esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Gravity and gravity gradient data serve as fundamental inputs for geophysical resource exploration and geological structure analysis. However, traditional denoising methods--including wavelet transforms, moving averages, and low-pass filtering--exhibit signal loss and limited adaptability under complex, non-stationary noise conditions. To address these challenges, this study proposes an improved U-Net deep learning framework that integrates multi-scale feature extraction and attention mechanisms. Furthermore, a Laplace consistency constraint is introduced into the loss function to enhance denoising performance and physical interpretability. Notably, the datasets used in this study are generated by the authors, involving simulations of subsurface prism distributions with realistic density perturbations (\(\pm\)20% of typical rock densities) and the addition of controlled Gaussian noise (5%, 10%, 15%, and 30%) to simulate field-like conditions, ensuring the diversity and physical relevance of training samples. Experimental validation on these synthetic datasets and real field datasets demonstrates the superiority of the proposed method over conventional techniques. For noise levels of 5%, 10%, 15%, and 30% in test sets, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNR) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB, respectively, outperforming wavelet transforms, moving averages, and low-pass filtering by 10-30 dB. In multi-component gravity gradient denoising, our method excels in detail preservation and noise suppression, improving Structural Similarity Index (SSIM) by 15-25%. Field data tests further confirm enhanced identification of key geological anomalies and overall data quality improvement. In summary, the improved U-Net not only delivers quantitative advancements in gravity data denoising but also provides a novel approach for high-precision geophysical data preprocessing.  
U-Net; gravity; gravity gradient; data noise reduction 2025
# Service-Driven Dynamic Beam Hopping with Resource Allocation for LEO Satellites
## 2 System Model and Problem Formulation
### Channel Model
\[G_{i}(\theta)=G_{0}\bigg{\{}\frac{J_{1}[u(\theta)]}{2u(\theta)}+36\frac{J_{3}[u (\theta)]}{u^{3}(\theta)}\bigg{\}}^{2} \tag{7}\]  
Figure 1:0  
Figure 1:1  
where \(G_{0}\) represents the peak value of the beam gain. \(J_{1}(\cdot)\) and \(J_{3}(\cdot)\) represent the first-order and third-order Bessel functions of the first kind, respectively. \(\eta\) represents the antenna efficiency. \(N\) represents a constant related to the antenna radiation pattern. \(\theta\) represents the angle between the signal emission direction and the pointing direction of the beam center. \(\theta_{3 dB}\) represents the 3 dB beamwidth. It can be calculated from the trigonometric formula as  
Figure 1:2  
where \(d_{m}\) represents the geometric distance from the satellite to the center of beam position \(m\). Similarly, \(d_{i}\) represents the geometric distance from the satellite to the center of beam position \(i\), and the distance between the centers of the two beam positions is \(d\). When the Earth's radius \(R\) and the satellite's orbital height \(h\) are fixed, the magnitudes of \(d_{m}\) and \(d_{i}\) are also fixed, and then the magnitude of the angle \(\theta\) is determined by the distance \(d\) between the center points of the two beam positions. The Signal-to-Interference-plus-Noise-Ratio (SINR) of beam position \(m\), denoted by \(SINR_{m}\), can be expressed as  
Figure 1:3  
where \(P_{m}^{k}\) is the transmit power of the satellite beam for beam position \(m\). \(h_{m}^{k}\) represents the channel coefficient of service beam \(k\) illuminating beam position \(m\). \(B_{tot}\) represents the total on-board bandwidth. \(N_{0}\) represents the noise power spectral density. \(I_{i}\) is the co-channel interference that co-channel beam position \(i\) causes to beam position \(m\). Taking the distance \(d\) between the centers of beam position \(m\) and beam position \(i\) as a variable, the SINR of beam position \(m\) is obtained as shown in Figure 1:4  
