esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wind Estimation Algorithms
### Method Based on Extended Kalman Filter for Wind Estimation  
Another method for estimating wind speed utilizes an extended Kalman filter proposed by NREL [21] and currently applied in ROSCO to calculate and provide estimated wind speeds. The extended Kalman filter leverages a covariance matrix based on the anticipated 3D wind field and defines a nonlinear rotor model to predict wind speed.  
For wind speed estimation, the nonlinear continuous time state-space model used in the continuous-discrete extended Kalman filter can be defined as shown in Equations (6) and (7). Here, \(\eta_{s}\) represents system noise defined as white noise, and \(\eta_{mc.}\) denotes measurement noise assumed to be constant white noise.  
\[x_{k}=f(x_{k-1})+\eta_{s} \tag{6}\]  
\[y_{k}=h(x_{k-1})+\eta_{mc.} \tag{7}\]  
The wind speed estimation based on the extended Kalman filter proceeds through the steps outlined in Figure 4; this process is divided into state prediction, covariance prediction, Kalman gain computation, result prediction, and covariance error computation. In Step 1, state prediction of the nonlinear wind field model is performed, expressed as Equation (8), in which \(\hat{x}_{k}^{-}\) denotes the predicted value of \(x_{k}\), which is subsequently used in the wind speed estimation step. In Step 2, the covariance error is predicted, expressed as Equation (9), where \(P_{k}^{-}\) represents the predicted value of the error covariance \(P\), used in the Kalman gain computation step, \(J_{f}\) represents the Jacobian of the process, and \(Q\) denotes the process noise covariance. In Step 3, the Kalman gain is computed, expressed as Equation (10), where \(K_{k}\) represents the Kalman gain, \(J_{h}\) denotes the Jacobian of the observation, and \(R\) signifies the measurement noise covariance. In Step 4, the result based on previously predicted and computed values is predicted using Equation (11), where \(\hat{x}\) represents the predicted result; \(Z_{k}\) denotes the measurement values including rotor speed, generator torque, and pitch angle; and \(h\) signifies the rotor system model. Through the above-described steps, the final wind speed estimation is obtained, whereafter, in Step 5 the covariance error is computed, expressed as Equation (12). The computed covariance error is fed back to Step 1, iterating the entire process for each \(k\), as previously described [20; 28; 29]. In this study, we designed and applied the method described above to assess prediction accuracy based on different wind speed estimation methods.  
Figure 4: Prediction process and sequence of the Kalman filter.  
\[\hat{x}_{k}^{-}=f(\hat{x}_{k-1}) \tag{8}\]  
\[P_{k}^{-}=J_{f}P_{k-1}J_{f}^{T}+Q_{k-1} \tag{9}\]  
\[K_{k}=P_{k}^{-}J_{h}^{T}\Big{(}J_{h}P_{k}^{-}J_{h}^{T}+R_{k}\Big{)}^{-1} \tag{10}\]  
\[\hat{x}_{k}=\hat{x}_{k}^{-}+K_{k}\big{(}z_{k}-h\big{(}\hat{x}_{k}^{-}\big{)} \big{)} \tag{11}\]  
\[P_{k}=P_{k}^{-}-K_{k}J_{h}P_{k}^{-} \tag{12}\]
## 5 Instrument Used in Milan and the Specific Mapping Approach Solution Applied to the Case Study
### Heron(r) Backpack--Mobile Mapping System
The entire global optimization results in an optimized estimate of the survey trajectories. In cases of low overlap and point matching, no connections are made between local maps. This approach avoids the restriction, often found in SLAM tools, that the survey start position must coincide with the survey end position [93; 94]. If, as designed in the survey phase, the survey paths have limited overlap between trajectories, the algorithms in Heron Desktop(r) allow the merging of these trajectories. This enables the generation of a single point cloud with associated spherical images for the entire complex. This process requires significant expertise and experience from the operators. The final point cloud, along with the corresponding spherical images, constitutes the final deliverable of the survey. The mobile mapping device and Heron Desktop processing software can also incorporate point cloud data acquired from other instruments to support the acquisition or processing phases. This can be done by loading the external point cloud into the system's memory during real time acquisition or into the software during post-processing. The sensors have a nominal accuracy of \(\pm 3\) cm, attributed to the Velodyne(r) VLP-16 sensors used [91]. Enhanced local accuracy can be achieved with the Hesai(r) 32-line sensors (Shanghai, China) (Figure 14) [95], which offer a nominal range precision of 5 mm (1 \(\sigma\)) and an accuracy of \(\pm 1\) cm. These sensors were recently introduced in the Heron(r) device (Figure 15) by the manufacturer, Gexcel. Figure 13: The centroids of the local maps, represented by colored dots, are automatically linked for the final bundle adjustment and optimization procedure. The methods implemented to ensure adherence to MM requirements are outlined below. Due to the project's significant innovation, the technical specifications were defined through the execution of a prototype project conducted prior to the commencement of the survey operations on a building complex. This facilitated the development of methods for field surveying, data processing, and the evaluation of ongoing activities.
