esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Validation of a Gyroscope Wave Energy Converter for Autonomous Underwater Vehicles
## 5 Parametric Analysis
### Stiffness Coefficient  
Figure 7 shows the effect of stiffness on the output power for different damping coefficients. The stiffness coefficient behaves the same for the different damping coefficients chosen, and the output power peaks at \(k_{y}=0.17\) for all of them. However, the further the stiffness is away from the optimum, the lower the output of the system is. Additionally, when the damping coefficient is near the optimal value, \(k_{y}\) has a greater effect on the output  
Figure 6: Performance of main parameters under rated conditions: (**a**) pitch amplitude; (**b**) flywheel precession angular displacement; (**c**) flywheel precession angular velocity; (**d**) torque in \(x\) direction; (**e**) torque in \(y\) direction; (**f**) torque in \(z\) direction; (**g**) output power (blue line is the average power, black line is the instantaneous power).  
power, while when \(c_{y}\) is far from the optimum, \(k_{y}\) has an insignificant effect on the output. It is also worth noting that if the PTO had no stiffness, the system would stabilize at a 90-degree angle of precession, and the gyroscopic effect would disappear and no precession torque or power would be generated.  
Figure 8 shows the effect of the stiffness coefficient on the main parameters of the system at rated condition. The precession angular displacement decreases as the stiffness coefficient increases, but the output power still reaches its maximum at \(k_{y}=0.17\). The peak voltage fluctuates with a sinusoidal cycle at the beginning of the simulation and stabilizes gradually with time. The torque in the \(x\) direction is completely independent of \(k_{y}\).
# Using Ground-Based Passive Reflectors for Improving UAV Landing
## 2 Navigation Systems for UAV Landing
### Satellite Landing System (SLS)  
Approaches relying on satellite navigation are a type of area navigation, meaning navigation that allows an aircraft to choose any course within a network of navigation beacons, within the range of onboard equipment or within a combination of the two [47].  
The equipment used in satellite landing systems can be divided into three categories (see Figure 3):  
1. Space equipment, that consists of the GPS and GLONASS satellite networks.
2. Ground equipment, a supplementary Ground-Based Augmentation System (GBAS), which enables the differential mode.
3. Airborne equipment, which includes the GNSS (Global Navigation Satellite System) receiver that picks up information from the satellites and local augmentation stations.  
Figure 2: The contents and the disposition of the elements in MLS with directional beacon (DB), azimuthal station transmitter (ATx), and elevation station transmitter (ETx).  
It is important to notice that Category 3 is mandatory, while Category 2 is used for improving the accuracy of the system. However, this equipment may be absent if its deployment does not happen to be possible due to any reasons, either technical or funding. The GBAS add-on includes a local augmentation station and a receiver for GPS and GLONASS signals that is placed at the precisely measured (centimeter accuracy) coordinates. Navigation signals emitted by the GNSS are received and processed by the local augmentation stations. After that, the differential corrections, system integrity data, and other service messages are transmitted via a VHF band into the onboard GNSS. With that stated, SLSs do have some serious disadvantages:  
* They are sensitive to weather interference.
* The antennae might become shadowed by the aircraft structures during maneuvers.
* The SLS is sensitive to jamming that could limit its effectiveness.
* The accuracy the SLS provides is insufficient for precision landings.
* The SLS is incapable of providing accurate measurements of the aircraft altitude.  
The precision of the GNSS space equipment fluctuates over time, and the system is prone to occasional lapses in monitoring, that might occur, for instance, when the onboard receivers are switching to different navigation stations. The fact is that the satellites orbiting the Earth and the occasional GNSS errors lead to errors in target location, whose values might change every several hours. Additionally, the precision level of the SLS (95%) fluctuates depending on the constellation geometry. The augmentation stations help rectify most of these errors, but the augmentation stations of today do not meet the criteria for the first ICAO category in accuracy and, more importantly, integrity. Namely, the SLS-x00 augmentation stations by Honeywell meet the requirements for Special Category 1, which has relaxed access to the vertical channel compared to the ICAO Category 1.
## Materials and Methods
### Surface Quality Measurements  
Surface quality measurements were performed using a MarSurf XT20 instrument manufactured by MAHR Gottingen GMBH (Gottingen, Germany), fitted with a MFW 250 scanning head with a tracing arm in the range of \(\pm\)750 \(\upmu\)m and a stylus with a 2-\(\upmu\)m tip radius and 90\({}^{\circ}\) tip angle, which measured the specimens along the processing direction, at a speed of 0.5 mm/s, a low scanning force of 0.7 mN, and a lateral resolution of 5 \(\upmu\)m. Two processed edges per category provided 3 profile measurements each, with a length of 50 mm, so that a total of six profiles were analyzed for each processing combination. This meant a total of 252 measured profiles for both processing methods (210 for CNC router and 42 for laser).  
