esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
### Datasets  
We performed experiments on three public databases.  
**BrainWeb dataset**[37] ([[http://www.bic.mni.mcgill.ca/BrainWeb/](http://www.bic.mni.mcgill.ca/BrainWeb/)]([http://www.bic.mni.mcgill.ca/BrainWeb/](http://www.bic.mni.mcgill.ca/BrainWeb/))) is a publicly available MRI dataset that includes normal and multiple sclerosis simulated images. It contains a set of realistic MRI data volumes produced by an MRI simulator. The voxel dimensions of the synthetic brain MRI is \(1\times 1\times 1\) mm\({}^{3}\) and the data size is \(181\times 217\times 181\).  
**NAMIC dataset** ([[http://hdl.handle.net/1926/1687](http://hdl.handle.net/1926/1687)]([http://hdl.handle.net/1926/1687](http://hdl.handle.net/1926/1687))) consists of real MRI data that were acquired using a 3T GE at BWH Hospital in Boston, MA. The voxel dimensions are \(1\times 1\times 1\) mm\({}^{3}\) and the data size is \(256\times 256\times 176\).  
**IXI dataset** ([[https://brain-development.org/ixi-dataset/](https://brain-development.org/ixi-dataset/)]([https://brain-development.org/ixi-dataset/](https://brain-development.org/ixi-dataset/))) consists of real MRI data collected from three hospitals in London. We evaluated on the MRI images from Guys hospital. The voxel dimensions are \(1\times 1\times 1\) mm\({}^{3}\) and the data size is \(264\times 255\times 186\).  
All 3D MRI data were split into 2D image sequences along the transverse, sagittal, and coronal planes. The obtained 2D data were all normalized to \([0,1]\).
# Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving
## 2 Fuzzy Cooperative Control Strategy Based on LTV-MPC
### Vehicle Steady State Analysis Based on Phase Plane
The \(\alpha_{f}\) corresponding to the stable state exceeds the saturation limit, while the fifth axle with the largest side slip angle among the rear three axles still remains within the boundary of the tire side slip angle, which is caused by the understeer characteristics of the five-axle heavy vehicle. In this case, the vehicle state is difficult to quickly converge, and such a state can also be referred to as instability for the vehicle. Based on the above phase plane analysis, it can be seen that when the steering angle of the first axle of a multi-axle vehicle exceeds the critical stable steering angle \(\delta_{1c}\), the vehicle will enter an unstable state. Selecting a vehicle speed range of 10-30 m/s and a road adhesion coefficient variation range of 0.1-1, the critical stable steering angle of multi-axle heavy vehicles under multiple operating conditions is calibrated based on the phase plane analysis method. The calibration results are shown in Figure 5. It can be seen that as the road adhesion coefficient decreases and the vehicle speed increases, the critical stable steering angle \(\delta_{1c}\) gradually decreases. Figure 4: Schematic diagram of phase plane. (**a**) \(\delta_{1}=0.1\) rad. (**b**) \(\delta_{1}=0.188\) rad. (**c**) \(\delta_{1}=0.3\) rad. Figure 5: The critical stable steering angle \(\delta_{1c}\). The tires linear working area boundary of \(\alpha_{f}\) and \(\alpha_{5}\) is introduced for phase plane analysis, and the linear critical steering angle \(\delta_{l1}\) is calibrated. The specific boundaries introduced are as follows:  
\[ \begin{cases}\left|\alpha_{f}\right|\leq\left|\alpha_{film}\right|\\ \left|\alpha_{5}\right|\leq\left|\alpha_{5 lim}\right|\end{cases}  \tag{12}\]  
where \(\alpha_{2 lin}\) and \(\alpha_{5 lin}\) are the lateral slip angle boundaries corresponding to the linear working area of the tire. Under the conditions of road adhesion of 0.8 and vehicle speeds of 15 m/s and 20 m/s, the linear critical steering angle analysis is performed using the phase plane method, as shown in Figure 6. It can be seen that at a speed of 15 m/s, the corresponding linear critical steering angle \(\delta_{l1}\) is 0.203 rad; at a speed of 20 m/s, the corresponding linear critical steering angle \(\delta_{l1}\) is 0.138 rad. Based on this, the stable state of the vehicle is defined. When \(\delta_{1}\in[-\delta_{l1},\delta_{l1}]\), the vehicle is in a stable state, when \(\delta_{1}\in[-\delta_{1c},\delta_{l1}]\cup(\delta_{l1},\delta_{lc})\), it is in a transition state, and when \(\delta_{1}\in(-\delta_{1 min},-\delta_{1c})\cup(\delta_{1c},\delta_{1 max})\), it is in an unstable state.