Figure 1:5 Influence of the spacing between the centers of simultaneously operating beams on SINR. According to the Shannon formula, the channel capacity of beam position \(m\) in time slot \(t\) can be obtained by  
Figure 1:6  
If \(x_{t}^{m}=0\), indicating that beam position \(m\) is not illuminated in time slot \(t\), the channel capacity becomes 0. The throughput of beam position \(m\) in time slot \(t\) can be described as  
Figure 1:7
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 3 System Architecture for Autonomous Vehicles
### LiDAR: Light Detection and Ranging  
LiDAR utilises the 905 and 1550 nm spectra [58]. The 905 nm spectrum may cause retinal damage to the human eye, and, therefore, the modern LiDAR is operated in the 1550 nm spectrum to minimise the retinal damage [43]. The maximum working distance of LiDAR is up to 200 m [15]. LiDAR can be categorised into 2D, 3D and solid-state LiDAR [59]. A 2D LiDAR uses the single laser beam diffused over the mirror that rotates at high speed. A 3D LiDAR can obtain the 3D image of the surrounding by locating multiple lasers on the pod [60]. At present, the 3D LiDAR can produce reliable results with an accuracy of few centimetres by integrating 4-128 lasers with a horizontal movement of 360 degrees and the vertical movement of 20-45 degrees [61]. The solid-state LiDAR uses the micro-electromechanical system (MEMS) circuit with micro-mirrors to synchronise the laser beam to scan the horizontal FoV several times. The laser light is diffused with the help of a micro-mirror to create the vertical projection of the object. The received signal is captured by a photo-detector and the process repeats until the complete image of the object is created. LiDAR is used for positioning, obstacle detection and environmental reconstruction [15]. 3D LiDAR sensors are playing an increasingly significant role in the AV system [62]. As a result, the LiDARs can be used for ACC, 2D or 3D maps and object identification and avoidance. A roadside LiDAR system has shown to reduce the vehicle-to-pedestrian (V2P) crashes both at intersections and non-intersection areas [63]. In [63], a 16-line real-time computationally efficient LiDAR system is employed. Deep auto-encoder artificial neural network (DA-ANN) is proposed, which achieves an accuracy of 95% within a range of 30 m. In [64], a 64-line 3D LiDAR utilising a support vector machine (SVM)-based algorithm is shown to improve the detection of the pedestrian. Although LiDAR is superior to a mm-Wave radar in measurement accuracy and 3D perception, its performance suffers under severe weather conditions such as fog, snow and rain [65]. In addition, its operating range detection capability depends on the reflectiveness of the object [66].
## 4 Materials and Methods
### Transcriptome Sequencing and Functional Annotation  
The RNA Prep Pure Plant kit (Tangen Biotech, Beijing, China) was used to extract total RNA from the leaf, stem, and root of _A. paniculata_ (three biological replicates per tissue). The concentration and integrity of RNA samples were assessed using an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Nine RNA samples from _A. paniculata_ that met the transcriptome sequencing requirements were sent to Nuobe Zhiyuan Biotechnology Co., Ltd, Shanghai, China. for sequencing on the Illumina Novaseq 6000 platform. The raw data were cleaned to remove adapters, N bases, and low-quality reads. The GC percentage, Q20, and Q30 were also calculated. Clean reads were de novo assembled into transcripts and unigenes using Trinity 2.4.0 software [62]. The blast2 go tool [63] was used to perform functional annotation against various databases, including NCBI non-redundant protein sequences (NR), NCBI nucleoside sequences (NT), KEGG, GO, euKaryotic Ortholog Groups/Clusters of Orthologous Groups of proteins (KOGs),Pfam, and Swiss prot. The iTAK 1.2 software [64] identified transcription factors (TFs) in _A. paniculata_.
