esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of CubeSat Missions and Applications
## 9 Review Conclusions and Discussion
### The Role of CubeSats in the Telecommunication and Asset-Monitoring Fields  
Despite the fact that CubeSats, due to their small size and subsequent low power generation and storage capability, are not able to directly compete with their larger counterparts for applications that require high bandwidth and data-rate capabilities, especially in the radio-spectrum telecommunication field, gradually, communication hardware miniaturization and developments made through technology demonstration missions have enabled CubeSats to find a role in certain telecommunication applications such as IoT, AIS, and ADS-B services that mainly require less bandwidth and data-rate hardware performance compared to other telecommunication applications. For such applications, CubeSats represent excellent choices as they enable the fast and affordable creation of large constellations that consist of dozens or even hundreds of CubeSats and are therefore capable of providing global connectivity coverage. With the inevitable further refinement of miniaturized communication technologies, the number of radio-based telecommunication CubeSats in orbit is most certainly expected to rise in the following years. However, the emergence of dedicated large telecommunication constellations that consist of larger and more capable satellite platforms that provide global coverage, such as SpaceX's Starlink and Amazon's Project Kuiper, should be expected to significantly hamper the widespread adoption of CubeSats as space-based telecommunication platforms.  
On the other hand, as demonstrated by the series of successful and sometimes even record-setting laser-optical communication demonstration missions that so have so far been completed, the emergence and proliferation of miniaturized laser-optical communication technologies which are capable of enabling very-high data transmission rates with regards to hardware size and power consumption when compared to radio wave telecommunication hardware has the potential to enable CubeSats in undertaking a much more significant role in future space-based telecommunication networks as low-cost distributed instruments of large telecommunication constellations comprising solely by CubeSats or by a mixture of CubeSats and large satellite platforms. Furthermore, should proposed CubeSat technology demonstration mission concepts around omnidirectional laser-optical communication technologies such as the Q4 materialize and prove successful, new horizons in the field of laser-optical communication will be opened, enabling CubeSats to form large laser-optical communication constellations capable of simultaneously performing and maintaining inter-satellite and space-to-ground laser-optical communication links.
## 3 Results and Discussion
### \(P_{\text{Cl}}\) Analysis of Impact Process  
Based on the assumption of the impact velocity and energy released by chemical reactions, the energy released by reactive fragments triggered by impact at different impact velocities could be obtained. By analyzing the simulation results, the rationality of the theoretical analysis of the shock wave pressure calculation was verified. The impedance matching method provided an important reference for predicting the impact pressure. The shock wave pressure and particle relationships were obtained at different impact velocities through the impedance matching method. If chemical reactions were added, their effects would be affected. The curve shifted upward, and the pressure generated by the chemical reaction occurred [47].  
Figure 12: Pressure and particle velocity curves.  
For the convenience of the numerical simulation calculation, the average value of \(P_{C\overline{\zeta}}\) was taken as 2.45 GPa, the average energy per unit volume was taken as 0.056 Terg/cm\({}^{3}\), and the detonation wave velocity was 0.123 cm/ us. Based on the above results, it was found that the reactive fragments induced chemical reactions under impact conditions, causing gas expansion that was similar to combustion to work externally, thereby releasing energy and generating pressure enhancement. This was consistent with the phenomenon observed in the impact experiment. According to Figure 13, the intersection of the P-up (red lines) and Rayleigh tangents (blue lines) was the bursting wave pressure. This process did not produce the bursting process but instead produced a slow gas expansion to release the chemical energy process, but the bursting wave theory and the existence of similarity to this process (that is, the enhancement effect on the impact process) would produce a certain pressure, so this paper adopted the bursting wave theory to describe the process, which has a certain degree of reasonableness with a certain value of reference.
## 2 Preliminaries and Problem Formulation
### Path Following Error Dynamics and Control Target  
To design the sideslip observer, the path following error dynamics are calculated. Since the desired path is a straight-line, the path tangential angle is zero. For a vessel located at \((x,\,y)\), the cross-track error is defined as  
\[y_{e}=y-y_{d}. \tag{14}\]  
Therefore, the path following goal turns into stabilizing \(|y_{e}|\) to zero.  
