esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Use Cases of Graphs
### Deduplication Domain Partitioning  
Motivation  
In deduplication-enabled storage servers, there are cases when some subsets of the entire deduplicated set of files need to be transferred to different servers or storage tiers, in deduplicated form, to provide scalability and load balance. One concrete case is in commercial backup/archival servers that deduplicate data on disk to save space and at some intervals (weekly, monthly) the deduplicated data need to be transferred to tapes for off line (or even off site) storage. There are a couple of constraints to be satisfied when storing deduplicated files on tapes. The first is to store all the (unique) chunks needed to reconstruct a file on the same tape (_i.e._, chunks of one file are not spread across many tapes). This will minimize the number of tape mounts needed when reading files from tape, and simplify the bookkeeping and transportation, especially when tapes are kept off-site. The second is to maintain the deduplication ratio close to that on disk (the original deduplication domain) when we place subsets of files on tapes. For this, each subset of files that are placed on the same tape should share most (all) of the chunks between themselves; _any chunk shared with other subset going on a different tape needs to be replicated on both tapes_. In this concrete case, the disk represents the original deduplication domain and tapes represent partitions of the disk domain into smaller, independent deduplication domains. These two constraints apply to many situations of deduplication domain partitioning. Thirdly, the sizes of the resulting partitions are roughly equal as default. But we usually relax this constraint in favor of first minimizing the deduplication loss due to partitioning. In another word, this constraint is optional.  
\begin{table}
\begin{tabular}{l r r} \hline \hline
**Workload** & **WL1** & **WL2** \\ \hline Total Size & 3,052 GB & 1,532 GB \\ Removable Duplicates & 978 GB & 460 GB \\ Deduplication Ratio & 68\% & 70\% \\ Number of Files & 289,295 & 201,406 \\ Average File Size & 10 MB & 7.79 MB \\ Median File Size & 82 KB & 18 KB \\ Number of Chunks & 17,509,025 & 12,021,126 \\ Average Chunk Size & 182 KB & 102 KB \\ Median Chunk Size & 71 KB & 52 KB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Workload Summary.
## 4 Experimental Results and Discussion
### Dataset and Metrics  
The proposed method is tested on the public benchmark dataset IARPA Multi-View Stereo 3D Mapping (MVS3 DM) Challenge [39]. The dataset contains 50 panchromatic images captured by the WorldView-3 satellite with 30 cm nadir resolution, covering an area of 100 km\({}^{2}\) near San Fernando, Argentina. These satellite images were acquired from November 2014 to January 2016. The dataset also provides the airborne lidar data with 20 cm nadir resolution that can be used as the ground truth DSM. We selected 4 sites for the experiments of the multi-date satellite images, including low-rise buildings, medium-tall buildings, and parks, which are rich in building types and can fully verify the effectiveness of the proposed method. Among these sites, the geographic range of Site 1 is 0.9 \(\times\) 0.9 km\({}^{2}\), and each of Site 2, Site 3, and Site 4 is 0.6 \(\times\) 0.6 km\({}^{2}\). In addition, we also selected 10 image pairs from the multi-date satellite images to test the DSM generation performance of the proposed method on satellite image pairs.  
For results evaluation, we follow the previous quality evaluation metrics [25; 26; 27]: completeness (CP) and median error (ME). Among them, CP is defined as the percentage of the altitude errors less than 1 m in a 3D point cloud, and ME stands for median elevation  
Figure 3: The stereo rectification of the proposed method.  
error, which is generated by aligning the generated DSM with the ground truth DSM and comparing them pixel-wise.
## 4 Results and Discussion
### Sensitivity Analysis of Basic Parameters  
#### 4.1.1 Distribution Pattern of Formation Pressure  
The drilling fluid infiltrated into the formation constantly and the pore pressure in the formation increased gradually. When the two reached a balance, the drilling fluid stopped infiltrating. Figure 5 shows the distribution nephograms of the reservoir pressure and near-wellbore zone pressure at different moments; Figure 6 shows the distribution of the formation isobaric lines and the distribution of formation fluid streamlines 20 h after the drilling invasion; and Figure 7 shows the distribution patterns of the pressure around the well at different moments. As can be seen from the graph, with the increase in time, the wellbore pressure continues to propagate further into the formation, the pressure front continues to advance, and the pore pressure of the formation continues to increase, and in the early stage of drilling fluid invasion, due to the large pressure difference, the speed of pressure wave propagation is fast. After entering the middle and late stages of invasion, the pressure difference gradually decreases, the seepage effect weakens, and the speed of pressure propagation slows down.