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 1 Introduction
It is also unknown whether we are acquiring the best pose to estimate the fish's size. Therefore, long-term continuous monitoring of fish is necessary to obtain an adequate number of frames to take measurements. A complex sequence of steps and essential procedures, such as camera calibration, the acquisition of hours of video, video preprocessing, object detection, instance segmentation, stereoscopic correspondence in pairs of images, and 3D triangulation, must be usually performed to estimate individual measurements. Against that background, this paper presents the findings of the BFT Growth in Farms Pilot Study (ICCAT GBYP 10/2020) and reports on the evolution of length and width distributions. The project's full report is accessible online [15]. Measurements were estimated using software that automatically processes data collected from a stereoscopic vision system from a ventral perspective (sensors placed at the bottom of the cage, facing towards the surface). This work validates the computer vision procedure developed by the authors and previously presented in works [13; 14] as a valuable tool to study the growth of BFT in terms of length and width within farms. Our study offers valuable data on tuna growth in captivity, of significant interest to both fish farmers and researchers, filling a gap that has persisted until now. The large number of monitored day, as well as the extensive period of months required considerable effort but simultaneously enhanced the validity of the results. Specifically, we investigated the hypothesis of accelerated growth in cages and found an affirmative answer to the following question: Does annual growth in cages surpass that in the wild due to the special conditions within the cage, as previously demonstrated in juvenile BFT [16]? Using a cohort-based approach, we demonstrated that each modal length surpassed the length of the next cohort, indicating accelerated growth in cages compared to the wild. In addition, we established that using a length-width-weight relationship to estimate fish weight yields more accurate results than traditional length-weight relationships.
# An IoT-Based Solution for Intelligent Farming +
## 3 IoT-Based Stack Architecture for Intelligent Farming Solutions
### Gateway
The left side one corresponds to the Localization procedure. To improve the accuracy and stability, the localization procedure filters and combines the RSSI data from both slaves and beacons [74]. The improved animal's estimated position is then stored on the local database. The right side flow corresponds to the alarm generation. The incoming sensor data is analyzed to detect abnormal behaviors. Relevant data includes localization, posture and activity type, penalization counters, inertial sensors readings, among others. A historical record of this data is maintained for each one of the animals, and the algorithms can access both individual and global data. Different algorithms can be applied, according to the specific use-case requirements, but common alarms include panic situations and missing animals. Eventual alarm events are stored at the local database. When the incoming data is completely processed, the relevant fields (sensors, timestamp, error counters, alarms, etc.) are fetched from the database and sent to the CP. The CP is designed to receive data using the AMQP protocol, thus, the gateway implements this protocol at the Internet interface. Further details on the CP operation are given in Section 3.3. Figure 9b illustrates the operation of the technical and management web interfaces. After system initialization, the gateway awaits for the arrival of a frame from the WiFi/Bluetooth interface. When a frame arrives, it is validated and parsed. Frames can be of one of two classes, _GetData_ and _SetData_. _GetData_ frames are sent by the user device and correspond to data requests. Conversely, SetData frames correspond to parameter update commands. When a _GetData_ frame is received, the Gateway  
Figure 8: Gateway internal modules. fetches the corresponding data from the internal database and reports it via the same interface. _SetData_ frames correspond to modifications to the system operation (e.g., activate or deactivate the posture control, set the height below which animals are allowed to feed, define the radio transmission power, etc.). When one of these frames is received, the corresponding parameters are stored on the local database and then forwarded to the WSN, thus reaching beacons and collars.
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 4 Experimental Results and Analyses
### Influence of Variable Factors on the Experiment
#### 4.3.2 CNN Architecture  
Here, we conduct comparative experiments on the dataset by using different CNN architectures. The deep features are extracted in the Fc layer. AlexNet, ResNet50, GoogleNet, GooglePlaces, and ResPlaces are used for comparison experiments. GooglePlaces and ResPlaces are pre-trained networks with training on the places dataset. Table 4 lists the APs and ARs by the deep features extracted from different networks. The NR is set to 20, and the Canberra metric is used for the similarity measure.  
It can be seen that the network structure has a great influence on the results, and good network architecture is more conducive to image retrieval.
# Carrier Phase Dual One-Way Ranging Method Based on a Frequency Hopping Signal
## 3 The FH Dual One-Way Ranging System
### The Acquisition Algorithm of the FH DOWR Model  
For satellites in TDD mode, the transmitting and receiving process is accompanied by signal reacquisition and retracking. The acquisition process obtains Doppler and code phase coarse estimate results for the receiving system. The steps are as follows:  
Step 1. Downconverting the received radio frequency (RF) signal to the intermediate frequency (IF). The receiver generates the local IF carrier according to the hopping pattern and downconverts the signal to the baseband;  
Step 2. Multiplying the fast Fourier transform (FFT) result of the local PN code by the FFT result of the baseband signal. The results were processed using the inverse fast Fourier transform (IFFT) [27].  
