esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Relation Between Migration Distance and Lens  
To analyze the effect of the lens on the distribution characteristics of the LNAPL during the leakage process, the maximum migration distances of LNAPL in the horizontal and vertical directions were compared in the absence of a lens and the presence of a weak and a high-permeability lens in the vadose zone. As shown in Figure 6, vertical migration distances were greatest with a high-permeability lens (45 cm), followed by no lens (40 cm), and least with a low-permeability lens (35 cm). Times to reach 33 cm were shortest for the high-permeability lens (70 min), followed by no lens (120 min), and longest for the low-permeability lens (320 min). For high-permeability lenses, vertical migration rates are high, peaking initially and then declining over time. Rates decreased from 0.17 cm/min to 0.142 cm/min for high-permeability lenses and from 0.12 cm/min to 0.043 cm/min for low-permeability lenses. Low-permeability lenses significantly retard the LNAPL's downward migration, while high-permeability lenses facilitate it.  
The LNAPL migrated furthest horizontally in low-permeability lenses (over 52 cm), followed by no lenses (44 cm), and least in high-permeability lenses (40 cm). The presence of low-permeability lenses accelerated horizontal migration but impeded vertical migration. In contrast, high-permeability lenses facilitated vertical migration at the expense of horizontal spread. Migration rates declined over time in non-lens and high-permeability lens media, while in low-permeability lenses, rates initially decreased and then increased after reaching 33 cm below the lens. This phenomenon is primarily due to the LNAPL first \"bypassing the flow\" in the aggregation below the lens, and then accelerating the diffusion after reaching a certain amount.  
Figure 5: Variation in the LNAPL migration velocity (high-permeability lens). (**a**) Migration process. (**b**) Lateral migration velocity at different heights. (**c**) Vertical migration velocity.
## Abbreviations  
* Time-interleaved analog-to-digital converters LS Least squares ADCs Analog-to-digital converters SAR Successive-approximation-register ADC Analog-to-digital converter FPGA Field-programmable gate array WSS Wide-sense stationary DTFT Discrete-time Fourier Transform IDTFT Inverse discrete-time Fourier transform SNDR Signal-to-noise distortion ratio SFDR Spurious-free dynamic Range
# Changes in Elderberry (_Sambucus nigra_ L.) Juice Concentrate Polyphenols during Storage
## 3 Results and Discussion
### Characterization of the Initial Eldeberry Concentrates
At the same time, the percentage of polymeric color was slightly affected at 5 \({}^{\circ}\)C for both juices, while at room temperature increased from 26.2% to 43.0% in concentrate I and from 28.3% to 47.9% in concentrate II. Similar observations were made by [PERSON] et al. [28] in blueberry juices with 64-68% losses of anthocyanins after six months of storage at 25 \({}^{\circ}\)C accompanied by a linear increase in percent polymeric color values. In addition, losses up to 75% in monomeric anthocyanins and a marked increase in percent polymeric color in blackberry [29] and in raspberry juices [30] after six months of storage at 25 \({}^{\circ}\)C were reported. Our results are also consistent with the kinetic studies developed by [PERSON] et al. [31] that predict a reduction of 50% of the monomeric anthocyanins in elderberry juice after 120 days of storage at 25 \({}^{\circ}\)C.  
The total phenolic content of the juice concentrates measured by the Folin-Ciocalteu method, whether stored at room temperature or at refrigerated temperature, does not follow the decrease in total monomeric anthocyanin content, and small variations over  
Figure 3: Evolution during storage at 5 \({}^{\circ}\)C and at room temperature (RT) for total monomeric anthocyanins (TMA) and percent polymeric color (%PC) in (a) concentrate I, (b) concentrate II; and total phenolic content (TPC) and antioxidant activity (AA) in (c) concentrate I and (d) concentrate II. time were observed. The higher stability observed in the total phenolic content may be explained by the formation of polymerized phenolic compounds that are detected by the Folin-Ciocalteu method [32]. This hypothesis is corroborated by the increase in the percent polymeric color. In fact, the decrease in monomeric anthocyanins content and the increase in percent polymeric color indicates that anthocyanins were transformed into polymeric compounds. For total phenolic compounds, after seven months of storage losses of about 13% and 14% were obtained for concentrate I and 10% and 8% of losses were found for concentrate II, at room temperature and at 5 \({}^{\circ}\)C, respectively. The results demonstrate that the total phenolic content is not much affected by the storage temperature, contrary to what happens with the monomeric anthocyanin content. The antioxidant activity, in general, follows the same profile of variation of total phenolic content and remained approximately stable over the seven months of storage despite the losses of total monomeric anthocyanins (Figure 3). Other authors [28; 31] have shown no significant changes in antioxidant capacity of juices during storage, despite a marked loss of anthocyanins. This may be related to the presence of polymeric compounds which have been associated with antioxidant capacity [33].
