esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Results  
### Test System  
The proposed method was tested using the IEEE Reliability Test System [20]. In the base case, three wind farms are included in place of hydro generators of the original RTS-96. The total installed capacity of the three wind farms is 450 MW, which is 12% of the installed generation capacity. The yearly peak load is set to be 2850 MW, and a load profile of first week of [20] was used. The fuel price is obtained from [21], but the fuel price of number 2 oil was set to be the same as that of number 6 oil. Upward and downward reserve costs of every generators are assumed to be 10 $/MW and 5 $/MW, respectively. The day-ahead load forecast error was assumed to be a zero-mean normal distribution with a standard deviation of 2% of the forecasted load. The maximum short-term load variations were calculated from the historical data of Korea during 2010, and were set as 1.2% of the hourly load for both directions. The time step (\(\tau\)) was set to be five minutes, which is equal to the dispatch interval of automatic generation control.  
Furthermore, the hourly wind power forecast was obtained by utilizing a moving average model with hourly measurements of five wind farms in Korea in 2010. The available wind generation range was set between three times of the standard deviations. The maximum wind power variations were also calculated using the historical data of Korea as shown in Figure 2.  
The total number of private vehicles was assumed to be seven hundred thousand, which is scaled down from that of the private vehicles in Korea considering the total capacity of the test system. In the base case, the number of EVs was assumed to be 20% of private vehicles. The battery capacity of an EV was assumed to be 16 kWh. It was assumed that the EVs are charged only at home with 1.44 kW rated power. The daily travel distance is assumed to be log-normally distributed with a mean of 36.9 km and with a standard deviation of 23.4 km obtained from [22]. The start time of charging is obtained from the distribution of departure time [22] with an assumption that the EVs begin charging one hour after their departure from work.
## 4 Application
### Comparing Results
Furthermore, the enhanced robustness to noise in PET/CT imaging is essential for achieving reliable diagnostic outcomes in clinical settings, even when dealing with low-quality or noisy medical images.
## 2 Methodology
### Game Theory Combined Weighting Method  
GTCWM aims to determine the optimal combination of weights derived from multiple methods by minimizing the deviation between the combined weight and each individual weight vector [18]. By integrating the advantages of both subjective and objective weighting approaches, GTCWM effectively mitigates the bias that may arise from relying on a single method, thereby enhancing the objectivity and scientific rigor of the final weight allocation [10]. The specific steps of the GTCWM are as follows:  
1. Linear combination of weights:  
Linearly combine the weight vectors \(\mathbf{\eta}=\left(\eta_{1},\eta_{2},\ldots,\eta_{n}\right)\) and \(\mathbf{\theta}=\left(\theta_{1},\theta_{2},\ldots,\theta_{n}\right)\) obtained from the G1 method and EWM, respectively. Let the resulting combined weight vector be \(\mathbf{\delta}\), as shown in Equation (6), where \(\beta_{1}\) and \(\beta_{2}\) represent the weighting coefficients for the subjective and objective methods, respectively. \[\mathbf{\delta}=\beta_{1}\mathbf{\eta}+\beta_{2}\mathbf{\theta}.\] (6)Construct the objective function:  
The goal is to minimize the deviation among \(\delta\), \(\eta\), and \(\theta\). Using the linear combination coefficients \(\beta_{1}\) and \(\beta_{2}\) from Equation (6), the optimal weight \(\delta\) is obtained. The objective function is formulated as follows:  
\[\left\{\begin{array}{l}\min\|\delta-\eta\|_{2}\\ \min\|\delta-\theta\|_{2}\end{array}\right.. \tag{7}\]
3. Solve the subjective and objective weight coefficients \(\beta_{1}\) and \(\beta_{2}\). According to the differential properties of matrices and the first-order optimality conditions derived from the objective function in Equation (7), the problem can be transformed into the following system of linear equations:  
\[\begin{bmatrix}\eta\eta^{T}&\eta\theta^{T}\\ \theta\eta^{T}&\theta\theta^{T}\end{bmatrix}\begin{bmatrix}\beta_{1}\\ \beta_{2}\end{bmatrix}=\begin{bmatrix}\eta\eta^{T}\\ \theta\theta^{T}\end{bmatrix}. \tag{8}\]  
By solving Equation (8), \(\beta_{1}\) and \(\beta_{2}\) are obtained. \(\xi_{1}\) and \(\xi_{2}\) are the normalized values of \(\beta_{1}\) and \(\beta_{2}\). Finally, the combined weight is shown in Equation (9):  
\[\delta=\xi_{1}\eta+\xi_{2}\theta. \tag{9}\]
## 3 Methodology  
Our proposed methodology integrates two complementary optimization techniques--Neural Architecture Search (NAS) and structured pruning--in a sequential pipeline to develop efficient deep learning models for WiFi sensing applications. This integrated approach is motivated by several key observations:  
* **Architectural Efficiency vs. Parameter Efficiency:** While NAS focuses on finding optimal macro-level architectural configurations (layer types, connectivity patterns,etc.), pruning optimizes micro-level parameter efficiency within a given architecture. This complementary relationship allows us to address model efficiency from both structural and parametric perspectives.