The scanned profiles were processed with the MARWIN XR20 software provided by the instrument supplier (Gottingen, Germany). After removing the form error, the primary profiles were obtained, out of which, P parameters were calculated. From these, other two types of profiles were generated by separating the irregularities in two types of wavelengths: waviness profiles and roughness profiles, which issued the W parameters and R parameters. The longest wavelengths correspond to waviness and the shortest to roughness. The separation was made with a robust Gaussian regression filter [25], with a cut-off length of 2.5 mm recommended for wood [26; 27]. Mean values parameters were calculated, such as: Ra (arithmetic mean deviation of the roughness profile), Rp (the maximum profile peak height), Rv (the largest absolute profile valley depth), Rsk (skewness of the profile), Wa (arithmetic mean deviation of the waviness profile), Wt (total height of the waviness profile), Pa (arithmetic mean deviation of the primary profile), Pt (total height of the primary profile) from [23]. Other parameters were the Abbot-curve parameters: Rk (the core roughness depth), Rpk (the reduced peak height), and Rvk (the reduced valley depth) from [24]. For clarity, the calculation of Abbot-curve parameters is illustrated in Figure 3.  
Rk is the parameter that best characterizes the processing roughness and is the least influenced by wood anatomical cavities [19]. It is calculated from the Abbot-curve and measures the height of the core roughness, region defined by the highest concentration of data points in a measured profile, which excludes isolated peaks and isolated valleys. The calculation of Rk is represented in Figure 4[19]. Rpk in Figure 4 is a measure of the isolated peaks, which for a processed wood surface could be raised grain and fuzziness. Rvk is a measure of isolated surface gaps caused by material being pulled-out during processing and/or anatomical cell lumens that exceed the depth of the processing marks.  
Figure 4: The calculation of the Abbot-curve parameters: Rk, Rpk and Rvk [19].
## 6 Conclusions  
Facing the current situation of large-scale renewable energy integration, this paper considers the uncertainty of wind power, photovoltaic, load, and demand response virtual units in different time scales, and the depth peak regulation characteristics of the thermal power unit. In addition, then a low-carbon economic scheduling model with day-ahead and intraday stage is built for wind, solar, and thermal storage power systems. The validity of this model is verified by examples. The conclusions are as follows:  
(1) For wind, solar, thermal power, and storage systems, the two-stage coordination scheduling model including day-ahead and intraday is proposed in this paper, which is less affected by the uncertainty of wind, solar, load, and demand response than the day-ahead scheduling model. In the simulation results, the total system cost is reduced by 16.2% compared with the day-ahead scheduling method. This scheduling model has a significant advantage in reducing the total system cost.  
(2) Based on the response of PDR and the IDR virtual unit in the day-ahead and intraday stage, the carbon processing cost is introduced into the economic dispatch model, which is effective to achieve low-carbon economic dispatch of the power system. The introduction of demand response can adjust the power consumption time of users to some extent, and the source-load interaction can greatly reduce the limited power generation cost of renewable energy.  
(3) Considering the normal operation state and deep peak regulation state of thermal power units, the generation cost of thermal power units is quantified when large-scale wind power and PV power is incorporated into the grid, so that the model can reflect the actual working conditions more authentically. With the increase of peak regulation depth, the start-stop frequency of thermal power units decreases, and it is conducive to absorbing the intermittency and volatility of renewable energy power generation.  
With the change of electricity price mechanism and market mechanism, the power system dispatching method proposed in this paper will have a great application prospect in absorbing large-scale renewable energy generation. The next step is to study the influence of the correlation  
Figure 9: The change curve of integrated scene system operation cost under different load prediction errors.  
between wind power and PV power generation on day-ahead and intraday scheduling of wind, solar, thermal power, and storage systems with demand response.  
[PERSON] and [PERSON] conceived the idea of the study and conducted the research. [PERSON], [PERSON] and [PERSON] analyzed most of the data and wrote the initial draft of the paper. [PERSON], [PERSON], and [PERSON] provided data for the study and contributed to finalizing this paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Fund of State Key Laboratory of Power Grid Safety and Energy Conservation (China Electric Power Research Institute) (No. JBB51201801311).  
The authors declare no conflicts of interest.
