esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Remote Sensing Data
#### 2.2.3 LAI and Photosynthetically Active Radiation (PAR) Data  
LAI and PAR were utilized for downscaling purposes, with LAI serving as an indicator of vegetation structural distribution while PAR delineates the spatial distribution of photosynthetically available radiation. The LAI data employed originate from the Global Land Surface Satellite (GLASS) series [43], with a temporal resolution of 8 days. Among the available spatial resolutions for LAI products, the finest resolution of 250 m was selected. The PAR data were derived from the Breathing Earth System Simulator (BESS) PAR product [44], with a temporal daily resolution and a spatial resolution of 0.05\({}^{\circ}\), aggregating every 8 days.
## 2 Modeling of Five-Phase Induction Motor  
The functioning of a five-phase induction motor is the same as that of a three-phase machine. As a result, modeling a five-phase induction motor follows the same principles as modeling a three-phase motor. The only difference is the number of windings; a three-phase motor has three stator windings that are 120\({}^{\circ}\) apart in space and time, whereas a five-phase motor has five windings that are 72\({}^{\circ}\) apart in space and time. It is possible to build any n-phase machine in which the angle between the windings is determined by the number of phases, as defined by the relation:  
\[\alpha=\frac{2\pi}{n}. \tag{1}\]Figure 1 depicts the mechanical distribution of the winding in a five-phase induction machine, with the phase shift between each adjacent phase equal to 72\({}^{\circ}\).  
The five-phase motor, as noted in the introduction, opens up additional possibilities for connecting stator windings. These must be envisioned, and the distinction between these relationships must be expressed. Figure 2 depicts the stator winding connections. This is the fundamental winding connection to the star, as well as the pentagon connection, which is comparable to a triangle connection in a three-phase system. The pentacle connection is another connection that is not common in three-phase machines [29].  
It should be noted that by changing the individual winding connections, the motor will achieve different properties. This fact is given by the phase distribution between the individual connections. This is nice to see in the phase diagrams of the individual connections. It is not necessary to present a phase diagram of the star connection, because the supply voltage applied to the winding phase in this circuit is equal to the supply voltage. When the windings are connected to the pentagon, the voltage from the source of phases \(a\) and \(b\) is closed on the \(W_{A}\) phase of the winding, as can be seen from Figure 3a. The vector voltage sum of this connection will be on the motor phase 1.1756 times higher than the voltage of the source. Figure 3b is a phase diagram for a pentacle connection. Here, the voltage between the \(a\) and \(c\) phases of the source is closed on the \(W_{A}\) phase. In this case, the voltage on the motor winding will be 1.902 times higher than the supply voltage. It is because of these facts that it is necessary to realize that the motor in each connection will have different properties [30; 31].  
Figure 1: The mechanical winding layout of a five-phase induction machine.  
Figure 2: Possibilities of stator windings connection of five-phase induction machine: (**a**) star connection; (**b**) pentagon connection; (**c**) pentacle connection.
