esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and Case Study
### Selection of M-A Sequences
Therefore, it is necessary to study different intensity combinations of mainshock and aftershock on the failure probability of a structure. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Num.**} & \multicolumn{3}{c}{**Mainshock**} & \multicolumn{3}{c}{**Aftershock**} & \multirow{2}{*}{**Earthquake Event**} \\ \cline{2-2} \cline{5-6}  & **Sequence** & **M** & **R_rup_ (**km**) & & **Sequence** & **M** & **R_rup_ (**km**) \\ \hline
50 & 978 & 6.69 & 23.07 & 1701 & 5.28 & 19.62 & Northridge \\ \hline
51 & 981 & 6.69 & 42.2 & 1655 & 6.05 & 39.16 & Northridge \\ \hline
52 & 982 & 6.69 & 5.43 & 1703 & 5.28 & 14.69 & Northridge \\ \hline
53 & 984 & 6.69 & 41.17 & 1656 & 6.05 & 38.69 & Northridge \\ \hline
54 & 985 & 6.69 & 29.88 & 1657 & 6.05 & 27.19 & Northridge \\ \hline
55 & 988 & 6.69 & 15.53 & 1658 & 6.05 & 20.47 & Northridge \\ \hline
56 & 990 & 6.69 & 36.62 & 1659 & 6.05 & 33.8 & Northridge \\ \hline
57 & 995 & 6.69 & 24.03 & 1660 & 6.05 & 20.68 & Northridge \\ \hline
58 & 996 & 6.69 & 20.81 & 1711 & 5.28 & 17.14 & Northridge \\ \hline
59 & 999 & 6.69 & 37.36 & 1661 & 6.05 & 34.36 & Northridge \\ \hline
60 & 1004 & 6.69 & 8.44 & 3771 & 5.28 & 11.66 & Northridge \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 3: Mainshock-aftershock (M-A) sequence with mainshock intensity scaled to 0.8 g and aftershock intensity scaled to 0.5 g.
# Research on Optimized Energy Scheduling of Rural Microgrid
## 6 Solution Procedure
### Scheduling Results and Analysis
In the process of dispatching, we adopt the real-time price (RTP) of the grid [28], and fully consider the users' power generation and power consumption demand. The total electricity cost of case 1 is 1627 RMB and the cost of case 2 is 1531 RMB. Therefore, the rural microgrid architecture with CES proposed in this paper has better scheduling results. Their scheduling results are shown in Figure 10.  
Since the CES has economies of scale, the unit cost of setting CES investment is 20% lower than the unit cost of distributed energy storage (DES) investment [25]. Therefore, when calculating the energy-storage cost according to Equation (20) in scenario 1, the parameter \(\alpha\) needs to be changed to 0.586. This means that the unit storage cost of DES is higher than CES. Comparing Figure 10b with Figure 10d, we can find that the state of charge (SOC) of DES equipment at 3:00-8:00 is lower than that of CES. Higher energy-storage costs result in DES reducing the utilization of energy-storage space. After renting 57 kWh of energy-storage capacity from CES, the dispatching result of rural microgrid is solved, as shown in Figure 10c. By analyzing the generation power curve in Figure 10c, it can be found that the generation peak is located at 4:00 and 14:00. This is because wind-power generation is relatively large due to high wind speed in the early morning, and the light at noon leads to more photovoltaic power generation. However, it does not generate enough electricity to meet the demand of the microgrid. Therefore, users still need to purchase electricity from the grid and the total power purchased is shown as the grid power curve in Figure 10c. In addition, CES can be used as a shared energy-storage unit for the rural microgrid to meet the energy-storage needs of all users. The charging/discharging behavior planning of CES batteries is also a part of the energy dispatching. From the SOC curve of CES batteries in Figure 10d, it can be found that the available  
Figure 9: Electricity costs of different storage capacity. stored energy in the battery is completely used up at the end of the day, and the battery has experienced the maximum SOC and the minimum SOC during the day's use, which indicates that the space is fully used. Therefore, the new microgrid architecture with CES can optimize the energy dispatching of a rural microgrid.
# DFD-SLAM: Visual SLAM with Deep Features in Dynamic Environment
## 3 Materials and Methods  
### System Architecture  
Our proposed method enhances the ORB-SLAM3 framework by incorporating three core modules: the feature point and descriptor extraction module, the semantic segmentation module, and the geometric detection module. The feature point and descriptor extraction module uses HFNet to replace traditional ORB feature extraction, generating both local and global descriptors. The semantic segmentation module generates initial masks using YOLOv8-seg. The geometric detection module employs Lucas-Kanade optical flow tracking combined with motion consistency checks for tracking feature points and executing frame rotation. The system framework is illustrated in Figure 1.  
