esa-sceva/satcom-chunk-collection huggingface dataset:
# CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
## 4 Experiments
### Objective Evaluation
#### 4.1.2 Linguistic Information Preservation Test  
Effective voice conversion requires not only a change in speaker characteristics but also the preservation of linguist information. Automatic speech recognition (ASR) can be utilized to quantify how well the linguistic information is maintained in the converted speech by measuring the speech recognition accuracy of the converted speech. We used a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **DiffVC** & **CycleDiffusion** \\ \hline  & i-vector & 0.4850 \(\pm\) 0.0049 & 0.5376 \(\pm\) 0.0047 \\  & x-vector & 0.8909 \(\pm\) 0.0033 & 0.9070 \(\pm\) 0.0020 \\  & Average & 0.6880 \(\pm\) 0.0041 & 0.7223 \(\pm\) 0.0034 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average cosine similarity values along with the 95% confidence intervals for the speech samples converted by DiffVC (baseline) and CycleDiffusion (proposed).  
publicly available ASR system--Whisper [35]--to measure the speech recognition accuracies. To account for the imperfections of ASR technologies, the speech recognition accuracy of the converted speech is calculated by comparing it against the recognized text of the original input speech, which serves as the reference transcription. An accuracy of 100% signifies complete preservation of the linguistic information, whereas an accuracy of 0% indicates that all linguistic information has been lost during the voice conversion process. Table 2 summarizes the average speech recognition accuracies, along with 95% confidence intervals for the converted speeches generated by DiffVC and CycleDiffusion.  
The ASR system achieved a recognition rate of 71.3% on the speech transformed by DiffVC, while the speech converted through CycleDiffusion yielded a superior recognition rate of 74.4%, resulting in a statistically significant relative error reduction rate of 10.8%. The higher speech recognition accuracy achieved by CycleDiffusion can be credited to enhanced intelligibility and better preservation of linguistic information, facilitated by the integration of cycle consistency loss during the training process.
## 3 Results and Discussion
### Melting Profile  
The melting point is one of the main indicators characterizing the consistency of fats and oils, and consequently, also their possible uses. DSC melting curves of amaranth and quinoa oils are presented in Figure 3. In the quinoa oil, three endothermic peaks were observed at temperatures: \(-44.81\)\({}^{\circ}\)C, \(-20.34\)\({}^{\circ}\)C, and \(9.08\)\({}^{\circ}\)C. For amaranth oil, three endothermic peaks can also be observed, but in a different temperature range. The maximum of the first peak was recorded at temperature of \(-26.10\)\({}^{\circ}\)C, the second at \(-7.05\)\({}^{\circ}\)C, and the third at \(3.58\)\({}^{\circ}\)C. The occurrence of maximum peaks at low temperatures was attributed to the high content of low-melting triacylglycerlycords with a high proportion of mono- and polyunsaturated fatty acids [21]. The performed determination of the fatty acid composition of the analyzed amaranth and quinoa oils confirmed the predominant share of unsaturated fatty acids. Generally, vegetable oil with a high degree of unsaturation can remain liquid over a wide temperature range [37]. Our results are in agreement with those reported by [PERSON] et al. [21], [PERSON] et al. [38], and [PERSON] et al. [39], who observed that vegetable oils were characterized by the presence of peaks in the low temperature range on the melting curve, which corresponded to the presence of low-melting TAG fractions.
