esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of the Annual Trend and Other Analyses  
The trend in the annual number of the selected papers was computed by using join-point analysis (Joinpoint Regression Program software, version 4.9.1.0--April 2022; Statistical Methodology and Applications Branch, Surveillance Research Program, National Cancer Institute) and the Dickey-Fuller test. According to this approach, the average annual percent change (APC) in publication amount estimates the weight of the time variation. The Dickey-Fuller test is a commonly adopted unit root test. According to this approach, the null hypothesis is \((1-\varphi)=0\) versus the alternative \((1-\varphi)<0\) (where \(\varphi\) is the first-order lag coefficient of an AR model \(yt=a+qyt-1+\epsilon t\)) and hence large negative values of the test statistic led to the rejection of the null. The join-point regression (change point regression or segmented regression) estimates that data can be split into subsets. For example, in a time series with two distinct trends, it can be found that when \(t\leq t^{*}\), the model has a precise intercept and slope; when \(t>t^{*}\), the model would have a different intercept and slope. Thus, \(t^{*}\) represents a join-point. APC is a possible characterization of trend in study-focus rates over time and for discrete-scale counts. Assumption requires that rates change at a constant percentage of the rate of the previous scale period. Rates that change at a constant percentage every year change linearly on a log scale. Thus, to calculate the APC for a series of values, the following regression model is implemented:  
\[\textbf{ln}(r_{t})\textbf{=}\alpha\textbf{+}\beta\;t\]  
where \(r_{t}\) is the rate of the \(t^{th}\) year. The APC from that year to the next one is calculated as:  
\[APC(t,t+1)\textbf{=}\frac{r_{t+1}-r_{t}}{r_{t}}\cdot\textbf{100}\textbf{=}(e^ {\beta}-1)\cdot\textbf{100}\]  
The model focuses on linear segments connected at join points that express the optimum fit of the observed data. These elements reduce the amount of the square of the differences between estimated and observed data. Consequently, the join-point year represents the point in time where the trend change is evaluated [15].  
The suite ggplot2 of the R software, version 4.1.3 (R Core Teams, R Foundation for Statistical Computing, Vienna, Austria), was adopted for graphical visualization (quartiles, article types, topics, citations).
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
###### Abstract  
The accurate prediction of the residual strength of defective pipelines is a critical prerequisite for ensuring the safe operation of oil and gas pipelines, and it holds significant implications for the pipeline's remaining service life and preventive maintenance. Traditional machine learning algorithms often fail to comprehensively account for the correlative factors influencing the residual strength of defective pipelines, exhibit limited capability in extracting nonlinear features from data, and suffer from insufficient predictive accuracy. Furthermore, the predictive models typically lack interpretability. To address these issues, this study proposes a hybrid prediction model for the residual strength of defective pipelines based on Bayesian optimization (BO) and eXtreme Gradient Boosting (XGBoost). This approach resolves the issues of excessive iterations and high computational costs associated with conventional hyperparameter optimization methods, significantly enhancing the model's predictive performance. The model's prediction performance is evaluated using mainstream metrics such as the Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R\({}^{2}\)), Root Mean Square Error (RMSE), robustness analysis, overfitting analysis, and grey relational analysis. To enhance the interpretability of the model's predictions, reveal the significance of features, and confirm prior domain knowledge, Shapley additive explanations (SHAP) are employed to conduct the relevant research. The results indicate that, compared with Random Forest, LightGBM, Support Vector Machine, gradient boosting regression tree, and Multi-Layer Perceptron, the BO-XGBoost model exhibits the best prediction performance, with MAPE, R\({}^{2}\), and RMSE values of 5.5%, 0.971, and 1.263, respectively. Meanwhile, the proposed model demonstrates the highest robustness, the least tendency for overfitting, and the most significant grey relation degree value. SHAP analysis reveals that the factors influencing the residual strength of defective pipelines, ranked in descending order of importance, are defect depth (\(d\)), wall thickness (\(t\)), yield strength (\(\sigma_{y}\)), external diameter (\(D\)), defect length (\(L\)), tensile strength (\(\sigma_{u}\)), and defect width (\(w\)). The development of this model contributes to improving the integrity management of oil and gas pipelines and provides decision support for the intelligent management of defective pipelines in oil and gas fields.  
