esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Control of the Aerodynamic Flaps of the Savonius
## 4 Results and Discussion
### Kinematic and Dynamic Characteristics
The insights gained from the simulations form a solid basis for the practical implementation of the proposed control algorithm in future prototypes. These findings validate the importance of dynamic tuning in maintaining optimal performance and minimizing mechanical stress in real-world environments. The insights gained from the simulations form a solid basis for the practical implementation of the proposed control algorithm in future prototypes. As the present work is based entirely on numerical simulation, the absence of experimental validation constitutes a limitation. Nevertheless, all system parameters--including servo dynamics, mechanical dimensions, and wind loads--were derived from manufacturer specifications and established aerodynamic models, ensuring physical consistency. While the simulations provide insight into transient performance under varying wind scenarios, real-world deviations may arise due to unmodeled turbulence, component wear, or sensor noise. These factors may affect the stability and accuracy of the control system, particularly under prolonged operation or extreme conditions. Future work will address these aspects through laboratory-scale validation using a low-power VAWT prototype equipped with real-time wind and position sensors. This will allow for direct comparison between simulated and measured flap responses and enable refinement of the control model under realistic disturbances. All simulations presented in this study were carried out using Python 3.11 with a custom simulation framework developed by the authors. The dynamic modeling, numerical integration of differential equations, and adaptive PID control logic were implemented using standard scientific libraries, including NumPy 2.2.6 and SciPy v 1.15.3 for numerical computations, SymPy 1.14.0 for symbolic verification of equations, Matplotlib v3.10.3 for result visualization, and the python-control library for control system analysis. This environment provided sufficient flexibility and precision to simulate mechanical nonlinearities such as backlash, friction, and wind disturbances with time-step resolution ranging from 1 to 10 milliseconds depending on scenario complexity.
## 1 Introduction
With the various assumptions made during attenuation expression derivations, it is necessary to minimize biases that may arise from the encapsulated parameters due to their associated uncertainties. Variations in \(M\) will result in different values for \(V\) and, consequently, for attenuation (\(A\)) in Equation (3) via Equations (2) and (4), or in Equation (11) via Equations (5) and (7)-(10). For convenience, the equations in Table 1 will be referred in discussions and some graphs in this paper as denoted under the third column, where each equation is assigned an alphanumeric character (e.g., E1 corresponds to [PERSON] and [PERSON]'s equation). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author(s)** & **Visibility, km** & **In-Paper Ref.** & & **Relationship Development Highlights** \\ \hline \multirow{6}{*}{[PERSON] and [PERSON] [12]} & & & & Technique employed: field experiments. \\  & & & & Monitoring period: 2 years. \\  & & & & Location: near Memo, Kansas, USA. \\  & & & & Field Observations: 22 averaged measurements at different heights. \\  & & & & Distance from targeted dust source: at, or very close to, encoding dust source. \\  & & & & Technique employed: field experiments exploring soil-derived aerosols. \\  & & & & Monitoring period: 2 years. \\  & & & & Location: rural areas of west Texas, USA. \\  & & & & Field Observations: 13 measurements at a fixed height. \\  & & & & Distance from targeted dust source: at, or very close to, encoding source. Data best fit: [11] (min: [35], max: [35]) \\  & & & & \\  & & & & Information inaccessible. \\  & & & & Technique employed: datasets from weather stations. \\  & & & & Monitoring period: March/April 2002. \\  & & & Location: China, Japan, and Korea. \\  & & & & Field Observations: Synoptic records and visibility observed at around 1200 weather stations - near-surface dust concentrations measured at 12 monitoring sites. \\  & & & & 2 likely empirical relationships were found, with respective R\({}^{2}\) being 0.68 (\(\sim\) 3.5 km) and 0.72 (\(V\) 3.5 km). \\  & & & & Technique employed: datasets from weather stations. \\  & & & Monitoring period: records of 24 years. \\  & & & & Location: rural Mid/Burong. \\  & & & Distance from targeted dust source: 10-100 km from the sandy soils of the Mallee region. \\  & & & & An empirical relationship with an R\({}^{2}\) = 0.79. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Visibility empirical equations, relationship development highlights, and their in-paper references.
# Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking
## 3 Trajectory Planning
### Trajectory of the Hip  
Hip horizontal trajectories in DSP and SSP are designed as two third-order polynomials, respectively.  
\[x_{h}^{DSP}(t)=c_{0}+c_{1}t+c_{2}t^{2}+c_{3}t^{3},\ \ 0\leq t\leq T_{d} \tag{10}\]  
\[x_{h}^{SSP}(t)=d_{0}+d_{1}t+d_{2}t^{2}+d_{3}t^{3},\ \ 0\leq t\leq T_{s} \tag{11}\]  
The hip trajectory should be continuous during the walking step. Therefore, the displacement and velocity of the hip at the end of DSP should be equal to those at the beginning of SSP. Furthermore, to keep ZMP continuous, the acceleration of the hip is also required to be continuous. Thus, the following continuity condition should be satisfied:  
\[\left\{\begin{array}{l}x_{h}^{DSP}(T_{d})=S_{1}\\ x_{h}^{SSP}(0)=S_{1}\\ \dot{x}_{h}^{DSP}(T_{d})=\dot{x}_{h}^{SSP}(0)\\ \dot{x}_{h}^{DSP}(T_{d})=\dot{x}_{h}^{SSP}(0)\end{array}\right., \tag{12}\]  
where \(S_{1}\) is the hip position with respect to stance ankle in the horizontal direction at the end of DSP, as shown in Figure 2.  
To keep the gait repeatable, the configuration at the beginning of DSP should be identical to the one at the end of SSP and the velocity of the hip at the beginning of DSP should be the same as the one at the end of SSP. In addition, to make the motion smooth, the acceleration of the hip at the beginning of DSP should be equal to the one at the end of SSP. Therefore, the following cyclic condition should be satisfied:  
\[\left\{\begin{array}{l}x_{h}^{DSP}(0)=S_{2}-L\\ x_{h}^{SSP}(T_{s})=S_{2}\\ \dot{x}_{h}^{DSP}(T_{d})=\dot{x}_{h}^{SSP}(0)\\ \dot{x}_{h}^{DSP}(T_{d})=\dot{x}_{h}^{SSP}(0)\end{array}\right., \tag{13}\]  
where \(S_{2}\) is the hip position with respect to stance ankle in the horizontal direction at the end of SSP, as shown in Figure 2.  
Constant polynomial coefficients \(c_{i}\) and \(d_{i}\) (\(i=0,\,1,\,\ldots,\,3\)) are determined by Equations (12) and (13), respectively.  
In this paper, hip vertical motion is not our focus. Therefore, hip height keeps unchanged during walking. The trajectory of \(z_{h}(t)\) is described as follows:  
\[z_{h}(t)=H,\ \ 0\leq t\leq T_{c} \tag{14}\]  
where \(H\) is also a design parameter that will be determined by optimization.
