esa-sceva/satcom-chunk-collection huggingface dataset:
# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 2 Motivation
### Convergence of Contours  
The level set method remains a widely used technique for aligning the initial contour to the target boundary. It models a closed curve \(S\) as the zero level set of a Lipschitz function \(\phi\). The convergence of \(S\) involves minimizing an energy function \(E(\phi)\) with respect to the level set function [31]:  
\[S(t)=\Big{\{}(x,y,t)\big{|}\phi(x,y,t)=0\Big{\}}, \tag{6}\]  
\[\frac{\partial\phi}{\partial t}=-\frac{\partial E(\phi)}{\partial\phi}, \tag{7}\]  
where \(t\) represents the evolution time, and \((x,y)\) are the Cartesian coordinates of a pixel in the image.  
Existing LSE models can be generally grouped into region-based and edge-based models. The distance regularized LSE (DRLSE) method slows down the shrinkage or expansion of \(S\) at image edges [32]. The region-based Chan-Vese model deforms \(S\) according to the gray difference between regions inside and outside the contour [33]. A kernel function was integrated into the Chan-Vese model for better effectiveness in inhomogeneous images [34]. A combined data fidelity term was integrated into the energy function for efficiently handling images with additive and multiplicative noise [35].  
Level set techniques often require initialization and do not guarantee a global minimum. After obtaining the initial contour from superpixel-based clustering results, we choose edge-based LSE with a new edge indicator to refine the contour.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation
### Communication Time between the Satellite and GST(\(\tau\)(i))
#### 3.1.1 Antenna Radiation Pattern  
The radiation pattern of the receiver antenna was measured in the anechoic chamber tests. The antenna radiation pattern was measured for three planes such as X-Y plane, Y-Z plane, and X-Z planes, and in each plane for vertical and horizontal orientation of the reference antenna. Figure 9 shows the rotation axis of the antenna for determining the antenna radiation pattern.  
The maximum gain of the receiver antenna integrated together with the satellite body was 0.27 dBi for 400 MHz and 0.36 dBi for 433 MHz as in Figure 10.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Mobile Cloud Computing (MCC)
#### 3.4.1 Actors Involved in MCC  
In MCC, various key actors play a significant role in delivering smooth services, including both end-users and the infrastructure:  
1. Mobile Devices: Smartphones, tablets, wearables, and other portable gadgets form the primary end-user interface. These devices execute applications, generate data, and connect to the cloud, acting as a bridge between users and the cloud infrastructure.
2. Cloud Servers: These servers process applications and data, store and manage vast amounts of information, perform complex computations, and ensure the reliability of services provided to users.
3. Cloudlets: Acting as intermediary servers between mobile devices and the cloud, cloudlets enable computation offloading, reduce latency, and improve the performance of real-time applications.
4. Network Infrastructure: Comprising the network components and protocols, this infrastructure facilitates communication between mobile devices, cloudlets, and cloud servers, ensuring seamless data transmission and connectivity.
5. Service Providers: Service providers manage the cloud infrastructure and its applications, ensuring reliability, security, and optimal performance for end-users.
## 2 Materials and Methods  
### Study Site Description  
The study area is located in an Environmental Protection Area at the Pacoti River estuary, Ceara state, NE-Brazil (Figure 1A). The Pacoti River extends for 150 km of stream and contains two large reservoirs built to supply freshwater for human consumption. These reservoirs decrease the freshwater flow resulting in an increase of soil salinity at the estuary [30].  
The studied HTF is influenced by a tropical semi-arid climate, with mean annual temperatures ranging from 26 to 28 \({}^{\circ}\)C [18; 31]. The rainfall is distributed in two seasons, as follows: a rainy summer (from February to June) and a dry season (from July to January) with mean annual rainfall of up to 1200 mm [30]. The tides on the estuary are semidiurnal, with maximum tidal amplitudes of about 3.1 m and a minimum of 0.9 m [32]. In a previous study, the soils of the studied HTF were classified as Tidalic Gleyic Sodic Protein Arenosol (Protocalic), according to the WRB-FAO soil classification system, and as _GLEISSOLO SdLICO sdtico tipico_ according to the Brazilian soil classification system [33].  
