esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Multivariate Data Analysis of Allium Species Analysed via SPME-GC/MS
The OPLS model was evaluated by the two parameters, Q\({}^{2}\)Y and R\({}^{2}\)X, where R\({}^{2}\)X is used to quantify the goodness-of-fit, whereas Q\({}^{2}\)Y is employed to assess the predictability of the model [8]. An OPLS-DA model was constructed by modelling freeze dried garlic against air-dried and microwave-dried samples grouped together in another class group with clear separation between freeze dried and other drying methods (Supplement Figure S2A). The S-loading plot of the OPLS-DA model revealed for the freeze-dried garlic samples enrichment in diallyl disulphide, diallyl trisulphide, and 3-vinyl-1,2-dithiacyclohex-5-ene (Figure S2B) and concurring the PCA results (Figure 2B). Likewise, a PCA model was constructed for freeze-dried, shade air-dried, and microwave-dried onion samples with variance explained by PC1 = 78% and PC2 = 13.5%. Microwave-dried onion samples were clustered closely with freeze-dried samples (negative score values), whereas air-dried samples were positioned on the other side with positive score values of PC1 (Figure 2C). The PCA loading plot revealed that 2-acetylpyrrole, methyl pentyl disulphide and allyl methyl trisulphide were the most representative metabolites detected in the air-dried samples (Figure 2D). Figure 1: Solid-phase microextraction coupled to gas chromatography mass spectrometry (SPME-GC/MS) based principal component analysis (PCA) of fresh and dried _A. sativum_ and _A. cepa_ red cv. specimens. **(A)** Score plot of PC1 and PC2 scores; **(B)** Loading plot for PC1 components contributing peaks and their assignments, with each metabolite denoted by its mass/r.t. (min) value: M6; diallyl disulphide, M21; 3-Vinyl-1,2-dithiacyclohex-5-ene, M28; diallyl trisulphide and M40; diethyl phthalate. Peak numbering follows that listed in (Table 1) for volatiles identification using SPME-GC/MS. Discrepancy between the _Allium_ species drying model results suggest that drying affects _Allium_ sp. in different ways as monitored via GC/MS. To confirm such a hypothesis, a different analytical platform was adopted for _Allium_ metabolites profiling. Heat applied during evaporation of _Allium_ sulphur compounds in GC/MS could have led to degradation of its native thiosulphinates [9]. Indeed, thermal instability of thiosulphinates warrants the utilization of a less artifact prone methodology viz. ultra performance liquid chromatography (HPLC) coupled to MS. Compared to GC/MS, UPLC/MS is more suited for the analysis of non-volatile polar constituent viz., glycosylides, peptides found in _Allium_[2].
## 5 Traffic Models and Utility Functions
### Best Effort Traffic for Mobility Dependent Predictive Services  
As earlier introduced, the BE traffic has been associated with NSIS-MDP users, because their services cannot be independent from users mobility. An important characteristic of BE traffic is represented by its capability to adapt to the available bandwidth: all the applications that belong to BE traffic are able to use the entire available bandwidth during data transfers, always respecting the existing upper bound (no guarantees are given for the lower bound, so a flow may be easily dropped).  
In this way, transmission speeds are highly time variant. Utility functions for elastic applications are concave over the entire range of bit-rate, approaching their maximum only when the perceived rate tend to infinite [45]. Utility curves can be implemented for any given network performance matrices, such as rate and delay. The most used way for building-up utility curves is based on subjective surveys: users are asked to give an opinion  
Figure 2: Utility functions for Real Time (**a**) and Best Effort (**b**) traffic.  
on the application performance under a wide range of network conditions. We based our choice on the surveys conducted in [45], so an exponential function can be considered:  
\[U_{BE}(r_{s})=a+b{\cdot}r_{s}{}^{q} \tag{5}\]  
where \(q<0\) and \(b<0\), because \(U_{BE}\) must be a monotonically increasing function of \(r_{s}\); a is the maximum desired utility value and when \(r_{s}\) approaches to infinity, \(U_{BE}(r_{s})\) approaches to \(a\); from [45], generally \(a=5\). Figure 2b illustrates the trend of \(U_{BE}\) for different values of \(q\) with \(a=5\) and \(b=-9\).
