esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In response to the potential impact of the gradual integration of intermittent renewable energy sources into the power grid, this study has developed a control system specifically for the E-dReg controller. The purpose of this controller is to effectively mitigate the potential impacts and disturbances caused by the integration of a large amount of renewable energy into the grid. This is crucial to maintaining a balance between power generation and load consumption, ensuring the stable operation of the power system.  
In the E-dReg control system, a flexible energy transfer scheduling feature has been incorporated, building upon the foundation of dynamic regulation reserve (dReg0.5). The objective is to enhance the efficiency of dReg0.5 utilization and extend the primary operating range of the energy storage system. The E-dReg controller, developed in this paper, takes into account the charging state of the energy storage system. It can sense the frequency within one second and perform precise frequency regulation control actions. Moreover, it is equipped to execute peak-shifting energy transfer scheduling within specified time intervals. Furthermore, by integrating with an EMS capable of receiving emergency dispatch commands from the Taiwan Power Company through a telemetry communication module, the E-dReg controller can accurately execute the charge and discharge actions specified in the emergency dispatch instructions. The proposed E-dReg control strategy in this paper enables the controller to maintain the SOC of the ESS within a safe and ideal range while accurately performing frequency regulation, energy transfer, and emergency  
Figure 19: The frequency response output curve for E-dReg receiving the emergency dispatch command (test two).  
Figure 18: The HMI for E-dReg receiving the emergency dispatch command (test two).  
dispatch controls. Over extended periods of operation, the controller consistently achieves a high execution quality level of 100%, ensuring competitiveness in the future ancillary services market.  
Conceptualization, H.-C.C. and C.-C.-K.; methodology, H.-C.C.; software, Y.-S.T. and C.-W.C.; formal analysis, Y.-S.T. and C.-W.C.; investigation, Y.-S.T. and C.-W.C.; resources, H.-C.C. and C.-C.K.; writing--original draft preparation, Y.-S.T.; writing--review and editing, H.-C.C. and C.-C.K.; visualization, H.-C.C.; supervision, H.-C.C. and C.-C.K.; project administration, H.-C.C. and C.-C.K. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support of the National Science and Technology Council of Taiwan, under contract number: MOST 111-2221-E-167-005-MY2.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Object-Level Semantic Map Construction for Dynamic Scenes
## 4 Results
### Camera Pose Estimation Accuracy  
We evaluated the camera pose estimation accuracy in static and dynamic environments by employing the widely used TUM RGB-D dataset [52]. The dataset provides RGB-D sequences with ground truth camera trajectories recorded by a motion capture system. We report the commonly used root mean square error (RMSE) of the absolute trajectory error (ATE) and relative pose error. As shown in Figure 7, because our proposed method was developed based on the ElasticFusion framework, the camera pose estimation has accuracy comparable to that of the ElasticFusion [3] method in a static scene. Meanwhile, in a dynamic environment, inspired by the MaskFusion [38] and Co-Fusion [37] methods, our proposed method, which eliminates the dynamic part in the current frame and tracks all static objects models for camera pose estimation, achieves higher accuracy than the other methods. In general, the experimental results, which compare the proposed method to the related state-of-the-art approaches, demonstrate that our method achieves similar performance in static scenes, and improved accuracy and robustness in dynamic scenes.
## 4 Experiments and Results  
### Datasets  
For semantic segmentation, 4093 labeled images--3475 from the Cityscapes [54] dataset and 618 from a custom dataset--were used for training. The Cityscapes dataset provided sufficient labels for vehicles and roads. The custom dataset focused on capturing roads near Jeonbuk National University, especially pathways with trees and stairs. For annotation, the Computer Vision Annotation Tool was utilized.  
For the drivable region mapping, a total of eight paths were captured, as shown in Figure 5. Data collection was performed using a Zed2i RGB-D camera installed on a vehicle. Further details about the dataset are provided in Table 1. Additionally, the Complex Urban dataset [55], a benchmark dataset taken in an urban environment, was also used to evaluate the performance of the proposed method. Among the datasets, the Urban 28 sequence was used, as shown in Figure 6. Table 2 provides further details about the dataset. Unfortunately, the Complex Urban dataset does not contain depth images. Therefore, SLAM was performed in stereo mode, and the drivable region was confirmed through depth estimation. In the depth-estimation process, the Depth Anything [56] model was utilized. Through the evaluation of these two datasets, we demonstrated that the proposed method is effective in large-scale environments.
# Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition
## 3 Algorithm Description
#### 3.1.2 Spatial Spectrum Reconstruction of the Interference Signal based on the Sparse Estimation
\begin{array}{c}\min\left\|\widetilde{\mathbf{A}}\left[\begin{array}{c}x_{ \text{Re}}\\ x_{\text{Im}}\end{array}\right]-\widetilde{\gamma}\right\|_{2}^{2}+\lambda\bm {1}^{\mathsf{T}}\mathbf{u}\\ \text{s.t.}\sqrt{x_{\text{Re}}[m]^{2}+x_{\text{Im}}[m]^{2}}\leq u[m],\;m=1, \cdots M\end{array}  \tag{13}\]  
where:  
\[\widetilde{\mathbf{A}}=\left[ \begin{array}{cc}\text{Re}(\mathbf{A})&-\text{Im}(\bm {A})\\ \text{Im}(\mathbf{A})&\text{Re}(\mathbf{A})\end{array} \right]\]  
\[\widetilde{Y}=\left[ \begin{array}{c}\text{Re}(Y)\\ \text{Im}(Y)\end{array} \right]\]  
\(\text{Re}(A)\) means taking the real part of matrix A and \(\text{Im}(A)\) means taking the imaginary part of matrix A. The expression \(\mathbf{u}=[u[1],\cdots,u[M]]\) is an auxiliary variable; \(x_{\text{Re}}\) and \(x_{\text{Im}}\) are the real and imaginary parts of the \(\mathbf{x}\). Equation (12) can further be written by using the interior point method:  
\[\text{minimize }t\left\|\widetilde{\mathbf{A}}\left[ \begin{array}{c}x_{\text{Re}} \\ x_{\text{Im}}\end{array} \right]-\widetilde{Y}\right\|_{2}^{2}+t\lambda\mathbf{1}^{ \mathsf{T}}\mathbf{u}+\mathbf{\phi}(x_{\text{Re}},x_{\text{Im}},\mathbf{u}) \tag{14}\]  
where \(\mathbf{\phi}(x_{\text{Re}},x_{\text{Im}},\mathbf{u})=-\sum\limits_{m=1}^{M}\log(u[m]^ {2}-x_{\text{Re}}[m]^{2}-x_{\text{Im}}[m]^{2})\), \(t\) is a scalar. Equation (13) is a quadratic convex optimization problem and can be solved by the Newton method [12]. We can get \(\mathbf{x}=\sqrt{x_{\text{Re}}^{2}+x_{\text{Im}}^{2}}\) by solving Equation (13), then the direction of the interference signal can be determined according to the magnitude of each entity in \(\mathbf{x}\). We denote the estimated DOA by \(\theta_{r}\), \(r=1,2,\cdots,N_{R}\) with \(N_{R}\) equal to the number of non-zero entities in \(\mathbf{x}\) reconstructed. According to the RIP criteria, the relationship between \(N_{R}\) and the dimension of matrix \(\mathbf{A}\), i.e., \(M\times 2N_{A}\), can be described as \(M\geq cN_{R}\log(2N_{A}/N_{R})\), where \(c\) is very small constant.