Due to the hypersaline conditions, the HTF present a patchy-distributed vegetation, which allowed the distinction of vegetated and bare soil (Figure 1B). We studied the three most dominant plant species in HTF of Ceara coastal region, as follows: _Batis maritma_ ([PERSON]), _Blutaparon portulacoides_ ([PERSON]), and _Sorobolus virginicus_ ([PERSON]) [PERSON] (Figure 2). For each of these plants, rhizosphere and nonrhizophrenic (bulk) soils were sampled. The studied plant species are clonal plants typical from these saline environments and normally considered as pioneer species able to colonize and stabilize flats and dunes [34; 35; 36].  
In order to facilitate comparisons and interpretations, the samples were named according the species (i.e., _Batis maritima_--Bm; _Blutaparon portulacoides_--Bp; and _Sorobolus virginicus_--Sv), with the indication of rhizosphere (R) or bulk (B) soil sample (e.g., BmR refers to rhizosphere samples from _Batis maritima_ whereas SvB refers to bulk soil of _Sorobolus virginicus_).  
Figure 1: (**A**) Map of the study site (Pacoti River estuary, Aquiraz, Ceará state, Brazil), distinguishing the coastal tableland (CT), the studied hypersaline tidal flat (HTF), the associated mangrove (M), the Pacoti river (R), and the costal dunes (D). (**B**) Landscape of the studied site, showing the patchy distribution of _Blutaparon portulacoides_ (Bp) and mangrove forest at the background. (**C**) Average monthly precipitation of the Pacoti estuary for 2012 and 2013 (studied years). Data from FUNCEME database of historical precipitation records. The * represents missing data.
## 4 Discussion
### Spatial Heterogeneity of Vegetation Resistance and Resilience to Drought  
Vegetation resistance and resilience in the West Liao River Basin show significant spatial heterogeneity, with higher levels in the west and lower levels in the east and south. This variation is driven by natural and anthropogenic factors. Western vegetation, dominated by drought-tolerant shrubs and grasslands, exhibits strong drought resistance due to physiological adaptations [51]. In contrast, eastern and southern croplands are more sensitive to drought, influenced by both climatic and human factors (e.g., irrigation, land-use changes) [7]. Future research should integrate high-resolution remote sensing and ground observations to quantify these influences.  
High resilience enables the rapid restoration of ecological functions (e.g., carbon uptake, water cycling) [52], mitigating cumulative drought impacts. However, extreme droughs exceeding recovery capacity can lead to ecosystem degradation [53]. The spatial patterns of resistance and resilience inform ecosystem management. In resilient western regions, maintaining ecosystem stability through measures like ecological reserves and grazing restrictions is crucial [5]. In vulnerable eastern and southern regions, targeted interventions--such as drought-resistant crops and ecological water replenishment--are needed to enhance resilience [54]. For example, deep-rooted legumes (such as alfalfa) sustain productivity under drought conditions, while intercropping drought-resistant legume species improves crop resilience to aridity [55]. Furthermore, drought during the flowering stage of maize can reduce yields by up to 50%, whereas sorghum exhibits higher drought tolerance due to its waxy cuticle. Therefore, adopting drought-resistant crop varieties (e.g., substituting maize with sorghum) and implementing precision irrigation can significantly enhance agricultural resilience to water stress [56]. Additionally, climate change scenarios should be integrated to predict long-term impacts and guide sustainable management.
## 1 Introduction
### Related Works  
In recent studies referenced as [7; 8], the researchers delve into the design of intelligent waveforms capable of facilitating both data transmission and radar sensing simultaneously, a topic highly pertinent to this paper. Additionally, [9] explores the joint receiver design for ISAC systems, where both communication and target echo signals are received concurrently. These signals undergo processing to optimize the performance across both functionalities, with two distinct design schemes considered.  
