esa-sceva/satcom-chunk-collection huggingface dataset:
# A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines
## 5 Conclusions  
Based on SAM, a LoRA fine-tuning model, ASAM, is constructed for image segmentation of aircraft parts. An innovative fusion loss function has been proposed to enhance the model's applicability to the target task. Taking the fire detection line as an example, a custom dataset is formed through collection and annotation. Experiments are conducted to compare the performance of the proposed model with K-Net quantitatively. The robustness of the model is also qualitatively analyzed. Meanwhile, the effects of different SAM weights and ranks on model performance are discussed from both quantitative and qualitative aspects to support subsequent deployment inference.  
Given that fire detection lines are elongated and challenging to segment, the proposed ASAM model demonstrates a certain level of generalization in the field of aircraft com  
Figure 9: As the rank increases, the model may incorrectly identify some interference as fire detection lines.  
ponent image segmentation. In future work, different LoRA fine-tuning models can be developed for various inspection tasks, enabling input routing within a single batch to construct a unified agent capable of handling multiple segmentation tasks for different aircraft parts. Furthermore, considering the multi-dimensional nature of aircraft inspection tasks, it is also promising to design task-specific fusion loss functions and model architectures to enhance adaptability across diverse application scenarios.  
Despite its effectiveness, the proposed method still has several limitations. (1) To accurately calculate the distance between fire detection lines, the input image must be captured from a viewpoint perpendicular to the plane formed by the two lines during inference. (2) The current model does not yet support scenarios involving multiple pairs of fire detection lines in a single image. (3) The segmentation performance may be affected by occlusion from pipelines, shadows, and image overlap, especially in complex environments. Future work will focus on addressing these issues by exploring spatial distance estimation based on multi-view monocular images, as well as developing more advanced image enhancement and denoising techniques. These efforts are expected to improve the model's robustness and applicability further.  
Conceptualization, M.C. and H.Z.; methodology, M.C. and Y.L.; software, S.C. and Q.Y.; validation, H.Z.; writing--original draft preparation, M.C.; writing--review and editing, S.C.; supervision, H.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company COMAC Shanghai Aircraft Manufacturing Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Discussion
applied GOME-2 _SIF_ to monitor the Texas droughts in 2011 and the central Great Plains drought in 2012 and found the spatial patterns of _SIF_ anomalies very much resembled drought intensity maps from the U.S. Drought Monitor for both events and that _SIF_ is sensitive to both structural and physiological/biochemical variations of vegetation caused by drought [3]. [PERSON] et al. used OCO-2 _SIF_ to monitor the 2018 European drought and found that _SIF_ is more sensitive to drought than MODIS's fPAR and NDVI [4]. In fact, _SIF_ has been also used to monitor the Russian drought in 2010 [5], and the northern Great Plain drought in 2017 [6]. The results show that _SIF_ is a promising indicator of drought. However, there are very few studies using the relatively high-resolution _SIF_ (e.g., 500 m) for drought monitoring due to the lack of high-resolution downscaled SIF, and because TROPOSIFhas a very short mission length, we cannot obtain a long time series of TSIF dataset which hinders TSIF applications in drought monitoring. Thus, we used a relative difference as evaluation metrics for drought monitoring, which only uses two years of data, overcoming this problem to some extent. Similar methods have also been used in vegetation growth monitoring studies [32] and drought monitoring [14]. The results show that TSIF-Diff can capture almost the same spatial and temporal patterns as TROPOSIF-Diff and can capture the spatiotemporal patterns of drought. In addition, TSIF-Diff is more sensitive to drought and precipitation compared to USIF-Diff and EVI-Diff. In the future, the TSIF anomalies can also be calculated when TROPOMI has 5 years of _SIF_ observations. TSIF has many data gaps caused by MODIS data gaps. In the future, we will test different methods for gap-filling without introducing large uncertainties. Moreover, because we aim to develop an operational downscaling method, we did not perform strict quality control on data to avoid too many data gaps, which will introduce some uncertainties. TSIF has a temporal resolution of 16-day, finer temporal resolution downscaled _SIF_ (e.g., 8-day and 4-day) are also needed. A suitable downscaling method will be tested in the future. Finally, different land covers may represent different relationships between explanatory variables and _SIF_ in different regions and times, these factors will also be allowed for in the future. Because our method only needs regional data instead of global data for training the downscaling models, the downscaling models can be trained quickly and easily. We can generate downscaled _SIF_ in a given area without having to collect and process significant amounts of data. Our method is very practical.