## 3 Results and Discussion
### Evaluation of Potentially Robotic Traits
#### 3.2.4 Hydrophobicity Assay  
The hydrophobicity assay, next to autoaggregation, allows us to estimate the microorganisms' ability to survive and adhere to the mucosa due to the high correlation between high hydrophobicity assay values and the strains' ability to adhere to intestinal cell lines [37; 38; 39; 40]. The ability of probiotics to adhere to the host intestinal mucosa is one of the mechanisms of protection against enteropathogens by competing for attachment sites to host cells. Additionally, the adhesion ability of a probiotic may prolong the time of interaction with the cells of the gastrointestinal tract and thus prolong the time of the intended beneficial effect [39]. [PERSON] et al. [15] stated that yeast isolates with hydrophobicity levels exceeding 40% may be considered hydrophobic. In our research, the yeast strains showed moderate to high hydrophobicity in xylene, with values ranging from 54.35% to 98.16%, except for 107_Metsch_pulcherrima, which showed weaker hydrophobicity. Detailed results of the assay are presented in Figure 4. Seven strains presented with lower values than the control and only four strains showed hydrophobicity values higher than _S. cerevisiae_ var. _boulardi_: 110_Metsch_pulcherrima (88.44%), 111_Metsch_pulcherrima (82.97%), 113_Metsch_pulcherrima (83.16%), and 058_[PERSON]_metapsilosis (98.16%), which is consistent with the observations of other authors for both _M. pulcherrima_ and _C. metapsilosis_[30; 41; 42; 43]. In turn, _P. kudriavzevii_ strains showed hydrophobicities in the range of 54-67%, which was lower than the result obtained for _P. kudriavzevii_ isolated from kombucha by [PERSON] et al. [10]. On the other hand, [PERSON] et al. [13] isolated six strains of _P. kudriavzevii_, which showed hydrophobicities in the range of 18.56-61.12%. They stated that large differences in the hydrophobic capacity of yeasts indicate that this feature is strain-dependent. [PERSON] and [PERSON] [19] reported that 23 isolated yeast strains (38.3%) showed moderate to high hydrophobicity, with values ranging from 25% to 50%, and 21 strains (35%) had a high hydrophobicity rate above 50%, with many isolates outperforming _S. cerevisiae_ var. _boulardii_.  
Figure 4: Results of the hydrophobicity assay of tested strains (One-Way ANOVA: strain: F (27,56) = 38.982, \(p\) = 0.0000, se \(\pm\) 0.027319).
## 1 Introduction
The slope sensor is based on the principle of the autocollimator and has been optimized to achieve higher precision measurement of two-dimensional displacement. The rotating paraboloid array is precisely calibrated for the purpose of rapid measurement, which expands the measurement range of the system in a discrete manner. To verify the feasibility and accuracy of the proposed method, a series of experiments were carried out. The experimental results show that the optimized optical system enhanced the measurement accuracy to \(\pm 0.4\)\(\upmu\)m within the range of 1500 \(\upmu\)m and the overall measurement error was approximately \(\pm 2\)\(\upmu\)m when measured within the range of 450 mm. The system has the advantages of a non-contact, simple structure and a flexible range expansion, and is especially suitable for the detection of errors, such as the position error and straightness error of a multi-axis system.
## 5 Security Analysis
### Heuristic Analysis
#### 5.2.4 Replay Attacks  
In replay attacks, \(\mathcal{A}\) has the capability to intercept login request data \(\{M_{2},K_{1},K_{2}\}\) from a prior session and \(\mathcal{A}\) attempts to resend this information \(\{M_{2},K_{1},K_{2}\}\) to the aggregation server \(S_{j}\); the server will find it impossible to extract any meaningful user identity ID from the replayed data due to the session-by-session refresh of the random parameter \(R_{i}\). Likewise, if \(\mathcal{A}\) tries to resend \(\{K_{3},M_{3}\}\) to user \(U_{i}\), the effectiveness is nullified, as \(M_{3}\) relies on a session-unique dynamic parameter. Consequently, the adversary will fail to recognize \(M_{3}\) messages from past sessions in the current context. This mechanism robustly shields against replay attacks.
# Context-Based Energy Disaggregation in Smart Homes
## 1 Introduction
On the other hand, low-frequency measurements contain less information useful for load disaggregation than high frequency ones. To cope with this issue, in this paper, we enhance state of the art disaggregation approaches based on Factorial Hidden Markov Models (FHMM) [8] with the use of context information, _i.e._, information that can be gathered by home sensors on relevant events in the domestic environment to improve the accuracy of the disaggregation algorithm. Our context-based energy disaggregation approach uses probabilistic models representing the appliances consumption behavior. More specifically, we adopted the additive Factorial Hidden Markov Model (FHMM) [9], where the observed variables represent the aggregated power consumption profile, while the hidden variables represent the states of appliances. Context information (namely user consumption patterns and users presence in a room) is exploited to vary the state transition probabilities of device models in order to improve the accuracy of results. Moreover, the proposed approach has been tested using data gathered from real home environments and made available as an open dataset by the Technische Universitat Darmstadt (_i.e._, Tracebase [10]). In our opinion, this choice may be scientifically relevant since it eases the comparison of results with future work and encourages further improvements. The paper is structured as follows: in Sections 2 and 3, we discuss Background and Related Work, respectively. Section 4 describes the disaggregation algorithm focusing on our context conditioning approach. In Section 5, we describe the testing activities and discuss related results. Section 6 concludes the paper with final considerations.