# Clinical Applications of In Vivo and Ex Vivo Confocal Microscopy
## 3 Ex Vivo Confocal Laser Scanning Microscopy
### Main Ex Vivo CLSM Clues of Skin Cancer and Inflammatory Skin Diseases
showed that skin features such as epidermal alterations, including hyperkeratosis, parakertosis and spongiosis, as well as variations at dermal level, like fibrosis or vascular variations can be clearly visualized with ex vivo CLSM [111]. The future purpose of ex vivo CLSM is to fill the time frame that separates the biopsy from diagnosis and therefore from therapy [99].
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art
### Application Case Studies in Industrial Environments
#### 3.2.3 NB-IoT Industrial Application (NB-IIoT)  
NB-IoT has been used in Power Wireless Private Networks (PWPN) in power industries [11] to achieve deep and large coverage. The analysis made by the authors also shows that NB-IoT is suitable for the latency tolerance services required in PWPN. To improve the efficiency of next-generation aircraft industries, the low power consumption feature of NB-IoT has enabled its usage in aircraft industries as authored by [93]. The integration of NB-IoT into LTE functionalities has enabled this technology to co-exist with the LTEinfrastructure, and its application in smart cities is encouraging [19]. More applications of NB-IoT are found in e-Health [94] and utility industries such as smart metering and tracking [95]. The suitability of NB-IoT deployment in smart energy distribution networks has been tested and optimized in [96].
## 5 Conclusions  
1. By changing the Joukowsky airfoil and increasing the tail edge thickness control parameter K, the airfoil tail thickness can also be parameterized, making the blunt trailing edge more flexible and accurate in the Joukowsky airfoil. It is very convenient to optimize the aerodynamic performance of the airfoil by adjusting various parameters.
2. The experimental results are in good agreement with the simulation results, indicating that the CFD model and simulation method are highly reliable.
3. The aerodynamic characteristics of the airfoil are unsteady and periodic and are mainly affected by the size of vortices and the position of vortex generation/shedding. At the same angle of attack, the K-value can affect the size of vortices and the position of vortex generation/shedding. When \(\alpha=0^{\circ}\), \(\alpha=4^{\circ}\), and \(\alpha=8^{\circ}\), the blunt trailing edge can improve the aerodynamic performance of the airfoil; when \(\alpha=12^{\circ}\), the position of vortex generation changes, which reduces the aerodynamic performance of the airfoil. Therefore, when designing the trailing edge of an airfoil, the thickness of the trailing edge can be designed according to the specific working conditions.
4. This article only proposes a parameterization method for the K-value to enable the optimization of a Joukowsky airfoil. No further specific analysis was performed. In the future, the optimal K-value can be obtained for specific working conditions to optimize the aerodynamic performance of airfoils.  
Conceptualization, P.X. and L.W.; methodology, P.X. and X.C.; software, P.X.; validation, Y.W. and W.Y.; resources, X.C.; data curation, P.X. and L.W.; writing--original draft preparation, P.X.; writing--review and editing, L.W.; supervision, X.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data availability StatementData is contained within the article.  
The authors declare no conflict of interest.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 4 Outlook
### Trends Toward Increasing the Amount of RO Soundings  
#### 4.1.1 Multi-System-Compatible RO Receivers  
The authors of [47] demonstrated that there is no saturation in NWP, even with 128,000 RO observations assimilated. However, there may be only 18,400 soundings per day in 2027, as estimated in Section 2.2. Therefore, the frequency of RO soundings should be enhanced. Other than more missions, a multi-system-compatible RO sounder is an economical and efficient strategy to improve the frequency of RO soundings.  