# Magnetic and Hydrophobic Composite Polyurethane Sponge for Oil-Water Separation
## 2 Experimental
### Preparation of Fe\({}_{3}\)O\({}_{4}\) Nanoparticles  
Magnetic ferroferric oxide (Fe\({}_{3}\)O\({}_{4}\)) nanoparticles were prepared by chemical co-precipitation method [41; 42]. The reaction principle is: Fe\({}^{2+}\) + 2 Fe\({}^{3+}\) + 8 OH\({}^{-}\)\(\rightarrow\) Fe\({}_{3}\)O\({}_{4}\) + 4H\({}_{2}\)O. Fifty milliliters of distilled water was added to the three-necked round bottom flask, and then FeCl\({}_{2}\) and FeCl\({}_{3}\) were quickly weighed in a mole ratio of 1:2 and added to the three-necked round bottom flask. A measuring cylinder was used to measure 50 mL ammonia water (25%-28%) and added to a three-necked round bottom flask. After vacuuming, nitrogen (N\({}_{2}\)) was introduced as a protective gas. The three-necked round bottom flask was placed on a magnetic stirrer at a temperature of 90 C and stirred at 800 r/min to 900 r/min for 24 h. After 24 h, the reaction product was magnetically separated with a strong magnet and washed three times with absolute ethanol. And the reaction product was dried in a vacuum oven for 12 h to finally obtain magnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles.
## 2 Materials and Methods
### The Forest Model FORMIND  
#### 2.5.1 General Model Description  
FORMIND is an individual- and process-based forest gap model which describes the succession of a forest in patches with a size of 20 m \(\times\) 20 m [40]. These patches are typical for gap models that are describing the succession of a forest in subareas with different successful stages. The falling of big trees creates gaps in a forest stand which drives the dynamic of species succession. Up to 100 ha of forest area with a daily time step can be simulated, whereby the growth is determined for each individual tree. For each time step, four main processes are calculated: establishment, tree growth, mortality and competition (for light, space, and soil water). Main driving forces of these processes are the PPFD, the soil water content and the air temperature. While the vertical light condition for one tree is influenced by the shading of larger tree crowns, horizontal light conditions are homogenous within one patch. The soil water content of each patch is mainly driven by precipitation, interception, transpiration, and run-off characterised by soil properties as well as stand characteristics. Therefore the soil water content is continuously calculated and patch specific [40].  
Input data of the model includes daily climate data: daytime mean PPFD, daily precipitation, the daytime mean of air temperature and the day length, where daytime was defined as the time when PPFD \(>\) 20 \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)[41]. All input variables affect processes of each tree individually, therefore photosynthesis and respiration are calculated for each tree separately. Thus, the influence of variable climate conditions on the carbon balance on tree and ecosystem level can be analysed.  
Species-specific parameters for the four occurring tree species within the 1 ha research area (see Appendix C, Table A3) as well as the allometric equations to describe the geometry (dbh, tree height, crown diameter) and the above-ground biomass of a tree were taken from [PERSON] et al. (2018).  
In the FORMIND model, a carbon flux module was used ([PERSON], 2017), which calculates the full carbon balance of a forest stand on a daily time step (see Appendix B).
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**CG\&W (\(n=53\))**} & \multicolumn{2}{c}{**CS\&W (\(n=41\))**} & \multicolumn{2}{c}{**CG\&W vs. CS\&W**} \\ \cline{2-9}
