esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Multi-Objective Optimal Design Approach for Combined Water Systems
## 2 Optimal Design of Combined Water Systems  
### Concept and Modeling for the Combined Water System  
To design the combined water system, this study designed the WRS and WDS separately according to the use of water. Figure 1 shows the process of differentiating between water for use in water distribution systems and water for use in reused-water systems. Water that is treated at a water purification plant can be used in WRSs and water distribution networks; however, reused water, which is rainwater or wastewater that has gone through a sewage treatment facility, cannot be used as drinking water in a water distribution system despite chemical treatment. Therefore, modeling was performed so that the water for the existing water distribution system and water for the reused water system were not mixed.  
Furthermore, in this study, a plan was developed for restricting reused-water systems to rainwater only and using this as toilet water, landscaping water, and firefighting water to reduce the water used by existing water distribution systems. Therefore, EPASWMM, which is an urban runoff analysis program provided for free by developers at the EPA, was used to calculate the capacity of rainwater storage tanks that can be used in the reused-water system. The capacity of the rainwater storage tanks was set to that of multiple water sources in the water distribution system analysis program EPANET. The analysis was performed by differentiating between nodes in the water distribution system and nodes in the reused-water system, as shown in Figure 2.  
According to the data published by the Korea National Sewer Information System ([[https://www.hasudoinfo.or.kr/](https://www.hasudoinfo.or.kr/)]([https://www.hasudoinfo.or.kr/](https://www.hasudoinfo.or.kr/)), accessed on 2 January 2023), the water reuse rate is approximately 30%. Therefore, in this study, 70% of the existing WDS demand is set as the demand of the WDS for drinking water, and the remaining 30% is set as the demand of the WRS, mainly used for nonpotable uses such as flushing toilets, cooling of industrial units, watering gardens, etc.  
Furthermore, to allow water for the water distribution system to be used when the water for the reused water system is exhausted, EPANET's control tool was used to install nodes that have the same properties as conventional nodes but no demand (dummy nodes)  
Figure 1: Flowchart of combined water systems and urban drainage systems.  
Figure 2: Schematic view of water reuse systems modeling using EPANET 2.2.  
and flow control valves (check valves) so that water for the water distribution system can be used when the water in the rainwater storage tanks is exhausted. The capacity of the rainwater storage tanks was set as the amount of stormwater that cannot be accommodated by the stormwater drainage system, and the pipes that combined the water distribution system and the reused-water system were set to the same length, assuming that the nodes were the same as the existing nodes.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 6 Performance Evaluation
### Performance Comparison for Three Different Scenarios
On average, the bandwidth costs of each user service for _cooperative caching_ can reduce by 83.9% for the _cloud center_ and just increase by 32.36% for _edge caching_. Furthermore, the average total deployment costs of each user service are provided in Figure 4c, where the total deployment costs are composed of storage resource usage and network bandwidth consumption based on a weighted sum approach, as shown in Equation (10). It can be found in Figure 4a,b that there are different influences for the three scenarios on storage costs and bandwidth costs. However, the total deployment costs of each user service for _cooperative caching_ are less than that of _cloud center_ and _edge caching_, where the total deployment costs of each user service for _edge caching_ are also less than that of _cloud center_. Compared with _cloud center_ and _edge caching_, the average total deployment costs of each user service for _cooperative caching_ can reduce by 56.3% and 15.79%, which also demonstrates that _cooperative caching_ can outperform both of _cloud center_ and _edge caching_ in decreasing the total deployment costs. Moreover, the performance evaluation for normalized costs in three different scenarios is also provided in Figure 5, where the weighted factors for storage resource usage and network bandwidth consumption are indicated as 0.3 and 0.7. Similar to the results in Figure 4, for _cooperative caching_, the normalized storage costs of each user service are less than that of _edge caching_, and the normalized bandwidth costs of each user service are between that of the _cloud center_ and _edge caching_. However, the normalized total costs of each user service for _cooperative caching_ are less than that of the _cloud center_ and _edge caching_.