Table 3 lists some of the primary GNSS sounders in operation or in the planning stage. The popular TriG (Tri-GNSS) receiver on COSMIC-2 and GRACE collects signals from GPS and GALILEO with approximately 500 soundings per day per satellite. The next-generation, TriG (JASON-CS), can receive  
Figure 14: Differences between the excess phase operator (EPH) and local refractivity operator (LOC) experiments in a time–pressure section from –72 to 0 h for Typhoon Nuri (2008) in (**a**) vertical velocity (colors; m s\({}^{-1}\)) and water vapor mixing ratio (contours; g kg\({}^{-1}\)), and (**b**) vertical velocity (colors; m s\({}^{-1}\)) and vorticity (contours, \(10^{-5}\)s\({}^{-1}\)) [75].  
additional data from GLONASS, with the amount of data reaching 1000 soundings per day. According to [77], the next-generation TriG will be compatible not only with GPS, Galileo, and GLONASS, but also with BeiDou, DORIS (The Doppler Orbitography by Radio positioning Integrated on Satellite), Japan's QZSS (Quasi-Zenith Satellite System), and IRNSS (India's Regional Navigation Satellite Systems). Moreover, China's GNOS (GNSS Occultation Sounder) onboard FY-3C/D is the first BeiDou and GPS-compatible RO sounder [17; 18; 19; 20]. With the complete deployment of BeiDou satellites, the soundings have reached 1000 soundings per day. Furthermore, the next sounder for MetOp, namely the RO Sounder, may increase the tracked GNSS constellation to three constellations. More multi-system-compatible RO receivers are required. With the development of multi-system RO receivers, a further increment in the amount of RO soundings is foreseen. As a result, this will further improve the NWP and TC forecast.
## 5 Conclusions  
(1) An intelligent ventilation system construction method based on the global scheme is proposed. The method mainly sets several monitoring points in the main roadway of the mine and then predicts the airflow parameters at other unknown points by an artificial intelligence algorithm. Real-time intelligent control is carried out according to the data of sensing points. It can solve the problem of the sensor not being installed in the airflow disorder and under the harsh environmental conditions of construction.  
(2) The 3D simulation system software of mine ventilation is used to obtain airflow parameter data of each ventilation roadway under different natural wind pressures and different operating conditions of the main fan, and a huge database for the training and learning of neural networks is constructed. This method can compensate for the shortage of fully connected neural networks, which are not easy to train.  
(3) A method based on a convolutional neural network model for wind flow parameter prediction is proposed. The practical application is divided into two stages. At the beginning of the application, the database obtained by the network solution is used for training and prediction. In operation, the perceived points are predicted by accumulating and rolling the observed point data.  
(4) Based on solving the key modules of intelligent perception prediction, the real-time monitoring and intelligent control modules are integrated, and the intelligent ventilation system suitable for a mine is developed and put into trial operation. According to the field feedback, an intelligent ventilation system based on a global scheme can effectively improve the efficiency of the ventilation system.  
Y.C.: conceptualization, writing--original draft. Z.L.: supervision, resources, methodology. X.L.: data curation, validation. W.T.: software, visualization. Q.Z.: formal analysis, writing--review and editing. H.W.: validation, project administration, visualization. W.H.: review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (52274247) and the Natural Science Foundation of Hunan Province (2021)J40799).  