* **Search Space Reduction:** NAS first identifies promising architectural candidates, effectively reducing the search space for subsequent pruning operations. This makes the pruning process more focused and computationally efficient compared to pruning arbitrary architectures.
* **Baseline Quality Assurance:** By using NAS as the first step, we ensure that pruning starts from architectures that are already well-suited for the target task, rather than attempting to optimize potentially suboptimal baseline architectures.  
This two-phase approach is particularly relevant for WiFi sensing applications, where both architectural design and parameter efficiency are crucial for real-world deployment. The following sections detail each component of our methodology.
## 2 Materials and Methods
### Neural Networks Architectures for TEC Forecasting
#### 2.2.1 Data Preprocessing  
For each year and each station, the data preparation procedure was as follows. Datasets for each year considered consisted of samples with a time step of 2 h for both TEC values and solar and geomagnetic activity indices. For indices that are provided only on a daily basis, daily values were used in 2 h increments throughout the respective day.  
Each dataset was split into training, validation, and test subsets. The first 40% of the samples from yearly datasets were used as training data, the next 10% were used for validation, and the last 50% of the samples corresponding to the second half of the year were used for testing. TEC samples are organized into subsequences formed by the applied sliding window with a width equal to 12 samples. All the architectures in this paper are considered for two sets of indices of solar and geomagnetic activity. One set included only Np, 10 Kp, Dst, and F10.7; the second set included Np, 10 Kp, Dst, F10.7, and Vsw. One of the tasks of this work was to determine the possibility of improving the accuracy of TEC values' prediction by including Vsw in the training set due to a high correlation with TEC in this case (Figure 3). The indices are organized into individual sequence features, but no sliding window is applied. Each index array is shifted so the TEC value at time step \(t\) was predicted using the index at time step \(t\)\(-\)1. Thus, the TEC value at the time step \(t\) is predicted using 12 previous TEC values and a single previous value of the respective included indices. Data arrays are truncated appropriately to maintain the same array lengths for all features.
## 6 Space-Segment Optimization Aspects
### Leo Satellite Constellations  
In the literature, we can find many constellations that have most of their satellites already in the sky (e.g., Globalstar, OrbComm, Iridium,...). However, not all LEO constellations are fully operational. Some of them are partially deployed (e.g., Starlink, Oneweb), and some others are only planned to be launched in the relatively near future (e.g., Ama  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Topology** & **Design** & **Common** & \\
**Parameters** & **Parameters** & **Flane and Phase Separation** \\ \hline Walker Constellation & \(i:N_{T}/N_{p}/F,h\) & \(i,a\) & \(\Delta\Omega_{jk}=2\pi\frac{(j-1)}{N_{p}}\) & (2) \\  & & & \(\Delta M_{jk}=2\pi\frac{N_{p}}{N_{T}}(k-1)+2\pi\frac{F}{N_{T}}(j-1)\) & (3) \\  & & & \(\Delta\Omega_{k}=2\pi\frac{F_{e}}{F_{k}}(1-k)\;mod(2\pi)\) & (4) \\  & & & \(\Delta M_{k}=2\pi\frac{F_{e}N_{p}+F_{e}N_{e}}{F_{k}N_{d}}(k-1)\) & (5) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Walker and Flower Constellations Modeling Parameters.  