Step 3. Recording the maximum position and maximum value of the IFFT value [27].  
Step 4. Setting the local intermediate frequency as the center frequency point, recording the IFFT results at a Doppler frequency offset of \(-12\) kHz\(\sim\)12 kHz [27].  
Figure 4: FH DOWR system: (**a**) transmitter and (**b**) receiver.  
Step 5. Outputting the code offset and Doppler frequency offset with the maximum correlation peak to the tracking stage.  
Step 6. Switching to the next FH frequency point, after which the acquisition starts based on the hopping pattern.  
Step 7. Since we have pre-estimated the Doppler frequency offset at the first frequency point, the Doppler offset value at the second frequency point can be estimated according to (10).  
\[f_{\text{d(n+1)}}=\frac{f_{n+1}}{f_{n}}f_{\text{dn}} \tag{10}\]  
\(f_{n}\) and \(f_{dn}\) are the first FH frequency point and Doppler frequency, respectively. \(f_{n+1}\) and \(f_{d(n+1)}\) are the next FH frequency point and Doppler frequency, respectively. The acquisition process is shown in Figure 5.  
However, because the Doppler frequency searching step is 500 Hz, the FH Doppler frequency \(f_{dn}\) estimation error is \(-250\) Hz\(\sim\)250 Hz. Therefore, it is necessary to detect the Doppler frequency of the next FH frequency point. The steps are listed as follows.  
Step 1. The initial frequency is calculated as \(f_{d(n+1)}\).  
Step 2. The Doppler frequency was searched for at both sides of \(f_{d(n+1)}\). The frequency searching order is shown in (11). The maximum correlation peak is calculated as (12):  
\[\left[f_{d(n+1)},\ \ f_{d(n+1)}+500\;\text{Hz},\ \ \ f_{d(n+1)}-500\; \text{Hz},\ \ \ f_{d(n+1)}+1000\;\text{Hz},\ \ \ f_{d(n+1)}-1000\;\text{Hz},\ \ \ \dots\ \ \ \ \right], \tag{11}\]  
\[\left[P_{n}\ \ \ P_{n+1}\ \ \ \ P_{n-1}\ \ \ P_{n+2}\ \ \ P_{n-2}\ \ \ \dots\ \ \right]. \tag{12}\]  
Step 3. When the searching range exceeds \(-12\) kHz\(\sim\)12 kHz, the \(f_{dn}\) is recorded as the Doppler frequency estimation result with \(\max(P_{n})\). When the correlation peak at different frequencies satisfies \(P_{n}\geq P_{n+1}\) & \(P_{n}\geq P_{n-1}\), the search is stopped, and \(f_{dn}\) is recorded as the Doppler frequency estimation result with \(P_{n}\).  
Figure 5: FH DOWR signal acquisition process.
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
###### Abstract
In addition, those models assume that the coldest pixel in the image means potential transpiration, and the hottest pixel means zero transpiration which is not always the case (e.g., in humid and sub-humid areas). In this study we will evaluate three different ET models driven by Sentinel-2 and Sentinel-3 imagery: METRIC [32] is a one source energy balance model that is less sensitive to heat transfer coefficient parametrizing than other OSEB model such as SEBS [33]; TSEB-PT [36] as a widely used two source energy balance model; and ESVEP [44] as a hybrid contextual-two source energy balance model.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 7 Open Issues and Future Research Directions
### Future Research Directions
#### 7.4.3 Machine Learning Integrated with Blockchain for 5G-Based Mec  
Machine Learning (ML) techniques create new opportunities for the existing and future V2X technologies, enabling them to learn from data, provide predictions and support decisions [120]. In the integrated system, the ML transforms the way data analytics are performed to help intelligent services, such as traffic data estimation, for avoiding network congestion. There is a lot of research based on integrating machine learning such as DRL with blockchain, 5G-based edge computing in V2X to ensure security, and intelligent resource management [88] in dynamic vehicular network with high mobility. The DRL helps in analyzing the channel interference, channel assignments, and wireless network topology. It helps to choose the most suitable access mode, such as cellular or V2V, to enhance the V2X connection or improve the intelligent vehicle experience. However, the resolution on offloading optimization needs to be further researched to balance the integrated blockchain and 5G-based edge computing.