defective pipelines; residual strength; prediction method; machine learning; explainable +
Footnote †: journal: Applied sciences
###### Abstract  
Torsional deformities of the lower limb are common in children with cerebral palsy (CP)-determining gait problems. The mechanisms underlying transverse plane gait deviations arise from a combination of dynamic and static factors. The dynamic elements may be due to spasticity, contractures and muscle imbalances, while the static ones may result from excessive femoral anteversion, which decreases the efficiency of the hip abductors by reducing the muscular lever arms. A therapeutic approach has been identified in multi-level functional surgery for the lower limb. Treating the malalignments of the lower limb with femoral or tibial derotation provides optimal results, especially when supported by adequate biomechanical planning. This planning requires an integrated static-dynamic approach of morphological and functional evaluation, based on radiological measurements, physical examination and gait analysis. Instrumented gait analysis has been confirmed as essential in the evaluation and surgical decision making process for children affected by CP with transverse plane deformities. Computational simulations based on musculoskeletal models that integrate patient-specific CT morphological data into gait analysis can be used for the implementation of a surgical simulation system in pre-operative planning to test the possible effects of the different surgical treatment options on the torsional defects of the lower limbs. Recently, a computer-aided simulation process has been implemented in the preoperative planning of complex osteectomies for limb deformities in children. Three-dimensional (3D) digital models were generated from Computed Tomography (CT) scans, using free open-source software. The aim of this study is to integrate the patient-specific CT musculoskeletal model with morphological data and gait analysis data, with the personalized calculation of kinematic and kinetic parameters, which allow us to generate an \"avatar\" of the patient for a more in-depth evaluation of the gait abnormalities. The computational simulation platform proposed provides a realistic movable musculoskeletal model in a virtual environment, with the possibility of planning and monitoring the effects of virtual three-dimensional surgical corrections.  
cerebral palsy; torsional deformities; derotation; musculoskeletal modeling; virtual surgical planning; avatar; gait analysis; computed tomography +
Footnote †: journal: Applied Sciences
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.9 Smart Grid Hyllie  
The \"Smart Grid Hyllie\" project, which is realized in Sweden, examines an energy monitoring system that optimizes the energy utilization. In addition, the integration of renewable energy production is facilitated through monitoring and measuring the power output. Through this project, Hyllie will become an energy efficient town district via the construction and operation of a large scale smart grid [106].
## 6 Conclusions and Future Work  
### Research Contribution  
Through the research and analysis of various cases and the trial evaluation of advertising effectiveness, we can find that the application of 5G technology can indeed fill the current advertising market gap and provide a time and location-based audience exposure evaluation tool. There are three main contributions of this research:  
* This research has sorted out the needs of business application scenarios in various markets and specifically explained the customer groups that different application scenarios hope to target and the difficulties encountered.
* This study presents a generic 5G data collection and processing mode, demonstrating how to organize time and location data and implement it in advertising contextual applications.
* This study evaluated the possible benefits of various deployment scenarios in conjunction with the use of 5G data and discussed the practical problems and improvement directions that may be encountered in practical deployment.
* This study provides additional 5G footprint tracking technology applications by reference, including market research, urban transport, security, epidemic prevention, and other fields.