# A Method of Aerial Multi-Modal Image Registration
## 3 Multi-Modal Image Registration Based on Virtual Scene ROI Drive  
Image registration is the process of superimposing two or more images in different times, spaces and modes, which is shown as finding the best geometric transformation relationship between different images. Suppose \(I_{1}\) is the reference image and \(I_{2}\) is the registration image to seek the transformation between them. The mathematics can be defined as:  
\[I_{2}(x,y)=g(I_{1}(f(x,y))) \tag{9}\]  
\((x,y)\) represents image coordinates, \(f(\cdot)\) represents geometric transformation, and \(g(\cdot)\) represents grayscale transformation.  
According to the different image information used in the registration process, the registration methods are mainly divided into the region (pixel) based registration and feature-based registration [7]. The region (pixel) based registration technology is to use the global or local gray-level information of the image for registration. The advantage is that it is simple to implement and does not need to extract image features. The disadvantage is that the gray information of the whole image is involved in the calculation, which requires a large amount of calculation and high implementation complexity. Region (pixel) based registration technology can be roughly divided into three categories: cross-correlation method, mutual information method and transform domain method [7]. Feature-based registration technology aims to extract some distinctive features of the image, use the corresponding relationship between features to match features, and then obtain the spatial transformation of the image to be registered. Features mainly include point features, line features and area features. Because the number of features in the image is far less than the number of pixels in the image, the amount of calculation is greatly reduced.  
In the aircraft approach phase, the airborne infrared convertible optical camera platform system is generally prone to cause differences in translation, rotation, scaling and shearing between two images due to aircraft jitter, weather, shooting angle and other factors. Therefore, real-time and high-precision registration is a necessary prerequisite for the fusion of airborne approach images. This paper proposes the generation of airport ROI region based on virtual scene driving and realizes the registration based on ROI feature extraction and RANSAC (Random Sample Consensus), which can greatly reduce the amount of  
Figure 3: Visual analysis of flight data and virtual scene generation framework.  
computation, improve the relevance of the images to be registered, and further improve the real-time registration accuracy.
# The Role of Cryptogams in Soil Property Regulation and Vascular Plant Regeneration: A Review
## 3 Effects of Cryptogam on Vascular Plants Regeneration
### Other Effects  
In addition to the above three cases, other studies found a competitive relationship between cryptogam and vascular plants. Greenhouse experiments showed that the moss _Didymodon toplacus_ significantly improved the seed germination, seedling growth, and individual reproduction of the herb _Delphinium ulginosum_. However, in the later life stages of _Delphinium ulginosum_, _Didymodon toplacus_ exerts a competing effect on it [39]. In [PERSON]'s study, when the biomass of the four mosses reached its highest level, the growth of _Pinus sylvestris_ seedlings was significantly reduced due to the competition between seedlings and moss for light. Additionally, the height and above-ground quality of the seedlings increased. The morphologically taller mosses _Hylocominium splendens_ and _Polytrichum commune_ competed with _Pinus sylvestris_ the most intensely [117]. In addition, there are also some reports that cryptogams inhibit vascular plant regeneration in other ways, such as lichen. This prevents ectomycorrhiza from infecting seedling roots, leading to seedling stunting [36]. The decreasing effect of cryptogams on soil temperature will also reduce the accumulated temperature of the site environment, thus shortening the growing season of _Picea crassifolia_ and limiting its regeneration [34]. It was also found that moss crust alleviates the effects of climate change on the establishment of vascular plant seedlings in the alpine forest line region. This limits the expansion of forest lines caused by climate warming [118]. Mosses in the desert have also been found to significantly reduce the reproductive capacity of _Echinops gmelini_ individuals, thereby limiting their regeneration ability [19]. To sum up, physical barriers, allelopathy, regulating soil conditions, competition, inhibiting ectomycorrhiza development, and reducing plant reproductive capacity are the ways in which cryptogams affect vascular plant regeneration (Figure 1).
