esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Phase Bias Estimation for BeiDou Satellites Based on Consideration of Orbit Errors
## 3 Data and Processing Strategy  
The Positioning and Navigation Data Analyst (PANDA) software [40] that was developed at Wuhan University was used in this study and the proposed strategy is thus implemented in PANDA. Observations collected with 60 stations on days of the year (DOYs) 74-80, 2015 were processed, where 40 stations were used as reference stations for the satellite clock and FCB estimation, and the remaining 20 stations were used as rover stations to perform the PPP ambiguity resolution. Figure 1 shows the distribution of the stations used. The detailed processing strategies used for GPS + BDS FCB estimation and PPP ambiguity resolution are summarized in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Observation or Parameter** & **Model** & **Constraint** \\ \hline \multirow{3}{*}{Observations} & Undifferenced ionosphere-free code and carrier-phase combination; 30-s interval; 7\({}^{\circ}\) elevation cutoff & 1 cm for carrier-phase and 1 m for pseudorange \\ \hline \multirow{3}{*}{Weight} & Assign 100 times more weight for GPS in FCB estimation; equal weight for each system in PPP-IAR; elevation-dependent weighting & E \textgreater{} 30\({}^{\circ}\), 1; else sin(E) \\ \hline \multirow{3}{*}{Phase center offset and variation of Satellite antenna} & \multirow{3}{*}{igs08\_1899.atx for GPS and BDS} \\ \cline{1-1}  & &  
To assess the benefits of ambiguity resolution when using GPS + BDS, we performed PPP ambiguity resolution for all 20 rover stations using data from a seven-day period. The daily observations were divided into 24 pieces composed of hourly sets; therefore, there were generally 168 hourly solutions for each station, which gave an overall total of 3360 solutions if there were no data losses. We assumed that the ambiguities could be fixed to the correct integers using the daily observations, and then the hourly ambiguities were compared with the daily \"truth\" to check its correctness. The WL ambiguity was fixed by rounding using a threshold of 0.25 cycles [5; 6; 41]. Because of the strong correlation, the least-squares ambiguity decorrelation adjustment (LAMBDA) method was used to search the NL ambiguity, and a ratio test was used to validate the ambiguity resolution using a threshold of 2.0 [14; 18]. The initial fixing time attained in each hourly session was recorded and analyzed and we then obtained the fixing percentages for different observation durations via calculation of the cumulative distribution of the initial fixing times.
# Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications
## 1 Introduction  
One-dimensional (1D) nanostructures have attracted growing interest in the past few decades due to their fascinating properties and unique applications in areas ranging from physics, chemistry, biology, and material science [1; 2; 3; 4; 5]. Among different kinds of preparation methods for 1D materials, such as arc discharge, chemical vapor deposition (CVD), laser sputtering, and liquid-phase synthesis, the electrospinning method has been a standout for its simplicity and cost-effective characteristics for producing nanofibers. Currently, hundreds of polymers, inorganic compounds, and composites with one-dimensional nanostructures have been successfully developed based on electrospun materials. With the continuous research, it is believed that more and more types of 1D nanomaterials will be fabricated by the electrospinninging method, which will play an increasingly important role in the field of science and technology shortly.  
Electrospinning evolves from electrospray technology, and the key technique of electrospray is to destroy the equilibrium of the droplet: the electric field force must be increased to exceed the droplet surface tension so that the bigger droplet will split into smaller charged droplets, which is also known as \"Rayleigh instability\" phenomenon [6]. Based on this principle, electrospinningning produces nanofibers by volatilization or solidification of the jet stream. Because of high aspect ratio, high specific surface area, and porosity of obtained nanofibers, together with effective control of the fiber arrangement and size, electrospinningning would havegreat prospects for applications in electronic devices, energy storage, and biomedicine. This review paper aims to introduce the fundamentals of electrospinningning and the current development of electrospun nanofiber-based electronics consisting of field-effect transistors, memory devices, and sensors, which suggests great possibilities for further research. State-of-the-art fiber producing methods, including coaxial electrospinningning, near-field electrospinningning, and corresponding complex fiber structure, are presented carefully. Furthermore, electrospun nanofiber-based electronic devices consisting of field-effect transistors, memory devices, sensors, and other electronic applications are discussed in detail. Finally, an outlook on the potential for comprehensive systems and some drawbacks are provided.