Figure 5: Examples of Constellations: **A**. Single orbital plane with 10 satellites at 700 km. **B**. Star constellation or Walker constellation with 6 polar orbital planes, with 10 satellites at 700 km. **C**. Walker constellation with 6 orbital planes at a 53 degree inclination and 10 satellites in every orbital plane. **D**. Flower constellation with 6 orbital planes at a 63 degree inclination, with 10 satellites on elliptical orbit with apogee of 3000 km.  
zon Kuiper or Facebook Athena). In Table 7, we have listed some of the most promising/currently relevant LEO constellations. Currently, LEO constellations are typically used for narrowband and broadband communications and Earth observation. The main advantage of LEO lower altitudes (compared to MEO) is the relatively low latency, fundamental for voice applications and high performance internet connection. Traditionally, LEO constellation were placed in the higher end of LEO altitudes (e.g., >800), e.g., Globalstar, Orbocomm, and Iridium. On the contrary, due to the latest progress in satellite construction and launching, we are able to put in orbit smaller satellites in a more efficient way, placing in an orbit multiple satellites in a single launch. Besides the constellation names, Table 7 also lists some relevant parameters describing these constellations, as well as some references for the most advanced readers. Table 7 summarizes fundamental orbit parameters, such as the total number of satellites in the constellation, the number of independent orbital planes \(N_{P}\), the altitude (or altitudes) \(h\) considered during the constellation design, and the orbital plane inclination \(i\). In addition, Table 7 also shows the frequency band used for the considered constellations, as well as the typical satellite mass and the main purpose of the constellation.
# Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps
## 3 Improved A* Algorithm Based on Local Obstacle Rate and Dynamic Neighbourhood
### Improved A* Algorithm Based on Local Obstacle Rate Search and Dynamic Weighting
#### 3.2.1 Localised Obstacle Rates  
The accurate acquisition of local obstacle rates relies on a dynamically constructed drift matrix. The movement mechanism of this matrix is similar to the expansion process of nodes in the A* algorithm, both of which are traversed and evaluated in space. Within a given map, the smaller the size of the drift matrix, i.e., the shorter the length of its edges, the richer the details of the local environment it captures, which can more accurately reflect the distribution of obstacles in the region surrounding the current node.  
Taking the initial A* algorithm as an example, when the node extension adopts a neighbourhood range of 3 \(\times\) 3, in order to match it and obtain an accurate local obstacle rate, this paper chose a drift matrix corresponding to this neighbourhood size, i.e., the length of its edges equals the size of three raster map units. In this way, the drift matrix accurately covers the 3 \(\times\) 3 raster area around the current node, allowing the computed local obstacle rate to reflect the obstacle information in this area in a detailed and accurate manner.  
The process of obtaining the local obstacle rate based on the matrix is as follows. As shown in Figure 4, let the modelling be according to the raster method, wherein the map size obtained is 10 \(\times\) 10, the starting point coordinates are S (1, 1), the end point coordinates are E (10, 10), and the drift matrix is a raster matrix of 3 \(\times\) 3 size. Then, the initial node S goes to the next node \(a_{1}\), at this time to \(a_{1}\) as the centre, the 3 \(\times\) 3 drift matrix is expanded, the obstacle rate \(p\) is calculated, and the obstacle rate formula for \(p\) equals the number of obstacle grids divided by 8. According to the evaluation function to select the node with the smallest cost after the node, from the node \(a_{1}\) to the node \(a_{2}\), \(a_{2}\) at the local obstacle rate is calculated in the same way as the above. Keep cycling through the process until the extension of the end point E, where the local obstacle rate \(p\) value takes the value range [\(p_{min}\), \(p_{max}\)] = [0, 1].