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 5 Results and Discussion
### Limitations  
When evaluating the performance of the inpainting results across diverse datasets, it was observed that the model achieving the optimal MOS contained a different number of LSTM layers. In the time domain, the proposed models proficiently restored the waveform envelope trends of the lost speech signals; however, a discernible loss in amplitude was observed. As for the frequency domains, it was observed that the proposed models effectively restored the speech signal's lower frequency range below 2.0 kHz for both single-speaker and multi-speaker datasets with sufficient accuracy. However, the efficacy of the model was limited at higher frequency ranges exceeding 2.0 kHz, especially for the multi-speaker datasets, exhibiting a significant loss in amplitude and introducing more pitch frequencies to the spectrograms. However, the listening quality was still understandable and acceptable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Gap Length (ms)** & **MOS (NB)** & **MOS (WB)** \\ \hline Context-Encoder [22] & 64/128 & 4.02/3.57 & 3.95/3.48 \\ \hline SpeechPainter [27] & 750–1000 & \(\backslash\) & 3.48 \(\pm\) 0.06 \\ \hline Audio-Visual (A+V+MTL) [31] & 100/200/400/800/1600 & \(\backslash\) & 4.10/3.82/3.43/2.49/1.56 \\ \hline TF-Masks-informed (Avg. of 3 intrusions) [23] & 100/200/300/400 & \(\backslash\) & 3.20/2.57/2.09/1.78 \\ \hline TF-Masks-blind (Avg. of 3 intrusions) [23] & 100/200/300/400 & \(\backslash\) & 3.17/2.75/2.46/2.21 \\ \hline Proposed method & 20/40/50/100 & 4.19/4.03/3.98/3.84 & 4.18/4.02/3.97/3.82 \\ (single-speaker) & /200/500/1000 & /3.64/3.17/2.64 & /3.58/3.01/2.36 \\ \hline Proposed method & 20/40/50/100 & 4.14/3.98/3.94/3.81 & 4.01/3.91/3.86/3.72 \\ (multi-speaker) & /200/500/1000 & /3.60/3.15/2.58 & /3.47/2.91/2.27 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of MOSs between the proposed method and other algorithms.  
It is worth mentioning that we only used a compact training dataset built from the RyanSpeech dataset for training and testing during the fine-tuning of the hyperparameters. Subsequently, the optimised hyperparameters were applied to all the other datasets. This means that the optimal hyperparameters based on the RyanSpeech dataset may not be the best-fitting choice for all datasets. To achieve better inpainting performance, fine-tuning for different datasets should be considered. We systematically tested various hyperparameter configurations, including a trial employing the five-layer LSTM model with specific settings: batch size = 512, dropout rate = 0.2, and dropout layers placed after the first and last LSTM layers. Notably, this configuration also achieved ideal inpainting results.  
We conducted additional tests to assess the inpainting capability of models trained on specific datasets when applied to different datasets. The inpainting performance exhibited a diverse range of outcomes.
# An Adaptive Linear Programming Algorithm with Parameter Learning
## 3 Parameter Learning to Dynamically Change the RMC
### Step 1--Identifying the Criteria  
In ALPPL, we add more criteria to the two criteria considered in the golden section search--deviation and constraint violation. We use a list, EIs[best], to record all the evaluation indices.  
* Criterion 1--deviation.  