We utilize TensorRT on the GPU for parallel inference of HFNet and YOLOv8 to conduct feature extraction and semantic segmentation. This process results in obtaining feature points along with their HFNet descriptors, as well as the semantic information and masks provided by YOLOv8-seg. The RGB input image is converted to grayscale, and an image pyramid is constructed, where HFNet extracts local features at each level. YOLOv8 infers the semantic information of the input RGB image. Subsequently, feature points are tracked using Lucas-Kanade optical flow and undergo moving consistency checks to accurately remove dynamic feature points. Optical flow vectors are also used to estimate frame rotation angles, determining whether to employ HFNet's local descriptors or recalculate ORB descriptors. If the current frame is set as a keyframe, the system utilizes HFNet to extract global features for it. The global descriptors from HFNet replace the bag-of-words approach of ORB-SLAM3 for describing keyframes, with each new keyframe being described by a 4096-dimensional floating-point vector. The system calculates the Euclidean distance between the new keyframe and the keyframes stored in the keyframe library to identify the closest keyframes as candidate keyframes. Subconsistent with ORB-SLAM3. This method supports both monocular and monocular-inertial modes, with all other algorithms and processes remaining as per ORB-SLAM3. Our approach is a visual SLAM system that combines deep features with dynamic SLAM, which we call DFD-SLAM.
## 2 Application Scenarios and Key Concepts/Enablers for RF Hardware in JCRS
### Key Enablers and Driving Factors  
The advancement of complementary metal-oxide semiconductor (CMOS) and silicon germanium (SiGe) Bipolar CMOS (BiCMOS) technologies have boosted the performance of RF front-ends in the last decade, thus making it possible to have low-cost consumer-ready solutions [15; 16; 17]. A big driving factor has been the spectrum crunch in sub-8 GHz bands. The 5G-NR bands at cmWave, the increasing interest in the 6-16 GHz bands [18] and available hardware at 60 GHz can be identified as the key enablers bringing JCRS systems to the consumer market. In the radar domain, range resolution depends on the usable bandwidth. The mm-scale range resolution is possible while using 60/77 GHz frequency bands. The higher frequencies, namely, 110-140 GHz bands [19], can also effectively enable new use cases in AR/VR and Industry 4.0 use cases. Innovative technologies, such as metasurfaces and intelligent reflective surfaces [20], add new dimensions. The new hardware technologies are discussed in Section 3. The trends of cellular networks going into 6G and beyond are discussed in [21]. One trend is to move into higher frequency bands that support a wider bandwidth. As the radar range solution becomes better with a higher bandwidth, this trend will facilitate JCRS systems greatly.  
In-band full-duplex operation is also envisioned as one of the key enablers. It will enable better spectral re-use, adding minimal channel interference considering spatial diversity at mmWave frequencies [22]. Figure 2 depicts the classification of different applications in terms of cost and complexity, which will potentially operate in full-duplex mode. For applications such as IoT routers, a single antenna communication link will be sufficient, as it does not require a great cost. On the other hand, high-cost AR/VR applications require multiple-input and multiple-output (MIMO) capabilities with integrated sensing in order to enable real-time interaction with the additional realities. For some applications, such as machine-to-machine (M2M) or radio frequency identification (RFID), the classification border is vague; however, it helps to identify niche solutions for those.
## 5 Comparison of Different Decomposition Methods
### Recursive Cosine Sine Decomposition  
With the circuit presented in [18], an \(n\)-qubit gate is decomposed into multi-controlled rotation gates. Cosine sine decomposition (CSD) is applied recursively until all the matrices are diagonal.  
With CSD, any even-dimensional unitary matrix \(U\) can be decomposed into real diagonal matrices \(C\) and \(S\) and smaller unitary matrices \(L_{0}\), \(L_{1}\), \(R_{0}\) and \(R_{1}\) as shown in Equation (15) [19].  
\[U=\begin{bmatrix}U_{00}&U_{01}\\ U_{10}&U_{11}\end{bmatrix}=\begin{bmatrix}R_{0}&0\\ 0&R_{1}\end{bmatrix}\begin{bmatrix}C&-S\\ S&C\end{bmatrix}\begin{bmatrix}L_{0}&0\\ 0&L_{1}\end{bmatrix} \tag{15}\]  
The left and right matrices are uniformly controlled gates; see Section 3.5. \(C\) and \(S\) are diagonal matrices with the cosines and sines of angles \(\theta_{j}\) as the diagonal elements, respectively, between the subspaces, as shown in Equations (16) and (17).  
\[C =diag(cos(\theta_{0}),\ldots,cos(\theta_{n})) \tag{16}\] \[S =diag(sin(\theta_{0}),\ldots,sin(\theta_{n})) \tag{17}\]  
where the values \(\theta\) are ordered from large to small and are between \(\frac{1}{2}\pi\) and \(0\).  