## 4 MIL Verification  
The proposed solar arrays and angular rates only ADCS has been developed for practical applications in real space software and hardware. Then, the verification of the control performance has been carried out with model-in-the-loop testing campaigns, exploiting a functional engineering simulator (FES) developed for small satellite applications in Earth orbits. In particular, the spacecraft considered in the verification campaign has moments of inertia in the order of \(6\times 10^{-2}\) kgm\({}^{2}\), dimensions of a \(3U\) CubeSat and solar arrays producing a maximum power of \(32\,\mathrm{W}\). The MIL results are analyzed and discussed in terms of generated power from the solar arrays, being the primary objective of the proposed ADCS. Moreover, a direct relationship between the power generation and the Sun angle is available. The actuators considered in the simulations are reaction wheels with a maximum torque of \(2\,\mathrm{m}\mathrm{N}\mathrm{m}\), or magnetic torquers with a maximum dipole moment of \(0.5\,\mathrm{A}\mathrm{m}^{2}\). The MIL verification exploited Monte Carlo analyses with dispersion on the simulation parameters. The verification has been conducted on more than 500 runs, but a reduced subset of the worst-performing 10 samples is reported in the figures of this paper. The simulation parameters affected by dispersion are all the initial conditions for the dynamics, the inertia, the position of the center of mass, the ballistic and the reflectivity coefficients, the measurement noise, the actuators errors. The dispersion is random at 50% of the nominal value at \(3\sigma\) for normal distributions, and at 25%  
Figure 5: Specific solar arrays current, with eclipses.  
Figure 6: Control torques, with eclipses.  
The effect of measurement noise is shown in Figure 7, where the input and the output of the attitude determination algorithms are reported. For coherence with all the results shown in this paper, the specific current is shown in the figure. Reminding Equation (8), a specific current equal to 1 corresponds to aligned solar arrays, with Sun angle equal to 0. The noisy current measurements are averaged over the last two batches of \(K\) samples to detect a rising or decreasing trend in the Sun angle. The output of the attitude determination is the two smoothed lines, whose direct numerical comparison allows the estimation of the derivative of the Sun angle, as discussed in the determination algorithm section.
## 3 Observational Data Description, Assimilation and Experimental Design  
### The EO Variables of Interest  
As mentioned in the Introduction, this research focuses on Sentinel-derived and GNSS-derived data. The family of missions called Sentinels were specifically developed for the operational needs of the Copernicus programme. Each mission is based on a constellation of two satellites to ensure that the revisit time (less than 6 days) and the spatial coverage of the data are compliant with the requirements of the programme. In this study, Sentinel-1 and Sentinel-3 data were used. Sentinel-1 is apolar-orbiting, all-weather, day-and-night C-band (5.4 GHz) SAR mission for land and ocean services. Sentinel-3 is a multi-instrument mission to measure sea and land surface temperature, ocean colour and land colour with high-end accuracy and reliability.  
For a detailed description of the reasoning behind the selection of the EO variables to be ingested into the WRF model, that was based on the Observing Systems Capability Analysis and Review (OSCAR) tool, the reader is referred to Reference [12]. Through OSCAR, which is a resource developed by World Meteorological Organization (WMO) in support of Earth Observation applications, studies and global coordination ([[https://www.wmo-sat.info/oscar/](https://www.wmo-sat.info/oscar/)]([https://www.wmo-sat.info/oscar/](https://www.wmo-sat.info/oscar/))), a set of requirements for observation of physical variables in support of WMO programs is available.  
By giving priority to the availability of data at high spatial resolution among those indicated by OSCAR as the most relevant for NWP applications, we selected the following variables: ZTD (that can be retrieved using SAR interferometric technique from Sentinel-1 or GNSS data), surface soil moisture and surface wind vector over the sea (that can be retrieved using Sentinel-1 data), sea and land surface temperature (that can be retrieved using Sentinel-3 data). Some of these variables (wind vector, sea and land surface temperature) are directly available as Sentinel products, while others (soil moisture and integrated water vapour) have to be retrieved. A description of the methods used to estimate these variables is provided in the Appendices A and B.
# Effect of Phase Noise on the Optical Millimeter-Wave Signal in the DWDM-RoF System
## 3 Simulation Setup  
The DWDM-RoF architecture was simulated using Optisystem software version 18 (Ottawa, ON, Canada). In the simulation, the optical mm-wave generation was set up using a single MZM with three input sources, as explained in the prior section.  
At the central office (CO), an array of CW lasers starting at 1550 nm and spaced by 0.8 nm with a power of 10 dBm was used. The CW lasers were connected to wavelength division multiplexing (WDM) to multiplex the wavelengths together before being modulated in the MZM, as shown in Figure 3.  
The WDM multiplexer represented the DWDM technique in the architecture. The DWDM technique is flexible, allowing narrowing of the spacing of channels to increase the capacity of the transmitted signal as well the number of channels for the same purpose [33]. In this work, eight channels were used with a channel spacing of 0.8 nm. To generate the  
Figure 3: The block diagram of the DWDM-RoF system.  
60 GHz mm-wave, the RF signal generator was set to 30 GHz and followed by a PS to control the phase imbalance.  
The lasers, RF signal generator, PS, and a binary signal of 2.5 Gb/s in PRBS and NRZ format were connected to a single MZM modulator. The MZM was then connected to an optical fiber before being connected to a positive intrinsic negative (PIN) receiver and lowpass filter (LPF) in the base station (BS).  