**Polymorphism** & \(n\) & \% & \(n\) & \% & \(p\) a  & \multicolumn{2}{c}{**Compared**} & \multicolumn{2}{c}{**Genotypes or**} & \multicolumn{2}{c}{**OR (95\% CI)**} & \multicolumn{1}{c}{\(p\) b **} \\ \hline Th1111 & & & & & 0.944 & & \\ genotype & & & & & & \\ \hline CC & 24 & 45.28\% & 20 & 48.78\% & CC + CT vs. TT & \begin{tabular}{c} 0.967 \\ (0.134–6.096) \\ \end{tabular} & \(1\) \\ \hline CT & 25 & 47.17\% & 18 & 43.90\% & CC vs. CT + TT & \begin{tabular}{c} 0.87 \\ (0.355–2.129) \\ \end{tabular} & \(0.836\) \\ \hline TT & 4 & 7.55\% & 3 & 7.32\% & CC vs. TT & \begin{tabular}{c} 0.902 \\ (0.118–6.035) \\ \end{tabular} & \(1\) \\ \hline Th1111 allele & & & & & & \\ \hline C & 73 & 68.87\% & 58 & 70.73\% & C vs. T & \begin{tabular}{c} 0.916 \\ (0.463–1.795) \\ \end{tabular} & \(0.873\) \\ \hline T & 33 & 31.13\% & 24 & 29.27\% & & \\ \hline N363S genotype & & & & & & \\ \hline AA & 49 & 92.45\% & 38 & 92.68\% & AA vs. AG & \begin{tabular}{c} 0.967 \\ (0.134–6.096) \\ \end{tabular} & \(1\) \\ \hline AG & 4 & 7.55\% & 3 & 7.32\% & & \\ \hline GG & 0 & 0.00\% & 0 & 0.00\% & & \\ \hline N363S allele & & & & & & \\ \hline A & 102 & 96.23\% & 79 & 96.34\% & A vs. G & \begin{tabular}{c} 0.969 \\ (0.138–5.905) \\ \end{tabular} & \(1\) \\ \hline G & 4 & 3.77\% & 3 & 3.66\% & & \\ \hline ER22/ER23 EK genotype & & & & & \\ \hline AA & 0 & 0.00\% & 0 & 0.00\% & AG vs. GG & \begin{tabular}{c} 1.561 (0.079– \\ 94.772) \\ \end{tabular} & \(1\) \\ \hline AG & 2 & 3.77\% & 1 & 2.44\% & & \\ \hline GG & 51 & 96.23\% & 40 & 97.56\% & & \\ \hline ER22/ER23 EK allele & & & & & \\ \hline A & 2 & 1.89\% & 1 & 1.22\% & A vs. G & \begin{tabular}{c} 1.554 \\ (0.08–92.968) \\ \end{tabular} & \(1\) \\ \hline G & 104 & 98.11\% & 81 & 98.78\% & & \\ \hline Th1111/N363S/ & & & & & \\ ER22/23 EK & & & & & \\ haplotype & & & & & \\ \hline CAG & 68 & 64.15\% & 55 & 67.07\% & CAG vs. other & \begin{tabular}{c} 0.879 \\ (0.456–1.684) \\ \end{tabular} & \(0.758\) \\ \hline TAG & 32 & 30.19\% & 23 & 28.05\% & TAG vs. other & \begin{tabular}{c} 1.109 \\ (0.561–2.212) \\ \end{tabular} & \(0.872\) \\ \hline CGG & 4 & 3.77\% & 3 & 3.66\% & CGG vs. other & \
\begin{tabular}{c} 1.033 \\ (0.169–7.252) \\ \end{tabular} & \(1\) \\ \hline TAA & 1 & 0.94\% & 1 & 1.22\% & TAA vs. other &
\begin{tabular}{c} 0.773 \\ (0.01–61.267) \\ \end{tabular} & \(1\) \\ \hline CAA & 1 & 0.94\% & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Th1111, N363S, and ER22/23 EK genotypes in CG&W and CS&W. Association among several metabolic profile parameters and the Thh111I, N363S, and ER22/23 EK variants was assessed (Table 7). We did not observe any significant association between studied polymorphisms and BMI, dyslipidemia, DM, or HT.
## 3 Proposed TD3-Based MMC-VSG  
### Markov Decision Process  
The MDP is used in RL to model the interaction between an agent and the environment, which is depicted in Figure 8. The TD3 technique suggested in this study can also be thought of as an MDP since almost every RL issue can be classified as such. MDP can generally be expressed as \(<\)_S_, \(A,P,R\)>, where  
Figure 6: Step response for different values of \(J\) (with \(D=8\)).  
Figure 7: Step response for different values of \(D\) (with \(J=0.4\)).  
\(S\) is the name of the state. This includes the environmental conditions at time \(t\) examined by the agent. The agent requires proper state information to reasonably manage the VSG control strategy. Here, the state of the active power and reactive power as well as the angular frequency are needed at time \(t\). Therefore, the state \(S\) is defined as: \(S(P_{tn}\),\(Q_{tn}\),\(\omega_{tn})\). \(A\) represents the action, it stands for the action at time \(t\). In this case, the action can be set as the value of the inertia coefficient (\(J\)), the damping (\(D\)), and the voltage (\(E\)). After each individual action, the next state will be performed by the environment, which can be referred to as \(S^{{}^{\prime}}\). \(P\) is the transfer probability parameter, which is relevant to the uncertainty rate of the system and action value. \(R\) is the reward function induced as \(R(s,a)\), which is the environment feedback value since the environment implements action \(A\) at state \(S\).  
