esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Error Introduced by LEO Satellite Assistance  
To determine the parameters of acquisition and tracking and analyze the performance of LEO positioning assistance, it is necessary to consider the errors introduced by the assistance information from the LEO satellite. These errors include the clock accuracy of the satellite and receiver, the clock stability error of the satellite and receiver, and the Doppler deviation caused by the position error introduced by the positioning error from LEO satellite. The Doppler deviation and the clock accuracy of the receiver mainly affect the acquisition of a GNSS signal. A larger deviation increases the search range, resulting in low efficiency. On the other hand, the clock stability of the receiver prevents excessive compression of the coherent integration time. This section will analyze each error in detail. In the analysis, the receiver is assumed to be static. In the analysis and simulation, GPS constellation is considered for GNSS. Moreover, the parameters for LEO positioning are mainly derived from the Iridium satellite, whose positioning technology is mature enough. The only difference is that the LEO is assumed to transit GNSS ephemeris in this work. GPS satellites follow near-circular orbits at about 20,200 km altitude, and Iridium uses 66 spacecraft orbiting at an altitude of 780 km, which is much lower than the GPS orbit altitude [23; 24]. The velocities of a GPS satellite and an Iridium satellite relative to the earth are about 3862 m/s and 7500 m/s, respectively. The GPS L1 C/A signal uses a frequency of 1575.43 MHz, while Iridium uses the frequency 1624 MHz.
## 2 Related Work
### Sentiment Lexicon and Machine Learning  
Early research primarily focused on sentiment lexicon (SL) and ML methods. [PERSON] and [PERSON] [17] proposed an approach based on the SentiStrength lexicon [18], providing coherent summaries of user sentiments related to specific app features. [PERSON] et al. [19] introduced the SAR model, which combines entity discovery, review stratification, and mixed-sentiment computation to provide developers with insights into user reactions. [PERSON] et al. [20] utilizes frequent item-set mining and SL-based method to compare competing mobile apps by highlighting strengths and weaknesses in user sentiment expressions. [PERSON] et al. [21] presented a semi-supervised methodology combining syntactic features to identify and compare \"hot\" app features. The eBTM model [22] combined support vector machines (SVM) with a feedforward neural network to summarize multiple short online reviews. [PERSON] et al. [8] proposed the first dataset (AWARE) covering three application review domains with systematic aspects and sentiments and provided a baseline based on various ML models. The AWARE dataset is one of the benchmark datasets used in this study.  
While lexicon-based methods rely on predefined vocabulary and rules, they struggle to handle new words and diverse contexts and are limited by the coverage of words and phrases. Although ML models can automatically learn features, they depend heavily on manual feature engineering and a large amount of labeled data, making it difficult to capture complex nonlinear relationships.
# BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions
## 4 Experimental Results and Discussion
### Classifier Model Optimization
In this work, the range of the penalty factor C is set as [\(2^{-15}\), \(2^{-14}\), \(2^{-13}\), \(\cdots\), \(2^{-1}\),\(2^{0}\),\(2^{1}\), \(\cdots\), \(2^{13}\), \(2^{14}\), \(2^{15}\)] while the parameter \(\sigma\) range is set as [\(2^{-5}\), \(2^{-4.5}\), \(2^{-4. }\), \(\cdots\), \(2^{4}\),\(2^{4.5}\),\(2^{5}\)]. Then, the quadratic grid search algorithm is utilized for optimization. Figure 9a shows the contour map for the searching process. Observed from Figure 9b, the optimized point appears in (7.914, 3.297) for the first rough grid searching. Meanwhile, the classification accuracy achieves 92.4%. Based on the initial optimization results, the grids are refined, shown as Figure 10, where the optimized point appears in (8.235, 2.814). After, the optimal parameters are obtained for C = 301.2, \(\sigma\) = 7.03 while the classification accuracy achieves 94.8%. The experimental result shows that the grid search algorithm has achieved a better accuracy for the training model. Figure 9: Parametric optimization of contour map by quadratic grid search method: (**a**) parameter selection for initial grid partitioning and (**b**) parameter selection of secondary meshing.