# Ionic Liquids Hybridization for Carbon Dioxide Capture: A Review
###### Abstract
Since then, ILs have been applied in various applications including, but not limited to, wastewater treatment, drug design, biodiesel production, biomass pretreatment and as additives in lubricants [10; 11; 12; 13; 14]. In the meantime, ILs have emerged as a promising alternative sorbent for CO\({}_{2}\) capture in the effort to find innovative solutions that are able to address the challenges associated with traditional amine-based capture methods. The development of ILs as sorbents in the field of CO\({}_{2}\) capture is mainly attributed to their low vapor pressure values [15]. The utilization of non-volatile material such as ILs has garnered significant attention in addressing the solvent loss issue during absorption and regeneration process. Moreover, ILs are also known as designated solvents due to the wide range of possible combinations of anions and cations as depicted in Figure 1. This allows the properties of ILs to be tailored, thereby providing an opportunity to enhance the efficiency and sustainability of CO\({}_{2}\) capture. Apart from that, ILs also possess several other unique properties including good thermal and chemical stability, broader liquidous range and wide electrochemical window [16; 17]. To date, a wide range of ILs from different classes, such as Room-temperature ILs (RTILs), Task-specific ILs (TSILs), Dual-functionalized ILs (DFILs) and amino-acid ILs (AAILs), have been synthesized and investigated for CO\({}_{2}\) capture. These ILs encompass various cations, including imidazolium, pyridinium, phosphonium, cholinium and ammonium [18; 19; 20; 21]. Figure 1: Examples of IL cation and anion.
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 3 Experiments and Discussions
### 3D Displacement Measurement of Log Specimen
#### 3.3.3 3D Displacement Error  
The linear translation stage was used to translate the log specimen with the natural texture in five-step increments of 2 mm, and the displacement measured by the panoramic DIC method was compared with the displacement applied by the translation stage to calculate the displacement error. To verify the accuracy of the displacement measurement by the panoramic DIC method, based on the aforementioned verification, the displacement field of the translation experiment was retrieved and analyzed, as shown in Figure 7. In Figure 7a, The horizontal coordinates are the applied displacements and the vertical coordinates are the measured displacements by the panoramic DIC method, the measured displacements are averaged and plotted against the applied displacements, which, at first glance, almost coincide with the applied displacements. Subsequently, the errors of the displacement, including the mean error and standard deviation, are obtained, as shown in Figure 7b. In Figure 7b, the horizontal coordinates are the same as in Figure 7a. Quantitatively, for the translation experiment, the maximum absolute mean bias and maximum standard deviation were 0.0716 mm and 0.1066 mm, respectively. Both the mean bias and standard deviation in the translation experiment were small, as with the regular Stereo-DIC error, which provide a good validation of the displacement measurement capability of the panoramic DIC method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Camera Pair**} \\ \cline{2-4}  & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) \\ \hline average error & 1.47 mm & 1.61 mm & 1.72 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: The average error for each single surface.  
Figure 6: 3D reconstruction result and error. (**a**) reconstruction point cloud matched with the laser point cloud. (**b**) the corresponding point distance error maps, the units in the diagram are in mm.
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 2 Creation of Kinematic Pair Clearance Model
### Creation of Kinematic Model for Revolute Joint Clearance
#### 2.2.2 Constructing a Model for Tangential Friction  
The Coulomb model is a commonly selected force model for considering friction in revolute joints. Friction in this model is proportional to the applied pressure, and its opposite of the direction of the sliding speed relative to it. This study adopts the modified Coulomb friction model proposed by [PERSON], which introduces a dynamic correction factor to solve the problem of numerical integration instability caused by friction directions close to zero velocity [44]. This correction method can effectively smooth the transition process of friction force and avoid the numerical divergence phenomenon when the velocity crosses zero. This law has been used in many studies [45; 46; 47].  
The corrected model of Coulomb friction is denoted as [44]:  
\[F_{t}=-\mu c_{d}F_{n}\frac{v_{t}}{|v_{t}|} \tag{15}\]where \(F_{n}\) represents the normal impact force, \(\mu\) denotes the friction coefficient, and \(c_{d}\) is the correction factor for dynamics, whose expression is given by:  
\[c_{d}=\begin{cases}0\,,\,|v_{1}|<v_{0}\\ \frac{|v_{1}|-v_{0}}{v_{T}-v_{0}},v_{0}<|v_{t}|<v_{1}\\ 1\,,\,|v_{t}|>v_{1}\end{cases} \tag{16}\]  
where \(v_{t}\) represents relatively sliding speed and \(v_{0}\) and \(v_{1}\) denote the speed thresholds for static friction and kinetic friction, respectively.  
Force and moment which is operating on center point of bearing can be expressed as:  
\[F_{12}=F_{n}\mathbf{n}_{R}+F_{t}\mathbf{t}_{R}=\left[F_{12}^{x}F_{12}^{y}\right]^{ \mathsf{T}} \tag{17}\]  
\[M_{12}=-\left(y_{1}^{Q}-y_{1}\right)F_{12}^{x}+\left(x_{1}^{Q}-x_{1}\right)F _{12}^{y} \tag{18}\]  
where \(F_{12}^{x}\) and \(F_{12}^{y}\) represent the contact components acting in \(X\) and \(Y\) bearing directions, respectively, and \(M_{12}\) denotes moment acting on centroid of the bearing.  