# A Complete and High-Resolution Estimate of Sardinia's Rootrop Photovoltaic Potential
## 3 Methodology
### Estimation of Incident Radiation  
The mean annual irradiance on the plane of the array \(E_{\text{FOA}}^{\text{y}}\) was obtained from the sum of several contributions, i.e., the mean annual irradiances due to the direct component \(E_{b}^{\text{y}}\), the diffuse component from the sky \(E_{d}^{\text{y}}\) and the diffuse component from the ground \(E_{g}^{\text{y}}\):  
\[E_{\text{FOA}}^{\text{y}}=E_{b}^{\text{y}}+E_{d}^{\text{y}}+E_{g}^{\text{y}} \tag{1}\]  
The individual addends of Equation (1) could be approximated to summations:  
\[E_{b}^{\text{y}}=\int_{0}^{T}E_{b}(t)dt\approx\Delta t\sum_{i=1}^{N}E_{b,i}= \Delta t\sum_{i=1}^{N}F_{i}\text{DNI}_{i}\cos(\text{AOI}_{i}) \tag{2}\]  
Equation (2) calculates the average annual contribution \(E_{b}^{\text{y}}\) due to direct irradiance \(E_{b}(t)\) over a year (\(T\)), which was discretized into one-hour time intervals \(\Delta t\). In Equation (2) DNI\({}_{i}\) is the mean direct normal irradiance, \(F_{i}=\{0,1\}\) indicates whether the point at time instant \(t_{i}\) of the year is in shadow or is illuminated by the sun, and \(\text{AOI}_{i}\) is the mean angle of incidence of the solar ray with respect to the panel surface at time \(t_{i}\). \[E_{d}^{\text{y}}=\int_{0}^{T}E_{d}(t)dt\approx\Delta t\sum_{i=1}^{N}E_{d,i}= \Delta t\frac{1+\cos\beta}{2}\sum_{i=1}^{N}\text{DHI}_{i} \tag{3}\]  
Equation (3) is relative to the average annual contribution \(E_{d}^{\text{y}}\) due to diffuse irradiance \(E_{d}(t)\) from the sky on a surface tilted with angle \(\beta\) with the isotropic sky model [45]; \(\text{DHI}_{i}\) is the diffuse horizontal irradiance. \[E_{g}^{\text{y}}=\int_{0}^{T}E_{g}(t)dt\approx\Delta t\sum_{i=1}^{N}E_{g,i}= \Delta t\frac{1-\cos\beta}{2}\sum_{i=1}^{N}\rho\text{GHI}_{i} \tag{4}\]  
Equation (4) is relative to the average annual contribution \(E_{g}^{\text{y}}\) due to diffuse irradiance from the ground \(E_{g}(t)\) on a tilted surface; \(\text{GHI}_{i}\) is the global horizontal irradiance, and \(\rho\) is the albedo. In our model, the values of GHI, DHI, and DNI depend solely on the position of the Sun with respect to the panel. Each instant \(t_{i}\) of the year corresponds to a specific position of the sun in the sky, as defined by azimuth angles \(\alpha_{i}\) and zenith angles \(\theta_{i}\). Performing the calculation described in Section 3.2 for millions of pixels and for hundreds of thousands of possible positions of the Sun in the sky (a year consists of over 500,000 min) is too computationally onerous. With the objective of keeping the spatial resolution of 1 m unchanged, it is possible to significantly reduce the number of sun positions for which to calculate whether a point is illuminated by the sun or is in shadow. The employed strategy was to divide the sky into sectors with a resolution of \(\Delta\alpha=\Delta\theta=5^{\circ}\) (both in azimuth and zenith) without sacrificing accuracy [32].
## 3 Methodology and Accuracy Assessment
### Pan-Sharpening  
In the Pan sharpening procedure, the MS image should be resampled to the same pixel size of the Pan image before converting it to CIELab color space. In this research, the bicubic interpolation method was used to resample 2 m resolution Pleiades MS images into 50 cm resolution to match the pixel size of Pleiades Pan image. This resampled dataset is used in all Pan sharpening methods used in this research, including the proposed one. After converting the MS image from RGB to CIELab space, the Pan sharpening process continues with replacing the Pan image with the L* component. Unlike the proposed method in [29] study, there is no need for color space conversion of Pan image in the proposed method, which leads to low computation and less data distortion. Before replacing the L* band of MS with the Pan image, there is a histogram matching step that could be considered as preprocessing step. After resampling the MS image to the same size of Pan image and converting the MS image color space, the histogram of Pan image has to be matched with the histogram of L* component in order to minimize the spectral differences [42]. For performing histogram matching task, mean and standard deviation normalizations were used [43]:  
\[Pan^{HM}=\left(Pan-\mu_{Pan}\right)\frac{\sigma_{I}}{\sigma_{Pan}}+\mu_{I}, \tag{8}\]  
where \(Pan^{HM}\) stands for histogram matched Pan image, \(\mu\) stands for mean and \(\sigma\) represents standard deviation. After these preprocessing steps, the L* component is replaced with a Pan image. The Pan sharpened image is then produced by implementing inverse conversion of CIELab color system on the Pan*a*b* image and results in a new MS image with high spatial resolution.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 4 Experiments and Results
### Verification Experiment under Simulated Smoke and Water Mist Environment  
In order to verify the robustness of the proposed method under smoke and water mist environment, the following experiments were conducted.