# A Near-Far-Field Model for Bubbles Influenced by External Electrical Fields
## 5 Coupling Near-Field and Far-Field
### Bubble Transport: Validation of the Far-Field  
In the following, we validate the far-field model, which is a model based on the transport of bubbles.  
We define \(N\) grid points for the \(x\) and \(y\) direction in the area of the bubble transport. Then, we deal with different space-steps, such as \(\Delta x,\Delta x/2,\Delta x/4,\Delta x/8\).  
We apply the numerical error based on the \(L_{2}\)-norm:  
\[err_{L_{2},\Delta x,\Delta x/2}(t)=\frac{1}{N}\sum_{i=1}^{N}(u_{i,\Delta x}(t )-u_{i,\Delta x/2}(t))^{2}, \tag{64}\]  
where \(N\) number of grid-points on which we compute the solutions. \(\Delta x\) spatial step of the error analysis. \(t\) time, where we apply the convergence study, e.g., \(t=t_{end}\).  
Furthermore, we apply the experimental order of convergence, see [40], which is given as:  
\[\rho_{L_{2},\Delta x,\Delta x/2}(t)=\frac{\log\left(\frac{err_{L_{2},\Delta x /2\Delta x/4}(t)}{err_{L_{2},\Delta x,\Delta x/2}(t)}\right)}{\log(0.5)}, \tag{65}\]  
where we can see a convergence rate based on the refinement of the spatial grid.  
We have the following Table 1 for the validation of the far-field:  
In the following Figure 14, we see the convergence rates with the higher resolution of the computations.  
We see a convergence based on the \(L_{2}\)-error with finer resolutions in Figure 14. Therefore, we obtain with finer grids a higher resolution for the far-field computations. Such a convergence analysis allows us to validate the far-field computations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\Delta x\) & \(err_{L_{2},\Delta x,\Delta x/2}(t)\) & \(\rho_{L_{2},\Delta x,\Delta x/2}(t)\) \\ \hline \(1/N\) & \(1.2\ 10^{8}\) & \\ \(1/(2N)\) & \(7.8\ 10^{7}\) & \(0.621\) \\ \(1/(4N)\) & \(2.1\ 10^{7}\) & \(1.893\) \\ \(1/(8N)\) & \(3.2\ 10^{6}\) & \(2.714\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validation for the far-field solvers with \(N=N_{x}=N_{y}=41\).  
Figure 14: Convergence analysis of the finer resolution with respect to the far-field validation with \(N_{start}=41\) and \(N_{i}=i\)\(N_{start}\).