## 2 System and Channel Models
### Outage Probability
Similar to the conclusion in Figure 7, it can also be seen that under the consideration of pointing errors, compared to the hybrid FSO/RF direct link, the UAV-assisted hybrid system significantly improves the outage performance. As \(D_{x,y}\) increases, or \(\sigma_{x,y}^{s}\) decreases, it can further improve the outage performance of UAV-assisted hybrid systems and hybrid FSO/RF direct links. Figure 6: The relationship between outage probability and transmitted transmission power of UAV-assisted hybrid systems and hybrid FSO/RF direct links without PE. (**a**) \(m_{x,y}=1m=1\). (**b**) \(D_{x,y}=10\) cm. Figure 8: The relationship between outage probability and transmitted power of UAV-assisted hybrid systems and hybrid FSO/RF direct links with PE. (**a**) Different \(D_{x,y}\). (**b**) Different \(\sigma^{s}_{x,y}\). Figure 7: The relationship between BER performance and transmitted power of the hybrid FSO/RF direct links and UAV-assisted hybrid systems with PE under different parameters. (**a**) Different weather. (**b**) Different \(m_{x,y}\). (**c**) Different \(D_{x,y}\). (**d**) Different \(\sigma^{s}_{x,y}\).
## 7 Discussion
### Trends and Future Challenges  
In recent years, there have been more and more papers using deep learning to achieve EAT segmentation. This is a notable trend in the field and one that will probably continue as deep-learning-based methods develop and mature. Deep learning methods achieve state-of-the-art results in other cardiac imaging segmentation tasks [55], using very similar data to data used for EAT segmentation, which might indicate that it is possible to further improve the results of deep-learning-based methods.  
Another important dimension on which to validate EAT segmentation methods is model interpretability. Deep learning methods are notoriously hard to interpret, and their results can be surprising and inconsistent with expectations for a given CT scan. Slight variations in noise or structure that are easy to spot by humans could produce widely different results. Knowing these limitations ahead of time is hard because deep learning models are usually \"black boxes\". Once the model is trained, it is hard to determine exactly what the model is doing to achieve its results. Non-learning methods like active contours and region growing provide results that are more in line with expectations, and their limitations can often be tested and explained more easily. However, these approaches have lower limits on performance in other classification tasks compared to deep learning, and the same is probably true for EAT segmentation.
# Cross-View Attention Interaction Fusion Algorithm for Stereo Super-Resolution
## 3 Experimental Results
### Experimental Setup  
In order to test the effectiveness of the proposed network, the attention stereo fusion module proposed in this paper is applied to five single image super-resolution networks, such as SRCNN [7], VDSR [8], LapSRN [28], SRDenseNet [9] and SRResNet [26]. We use Flickr 1024 dataset [29] as the training set of the network. For the generation of training data, first, all images are down-sampled with sampling factors of 2 and 4 to generate low-resolution images. Then, these low-resolution images are cropped into small image blocks of the same size \(30\times 90\) at an interval of 20 pixels, and the corresponding high-resolution images are cropped into small image blocks of the same size. In addition, the training data are enhanced by random horizontal and vertical flipping to ensure the diversity of training data.  
All series of network operations were programmed by PyTorch and implemented with NVIDIA RTX 2080 ti GPU. Since our method is improved based on reference [19], in order to prove its superiority, the parameter settings of other networks are consistent with those of reference [19]. After loading the pre-trained single image super-resolution model, Adam optimization method is used to fine tune the proposed network, and the learning rate is set as \(1\times 10^{-4}\). When the PSNR value on the verification set converges, the training process is stopped. The Flickr 1024 dataset was adopted as the training set, consisting of 1024 pairs of images with different resolutions. The large scale of data and significant changes in scene types provide data support for model learning in stereo vision tasks. The KITTI dataset is a stereoscopic dataset for urban and rural streetscape roads. The difference from the Flickr 1024 dataset is that the KITTI dataset contains richer foreground and close-up images. For performance evaluation, we use 20 images from KITTI dataset [30; 31] and five images from Middlebury dataset as test datasets. The larger the sampling factor, the more difficult it will be to reconstruct the image. Therefore, the comparative experiment in this paper only uses the image with the sampling factor of four. PSNR and SSIM metrics are used to evaluate the performance of stereo super-resolution networks. The evaluation indicators PSNR and SSIM are both obtained by averaging the calculation results of multiple images, which is persuasive for the performance evaluation of the algorithm.