## 3 Results and Analysis
### Analysis of the Average RSEI in the Study Area
#### 3.2.2 Spatial Characteristics Analysis of Ecological Environment Quality in the Study Area
The bandwidths for NDVI and DEM showed a downward trend, indicating an increase in their spatial heterogeneity over time. The bandwidths for the land use and protected area factors exhibited significant variability over time. After 1995, the bandwidth for land use mostly presented a decreasing trend, except for a sudden increase in 2010, whereas the bandwidth for the protected area factor hit its lowest in the same period. In 2015, the bandwidth for land use diminished, and that for protected areas grew, but by 2020, the bandwidth for protected areas had gradually decreased once more. This suggests that, even with the expansion of the urbanization process and increased human disturbance, the impact of conservation policies continued to drive improvements in the ecological environmental quality of the study area.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
###### Abstract  
GNSS signals arriving at receivers at the surface of the Earth are weak and easily susceptible to interference and jamming. In this paper, the impact of jamming on the reference station in carrier phase-based relative baseline solutions is examined. Several scenarios are investigated in order to assess the robustness of carrier phase-based positioning towards jamming. Among others, these scenarios include a varying baseline length, the use of single- versus dual-frequency observations, and the inclusion of the Galileo and GLONASS constellations to a GPS only solution. The investigations are based on observations recorded at physical reference stations in the Danish TAPAS network during actual jamming incidents, in order to realistically evaluate the impact of real-world jamming on carrier phase-based positioning accuracy. The analyses performed show that, while there are benefits of using observations from several frequencies and constellations in positioning solutions, special care must be taken in solution processing. The selection of which GNSS constellations and observations to include, as well as when they are included, is essential, as blindly adding more jamming-affected observations may lead to worse positioning accuracy.  
GNSS jamming; carrier phase-based positioning; real-time kinematic; network RTK; reference station networks; precise positioning Article  
Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions  
1  
CitationLarsen[PERSON]; [PERSON]; [PERSON] Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions  
Remote Sens202132680[[https://doi.org/10.3309/rs13142680](https://doi.org/10.3309/rs13142680)]([https://doi.org/10.3309/rs13142680](https://doi.org/10.3309/rs13142680))
## 2 Flow Distribution Characteristics of the Straight Bartel Inlet Header PCHE
### Physical and Numerical Models
The PCHE is assumed to have \(i\) layers of heat exchanger plates (\(i\) = 1, 2,\(\cdots\), \(n\)), each layer has \(j\) flow channels (\(j\) = 1, 2,\(\cdots\), \(n\)), and the flow rate in flow channel \(j\) of layer \(i\) is denoted as \(c_{ij}\). Then, the average flow rate of the flow channels is  
\[c_{ave}=\frac{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{n}c_{ij}}{Nn} \tag{1}\]  
where \(c_{\text{ave}}\) is the average flow rate of the flow channels, kg/s. The average flow rate of the layers is  
\[p_{ave}=\frac{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{n}c_{ij}}{N} \tag{2}\]  
The channel non-uniformity of each flow channel is defined as  
\[\text{CN}_{ij}=\frac{\left|c_{ij}-c_{ave}\right|}{c_{\text{ave}}} \tag{3}\]  
where \(\text{CN}_{ij}\) is the non-uniformity of each flow channel. The plate non-uniformity of each layer is defined as  
\[\text{PN}_{i}=\frac{\left|\sum\limits_{j=1}^{n}c_{ij}-p_{\text{ave}}\right|}{p _{\text{ave}}} \tag{4}\]  
where \(\text{PN}_{i}\) is the non-uniformity of layer \(i\). The overall non-uniformity of the straight barrel inlet header PCHE is defined as  
\[\text{ON}=\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{n}\text{CN} \tag{5}\]  
where \(\text{ON}\) is the overall non-uniformity. The values of the above non-uniformities differ in size. For the convenience of understanding and analysis, the standard deviations of the various non-uniformities are adopted to normalize them. The standard deviation of the overall non-uniformity is  