## 4 Results and Discussion
### Correlation Between Molecular Electronic Properties and Emulsification Stability
Interestingly, AS2 (10.04 D) and AS3 (4.61 D) exhibited intermediate performance, suggesting that moderate polarity--rather than extremes--is optimal for achieving emulsifier stability in oil-water systems. These findings are reinforced by the quantitative data in Table 8 that support the utility of dipole moment as a reliable predictor. The role of CED, presented in Figure 9e, was also examined. CED quantifies internal molecular cohesion and is sometimes used to infer interaction potential with other phases. However, no clear correlation was observed between CED and HIPE formation. For example, Oil 11 and Oil 12 had high CED values but did not yield stable emulsions, indicating that bulk cohesive strength may play a secondary role compared to interfacial forces such as van der Waals interactions and dipole alignment. For structural context, Figure 10 depicts the all-atom molecular structures used in the DFT study, highlighting the polyaromatic cores and various side chains. These architectures influence both molecular polarity and spatial coverage at the interface. Moreover, Figure 11 visualizes the HOMO and LUMO distributions of representative emulsifiers. These orbitals were localized within the PAC region, suggesting a lack of orbital overlap with surrounding molecules. This supports the conclusion that emulsification in these systems is governed by non-bonding interactions--rather than chemical bonding or charge transfer. Figure 10: All-atom molecular structures of emulsifiers and oils used for DFT calculations. In summary, while the HOMO-LUMO energy gap and cohesive energy density provide some insights, it is the dipole moment that shows the strongest correlation with emulsifier performance. These findings suggest that appropriate polarity enables the formation of stable interfacial films through non-covalent interactions, particularly van der Waals forces. Although dipole moment has been previously linked to electro-coalescence phenomena in petroleum systems [51], this study presents the first application of dipole correlation to HIPE stabilization, offering valuable guidance for designing future emulsifiers with targeted electronic properties.
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 4 Experiments
### Dataset  
The audiovisual dataset we used is the AuDio Visual Aerial seeNe reCognition datasEt (ADVANCE) [66], a multimodal learning dataset, which aims to explore the contribution of both audio and conventional visual messages to scene recognition. This dataset in summary contains 5075 pairs of geotagged aerial images and sounds, classified into 13 scene classes, i.e., airport, sports land, beach, bridge, farmland, forest, grassland, harbor, lake, orchard, residential area, shrubland, and train station. Some examples in the dataset are shown in Figure 6. The number of image-audio pairs in each scene is shown in Table 1.  
Figure 6: Example aerial images of ADVANCE dataset: (**1**) airport, (**2**) sports land, (**3**) beach, (**4**) bridge, (**5**) farmland, (**6**) forest, (**7**) grassland, (**8**) harbor, (**9**) lake, (**10**) orchard, (**11**) residential, (**12**) shrubland, (**13**) train station.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 4 PEAR Solution: Model Design and System Implementation
The network topology also employs a hierarchical parent-child relationship between devices where the parent device routes the packets from its child devices, thus, increasing the traffic and energy load on the parent device. Moreover, a device can have simultaneously a parent and a child thus, the energy load is cascaded from the higher topology level to the lower ones.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 6 Implementation and Evaluation
### Evaluation
#### 6.3.3 Improving LoRaWAN Scalability  
The previous evaluation shows that network resources are important in LPWAN. According to [5], the adaptive data rate (ADR) can increase LoRaWAN scalability. The ADR mechanism adapts the data rate setting based on the received signal strength index (RSSI) to increase the data rate while preserving the coverage. We can utilize the relay to divide a single slow data rate transmission into multiple fast data rate transmission to increase network scalability further. This approach will use the network resources more efficiently as decreasing a step of SF cuts the transmission time doubled [35].  