Thus, under one MDP step, the agent perceives the state \(S\) of the environment and takes action \(A\) based on the policy \(\pi\). Then, the environment transfers to the next state \(S^{{}^{\prime}}\), and the feedback value \(R\) is sent to the agent. According to this feedback, policy \(\pi\) will be updated by the agent to maximize the \(Q\) function, which is defined as the expected cumulative reward value  
\[Q(s,a)=r_{t+1}+\gamma r_{t+2}+\ldots+\gamma^{k}r_{t+k}=\sum_{k=0}^{\infty} \gamma^{k}r_{t+k} \tag{27}\]  
where \(\gamma\) represents the discount rate (\(\gamma\in[0,1]\)), which reflects the influence of the future reward value on the current action.  
The optimization objective in this paper is to suppress active power oscillation and primary frequency change; hence, the reward function \(r\) can be modeled as  
\[r_{t}=-\left(k_{1}*{p_{error}}^{2}+k_{2}*{q_{error}}^{2}+k_{3}*{f_{error}}^{2 }+R*\sum_{j}{u_{t-1}^{j}}^{2}\right)-100d \tag{28}\]  
where \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(R\) are coefficient factors, which can be assigned as values of 0.5, 1, 0.5, and 0.01, respectively. \(p_{error}\) is the active power error, \(q_{error}\) is the reactive power error, \(f_{error}\) is the frequency error of the primary frequency modulation, \(u_{t-1}^{j}\) are the previous time step actions, and \(d\) is a flag equal to 1 when the emulation is accidentally terminated.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 2 Room Acoustic Solver Using Conventional Explicit TD-FEM
### Basic Equation and Its Discretization in Space and Time  
We consider the following nonhomogeneous wave equation to simulate sound propagation in a closed sound field \(\Omega_{\mathrm{f}}\) with a boundary \(\Gamma\).  
\[\frac{\partial^{2}p}{\partial t^{2}}-c_{0}^{2}\
abla^{2}p=\rho_{0}c_{0}^{2} \frac{\partial q}{\partial t}, \tag{1}\]  
where \(p\), \(c_{0}\), \(\rho_{0}\), \(q\), and \(t\) respectively represent the sound pressure, the sound speed, the air density, the added fluid mass per unit volume, and the time. \(\
abla^{2}\) represents the Laplacian of \(p\). Using [PERSON]'s theorem, the weak form of the nonhomogeneous wave equation is expressed as  
\[\int_{\Omega_{\mathrm{f}}}\phi_{\mathrm{f}}\frac{\partial^{2}p}{\partial t^{2} }dV+c_{0}^{2}\int_{\Omega_{\mathrm{f}}}\
abla\phi_{\mathrm{f}}\
abla p\ dV=c_{0}^{2} \int_{\Omega_{\mathrm{f}}}\phi_{\mathrm{f}}\frac{\partial p}{\partial n}dA+ \rho_{0}c_{0}^{2}\int_{\Omega_{\mathrm{f}}}\phi_{\mathrm{f}}\frac{\partial q }{\partial t}dV. \tag{2}\]  
Here, \(\phi_{\mathrm{f}}\) denotes the arbitrary weight function. Applying the [PERSON] method and incorporating three boundary conditions (a rigid boundary, a vibrating boundary, and an impedance boundary) engender the semi-discretized matrix equation as shown below.  