Figure 13: Verification of intelligent ventilation system sensing module.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
[PERSON] was employed by the Xinjiang Kalatongke Mining Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 3 Case Study
### Gas Path Fault Feature Analysis  
Based on the GPA method introduced in the methodology, the degradation calculation of flow rate and efficiency is carried out. Figure 6 shows the flow and efficiency degradation results of the fan, low-pressure compressor, high-pressure turbine, and low-pressure turbine. In Figure 6a, the fan flow rate degradation remains relatively stable until around 1736 s, after which it drops sharply to \(-\)4.78% due to the shutdown. The efficiency degradation is smaller in magnitude but still noticeable, peaking at 0.94%. This sudden decline is indicative of a critical failure, later confirmed to be a fan baffle detachment. Figure 6b illustrates the LPC degradation. Following the fan failure, its flow rate also declines rapidly, reaching \(-\)4.00%. The efficiency degradation is limited in magnitude, with transient fluctuations that reflect sensitivity to upstream flow disturbances. As shown in Figure 6c, the HPC experiences a similar pattern. The flow degradation reaches \(-\)3.29% with a slight time delay compared to the LPC. The efficiency degradation remains relatively stable, indicating the compressor maintained operational integrity despite upstream disruptions. Figure 6d presents the HPT degradation. While the flow and efficiency curves are generally stable, they display noticeable oscillations. These fluctuations result from dynamic changes in pressure and thermal conditions propagating downstream, yet no catastrophic drop is observed. Figure 6e shows that the LPT experiences the most gradual and minimal changes. The flow degradation remains under \(-\)0.45%,  
Figure 4: The speed of the turbofan engine. (**a**) The relative value of N\({}_{1}\). (**b**) The relative value of N\({}_{2}\).  
and the efficiency degradation is approximately 0.02%. As a downstream component, the LPT is indirectly affected by upstream failures and exhibits a delayed and attenuated degradation response.  
Comparing the overall degradation patterns, it is clear that the fault originated at the fan, then propagated through the compression system and gradually reached the turbines with decreasing intensity. This confirms the role of airflow disruption in fault propagation. In addition, comparing with the vibration measurement results in Figure 5, gas path degradation signals appear earlier, making them more suitable for early-stage fault diagnosis. These degradation characteristics provide strong evidence of a fault propagation mechanism starting from the fan and moving downstream, with the severity of impact diminishing across the system.
# Lightweight Detection Method of Obfuscated Landing Sites Based on the AST Structure and Tokens
## 4 Experiment
### Lightweight AST
#### 4.2.2 Analysis of the Structure Similarity Computation Time  
Table 5 shows the comparison results of the structure similarity computation time. On average, the lightweight AST requires 3.021 s, which is 24.15% less than the 3.983 s of the conventional AST. In particular, the obfuscated JavaScript comparison time is reduced more than 47%. This result is in accordance with those of Theorem 2 and \(t\)-test. The effect is small in the unobfuscated JavaScript because it does not have a special structure compared to its size. However, in the obfuscated JavaScript case, the effect is large when using the lightweight AST because it has many tokens by which words are divided by obfuscation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Obfuscation** & **Number of AST** & **Number of AST** & **Reduction** \\
**Type** & **Structural Nodes** & **Nonstructural Nodes** & **Rate** \\ \hline Type 1 & 52 & 49 & 48.51\% \\ Type 2 & 30 & 28 & 48.28\% \\ Type 3 & 36 & 32 & 47.06\% \\ Type 4 & 22 & 23 & 51.11\% \\ Type 5 & 238 & 237 & 49.89\% \\ Type 6 & 147 & 130 & 46.93\% \\ Type 7 & 23 & 22 & 48.89\% \\ Type 8 & 91 & 84 & 48.00\% \\ Type 9 & 45 & 39 & 46.43\% \\ Type 10 & 30 & 24 & 44.44\% \\ Type 11 & 33 & 27 & 45.00\% \\ Type 12 & 50 & 43 & 46.24\% \\ Type 13 & 49 & 47 & 48.96\% \\ Type 14 & 33 & 30 & 47.62\% \\ Type 15 & 30 & 25 & 45.45\% \\ Type 16 & 206 & 148 & 41.81\% \\ Type 17 & 36 & 38 & 51.35\% \\ Type 18 & 36 & 38 & 51.35\% \\ Type 19 & 72 & 60 & 45.45\% \\ Type 20 & 83 & 78 & 48.45\% \\ Type 21 & 72 & 57 & 44.19\% \\ Type 22 & 18 & 16 & 47.06\% \\ Type 23 & 125 & 100 & 44.44\% \\ Average & & & 47.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Practical minimum node reduction rate according to obfuscation type.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 2 Data and Methods
### Methodology
#### 2.3.4 Response of Deciduous Forest Phenology to Climatic Factors  
Because vegetation phenology is strongly influenced by the preseasonal climate, it is commonly used to examine how phenology responds to various climate variables [60; 61]. The preseason length is determined by calculating [PERSON]'s correlation coefficient and is defined as the period from the most sensitive period of vegetation phenology to the month in which the phenological metric occurs. This period occurs 1 to 4 months prior to the multi-year average of the start or end of the growing season (SOS/EOS) [62; 63; 64]. Preseason duration for 2m temperature is defined as the period (in steps of 1 month) before the multi-year average of SOS/EOS during which the maximum bias correlation coefficient occurs between SOS/EOS and 2m temperature after excluding the influence of climaticfactors such as precipitation and soil temperature during the same period. We performed similar steps for other climatic factors.  