In this evaluation, we use network topology 2 to compare the performance of network reliability and scalability. Not only comparing our proposed solution with fixed-path based forwarding, we evaluate the relay performance when exploiting the benefit of packet duplication to increase the network reliability. The mechanism took a small part of the cooperative relaying scheme [7] without calculating redundancy allocation; we named it CPR. While OOD, OODC, and FP include the relay for ADR selection, CPR only considers gateway as the destination. In CPR, the relay selects and combines packets randomly before transmission. Following 1% duty cycle on a relay running on SF 9 and BW 125 kHz, the relay has to transmit at max ten packets (20B for each packet) for approximately 1 s. Then, it has to wait for 100 s for the next transmission.  
The results is shown in Figure 9a. As expected, ADR greatly improves performance compared to the previous evaluation. In this result, we are using 95% confidence interval, which resulted in less than 1 margin of error. Our mechanism shows reliability improvement above 90% of PDR. Conversely, both FP and CPR reliability performance degrade faster, although they provide good reliability for a small number of end-devices. In this evaluation, the difference in packet delivery ratio for the ADR mechanism is not that great. This is mainly caused by multiple channel utilization. The spreading factor for each end-device is different according to the distance. As the spreading factor in LoRa is orthogonal, the packet collision between relay and end-device highly occurs when they use a similar spreading factor.  
Figure 8: Performance evaluations on network topology 1. Reliability comparison between OOD, OODC, and FP forwarding on slow data rate and fast data rate scenarios. Here, multi-reception approach improves the reliability of OOD and OODC.
## 4 Results and Discussion
### Cavitation Characteristics
#### 4.2.2 Internal Flow Characteristics under Different Cavitation Numbers  
Figure 19 presents the distribution of the refrigerant bubble volume fraction within the inducer and the impeller for different cavitation numbers under the designed condition. It can be observed that as the cavitation number decreases to 0.705, a small amount of cavitation cloud appears near the hub of the impeller on the suction side, and according to Figure 20b, a temperature drop of less than 0.1 K is observed at the center of the cavitation cloud. As the cavitation number decreases to 0.67, the cavitation cloud develops downstream along the suction side of the impeller, and a small amount of cavitation cloud starts to appear in the tip clearance region. When the cavitation number reaches 0.635, a vapor cloud appears on the outer side of the leading edge of the inducer suction side blade. As shown in Figure 19d, when the cavitation number drops below the critical cavitation number of 0.496, the cavitation cloud at the inducer leading edge merges with the one at the tip clearance region and then develops downstream along the inducer blade. After integrating, bubbles appear at the trailing edge of the inducer. Still, these cavitation clouds collapse quickly before entering the impeller blade due to the pressure rise in the low-pressure region. Figure 20b shows the refrigerant temperature rise detaching from the inducer blades. As the cavitation number further decreases, rapidly developing cavitation clouds block most of the inducer passages, and a small amount of cloud separates from the inducer trailing edge and enters the impeller.  
Figure 19: Diagram of changes in bubble volume fractions at different degrees of cavitation.  
Figure 18: (**a**) Bubble volume fractions along the direction of streamline, (**b**) bubble volume fractions in the impeller, (**c**) temperature distributions along the direction of streamline and (**d**) temperature distributions in the impeller.  
The low-pressure region formed at the leading edge of the impeller due to high-speed rotation, and an intense cavitation cloud of the working fluid generated a \"horseshoe-shaped\", as shown in Figure 18b. Meanwhile, the cavitation clouds have not entirely blocked the impeller passages, and the core region of the cavitation cloud appears to have a temperature drop of approximately 0.3 K. At this cavitation number, it is noteworthy that the maximum temperature drop occurs in the front section of the inducer blades, at about 0.9 K.