The central matrices from each recursive step correspond to multi-controlled \(R_{y}\) gates which are decomposed as in Section 3.6. The other diagonal gates can be decomposed into a circuit consisting of \({}^{1}\!/_{2}\cdot n\cdot 4^{n}-{}^{1}\!/_{2}\cdot 2^{n}\) CNOTs and \({}^{3}\!/_{2}\cdot 4^{n}-{}^{1}\!/_{2}\cdot 2^{n}\) one-qubit rotation gates [20].  
This is significantly improved upon in [21], which stops the recursion at uniformly controlled one-qubit gates.  
Furthermore, this proves that any uniformly controlled two-qubit gate \((F_{n}^{n-1}(U(2)))\) can be decomposed into a specific sequence of \(2^{n-1}-1\) CNOT gates, \(2^{n-1}\) one-qubit gates and one total global phase gate expressed as \(\Delta_{n}\).  
Furthermore, this proves that each multi-controlled two-qubit gate can be decomposed into a diagonal gate (\(\Delta\)) and a Gray code sequence of CNOTs and one-qubit gates. The diagonal gates are folded into the central matrix from the CSD, so the total decomposition is  
\[U=\Delta_{n}\tilde{F}_{n}^{n-1}(U(2))\prod_{i=1}^{2^{n-1}-1}\tilde{F}_{n- \gamma(i)}^{n-1}(U(2))\tilde{F}_{n}^{n-1}(U(2)) \tag{18}\]  
Each \(\tilde{F}_{n}^{n-1}(U(2))\) is decomposed with \(2^{n-1}-1\) CNOTs, and the \(\Delta_{n}\) gate is implemented with multi-controlled \(R_{Z}\) gates. This results in \(2^{n}-2\) CNOTs, which makes the total CNOT count \({}^{1}\!/_{2}\cdot 4^{n}-{}^{1}\!/_{2}\cdot 2^{n}-2\). The resulting circuit is shown in Figure 9.  
Figure 8: Decomposition into the Givens rotations [16].
## 4 Experiments and Discussion
### Datasets  
#### 4.1.1 DIOR Dataset  
DIOR [2] is a large ORSIs dataset that was established in 2020 to develop and validate data-driven methods. It contains 23,463 images and 192,472 objects in total, covering 20 categories in optical remote sensing field. Images in this benchmark dataset have been clipped into 800 \(\times\) 800 pixels. There are vast scale variations across objects in DIOR because it contains images with spatial resolutions ranging from 0.5 m to 30 m. According to the definition of COCO [15], objects with area of ground truth less than 32 \(\times\) 32 pixels, between 32 \(\times\) 32 pixels and 96 \(\times\) 96 pixels and larger than 96 \(\times\) 96 pixels are taken as small, middle and large-sized objects, respectively. Each category and the size distribution of objects in DIOR is shown in Figure 10. It can be seen that objects in DIOR possess great size difference and are concentrated in small and middle-sized.  
Figure 10: Each category and the size distributions of objects in DIOR.  
Moreover, since images in DIOR are carefully collected under various environment conditions, such as different weathers and seasons, these images possess richer variations in viewpoint, background, occlusion, etc. Problems of intra-class diversity and intra-class similarity are more laborious due to the above characteristics. The main difficulties in real-world tasks can be well reflected by DIOR; thus, ablation experiments of YOLO-DSD and comparative experiments with SOTA detectors were conducted in DIOR dataset.
## 2 Dynamical Models
### Circular Restricted Three-Body Problem  
The circular restricted three-body problem model describes the orbital motion of a body of negligible mass (e.g., the spacecraft) in the gravitational field of two primaries (in our case, the Earth and the Moon) revolving with a constant angular velocity in circular orbits around their center of mass \(C\). We denote by \(m_{E}\) and \(m_{M}\) the masses of the major primary (the Earth) and the minor primary (the Moon), respectively. The CR3 BP equations of motion are usually written in the rotating reference frame with the origin at \(C\): the \(x\)-axis connects the primaries and points toward the minor primary, the \(z\)-axis is directed along the angular momentum of the primaries, and the \(y\)-axis completes the right-handed system (Figure 1).  
Figure 1: Rotating reference frame in the Earth–Moon circular restricted three-body problem.  