# A Simplified CityGML-Based 3D Indoor Space Model for Indoor Applications
## 3 Simplified 3D Indoor Space Model (3 DSISM) Based on CityGML
### Simplified Representation for Indoor Spaces  
CityGML2.0 LOD4 describes building information at the highest level of detail, including detailed exterior and interior building information. Although the 3D indoor space model proposed in this paper does not describe exterior information, not all indoor applications require the most detailed indoor information [39]. Therefore, the simplification of building components to generate lightweight  
Figure 6: UML diagram of Simplified 3D Indoor Space Model (3 DSISM) based on CityGML.  
but efficient indoor spatial data is an appropriate approach to model 3D indoor space for indoor navigation. The simplification of indoor spaces is largely different from that of the exterior shape of a building. The simplification of a building's exterior is mostly a simplification of its exterior contours, removing unnecessary and redundant data to achieve a low level of detail to describe the building exterior. While the simplification of interior spaces requires a consideration of the geometric representation of building components, descriptions of the indoor spaces and relationships among indoor spaces according to the requirements of different interior applications. Therefore, principles of simplification are needed in the descriptions of interior spaces. It should be noted that we put emphasis on a simplified representation for building elements and indoor spaces, not a methodology for simplification.
# RE-PU: A Self-Supervised Arbitrary-Scale Point Cloud Upsampling Method Based on Reconstruction
## 4 Experiments
### Datasets and Metrics  
The experiments in this study were performed and tested on the dataset proposed in [25] (for convenience of description, denoted as PU-GAN) and the dataset PU1K proposed in [17]. The point cloud data were obtained from the surface of original 3D triangle meshes by Possion Disk Sampling. Since our method is self-supervised, we only utilized the testing set of the dataset during training, rather than the training set.  
**PU-GAN**: The PU-GAN [25] dataset consists of a total of 147 models, with 120 designated for training and 27 for testing. This dataset is widely employed in point cloud upsampling tasks. The training data had 40 simple models, 40 medium models, and 40 complex models, covering point clouds of diverse complexities. During the training phase, each model contained 200 point clouds patches, resulting in a total of 24,000 point cloud pairs.  
**PU1K**: The PU1K [17] dataset is currently the largest dataset in point cloud upsampling, approximately 8 times larger than the PU-GAN dataset, of which the PU-GAN dataset is a subset. The PU1K dataset comprises 1020 training models and 127 test models, with the majority sourced from ShapeNetCore [56]. During the training phase, each model contains 50 point clouds patches, resulting in a total of 69,000 point cloud pairs.  
**Evaluation Metrics**: We utilized five quantitative evaluation metrics to evaluate the experimental results: (i) CD (Chamfer Distance, describing the average distance of the closest points of two point clouds); (ii) HD (Hausdorff Distance, describing the farthest distance between the closest points of two point clouds); (iii) EMD (Earth Mover Distance, describing the average distance of optimal transportation between two point clouds); (iv) P2F (Point-to-Surface Distance, describing the distance from the generated point to the original surface). Among them, P2F includes the mean and variance, representing its mathematical expectation and degree of dispersion, respectively. For all five metrics, smaller values indicate better performance.
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design
## 4 Numerical Examples
### \(H^{\infty}\) Static Controllers  
Then, we consider the reduced-order \(H^{\infty}\) control problem formulated in Problem 2. Here we choose AC4, NN1, NN12, and HE6 from COMPLib. AC4 is from a autopilot control problem for an air-to-air missile discussed in [37]. NN1 and NN12 are academic test problems proposed in [38] and [39], respectively. HE6 is a helicopter model that has four inputs, 20 states, and six outputs [40]. For these plant models, we seek the \(H^{\infty}\) static controller by using the bisection method on \(\gamma\). Namely, we first give a sufficiently large upper bound \(\overline{\gamma}\), for example \(\overline{\gamma}=100\), and a sufficiently small lower bound \(\underline{\gamma}\) (e.g., \(\underline{\gamma}=0\)). Then we set \(\gamma=(\underline{\gamma}+\overline{\gamma})/2=50\) and solve Problem 2. If there is a feasible solution, then we update the upper bound to \(\overline{\gamma}=\gamma=50\), otherwise we set the lower bound to \(\underline{\gamma}=\gamma=50\). We note that the problem is assumed to be infeasible if a solution of Problem 2 is not found after 15 iterations of (16). We repeat this process until sufficient accuracy is achieved.  