## 3 OCC Performance Improvement
### LED Size  
The LED size significantly determines the communication distance. The number of strips is reduced when the LED is too small. The number of strips for a circular-shaped LED can be expressed as  
\[\mathrm{n_{s}}=\frac{\mathrm{A_{i}f_{o}}^{2}(\mathrm{f_{on}}+\mathrm{f_{off}} )\mathrm{t_{r}}}{\rho^{2}\mathrm{d}_{\alpha,\beta}^{2}}. \tag{14}\]  
For the OCC system, there is a minimum number of strips below which the data bits cannot be extracted. The full LED does not need to appear inside the image sensor. The minimum area  
Figure 5: The relationship between camera exposure time and the number of detectable bits.  
that should appear depends on the LED size. As shown in Figure 6, the minimum area and the corresponding number of strips considering a circular LED can be expressed as  
\[\mathrm{A_{m}}=\left\{\begin{array}{ll}2\int\limits_{\mathrm{r_{1}-r_{m}}}^{ \mathrm{r_{1}}}\sqrt{\mathrm{r_{1}^{2}-x^{2}}}\mathrm{d}x&:\mathrm{r_{1}}> \mathrm{r_{m}},\\ \frac{1}{2}\mathrm{A_{l}}&:\mathrm{r_{m}}=\mathrm{r_{1}},\\ 2\int\limits_{\mathrm{r_{m}-r_{1}}}^{\mathrm{r_{m}}}\sqrt{\mathrm{r_{1}^{2}-x^ {2}}}\mathrm{d}x&:\mathrm{r_{m}}>\mathrm{r_{1}},\\ \mathrm{A_{l}}&:\mathrm{r_{m}}=2\mathrm{r_{1}},\end{array}\right. \tag{15}\]  
\[\mathrm{n_{smin}}=\frac{\mathrm{A_{m}f_{0}}^{2}(\mathrm{f_{on}}+\mathrm{f_{off} })\mathrm{r_{r}}}{\rho^{2}\mathrm{d_{\alpha,\beta}^{2}}} \tag{16}\]  
where \(\mathrm{r_{1}}\) and \(\mathrm{r_{m}}\) represent the LED radius and the minimum portion of it that should appear inside the image sensor, respectively. It is worth noting here that a low \(\mathrm{n_{smin}}\) decreases the overall received power of the image sensor that can contribute to an increase in the BER.
## 5 Verification
### Verification Process
#### 5.2.2 Verification of S-L Analysis Method  
In this section, the frequency characteristics of the above operating conditions (in Section 6.1) is analyzed with an S-L analysis method. The feasibility of the S-L analysis method can be verified by comparing the analysis results with the actual operation results. There are three reasons for choosing the above operating conditions, which are detailed below.  
1. The above operating conditions contain an input/output bidirectional devices (PV storage system) and involve bidirectional frequency regulation, which is possible to verify whether S-L analysis method is suitable for input/output bidirectional devices.
2. The above operating conditions not only include the PV storage virtual synchronous machine system, but also include the traditional PV storage system, which is possible for verifying whether the S-L analysis method is applicable to different control strategies and devices.
3. There are two power supplies involved in frequency regulation jointly in above operating conditions through which the S-L analysis method is available to a multi-power supply.  