## 2 Pre-Existing Circuit Structures of ESD for Distributed ERT System
### Present ESDs  
In 2004, [PERSON] designed an ESD for a distributed ERT, which is the most used model. Figure 2 shows a SS1 schematic. It takes SPDT (Single Pole Double-Throw) and DPDT (Double Pole Double-Throw) switches to connect/disconnect the four ports of the AMNB to the bus lines. Figure 3 is the connection map of one of the electrodes in one distributed structure. It shows that four switches are required for each electrode. J1 in Figure 2 is connected to J1 in Figure 3 through bus lines (Figure 1), and J2 is connected to J1 of the next electrode through the bus. Via this manner, all the electrodes are lapped to bus. Four switches are responsible for the connection between the electrode and one of the four connectors (AI, BI, MI, NI), and via the bus they set up a connection relationship with one of AMNB [23].  
Figure 1: Structure of ESD for the distributed ERT system.  
Figure 3: Circuit schematic for one electrode connection in the ESD, designed by [PERSON] [23].  
Figure 2: Power supply interface schematic for ERT in ESD, designed by [PERSON] [23].  
As shown in Figure 1, present traditional ESDs (TESD) have the same bus [23; 24; 25]. The four lines on the bus are just one-to-one corresponding to AMNB four ports. In other words, SS1 just has the function of on and off, which is not a real structure. In SS2, an electrode must have four switches to choose one of the four lines (i.e., four ports). Analyzing all the information above, to improve the system structure SS1 must has its own function and not just act as a simple on-off to AMNB. It needs to set AMNB to not be directly connected to the fixed lines on the bus. Thus, it is possible that SS2 has other options, such as one electrode with less switches. This would divide the whole structure into two layers, which is completely different from the existing scheme. Accordingly, the newly designed ESD was named hierarchical ESD (HESD).
# A Shearlets-Based Method for Rain Removal from Single Images
## 4 The Proposed Method
### Solving the Proposed Model
```
Input:\(o^{0}\), \(M_{iter}\), \(tol\), \(\gamma_{1}\), \(\gamma_{2}\), \(\gamma_{3}\), \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\) Output:\(r\), \(b\)  Initialization: \(b_{v_{1}}^{0}=b_{v_{2}}^{0}=b_{v_{3}}^{0}=0\); while\(\|o-r^{k+1}\|_{2}/\|o-r^{k}\|_{2}>tol\) or \(k<M_{iter}\)do (1) Solving \(v_{1}\)-sub-problem by \[v^{k+1}=\text{Sh}\Big{(}(S_{r}(o-r^{k})+b_{v_{1}}^{k})(r),\frac{\gamma_{1}}{ \alpha_{1}}\Big{)};\] (2) Solving \(v_{2}\)-sub-problem by \[v^{k+2}=\text{Sh}\Big{(}(S_{b}r^{k}+b_{v_{2}}^{k})(r),\frac{\gamma_{2}}{ \alpha_{2}}\Big{)};\] (3) Solving \(v_{3}\)-sub-problem by \[v^{k+3}=\text{Sh}\Big{(}(r^{k}+b_{v_{3}}^{k})(r),\frac{\gamma_{3}}{\alpha_{3} }\Big{)};\] (4) Solving \(r\)-sub-problem using FFT by \[r^{k+1}=\mathcal{F}^{-1}\Big{(}\frac{\alpha_{1}\mathcal{F}(S_{r}^{*}S_{r})+ \alpha_{2}\mathcal{F}(S_{b}^{*}S_{b})+\alpha_{3}\mathcal{F}(I)}{\alpha_{2} \mathcal{F}(S_{b}^{*}(v_{2}^{k}+b_{v_{2}}^{k}))+\alpha_{3}\mathcal{F}(v_{3}^ {k}+b_{v_{3}}^{k})-\alpha_{1}\mathcal{F}(S_{r}^{*}(v_{1}^{k}-b_{v_{1}}^{k}-S_ {r}o))}\Big{)};\] (5) Output \(r,b\) are given by \[r^{k+1} =\min(o,\max(r,^{k+1}0));\] \[b^{k+1} =\min(o,\max(o-r^{k+1},0));\] (6) Updating \(b_{v_{1}}^{k},b_{v_{2}}^{k},b_{v_{3}}^{k}\) by \[b_{v_{1}}^{k+1} =b_{v_{1}}^{k}+S_{r}(o-r)-v_{1};\] \[b_{v_{2}}^{k+1} =b_{v_{2}}^{k}+S_{b}r-v_{2};\] \[b_{v_{3}}^{k+1} =b_{v_{3}}^{k}+r-v_{3};\] (7) Computing the relative error by \[tol^{k+1} =\|o-r^{k+1}\|_{2}/\|o-r^{k}\|_{2}\]  end while