## 5 Discussion
### Comparison of Methods in the Gejiu Area  
In our case illustration for the Gejiu area, values in the same level of the 19 levels for the four methods may be close or different depending on the case data. For example, the 9 th values of 6.0, 6.3, 7.6, and 10 \(\upmu\)g/g for the cumulative frequency, logarithmic interval, Avg\(\pm\)k*Std, and fixed-value methods, respectively, which are relatively close, and the differing values of the 16 th level, namely 1200, 32, 74, and 400 \(\upmu\)g/g, respectively.  
Furthermore, the geochemical map colors indicating Sn deposits in the Gejiu area may be similar or different. For instance, the Gejiu super-large Sn deposit cluster is located in a high-value area in red tones using the three traditional methods (Figures 3-5) and in gray and black tones using the fixed-value method (Figure 6). The Bainiuchang deposit area and Guanfang-Bozhushan area are located in the yellow-to-red tone and pink-to-red tone areas. For further comparison, the Bainiuchang and Guanfang deposit areas are colored in a way that indicates relatively lower values than the Gejiu super-large Sn deposit cluster using cumulative frequency (Figure 3) and fixed-value methods (Figure 6). However, they may indicate similar values to the logarithmic interval (Figure 4) and Avg\(\pm\)k*Std methods (Figure 5). In any case, geochemical map depictions of Sn deposits in the Gejiu area are basically consistent with all four methods. These similar results indicate that all four methods are feasible for geochemically mapping Sn in this case illustration. However, the presented fixed-value method is superior to the other three traditional methods.
## 4 Discussion
### Implications for Ecosystem Management  
Our results have important consequences for the conservation and adaptive management of coastal forested wetlands, particularly in the face of rapid sea level rise and increasing climate variability. First, the central role of GWT suggests that local hydrological management (e.g., culvert modification, drainage ditch blocking/opening, or groundwater buffering) may be more immediately effective than broader coastal defenses in sustaining forest resilience. Second, the identification of LAI and NEE as early indicators supports the integration of remote sensing and flux tower networks into early warning systems, allowing managers to identify at-risk areas before visible decline occurs. Finally, this study reinforces the concept that ecosystem collapse is not random but predictable when key stressors and feedbacks are adequately monitored. Anticipating rather than reacting to ecological thresholds offers the best hope for sustaining biodiversity, carbon storage, and ecosystem services in vulnerable coastal landscapes.  
By translating complex statistical relationships into simple, interpretable thresholds, this study bridges the gap between scientific understanding and actionable decision-making. Monitoring when ecosystems cross these biophysical boundaries can support proactiveadaptation, such as hydrological buffering, assisted migration, or targeted conservation, ultimately helping to sustain carbon sinks and biodiversity in vulnerable coastal regions.  
Recent studies underscore the presence of ecological tipping points in coastal and wetland forests, where chronic sea level rise, groundwater salinization, and increasing climatic extremes (e.g., drought, storm surges) trigger abrupt shifts from tree-dominated ecosystems to open marshes or ghost forests--landscapes dominated by standing dead trees and herbaceous vegetation [4, 7]. These transitions are frequently abrupt and irreversible, marking critical thresholds beyond which forest productivity, structure, and biogeochemical functioning collapse [88].  
Our findings align with this growing body of evidence, suggesting that wetland tree mortality may be an early indicator of broader ecosystem regime shifts under compound hydroclimatic stress. Such transitions are not isolated; similar thresholds have been observed in tropical, boreal, and temperate forests globally, highlighting the universal vulnerability of forest systems to climate-driven perturbations [89, 90]. These insights reinforce the need to incorporate hydrological, salinity, and disturbance dynamics into predictive models of forest resilience to capture the full range of possible future ecological states.