Table 4 shows the obtained upper bounds of \(\gamma\) for the chosen models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & AC4 & NN1 & NN12 & HE6 \\ \hline \(\gamma\) & 1.000 & 74.72 & 28.09 & 520.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Upper bounds \(\gamma\) of the \(H^{\infty}\) norm by static controller.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\mathbf{N}\mathbf{H}^{\infty}\)[36] & **CCL [12]** & **NNM [15]** & **GS96 [13]** & **Proposed** \\ \hline TF1 (\(n_{c}=1\)) & unstable & **stable** & **stable** & **stable** & **stable** \\ CPU time [s] & 0.016910 & 0.214947 & 0.177307 & 0.194252 & 1.836873 \\ \hline TF1 (\(n_{c}=2\)) & unstable & **stable** & **stable** & **stable** & **stable** \\ CPU time [s] & 0.016012 & 0.263448 & 0.205307 & 0.206729 & 4.250059 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of stabilizing low-order controllers with \(n_{c}=1\) and 2 for TF1.  
The obtained static controllers are given as follows:  
\[\begin{split} K_{\text{AC4}}&=\left[-0.3228\quad-0.07534 \right],\\ K_{\text{NN1}}&=\left[3.5\quad 60.22\right],\\ K_{\text{NN12}}&=\left[\begin{matrix}22.81&-35.61\\ -5.179&8.137\end{matrix}\right],\\ K_{\text{HE6}}&=\left[\begin{matrix}83.25&-0.5581&-0.5931&0.1238&0.1546&-0.025 33\\ -24.27&7.876&0.7263&0.0309&0.3272&-0.6042\\ -15.85&0.1609&-6.578&-2.062&1.677&0.1478\\ 65.77&-1.413&9.07&-16.61&-0.9596&-0.04191\end{matrix}\right].\end{split} \tag{34}\]  
It is easy to check that the obtained static controllers really achieve the \(H^{\infty}\) norm listed in Table 2. These numerical examples demonstrate the effectiveness of the proposed method.
## 3 EV Battery Chargers and Their Architectures
### Based on Power Flow  
The EV charger can be either unidirectional or bidirectional, depending on the direction of the power flow. Unidirectional chargers are used for charging EVs, and bidirectional chargers are used for both charging and discharging. Bidirectional chargers can be used for vehicle-to-grid (V2G) applications, where electricity stored in the EV battery is used to feed power back into the grid [60].  
Unidirectional Charger: The most popular type of EV charger is a unidirectional charger. In one-way charging, electricity is supplied from the grid to the EV and does not flow back. Most EV drivers have experience with this type of charger. In unidirectional charging, electricity is supplied solely from the grid to the EV. In most cases, the unidirec  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technology** & **Description** & & **Pros** & **Cons** \\ \hline Hybrid Electric Vehicles (HEVs) & Features a combination of a conventional gasoline engine with an electric motor and battery. & •  
tional charge is used for home charging and public charging stations since it is easy to implement and inexpensive to maintain.  
Bidirectional Charger: The bidirectional EV charger is a newer type of EV charger that facilitates two-way electricity flow. This means that an EV can be used to charge up from the grid and then discharge the energy back into the grid later when it is no longer needed. Both the EV and the grid can benefit from a bidirectional charger's ability to send and receive power. A bidirectional charger can serve as a backup power source in the event of an outage or be used for charging at home. There are several advantages to bidirectional charging over traditional unidirectional charging, including improved efficiency and dependability in terms of both energy use and business operations. However, there are also drawbacks to bidirectional EV charging, including a large initial investment, incompatibility with EVs, and obstacles posed by regulators. When compared to the conventional one-way EV charger, the bidirectional charger has many advantages. But before broad use of bidirectional EV charging can occur, there are various obstacles that must be overcome.  
Figure 11: Different architectures of EV battery chargers.  
Figure 10: Block diagram of on-board and off-board charging system for electric vehicles.  
Table 6 presents a comparative analysis of unidirectional and bidirectional chargers. This comparative analysis offers a concise examination of the differences between unidirectional and bidirectional chargers.