## 3 Materials and Methods
### Experimental Workflow
#### 3.4.1 Data Augmentation Procedures  
Due to the unavailability of large datasets, two types of augmentation were considered, i.e., basic and advanced. The basic augmentation comprises rotation and flip, whereas the advanced augmentation also includes color jittering. Numerous configurations with respect to data augmentation were considered, as reported in the Table 1 and described hereunder. By exploiting the _transforms.Compose_ interface provided by PyTorch [59], the augmentations are sequentially performed on the fly, each with a given probability that has been set to 0.25.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Transform** & **No Aug** & **Basic Aug** & **Adv Aug** \\ \hline RandomRotation90 & ✗ & ✓ & ✓ \\ RandomRotation180 & ✗ & ✓ & ✓ \\ RandomRotation270 & ✗ & ✓ & ✓ \\ RandomHorizontalFlip & ✗ & ✓ & ✓ \\ RandomVerticalFlip & ✗ & ✓ & ✓ \\ ColorJitter & ✗ & ✗ & ✓ \\ Normalization & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data augmentation summary. All augmentations are carried out on the fly with 0.25 probability in the order they are presented in the table. ’No Aug’, ’Basic Aug’ and ’Adv Aug’ stand for ’No Augmentation’, ’Basic Augmentation’ and ’Advanced Augmentation’. The ✓ and � symbols mean that the transform is included or excluded, respectively. Normalization is always performed at after all other augmentations. ColorJitter refers to the random alterations of the _brightness_, range: [0.8, 1.2]; _contrast_, range: [0.8, 1.2]; _saturation_, range: [0.8, 1.2]; and _hue_, range: [\(-0.2,0.2\)].  
Figure 5: The overall flow diagram of the experiments. The experimental setup starts by fine-tuning the considered pretrained networks with three different datasets, i.e., the original one and two datasets obtained with two different data augmentation procedures. Thereafter, the features extracted by the feature maps of all versions of fine-tuned and pretrained networks were analyzed with both t-SNE and UMAP. Finally, Grad-CAM and LIME were applied to the RoI images.  
**No Aug** refers to the adoption of no augmentation, with the exception of normalization, by rescaling intensity values of images from integers ranging \([0,255]\) to float values in \([0,1]\). **Basic Aug** consists of performing random rotation by the degrees in multiples of 90, and performing random horizontal and vertical flips. **Adv Aug** takes advantage of ColorJitter transformations in addition to the previous configuration of basic augmentation. The advanced augmentation comprises random perturbations of brightness, contrast, saturation, and hue. Finally, normalization is performed similar to **No Aug**.
# Antennas and Propagation: A Sensor Approach
### Innovative Antenna Technologies for Space Applications  
In Ref. [7], multibeam antenna systems are proposed with the aim to provide multispot coverage for broadband satellite communications in the Ka-band. Two multibeam reflectarrays are used, making it possible to halve the number of required antennas onboard the satellite.  
The work in [8] presents an antenna design that can be used as an array element for monitoring and detecting radio emissions resulting from cosmic particle interactions in the atmosphere. The proposed antenna provides a high gain over a large relative bandwidth, a narrow beamwidth, a small group delay variation and a very stable radiation pattern between 110 and 190 MHz.
# Automatic Decentralized Vibration Control System Based on Collaborative Stand-Alone Smart Dampers
## 5 Results
### Experimental Results
#### 5.3.2 Coordinated Control  
In this final part of the work, the experimental results of the coordinated control strategy are reported. First of all, a tuning procedure has been done to find out the maximum gain \(g_{max,ij}\). The results for each actuator-mode pair are shown in Table 5. Gains related to Actuator 2 results higher than the ones of the others actuators. The limitation on the maximum gain for actuator 1 and 3 is due to instability. On the Actuator 2, stability limitation is just applied on mode 4, but current saturation phenomenon limits the gains of mode 2 and 3.  
Once these gains have been set, a further tuning procedure is done, which is related to the setting of \(\gamma\) parameter in order to avoid system instability or actuators saturation. Experimentally, saturation has been detected on Actuator 2 by applying \(\gamma=1\) as a first trial. Then, \(\gamma\) has been decreased to 0.65 and the final gain applied.  