# Communication Interface Manager for Improving Performance of Heterogeneous UAV Networks
## 1 Introduction
The proposed IM is situated above the network link layer and contains a heuristic that decides in real-time between two or more wireless communication interfaces. It considers up-to-date monitoring data that represent the current state of the UAVs' communication links within a given environment. Currently, the heuristic accounts for parameters, such as the number of bytes received, number of bytes lost, throughput, received signal strength indication (RSSI), and signal to noise ratio (SNR). In its current version, the proposed IM is implemented using two wireless interfaces: IEEE 802.11n 2.4 GHz and IEEE 802.11p 5 GHz. The following characteristics summarize the advantages of adopting the proposed IM for multi-UAV communication: (i) provides more stable communication links, (ii) enhances communication connectivity, and (iii) increases message delivery levels. The proposed IM was validated with simulations conducted using a realistic (and complex) simulation setup based on the NS-3 network simulator, connected to the Gazebo multi-UAV simulator. This allows the UAV missions to be programmed within Gazebo, and leave all communication aspects to be processed within NS-3. Experimental scenarios involving different numbers of UAVs, flying at different speeds, traveling different distances and trajectories were adopted for analyzing the performance of the communication interfaces applied homogeneously (single interface) and heterogeneously (using the proposed IM). The remainder of this paper is organized as follows: Relevant related works that focused on homogeneous and heterogeneous communication between multiple UAVs are described in Section 2. Section 3 presents the architecture of the proposed IM. Section 4 describes the IM abstract model and the adopted heuristic. In Section 5, the experimentation setup and the created application scenarios are presented. Section 6 highlights the obtained results. Finally, Section 7 presents our conclusions and the future work directions.
## 3 Methodology
### Two-Layer Topological Model for Sparse Road Networks
#### 3.1.1 Trunk-Level Road Network Topology  
The sparse main network layer topology is composed of simplified basic modeling units of a sparse road network. The simplification of the main road network follows these principles:  
* Delete urban roads within the target area, retaining highways, national roads, and provincial roads;
* Remove internal road nodes and merge multiple connected roads without branches into a single structure;
* Eliminate auxiliary roads that are short and do not affect the overall connectivity of the main road network;
* Merge internal road nodes of cities (municipalities), counties (districts), and towns within the target area into a single node.  
Figure 2: Sparse road network with two layers.  
Based on these simplification principles and using complex network directed graph theory, a sparse main network layer topology model is constructed. This model includes two data elements: directed road segments and nodes. Road segments represent directed lines between nodes, such as high-level roads like highways, national roads, and provincial roads within the area. Nodes encompass major road intersections and local road networks, such as important national and provincial road intersections, as well as county and city road networks. The relationships between road segments and nodes reflect the overall macro topology of the road network. The basic topology of the main network layer is illustrated in Figure 3.
## 3 Methods
### Data Generation  
In the spectrum sensing process, the decision on the channel condition is binary, involving two hypotheses: \(H_{1}\) and \(H_{0}\)[25]. Here, \(H_{1}\) represents the hypothesis in which the PU is present, while \(H_{0}\) represents the absence of the PU [2]. For the purposes of this paper, we will only consider hypotheses involving the presence of the PU. We assume that \(N_{SU}\) SU and a single PU are moving at a speed \(v\), with their starting positions randomly chosen within a given area. As a result, the users' locations change over a time interval of \(\Delta t\). Additionally, we are considering a multi-channel system with \(N_{B}\) bands, each having a bandwidth of \(B_{W}\). Furthermore, we assume that the PU can utilize \(N_{B_{P}}\) consecutive bands [1]. Therefore, the received signal of the \(i\)-th SU on the \(j\)-th band at time \(n\) can be described as  
Figure 1: User position over time (initial state on the left; final state on the right).  
\[y_{i}^{j}(n)=\left\{\begin{array}{ll}s_{i}^{j}(n)+w_{i}^{j}(n),&\text{for $H_{1}$ and $j\in B_{p}$}\\ \sqrt{\eta}s_{i}^{j}(n)+w_{i}^{j}(n),&\text{for $H_{1}$ and $j\in B_{A}$}\end{array}\right. \tag{1}\]  
where \(s_{i}^{j}(n)=\kappa_{i}(n)g_{i}^{j}(n)x(n)\) and \(w_{i}^{j}(n)\) is the additive white Gaussian noise (AWGN), whose noise power density is \(N_{0}\), mean zero, and standard deviation \(\sigma=\sqrt{B_{W}10^{\frac{N_{0}}{10}}}\). With \(\eta\) being the proportion of power leaked to adjacent bands, \(B_{p}\) represents the bands occupied by the PU and \(B_{A}\) indicates the bands affected by the leaked power of the PU.  