## 4 Numerical Analysis and Discussion
### Parametric Study
#### 4.3.3 Effect of the Gap  
To obtain the effect of the gap between the mass block and the limiting device, nine different gaps are considered in this analysis, and the gaps are selected as 0.02-0.18 m, in increments of 0.02 m.  
Figure 11: Vibration reduction ratios of the maximum displacement with different intensities.  
Figure 10: Vibration reduction ratios of the maximum displacement with the different mass ratios.  
Figure 12 shows variation in the reduction ratios of the maximum displacement with different gaps. It can be seen that the reduction ratio increases first, and then decreases with the increase of the size of the gap, but the change of the reduction ratio is not obvious. The reason for this phenomenon is that the bounding energy dissipation is limited with few collisions when the gap is large. Therefore, the influence of the gap on the control effect of the PTMD is not significant.
# Deep Learning for Point-of-Care Ultrasound Image Quality Enhancement: A Review
## 4 Discussion
Studies focusing specifically on enhancing POCUS images commonly paired low-quality POCUS images with high-quality images from high-end machines. Although these image pairs are expected to contain the same locational information, they often suffer from locational differences due to acquisition challenges, which can only be partially mitigated by registration methods and consequently impact network training. In contrast, studies using Plane Wave Imaging (PWI) did not encounter this issue, as they used the same device with different numbers of angles, resulting in nearly identical locational information. Future research could benefit from developing more accurately paired datasets, particularly using ex vivo data, to improve image-to-image translation techniques for POCUS. Several limitations of this review should be noted. First, the selection of articles that addressed multiple quality enhancement aspects might have excluded relevant studies focusing on single aspects. Second, the heterogeneous nature of the included studies, with varying datasets and ultrasound devices, complicates direct and fair comparisons. Although we attempted to group datasets into in vivo, phantom, and simulation categories, diversity remained within these subgroups. Lastly, performing a meta-analysis for machine learning-based research presents unique challenges, as this method was originally designed for comparing cases and controls in medical treatments. Aspects such as the number of images in \"case\" and \"control\" groups, the use of cross-validation, and dataset similarities due to augmentation were not consistently accounted for. Therefore, the meta-analysisshould be seen primarily as an illustrative tool, and caution is needed when drawing firm conclusions about the precise effects of ultrasound image enhancement in terms of expected PSNR and SSIM gain.
# Location-Based Relay Selection in Full-Duplex Random Relay Networks
###### Abstract  
Full-duplex relay (FDR) has attracted considerable interest in enhancing the performance of relay networks by utilizing resources more efficiently. In this paper, we propose a framework for full-duplex random relay networks (FDRRNs), where relay nodes equipped with full-duplex (FD) capability are randomly distributed within a finite two-dimensional region. We first derive the outage probability of an FDRRN and then identify the potential relay location that minimizes the outage probability. Furthermore, we introduce a low-complexity relay selection algorithm that selects the relay node nearest to the potential relay location. Finally, simulation results show that the proposed relay selection algorithm achieves performance comparable to that of the _max-min_ relay selection algorithm.  
outage probability; random relay network; cooperative network; full-duplex relay; relay selection
# Native Smartphone Single- and Dual-Frequency GNSS-PPP/IMU Solution in Real-World Driving Scenarios
## 2 Mathematical Models and Data Processing Strategies
### York-PPP Engine and Processing Settings  
The York-PPP engine is a well-established software that is able to produce PPP/IMU tightly-coupled solutions for geodetic, low-cost, as well as smartphone devices. Table 1 highlights relevant engine settings for this current processing with corresponding products.  