Force and moment acted on the center point of shaft can be shown as:  
\[F_{21}=-F_{12} \tag{19}\]  
\[M_{21}=-\left(y_{2}^{Q}-y_{2}\right)F_{21}^{x}+\left(x_{2}^{Q}-x_{2}\right)F_ {21}^{y} \tag{20}\]  
where \(F_{21}^{x}\) and \(F_{21}^{y}\) represent the contact components acting in \(X\) and \(Y\) axial directions, respectively, and \(M_{21}\) denotes torque acting on the centroid of the axis.
## 3 Data and Methods
### Methodology
One of the primary advantages of GLM is its ability to handle nonnormal data distributions. In this study, greenhouse gas (GHG) emissions and renewable energy adoption, the key environmental performance indicators, exhibit skewed distributions and varying dispersion levels across observations. GLM allows for different error structures and link functions, ensuring that the relationships between variables are appropriately captured without imposing restrictive assumptions about normality. This flexibility enhances the robustness of the estimated coefficients and improves the interpretability of the results. Another critical factor in choosing GLM is its resilience to nonstationary data. As demonstrated by the stationarity tests conducted in this study, some of the independent variables exhibit unit roots, indicating potential nonstationary behavior. In contrast to conventional time-series models, which require strict stationarity conditions to avoid spurious regression results, GLM remains robust in the presence of nonstationary panel data. By using appropriate link functions and variance structures, GLM mitigates biases associated with time-dependent trends, allowing for more reliable inference. Moreover, the flexibility of GLM in modeling panel data structures makes it particularly relevant for this research. The dataset encompasses multiple countries over an extended period, requiring an analytical framework capable of capturing both cross-sectional and temporal variations. GLM accommodates varying data structures while maintaining statistical efficiency, making it an effective tool for evaluating the role of digital transformation in shaping environmental performance outcomes. Additionally, the precedent set by previous empirical research further supports the application of GLM in this context. Several studies examining the relationship between technological advancements and environmental sustainability have utilized GLM due to its ability to model complex interactions and account for nonlinearity in economic and environmental phenomena. The methodological rigor and empirical validity of GLM in similar domains reinforce its appropriateness for the current study. In alignment with the overarching objective of this research, we have developed two distinct econometric models, each specifically designed to address the key research questions identified in the earlier stages of our study:  
\[\text{M1}:\ lnGHG_{i}=\exp(\beta_{1}+\beta_{2}AFFORD_{i}+\beta_{3} INFR_{i}+\beta_{4}USAD_{i})+\epsilon_{i} \tag{3}\]  
\[\text{M2}:\ REN_{i}=\exp(\beta_{1}+\beta_{2}AFFORD_{i}+\beta_{3} INFR_{i}+\beta_{4}USAD_{i})+\epsilon_{i} \tag{4}\]
# Dynamic Relevance-Weighting-Based Width-Adaptive Auto-Encoder
###### Abstract  
This paper proposes a novel adaptive autoencoder model that autonomously determines the optimal latent width during training. Unlike traditional autoencoders with fixed architectures, the proposed method introduces a dynamic relevance weighting mechanism that assigns adaptive importance to each node in the hidden layer. This distinctive feature enables the simultaneous learning of both the model parameters and its structure. A newly formulated cost function governs this dual optimization, allowing the hidden layer to expand or contract based on the complexity of the input data. This adaptability results in a more compact and expressive latent representation, making the model particularly effective in handling diverse and complex recognition tasks. The originality of this work lies in its unsupervised, self-adjusting architecture that eliminates the need for manual design or pruning heuristics. The approach was rigorously evaluated on benchmark datasets (MNIST, CIFAR-10) and real-world datasets (Parkinson, Epilepsy), using classification accuracy and computational cost as key performance metrics. It demonstrates superior performance compared to state-of-the-art models in terms of accuracy and representational efficiency.  
deep learning; unsupervised learning; autoencoder; dynamic model; width learning Article
# A Study on the Electromagnetic-Temperature Coupled Analysis Method for In-Wheel Motors
## 1 Introduction
Based on establishing the finite element model of the IWM, the coupling factors between the electromagnetic and temperature fields, and the influence trend of the two fields are analyzed. According to the analysis, ignoring the influence factors of weak coupling, the coupling calculation of the electromagnetic field and temperature field of an IWM is carried out by using the unidirectional coupling method and bidirectional coupling method, respectively, and the calculation errors of different coupling analysis methods are predicted by comparing and analyzing the results.
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Machine Vision Methods
#### 2.4.2 Hardware and Imaging Platform
UAVs can also be flown between rows to achieve side views of tree canopies, as demonstrated in the detection of apple and citrus fruits [47]. However, further publications from the same group have utilized ground vehicles for image collection [48; 49]. One issue in the use of UAVs between rows is the unreliability of the GNSS signal, thus requiring the use of multiple cameras for navigation by vision.