The deviation in every iteration is recorded and ranked among all iterations. The best deviation in all iterations is stored in EIs[best]. Through implementing multiple design scenarios (lexicographic and/or weight scenarios), we obtain the deviations for all scenarios and iterations, and the desired range DEI is updated accordingly.  
* Criterion 2--robustness of solutions.  
We extend the notion of \"feasibility\" to \"robustness\", which means the solutions should be feasible under multiple design scenarios and relatively insensitive to model errors and uncertainties. We assume errors and uncertainties result in changes in the boundary of the feasible region. The nonlinear surface is a part of the boundary. Therefore,  
Figure 7: The concept of adaptive linear programming algorithm with parameter learning (ALPPL).  
the robustness of the solution can be measured by how far it is away from the nonlinear surface. In Figure 8, we show two situations. The arrows indicate the direction of the feasible space bounded by the inequality constraints while minimizing the deviation. In Figure 8a, the solution \(\mathrm{x}_{1}^{*}\) is not on the boundary, so it is relatively insensitive to errors and variations of the model, and the nonlinear constraint \(\mathrm{NF}_{\mathrm{j}}\) is not an active constraint. In contrast, in Figure 8b, the solution is on the boundary and \(\mathrm{NF}_{\mathrm{j}}\) is an active constraint. The RMC affects the approximation in the next iteration, so we adjust the RMC to obtain more robust solutions.
* Criterion 3--approximation accuracy. More accumulated constraints may not necessarily lead to a more accurate approximation. We want sufficient and useful accumulated constraints--Figure 9b--rather than many unnecessary accumulated constraints--Figure 9a.  
In Table 5, we summarize the criteria for approximation performance evaluation. Based on these criteria, we develop evaluation indices (EIs) in Section 3.2  
Figure 8: A relatively sensitive solution and a robust solution. Subfigure (**a**) shows the situation when the solution \(\mathrm{x}_{1}^{*}\) is not on the boundary - the surface of \(\mathrm{NF}_{\mathrm{j}}\), whereas Subfigure (**b**) shows the situation when the solution \(\mathrm{x}_{1}^{*}\) is on the boundary.  
Figure 9: Unnecessary accumulated constraints versus necessary accumulated constraints. Subfigure (**a**) illustrates the situation when the accumulated constraints are in the “wrong” region, which results from the linearization points in multiple iterations being in a region that gives poor achieved values of the goal. Subfigure (**b**) illustrates the opposite situation – when the accumulated constraints are in the “right” region.
## 3 Empirical Results  
In order to study the ability of the GPR index in forecasting a pool of selected variables, we train OLS (We use first differences in all estimations and Newey-West Robust Standard Errors) and SVR regression models in rolling windows. In each step we use 120 observations for training our models. Based on the trained model, we evaluate the forecasting ability in 1, 3, 6, 12, and 24 months ahead forecasting. Afterwards, we slide our training window one observation forward removing the first observation of the previous window as to maintain our training sample at 120 observations and retrain the model. We do so until the end of our sample. After collecting all out-of-sample forecasts, we measure the Mean Absolute Percentage Error (MAPE). All of the models include the first lag of the dependent variable (We also trained models of higher lag order, but the results were quantitatively similar). In the following tables we report the MAPE for the OLS model, the most accurate kernel for the SVR model (All results are available from the authors upon request.) and the MAPE of the Random Walk (RW) model (where we assume that the best forecast is the present value).
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 1 Introduction
### Conventional Shift Construction Structures
#### 1.1.1 Open Caissons  
Open caissons are utilized for constructing shafts in soft or loose soils. To stabilize the ground around the shaft periphery, sheet piles are first driven to reduce the risk of basal heave in soft clays by mitigating pressure differentials and to prevent hydraulic uplift in loose sands by increasing the groundwater flow path. Cast-in situ concrete caisson rings, typically 1.3 m high and 300 mm thick, are progressively installed from top to bottom, providing structural support to the surrounding soil, allowing excavation inside the shaft to proceed.  