\[\sigma_{o}=\sqrt{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{n}\left(\text{CN}_{ ij}-\text{ON}/Nn\right)^{2}/nN} \tag{6}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Pressure/MPa**} & **Flow Rate/(kg.s\({}^{-1}\))** & **Inlet Temperature/K** & **Wall Temperature/K** & **Re** \\ \hline
1 & & 0.01 & & & 1275.8 \\
2 & & 0.02 & & & 2551.6 \\
3 & 9.5 & 0.03 & 305 & 333 & 3827.4 \\
4 & & 0.04 & & & 5103.2 \\
5 & & 0.05 & & & 6379.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation working conditions. where \(\sigma_{o}\) is the standard deviation of the overall non-uniformity. The standard deviation of the non-uniformity of each layer is  
\[\sigma_{p}=\sqrt{\sum_{i=1}^{N}\left(PN_{i}-\sum_{i=1}^{N}PN/N\right)^{2}/N} \tag{7}\]  
where \(\sigma_{p}\) is the standard deviation of the non-uniformity of each layer. The standard deviation of the non-uniformity of each flow channel of the layer \(i\) heat exchanger plate is  
\[\sigma_{i,c}=\sqrt{\sum_{j=1}^{n}\left(CN_{tj}-\sum_{j=1}^{n}CN_{tj}/n\right)^ {2}/n^{2}} \tag{8}\]  
where \(\sigma_{i,c}\) is the standard deviation of the non-uniformity of each flow channel of the layer \(i\) heat exchanger plate.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 2 Materials and Methods
### Nationwide Flood Mapping Framework
#### 2.2.3 Annual Flood Mapping: Scheme C  
In Figure 1c, a robust pixel-based detection of surface water changes is widely applicable to create an accurate nationwide flood map at both national and transboundary river scales using a synchronized multiple index such as MODIS-derived annual Synchronized floodwater index (SfWi) based on MLSWI [25; 26]. In particular, a time-series change detection of pixel-based classification is connected with annual hydrological data, i.e., water level, to identify and estimate the flood duration and the starting and ending time of the event. In spite of the limitations of satellite data--e.g., cloud  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Contents** & **Used Band** & **Resolution (Spatial/Time)** \\ \hline MODIS (MOD09A1) [1] & Land surface reflectance & Band 2 \& 7 & 500 m/eight-day \\ MODIS (MOD11A2) [1] & Land surface temperature & & 1000 m/eight-day \\ Water level [2] & Meter & - & -/one day \\ ALOS-2 [3] & Backscattering coefficient & HH & 100 m/one day \\ HIMAWARI 8 [4] & Shortwave-IR & Band 5 \& 6 & 2000 m/10 min \\ \hline \end{tabular} \({}^{1}\) NASA-USGS, 2015. \({}^{2}\) BWDB, 2015. \({}^{3}\) JAXA, 2015. \({}^{4}\) JMA, 2015.  
\end{table}
Table 1: Used data including designed process for 2015 flood detection.  
Figure 1: Framework of nationwide monitoring for supporting emergency risk management.  
cover, mixed pixel, and spatial resolution--the annual flood map is of great value to overview and understand an actual flood situation to assist disaster monitoring and response efforts to minimize disaster risk and damage.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 2 Research Status of the Precision Formation of Distributed Spacecraft Project
### Taiji  
The Taiji project was proposed and developed by the Chinese Academy of Sciences in 2016. As shown in Figure 10, similar to the LISA scenario, Taiji also forms an equilateral triangle formation on a heliocentric orbit, deviating from the Earth-sun direction by 18-20\({}^{\circ}\). The distance between the spacecraft is 3 million km, and the orbit height is approximately 50 million km [24]. The project completed the 0.1 mHz-1 Hz mid- and low-frequency gravitational wave detection plan by establishing a laser link. The difference is that the Taiji program formation configuration has a side length of 3 million km. It is more technically difficult to maintain the necessary coordinated ultra-stable and ultra-quiet platform of the spacecraft formation. The laser interferometric system consists of a highly stable laser light source, a highly stable laser telescope, a laser interferometer, a phase meter, an ultra-stable clock, a laser capture system, and a noise suppression system. The program was divided into three phases, first launching the Taiji-1 satellite, further completing the double-satellite formation, and finally completing the triple-satellite formation. Taiji-1 was launched on 31 August 2019 and completed the in-orbit test [25].