In this study, we used SHapley Additive exPlanations (SHAP) analysis to explore the response of the 1-km and 5-km SIF products' extracted phenological SOS/EOS metrics to individual climatic factors. SHAP analysis is a machine-learning interpretation tool based on the principles of cooperative game theory [65], which can reveal the complex relationships in deciduous forests between phenology and climatic factors. By assessing the relative importance of each climatic factor in phenological change and evaluating the influence of each climatic factor on the phenological prediction model, the method provides insights into how climatic factors drive phenological changes in deciduous forests, offering a comprehensive and detailed explanatory framework for analysis and comparison at different scales.
## 3 Architecture Design  
### Satellite Routing  
Recently, MLSNs (Multilayered Satellite Networks) have been put forward as a feasible design for next-generation satellite networks. Hierarchical architecture and many satellite networks are combined to create MLSNs. A typical bilayer MLSN consists of a low earth orbit (LEO) constellation and a medium earth orbit (MEO) constellation interconnected by intersatellite connections (ISLs) to create a mesh or ring topology. Furthermore, interlayer linkages (ILLs) connect satellites at various levels. Ground-satellite linkages (GSLs) allow terrestrial users to connect satellites at various levels. By integrating these diverse networks, we can improve the capacity, dependability, and performance of global communications. Figure 1 shows a portion of a multilayer satellite network, where GEO, MEO, and LEO satellites cover the terrestrial gateways. Packets are routed independently to the target gateway after being delivered to the satellite network across a number of satellite hops, perhaps in various layers. Routing decisions made in satellite networks are separate from those in the terrestrial networks.  
The satellite network is hierarchically structured to decrease computational complexity and reduce communication burden across the network. Satellites are organized into groups and managed by a satellite in the highest tier in this structure. Routing table computations are performed using a hierarchical structure.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 2 System Model Characterization
\(\mathbf{A}_{tx,u}=[\mathbf{a}_{tx,u}(\phi_{1,1}^{tx,u},\theta_{1,1}^{tx,u}), \ldots,\mathbf{a}_{tx,u}(\phi_{N_{cl},N_{ny}}^{tx,u},\theta_{N_{cl},N_{ny}}^{ tx,u})]\) and \(\mathbf{A}_{rx,u}=[\mathbf{a}_{rx,u}(\phi_{1,1}^{rx,u},\theta_{1,1}^{rx,u}), \ldots,\mathbf{a}_{rx,u}(\phi_{N_{cl},N_{ny}}^{rx,u},\theta_{N_{cl},N_{ny}}^{ rx,u}))]\) hold the transmit and receive array response vectors of the \(u\)th user, respectively. At the \(k\)th subcarrier, the received signal is given by  
\[\mathbf{y}_{k}=\sum_{u=1}^{U}\mathbf{H}_{u,k}\mathbf{x}_{u,k}+ \mathbf{n}_{k}, \tag{6}\]  
where \(\mathbf{n}_{k}\in\mathbb{C}^{N_{rx}}\) denotes the zero mean Gaussian noise, with variance \(\sigma_{n}^{2}\), and \(\mathbf{x}_{u,k}\in\mathbb{C}^{N_{tx}}\) represents the discrete transmitted complex baseband signal of the \(u\)th user at subcarrier \(k\).