## 7 Conclusions  
This study proposes an integrated method for the three-dimensional modeling of structural planes in tunnel surrounding rock, combining deep learning with surface interpolation techniques. A complete workflow--from image recognition and geometric extraction to 3D reconstruction--was developed to address the limitations of traditional methods in terms of efficiency and modeling accuracy. The main conclusions are as follows:  
1. A deep learning-based structural plane recognition method was developed. By integrating YOLOv5 for rapid object detection and DeepLabV3+ for precise semantic segmentation, the method enables accurate localization and boundary extraction of structural planes. Morphological skeletonization was applied to extract the centerlines, providing a clear and continuous geometric basis for subsequent modeling.
2. A geometric mapping model from image to spatial coordinates was established. Using color feature recognition and scale factor conversion, the extracted centerlines were converted into 3D spatial coordinates with physical significance. This ensured spatial consistency and improved modeling accuracy.
3. A B-spline interpolation-based method for 3D surface reconstruction was constructed. By fitting structural plane curves across multiple tunnel stations and building a two-dimensional control point network, smooth and continuous 3D surfaces were generated, meeting the modeling requirements in complex geological conditions.
4. The proposed method was validated in a real-world engineering project. Taking the Paiyashan Tunnel as a case study, multiple structural planes were successfully reconstructed in three dimensions. The modeling accuracy was quantitatively verified using multiple error metrics, including MRE, RMSE, and Hausdorff Distance, confirming both global and local fitting performance.  
In summary, the proposed approach offers an intelligent, efficient, and accurate solution for the identification and 3D reconstruction of tunnel structural planes, with promising potential for application in geotechnical engineering and underground construction projects.  
Conceptualization, H.L. ([PERSON]); methodology, H.L. ([PERSON]) and M.Z.; software, M.Z. and Y.L.; validation, M.Z. and Y.L.; formal analysis, L.L. and J.L.; investigation, Z.L., H.L. ([PERSON]) and P.L.; resources, M.Z. and Y.L.; data curation, M.Z. and Y.L.; writing--original draft preparation, H.L. ([PERSON]), M.Z. and Y.L.; writing--review and editing, M.Z. and Y.L.; visualization, M.Z.; supervision, H.L. ([PERSON]); project administration, H.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Provincial Natural Science Foundation of Hunan (Nos. 2024 JJ5036).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Methodology
### Visual Style Errors Validation  
To demonstrate the effectiveness of our algorithm in mitigating visual style discrepancies, we propose a novel visual style error validation method (Figure 4), leveraging cosine similarity to measure both content and style similarity between pyramid features extracted using a pre-trained backbone network (without fine-tuning). We then employ [PERSON]'s method [41] to segment these similarity values, yielding two subsets: the large visual style difference (Large-VS) subset (low content and style similarity) and the small visual style difference (Small-VS) subset (high content and style similarity). Improved algorithm performance on the Large-VS subset, compared to the Small-VS subset, demonstrates effective mitigation of visual style discrepancies and robust CD performance. The detailed procedure is as follows:  
1. Extract multi-level features: utilize a pre-trained backbone network to extract pyramid features from each image pair: \[\{F_{El}^{t},F_{El^{\prime}}^{t},F_{E3}^{t},F_{E4}^{t}\}=\mathrm{Backbone}(T^ {t}),\;t\in\{1,2\}\] (21)
2. Calculate mean content similarity (\(\bar{C}\)): average the cosine similarity between corresponding feature maps across all levels for both temporal phases: \[\bar{C}=\mathrm{CosSim}(F_{El}^{1},F_{El}^{2})=\frac{1}{4}\sum_{l=1}^{4}\frac{ F_{El}^{1}\cdot F_{El}^{2}}{\|F_{El}^{1}\|\|F_{El}^{2}\|},F_{El}^{t}\in \mathbb{R}^{\mathbb{C}_{l}H_{l}W_{l}}\] (22)Calculate mean style similarity (\(\bar{S}\)): average the Gram matrix-based cosine similarity (Equation (20)) between corresponding feature maps across all levels:  
\[\bar{S}=\frac{1}{4}\sum_{l=1}^{4}\frac{GS(F_{EI}^{1})\cdot GS(F_{EI}^{1})}{\|GS( F_{EI}^{1})\|\|GS(F_{EI}^{1})\|},GS(F_{EI})^{t}\in\mathbb{R}^{\mathbb{C}\subset \mathbb{C}_{l}} \tag{23}\]  
4. Apply [PERSON]'s method: utilize [PERSON]'s method to determine the optimal segmentation thresholds \(T_{C}\) and \(T_{S}\) for content and style similarity, respectively, based on the sets \(\{C_{1},C_{2},\ldots,C_{n}\}\) and \(\{\bar{S}_{1},\bar{S}_{2},\ldots,\bar{S}_{n}\}\).