## 1 Introduction
The homogenized model is a simplified representation of the battery's layered structure, which enables efficient computation while capturing essential mechanical behavior densification under compression. In this homogenized model, the complex multi-layered structure of the cells, including the active material layers, current collectors, separators, and the shell, is represented as a single equivalent material with average properties. This approach reduces computational costs significantly while still accurately reflecting the mechanical behavior of the battery pack under compression loads. The models are calibrated using experimental data to ensure accuracy, focusing on key parameters such as stress-strain responses and load-deformation correlation. The paper aims to provide comprehensive insights into the crashworthiness of lithium-ion battery packs and propose effective design modifications to mitigate risks associated with mechanical failures and thermal runaway. The homogenized FEM models are par ticularly valuable in simulating large-scale impacts and complex load scenarios where detailed modeling would be computationally expensive and require too many resources. This approach of using FEM homogenized models allows for a robust evaluation of battery pack safety and performance under various compression conditions [20]. When cylindrical lithium-ion battery cell packs are subjected to compression loads, significant radial deformation occurs, which can critically affect their structural integrity and safety. Radial deformation involves the outward expansion of the cell walls, leading to potential buckling and crushing of the internal components. This deformation is particularly pronounced at the contact points between adjacent cells and between the cells and the housing of the battery pack. At these contact points, the stress concentrations can be substantial, initiating localized deformation that propagates through the battery pack. Understanding the radial deformation and its propagation through contact points is essential for designing safer and more robust cylindrical battery cell packs. Our proposal is to fill up the empty spaces left by the stacking of the cylindrical battery cells. By blocking these deformation spaces, we aim to increase the internal energy required to achieve the same level of deformation, thereby enhancing the pack's structural integrity. To validate this approach, a series of investigations was conducted using both physical testing and simulation. The paper is structured as follows: the Materials and Methods section details the numerical modeling approach and validation process; the Results and Discussion section presents compression tests using a plate and a cylinder as well as dynamic impact simulations, demonstrating that spacers significantly improve crash performance. Additionally, different spacer configurations and materials--aluminum, printed PLA, and conditioned PLA--are evaluated, highlighting the effectiveness of stiffer materials in reducing deformation.
## 4 Conclusions  
Both the flours and the protein concentrates analyzed in this study meet current European labeling regulations. Hemp flours and hemp concentrates had a similar energy intake for the consumer; however, their composition showed differences. Carbohydrates were, thus, the main component of the flours, which essentially consisted of fiber; flours also showed a high antioxidant activity with a high concentration of total phenolic compounds. In the case of concentrates, the carbohydrate content was reduced by half, and the amount of protein increased. Hemp flours and concentrates were marketed partially defatted, with a 50% reduction in the fat content of the unprocessed hemp seeds, such that both flours and concentrates had low fat values. This fat was mainly composed of polyunsaturated fatty acids (linoleic and linolenic acids). Both flours and concentrates had an excellent amino acid profile, with the presence of all essential amino acids in large amounts. Both in the flours and in the concentrates, mineralization stood out in terms of its high K and P content. On the basis of the results obtained, hemp flour could be of great interest for application in the fortification of bakery products and breads. They offer an interesting nutritional composition, in addition to bioactive compounds, while showing an excellent swelling capacity. Protein concentrates showed higher WHC, FC, FS, and GF than the flours. For this reason, protein concentrates appear to be very interesting products for application in meat products and sauces. Organic products showed an influence on the nutritional composition, with lower concentrations of proteins and starch but a higher micronutrient content and total phenolic composition. However, no differences were found in the profile of the fatty acids, amino acids, and techno-functional properties. On the basis of these results, organic flours seem to be a good alternative to be included in the diet of consumers. In those cases where a higher protein intake is necessary, especially in elderly people or athletes, organic hemp concentrate could be a very suitable product.  
**Author Contributions:** Conceptualization and methodology, A.M.V.-Q. and I.R.; formal analysis, Y.A.; data curation, Y.A.; writing--original draft preparation, Y.A. and A.M.V.-Q.; writing--review and editing. A.M.V.-Q. and I.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available on request.  
**Acknowledgments:** [PERSON] is grateful to the Algerian government for the grant supporting a long-term residential doctoral program abroad.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Evaluation of Remote Sensing (RS)  
As discussed in the Introduction, historical time series of an NDVI\({}_{\text{RS}}\) enable one to follow the evolution of vegetative vigor and, therefore, the quality of pasture throughout the year. The significant correlations obtained in this work between the PQI and the NDVI\({}_{\text{RS}}\) (R\({}^{2}\) = 0.77; \(p<0.01\)) confirmed previous studies. According to [PERSON] et al. [35], the NDVI has been recognized as an excellent proxy for both the chlorophyll content and the intracellular spaces of plant leaves [36]: in general terms, higher NDVI values are indicative of greater vigor and photosynthetic activity, whereas lower NDVI values are associated with stress phenomena that result in decreased presence of chlorophyll and wilting or senescence of the leaves. The nonlinear behavior of this relationship seems to indicate the lower sensitivity of the NDVI to variations in the quality of pasture (PQI) in periods of more feeble vegetative vigor (NDVI values of approximately 0.4-0.6; Figure 6a), an aspect to be further explored in future works. [PERSON] et al. [1] observed significant correlations between the NDVI and pasture quality parameters, showing that this index can be used to develop a system of alarms that can inform a farm manager of the need for providing supplementary feed to the animals.  