Friction between the caisson wall and the soil helps support the weight of the concrete rings during excavation. The primary advantage of this construction method is that the completed shaft remains strut-free, facilitating construction activities within the shaft, such as removing excavated material. However, limitations of open caissons include high friction resistance in dense sands, improper sinking techniques, challenges with excavation bed cleaning, underwater concrete placement, and the risk of tilting due to unsymmetrical work, which can result in construction delays and increased costs [7].
###### Abstract  
This paper investigates the path following control problem of an unmanned surface vessel (USV) subject to input saturation and uncertainties including model parameters uncertainties and unknown time-varying external disturbances. A nonlinear robust adaptive control scheme is proposed to address the issue, more specifically, steering a USV to follow the desired path at a certain velocity assignment despite the involved disturbances, by utilizing the finite-time currents observer based line-of-sight (LOS) guidance and radial basis function neural networks (RBFNN). Backstepping and Lyapunov's direct method are the main design frameworks. Based on the finite-time currents observer and adaptive control technique, an improved LOS guidance law is proposed to obtain the desired approaching angle to the desired path, making compensations for the effects of unknown time-varying ocean currents. Then, a kinetic controller with the capability of uncertainties estimation and disturbances rejection is proposed based on the RBFNNs, where the adaptive laws including leakage terms estimate the approximation error and the unknown time-varying disturbances. Subsequently, sophisticated auxiliary control systems are employed to handle input saturation constraints of actuators. All error signals of the closed-loop system are proved to be locally uniformly ultimately bounded (UUB). Numerical simulations demonstrated the effectiveness and robustness of the proposed path following control method.  
unmanned surface vessel; path following; integral line-of-sight; finite-time currents observer; radial basis function neural networks; input saturation +
Footnote †: journal: Applied Mechanics and Physics Letters
## 1 Introduction
* Optimal material distribution: It reveals that in conventional copper coils, over 70% of losses are concentrated near the slot opening, creating an imbalance in loss distribution. * Hybrid coil solutions: New hybrid coils are proposed, featuring combinations of materials such as copper, aluminum, and 3D-printed alloys to achieve more balanced loss distribution and weight reduction. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Winding Topologies** & **Method** & **Frequency Range** & **Application** \\ \hline
[13] & Round and Litz wire & Analytical & 800 Hz & PM starter generator \\
[14] & Flat wire & FEA & 50–500 Hz & Fractional slot concentrated winding (FSCW) \\
[15] & Flat and stranded wires & FEA & 10–10\({}^{4}\) Hz & Interior PM synchronous machine (IPMSM) \\
[16] & Stranded wire & FEA and Analytical & 666.67 Hz & IPMSM \\
[17] & Multi-strand round wire & Analytical and experimental & 0–500 Hz & IPMSM \\
[12] & Flat wire & FEA and experimental & 0–1000 Hz & High-speed PM machines with form-wound windings \\
[18] & Round wire & Analytical and experimental & 0–1333.3 Hz & Slotless PM machines with concentrated winding \\
[11] & Flat wire & Analytical and experimental & 10 kHz, 20 kHz & Air-cored axial flux PM machine \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature on evaluation of AC winding losses. * Loss reduction: The hybrid coils significantly reduce losses, with up to 41% lower losses compared to traditional copper coils, while also achieving substantial weight savings. * Strand transposition: This study introduces strand transposition techniques, further reducing losses and enhancing current density. * Practical core loss measurement: A practical approach for core loss measurement and iron loss separation is introduced, enhancing the accuracy of loss assessments. * Frequency impact: The impact of operating frequency on losses is examined, providing insights into the performance of different coil designs under various conditions. * Novel hybrid coil topologies: This paper presents innovative hybrid coil designs that incorporate multiple magnet wire materials within the same slot, offering improved performance and efficiency. In summary, the novelty of this paper lies in its comprehensive exploration of winding topologies, innovative hybrid coil solutions, practical measurement techniques, and the efficient reduction of AC losses and weight in electrical machine windings. These findings have significant implications for improving the performance and efficiency of inverter-fed AC drive systems.
