esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization
## 3 Optimization Methods
### Reference Methods
#### 3.2.3 Scmopso  
The SCMOPSO algorithm was designed to solve the VND MO wireless network sensor deployment by [PERSON] et al. in [30]. It works with a population of particles that are clustered into different so-called classes based on the dimension of individual particles. The movement of a \(p\)-th particle is driven by the so-called exemplar. The exemplar is selected from the other particles that are in the same class as particle \(\mathbf{u}_{p}\). The particles with better values of the objective function are favored to be selected as exemplars. If a particle does not improve for a pre-defined number of iterations, it can move to another class. However, there is a restriction on the minimal size of the class. The new class is selected based on the probability density function that is constructed over the values for one randomly selected objective function for the best particles from different classes. Finally, SCMOPSO adds the mutation operation to the PSO algorithm. A randomly chosen variable of a particle can be mutated with a certain probability, which means that the variable will be generated randomly in the feasible decision space.
# A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities:
## 4 Hansel Services
### GNSS Orbit Visibility Service (VIZ)  
The orbit service is in charge of providing the visibility status of the _testbed_ in terms of GNSS satellites. It routinely fetches the most recent broadcast ephemeris that contains the orbital parameters for all satellites of the GPS, Galileo, Beidou, and GLONASS systems and generates the information to display the satellite visibility in terms of azimuth and elevation angles (so that a skylpot can be plotted). A skylpot can be generated according to any input reference position provided by the testbed operator or it can be generated according to the testbed location. The HANSEL testbed is indeed equipped with a high-end GNSS receiver to provide local observations and measurements from the testbed location.  
This service allows also to override the broadcast ephemeris file fetched by the service in favour of other broadcast files provided by the user or SP3 files. In addition, while the visibility is computed in the location and local time of the _testbed_, other locations and epoch ranges can be specified.
## 3 Results
### Fault Diagnosis
#### 3.2.4 Time-Synchronous Average (TSA)  
While fault frequency components have already been recognized thanks to the techniques applied so far, in order to accomplish a comprehensive and detailed study it is necessary to consider pre-processing techniques with the aim of denoising and filtering the raw signal to remove the presence of noise and masking components unrelated to faults, such as the harmonic series of the frequencies at 160 Hz and 505 Hz, making fault signatures even more easy to detect.  
The following techniques will be applied, re-starting each time from the original signal, and will be followed by the use of the SES to make a consistent comparison among their effect.  
The Time-Synchronous Average is a common time-domain diagnostic tool used as a pre-treatment technique to remove the deterministic components from the signal, bringing to light in the residual part those connected to bearing faults, which are cyclostationary; for the signal under test, the average has been made on time intervals that are synchronous to the shaft rotation for bearing 3 and to the cage rotation for bearing 4. The resulting graphs  
Figure 24: SES applied to the signal from sensor 6 on bearing 3 (last 49 s).  
Figure 25: SES applied to the signal from sensor 7 on bearing 4 (last 163 s).  
(Figures 26 and 27) are similar to those already obtained (Figures 24 and 25, respectively), with no added information, making the technique not useful.
## 4 Results
### Data of Bands with a Spatial Resolution of 10 m  
#### 4.1.1 Multivariate Analyses  
The results of multivariate analyses are presented in Figure 3. In Figure 3, we can see the dispersion and the correlation matrix. In the correlation matrix, the values range from \(-1\) to \(1\); the values closer to \(0\) indicate low correlations. The symbol (positive or negative) indicates if there is a positive or negative correlation between the variables. Moreover, the significance of the correlation, in terms of p-value is indicated in Figure 3. We can identify in Figure 3a the results of including all soil types, in Figure 3b the results for clay soil, in Figure 3c the results for sandy soil, and in Figure 3d the results for sandy-clay soil. According to the results, we can affirm that it is better to analyze the soils individually. For clay soil, band 4 has a negative correlation, and band 8 has a positive correlation, both statistically significant. Regarding clay-sandy soil, only band 8 correlates with the soil moisture values. In this case, is a positive and strong correlation. For the sandy soils, no correlation is found.
## 4 Method of Case Study
### Assumption-Setting
#### 4.2.2 Navigation Area, Bridge Manning and Autonomy Level  
The STCW Convention requires proper arrangement for watchkeeping personnel following the situation considering the limitation of individuals' qualifications or fitness (A-VIII/2 Part3). Based on that, the bridge manning level is provided in the Safety Management System (SMS) developed by the shipping company individually in accordance with Chapter IX of the SOLAS Convention and the ISM Code. The level decides the arrangement for watchkeeping, depending on, for instance, the navigational condition and navigation area. The style and contents have not been unified and standardised; some companies use three categorised colours, red, yellow and green. Other companies use four categorised number style [42]. As an international guide, ICS [22] shows an example of a bridge manning matrix. The matrix categorises the manning level by three factors, navigation area (port(entering and leaving), restricted water, coastal water, ocean water and anchorage), visibility (clear and restricted) and daylight (daylight, darkness and night). This study utilised ICS's manning matrix and excluded the port area (entering and leaving) and restricted water since these areas require complicated navigation, including frequent communication with vessel traffic service (VTS) centre and other vessels and pilots. It finally chose a coastal area with clear visibility and daytime based on the project's information described in Section 4.2. According to the matrix, the manning of this case study is the team of OOW (i.e., [PERSON]) and Lookout. Both of them were assumed to be on duty at RCC to simplify the situation.  