5. Construct visual style subsets, \(\wedge\) denotes the intersection of sets: Large-VS Subset (low content and low style similarity): \[\bar{C}<T_{C}\wedge\bar{S}<T_{S}.\] (24)  
Small-VS Subset (high content and high style similarity):  
\[\bar{C}\geq T_{C}\wedge\bar{S}\geq T_{S}. \tag{25}\]
## 5 Conclusions  
It has been shown that the fracture rate for all the considered alloys (EN AW-6060, EN AW-1050A and EN AW-5754) is higher for the measurements of the alloy subjected to hydrogenation. The value of fracture toughness is lower in all considered cases after the hydrogenation of alloys. In all cases, the value of the threshold stress intensity factor (the value of the stress intensity factor above which the crack propagation nucleation takes place) reaches a lower value after hydrogenation.  
In fact, in real conditions (operating conditions) the alloy hydrogenation will be on the lower level. However, apart from the level of hydrogenation, the effect of hydrogen on the considered alloys (EN AW-1050A, EN AW-5754 and EN AW-6060) is undeniable.  
In the study the deterioration of the mechanical properties of EN AW-1050A, EN AW-5754 and EN AW-6060 aluminum alloys after hydrogenation was shown. Additionally, analyzing the results from this study and previous results of electrochemical measurements [7], it should be clearly stated that the hydrogenation of EN AW-1050A, EN AW-5754 and EN AW-6060 alloys causes not only deterioration of their mechanical properties, but also a reduction in corrosion resistance.  
It was found that the aluminum alloy (EN AW-1050A) with the lowest amount of alloying elements was the least exposed to the effect of hydrogenation. In this case (lowest amount of alloying elements), the mechanical properties of the aluminum alloy changed the least. It was therefore found that increasing the amount of alloying elements lowers the mechanical properties of aluminum alloys after hydrogenation.  
The conducted research shows the way of selecting the aluminum alloy to extending the operational reliability time of aluminum structures exposed to hydrogenation. In the era of the development of zero-emission vehicles, the use of hydrogen as fuel is gaining more and more importance, where the influence of hydrogen on the properties of materials is an important issue. Therefore, when designing of the hydrogen installations from the analyzed alloys, the influence of hydrogen on the deterioration of the parameters of aluminum alloys should be considered. Moreover, on the basis of research from this work and previous work [7], due to the better reference to real conditions, future research will be conducted with the same aluminum alloys, but with gaseous hydrogenation.  
Data curation, P.P.W. and B.W.; investigation, P.P.W. and B.W.; methodology, P.P.W.; writing--original draft, P.P.W. and B.W.; writing--review and editing, P.P.W. and B.W.; supervision, P.P.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
## 4 Simulation Results and Analysis
### Three-Dimensional Simulation Parameters and Results
The proposed algorithm, by using the optimal node subset strategy, ensures the reliability of the selected beacon nodes, thereby reducing the sensitivity of localization errors to irregular communication. This highlights the superior robustness and reliability of the proposed algorithm for practical applications.
