esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Recursive Star Identification
### Star Trackers for Different Applications  
Although Star-ID is predominantly used for attitude determination, it can be used for other spacecraft related tasks. Here are some examples:  
1. _Star Gyros._ With appropriate algorithms, images from star cameras may also be used for estimating the angular velocity of the spacecraft [32].
2. _Space Surveillance._ It can be used for space situational awareness to estimate the orbit of other visible spacecraft [33, 34, 35].
3. _Space Navigation._ If placed on an interplanetary probe, it could observe visible planets and estimate the location of the probe [36, 37].
4. _Positioning System._ If carried on a planet or moon, it could be used to estimate its position on the body when combined with a clock and two inclinometers [30, 31].  
Interesting research have been carried out to increase star sensor accuracy as well as to simplify the Star-ID problem. Here are two examples:  
1. _Multiple Fields-of-View system._ While attitude determination from a single star camera image produces very accurate information about the direction of the camera boresight, the estimate of the rotation about the camera's boresight axis is less accurate. In order to solve this problem, a second star camera is sometimes used. There is another method, which uses a single star camera to record a combination of multiple star images simultaneously. For a two fields-of-view camera the light are preferentially smeared by the optics (e.g., by adding astigmatism) so that stars from one aperture are smeared in a horizontal direction in the image plane, while light from the other aperture is smeared in the vertical direction [38, 39]. Image filtering algorithms can detect the direction of the smearing and separate the stars according to which aperture they entered. If, however, the Star-ID technique is very robust to the presence of non-stars, the Star-ID algorithm may be run many times on the same image, perhaps on stars from three apertures, all in orthogonal directions [40, 41, 42]. In these cases it is possible to separate the stars without the need for smearing the stars in a given direction.
2. Techniques requiring multiple images as well as attitude maneuvers have been implemented [43].
3. _Uniform Star Catalog._ In order to develop optimized star sensing and star identification with respect to continuous operation and reliability, the concept of star catalogs with near uniform angular spacing between stars has been proposed [44]. These catalogs are not characterized by constant magnitude cutoffs. They are reference star catalogs where the expectation of the number of stars that fall in a given field of view is approximately constant (i.e. 5 or 6) (minimum standard deviation), independently which region of the sky the sensor optical axis is pointing.
# Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone
## 2 Methods  
A Broadcom 47755 dual-frequency GNSS chip is embedded in Mi 8, which is capable of tracking GPS L1 C/A, GLONASS L1, BeiDou (BDS) B1, QZSS L1, Galileo (GAL) E1, GPS L5, Galileo E5a and QZSS L5 signals [18]. The GPS L1/L5 and Galileo E1/E5a dual-frequency PPP algorithm will be described in detail.
# An Unequal Clustering and Multi-Hop Routing Protocol Based on Fuzzy Logic and Q-Learning in WSNs
## 2 Related Works
As research has progressed, it has become evident that WSNs often operate in rapidly changing environments, requiring the development of routing algorithms that can quickly adapt and make decisions. Reinforcement learning (RL) aims to maximize an agent's reward by taking a series of actions based on feedback from a dynamic environment. RL is particularly well suited for WSN routing as it can effectively adjust to the network's evolving conditions [31]. [PERSON] et al. [19] introduced an innovative routing algorithm that leverages Q-learning to enhance energy efficiency while incorporating data aggregation awareness. This strategy effectively cuts down on redundant data transmissions, contributing to a longer network lifespan. Nevertheless, it does not adequately tackle the challenge of reducing delays in data aggregation. [PERSON] et al. [32] offer a method for extending network lifetime and improving energy efficiency in WSNs through Q-learning-based routing. By taking into account different energy consumption criteria, it enables nodes to choose nearby nodes for transmission, leading to a more balanced and less energy-consuming process. [PERSON] et al. [33] proposed MRP-ICHI, a multi-hop clustering routing protocol that combines an improved Coronavirus Herd Immunity Optimizer (CHIO) with Q-learning to enhance network performance.The improved CHIO algorithm optimizes network clustering by formulating a fitness function that incorporates node energy levels and location attributes. To ensure efficient data transmission, the protocol also utilizes Q-learning to establish an effective multi-hop routing mechanism between CHs and the BS. Using grey wolf optimization and Q-learning, [PERSON] et al. [34] developed an innovative routing system for wireless body area networks (WBANs). By optimizing the network lifetime through these techniques, the protocol adjusts routing paths in response to energy depletion in various network nodes, increasing energy efficiency.