As for the numerical case, the results have been reported in Table 7, showing the percentage of reduction in terms of acceleration amplitude. In Figure 7 the FRFs in the sensors positions are depicted, both for the uncontrolled and controlled case. In these plots, some fluctuations on the curves can be appreciated with respect to the numerical ones, due to the noise which affects the measurements. Then, the numerical results have been validated. The same vibration reduction has been achieved for the most excited mode (mode 2) while, for the other modes, a lower reduction has been observed. This behaviour is due to a lower value of \(\gamma\) found in the experimental tests, which comes from some approximations made on the numerical model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Mode 2** & **Mode 3** & **Mode 4** \\ \hline
**Actuator 1** & 352.1 & 63.7 & 22.7 \\
**Actuator 2** & 2308.0 & 112.5 & 0.8 \\
**Actuator 3** & 202.2 & 7.3 & 53.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Weighted gains \(g_{i,j}\) evaluated for the \(i\)-th vibration mode and the \(j\)-th actuator.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mode** & **Sensor 0** & **Sensor 1** & **Sensor 2** \\ \hline \multirow{3}{*}{**Uncontrolled [m s\({}^{-2}\)]**} & **2** & 1.9540 & 0.1205 & 1.463 \\ \cline{2-5}  & **3** & 0.2826 & 0.4863 & 0.1439 \\ \cline{2-5}  & **4** & 0.0426 & 0.0074 & 0.1585 \\ \hline \multirow{3}{*}{**Controlled [m s\({}^{-2}\)]**} & **2** & 0.1868 & 0.0154 & 0.1839 \\ \cline{2-5}  & **3** & 0.1249 & 0.2201 & 0.0604 \\ \cline{2-5}  & **4** & 0.0247 & 0.0043 & 0.0860 \\ \hline \multirow{3}{*}{**Reduction [\%]**} & **2** & 90.44 & 87.22 & 87.43 \\ \cline{2-5}  & **3** & 55.79 & 54.74 & 58.01 \\ \cline{1-1} \cline{2-5}  & **4** & 42.08 & 41.76 & 45.74 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between the vibration of the uncontrolled (blue line) and controlled (red line) system on the three sensors locations in experimental tests.
## 2 Industry 4.0 Background
### Ubiquitous Internet Access  
Industrial environments have been characterised by devices connected through wired communication media working over industrial protocols [15]. Indeed, considering the stringent requirements related to the control of industrial processes, cables are the most straightforward options in terms of reliability, security, latency and resilience to interferences. Nevertheless, wireless networks offer significant advantages with respect to wired ones [16; 17]:  
* Since they are not limited by a cable, they are quick and easy to deploy, even for hard to reach and remote locations.
* They offer more flexibility and easily adapt to changes in the network configuration, thus improving scalability.
* They enable device mobility, providing real-time Internet access anywhere and anytime.
* They are available in personal smart devices such as smartphones, tablets, and smartwatches, which are equipped with a large number of sensors. Therefore, they can provide relevant information about the owner, enabling applications related to, for example, employee monitoring or customer feedback collection.  
For these reasons, and in order to meet the requirements that are typical of industrial environments, specific Industrial Wireless Network (IWN) solutions have been developed. Compared to traditional wireless networks, IWNs have to address the following issues [17]:  
* **Interference and path loss:** industrial environments are characterised by challenging conditions, due to the presence of dust, vibrations, heat, obstacles, critical temperatures and humidity levels. Furthermore, the presence of motors, metal obstacles and other wireless communications introduces severe signal interference.
* **Latency:** industrial processes are typically required to have real-time or quasi-real-time performance, hence latency has to be kept low. Nevertheless, higher energy amounts and costs are needed to reduce latency, and this might affect nodes' lifetime.
* **Changing location:** industrial processes may be location-dependent, meaning that node location may be a parameter for the process to be carried out. Therefore, node location should be always known, even when they are mobile. Accordingly, localization and tracing mechanisms need to be introduced.