## 2 Seismological Data and Results
### Seismicity Results
#### 2.1.1 The 2014 Cephalonia Sequence  
The 2014 Cephalonia sequence was initiated by an Mw6.1 mainshock that occurred on 26 January 2014, \(\sim\)2 km NE of the city of Lixouri, on the Paliki peninsula [44; 45; 46; 60; 64]. About one week later, on 3 February 2014, another significant earthquake of magnitude Mw5.9 occurred at the NW part of Paliki. The seismicity on Cephalonia Island during 2014 was relocated using the double difference method (HypoDD; [70]) and a minimum 1D velocity model [64; 65]. The aftershock distribution extended \(\sim\)32 km in a N20\({}^{\circ}\) E direction, covering the entire Paliki peninsula (group #1), but seismic activity was also triggered in a spatially separated cluster inside Myrtos Gulf (group #2). The southern half of the onshore seismicity, related mostly to the first major earthquake of 26 January 2014 at a depth of 16 km, appeared more complex, being distributed between 5 and 17 km and extending to a width of 15 km in a N110\({}^{\circ}\) E direction, whereas the northern half, mostly related to the second major earthquake, was more linearly distributed (width \(\sim\)5 km) and shallower, at focal depths between 5 and 12 km [65]. Seismicity after the first earthquake was mainly concentrated near its hypocenter during the first hours, but quickly spread throughout the aftershock zone. Some clusters further south, offshore Cephalonia, were triggered at a later stage. A significant cluster occurred south of group #1 after an M5.0 event on 8 November 2014, soon followed by clustered activity in Myrtos Gulf (group #2). The focal mechanisms of the two major events and most of the major aftershocks indicate dextral strike-slip faulting along SSW-NNE-trending, subvertical faults. However, seismicity inside the Myrtos Gulf is characterized by several smaller clusters trending E-W, i.e., transverse to the axis of the CLTFZ, which suggests antithetic sinistral faulting. The 2014 sequence temporarily increased the seismicity rate of the neighboring groups #7 and #8. The temporal evolution of the post-seismic activity in Cephalonia (group #1) shows a long relaxation period that lasted up to July 2017, considering a relatively high background seismicity level (0.3 events/day) for \(M\geq M_{\rm c}=2.0\) (Figure 4). In the northern area of Cephalonia (group #2), with a smaller background seismicity rate (0.1 events/day prior to 2014), increased activity was observed up to September 2017, due to its reactivation after the 2015 Lefkada mainshock, which is presented in the following Section 2.1.2.
# An Improved Neural Network Cascade for Face Detection in Large Scene Surveillance
## 3 Cascade Framework with Head-Shoulder Information
### Overall Framework
#### 3.2.1 Face-12-Small  
Face-12-small refers to the first CNN in the \"small size cascade\". Its structure is shown in Figure 3. Face-12-small is a shallow binary classification CNN for quickly scanning the test image and rejecting the non-face regions. Given a test image of size \(W\times H\), first we build it into an image pyramid (the scale factor between each pyramid level is \(f\)) to cover faces at different scales. If the minimum face size that we need to detect is \(a\times a\), we resize images at each level of the image pyramid by a coefficient of \(\frac{12}{a}\), so that the smallest patch becomes \(12\times 12\). We feed the resized images into Face-12-small. Then, the Face-12-small net will scan the input image with two-pixel spacing for \(12\times 12\) detection windows and reject the non-face windows. After that, we employ non-maximum suppression (NMS) to merge highly overlapped candidate windows.
# Enhancement of Underwater Images with Retinex Transmission Map and Adaptive Color Correction
## 4 Results
### Quantitative Results  
To quantitatively evaluate the performance of different methods, we performed not only non-reference evaluation, but also full-reference evaluation since there are ground-truth images in the UIEB testing dataset.  