In the CR3 BP, it is convenient to use a dimensionless system of units: the masses of the bodies are normalized so that \(m_{E}=1-\mu\) and \(m_{M}=\mu\), where \(\mu=m_{M}/(m_{E}+m_{M})\) is the CR3 BP mass parameter, the angular velocity of the rotating frame is normalized to one, and the distance between \(m_{E}\) and \(m_{M}\) is normalized to 1. The dimensionless gravitational constant \(G\) is also identically equal to 1. For the Earth-Moon system, the corresponding units of distance, velocity and time are adopted as follows:  
\[\begin{split}\text{DU}&=384402\text{km},\\ \text{VU}&=1.0245441823\text{km}/\text{s},\\ \text{TU}&=4.3425137728\text{days},\end{split} \tag{1}\]  
and the CR3 BP mass parameter \(\mu=0.0121505845\). In this system of units, the two primaries are at fixed positions \(\left[-\mu,0,0\right]\) and \(\left[1-\mu,0,0\right]\) along the \(x\)-axis. The equations of spacecraft motion are written in the nondimensional form as  
\[\ddot{x}-2\dot{y} =\frac{\partial\Omega_{3}}{\partial x},\] \[\ddot{y}+2\dot{x} =\frac{\partial\Omega_{3}}{\partial y}, \tag{2}\] \[\dot{z} =\frac{\partial\Omega_{3}}{\partial z},\]  
where  
\[\Omega_{3}(x,y,z)=\frac{x^{2}+y^{2}}{2}+\frac{1-\mu}{r_{1}}+\frac{\mu}{r_{2}}+ \frac{\mu(1-\mu)}{2} \tag{3}\]  
is the effective potential. The distances \(r_{1}\) and \(r_{2}\) from the spacecraft to \(m_{E}\) and \(m_{M}\) can be expressed by the formulas \(r_{1}^{2}=(x+\mu)^{2}+y^{2}+z^{2}\), \(r_{2}^{2}=(x-1+\mu)^{2}+y^{2}+z^{2}\).  
It is well known that the CR3 BP system (2) has an energy integral of motion, the Jacobi integral  
\[J(x,y,z,\dot{x},\dot{y},\dot{z})=2\Omega_{3}(x,y,z)-(\dot{x}^{2}+\dot{y}^{2}+ \dot{z}^{2}). \tag{4}\]  
As a result, every CR3 BP solution in the 6D phase space belongs to the 5D integral manifold \(\mathcal{J}=\{[x,y,z,\dot{x},\dot{y},z]\in\mathbb{R}^{6}|f(x,y,z,\dot{x},\dot{y },z)=\bar{f}\}\) of level \(\bar{f}\). The planar motion is reproduced by setting \(z\equiv 0\). Each planar solution lies on a three-dimensional Jacobi integral manifold in the four-dimensional phase space of the planar CR3 BP.
## 2 Positioning Method
### Fuzzy Positioning System  
In the constructed simulator, three LEDs with different colored temperatures in the x-axis direction were installed on the ceiling. The overlapping of light was considered, following which different chromaticity values were measured depending on the position of the RGB sensor in the x-axis direction.  
We first collected reference chromaticity data corresponding to the positional coordinates in the x-axis direction. The red dot on the mesh grid in Figure 1 denotes the location of the collected reference data. Thus, for obtaining reliable measurements of the x-axis in the interior of the simulator, the x coordinate was increased from zero to 60 cm in increments of 10 cm, and the chromaticity data were collected at each point on the lines indicated by \"close\", \"med\" and \"far\" intersecting each x-axis coordinate. Figure 2(a) shows the variation in the mean values of chromaticity data along the x-axis.  
As shown in Figure 1, the bar-shaped LED illuminators installed on the ceiling of the tunnel simulator were driven by power-switching methods at 1.75 and 4.75 kHz for each section. In this study, the y-axis position coordinate was calculated using the 1.75 kHz frequency-component ratios included in the sensor output signal, corresponding to the position change along the y-axis.  
For collection of the reference data, the RGB sensor was fixed at 30 cm on the x-axis. As the sensor was moved along the y-axis from 5 to 85 cm in increments of 10 cm, the magnitudes of the 1.75 and 4.75 kHz signals, subject to the bandpass filter and smoothing circuit, were measured. These measurement values were obtained to calculate the frequency-component ratios (\(\,\mathrm{\mid}\)VR(1.75 kHz)\(\,\mathrm{\mid}\) / \(\,\mathrm{\mid}\)VR(4.75 kHz)\(\,\mathrm{\mid}\) ). Figure 2(b) shows the frequency-component ratios corresponding to the y-axis coordinates at which the sensor was located. Based on the reference data shown in Figure 3, a fuzzy positioning system was developed [24]. In this study, a fuzzy-logic positioning system was designed with MATLAB to calculate the x and y-coordinates corresponding to the position of a mobile robot from data pertaining to the chromaticity and the frequency-component ratio of the  
Figure 3: Reference-data variations along x- and y- axes: (**a**) Chromaticity variation along x-axis; (**b**) frequency-component-ratio variation along y-axis.  