Taking the time derivative of \(y_{e}\), it follows from Equation (12):  
\[\dot{y}_{e}=u\sin\psi+u\cos\psi\tan\beta, \tag{15}\]  
where \(\beta=\text{atan}\big{(}\frac{v}{u}\big{)}\) is the sideslip angle (see Figure 1), denotes the angle between the vessel's velocity vector and the \(x_{b}\)-axis, the positive direction is defined according to Figure 1, which is unknown in practice. Without compensating the unknown sideslip angle for an underactuated vessel, it will cause a large tracking error. When a vessel sails along the bank in restricted waters, bank effect with varying water depths in shallow water results in time-varying sideslip angles. To accurately estimate the time-varying sideslip angles, a HGESO was designed.  
The control goal is to design a HGESO-ASMPFC scheme, such that the vessel with kinematics (12) and dynamics (13) converges to and precisely follows the desired straight-line path subjecting to vessel-bank interaction with varying water depths in shallow water, namely fulfills \(\underset{t\rightarrow\infty}{\lim}y_{e}=0\).
## 4 Prediction of Mechanical Properties of UHPC Based on AOA-SVR
### Comparative Analysis of Prediction Results
#### 4.4.2 Model Explanation  
In this study, the SHapley Additive exPlanations (SHAP) method is utilized to explain the model prediction results. The contribution analysis of the prediction results based on SHAP values can be divided into two levels according to the established model. Figures 7 and 8 provide a global interpretation of the AOA-SVR model. The results indicate that cement content has the most significant impact on the mechanical properties of UHPC, while slag powder has the least effect.  
Figure 7 illustrates the distribution of SHAP values for each characteristic parameter and indicates their corresponding influence trends. The analysis reveals that higher values of steel fiber content, cement content, fly ash content, and silica fume content are associated with larger SHAP values. Conversely, higher values of water content, superplasticizers content, and slag powder content correspond to smaller SHAP values. This indicates that steel fiber content, cement content, fly ash content, and silica fume content are positively  
Figure 8: Feature importance analysis.  
Figure 7: Evaluation summary chart.  
correlated with compressive strength, whereas water content, superplasticizers content, and slag powder content are negatively correlated. This detailed SHAP analysis further demonstrates the reliability and robustness of machine learning methods for predicting the compressive strength of UHPC.  
The AOA-SVR model can be applied to the performance prediction of other concrete materials through the steps of data collection, feature selection, model training, and validation; the prediction accuracy can be further improved, and the material ratio can be optimized by comparing with the performance of other models.
###### Abstract  
A method for separating multisource partial discharges (PDs) in a substation is proposed based on selected bispectra of ultrahigh frequency (UHF) electromagnetic waves. Bispectra are sensitive to Gaussian noises and processes of symmetrical distribution. The phase information contained in bispectra can be useful and important for further signal processing. Bifrequencies where Fisher-like class separability measures between signals' bispectra achieve their maximums are selected as characteristic parameters of the signals. Then, the selected bispectra are utilized for training the radial basis neural network to separate PD UHF signals in a substation. The method is used to analyze simulated UHF signals mixed with Gaussian white noise and frequency-fixed interference, and to separate PD UHF signals that are collected in a 500 kV substation. In order to prove the validity of the proposed separation method, the localization results are compared with the results calculated by time delay sequence, and the proposed separating algorithm is verified in the interference circumstances of a substation. However, the exact location of PD sources cannot be calculated according to the time delay sequence when the PD sources in a substation are close to each other or there are fewer than four antennas for receiving signals.  
partial discharge; ultrahigh frequency (UHF); electromagnetic wave; selected bispectra; substation +
Footnote †: journal: Applied Physics Letters
## 4 Discussion
### Fatty Acids Distribution in Triacylglycerglycerol Structure  
Triglycerides (TGs) are the main constituents of oils and can contain various fatty acids esterified with glycerol leading to several positional isomers. Triglyceride biochemistry and physical properties are determined by the stereospecific structure of TG and, thus, have an influence on lipid metabolism. Acidic radicals can occupy specific positions in the triacylglycerglycerol molecule. In order to correctly determine the structure of triglycerides, methods with enzymes from the lipase group are used. These enzymes are characterized by high regio- and stereospecificity. Because of that, these substances can be successfully used to selectively cleave acidic radicals from triglyceride molecules [39].  
TGs can also be considered the characteristic indicators of oil quality control [40].  