All smartphone collected raw measurements were processed in the aforementioned PPP-IC mode with and without IMU integration. Besides correcting the satellite orbits and clocks with GFZ (Geo-ForschungsZentrum) rapid products, the satellite DCBs are corrected using CAS (Chinese Academy of Sciences) products. The combined final IGSG GIM products served as the external constraints for ionospheric error estimation. Meanwhile, a carrier-to-noise (\(C/N_{0}\)) based stochastic weighting scheme was adopted since the smartphone signals contaminated with significant multipath errors tend to have lower \(C/N_{0}\) ratios [5], and corresponding standard deviation of the code and phase observation \(\sigma\) can be estimated as Equation (3) [48]:  
\[\sigma=a+b*10^{-\frac{1}{2}*\frac{C/N_{0}}{10}} \tag{3}\]  
where coefficient \(a\) is 4 m and 6 cm for pseudorange and carrier-phase observations, respectively. These values are empirically derived from the residuals. In addition, \(b\) is the pseudorange chipping length and carrier-phase wavelength for pseudorange and carrier-phase measurements, respectively [7].  
In terms of measurement quality control, the satellite elevation cutoff angle is chosen as 10\({}^{\circ}\)and, on average, 0.7 satellites are rejected per epoch for four constellations. This selection ensures that low-elevation satellites will be removed owing to their high noise and multipath. Additionally, the PPP engine is automatically configured to screen out satellites with post-fit residuals tenfold larger than the standard deviations of measurements to mitigate the impacts of outliers.
## 2 Related Work
### Unmixing of Hyperspectral Images  
High spectral resolution in hyperspectral images makes it possible to identify materials by analyzing the spectral signatures of each pixel. This analysis can be compromised if there is not enough spatial resolution, a problem typically found in airborne and low-cost sensors. Hence, the spectral unmixing of the pixels has proven to be necessary to address the aforementioned problem [20].  
In remote-sensing scenarios, hyperspectral unmixing has been traditionally done in Earth facilities after compression/decompression on the satellite link. In order to achieve real-time execution performance, and due to high computational loads, High-Performance Computing systems are commonly used [21; 22]. In particular, CPUs, GPUs or CPU/GPU architectures have been used in most solutions. For instance, a real-time implementation of endmember extraction was reported in Reference [23], and, although part of the unmixing chain (i.e., abundance calculation) was not present, it marked the path to follow. One year later, the same group presented the first real-time implementation of the complete unmixing chain, using only one GPU and combining different algorithms for each part of the chain [24]. However, one of the weak points of the proposed implementation was the accuracy of the endmember extraction stage. In Reference [19], a new linear unmixing algorithm that provided better accuracy and performance results than state-of-the-art alternatives was proposed. Optimized implementations of the algorithm were presented in Reference [25] for GPUs and Reference [26] for FPGAs. However, none of these implementations considers on-board processing.  
In fact, the concept of on-board processing of hyperspectral images usually refers to data compression on the edge. In this context, where flexibility and reliability become as important as real-time execution, FPGAs have been the most-used devices. Many theoretical approaches have proposed different compression algorithms, but their evaluation is not done on space-compliant devices [27; 28]. Hence, no experimental data or performance metrics under space constraints can be found in those works. However, it is also possible to find solutions throughout the literature using GPUs or FPGAs showing potential real-time results [8; 9; 29; 30; 31].  
In this work, as opposed to other state-of-the-art solutions, the data-parallel and scalable approach of the modified FUN algorithm enables the implementation on a multi-SoPC cluster. As such, the cluster emulates a satellite constellation, where the hyperspectral linear unmixing is done on the edge (avoiding the downlink transmission overhead).
# On the Communication Performance of Airborne Distributed Coherent Radar
###### Abstract  
Compared with a monostatic radar, airborne distributed coherent radar (ADCR) has been widely applied thanks to its flexibility, greater degree of freedom, stronger detection, and anti-jamming ability. Unlike distributed ground-based radar, the precondition for ADCR to perform tasks is maintenance of stable wireless communication links among the unmanned aerial vehicles (UAVs). Therefore, the communication channel modeling of ADCR is very important. This paper mainly analyzes the performance of a communication system composed of radar UAVs, communication UAV (relay), and ground base station. The probability density function (PDF) and outage probability (OP) of signal-to-noise ratio (SNR) at the ground terminal are derived analytically in the cases of transmission power error, UAV position error, and multi-path fading. Numerical simulation shows the validity of the derived results.  