* **Security and privacy:** since wireless signals can be easily intercepted, they are much more susceptible to security and privacy attacks (e.g., jamming, spoofing, packet sniffing) with respect to wired signals. For this reason, specific security and privacy-preservation technologies have been developed, so that industrial processes and employee/customer data are not threatened.  
The evolution of wireless solutions to meet these requirements enables ubiquitous Internet access even for harsh scenarios and demanding processes such as the industrial ones. Therefore, everyone and everything will be connected: people to people, people to objects, people to services, and objects to objects [11]. A further evolution would be the introduction of hardware and software handover mechanisms, which would make seamless communication possible.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 1 Introduction
### Literature Review
[35] collected and analyzed local gas load and meteorological data, proposed a daily average temperature correction algorithm based on the cumulative effect of temperature, and built a back-propagation-improved complete ensemble empirical mode decomposition with adaptive noise-gated recurrent unit combined prediction model. In addition to temperature, weather variables such as humidity, wind speed, and precipitation were also incorporated into the forecast model. [PERSON] et al. [36] proposed a multi-component natural gas load forecasting method based on a residual recurrent neural network by integrating historical, climatic, and holiday factors. However, more studies have been conducted on the relationship between daily gas load and temperature, [PERSON] et al. [37] analyzed the relationship between load variation and meteorological factors using the gas load and meteorological data of Hangzhou City, established a prediction model based on SVM, and found that the load had seasonal and daily variation characteristics, and the temperature and pressure were significantly correlated with the load rate. [PERSON] et al. (2016), aiming at the forecasting error of gas load under extreme weather in Chinese cities in winter, proposed a forecasting method based on short-term temperature and load data combined with one-week weather forecast. [PERSON] and [PERSON] (2017) used K-means clustering combined with temperature estimation to identify daily load categories and then independently predicted different daily load types by stacking noise reduction autoencoders based on Algerian hour data verification, effectively improving the prediction accuracy. But less consideration has been given to the effect of comprehensive weather variables on daily gas load. To sum up, although traditional statistical methods and artificial intelligence methods have made some progress, there are generally two limitations. First of all, the traditional statistical method has application value when the data amount is small and the rule is simple, but it is difficult to adapt to the nonlinear, multi-variable coupling and high-frequency fluctuation characteristics of gas load, and the prediction ability is poor and the prediction accuracy is low. Secondly, although the artificial intelligence method has strong fitting ability, it lacks the support of physical mechanism and has difficulty explaining the actual impact of meteorological variables on load. In this paper, an MWP-DLP model based on composite weather variables was proposed. For the first time, cumulative temperature, solar radiation, wind speed, and building foundation temperature changes were incorporated into the unified modeling system, and multi-factor influence coefficients were dynamically modified by genetic algorithm, providing a new path for solving nonlinear and multi-variable coordination problems in urban gas load prediction.
## 6 Conclusions  
This paper focuses on addressing the problem of collaborative DL inference in LEO satellite edge computing. We propose the COIN-LEO algorithm, specifically designed for efficient model partitioning and task assigning among satellites. To achieve this, we trained a five-layer fully connected NN to accurately predict inference time based on the remaining resources of each satellite. Through a series of experiments, we evaluate the performance of our proposed COIN-LEO algorithm in different resource-occupation scenarios. The results demonstrate that COIN-LEO effectively improves inference efficiency by intelligently assigning tasks based on resource-occupation rate.  
In future research, we plan to further enhance our approach by incorporating a broader range of DL models and diverse datasets. This expansion aims to explore the strategy's adaptability and generalization across various real-world scenarios, ensuring robustness and effectiveness in different satellite constellations and dynamic environments.  