\[\mathbf{M}\ddot{\mathbf{p}}+c_{0}^{2}\mathbf{K}\mathbf{p}+c_{0}\mathbf{C}\dot{\mathbf{p}}=\mathbf{f}, \tag{3}\]  
with  
\[\mathbf{M}=\sum\limits_{\mathbf{\mathrm{c}}}^{N_{\mathrm{c}}}\mathbf{M}_{\mathrm{e}}=\sum \limits_{\mathbf{\mathrm{c}}}^{N_{\mathrm{c}}}  
backward difference [25]. Consequently, the time marching scheme for the explicit TD-FEM is expressed as  
\[\mathbf{p}^{n}=\mathbf{p}^{n-1}+\Delta t\mathbf{D}^{-1}\mathbf{M}\mathbf{v}^{n-1}, \tag{7}\]  
\[(\mathbf{D}+\Delta t{c_{0}}\mathbf{C})\mathbf{v}^{n}=\mathbf{D}\mathbf{v}^{n-1}+\Delta t(\mathbf{f}^{n} -{c_{0}^{2}}\mathbf{K}\mathbf{p}^{n}). \tag{8}\]  
Here, \(\Delta t\) and \(n\) respectively denote the time interval and the time step. Equation (8) includes an implicit expression, but it is calculable explicitly by the diagonalization in \(\mathbf{C}\) with the row-sum method. Two sparse matrix-vector products (MVP) per time step are the main operation of the scheme.
# Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network
###### Abstract  
Soil temperature (ST) plays a key role in the processes and functions of almost all ecosystems, and is also an essential parameter for various applications such as agricultural production, geothermal development, and their utilization. Although numerous machine learning models have been used in the prediction of ST, and good results have been obtained, most of the current studies have focused on daily or monthly ST predictions, while hourly ST predictions are scarce. This paper presents a novel scheme for forecasting the hourly ST using weather forecast data. The method considers the hourly ST prediction to be the superposition of two parts, namely, the daily average ST prediction and the ST amplitude (the difference between the hourly ST and the daily average ST) prediction. According to the results of correlation analysis, we selected nine meteorological parameters and combined two temporal parameters as the input vectors for predicting the daily average ST. For the task of predicting the ST amplitude, seven meteorological parameters and one temporal parameter were selected as the inputs. Two submodels were constructed using a deep bidirectional long short-term memory network (BiLSTM). For the task of hourly ST prediction at five different soil depths at 30 sites, which are located in 5 common climates in the United States, the results showed the method proposed in this paper performs best at all depths for 30 stations (100% of all) for the root mean square error (RMSE), 27 stations (90% of all) for the mean absolute error (MAE), and 30 stations (100% of all) for the coefficient of determination (R\({}^{2}\)), respectively. Moreover, the method adopted in this study displays a stronger ST prediction ability than the traditional methods under all climate types involved in the experiment, the hourly ST produced by it can be used as a driving parameter for high-resolution biogeochemical models, land surface models and hydrological models and can provide ideas for an analysis of other time series data.  
soil temperature; machine learning; weather forecasting data; BiLSTM; soil depths +
Footnote †: journal:
## 3 Data and Methods
### Cwt  
CWT is a widely spread spectral analysis method for nonstationary signals, in particular GICs [31; 32; 33; 34]. In the case of intellectual fault diagnosis for power systems, CWT is often used as a feature extraction method from different transformer signals such as vibrations, currents and voltages [14; 15; 16]. CWT provides an accurate representation of changes in the frequency characteristics of a signal over time. The essence of the method is to convolve the analyzed signal with a scaled and translated version of the analyzing function, which is called the mother wavelet. The wavelet is a mathematical function that most frequently has a wave-like form, and its amplitude tends to zero with distance from the origin of the coordinates. An important property of wavelets is time-frequency localization, which makes it possible to extract frequency characteristics of a signal with good localization in time. As a result of CWT, the wavelet coefficients are calculated, the values of which are directly proportional to the degree of similarity between the analyzed signal and the selected wavelet. The CWT of a discrete time series, \(x_{n}\), is defined by the following equation [35]:  
\[W_{n}(s)=\sum_{n^{\prime}=0}^{N-1}x_{n^{\prime}}\psi^{*}\left[\frac{(n^{\prime }-n)\delta t}{s}\right], \tag{3}\]  
where \(n\) is the localized time index, \(s\) is the wavelet scaling factor, \(\psi\) is the mother wavelet, * symbolizes a complex conjugation, and \(\delta t\) is the sampling interval.  