(1) Generator Mode  
The S-L analysis of the multi-power system including the PV system (in generator mode) is shown in Figure 23. L\({}_{\mathrm{L}}\)\({}^{0}\) is the initial frequency characteristic of total load and L\({}_{\mathrm{L}}\)\({}^{1}\) is the frequency characteristic of the total load after sudden change. L\({}_{\mathrm{G-N}}\), L\({}_{\mathrm{G-T}}\), L\({}_{\mathrm{G}}\)\({}^{\prime}\) are the frequency characteristics of the PV storage virtual synchronous generator system, the traditional PV storage system and the microgrid, respectively. The input/output bidirectional device (PV storage system) is operating in the generator mode and its frequency characteristic are located in the second quadrant, which would send power to the load together with the actual grid (Synchronous Machine Grid Equivalent Model). The blue elements are related to the initial power balance before the load is reduced. The red elements are related to the final power balance after the load is reduced. The bold black elements are related to the traditional PV storage system. For ease of reading, the slope of the Synchronous Machine Grid Equivalent Model was slightly increased.  
Learning from Equations (18) and (19), with \(\Delta P_{\rm G-T}~{}=~{}0\), \(\Delta P_{\rm L}{}^{1}~{}=~{}0\), the frequency regulation equation of the traditional PV system is obtained below.  
\[\left\{\begin{array}{c}\Delta P_{\rm G}{}^{\rm\prime T}~{}=~{}20000\\ 0.000025\Delta P_{\rm G}{}^{\rm\prime T}~{}=\left|\Delta f_{\rm T}\right|\end{array}\right. \tag{39}\]  
From Equation (39), we can get \(\Delta P_{\rm G}{}^{\rm\prime T}\)= 20 kW, \(|\Delta f_{\rm T}|\) = 0.5 Hz, which indicates a good agreement between the above results of S-L analysis and the simulation results in Figure 18a.
## 3 Experimental Setup and Measurement Campaigns
Measurements were made using a non-uniform sampling frequency, with an average of 1 KHz during 90 s. The reference velocity was \(U_{\infty}=10\) m/s for the cases Ti3 and \(U_{\infty}=6\) m/s for Ti12. As it was shown by [41] and later work, various estimations in grid-generated turbulence can be considered Reynolds independent (but not for observations such as the scaling region of the spectrum, as shown by [42]. Therefore, non-dimensional results of mean velocities and root-mean-square (RMS) statistics obtained with the LDA technique will be used despite the differences in reference velocity, set in the simulations to \(U_{\infty}=3\) m/s. LDA data are used to compare with the obtained results of velocity deficit and \(k\), while the velocity time-series from HWA are used to compute the other quantities examined in this work.
# Research on Narrow Linewidth External Cavity Semiconductor Lasers
## 2 FSBDG Structure of NLECSLs  
The FSBDG is one of the most widely used external mirrors. It has good performance, especially in its wide tuning range, high spectral resolution, and flexible and precise tunability. Great progress in the ECSL with FSBDG mirror have been achieved, and the results of research and development in the field are continuously transferred to industrialization. Wavelength selectivity and tunability of FSBDG ECSL can be realized by adjusting the incident angle to the FSBDG plane. Different applications put forward different tuning requirements; some require a large tuning range with hopping allowed, whereas others require fine continuous tuning without hopping. This paper focuses mainly on the latter. The continuous tuning range and precision are dependent on the design of optics and related mechanics. Two configurations are well developed and widely used, including the Littrow structure and Littman structure, which are introduced as follows.
## Result and Discussion  
### Characterisation of CaMn\({}_{2}\)O\({}_{4}\) Powder  
Figure 1 shows the X-ray diffraction pattern of the synthesised CaMn\({}_{2}\)O\({}_{4}\) powders, where the diffraction peaks can be indexed to the orthorhombic phase of CaMn\({}_{2}\)O\({}_{4}\) (JCPDS card No 74-2293) [14].  
Figure 2 shows a typical SEM and TEM images of CaMn\({}_{2}\)O\({}_{4}\) powders. The CaMn\({}_{2}\)O\({}_{4}\) powder made by mechanochemical processing were irregular in shape with sizes from 200 nm to 2 \(\upmu\)m (Figure 2a,b). The large particle sizes can be attributed to the higher temperature required to decompose the carbonate precursors. As evidenced in Figure 2c, irregular-shaped CaMn\({}_{2}\)O\({}_{4}\) particles showed high crystallinity with clearly distinguished lattice fringes. The CaMn\({}_{2}\)O\({}_{4}\) powder made using the hydrothermal method had wire shapes with diameters of \(\sim\)500 nm and varying lengths (Figure 2d,e). The inset in Figure 2e shows the electron diffraction pattern of the wires, from which the growth direction of nanowires was identified as [001] (Figure 2f). The BET surface areas of the irregular-shaped powder and nanowire powder were 4.7 m\({}^{2}\) g\({}^{-1}\) and 5.4 m\({}^{2}\) g\({}^{-1}\), respectively.  