light. Data points corresponding to x-chromaticity and the frequency-component ratio (i.e., points other than those shown in Figure 3) were estimated by using linear approximation between the two-boundary data, which involved every two-neighbored reference data point in Figure 3. In this way, all the data points corresponding to the x-chromaticity and frequency-component ratio at the location of the mobile robot in the MATLAB-based simulation were estimated; these were then utilized as inputs to the fuzzy positioning system which calculates the 2-D Cartesian coordinates. Figure 4 summarizes the fuzzy positioning process [24].
### muliresolution modeling  
A polygonal model \(M\) can be considered as the composition of a fixed set of vertices \(V=(v_{1},v_{2},...,v_{r})\) and a fixed set of faces \(F=(f_{1},f_{2},...,f_{n})\). It provides a single fixed resolution representation of a surface. Suppose we have a polygonal model \(M\) and we want an approximation \(M^{\prime}\). While \(M^{\prime}\) will have fewer polygons and vertices than the original one, it should be as similar as possible to \(M\). A multiresolution model is a model representation that covers a sequence of approximations of a surface and can be used to reconstruct any one of them on demand.  
There are two most common methodologies in multiresolution modeling: decimation and refinement. A decimation algorithm starts from the original surface and removes elements from the model. For example, from the high detail model \(M^{n}\), we can get a sequence of models in different levels of details  
Figure 1: Surface sequences in different resolution: (a) original surfaces, (b) and (c) surfaces by compound subdivision wavelet analysis once and twice; (d) meshes in lowest resolution.  
by iteratively decimation.  
\[M^{n}\to M^{n-1}\rightarrow...\to M^{0}\]  
In the opposition to the decimation, the refinement algorithm begins from an initial coarse approximation and adds supplements to the model. We can retrieve the high detail model \(M_{n}\) by iterative refinement.  
\[M^{0}\to M^{1}\rightarrow...\to M^{n}\]  
The cost of surface decimation and refinement should be low because we will often need to use many different approximations at run time. In some applications, the low cost of computation is much more important than the similarity of the objects in different resolutions.
# Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques
## 3 The Augmented Digital Control Room
### Augmenting the Digital Control Room
#### 3.3.3 Immersion and Collaborative Experience  
In the context of the augmented digital control room, one interesting subject is synchronous remote collaboration, where users can discuss relevant issues without being physically at the same place. One such situation could arise in the industrial context, when a worker (wearing MR glasses) realizes some issues with a machine and wants to update machine tasks in accordance with the planning office. With respect to the augmented digital control room, this has some special properties compared to the classical digital control room as described in Section 3.2. The immersive experience changes the way of interaction as well as the potential of collaboration compared to the desktop PC. One specifically interesting property is the enhanced ability of creating a feeling of co-presence through the visualization of aspects such as gaze focus and hand visualization. [PERSON] et al. [27] investigated a setup with an MR user and remote desktop expert and evaluated how different cues in the collaboration process impacted different aspects such as connectedness, fun, and co-presence. Both collaborators communicated through voice chat and the PC user could see a live video from the MR view. The MR user could see the pointer of the mouse position and the PC user could see the gaze focus of the MR user. As a result, these cues increased quality of work, communication and co-presence. Moreover, the gaze focus made the PC user feel the focus of the MR user and increased the fun of collaboration. Recently, [PERSON] et al. [28] published a work, where the MR user collaborated with a VR user. They explored how cues improve the speed, the feeling of co-presence and the mental load. Key findings were that the visualization of a hand-pointer did not significantly affect the task result, while drawing into the field of view improved the speed of work. Both, pointer and drawing, increased the mental load for the users. Our research shall tighten the aforementioned research and includes a similar setting to [PERSON] et al., but adds visual cues of the MR user's hands and pointers. Moreover, we use the Hololens 2 as a more modern and powerful device for MR applications compared to a hand-crafted device by [PERSON] etal. and the Meta2 device by [PERSON] et al., which, for example, does not support eye tracking and does not have a build-in microphone compared to the Hololens 2 [29].  
In this work, we focus on the following research question with respect to collaboration in the augmented digital control room.  
_RQ 3: How do visual cues of hands and eyes improve the experience of collaboration between PC user and Hololens 2 user?_