# Enhancing the Iterative Smoothed Particle Hydrodynamics Method
## 3 The Iterative Approximation
### The Enhanced Iterative Formulation (E-ISPH)  
In order to speed-up the process, the computation is revisited focusing on  
\[\mathbf{S}^{(n)}=\sum_{k=0}^{n}(\mathbf{I}-\mathbf{A})^{k}\qquad\qquad n=0,1,2,... \tag{8}\]  
For each \(n\), the summation \(\mathbf{S}^{(2^{n}-1)}\) of \(2^{n}\) terms is taken into account. Let \(\mathbf{\tilde{S}}^{(n)}:=\mathbf{S}^{(2^{n}-1)}\), i.e.,  
\[\mathbf{\tilde{S}}^{(n)}=\sum_{k=0}^{2^{n}-1}(\mathbf{I}-\mathbf{A})^{k}=\sum _{k=0}^{2^{n}-1-1}(\mathbf{I}-\mathbf{A})^{k}[\mathbf{I}+(\mathbf{I}-\mathbf{ A})^{2^{n-1}}]. \tag{9}\]  
By considering that  
\[(\mathbf{I}-\mathbf{A})^{s}=\mathbf{I}-[\sum_{j=0}^{s-1}(\mathbf{I}-\mathbf{ A})^{j}]\mathbf{A} \tag{10}\]  
by substituting (10) in (9) with \(s=2^{n-1}\) the following relation holds  
\[\mathbf{\tilde{S}}^{(n)}=\sum_{k=0}^{2^{n}-1-1}(\mathbf{I}-\mathbf{A})^{k}\{ \mathbf{I}+\mathbf{I}-[\sum_{j=0}^{2^{n}-1-1}(\mathbf{I}-\mathbf{A})^{j}] \mathbf{A}\}. \tag{11}\]  
Consequently, the subsequent recursive formula can be written  
\[\mathbf{\tilde{S}}^{(n)}=2\mathbf{\tilde{S}}^{(n-1)}-(\mathbf{\tilde{S}}^{(n- 1)})^{2}\mathbf{A}. \tag{12}\]  
Now, the (12) is employed in the overall computation, giving rise to the new succession \(\{\tilde{f}^{(n)}(\mathbf{x})\}\). The described enhanced formulation is comparable in accuracy to ISPH, requiring a reduced number of iterations. In the next section, the behavior of the fast procedure E-ISPH is reported and compared with the standard SPH and ISPH formulations.
## Materials and Methods
### Electrochemical Measurements  
The electrochemical lithium storage performances were evaluated through a two-electrode system. Specifically, high-purity lithium foil (15.8 \(\times\) 0.5 mm) was employed as the counter and reference electrode. To fabricate the working electrode, the slurry was first made by mixing the MgCo\({}_{2}\)O\({}_{4}\) samples, Super P carbon and the polyvinylidene fluoride (PVDF) binder at a weight ratio of 70:15:15 in \(N\)-methyl pyrrolidone (NMP) solvent. After fully stirring, the steady slurry was coated onto conductive copper foil and dried at 80 \({}^{\circ}\)C for \(\sim\)12 h. The current collector then punched into a 12 mm-diameterdisk as working electrode, and the active material loading in each disk was weighed to be 0.9 mg to 1.1 mg, corresponding to 0.8-0.97 mg cm\({}^{-2}\). The coin cells (type CR2025) were assembled in an Ar-filled glovebox with low concentrations of water vapor and oxygen (lower than 0.5 ppm). During assembly, the Celgard 2400 was used as separator, while the electrolyte was made of dissolving 1M LiPF\({}_{6}\) into a solvent consisting of ethylene carbonate (EC) and dimethyl carbonate (DMC) (EC/DMC, 1:1 in volume ratio). The galvanostatic charge-discharge performances were evaluated by LAND system (CT2001A, Wuhan, China) between 0.01 and 3.0 V vs. Li/Li\({}^{+}\). Electrochemical impedance spectroscopy (EIS) was conducted ranging from 100 kHz to 0.01 Hz, and cyclic voltammetry was measured in the range of 0.01-3.0 V (vs. Li/Li\({}^{+}\)) at 0.5 mVs\({}^{-1}\), the above two electrochemical testing were carried out on electrochemistry workstation (CHI660C, Shanghai, China).
## 3 Fully Intelligent Accident Liability Determination Method Based on Collision Detection
### Vehicle Recognition and Tracking  
Accurate target recognition and effective tracking are crucial for the success of our proposed system in collision detection and liability determination. The process commences with the recognition of vehicles in traffic road videos using the trained YOLOv8 network model. The YOLOv8 model scans video frames to generate a tracking window that accurately outlines the recognized vehicle. This tracking window forms the basis for the subsequent tracking process, ensuring consistent monitoring of the identified vehicle across multiple frames.  