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 4 Case Study  
This case study considers a modified version of the well-known WSCC 9-bus, 3-machine test system [39], where the load at bus 6 is replaced with a 8-bus, 38 kV distribution system that includes a VPP [40]. The topology of the overall system is shown in Figure 5. Specific setups are as follows.  
* The VPP is connected through an Under-Load Tap Changer (ULTC) type step down transformer with TG.
* One SPVG, two WGs, and one ESS are connected at buses D8, D5, D2, and D2, respectively. Each DER uses the bus frequency signal by an Synchronous Reference Frame Phase-Locked Loop (SRF-PLL) installed at Bus D1 for frequency control. The initial active power generation of the WG and the SPVG are 15 MW each, whereas the power rate of the ESS is 10 MW.
* The total active and reactive power consumption of loads in the VPP are 57.8 MW and 11.7 MVar, respectively.
* Since the focus of the case study is to observe the power system short-term transient behavior (a few tens of seconds), the impact of the State of Charge (SoC) of ESS is neglected.  
In this case, the power injected by the VPP into the grid, namely \(p_{\text{inj}}\), is the power flow from Bus D1 to Bus 6. The stochastic processes of the wind speeds, load consumption and solar irradiance described in the previous section are included in all scenarios of the case study. Simulations are carried out using the co-simulation software tool described in [41] that combines the Python-based power system simulator Dome [42] and the communication network simulator ns-3 [43].
## 4 Recent Developments in IRT Detection  
IRT images provide indisputable advantages for direct retrieval of the surface temperature distribution of the observed object, but also for detection and identification of objects emitting thermal radiation under low-light conditions, when recording of the visible spectrum is insufficient [57; 58], or for detection of defects of the observed object that cannot be recorded by conventional photosensors [59; 60; 61]. However, images captured with thermal imaging sensors currently still have a significantly lower resolution than images taken with standard image sensors designed to capture the visible spectrum [62]. Table 1 shows that a significant number of authors had images with a resolution of 320 \(\times\) 240 pixels, which is less than 0.1 MP. The highest-resolution thermal image for the sources examined was 640 \(\times\) 512 pixels. Another characteristic drawback of IRT images is low contrast and a significant amount of noise, with the typical feature of IRT images being blurred edges of the observed object contrary to the image in the visual range [57; 63].  
There is quite a wide range of methods for IRT image processing. However, in many sources, their classification is not clear and comprehensive. In the literature, it is possible to encounter a categorization of methods into spatial and frequency domain [59; 68; 77] preprocessing, segmentation, feature extraction, and classification [97]. A different categorization can be found in [98], where a classification into histogram modification methods, point methods, and context-aware processing methods is presented, and [PERSON] et al. includes image segmentation and classification and specific features extraction among the main areas of image processing [99]. The sources also differ in indicating the sequence of the methods used. For example, [PERSON] et al. state that segmentation represents the first step in image processing [100]. In contrast, [PERSON] et al., [PERSON] and [PERSON], and [PERSON] et al. apply segmentation methods after other operations are completed [78; 80; 101]. Most sources agree in using a combination of multiple methods in IRT image processing.
## 3 Demonstration test flight  
Several demonstrations of the SITHON airborne imaging platform have been conducted during the project lifecycle. The first experiments were carried out in May 2005. During these operations new requirements for additional R&D developments were identified for enhancing system's capabilities meeting operational functionality needs. The system in its final configuration was flown over the Sithonia Peninsula of Chalkidiki in May 2006 [Figure 7(a)]. The system's sensing and imaging capability was evaluated in different conditions by changing the flight height and aircraft cruising speed, as well as the size and the intensity of fire flames detected. For this to be achieved, a number of different pre-designed navigation paths as in Figure 7(b), have been inserted into the aircrafts navigation control system to ensure that the entire Sithonia Peninsula is covered without gaps in image acquisition.  