## 4 Propagation Law of Near-Ground Explosion Shock Wave
### Reflection of Shock Wave above Ground  
When the explosive detonates at the height \(h\) above the ground, the air shock wave generated spreads in all directions in a spherical shape. When the shock wave reaches the ground, it will be reflected above it  
When the incident angle is approximately equal to zero, the front of the incident shock wave is parallel to the ground, so a positive reflection occurs. When the incident is greater than zero, an oblique reflection of the shock wave occurs. When the incident angle continues to increase to the limit, the Mach reflection occurs, and the incident wave and the reflected wave become Mach waves. Reference [47] presents the functional relationship between the limit incidence angle and the reciprocal of the proportional height measured by the test. According to law, the limit incidence angle of the models in this paper is about 40\({}^{\circ}\).
## 3 Methodology
### Feature Selection and Comparison of Techniques (Step 2)  
This step involves applying and comparing the techniques Principal Component Analysis (PCA), Minimum Redundancy Maximum Relevance (mRMR), Neighborhood Component Analysis (NCA), and Denoising Autoencoder (DAE) to select the best features related to machine failures.  
In this work, PCA was applied to the data as indicated by [19,20] to verify each variable's percentage of total variance using the main components. To do this, we employed Sklearn's PCA Decomposition library in Python, which has a function called explained_variance_ratio. This function calculates the cumulative proportion of the explained variance and returns the total number of principal components with the percentage contribution of each variable to each component. The mRMR was provided by pymrmr library in Python. It receives the input and output variables and returns the relevance level of each input variable about the output. The sklearn_neighbors library was used to provide  
Figure 1: Steps carried out in this work.  
the correlation levels between the input and output variables using NCA. The procedure involves showcasing input and output variables while partitioning 70% of the data for training and reserving 30% for testing. Finally, the DAE technique was applied using Keras and Tensorflow version 2.15.0. As mentioned before, it is a useful tool to deal with dimensionality reduction and feature selection.  
In this study, we employ the technique using TensorFlow's input, model, and dense libraries.Keras.layers and models. We employ a dense with nine inputs and relu activation function for the encoding layer, while we use the linear activation function for decoding. Furthermore, we applied the Adam optimizer with 100 epochs, a batch size 64, and the mean squared error metric as a loss function. To evaluate the contributions of each variable, we extract the weights from the autoencoder-encoded layer and apply them to the corresponding variables.  
Each of these techniques was employed to extract important features from the data and reduce the dimensionality of the feature set. We also sought to observe the level of importance attributed to each resource through these techniques. Subsequently, we compared the best features highlighted by each technique.  
It is important to highlight that the RF technique, being based on Decision Trees (DTs), can be used for feature selection by analyzing the contribution of each attribute (variable) to the model's prediction capability. During the training of the RF algorithm, attributes are evaluated in each individual DT to measure how much each one contributes to reducing impurity in the tree nodes. Then, the importance of each attribute is calculated by the average or sum of impurity reductions across all DTs in the forest. Thus, attributes with greater impurity reductions are considered more important for the model, while those with lesser impact are deemed less relevant and may even be discarded during the DT construction process. Therefore, we also included in the results an analysis of the importance of attributes based on RF.