In the expression \(s_{i}^{j}(n)\), a simplified path loss model is utilized, which can be written as follows:  
\[\kappa_{i}(n)=\sqrt{\frac{P}{\beta(d_{i}(n))*10^{\frac{b_{i}(n)}{10}}}}, \tag{2}\]  
where \(\alpha\) and \(\beta\) denote the path-loss exponent and path-loss constant, respectively. Here, \(d_{i}(n)\) represents the Euclidean distance between the PU and SU \(i\) at time \(n\). The shadow fading of the channel, indicated by \(h_{i}(n)\), between the PU and SU \(i\) at time \(n\) in decibels (dB) can be described by a normal distribution with a mean of zero and a variance of \(\sigma^{2}\). The term \(P\) denotes the power transmitted by the PU within a specified frequency band. Furthermore, the multipath fading factor, denoted as \(g_{i}^{j}(n)\), is modeled as an independent zero-mean circularly symmetric complex Gaussian (CSCG) random variable. Moreover, the data transmitted at time \(n\), represented by \(x(n)\), have an expected value of one [1; 2].
# Marine Internet for Internetworking in Oceans: A Tutorial
## 4 Communication Systems in Oceans
### High-Altitude Platform (HAP)  
A High-Altitude Platform (HAP) can provide more cost-effective communication services than satellites. As reported in [59], a 10-km-attitude HAP can provide downlink date rates up to 320 Mbps with a 50-MHz bandwidth at 28-GHz frequency. The main advantages of HAPs over satellites include easier and faster deployment, lower cost and larger communication capacity with shorter propagation delay [59]. Compared with a terrestrial BS, a HAP can cover much larger areas even in harsh environments.  
The power supply of HAPs is a challenging issue. Power is required for propulsion and station-keeping against mild wind and turbulence in the stratosphere. A balloon-based HAP requires wind compensation to stay still in the sky. An aircraft-based HAP has to fly along a circle to maintain services in certain area [59]. Power is also required for the payload, communication and networking. Unlike satellites that can be re-charged by solar energy frequently, for HAPs, enough power has to be stored during the day in order to maintain the operation throughout the whole night. Thus, a large capacity cell is required, resulting in more payloads and more power consumption. A possible solution is to bring a HAP back to the ground for service, which however disrupts service at increased deployment cost [20].  
HAPs are more suitable for large water areas near coastlines and can be used in the following situations [19]:  
* An instant demand as a short-time complementary to existing communication facilities. When the number of users goes beyond the expected situation, a HAP can be set up shortly and removed afterwards.
* A short-term solution where no other network services are available. A HAP can be in the place for a duration of months or longer, particularly useful for some events (e.g., scientific exploration in a remote water area) with a large amount of data traffic to be transferred to the BS on board or the land.
* Communication network infrastructure. Such kind of HAP can be based on solar-powered unmanned airships that can stay in the stratosphere for a long period. For example, the high-altitude unmanned airplane developed by DARPA can operate continuously on-station for five years [60], and Google Loon's high-altitude balloons aim to create an aerial wireless network in order to provide free global Internet access [61].  
Figure 4: Overview of MARS structure [55].
## 2 Materials and Methods
### Calibration and Tolerance Derivation  
A calibration procedure was performed using 60 frames captured in a known aligned pose. The angle between the prosthetic line and each wall reference line was computed, and the mean angle was stored as the nominal reference: _\(\theta\_middle\)_ = \(161.2^{\circ}\), _\(\theta\_upper\)_ = \(108.2^{\circ}\). For each set of frames, the standard deviation was computed, and tolerances were set as twice the standard deviation to cover 95% of natural variation. This yielded _tol_middle_ = \(\pm 0.073^{\circ}\), _tol_upper_ = \(\pm 0.90^{\circ}\). These tolerances were used as thresholds for detecting alignment in real-time operation.