```  
**Algorithm 1**The split Bregman algorithm for proposed model (13)
## 4 Experiments and Analysis
### Visual Comparison of Detection Results  
To qualitatively evaluate the detection performance of the proposed YOLO-AFR algorithm under real-world dangerous driving scenarios, we selected representative images containing six types of dangerous driving behaviors from the two datasets for testing. A comparative analysis was conducted against the baseline models YOLOv10n, YOLOv11n, and YOLOv12n, as illustrated in Figure 10.  
As depicted in the figure, our improved YOLO-AFR model demonstrates superior performance, notably in reducing missed and false detections, which contributes to a significant enhancement in overall accuracy. Crucially, the benefits of this performance uplift become particularly evident when analyzing specific detection instances. While baseline models might achieve high overall accuracy, they can falter on certain challenging targets, resulting in lower precision for those particular detections. YOLO-AFR addresses this by substantially improving performance, specifically on these previously lower-accuracy targets. This targeted improvement, in turn, markedly boosts the credibility and reliability of the final detection results. Consequently, the reduction in detection errors (both missed and false detections), coupled with this enhanced overall credibility, clearly underscores the practical advantages of YOLO-AFR in real-world applications. These comprehensive enhancements are attributable to the strong environmental adaptability and robust occlusion-handling capabilities endowed by the integrated FRFN, SC-Conv, and SEAM modules. Collectively, these modules empower YOLO-AFR to accurately detect dangerous driving behaviors across various scales with heightened robustness and precision, even when faced with complex and challenging conditions.  
Figure 9: Synergistic Interaction of FRFN, SC-Conv, and SEAM.
## 3 Results
### IOD of Arcs from GEO Objects
#### 3.2.2 FocusGEO  
The data were observed on 20, 22, and 23 October 2019, and 5002, 5093, and 4086 arcs were obtained from each day. Then the arcs are tried to match with the TLE data and the IOD elements are computed. The results are shown in Table 7 and Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Error (Deg)** & **Count** & **Rate** \\ \hline
**\textless{}1** & 523 & 97.03\% \\
**\textless{}0.5** & 515 & 95.55\% \\
**\textless{}0.2** & 488 & 90.54\% \\
**\textless{}0.1** & 444 & 82.37\% \\ \hline \end{tabular}
\end{table}
Table 6: The statistics for the IOD inclination errors of space objects in GEO.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Date** & **Number of Arcs** &
\begin{tabular}{c} **Number of Matched** \\ **Arcs** \\ \end{tabular} & **Rate (\%)** \\ \hline
6 February 2021 & 25,714 & 17,369 & 67.50 \\
7 February 2021 & 15,513 & 11,302 & 72.86 \\
8 February 2021 & 14,250 & 10,960 & 76.91 \\
9 February 2021 & 11,976 & 9355 & 78.11 \\
10 February 2021 & 16,709 & 12,734 & 76.20 \\ Mean & 16,832 & 12,344 & 74.32 \\ \hline \end{tabular}
\end{table}
Table 4: The matching result between the arc observation and TLE.  