During the demonstration campaigns the entire study area was surveyed by the SITHON system by capturing and processing acquisitions every 5-7 seconds. The exact time interval between successive acquisitions depended on the flying conditions (flight height, cruising speed, ground elevation). The computer system controlling the image capture during the flight was calculating and automatically adjusting the time interval between the successive frames. For as long as the level of recorded temperatures was within ordinary values the system was remaining in the so-called'silent mode' sending to the server PC every processed and geo-referenced thermal image frame.  
Figure 6: cont.  
As long as the first hot spot event with a temperature above the predefined threshold limit was detected, the system turned immediately to 'alarm mode' and the aircraft staff was alerted to the danger by a sound alarm. At the same time, the geographic coordinates of the fire event were shown on the display, indicating the existence and location of the fire as in Figure 8. The demonstration campaigns showed that the SITHON infrared platform has been sensitive enough to detect fires of low intensity and of a few square meters on the ground from the height of 1,000 m to 2,000 m above sea level (ASL). Real time direct image geo-referencing, using input only from the GPS-assisted INS, performed well and produced sufficiently accurate (\(\sim\)50 m) relationships between the captured images and the terrain system. Figures 8(a) and (b) illustrate the system's capability for dynamic surface representation, stereoscopic viewing with image overlap of 60% and image projection of the geo-referenced images onto a reference base map of the study area. An example of a low intensity and small size fire of about 3x3 m\({}^{2}\) on the ground as detected from 2,000 m and 1,000 m ASL is given in Figure 8(c) and (d), respectively.
## 4 Discussion
### Limitations  
As proposed in previous studies, the remote sensing data used for downscaling mainly included factors that impact BCEs. However, data directly related to buildings were not considered in the regression model due to data availability. We used prefecture-level nighttime light data as one factor to estimate the BCEs. This is a useful metric because nighttime light data not only reflects the intensity of public and residential buildings' activities but also includes light from roads and vehicles. Therefore, the grid-scale emissions in the BTH may be slightly underestimated. To improve the results using nighttime light data, distinguishing urban and rural areas before estimating and processing nighttime light data to remove the light that is not included in the estimation scope is recommended. In addition, there was no comparison between the results of the MLR model used in this study and the machine learning or Cubist model used in previous studies. Due to the focus on socioeconomic factors in the analysis of BCE growth, the impact of natural factors was not included. Moreover, specific energy consumption comparisons and the policy-making influence were not considered in the analysis of socioeconomic factors.
## 2 Related Work
### Background Information for Target Tracking  
With the depth of research, background cues have been incorporated into target appearance modelling as a factor to enhance tracking accuracy. In deep learning, a series of innovative algorithm designs further highlight the importance of background information. DaSiamRPN [50] combines the Siamese network with a local-to-global search strategy to pay more attention to semantic background information. [PERSON] et al. [51] transform the scene context into state representations, which are then integrated with target visual characteristics to effectively reduce the complexity of the background. And the CFBI method proposed by [PERSON] et al. [52] addresses the background confusion problem by dividing video frames into foreground and background regions for feature learning. To further distinguish and address the challenge posed by multiple interfering objects, [PERSON] et al. [53] use AOT network for feature extraction based on object classification, which provides a more refined processing capability for target tracking in complex scenes. These developments highlight the crucial contribution of background cues to enhancing the performance of target tracking. For the specific field of NIR target tracking, we propose the History Frame Background Extraction (HFBE) module. This module not only harnesses the abundant background information, but also integrates spectral properties to achieve effective foreground and background segmentation within video sequences.
## 3 Related Work on Correcting Typographical Errors
### Candidate Generation  
This is the task of finding the confusion set of real words for a given misspelled word. One can manually craft a confusion set or look for a publicly available one, such as [55] for the Chinese language. However, usually these sets are generated on the fly. The similarity measure between words is obtained by the phonetic or the Minimum Edit Distance algorithms.  
The most-known phonetic algorithm is Soundex [56; 57]. The cornerstone of the Soundex approach is that homophones (the same-sounding words) are encoded similarly, so that they can be matched regardless of subtle differences in their spelling. A Soundex code is computed from a misspelling, and words that have the same code are retrieved from the dictionary as correction candidates. A similar principle of misspelling encodingwas used in the first system by [50]. Nowadays, the Metaphone representations of words (as an improvement over Soundex) [58] are used in Aspell [51].  