Figure 3 shows the XPS spectra of CaMn\({}_{2}\)O\({}_{4}\) nanowires and irregular-shaped particles. As shown in Figure 3a, two strong peaks corresponding to Mn2p3/2 and Mn2p1/2 were observed at approximately  
Figure 1: X-ray diffraction instrument (XRD) patterns of (**a**) irregular-shaped CaMn\({}_{2}\)O\({}_{4}\) particles, and (**b**) CaMn\({}_{2}\)O\({}_{4}\) nanowires.  
641 and 653 eV, respectively. Figure 3b shows the spectra near Mn3s peaks. Average oxidation state (AOS) was calculated using the linear relationship with splitting energy between two Mn3s peaks [15]. AOS for irregular-shaped particles and nanowires was found to be 3.07 and 2.96, respectively, which is in good agreement with the theoretical value of 3.  
Figure 3: X-ray photoelectron spectroscopy (XPS) spectra of irregular-shaped CaMn\({}_{2}\)O\({}_{4}\) particles and CaMn\({}_{2}\)O\({}_{4}\) nanowires; (**a**) Mn 2p and (**b**) Mn 3s regions.  
Figure 2: Electron micrographs of CaMn\({}_{2}\)O\({}_{4}\) powder made by mechanochemical processing and subsequent heat treatment, (**a**) SEM, (**b,c**) TEM; CaMn\({}_{2}\)O\({}_{4}\) powder made using a hydrothermal method, (**d**) SEM and (**e,f**) TEM. The inset in Figure (e) is an electron diffraction pattern taken in the area indicated with a yellow square.  
Figure 4b shows transmittance spectra of irregular-shaped CaMn\({}_{2}\)O\({}_{4}\) particles and CaMn\({}_{2}\)O\({}_{4}\) nanowires, respectively. The Tauc plots in Figure 4c,d suggest an optical band gap of 1.5 eV and 1.6 eV for irregular-shaped particles and nanowires, respectively.
## 3 Results  
### Demographics  
Twenty persons with CNSLBP (ten women, age = 44.5 \(\pm\) 10.6 y) and twenty HCs (ten women, age = 44.5 \(\pm\) 10.3 y) participated in this study. Persons with CNSLBP displayed moderate pain intensity (NPRS = 5.2 \(\pm\) 2.3) and moderate disability levels (MODI = 10.3 \(\pm\) 5.7). No differences were found between CNSLBP and HCs in any outcomes except for better sleep quality in the latter (\(p\) < 0.001). The detailed characteristics of all participants are presented in Table 1. No dropouts were noted during the clinical phase of this trial.  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline  & **CNSLBP (n = 20)** & **HC (n = 20)** & \(p\)**-Value** \\ \hline Sex (F/M, \%) & \(10/10\) & \(10/10\) & 1.000 \\ Age (years) & \(44.5\pm 10.6\) & \(44.5\pm 10.3\) & 1.000 \\ \hline BMI (kg/m\({}^{2}\)) & \(26.3\pm 4.9\) & \(26.4\pm 4.0\) & 0.695 \(\pm\) \\ Working (yes/no) & \(19/1\) & \(19/1\) & 1.000 \\ Diet (yes/no) & \(0/20\) & \(1/19\) & 1.000 \\ Smoking (yes/no) & \(20/0\) & \(20/0\) & 1.000 \\ LBP onset (years) & \(12.7\pm 10.4\) & / & - \\ Pain intensity (NPRS, 0-10) & \(5.2\pm 2.3\) & / & - \\ Disability level (MODI) & \(10.3\pm 5.7\) & / & - \\ Fear avoidance (FABQ, 0-96) & \(29.8\pm 14.2\) & / & - \\ Activity level (IPAQ, METS) & \(4740\pm 2843\) & \(3413\pm 2666\) & 0.185 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Group demographics of CNSLBP and HCs.