\begin{table}
\begin{tabular}{c c c} \hline
**Errors of SMA** & **Count** & **Rate** \\ \hline \textless{}100 km & 962 & 70.58\% \\ \textless{}50 km & 743 & 54.51\% \\ \textless{}30 km & 546 & 40.06\% \\ \textless{}10 km & 232 & 17.02\% \\ \hline \end{tabular}
\end{table}
Table 5: The statistics for the IOD SMA errors of space objects in GEO.  
Selected for calculation were 881 arcs containing 9 or 10 data points from the observation file of 20 October 2019, and the arc lengths were all about 53 s. There were 868 arcs with successful orbit computations, and the success rate was 98.52%. The TLE data were used to evaluate the initial orbit errors, and the results are shown in Table 7. The corresponding error distributions of SMA, eccentricity, and inclination errors are shown in Figure 4. It can be found that over 55% of the IOD SMA errors are less than 100 km for GEO objects and about 53% of all the IOD eccentricity errors are smaller than 0.001.  
As to the IOD success rate, the rate of Changchun GEO is higher than that of FocusGEO. The reason should be that the average arc length is different. The average length of the former is about 77 s but the average length of the latter is about only 53 s. This is also the reason for the worse result of the IOD elements solution.
# Optimization of Redundant Degrees of Freedom in Robotic Flat-End Milling Based on Dynamic Response
## 2 Redundant Degrees of Freedom Optimization in Robotic Milling  
### Problem Description of Redundant Degrees of Freedom in Robotic Milling  
Typically, the posture of the robot end effector, such as milling tools, is derived from the cutter location data denoted as CL (\(x\), \(y\), \(z\), \(I\), \(J\), \(K\)), which are generated by a five-axis CAD/CAM software system. Here, (\(x\), \(y\), \(z\)) denotes the tool center point, and (\(I\), \(J\), \(K\)) represents the unit vector indicating the tool axis direction. Essentially, the CL file provides the tool's position and orientation parameters in the workpiece coordinate system [W]. Conversely, the tool coordinate system [T] is established in relation to the workpiece coordinate system [W] through a six-dimensional tuple (\(x\), \(y\), \(z\), \(\alpha\), \(\beta\), \(\gamma\)), where (\(\alpha\), \(\beta\), \(\gamma\)) represents the X-Y-Z Euler angles. Consequently, when executing a G code robot program for milling, the cutter location data CL (\(x\), \(y\), \(z\), \(I\), \(J\), \(K\)) and tool posture (\(x\), \(y\), \(z\), \(\alpha\), \(\beta\), \(\gamma\)), should satisfy the following conditions:  
\[\begin{bmatrix}I&x\\ J&y\\ K&z\\ 0&1\end{bmatrix}=M_{WT}\begin{bmatrix}0&0\\ 0&0\\ 1&0\\ 0&1\end{bmatrix} \tag{1}\]  
where \(M_{WT}\) is the homogeneous transformation matrix of the tool coordinate system relative to the workpiece coordinate system.  