The Minimum Edit Distance [59] measure is defined by the minimum number of edit operations needed to transform one string to another. As reported in [60], more than 80% of errors differ from the correct word by only a single letter; thus, the distance between them is low. There are several different edit distance algorithms: Levenshtein [61] (number of insertions, deletions, and substitutions), Damerau-Levenshtein [60] (treating transposition as a single edit), Hamming [62] (number of characters that differ between two equal-length strings), and the Longest Common Subsequence [63]. As an example, the widely-used Aspell uses the Damerau-Levenshtein distance between Metaphone representations of words.
## 2 Double-Ridged Waveguide Rotman Lens Design in the K/K\({}_{\text{a}}\)-Band  
The Rotman lens is a particular case of planar (2-D) PPW-constrained lens beamformer suitable for linear array designs that produces three true focal points, one on-axis and two symmetrically arranged off-axis [20; 24]. An adequate design of the focal arc enables us to reduce residual phase aberrations in between the focal points, resulting in a wide angular scanning range with stable radiation patterns. Figure 1 defines the main design parameters of a generalized form of Rotman lens [37], which introduces the scaling factor \(\gamma=\sin\beta/\sin\alpha\), where \(\alpha\) is the off-axis focal point angular position and \(\beta\) the corresponding beam pointing direction. The achievable scanning range is thus approximately \(\pm\beta\), as phase aberrations increase drastically beyond the off-axis focal points. In the original Rotman lens design, \(\gamma=1\). The optimization of the focal arc is obtained by tuning the parameter \(g=\text{GO}_{1}/\text{F}_{1}\text{O}_{1}\), corresponding to the ratio of the on-axis focal length over the off-axis focal length, \(F\).  
Here, the objective is to design a Rotman lens beamformer that covers both the downlink and the uplink frequency bands allocated to broadband satellite communications in the K/K\({}_{\text{a}}\)-band, 17.3-20.2 GHz, and 27.5-30 GHz, respectively. The detailed design of the Rotman lens is driven by the feeding port design, discussed in Section 2.1.
## 3 Application to an Octagonal Viscoelastic Bar  
### Plane Finite Element Mesh Design  
An octagonal steel bar of overall radius \(R=10\) mm is assumed (see Figure 2). The steel is considered as a hysteretic linear viscoelastic material with the following nominal properties [13]:  
\[\begin{array}{rl}\circ&\text{longitudinal bulk wave speed $c_{\text{L}}$ = 5,900 m/s,}\\ \circ&\text{shear bulk wave speed $c_{\text{T}}$ = 3,190 m/s,}\\ \circ&\text{density $\rho$ = 7,800 kg/m${}^{3}$,}\\ \circ&\text{longitudinal bulk wave attenuation $\kappa_{\text{L}}$ = 0.003 Np/$\lambda$,}\\ \circ&\text{shear bulk wave attenuation $\kappa_{\text{T}}$ = 0.008 Np/$\lambda$.}\end{array}\]  
Starting from these data, the viscoelastic constitutive operator \(\overline{\mathbf{C}}^{e}\) for the \(e\)-_th_ finite element can be easily computed [4]. In this case, since the octagonal bar is all made of the same material, the operator \(\overline{\mathbf{C}}^{e}\) results to be identical for each finite element of the mesh. In addition, since a hysteretic rheological model leads to a frequency independent constitutive operator, the \(\overline{\mathbf{k}}_{{}_{i}}\) matrices of Equation (12) need to be calculated only once and hold for any frequency in input.  
The Matlab \"_pdetool_\" mesh parameters were set in such a way to have a mesh of similar triangles for accuracy reasons. In Figure 4 four different mesh refinements are shown. In each refinement, a single finite element has been subdivided into four identical triangles.  
As indicated by the mesh accuracy criterion for SFE formulations proposed in Reference [14], the mesh of linear finite elements can yield accurate waves representation up to a maximum frequency \(f_{\text{max}}=c_{\text{T}}/(10L_{\text{max}})\), where \(L_{\text{max}}\) is the maximum finite element side length.  
The four meshes proposed in Figure 4, having elements with maximum edge length equal to 10 mm, 5 mm, 2.5 mm and 1.25 mm, guarantee accuracy results up to 31.9 kHz, 63.8 kHz, 127.6 kHz and 255.2 kHz, respectively.