Furthermore, the investigations highlighted in [10; 11] center around the channel estimation and signal detection within orthogonal frequency division multiplexing (OFDM) systems, leveraging a deep learning (DL) approach. This method is employed to comprehensively address wireless OFDM channels, from the estimation of channel state information (CSI) to symbol recovery. Various channel scenarios are examined to evaluate the efficacy of the DL approach.
# Single-Layer Pressure Textile Sensors with Woven Conductive Yarm Circuit
## 2 Sensor Design  
The conductive yarns are fabricated by impregnation to impart conductivity to the nonconductive wool yarns (about 10 yarns/sample). The impregnation ink is carbon nanotubes (CNTs) dispersed methyl ethyl ketone (MEK), which is easily agglomerated by the van der Waals force of the surface. Therefore, in order to ensure uniform dispersion, the CNT ink was stirred for at least 1 h at 700 rpm by a mechanical stirrer and treated ultrasonically for avoiding incorporation of air bubbles by an ultrasonic machine (Figure 1a,b). As shown in Figure 1c,d, the wool yarns are impregnated in CNT inks and dried for 48 h in order to fix CNT particles adhering to the wool yarns. Using a precision LCR meter (Keysight E4980 AL), we observed that the produced wool yarns have a resistance of about \(7\times 102\)\(\Omega\)/cm.  
As shown in Figure 1e, single-layer pressure sensors are fabricated by combining electrode threads and spacing threads through intertwining with conductive wool yarns via a hand process on a weaving tool (from Woven on looms Ltd., Seoul, Korea) [32]. The electrode threads are made of 70 deniers (D)  
Figure 1: Fabrication process of conductive yarns consisting of (**a**) stirring CNTs, (**b**) sonification CNTs, (**c**) CNT-impregnation yarns, (**d**) drying CNT-yarns, (**e**) weaving sensor layer, and (**f**) the sensor with two laminating layers.  
galvanized nylon plated yarn in a twist direction (S-twist) with a 140D metal plated gold yarn twined at a twisting rate of 10,000 rpm during manufacturing. In the process of electrode threads, the metal plating chamber is set at a very high electrical resistance (8.104 \(\Omega\)/m). Non-conductive PET 250D is used as the spacing yarns (about 10 yarns/sample). The sensor area is laminated by thermal pressing with a flexible thin film on both sides (Figure 1f) at a temperature of 135 \({}^{\circ}\)C and a duration of 20 s. These film layers cover the sensor surface and prevent CNT fall-off. The material used for laminating the sensor is a 100 \(\upmu\)m thin elastic film with 100% polyurethane (PU) from Sealon Ltd., Seoul, Korea. Because of the PU films, the sensor will no longer have a drape or feel of textiles normally, and it will have no breathability. However, the area size (10 mm) and the thickness (0.4 mm) of sensors are small; this can be accepted for the application in this paper. We suggest using other materials or structures for the cover layers (as highly stretchable PU/Silicone film, some holes on film) to improve the drape and breathability, but more experiments are needed for this. Finally, the one-layer-fabric pressure sensor of a small thickness of 0.4 mm and a size of \(10\times 10\) mm is prepared.
## 4 Conclusions  
In this paper, PCS and PCM were prepared successfully. The physicochemical properties, photocatalytic activity and long-term catalytic performance were studied comprehensively. 2 \(\upmu\)L benzene was degraded completely in 200 min by 5 g 4 PCS-1.25-2.35 and 300 min by PCM-5, and was still degraded over 80% in 400 min by PCM-5 after exposure to the natural environment for 6 months. The results showed that PCS presented an excellent photocatalytic activity due to abundant pores, stable structure and large reaction area of CS. The detailed results showed that the photocatalytic activity of PCS increased through adjusting CS in appropriate pore structure (more pores in the range of 10-1000 nm, appropriate total porosity and pore volume) and TiO\({}_{2}\) in appropriate ratio, which could provide an excessive exposure of TiO\({}_{2}\) on the surface and more gas diffusion channels. PCS exhibited an optimal degradation activity to benzene when TiO\({}_{2}\) concentration rose to 4 g/L. However, a fraction of the loaded TiO\({}_{2}\) was inaccessible to the reactants or light as the concentration of TiO\({}_{2}\) kept increasing (up to 5 g/L). Excessive TiO\({}_{2}\) might have serious agglomeration problems, leading to the decreased adsorption rate of air pollutants.  