Once the tracking window is established, the next critical step involves preprocessing the pixel values within this window to enhance the tracking filter's accuracy. Initially, each pixel value \(p\) in the tracking window undergoes a logarithmic transformation to yield \(p^{\prime}\).  
\[p^{\prime}=log(1+p) \tag{5}\]  
Subsequently, \(p^{\prime}\) is normalized according to Formula (6)  
\[p^{\prime\prime}=\frac{p^{\prime}-\mu}{\sigma_{p^{\prime}}} \tag{6}\]  
where \(p^{\prime}\) represents the normalized pixel value, with \(\mu\) and \(\sigma_{pt}\) being the mean and standard deviation of the pixel values post-logarithmic transformation, respectively.  
To focus on the center of the tracking window, a cosine window function is applied to taper the pixel values toward the edges. If the window \(W\) is an \(N\times M\) matrix, the cosine window function can be expressed as:  
\[\begin{array}{c}W(n,m)=0.5\big{(}1-cos\big{(}\frac{2\pi m}{N}\big{)}\big{)} \\ \cdot 0.5\big{(}1-cos\big{(}\frac{2\pi m}{M}\big{)}\big{)}\end{array} \tag{7}\]  
where \(n=0,1,...,N-1\) and \(m=0,1,...,M-1\) are the row and column indices of the pixel in the window, respectively. The final preprocessed pixel value is obtained using Formula (8):  
\[P_{final}(n,m)=p^{\prime\prime}(n,m)\cdot W(n,m) \tag{8}\]  
\(P_{final}\) at the pixel location \((n,m)\) forms a two-dimensional cosine window, peaking at the center and diminishing to zero at the edge. This preprocessing step effectively converts and optimizes the image data to meet the tracking algorithm's requirements, enhancing the central features of the tracked target while minimizing edge interference.  
Figure 2: Flowchart of the accident liability determination process.
###### Abstract  
Several non-functional objects are orbiting around the Earth and they are called space debris. In this work, we investigate the process of space debris mitigation from the GEO region using a solar sail. The acceleration induced by the solar radiation pressure (SRP) is the most relevant perturbation for objects in orbit around the Earth with a high area-to-mass ratio (\(A/m\)). We consider the single-averaged SRP model with the Sun in an elliptical and inclined orbit. In addition to the SRP effect, the orbital evolution of space debris is analyzed considering the perturbations due to the Earth's flattening and third-body perturbations in the dynamical system. The idea is to use the solar sail as a propulsion system using the Sun itself as a clean and abundant energy source so that it can remove space debris from the geostationary orbit and also contribute to the sustainability of space exploration. Using averaged dynamical maps as a tool, the numerical simulations show that the solar sail contributes strongly to exciting the eccentricity of the space debris, causing its reentry into Earth's atmosphere. To perform the numerical simulations, we consider data from real space debris. We also show that the solar sail can be used to remove space debris for a graveyard orbit. In this way, the solar sail can work as a clean and sustainable space-debris-removal mechanism. Finally, we show that the convenient choice of the argument of perigee and the longitude of the ascending node might contribute to amplify the growth of eccentricity. It is also shown that solar radiation pressure destroys the symmetry of the orbits that can be observed in keplerian orbits, so all the orbits will be asymmetric when considering the presence of this force.  
space debris; averaged model; solar radiation pressure; mitigation risk; debris removal; GEO region; astrodynamics; orbital perturbations +
Footnote †: journal: _Symmetry_  
0023  
[PERSON]  
1 Science and Technology Center in Energy and Sustainability (CETENS), Federal University of the Reconcavo of the Bahia, Feira de Santana 44042-280, Brazil; [EMAIL_ADDRESS] (J.P.S.C.); [EMAIL_ADDRESS] (J.S.L.)  