Emergencies such as search and rescue (S&R), a fire onboard and a cyberattack were excluded from this study to avoid complex factors of watchkeeping.  
It was also assumed that this case study's autonomy level was the same as the current common ships to minimise the variables to be considered. That means that [PERSON] navigates the ship with no autonomy at RCC.
# A Study of the Thermodynamic Properties of Nd-C and Ce-C TRISO Fission Product Binary Systems
## 2 Materials and Methods
### Crucible Selection  
Initially, alumina crucibles were employed with a thin layer of alumina powder applied to the crucible inner surface to prevent sample-crucible adhesion, which was applied successfully in previous campaigns [44]. Calibrations were completed successfully but chemical interactions occurred between the alumina crucibles, alumina powder, and the 10 mol%C and 90 mol%Nd sample. This was confirmed through review of the erroneous DSC signal response and visual confirmation of an interaction between the sample and crucible, as shown in Figure 6.  
The specimen could not be removed from the crucible, and the crucible developed a permanent blue and black discoloration through to the outer surface. Further experiments with alumina were halted and boron nitride (BN) crucibles were instead employed (sourced from Netzsch product no. NGB808836, Bavaria, Germany) along with boron nitride powder placed in the interior to prevent adhesion (sources from Sigma Aldrich product no. 255475-50G boron nitride powder, 1 \(\upmu\)m, 98%, St. Louis, MO, USA). Boron nitride crucibles have been shown to perform successfully with rare earth carbides in the open literature [23; 28]. Other crucibles were not considered due to potential chemical interactions. While the BN powder prevented adhesion, it also acted as a thermal barrier between the specimen and DSC thermocouple, significantly dampening the DSC peak response and hindering accurate phase transition identification. Therefore, the BN powder was not used, resulting in the samples adhering to the crucibles but providing clean and distinct DSC phase transition responses. A new crucible was employed for each test, which greatly added to the cost of the experimentation.  
Figure 6: Optical images of Nd1, 10 mol%C and 90 mol%Nd post-DSC test using alumina crucibles and alumina powder: (**a**) Nd1 Post-DSC Crucible Interior. (**b**) Nd1 Post-DSC Crucible Exterior.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 4 Proposed Approach
### Preliminaries
This enriched representation allows for a nuanced understanding of the edges, offering insights into the temporal patterns, intensity of connections, and spatial dynamics among significant locations._
* \(f_{\mathcal{M}}:(\mathcal{V}\cup\mathcal{E})\rightarrow\mathcal{P}(\mathcal{M})\) _is a function that associates each vertex_ \(v\in\mathcal{V}\) _or each edge_ \(e\in\mathcal{E}\) _with a subset of contextual metadata elements from the power set_ \(\mathcal{P}(\mathcal{M})\)_, which is the set of all subsets of_ \(\mathcal{M}\)_. This association allows for a highly flexible and comprehensive enrichment of each graph component, whether points of interest or their connections, with a wide range of contextual information._  
**Definition 2**. : _Geographical Coordinate is a tuple that contains latitude and longitude. This can be formally represented as follows:_  
\[c=\langle\text{lat},\text{lng}\rangle\mid\text{lat}\in\mathbb{R},\text{lng}\in \mathbb{R}\]  
**Example 1**. : _Consider a park bounded by the following geographical coordinates marking each of the four corners, thereby forming a trapezoid that outlines the park's perimeter:_  
(48.8566, 2.3522) (48.8568, 2.3624)  
**Definition 3**. : _Hierarchical Category consists of an ordered list of strings describing the category of the significant location at different levels of granularity. This is formally defined as follows:_  
\[CA=[c_{1},c_{2},...,c_{n}]\]  
_where_  
* \(c_{1}\) _represents the most general category, indicating the broad classification under which the significant location falls._
* \(c_{n}\) _is the most specific detail within the category, providing the finest level of classification detail for the significant location._
* _Each_ \(c_{i}\) _(for_ \(1<i\leq n\)_) refines the description provided by its predecessor, thereby offering a more detailed and nuanced classification at each subsequent level._  
**Example 2**. : _Using the North American Industry Classification System (NAICS) as an example, the hierarchical category structure can be illustrated as follows:_  
\[CA=[\text{``NAICS:452110''},\text{``Restaurant''},\text{``Italian''}]\]  
_where_  
* _NAICS:452110 identifies the entity within the general category of \"Full-service restaurants\"._
* _Restaurant further refines this category to denote the type of establishment._
* _Italian adds an additional level of specificity, indicating the specific cuisine offered by the restaurant._
## 3 Construction of Carbon Steel Corrosion Prediction Model
### Verification of Prediction Model
#### 3.2.1 BP (Backpropagation) Neural Network Prediction Model  
BP neural networks, also called backpropagation networks, were proposed in the late 1980s, when there was a calculation error lag on the Internet. To improve transmission, it propagates in the opposite direction after reaching the input layer through the calculation [29].  