In each of the mixture samples, palmitic acid (C16:0) was present only in the external position. [PERSON] et al. [41], in their work on the regiospecificity of triglycerides in human milk, found that in the case of vegetable oils, palmitic acid is usually in the sn-1,3 position, while unsaturated fatty acids such as oleic and linoleic acids are located at the sn-2 position. The results obtained in this study are comparable with the literature. Oleic and lauric acids were always present in the internal positions of TGs, whereas palmitic acid was present in the external positions of TGs.  
The composition of fatty acids in the internal position of TG does not change during interesterification in the presence of regiospecific lipases because the process undergoes in the external positions. Therefore, when this type of modification is carried out, there are mainly unsaturated fatty acids in the internal position of triacylglycerols in the mixtures, as in the initial raw oils. Minor changes in TG distribution may be associated with acyl migration during interesterification, which leads to the transfer of unsaturated fatty acids from the sn-1 and 3 positions to the sn-2 position [42].
## 5 Construction of Secure and Efficient Private Image Matching (SEPIM)
### Index Tree Building  
To speed up the image search process, the data owner _[PERSON]_ needs to construct an index tree _Treelndex_ for large-scale images dataset as an example shown in Figure 2. We follow the method similar to the TreeBASIS descriptor in the context of the search process using \(k\)-means clustering [35]. Particularly, we implement the \(k\)-means clustering algorithm in a process of classifying image dataset into various clusters. Specifically, the data owner runs Algorithm 2 recursively separating the images dataset into \(R\) clusters, stopping when no cluster has greater than \(R\) images. The feedback from Algorithm 2 provides the data owner with a base from which to connect each image's descriptor vector \(\overrightarrow{V_{i}}\) to a leaf node of _Treelndex_, as well as allowing those nodes within a same cluster to be able to connect to the identical non-leaf node as their father. A \(k\)-dimensional mean descriptor vector \(VF_{hi}\) can also be assigned to identical non-leaf father nodes by the data owner, with \(h\) representing the height of the father node within _Treelndex_, and \(I\) representing the index at height-\(h\).  
An element within \(VF_{hi}\) has its measure quantified as the mean value for the elements of its connected children. Such mean values will be employed during the search process of the tree up to leaf level, since the formula presented relies on the relevance of pair of images can be measured by the cosine similarity of their descriptor vectors. A formula representing \(VF_{hi}\) is:  
\[\overrightarrow{VF_{hi}}=\frac{\sum_{\overrightarrow{v_{i}}\in\mathit{Children }}\overrightarrow{v_{ij}}}{|\mathit{Child}{ren}|}. \tag{6}\]
# Traceability of Surgical Instruments: A Systematic Review
## 4 Results
### Medium-to-Long-Term Benefits: Economy and Environment
#### 4.2.2 Environmental Benefits  
Traceability systems promote environmental sustainability by reducing waste and energy consumption.
* Rationalizing surgical tray contents ensures that only used instruments are sterilized, reducing unnecessary sterilization processes. For instance, the Davis Ambulatory Surgical Center (2021) [31] achieved a 40.3% reduction in tray size, leading to decreased sterilization needs and associated energy consumption.
* Similarly, the Japanese Red Cross Wakayama Medical Center (2019) [22] reported sustainable waste management practices, where only necessary instruments were cleaned and reused, reducing overall waste.
## 3 Materials and Methods
### Step 3: Dynamic Life Cycle Assessment (LCA) Inventory  
The framework performs a dynamic LCA inventory analysis using real-time data to quantify environmental burdens at the process-instance level (S6, S7). This step moves beyond traditional static LCA by linking live data to environmental impacts, forming a basis for subsequent optimization. The analysis, which informs visualizations like the one presented in Figure 5 in the Section 4, includes the following:  
* Energy Consumption (EC): Calculated by integrating power readings \(P_{i}(t)\) over operational time \(t_{i}\): \[EC_{\text{instance/period}}=\sum_{i}\int_{t_{\text{start},i}}^{t_{\text{end},i}} P_{i}(t)\,dt\] (4)
* Carbon Emissions (CE): Estimated using activity levels (e.g., \(EC\)) and emission factors \(EF_{i}\): [2; 14; 29] \[CE_{\text{instance/period}}=\sum_{i}(\text{ActivityLevel}_{i}\cdot EF_{i})\] (5)
* Waste Generation (WG): Quantified from detected defects \(Q_{i}\) and conversion factors \(\delta_{i}\): \[WG_{\text{instance/period}}=\sum_{i}(Q_{i}\cdot\delta_{i})\] (6)
## 3 Mathematical Formulation
### Equations of Motion  
The well-known [PERSON]'s principle is used to derive the governing differential equation for the natural vibration of the orthotropic plate based upon CPT as given by Equation (3).  