## 3 Introduction of Input Data and Software
### Renewable Sources Assessment  
Solar irradiation and ambient temperature are unstable variables and, accordingly, the power of PV system is unstable. We use Homer to find the latitude and longitude of the site under study and access to National Aeronautics and Space Administration (NASA) surface meteorology and solar energy database for downloading solar irradiation and ambient temperature [75]. The monthly averages of these two meteorological data are defined in Homer resource inputs. The following two points provide more detailed analysis of these variables.  
* The solar irradiation:  
The monthly average solar irradiation is in the range from 3.51 kWh/m\({}^{2}\)/day to 7.89 kWh/m\({}^{2}\)/day, with the annual average of 5.8 kWh/m\({}^{2}\)/day as shown in Figure 6. The global horizontal irradiance (GHI) is risen above the average value in the months from April to September with the peak value recorded in June. The remaining months are shown relatively lower than the average value of solar irradiation with the lowest average value recorded in December.  
* The ambient temperature:  
The air temperature of the considered site is taken in to account. It significantly affects the output characteristics of the PV system. The monthly average air temperature is depicted in Figure 7, which ranges from 15.12 \({}^{\circ}\)C to 36.2 \({}^{\circ}\)C with an annual average of 26.85 \({}^{\circ}\)C. The ambient temperature is higher than the average value in the month from April-October, and is recorded highest in July. The remaining months are shown low average value with the lowest average value appeared in January.  
Figure 6: The monthly average solar global horizontal irradiance (GHI), clearness index in Alkharj.
## 4 Conclusions  
New and freely available Sentinel-2 data has the potential to estimate leaf N as an indicator of rangeland quality with the inclusion of red edge bands. The red edge based indices (especially simple ratio based on 705 and 783 nm), CI and MTCI based on simulated Sentinel-2 data showed higher potential to estimate leaf N. The latter has been achieved because the red edge position is known to relate to leaf N and chlorophyll. Even though spectral bands did not yield higher estimation accuracy, most of them were significantly related to leaf N. ASD reflectance data could be useful in the estimation  
Figure 6: Cross-correlations between leaf nitrogen concentration for various dates with various environmental variables (MAP = mean annual precipitation, DEM = digital elevation model, N = leaf nitrogen concentrations (%)).  
Figure 7: The analysis of the influence of geological types (top) and fire frequency (below) on the variation of leaf N data, mean, median and standard deviation, derived from nine months or granules of spatial data.  
of leaf N, provided there is a good red edge embedded satellite data such as Sentinel-2. Geology and fire frequency significantly influence the spatial distribution of leaf N. Topographic features such as altitude significantly influenced the spatial distribution of leaf N, while aspect and slope did not. Monitoring the spatial distribution of nutrients is crucial for understanding the feeding patterns and movements of the herbivores, and is useful to the improvement of rangeland management and planning tools such as stocking rates and rangeland carrying capacity.  
We would like to thank CSIR, National Research Foundation (NRF)--SASSCAL Project and European Union's Horizon 2020 research and innovation programme under grant agreement No. 641762 (ECOOPTENTIAL Project) for providing funds to undertake this work. I would like to thank SANPARKS scientific services at Skukuza for facilitating field work activities and providing other ancillary datasets. I would also like to thank CSIR's Meraka Institute, Terradue and South African National Space Agency for downloading and processing Sentinel-2 data. I am grateful to [PERSON] and [PERSON] (Bushbuckridge area) for field work assistance. I would further like to thank the Editor and anonymous reviewers for their constructive comments, which eventually improved the quality of this manuscript.  
[PERSON] (A.R.) conceptualized the idea, collected, analyzed the data and wrote the manuscript. [PERSON] (M.A.C.) contributed on the conceptualization of the idea and paper writing.  
The authors declare no conflict of interest.