## 3 Case Study
### Simulation Results
#### 3.7.2 Long-Term Prediction Performance Evaluation  
Most dams have less monitoring data. Therefore, less training data should be used to verify the stability of the prediction model for dam deformation. Two cases with different lengths of the training dataset were considered. In the first case, the monitoring dataset  
Figure 12: Long-term and short-term predictions for G3-HY through the different models: (**a**) LSTM-SSA with 30% data for training; (**b**) LSTM-SSA with 60% data for training; (**c**) GRU-SSA with 30% data for training; (**d**) GRU-SSA with 60% data for training; (**e**) CNN-LSTM-SSA with 30% data for training; (**f**) CNN-LSTM-SSA with 60% data for train-ing; (**g**) CNN-GRU-SSA with 30% data for training; (**h**) CNN-GRU-SSA with 60% data for training; (**i**) The hybrid model with 30% data for training; (**j**) The hybrid model with 60% data for training.  
was divided into 60% for training and 40% for testing. In the second case, only 30% of the data were used for training and 70% of the data for testing. The prediction results of the nine models are evaluated using measurement indicators, as shown in Table 2. Typically, the hybrid model outperforms the single-algorithm models (CNN, LSTM, and GRU). For example, compared with the CNN-GRU-SSA model, the proposed model reduces the _RMSE_ by 20.6% for G3-HY. Compared with the CNN-LSTM-SSA model, the prediction accuracy of the proposed model improves significantly in the aspect of R\({}^{2}\) promotion of up to 14.2% for G3-HY. Combined with the boxplot, as shown in Figure 11d, the single-algorithm models have some outliers, whereas the proposed model is within the 1.5 interquartile range, and there are only a few mild outliers, indicating that the hybrid model is more suitable for long-term prediction than the single-algorithm models.  
The five better models are selected here for comparison, as shown on the left side of Figure 12. The prediction accuracy of the proposed hybrid model reveals that using a shorter training dataset yields results similar to those using a longer training dataset. When the training data are reduced, the LSTM is more sensitive than the GRU, which shows that the prediction accuracy gradually decreases in the later stage, indicating that the GRU outperforms the LSTM in some tasks. Therefore, through an analysis of the long-term prediction ability of the model, it is proved that the effective combination of the method is beneficial to improve the generalization ability of the predictive model.
# Ultrasound-Guided Procedures in Common Tendinopathies at the Elbow: From Image to Needle
## 3 Common Elbow Pathologies  
### Tennis Elbow  
Tennis elbow (i.e., lateral epicondylitis) is a painful condition of the common extensor tendon (CET) in the proximal dorsal forearm. The problem was first described by [PERSON] [22] in 1873. The \"Lawn Tennis Arm\" was labeled and published in _The Lancet_ by [PERSON] in 1882. Patients usually complain of pain and tenderness at the lateral elbow. In most cases, the etiology is overuse due to the fact of repetitive strain from excessive grip or wrist extension, radial deviation, and/or forearm supination causing microtrauma [23]. In addition to mechanical forces, the unique origin of the extensor carpi radialis brevis muscle (ECRB) in the lateral part of the capitellum could influence repeated undersurface abrasion during repetitive flexion and extension [24]. Following the tendon's repetitive stretching, multiple microtears can cause irreversible denaturing of matrix proteins and fibrous tissue proliferation [7]. The annual incidence of tennis elbow was reported as 2.4 per 1000 people [25], the prevalence was estimated as 1-3% [26], and the peak incidence is between 40 and 50 years of age [27]. It seems to be independent of sex or ethnic background [28]. It is also a socioeconomic problem, because workers are absent from work [29]. The diagnosis is based on clinical evaluation, US examination, and eventually an X-ray.
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 4 Spatio-Temporal Analysis
### Europe
GNSS IWV trend ranged between \(-1.5\) and \(2.3\) mm decade\({}^{-1}\). However, longer time-series from ERA-Interim (\(\sim\)26 year) revealed positive trends in all sites, which also had a gradient from southwest to northeast. Trends obtained from dew-point temperature were comparable with the other estimates, and therefore can be used when observations are not available.