2 Grupo de Mecanica Espacial-UtMA, University of Zaragoza, 50009 Zaragoza, Spain 2
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 2 Related Work  
In the field of path planning, with the continuous advancement of automation technologies, particularly in autonomous driving and robotics, researchers have proposed various path-planning methods. These methods can primarily be categorized into traditional path-planning approaches and reinforcement learning (RL)-based methods. Traditional path-planning techniques, such as the A* algorithm and RRT, exhibit certain advantages in specific environments. On the other hand, RL-based path-planning methods demonstrate enhanced flexibility and adaptability, enabling online learning and adaptation in dynamic environments. Both of these approaches have been extensively studied in recent years.
## 2 Preliminaries and Problem Formulation
### The Finite-Time Attitude Constraint Controller Design
\begin{split}\dot{V}_{r}\leq&-k_{\psi}\psi_{e}^{2}- k_{\psi 1}|\psi_{e}|^{\frac{3}{2}}-(a_{r}-1)\zeta_{r}^{2}-a_{r1}\left|\zeta_{r}\right|^{ \frac{3}{2}}\\ &-\frac{\psi_{e}U\phi_{1}\psi_{e}}{k_{y}^{2}-y_{e}^{2}}-k_{r}r_{e }^{2}-k_{r1}|r_{e}|^{\frac{3}{2}}-r_{e}\tilde{d}_{r}+k_{\chi_{r}}\chi_{r}r_{e }\\ &+r_{e}\Delta\tau_{r}-k_{\chi_{r1}}\chi_{r}^{2}-k_{\chi_{r2}}| \chi_{r}|^{\frac{3}{2}}+\chi_{r}\Delta\tau_{r}\end{split}  \tag{52}\]  
According to the Theorem 1, for \(\forall t\geq T_{1}\), the disturbance observation error \(\tilde{d}_{r}=0\). Therefore, for \(\forall t\geq T_{1}\), there are  
\[ \begin{split}\dot{V}_{r}\leq&-k_{\psi}\psi_{e}^{2}- k_{\psi 1}|\psi_{e}|^{\frac{3}{2}}-(a_{r}-1)\zeta_{r}^{2}-a_{r1}\left|\zeta_{r}\right|^{ \frac{3}{2}}\\ &-\frac{y_{e}U\phi_{1}\psi_{e}}{k_{y}^{2}-y_{e}^{2}}-k_{r}r_{e}^{ 2}-k_{r1}|r_{e}|^{\frac{3}{2}}+k_{\chi_{r}}\chi_{r}r_{e}\\ &+r_{e}\Delta\tau_{r}-k_{\chi_{r1}}\chi_{r}^{2}-k_{\chi_{r2}}| \chi_{r}|^{\frac{3}{2}}+\chi_{r}\Delta\tau_{r}\end{split}  \tag{53}\]  
Based on the Young's inequality, we can obtain  
\[r_{e}\chi_{r}\leq\frac{1}{2}r_{e}^{2}+\frac{1}{2}\chi_{r}^{2} \tag{54}\]  
\[r_{e}\Delta\tau_{r}\leq\frac{1}{2}r_{e}^{2}+\frac{1}{2}\Delta\tau_{r}^{2} \tag{55}\]\[\chi_{r}\Delta\tau_{r}\leq\frac{1}{2}\chi_{r}^{2}+\frac{1}{2}\Delta\tau_{r}^{2} \tag{56}\]  
Substituting (54)-(56) into (53), we have  
\[ \begin{split}\dot{V}_{r}\leq&-k_{\psi}\psi_{e}^{2}- k_{\psi 1}|\psi_{e}|^{\frac{3}{2}}-(a_{r}-1)\zeta_{r}^{2}-a_{r1}|\zeta_{r}|^{\frac{3}{2}}\\ &-\frac{y_{e}U\phi_{1}\psi_{e}}{k_{y}^{2}-y_{e}^{2}}-\left(k_{r}- \frac{k_{\chi_{r}}}{2}-\frac{1}{2}\right)r_{e}^{2}-k_{r1}|r_{e}|^{\frac{3}{2}} \\ &-\left(k_{\chi_{r1}}-\frac{k_{\chi_{r}}}{2}-\frac{1}{2}\right) \chi_{r}^{2}-k_{\chi_{r2}}|\chi_{r}|^{\frac{3}{2}}+\Delta\tau_{r}^{2}\end{split}  \tag{57}\]