ADCR; generalized \(K\) distribution; Meijer-G function; multi-path fading; OP; coherent detection +
Footnote †: journal: Applied  
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Footnote 0: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).
# Sentence-Level Insights from the Martian Literature: A Natural Language Processing Approach
## 2 Data and Method
### Unsupervised Clustering  
This study employs the lightweight language model _all-MiniLM-L6-v2_, developed by Ubiquitous Knowledge Processing Lab within the Sentence-Bidirectional Encoder Representations from Transformers (SBERT) architecture, to generate sentence embeddings, mapping each sentence into a 384-dimensional vector. SBERT is an enhanced version of Bidirectional Encoder Representations from Transformers (BERT), utilizing a Siamese network structure to independently encode sentences while preserving their semantic meaning. Unlike the original BERT, which requires simultaneous input of sentence pairs, SBERT outputs sentence vectors that can be efficiently compared using cosine similarity, making it particularly suitable for large-scale corpus similarity calculations and clustering analyses [14]. Moreover, the _all-MiniLM-L6-v2_ model offers an effective balance between computational speed and performance, and demonstrates strong results on the Massive Text Embedding Benchmark (MTEB) tasks.  
After obtaining the semantic embeddings, this study applies unsupervised clustering to automatically group the text data. The clustering algorithm constructs a similarity graph based on cosine similarity between sentences and applies graph-based algorithms to identify \"community structures,\" representing semantically similar text clusters. Specifically, sentences are treated as nodes in the graph, and an edge is established between two sentences if their cosine similarity exceeds a defined threshold (set at 0.5 for Section 3.1, and 0.7 for Section 3.3). This forms a sentence similarity network. The Louvain community detection algorithm [14; 15] is then employed to identify clusters, with a minimum community size requirement of five sentences.  
To further analyze the distribution of clustering results, this study applies the t-distributed stochastic neighbor embedding (t-SNE) algorithm to reduce the dimensionality of high-dimensional sentence vectors into a two-dimensional space. t-SNE is a nonlinear dimensionality reduction technique that effectively preserves local data structures and is well-suited for visualizing clustering patterns [16]. The reduced sentence vectors are presented as scatter plots, with different colors used to distinguish the identified clusters, thereby visually highlighting the boundaries and concentrations of distinct semantic categories. To validate the reliability of the clustering results, a visual analysis was conducted, and each cluster was manually inspected to confirm high semantic coherence and good interpretability.
## 3 Context-Aware Positioning Model
### Management of Contextual Information  
This second stage is key for the model. In this phase the contextual elements are assessed and processed in order to eliminate redundant and irrelevant information related to the positioning of a device. Based on this processed information, a set of recommendations is assembled. These recommendations contain the positioning strategies that can be accessed by the device, prioritized based on the device's current context and expected energy consumption level. The reference values for this last feature were obtained from several studies reported in the literature.  
Three tasks can be performed during this stage: (1) request sensing the physical environment; (2) process context sensing feature vectors and assemble recommendations; and (3) manage positioning information. Next we describe these tasks.