On the other hand, the significant correlation between the NDWI\({}_{\text{RS}}\) and PMC was in agreement with the observations of [PERSON] et al. [37], who indicated that the spectral signature of vegetation in NIR and SWIR (short-wave infrared) bands can be related with the plant water status. The NDWIRS, known as a \"water index\" [28; 35], has shown sensitivity to changes in leaf water content. By using the NDWIRS, the amount of leaf material can be known in order to estimate vegetation dryness [38].  
These results show the practical interest of the satellite-derived indices (as the NDVIRS and the NDWIRS) in the site-specific management of the Montado ecosystem, namely to provide the farmer with the information needed to intensify this production system in a climate change scenario [1]. The RS-based models can more accurately assess ecosystem services when their indicators mainly depend on green vegetation, such as for erosion prevention and the provision of forage [17].  
This RS approach has the great advantage of not requiring displacement to the field to collect information, unlike the NIRS approach. However, it is affected by the presence of clouds, which are very common in this region in the period of greatest vegetative development of the pasture (spring); an additional limitation is the inaccessibility under the trees, a characteristic element of this ecosystem that induces specific microclimates, influencing soil fertility and productivity, as well as the quality of pasture [39]. Thus, there seems to be a high potential for future research studies to evaluate the complementarity between NIRS approach (especially based on proximal and portable technology (PS)) and RS for monitoring and supporting pasture and grazing management in the Montado ecosystem.
## 4 Experimental Results
Since LSTM shows strength in simultaneously learning and predicting multiple modes, it is effective even in complex systems with multiple frequency modes. The mechanical resonance frequency estimation of a piezo element using the LSTM model can show superior performance in various aspects, such as complex nonlinear system processing, flexible adaptation to environmental changes, robustness to noise, and real-time processing capability, compared to the existing method. In addition, it can reduce hardware complexity and integrate the influence of various environmental variables to enable more accurate and stable frequency estimation. The LSTM model used in the experiment is the result of creating a model for six parameters (ultrasonic system resonance frequency (\(f_{system}\)), piezo element mechanical resonance frequency (\(f_{piezo}\)), piezo element parameter values \(L_{1}\) and \(C_{1}\), and \(L_{f}\) and \(C_{f}\) of the \(LC\) filter). In other words, it is a limited experimental result in estimating the mechanical resonance frequency of the piezo element. The piezo element has various variables, such as mechanical quality factor Qm, loss factor Q, and piezoelectric coefficient. Therefore, there are limitations in this study, and it is a future task to study the discussion on the prediction and experimental verification of the LSTM model using various parameters of the piezo element. These experimental results validate the effectiveness of using the LSTM model to estimate the mechanical resonant frequency of the piezo element and deliver maximum power to it.
# Effect of Cohesive Properties on Low-Velocity Impact Simulations of Woven Composite Shells
## 2 Finite Element Models
### Finite Element Mesh  
Eight-node continuum shell elements (SC8R) with a characteristic length of \(l_{e}=0.3\) mm, reduced integration, and stiffness hourglass formulation were used to mesh the semicylindrical composite shell layers [17]. The equations proposed by [PERSON] et al. [30] were used to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Symbol** & **Units** & **Value** \\ \hline Stiffness properties & \(k_{n}=k_{s}=k_{t}\) & N/mm\({}^{3}\) & \(10\), \(10^{3}\), \(10^{5}\), \(10^{6}\) \\ \hline Strength properties & \(\tau_{n}^{0}(\tau_{s}^{0}=\tau_{t}^{0})\) & MPa &
\begin{tabular}{c} 15 (30, 45, 60) \\ 30 (30, 60) \\ \end{tabular} \\ \hline Fracture toughness & \(G_{Ic}\) & N/mm & 0.3 \\ \(G_{IIc}=G_{IIIc}\) & N/mm & 0.6 \\  & \(\eta\) & - & 1.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Interlaminar properties defined for the composite shell interfaces.  