**Author Contributions:** Formal analysis, H.Z.; Methodology, P.Z., C.L., G.X. and C.S.; Software, Z.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is partially supported by the Natural Science Foundation of Shandong Province under Grant ZR2022 LZH015 and ZR2020 MF006, partially supported by the Industry-university Research Innovation Foundation of Ministry of Education of China under Grant 2021 FNA01001, partially supported by the Open Foundation of State Key Laboratory of Integrated Services Networks (Xidian University) under Grant ISN23-09, partially supported by the Key Funding from National Natural Science Foundation of China under Grant 92067206.  
**Data Availability Statement:** Data sharing is not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Network overhead of different strategy.
# Coordinate Descent for Variance-Component Models
## Appendix B Woodbury Identity  
Recall from Section 3.3.2 that we have the definitions \(\boldsymbol{w}:=\mathbf{Z}_{k}^{\top}\mathbf{\Omega}^{-1}\boldsymbol{y}\); \(\mathbf{B}:=\mathbf{Z}_{k}^{\top}\mathbf{\Omega}^{-1}\mathbf{Z}_{k}\) and \(\mathbf{M}:=\mathbf{I}_{c_{k}}+(x-\gamma_{k}^{(t)})\mathbf{B}\) and the simplified univariate function,  
\[h_{k}(x)=-(x-\gamma_{k}^{(t)})\boldsymbol{w}^{\top}\mathbf{M}^{-1}\boldsymbol{w }+x\;\text{tr}(\mathbf{B})+\text{const}.\]  
We now derive the first and second derivatives of this function. The differentiation of an invertible symmetric matrix implies that  
\[\frac{\partial\mathbf{M}^{-1}}{\partial x}=-\mathbf{M}^{-1}\mathbf{B}\mathbf{ M}^{-1}. \tag{12}\]  
Then, from the product rule of differentiation,  
\[h_{k}^{\prime}(x)=(x-\gamma_{k}^{(t)})\boldsymbol{w}^{\top}\mathbf{M}^{-1} \mathbf{B}\mathbf{M}^{-1}\boldsymbol{w}-\boldsymbol{w}^{\top}\mathbf{M}^{-1} \boldsymbol{w}+\text{tr}(\mathbf{B}).\]  
If we then approximately solve the linear system \(\mathbf{M}\boldsymbol{d}=\boldsymbol{w}\) with CG, then  
\[h_{k}^{\prime}(x)=-\boldsymbol{w}^{\top}\boldsymbol{d}+(x-\gamma_{k}^{(t)}) \boldsymbol{d}^{\top}\mathbf{B}\boldsymbol{d}+\text{tr}(\mathbf{B}).\]Using the matrix product rule of differentiation, we have that  
\[\frac{\partial\mathbf{M}^{-1}\mathbf{B}\mathbf{M}^{-1}}{\partial x}=2\mathbf{M}^{ -1}\mathbf{B}\mathbf{M}^{-1}\mathbf{B}\mathbf{M}^{-1}. \tag{10}\]  
Then, using (11) and (10) to differentiate \(h_{k}^{\prime}(x)\), we obtain  
\[h_{k}^{\prime\prime}(x)=2\mathbf{w}^{\top}\mathbf{M}^{-1}\mathbf{B}\mathbf{M}^{-1} \mathbf{w}+2(x-\gamma_{k}^{(t)})\mathbf{w}^{\top}\mathbf{M}^{-1}\mathbf{B}\mathbf{M}^{ -1}\mathbf{B}\mathbf{M}^{-1}\mathbf{w}.\]  
If we solve \(\mathbf{M}\mathbf{d}=\mathbf{w}\) and \(\mathbf{M}\mathbf{j}=\mathbf{B}\mathbf{d}\) with CG then \(j\) will approximate the matrix-vector product \(\mathbf{M}^{-1}\mathbf{B}\mathbf{M}^{-1}\mathbf{w}\) and the second derivative can be evaluated as  
\[h_{k}^{\prime\prime}(x)=2\mathbf{d}^{\top}\mathbf{B}\mathbf{d}+2(x-\gamma_{k}^{(t)}) \mathbf{d}^{\top}\mathbf{B}\mathbf{j}.\]