## 4 Discussion
Thus, knowledge of the range of these complications, and especially the risk of SuCC formation, emphasizes the need for adequate surgical techniques, with the need for careful augmentation of the sinus membrane and long-term clinical and radiologic follow-up, even in cases where the clinical procedure of sinus floor augmentation did not reveal macroscopic breach of the Schneiderian membrane. Irrespective of the etiological factors, the frequent radiological features of SuCCs were of well-defined, radiolucent unilocular lesions adjacent to the maxillary sinus, albeit a few cases of multivolacular appearance and less defined margins were reported [33]. Depending on the age of the patients, the common presentation of SuCC can raise the possibility of several differential diagnoses, including odontogenic cysts (e.g., residual cyst, odontogenic keratocyst), odontogenic tumors (e.g., ameloblastoma, odontogenic fibroma) and non-odontogenic tumors (e.g., central giant cell granuloma), as well as fibro-osseous lesions. In general, the diameter of SuCC, as measured on 2D-images, was most commonly found to be about 20 mm in diameter, however lesions over 31 mm in diameter were also reported [32], which is also the range found in our study in regards to SuCCs associated with augmentation of the sinus floor. Small lesions are expected to remain asymptomatic; however, as SuCCs slowly enlarge, perforation of the sinus walls might result, and even expansion beyond the original walls of the sinus has been described [34; 35; 36]. We may raise the assumption that the apparent rarity of SuCCs in conjunction with sinus floor augmentation is linked to their being underreported for a number of reasons--both lesion- and clinician-related. The commonly small dimensions of the cysts and their slow development do not elicit clinical symptoms; therefore, these areas might not be regularly evaluated radiologically. In addition, many of the surgical procedures related to dental implants are not necessarily performed at present by specialist dental surgeons (e.g., oral and maxillofacial surgeons, periodontitis). Thus, their degree of awareness of the development of this type of cyst, differential diagnosis, clinical sequelae, and the need to submit lesions for microscopic examination might not be as high as that of specialist surgeons. Variations in dental practice patterns are similar to those encountered in other fields of medicine, and are usually dependent on the individual practitioner's knowledge, skills, attitudes, health system accessibility, time to receive biopsy results, and insurance issues [37]. Treatment of SuCCs, regardless of the etiologic factors, consists of enucleation; however, a more aggressive approach might be needed in the case of large lesions, bony perforation, or recurrence [34].
# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
## 2 Related Work  
Compared with the conventional attention mechanism, Transformer's multi-head self-attention mechanism [22] has the advantage of being able to focus adequately on various aspects of the data and the internal correlations of the data. More importantly, the mechanism as a joint strategy allows the corresponding neural network to learn rich associations and information from different sub-representation spaces at different positions.  
The forward propagation of the multi-head self-attention of Transformer is described as follows. First, in each head, input data \(X\) are transformed by three linear projections and then fed into the scaled dot-product self-attention to obtain the attention weights assigned to the corresponding data. Then, a concatenate layer is utilized to fuse the weighted data of all the heads, which forms the multi-head structure. After that, a linear projection is adopted to change the fused output \(\overline{Y}\) into the same shape as the input data \(X\). Subsequently, a residual connection [32] is applied to connect the reshaped output \(\overset{\sim}{Y}\) and the input data \(X\). Finally, layer normalization [33] is used to regulate the output of the residual connection operation to generate the final output \(Y\). The above processes can be mathematically expressed as Equations (1)-(6).  
\[Q^{i}=XW_{Q^{i}},\ K^{i}=XW_{K^{i}},V^{i}=XW_{V^{i}}, \tag{1}\]  
\[A_{-}W^{i}=\text{softmax}\Big{(}Q^{i}{K^{i}}^{T}/\sqrt{d_{K^{i}}}\Big{)}, \tag{2}\]  
\[head^{i}=A_{-}W^{i}V^{i}, \tag{3}\]\[\overline{Y}=\text{concatenate}\Big{(}head^{1},\ldots,head^{i},\ldots,head^{N_{h}} \Big{)}, \tag{4}\]  
\[\widetilde{Y}=\overline{Y}R, \tag{5}\]  
\[Y=\text{layernorm}\Big{(}X+\widetilde{Y}\Big{)}, \tag{6}\]  
where, in the \(i\)th head, \(Q^{i}\), \(K^{i}\), \(V^{i}\), \(T\), \(1/\sqrt{d_{K^{i}}}\), \(A\_W^{i}\), and \(head^{i}\) represent the query matrix, key matrix, value matrix, transpose operation, scaling factor, attention weights, and corresponding weighted data, respectively. \(W_{Q^{i}}\), \(W_{K^{i}}\), \(W_{V^{i}}\), and \(R\) denote linear projections that are trainable parameters. The multi-head self-attention is shown in Figure 1.