# Global Local Fusion Neural Network for Multimodal Sentiment Analysis
## 3 Proposed Model  
### Overview  
Text and image data in social media often exist at the same time. However, sometimes, a single text may correspond to multiple images, which is more complicated. Considering that text and image information are equally important, this paper mainly focuses on the social data that a text corresponds to an image, and the image-text multimodal sentiment classification task is defined as follows. Given image-text pairs \(P=\{(I_{1},\ T_{1}),\dots(I_{i},\ T_{i}),\dots(I_{n},\ T_{n})\}\) and the corresponding label set of the image-text pairs \(L=\{I_{1},\dots I_{i},\dots I_{n}\}\), \(n\) is the total number of pairs in the set. For an image-text pair \((I_{i},\ T_{i})\), \(I_{i}\) denotes a single image, and \(T_{i}\) denotes the corresponding texts. The sentiment label is \(I_{i}\in\)[positive, negative, neutral]. The goal of the multimodal method is to predict the sentimental polarity correctly. The framework of the proposed model is shown in Figure 2. GLFN is composed of three modules: global feature learning, local fusion learning, and an integration network. Global fusion learning first extracts the overall fusion features from the pre-trained image and text representation with an attention mechanism. Then, fine-grained local fusion learning explores the detailed correlation between image content and words. Finally, an integration network is applied to aggregate the sentimental information from global fusion features and local correlation features to conduct the result.
## 4 CRISPR/Cas Mechanism
### Expression, crRNA Generation and Interference
#### 4.2.3 Type III Systems  
In type III CRISPR/Cas systems, expression and interference are predominantly similar to those described for type I systems. However, the significant difference is that the endoribonuclease Cas6 does not bind to a repeat stem-loop but to the first bases of the repeat and that further crRNA maturation steps occur (Figure 3B) [86, 87]. Indeed, not all repeats in those systems contain a strong palindrome and those associated with type III-A are predicted to be mostly non-structured [34]. In _Pyrococcus furiosus_, the binding site of Cas6 is located within nt 2-8 of the repeat and cleaves at a distal site between bases 22 and 23 resulting in crRNAs with an 8 nt repeat handle on the 5' end, the spacer sequence and 22 nt of the following repeat at the 3' end [86, 87, 88]. Interestingly, this RNA is not the final mature crRNA product. Cas6, unlike the type I endoribonucleases, only transfers the crRNA to the targeting complex termed CMR for type III-B where they are further processed (Figure 3C) [10, 89, 90]. In the type III-A system of _S. epidermidis_, a similar mechanism for processing by Cas6 was observed and the _csm2_, _csm3_ and _csm5_ genes were required for further 3' processing and crRNA maturation [60].  
The _P. furiosus_ CMR complex includes the full subset of Cmr proteins (Cmr1-6) and can contain two different species of crRNA [90], both with the 8 nt 5' end derived from the repeat and either 31 nt or 37 nt of the spacer [91]. The type III-B CMR complex from _S. solfataricus_ contains an additional protein, Cmr7 and a stoichiometry of one protein of each of Cmr1-6 and 6 Cmr7 proteins [92]. Interestingly, the _P. furiosus_ CMR complex has been shown to target RNA [89, 90, 92]. Basepairing between the last 14 nt of the crRNA and the RNA target seem to be essential for cleavage [90]. Target RNA cleavage occurs 14 nt upstream from the 3' end of the two mature crRNA species, generating two cleavage sites [90]. In contrast to type III-B, DNA is the target of type III-A systems [27], but there is not yet data about complex formation for the Csm proteins.  
The ability of CRISPR/Cas systems to target DNA raised the question of how they avoid targeting their own CRISPR arrays, which have perfect complementarity to the crRNAs they produce. For the type III-A system, this is avoided by requiring spacer:protospacer complementarity and an absence of base-pairing to the 5' handle in the crRNA [93]. This ensures that only non-self targets are licensed for degradation, but whether the same principle applies to other types remains unknown.