# Initial Coin Offerings and Agile Practices
## 5 Analysis of Agile ICOs Projects
### Software Development  
In order to better understand the process of software development of ICOs, for each of the 55 ICO projects selected in step 2 we examined the documentation to find the availability of software project repositories in which the development team stores and manages the software under development. We found that:  
* 36 ICOs have a software project publicly available on the Github platform.
* 12 of the remaining ICOs published in the Ethereum blockchain explorer _Etherscan_ ([[https://etherscan.io/](https://etherscan.io/)]([https://etherscan.io/](https://etherscan.io/))) the solidity code of the smart contract used to implement the token selling.
* 7 ICOs do not have a publicly available software project nor provide any smart contract solidity file.  
Summarizing, 48 out of 55 Agile ICOs provide at least the smart contracts used to develop the token of the ICO. These smart contracts are written in Solidity [44], which is the most popular high-level language for implementing smart contracts.  
We analyzed the 32 Agile ICOs software projects available on Github. In particular, we counted the number of _repositories_ (the folders of the project), the typology of files, and the number of solidity files.  
In summary, the 32 Github projects contain a total of 14.199 files. The total number of folders is about 2800. On average, each project contains 5.8 _repositories_, and the maximum number of _repositories_ per project is 38.  
Regarding the contents, source code files represents the most of the files present in ICO software projects. In particular, _j_s files (javascript) dominate the scene with 5.015 files, equal to 35.32% of the total. The projects contain 786 smart contracts written in solidity (.sol), equal to 5.54% of the total.  
Figure 7: Histogram of the number of months passed after the end of the ICO.  
Graphics file formats (like png and svg formats) represent the second most frequent kind of files. Table 2 summarizes the number of occurrences of the ten most common file types found in Agile ICO software projects.  
To better understand the process underlying the development of Agile ICOs, we have verified for each project the use of specific development frameworks and in particular the use of _Truffle_. Truffle is a popular development framework for Ethereum that includes built-in smart contract compilation, linking, deployment, binary management, and automated testing. Truffle is available at [[https://truffleframework.com](https://truffleframework.com)]([https://truffleframework.com](https://truffleframework.com)). We found that Truffle is commonly used in Agile ICOs. Nineteen out of 36 projects include the typical Truffle elements.
## 3 Results and Discussion
### Inclinations of Nails  
#### 3.2.1 Lateral Movement of the Soil Nailing Slope  
Figure 15 represents the effects of the nail inclinations on the horizontal movement of the slope face with and without footing pressure. The lateral displacements ratio (D/H) at the mid height of the slope is taken for different nail inclinations. It is evident from the figures that the increase in the inclination of the nails results in a decrease in the horizontal movement of the slope face. The percentage decrease in lateral movement with the increase in surcharge loading and nail inclination ranges from 7.1% to 25.0%.
# Traffic-Aware Optimization of Task Offloading and Content
## 3 System Model and Problem Formulation
### Execution Time and Energy Consumption Model
#### 3.2.1 Execution Time and Energy Consumption of Local Task Computation  
If the task \(q_{i,n}\) is selected for local processing, then \(TL_{q}^{i,n}\) is defined as the local execution time. Due to the difference in its own computational power brought by vehicle heterogeneity, the local execution time delay of task \(q_{i,n}\) is  
\[TL_{q}^{i,n}(t)=\alpha_{i,n}^{l}\times\frac{c_{q}^{i,n}}{f \Pi_{n}^{l}} \tag{1}\]  
Figure 1: Three-tier cloud–edge–vehicle network framework with different tasks.  
The local energy consumption is calculated as  
\[EL_{q}^{i,n}(t)=\alpha_{i,n}^{l}\times c_{q}^{i,n}\times(fl_{n}^{i})^{2}\times\varsigma \tag{2}\]  
where \(fl_{n}^{i}\) is the computational power of the \(n^{th}\) vehicle in region \(i\). \((fl_{n}^{i})^{2}\times\varsigma\) is the energy consumption per CPU cycle.