Here, we briefly introduce the image quality assessment metrics used in quantitative evaluations. For full-reference evaluation, we used two popular metrics, Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) [27]. A higher PSNR score means that the enhancement result is closer to the reference image in terms of image pixel values, while a higher SSIM score indicates that the result is more similar to the reference image in terms of image structure and texture. By using the reference images, PSNR and SSIM metrics provide direct evaluation of the enhancement performance to some extent, although the real ground truth images might be different from the reference images.  
Underwater image quality was also evaluated by two commonly used metrics for non-reference images: Underwater Color Image Quality Evaluation (UCIQE) [28] and Metric and Underwater Image Quality Measurement (UIQM) [29] Metric. UCIQE quantifies the non-uniform color casts, blurring, and low contrast, and combines these three components in a linear manner. A higher UCIQE score means that the result has better balance among the chroma, saturation, and contrast. UIQM is comprised of three underwater image attributed measures: image colorfulness, sharpness, and contrast. A higher UIQM score indicates that the result is more consistent with human visual perception.  
We compared the proposed method with six typical and SOTA underwater image enhancement methods: Fusion [7], RGHS [22], UDCP [6], GDCP [16], ULAP [19], and [PERSON] et al. [20]. Among these six compared methods, Fusion and RGHS are independent of physical models, UDCP and GDCP rely on DCP-variant priors, ULAP and Peng et al. belong to the physical model based category of methods using other priors. Together, they form a representative group of traditional underwater enhancement methods. The quantitative results of both full-reference evaluation and non-reference evaluation are listed in Table 1.  
In Table 1, the proposed underwater image enhancement with Retinex transmission map and adaptive color correction obtains the highest scores of PSNR, SSIM, and UIQM. In terms of UCIQE, it obtains the highest score along with Fusion, RGHS, and ULAP methods. Thus, our method achieves the best performance in terms of full-reference image quality assessment. In addition, it also achieves superior performance in the non-reference evaluation.
## 2 Literature Review
### Quorum Graph  
According to the preceding discussion [11], the quorum system can be represented as a quorum graph, which is denoted as _G_(_V_,_E_). The quorum graph comprises a vertex, a subgraph, a supergraph, and edges.  
1. Vertex: Vertices can be used to represent active slots for each node.
2. Subgraph: For a quorum, all vertices to one node can be represented as a subgraph.
3. Supergraph: Subgraphs corresponding to all the nodes can be represented as a supergraph.
4. Edge: Two vertices correspond to the same active slot in two subgraphs. A horizontal edge is used to connect these two vertices, which indicates that two nodes in this slot can exchange neighborhood information bidirectionally. Conversely, if two nodes corresponding to the two active slots in the same subgraph can pass neighborhood information only unidirectionally, a unidirectional vertical dashed edge exists between these two nodes.  
Figure 2 illustrates the construction of a quorum graph based on a network schedule. The network consists of nodes A, B, C, and D, as shown on the left side of the figure, and the global time slots range from 0 to 5; each node has its own wakeup schedule denoted by a red brick. Hence, according to the previous definition, we designed the quorum graph, as shown on the right side of Figure 3. This figure has four subgraphs (quorums A, B, C, and D), and the vertices represent active time slots in the network schedule. In addition, the horizontal dashed edges, that is, A2, A4, and A5 from the early to later slots within the subgraph, can be used to pass neighbor information. Vertical solid edges, for example, A4, B4, and D4, are built for the vertices in different subgraphs but associated with the same slot; these three nodes are associated with the same (global) time slot 4.  
Figure 2: Example of quorum graph.
## 2 Results and Discussion
### CD-HIT and BlastP Analysis  
Unigene sequences were clustered by Cluster Database at High Identity with Tolerance (cd-hit) to reduce the redundancy. The 143,245 unigenes were divided into 143,095 clusters. The result was shown in Table S2. After doing the blast, the amino acid sequences of 407 CYP unigenes have some homology (from 21.15% to 86.55%) to the CYP450 proteins from _Arabidopsis thaliana_, while the amino acid sequences of 488 UGT unigenes show different sequence identities (from 19.57% to 83.33%) to the UGT proteins from _Arabidopsis thaliana_. The results were shown in Table S3 and Table S4, respectively.