Figure 2: Bilinear cohesive model for mixed modes (normal and shear modes).  
calculate the element's characteristic length \(l_{e}\), and its effect on the numerical predictions was studied by the authors in [16]. The elements' coordinate system was set up considering the material directions in the different composite layers. All the nine layers were modelled with the same thickness \(t=0.178\) mm, resulting in a total thickness of the composite shell of 1.6 mm. As mentioned before, the cohesive behaviour between the adjacent layers of the laminate was modelled using cohesive surfaces, therefore no elements were required. On the other hand, the impactor was meshed with discrete rigid elements (R3D4) with a characteristic length of \(l_{e}=0.3\) mm. Finally, the supports were modelled as analytical rigid bodies, therefore no element or mesh definitions were necessary.  
During the simulation process in ABAQUS/Explicit [17], the analysis automatically halts if severe distortion is detected in deteriorated elements. It is a common practice to remove degraded elements from the FE mesh once a damage variable, often associated with the fibre, reaches a predefined threshold close to one. However, it is crucial to define these thresholds carefully as they can significantly impact the shape of the constitutive law. In the case of the low-velocity impact simulations in this study, no puncture was observed in the experimentally tested specimens [2]. Therefore, element deletion was not implemented, ensuring that the simulations captured the behaviour without compromising the structural integrity of the model.
## 2 Spatiotemporal Filtering Considering Formal Errors
(2014), Equation (6) is written as:  
\[\mathbf{a}^{\prime}_{k}(t_{i})=\sum_{j\in R_{i}}\mathbf{X}^{\prime}(t_{i},j)\mathbf{v}^{ \prime}_{k}(j)+\sum_{j\
otin R_{i}}\mathbf{X}^{\prime}(t_{i},j)\mathbf{v}^{\prime}_{k} (j)\qquad k=1,2,\cdots,n \tag{8}\]  
where \(R_{i}\) is the station set, which has observations at the \(i\)th epoch. For stations without observations at epoch \(t_{i}\), these observations can be expressed with PCs and eigenvectors as:  
\[\mathbf{X}^{\prime}_{ \begin{subarray}{c}i\\ j\
otin R_{i}\end{subarray} }(t_{i},j)=\sum_{k=1}^{n}\mathbf{a}^{\prime}_{k}(t_{i} )\mathbf{v}^{\prime}_{k}(j)\qquad j=1,2,\cdots,n \tag{9}\]  
Incorporating Equation (9) into Equation (8) yields a linear system of \(n\) equations for solving PCs. If missing data exist, the equation is rank deficient. Therefore, the minimum norm criterion is introduced for solving PCs and details of the solution process can be found in [PERSON] et al. (2014). With PCs calculated, we can reconstruct the CME with the common modes ([PERSON] et al., 2015). Here, the PCs with consistent spatial responses are regarded as the common modes. If the first \(q\) PCs are identified as the common mode, then CME is:  
\[\mathbf{S}(t_{i},j)=\sum_{k=1}^{q}\mathbf{a}^{\prime}_{k}(t_{i})\mathbf{v}^{\prime}_{k}(j )\qquad j=1,2,\cdots,n \tag{10}\]  
Thus, we can update \(\mathbf{e}\) by subtracting \(\mathbf{S}\) from Equation (1). Then, we update the generated time series in Equation (3) and iteratively extract the CME until the difference \(\Delta\mathbf{S}(t_{k},i)\) between the two adjacent iterations satisfies the following threshold:  
\[|\Delta\mathbf{S}(t_{k},i)|<10^{-3}\text{ mm} \tag{11}\]  
The new spatiotemporal filtering considering formal errors is named Improved Principal Component Analysis (IPCA). For homogeneous time series, it has the same formal error for all stations and all epochs, thereby the weight factor is equal to 1 and Equation (3) and reduces to Equation (1).