## 4 Panel Data Analysis-Based Surface Texture Prediction Model
### Prediction Results of Short-Term Surface Texture  
The short-term SMTD development is calculated for three scenarios: (1) predicting the SMTD development from 2018 to 2020 based on the SMTD panel data of 2017; (2) predicting the SMTD development from 2019 to 2020 based on the SMTD panel data from 2017 to 2018; and (3) predicting the SMTD development in 2020 based on the SMTD panel data from 2017 to 2019.  
The predicted SMTD results are compared with the field measured SMTD data, as shown in Figure 8. Compared with SRT-14, SRT-7, and SRT-19 sections, the panel data analysis result in terms of short-term SMTD prediction is less accurate for the SRT-9 section. This is because SRT-9 is the only section paved with porous asphalt concrete PAC13-80% (see Table 3) and because of the abnormal increase of SMTD occurring in 2019 [26]. It is seen from Figure 8 that when predicting the SMTD development of the fourth year (2020) based on the SMTD panel data of the first three years (2017 to 2019), the accuracy is relatively high. When the SMTD panel data of the first year (2017) or two years (2017 to 2018) are used to predict the SMTD development of the next one year, the predicted results have a certain deviation from the measured values. Obviously, the longer the time scale of SMTD data used in the model development, the more accurate the model regression and the short-term SMTD prediction.  
Figure 7: Linear relationship between the measured data and the regression results of the model based on different time scales of panel data. (**a**) SRT-14 (Dense-graded asphalt concrete, SAC13-65%). (**b**) SRT-7 (Dense-graded asphalt concrete, SAC13-70%). (**c**) SRT-9 (Porous asphalt concrete, PAC13-80%). (**d**) SRT-19 (Dense-graded asphalt concrete, SMA13-75%).
## 2 Materials and Methods  
### Transparent Sensor Fabrication  
The transparent ultrasound sensor was based on a ITO-coated PVDF thin film (PolyK Technologies LLC, Philipsburg, PA, USA). The PVDF thin film had a thickness of 50 \(\upmu\)m and was coated with a 200-Ohm ITO electrode on each surface. The thin film was firstly cropped into a \(\sim\)4 mm \(\times\) 4 mm square, which was then affixed onto the tip of brass tube (diameter: 2 mm; length: 3 cm) with electrically conductive epoxy (RS PRO Liquid Adhesive, Northamptonshire, UK). After the conductive epoxy was cured (24 h at room temperature), the PVDF-ITO thin film around the brass tube was trimmed. The wall thickness of the brass tube was 0.5 mm resulting in a transparent window with a diameter of 1 mm through the brass tube (Figure 1). The core of a micro-coaxial electrical cable tip was connected onto the top surface of the PVDF-ITO thin film, whilst its outer electrical wire was connected to the brass tube wall with the electrically conductive epoxy. The other tip of the electrical cable was connected to a BNC connector which was then connected to a pre-amplifier (SPA.1411, Spectrum Instrumentation, Grosshansdorf, Germany). The transmittance of light (400-800 nm) through the sensor was measured using a custom spectrophotometer. The frequency response of the transparent ultrasound sensor was characterised by detecting optically generated ultrasound signals from a black tape.
## 2 Materials and Methods
### Cell Viability  
Cell viability was assessed using the colorimetric MTT assay. RAW 264.7 macrophages were seeded into a 96-well plate at a density of 6.25 \(\times\) 10\({}^{4}\) cells per well and exposed to various concentrations (ranging from 0 to 100 ug/mL) of both ACE and the EtOAc fraction. Following a 24 h incubation with the samples, a solution of MTT (500 ug/mL) was introduced to the cells and allowed to incubate at a temperature of 37 \({}^{\circ}\)C for 90 min. Subsequently, the MTT solution was removed, and any resulting insoluble formazan dye was dissolved using dimethyl sulfoxide. The absorbance at 570 nm was then quantified through spectrophotometric analysis employing a Thermo Fisher Scientific spectrophotometer (Waltham, MA, USA).