\[D_{x}\frac{\partial^{4}w^{0}}{\partial x^{4}}+D_{xy}\frac{\partial^{4}w^{0}}{ \partial x^{2}\partial y^{2}}+D_{y}\frac{\partial^{4}w^{0}}{\partial y^{4}}= \rho h\frac{\partial^{2}w^{0}}{\partial t^{2}} \tag{3}\]  
where the mass density and thickness of the orthotropic plate are represented by \(\rho\) and \(h\), respectively.  
\(D_{x}\), \(D_{y}\), and \(D_{xy}\) are flexural rigidities, which are given by Equation (4).  
\[\begin{array}{l}D_{x}=-\frac{h^{3}}{12}\mathcal{Q}_{11}\\ D_{xy}=-\frac{h^{3}}{6}\mathcal{Q}_{12}-4\frac{h^{3}}{12}\mathcal{Q}_{66}\\ D_{y}=-\frac{h^{3}}{12}\mathcal{Q}_{22}\end{array} \tag{4}\]
# Novel Design of Expandable Spinal Cage for Efficient Lumbar Spine Fusion Operation
## 5 Results and Discussions
Additionally, in the actual compression test, Load drop was observed due to material fracture, which is a typical failure behavior of metallic components under high stress. However, this sudden load drop is not captured in the FEM analysis, as the simulation does not account for load drop due to fracture. As a result, the FEM results show a continued increase in load with compression, rather than a decline, leading to a divergence from experimental curve in region just before load drop. Through FEM analysis, structural stress distributions and failure modes were predicted, demonstrating a high correlation with the failure locations and patterns observed experimentally. Under compression loading, FEM analysis predicted stress concentration and potential failure in the anterior linkage region. Correspondingly, experimental tests confirmed failure at the anterior sliding block shaft and its corresponding shaft in the upper plate, aligning with the FEM predictions. For compression-shear loading conditions, FEM analysis predicted failure at the lower plate shaft, closely matching observed experimental failures. These findings underscore the FEM model's capability to accurately predict structural stress distribution and failure modes, highlighting its potential for reliable performance forecasting and optimization in early design stages without relying on physical prototypes. Mechanical performance of the expandable cage was evaluated through actual mechanical tests. Due to the inability to determine yield loads experimentally, ultimate loads were used as a criterion to compare performance with existing cages. The cage demonstrated excellent load-bearing capacity under compression, surpassing the ultimate loads of the lowest 5% of currently FDA-approved spinal cages. Additionally, under compression-shear loading, the cage similarly exceeded the lowest 5% ultimate load threshold, confirming its structural stability under various loading conditions. These results suggest that the designed cage meets the clinical load requirements effectively. Despite these promising results, this study carries limitations. It was restricted to static load testing without performing dynamic fatigue testing. Considering that spinal cages in clinical settings endure millions of cyclic loading events, fatigue testing is essential to assess long-term durability. Future studies should perform fatigue testing following standards such as ASTM F2077. Moreover, the current study did not evaluate in vivo applicability through clinical or animal trials. Given the necessity of verifying mechanical stability in actual physiological  
Figure 19: Shifted compression–shear analysis results to compare trends in analysis and experimental results. conditions, conducting clinical or animal experiments is critical. Hence, future research should include these tests to confirm the cage's performance in real-world settings.