The graphical representation of a three-dimensional array of wavelet coefficients, \(W_{n}(s)\), is a scalogram: the \(x\) axis indicates time; the \(y\) axis represents a frequency, the value of which is inversely proportional to the wavelet scale (width); and colors indicate the wavelet coefficients, the greater the value of which, the more the wavelet matches the shape of the analyzed signal. The scalogram is calculated as the square of the modulus of the wavelet coefficients values, \(\left|W_{n}(s)\right|^{2}\). Two-dimensional images of wavelet spectra provide a visual representation of various phenomena in the researched signal. They are used in such areas of machine learning as classification and pattern recognition [16].  
To compute scalograms for the dataset, this paper uses the open-source library PyWavelets (version 1.3.0) [36], which contains a number of CWT-compatible mother wavelets.
## 4 Discussion
### Limitations  
The current study has several limitations that must be acknowledged. First, we did not validate the IMU-derived spatiotemporal metrics against any reference gold standard method for kinematic analysis, such as 3D motion-capture systems, because we wanted to obtain the data over a larger skating distance (i.e., 80 m). Second, neither joint kinematics nor muscle activation were examined, which could potentially provide some additional explanations of the underlying mechanisms that could affect the obtained spatiotemporal metrics, their relationship in different sprint phases, and their power to predict the maximal skating speed. Third, the study did not compare male and female players, playing levels, and positions, which could show whether the IMU-derived spatiotemporal metrics had appropriate discriminative validity. Furthermore, the study included only male bandy players, which might limit the potential to generalize the findings from the study to female players. However, future studies should be designed to explore the abovementioned limitations.
# Guidelines for Experimental Algorithmics:
## 7 Evaluating Results with Statistical Analysis  
Even if a result looks obvious (it fulfills the \"inter-ocular trauma test\", as the saying goes, the evidence hitting you between the eyes), it can benefit from a statistical analysis to quantify it, especially if random components or heuristics are involved. The most common questions for a statistical analysis are:  
* Do the experimental results support a given hypothesis, or is the measured difference possibly just random noise? This question is addressed by _hypothesis testing_.
* How large is a measured effect, i.e., which underlying real differences are plausible given the experimental measurements? This calls for _parameter estimation_.
* If we want to answer the first two questions satisfactorily, how many data points do we need? This is commonly called _power analysis_. As this issue affects the planning of experiments, we discussed it already in Section 4.3.  
The two types of hypotheses we discussed in Section 4.1 roughly relate to hypothesis tests and parameter estimation. Papers proposing new algorithms mostly have hypotheses of the first type: The newly proposed algorithm is faster, yields solutions with better quality or is otherwise advantageous.  
In many empirical sciences, the predominant notion has been _null hypothesis significance testing_ (NHST), in which a statistic of the measured data is compared with the distribution of this statistic under the _null hypothesis_, an assumed scenario where all measured differences are due to chance. Previous works on statistical analysis of experimental algorithms, including the excellent overviews of [PERSON] [38] and [PERSON] [15], use this paradigm.  
Due to some limitations of the NHST model, a shift towards _parameter estimation_[83; 84; 85; 86] and also Bayesian methods [87; 88; 89] is taking place in the statistical community.  
We aim at applying the current state of the art in statistical analysis to algorithm engineering, but since no firm consensus has been reached [85], we discuss both frequentist and Bayesian approaches. As an example for null hypothesis testing, we investigate whether the KADABRA and the RK algorithms give equivalent results for the same input graphs. While this _equivalence test_ could also be performed using Bayesian inference, we use a null hypothesis test for illustration purposes.  
It is easy to see from running time plots (e.g., Figure 5) that KADABRA is faster than RK. To quantify this speedup, we use Bayesian methods to infer plausible values for the algorithmic speedup and different scaling behavior. We further evaluate the influence of the graph diameter on the running time.