After PCS was loaded on the cement materials, PCM could still present a high catalytic activity. Even after exposure to the natural environment for 6 months, PCM still remained good photocatalytic activity comparing with the cement material with TiO\({}_{2}\) on the surface or incorporated. The excellent photocatalytic activity and long-term catalytic performance were attributed to the activation for surface groups and distinctive structure of PCS. The porous structure of PCS increased specific surface areas, gas diffusion and light transmittance channels of PCM, improving the reacting areas and utilization rate of TiO\({}_{2}\) effectively. Through this preparation route, more TiO\({}_{2}\) particles on PCS were exposed to environment without being influenced by the envelope of cement hydration products and the carbonation of cement.  
**Acknowledgments:** We gratefully acknowledge the financial support of the National Natural Science Foundation of China (Nos. 51502222 and 51461135005).  
**Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] designed experiments; [PERSON] and [PERSON] carried out experiments; [PERSON], [PERSON] and [PERSON] analyzed experimental results. [PERSON] wrote the manuscript.  
Figure 10: Time of fully degradation of 2 \(\upmu\)L benzene by mPCS-1.25–2.35 (m = 1–5 g/L) for 10 cycles.
# An Overview of GEO Satellite Communication Simulation Systems
## 5 Conclusions  
This article systematically reviews the overall architecture, core modeling (i.e., coverage analysis and resource management and scheduling), and current mainstream simulation platforms and tools for GEO satellite communication systems. Looking toward future developments, several research directions warrant further exploration: First, establishing a multi-dimensional, multi-layered comprehensive simulation framework to improve model granularity, thereby ensuring simulation outcomes more accurately reflect actual operational conditions. Second, integrating digital twin technologies and real-time feedback mechanisms to achieve closed-loop calibration between simulated and measured data, thus significantly enhancing simulation accuracy and reliability. Third, introducing AI-driven adaptive optimization frameworks to substantially strengthen the intelligent decision-making capabilities and self-learning characteristics of simulation systems. Fourth, leveraging high-performance computing (HPC) and cloud-based elastic resource scheduling through distributed parallel processing and GPU acceleration to enable efficient execution of large-scale simulation tasks. These research avenues will provide essential guidance for future design and optimization of GEO satellite communication simulation systems, driving technological innovation and practical advancements in related fields.  
Conceptualization, S.L. and Y.Q.; methodology, S.L.; formal analysis, K.S.; investigation, S.L.; resources, K.S.; writing--original draft preparation, Y.Q.; writing--review and editing, S.L. and K.S.; visualization, Y.Q.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
During the preparation of this manuscript, the authors used ChatGPT 4o for the purposes of polishing sentences for clarity, accuracy, and academic tone. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest.
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 2 Related Works
This motivates the generation of highly photorealistic views. Our approach of using high-fidelity LiDAR reconstructions is one way to mitigate this gap. Another powerful set of techniques is Neural Radiance Fields (NeRFs) [17] and its successors. Inspired by this, M2 Mapping [2] unifies Neural Distance Fields and NeRFs to generate realistic renderings with structural consistency. More recently, 3D Gaussian Splatting [18] has emerged as a method for achieving real-time, high-quality novel view synthesis from point clouds, representing the state-of-the-art in rendering fidelity and efficiency. These advances in photorealistic rendering are crucial for bridging the domain gap and enabling robust keypoint matching between synthetic and real views. Recent research efforts have sought to incorporate photorealistic rendering techniques into SLAM pipelines. Systems like VI-NeRF-SLAM [19] and NICE-SLAM [20] jointly optimize pose estimation and dense reconstruction using learned radiance representations. In a related direction, vMAP [21] introduces a vectorized object-centric representation for neural field SLAM, supporting efficient mapping and instance-level segmentation. In contrast to radiance-based methods, SLAM3R [22] proposes a feed-forward approach that directly regresses dense 3D point maps from monocular input. Gaussian-based frameworks, such as G\({}^{2}\)-Mapping [23] and MAGiC-SLAM [24], offer scalable and real-time dense reconstruction using differentiable splatting representations. These approaches reflect a shift toward unified representations and learning-enhanced pipelines that improve accuracy, scalability, and computational efficiency. While previous methods show powerful registration capabilities, they differ from our framework in key aspects. For instance, approaches like CMRNet are direct methods, employing a convolutional neural network to register projected LiDAR data with an image without explicit keypoint extraction. These learning-based systems can be computationally intensive, often requiring GPU acceleration, and their performance can be tied to the availability of representative training data. Similarly, emerging NeRF-based localization frameworks typically build their dense scene representation from images, which can involve a lengthy, per-scene training phase. Our framework, conversely, is designed to leverage a pre-existing geometric map from LiDAR, making it a feature-based approach that is computationally efficient at runtime and does not require per-scene training. Our method builds on this body of work by combining dense LiDAR-based rendering with image-based keypoint matching. We directly compare our approach against leading visual SLAM pipelines to demonstrate its competitive performance, particularly leveraging the geometric accuracy of LiDAR to overcome the typical failure modes of purely visual systems while addressing the sim-to-real challenge through high-quality rendering.