# Polarization Radiometric Calibration in Laboratory for a Channeled Spectropolarimeter
## 1 Introduction
### Establishment of Polarization Radiometric Calibration Model
Based on Equation (8), the nine phase terms represent nine distinct channels in Fourier domain which contain the data related to the input Stokes parameters \(S_{0}(\sigma)\), \(S_{1}(\sigma)\), and \(S_{3}(\sigma)\). Calculating the autocorrelation of \(B(\sigma,\theta)\) using the Fourier transformation, we can distribute the nine channels with nine phase terms  
\[ \begin{array}{ll}C^{\theta}(L)&=C^{\theta}_{0}(L)+C^{\theta}_{1}(L-L_{2})+C^{ \theta}_{-1}(-L-L_{2})\\ &+C^{\theta}_{2}[L-(L_{1}-L_{2})]+C^{\theta}_{-2}[-L-(L_{1}-L_{2})]\\ &+C^{\theta}_{3}(L-L_{1})+C^{\theta}_{-3}(-L-L_{1})\\ &+C^{\theta}_{4}[L-(L_{1}+L_{2})]+C^{\theta}_{-4}[-L-(L_{1}+L_{2})],\end{array}  \tag{11}\]  
where \(L\) denotes the variable conjugate to wavenumber \(\sigma\) under the Fourier transform, and the superscript \(\theta\) is the field angle. \(L_{j}=\Delta n(\sigma)d_{j}\), is the optical path difference (OPD) introduced by Rj, \(\Delta n(\sigma)\) is the birefringence of the birefringent crystal. By filtering out nine channels and performinginverse Fourier transformations independently, the whole Stokes parameters of the target light can be described as  
\[S_{0}(\sigma)=\frac{X_{0}(\sigma,\theta)-D_{fore}(\sigma,\theta)X_{1}(\sigma, \theta)}{1-D_{fore}^{2}(\sigma,\theta)}, \tag{12}\]  
\[S_{1}(\sigma)=\frac{X_{1}(\sigma,\theta)-D_{fore}(\sigma,\theta)X_{0}(\sigma, \theta)}{1-D_{fore}^{2}(\sigma,\theta)}, \tag{13}\]  
\[S_{2}(\sigma)=\frac{X_{2}(\sigma,\theta)-\delta_{fore}(\sigma,\theta)X_{3}( \sigma,\theta)}{1+\delta_{fore}^{2}(\sigma,\theta)}, \tag{14}\]  
\[S_{3}(\sigma)=\frac{X_{3}(\sigma,\theta)+\delta_{fore}(\sigma,\theta)X_{2}( \sigma,\theta)}{1+\delta_{fore}^{2}(\sigma,\theta)}, \tag{15}\]  
where \(X_{i}(\sigma,\theta)\) can be obtained by reference to the self-correction method. The interested reader can find more information in [17]. From Equation (6), we can find that the polarization properties of imaging optics and dispersive imaging spectrometer will not influence the measurement of the CDISP. The process of measuring polarization information is complicated due to diattenuation and retardance of the fore-optics, \(D_{fore}(\sigma,\theta)\) and \(\delta_{fore}(\sigma,\theta)\). In addition, this effect will be more serious with the increase of field angle. However, the fore-optics is indispensable because of its important role in the fieldable CDISP [16], so we need to calibrate the polarization properties of fore-optics accurately. Based on the derived theoretical model, the parameters, \(A^{k}(\sigma,\theta)\), \(C(\sigma,\theta)\), \(\varepsilon_{1}\), \(\varepsilon_{2}\), \(\exp[i\varphi_{2}(\sigma)]\), \(\exp[-i[\varphi_{1}(\sigma,\theta)+\varphi_{2}(\sigma)]]\), \(D_{fore}(\sigma,\theta)\), and \(\delta_{fore}(\sigma,\theta)\), are needed to be calibrated accurately to reconstruct the Stokes parameters accurately.
# Modeling a Pre-Touch Reaction Distance around Socially Touchable Upper Body Parts of a Robot
## 5 Discussion  
### Implications  
This study investigated and modeled the pre-touch reaction distance around socially touchable upper body parts, based on human-human pre-touch interaction observations. Even if we conducted our research with limited participants (e.g., number, age, and culture), our analyzed data provide useful knowledge for researchers interested in (pre-) touch interaction in both human-human and human-robot interactions.  
In human science literature, females are more receptive to being touched or to engage in close-distance interaction than males. They also are more willing to accept a same-gender touch than an opposite-gender touch [50; 51; 52]. Even though the data collection analysis did not show significant differences, the gathered data may be useful to study gender effects of pre-touch distance around body parts.  
Cultural differences are another possible factor that may have influenced the data collection results for pre-touch reaction distance. In fact, touch interaction is quite different due to cultural effects [53; 54] and situations [55; 56]. In this study, only Japanese participants joined the data collection in a laboratory experiment setting. However, our findings might encourage comparisons of cultural differences in the context of pre-touch interactions.  
In summary, our studies still identified a more complex phenomenon than past studies. These results suggest that robots might require a different strategy in (pre-) touch interaction situations due to the appearances and the genders of the interacting people.