The following equation can be derived from Equation (1):  
\[\begin{bmatrix}I&J&K\end{bmatrix}^{T}=\begin{bmatrix}-\cos\alpha\sin\beta&- \sin\beta\sin\alpha&-\cos\beta\end{bmatrix}^{T} \tag{2}\]  
According to Equation (2), given cutter location data CL (\(x\), \(y\), \(z\), \(I\), \(J\), \(K\)), only the initial five components of the six-dimensional tuple (\(x\), \(y\), \(z\), \(\alpha\), \(\beta\), \(\gamma\)) can be determined. This implies that the Euler angle \(\gamma\) remains arbitrary, introducing redundancy. As shown in Figure 1, when a six-axis industrial robot engages in a five-axis milling task, it introduces a redundant degree of freedom represented by the arbitrary Euler angle \(\gamma\). This redundancy arises because the task only requires determination of five components, leaving one degree of freedom unspecified. To solve the inverse kinematics problem for the robot, setting the Euler angle \(\gamma\) becomes essential. Addressing this challenge involves optimizing the robot's redundant degree of freedom to achieve an improved posture, consequently enhancing machining accuracy.
# Dark Solitons and Grey Solitons in Waveguide Arrays with Long-Range Linear Coupling Effects
## 3 Dark Solitons  
In numerical simulations, we apply the imaginary time propagation (ITP) method [21] to study the fundamental solution to Equation (1) for dark solitons. We find that the stability of the dark soliton solutions changes with the scaled total power \(P\) and the normalized decay length \(d\). When the eigenvalues \(\lambda\) have an imaginary part, this means that the solution of \(\phi\) is unstable. Figure 1a,b show that the value of the imaginary part of \(\lambda\) (Im(\(\lambda\))) varies with \(P\) and \(d\). From Figure 1a, we can see two traits of dark solitons in this long-range linear coupling waveguide array. First, the nonlinearity strengthens when \(P\) increases, and the system requires a stronger coupling effect to achieve a stable solution for dark solitons. It can be seen that \(d\) increases as \(P\) increases. For example, when \(P=0.5\) and \(d>1.5\), the solitons are stable (Im(\(\lambda\)) = 0); however, when \(P\) increases to \(P=1\), the coupling effect must be enhanced to \(d>2.2\) to achieve stable solitons. Second, when the effective length \(d\) of the coupling effect is fixed, Im(\(\lambda\)) increases with larger \(P\). This means that the instability is enhanced when the scaled total power \(P\) is higher. From Figure 1b, we can see when the system has either a strongly local effect (\(d=0.0001\)) or a strongly coupling effect (\(d=5\)); the dark soliton solution is stable regardless of the value of \(P\). However, for \(0.0001<d<5\), the system is subject to the combined action of the short-range and long-range effects, and the stability conditions for dark solitons are more complex. In this region of \(d\), the tendency is that dark solitons become more unstable as \(P\) increases. Figure 1c,d show the amplitude and intensity, respectively, of the fundamental solution for dark solitons with \(P=1\) and \(d=3\). In Figure 1e, we present an example of the evolution of stable dark solitons with \(P=1\) and \(d=3\). In Figure 1f, we present an example of the evolution of unstable dark solitons with \(P=0.5\) and \(d=0.8\).  
We also present the widths of dark solitons in this system for different \(d\) and \(P\) values, as shown in Figure 2. The dotted lines represent unstable dark soliton solutions (with Im(\(\lambda\))\(\
eq\)0), and the solid lines represent stable solutions.  
Figure 1: Dark solitons in a 513-waveguide array. **(a)** The imaginary part of the eigenvalues \(\lambda\) (Im(\(\lambda\))) for different \(d\) values; **(b)** The imaginary part of the eigenvalues \(\lambda\) (Im(\(\lambda\))) for different \(P\) values; **(c)** The amplitude of the fundamental solution for dark solitons (\(P=1\) and \(d=3\)); **(d)** The intensity of the fundamental solution for dark solitons (\(P=1\) and \(d=3\)); **(e)** The evolution of a stable solution for dark solitons (\(P=1\) and \(d=3\)); **(f)** The evolution of an unstable solution for dark solitons (\(P=0.5\) and \(d=0.8\)).  
Figure 2: Widths of dark solitons for different \(d\) and \(P\) values.