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 3 Methods
### Theoretical Analysis of the Improved BPA
#### 3.3.2 Back Projection and Phase Compensation  
The focus processing of BPA in the azimuth direction is carried out in the time domain and does not require Range Cell Migration Correction (RCMC). \(F(\eta,\tau)\) is the correlation value at different time delays relative to the reference point, and the process of back projection is to map the \(F(\eta,\tau_{mn})\) to different pixels \((x_{m},y_{n})\), (see Figure 4) \((m,n)\) is the image pixel index of the final image.  
Figure 4: Schematic diagram of imaging pixel distribution.  
Figure 3: A block diagram of the improved BPA.  
The propagation delay can be calculated according to the signal propagation distance when the three positions of the navigation satellite, the pixels, and the receiver are uniquely determined, which can be expressed as:  
\[\tau_{mn}(\eta)=\frac{R_{T}(\eta,m,n)+R_{R}(m,n)}{c} \tag{8}\]  
\[R_{T}(\eta,m,n)=\sqrt{\left[x_{T}(\eta)-x(m,n)\right]^{2}+\left[y_{T}(\eta)-y( m,n)\right]^{2}+\left[z_{T}(\eta)-z(m,n)\right]^{2}} \tag{9}\]  
\[R_{R}(m,n)=\sqrt{\left[x_{R}-x(m,n)\right]^{2}+\left[y_{R}-y(m,n)\right]^{2}+ \left[z_{R}-z(m,n)\right]^{2}} \tag{10}\]  
\[F(\eta,\tau_{mn})=P[\tau_{mn}-\frac{R_{T}(\eta,m,n)+R_{R}(m,n)}{c}]\exp[-j2\pi \frac{R_{T}(\eta,m,n)+R_{R}(m,n)}{\lambda}] \tag{11}\]  
\(\tau_{mn}(\eta)\) is the time delay of the echo signal generated at the pixel \((x_{m},y_{n})\). \(R_{T}(\eta,m,n)\) and \(R_{R}(m,n)\) are the distance from the satellite and receiver to the pixel \((x_{m},y_{n})\). Before \(F(\eta,\tau_{mn})\) is mapped to the pixel, the Doppler phases need be compensated. The Doppler phase compensation factor can be expressed as:  
\[h(\eta,m,n)=\exp[j2\pi\frac{R_{T}(\eta,m,n)+R_{R}(m,n)}{\lambda}] \tag{12}\]  
The Doppler phase compensation is realized by complex multiplication \(h(\eta,m,n)\) in the time domain according to the data selected by the slant range \(R_{T}(\eta,m,n)+R_{R}(m,n)\), and the result is used as the value of the pixel. An image can be generated at each azimuth time \(\eta\), which can be expressed as  
\[S(\eta,x_{m},y_{n})=F(\eta,\tau_{mn})\cdot h(\eta,m,n) \tag{13}\]