# An Attention-Based Full-Scale Fusion Network for Segmenting Roof Mask from Satellite Images
## 3 The Proposed Attention-Based Full-Scale Fusion Network
### Loss Function  
Similarly to [44], we adopt the following loss to train the model:  
\[\Gamma=\zeta_{fuse}+\sum_{k=1}^{K}\zeta_{ant}^{(k)}, \tag{3}\]  
where \(\zeta_{ant}\) and \(\zeta_{fuse}\) denote the loss of the auxiliary prediction mask and fusion mask, respectively. For the \(\zeta_{ant}\), \(K=6\), corresponding to the prediction mask output by five decoders and the prediction mask output by the last encoder. To calculate each term \(\zeta\), we adopt the standard binary cross-entropy function:  
\[\zeta=-\sum_{(r,\epsilon)}^{(H,W)}\Big{[}P_{G(r,\epsilon)}\log P_{S(r,\epsilon )}+\Big{(}1-P_{G(r,\epsilon)}\Big{)}\log\Big{(}1-P_{S(r,\epsilon)}\Big{)} \Big{]}. \tag{4}\]  
In addition, we apply a binary cross-entropy loss function [52]\(\Psi\) to the output of the roof discrimination module, thereby utilizing weakly labeled data to enhance the model's sensitivity to roofs and non-roofs.
## 5 Experimental Results  
### EMI Spectrum Measurements  
All the EMI measurements are taken by the Gauss EMI receiver. The average detector (AV) was used with IFBW = 200 Hz (following the CISPR A standard value) and a dwell time of 100 ms.  
\begin{table}
\begin{tabular}{c c} \hline \hline The central switching frequency & 63 kHz \\ \hline Duty cycle & 50\% \\ \hline Modulating signal \(\varepsilon(\tau)\) & PAM, sawtooth, and sinusoidal \\ \hline Modulating signal sampling frequency \(f_{ss}\) & Varies from 30 Hz to 60 kHz \\ \hline Modulating signal frequency \(f_{m}\) & Varies from 0.3 to 600 Hz \\ \hline Spreading factor (\(\alpha\)) & 15\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spread spectrum modulation settings [30].  
In this work, we generate the modulating signal using 100 points distributed between \(-0.5\) and \(0.5\), with a sampling interval \(T_{s}\) between successive points. Figure 3 illustrates the voltage spectrum of the spread spectrum-modulated EMI measured on the PLC side when no PLC signal is transmitted. This measurement shows the EMI spectrum for three different modulating signal patterns, each applied at a modulating frequency \(f_{m}\) of 30 Hz and with a fixed spreading factor of 15%. The bandwidth of the resulting EMI is regulated by adjusting the spreading factor \(\alpha\).  
To demonstrate how the modulating frequency of the SS modulation works, a 2D spectrogram is presented in Figure 4, showing the three modulation patterns used in the experiment at a low modulating signal frequency of 0.3 Hz. As a result, the switching frequency varies very slowly in time, forming the used profile.  
Figure 4: The voltage spectrum measured from the PLC circuit side in the case of using various profiles of \(\varepsilon(t)\) at \(f_{m}=0.3\) and \(\alpha=0.15\).  
Figure 3: The voltage spectrum measured from the PLC circuit side in the case of using various profiles of \(\varepsilon(t)\) and constant \(\alpha\).  
Figure 5 shows the output voltage spectrum of the SS-modulated EMI utilizing the RandomPAM, sawtooth, and sine wave modulating signals respectively while using different values of \(f_{m}\) and constant \(\alpha=0.15\). The bandwidth of the SS modulation \(\Delta f\) used is 63,000 \(\times\) 0.15 = 9450 Hz.  
Figure 6 shows the relation between the peak magnitude of the measured SS-modulated EMI in the case of using various values of \(\beta\) and \(\alpha\) = 0.15. From the EMI standards point of view, the relation between the peak magnitude of the spectrum and the modulation index is not linear, and there is an optimum value of \(\beta\) for which maximum EMI reduction is achieved.  
Figure 5: The voltage spectrum measured from the PLC circuit side in the case of using various profile and frequencies \(f_{m}\) of modulating signal \(\varepsilon(t)\).  
Figure 6: The spectrum peak amplitude vs. modulation index \(\beta\) values.