# Powdered Foods: Structure, Processing, and Challenges: A Review
## 6 Improvements and Challenges on Powdered Foods
### Undesirable Properties  
Among undesirable effects in powdered foods, [PERSON] [4] identifies three main properties: dustiness, stickiness, and caking. These properties not only lead to a loss of functionality and quality of the final product but also impact the powder formationprocess [45]. Powdered foods, due to their small size, contribute to dust formation, which is undesirable due to the loss of final product, greater difficulty in cleaning, and respiratory problems for workers. It is inversely proportionally affected by particle size, density, and cohesiveness [46].  
Stickiness is defined as an adhesive force in which powder particles adhere to different surfaces, which occurs in the drying process, processing, and handling of the powdered product. This phenomenon is often attributed to the chemical composition of foods (sugars, whey, high-fat milk, fruit juices, and others) and is also affected by the temperature and humidity of the environment [4]. Finally, the caking effect is defined as the agglomeration of powdered particles leading to the formation of a solid block and then a sticky material. This effect is generated from the melting of fats, moisture absorption, or sugar crystallization, which depends on some factors such as environmental temperature and humidity [4,45].  
To prevent the caking process, one effective approach involves the use of food additives, specifically anticaking agents, which are defined as substances with a very low particle size (between approximately 40 and 100 \(\upmu\)m) and a high surface area, enabling them to absorb significant amounts of water. These agents can be either synthetic or natural, and they need to have a smaller particle size compared to the applied food matrix [47]. In a study by [PERSON] et al. [48], the application of tricalcium phosphate (E 341) and calcium silicate (E 552) as anticaking agents was explored to enhance the stability of soursop powder produced via spray-drying. The findings revealed an increase in the powdered product yield, in addition to increasing stability in terms of moisture content, water activity (aw), and density. These salts demonstrated the ability to absorb moisture from the environment and prevent loss of product quality [48].
## 6 Simulation
### Single Particle Settlement  
To demonstrate the feasibility of IMB-DEM-LBM for particle movement, a particle that falls in a container filled with liquid under the effect of gravity is simulated to illustrate velocity accuracy of a moving particle.  
The square container's physical size \(6\times 10^{-4}\) m, and the domain boundary is no slip bounce back boundary. The gravity acceleration is \(g=9.8\) m/s\({}^{2}\), particle radius is \(r=5\times 10^{-5}\) m, particle density is \(\rho_{P}=2000\) kg/m\({}^{3}\), fluid density is \(\rho_{F}=1000\) kg/m\({}^{3}\), dynamic fluid viscosity is \(\eta=10^{-3}\) Pa-s, conversion factor \(C_{l}=5\times 10^{-5}\), \(\tau=1\), the lattice domain size is \(60\times 60\), and particle radius is 5. The initial particle state is stationary and the fluid velocity is zero. The particle position and velocity profile during settlement is shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Reynolds Number**} & **Simulation** & **Analytical** & **Error** \\  & **Drag Coefficient** & **Drag Coefficient** & **Error** \\ \hline
9 & 6.312 & 4.476 & 0.4076 \\
22.5 & 2.940 & 2.452 & 0.2121 \\
45 & 1.711 & 1.627 & 0.0518 \\
90 & 1.164 & 1.147 & 0.0149 \\
135 & 0.886 & 0.953 & 0.0711 \\
180 & 0.805 & 0.842 & 0.0437 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation and analytical drag coefficient of different Reynolds number.  
Figure 12: Drag coefficient with different Reynolds number.  
The drag force on particle increases as the sinking velocity increases, and the particle velocity reaches equilibrium when the drag force is equal to the gravity force. The analytical equilibrium sinking velocity is given by Equation (30) [29]. Therefore, the analytical sinking equilibrium velocity is \(u^{eq}=5.45\times 10^{-3}\) m/s. The simulation result using the method proposed in this study is shown in Figure 14, which indicates an equilibrium velocity of about 5.75 \(\times\)\(10^{-3}\) m/s.  
\[u^{eq}=\frac{2 gr^{2}\cdot(\rho_{P}-\rho_{F})}{9\eta} \tag{30}\]  
Figure 13: Sinking particle’s location and change in velocity with time wherein two eddies appear at the symmetry sides as the particle moves: (a) time step = 0; (b) time step = 20; (c) time step = 300; (d) time step = 500.