The eight transmitted channels were received at eight PIN receivers in the BS, each receiver for a specific wavelength. The optical fiber ranged from 0 km to 40 km with a dispersion of 16.75 ps/nm/km and attenuation of 0.2 dB/km. The PIN responsivity was set to 0.7 A/W, and the signal was filtered using the LPF of 0.75 cutoff frequency. Finally, the signal was sent to the antenna. An attenuator of 5 dB was used to represent the antenna in the BS. The signal analyzer was used at the output of the MZM to monitor the phase imbalance and phase noise of the mm-wave signal. Table 1 shows the parameters settings used in this work.
## 1 Introduction
### Summary of the Methodology  
In this paper, we discuss UAV motion modeling by developing dynamic-aware kinematic equations for path-plus-speed (trajectory) optimization in the context of future wireless networks. The methodology of the paper is summarized as follows:  
1. The paper studies the principles of UAV (notably quadrotors) motion and dynamics and develops two generic linear kinodynamic motion models (\(\mathcal{KM}1\) and \(\mathcal{KM}2\)) in the state space domain that can be used for trajectory optimization or in a sensor fusion algorithm like the Kalman filter for position estimation.
2. A systematic UAV trajectory optimization problem is presented (\(\mathcal{P}1\)) by utilizing the proposed kinodynamic models.
3. The optimality issue of the optimization problem (\(\mathcal{P}1\)) is discussed. The optimization problem is modified to be used in the finite receding horizon MPC optimization (\(\mathcal{P}2\)) to achieve a more optimal solution. Required formulations and appropriate matrix dimensions are presented.
4. A trajectory smoothing technique is proposed (\(\mathcal{P}3\)) to ensure that the discrete-time trajectory becomes dynamically feasible in continuous time.
5. Although communication channel modeling is not considered in this paper, the methodology is valid. Because it is the case in most of the relevant works that use sequential convex programming, in which the trajectory optimization and channel performance objectives are solved consecutively by relaxing other objectives and constraints [16].
6. Simulation results are provided to evaluate the crash-avoidance performance and the running time of the proposed trajectory optimization and are compared with sampling-based techniques.
## 3 Nature of Input Dimension and Overfitting
### The Nature of the Overfitting Phenomenon  
As seen in the above discussion, the change of input dimension affects testing accuracy through its influence on the frequency resolution of the weight matrix. When the input dimension increased, the testing accuracy also should have increased. However, as seen in Figure 6, when the input dimension was larger than 100, testing accuracy was reduced. Training accuracy did not reduce when the input dimension was increased, suggesting that sparse filtering cannot extract discriminative features for a testing dataset, even though the weight matrix has perfect frequency resolution. This phenomenon is called overfitting.  
To explain the nature of overfitting, we use \(\mathbf{W}\mathbf{W}^{\text{T}}\) to measure the similarity of the row vectors respectively when \(N_{in}=100\) and \(N_{in}=200\). The results can be seen in Figure 8a,b; when the input dimension is 100, the inner product of the same row vector of approaches 1, and the inner product between the two different vectors is close to 0. However, when the input dimension is 200, the inner product of vectors of the weight matrix indicates that they have similar patterns. This suggests that sparse filtering can encourage the learned weight matrix to display clear and distinct patterns when the input dimension is smaller. 15 row vectors of \(\mathbf{W}\), trained by sparse filtering with \(N_{in}=100\), were randomly selected and plotted in Figure 9a. The corresponding frequency spectra of these row vectors are displayed in Figure 9b. The row vectors show local striking in the time domain and they also  
Figure 6: Classification results of sparse filtering of various input dimensions.  
Figure 7: Selected weight vectors for the motor bearing dataset and the fitting vector by Gabor function: (**a**) Vectors in the time domain; (**b**) Fourier transforms.  
dominate the narrow spectral bandwidth in the frequency domain. Accordingly, these row vectors exhibit time-frequency properties and show similarities to one-dimensional Gabor functions, which serve as good band-pass bases for mechanical signals. For comparison, we randomly plotted 15 row vectors of \(\mathbf{W}\) trained by sparse filtering of \(N_{in}=200\) in Figure 9c,d. These vectors also exhibited some time-frequency properties and wide spectral bandwidth in the frequency domain. The results demonstrate that the clearer and more distinct the time-frequency properties of the trained weight matrix are, the better the diagnosis performance of the method. Therefore, sparse filtering encourages learned features to be discriminative to improve the testing accuracy.  
Figure 9: _Cont._