We separated the experimental data into training and test sets according to a certain proportion. If the training set is too small, insufficient training strength is likely to occur. If the training set is too large and the test set is relatively small, the model cannot be verified. Therefore, 80% of the total data was selected as the training set, and the remaining 20% as the test set to build the model. According to the experimental data, five influencing factors were received as inputs, and the uniform corrosion depth of the carbon steel served as output. Therefore, the input layer had five neurons, and the output layer had one neuron. Determining the number of neurons in the hidden layer is crucial to the accuracy of the model, which is calculated according to the following empirical formula:  
\[l=\sqrt{m+n}+a \tag{7}\]  
where \(l\) is the number of hidden layer neurons; \(m\) is the number of neurons in the input layer; \(n\) is the number of neurons in the output layer; and \(a\) indicates the empirical parameter, \(1\leq a\leq 10\).  
Figure 9: Predicted corrosion depth of carbon steel and error curve.  
We found that using the BP neural network prediction model achieved better accuracy with nine hidden layer neurons. According to the experimental sample size and desired accuracy, the maximum number of iterations was set to 300, with an expected error of 0.0001 and a learning rate of 0.01.  
Due to the inherent randomness in the training process of machine learning algorithms, the results vary each time. In this study, the model was trained 30 times, and the top five results were selected for plotting the predicted corrosion depth of carbon steel and the error curve, as shown in Figure 10.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 4 Analysis of Dynamic Response Characteristics of the Transmission System
### Load-Sharing Coefficients  
Using the previously described method in Section 3.4, the dynamic meshing forces of the transmission system were utilized to calculate the LSCs based on the analysis of the dynamic response.
## 4 Discussion  
From an increasing number of experiments and analysis, the performance of crop classification can be improved remarkably based on multi-temporal quad-pol SAR data. Nowadays, a great number of spaceborne SAR systems launched into orbit around the Earth can enhance the revisiting period of satellite constellation and obtain a growing amount of real data, which provides a tremendous chance for multi-temporal data analysis. Additionally, the wide application of neural network in remote sensing has shown great abilities. Based on these two attentions, this paper attempted to divide two steps which are dimensional reduction based on NCSAE and then classification with MSCDN to achieve the crop classification. The summary for experimental results of Section 3 is discussed in the following.
# Optimization of Redundant Degrees of Freedom in Robotic Flat-End Milling Based on Dynamic Response
## 2 Redundant Degrees of Freedom Optimization in Robotic Milling
### Solution of Redundant Degree of Freedom Optimization Model
#### 2.3.4 Dynamic Response Index in Robotic Milling  
Under ideal conditions, if there is no chatter and the motion is described by \(\mathbf{x}(t)=\mathbf{x}_{\text{d}}(t)\), the corresponding Equation (10) can be represented as  
\[\mathbf{M}(\mathbf{\theta})\mathbf{\tilde{x}_{\text{d}}}(t)\mathbf{+C}( \mathbf{\theta})\mathbf{\dot{x}_{\text{d}}}(t)\mathbf{+K}(\mathbf{\theta}) \mathbf{x}_{\text{d}}(t)\mathbf{=G}(t) \tag{31}\]  
By combining the cutting forces and the predicted frequency response function, the tool displacement can be calculated, as in \(\mathbf{x}_{\text{d}}(t)=\begin{bmatrix}\mathbf{x}_{\text{d}}(t)&\mathbf{y}_{ \text{d}}(t)\end{bmatrix}^{\text{T}}\).  
In robotic milling, the actual paths of the teeth are affected by the vibrations of the tool. If the cutting process is stable, the actual path of the \(j\)-th tooth will be determined by [43].  
\[\mathbf{x}_{j}(t)=\begin{bmatrix}x_{j}(t)\\ y_{j}(t)\end{bmatrix}=\begin{bmatrix}x_{\text{d}}(t)\\ y_{\text{d}}(t)\end{bmatrix}+\begin{bmatrix}-R\sin(\frac{2\pi(j-1)}{N}+\frac{2 \pi\Omega}{\Theta 0}t)\\ -R\cos(\frac{2\pi(j-1)}{N}+\frac{2\pi\Omega}{\Theta 0}t)\end{bmatrix}+ \begin{bmatrix}v_{f}t\\ 0\end{bmatrix} \tag{32}\]  
where \(R\) is the radius of the tool, \(N\) is the number of tool teeth, and \(\Omega\) is the spindle speed, and \(v_{f}\) is the feeding velocity.  
According to Equation (32), the dynamic response index can be determined by extracting the vibration signal peaks to evaluate the machined surface quality, as follows:  
\[\text{DRI}=\frac{1}{L}\sum_{l=1}^{L-1}\left|(P_{l+1}-P_{l})(y_{l+1}^{P}-y_{l}^{ P})\right| \tag{33}\]  
where \(P_{L}\) is the peak vector of the vibration signal, and \(L\) is the total number of elements in the peak vector.
## 2 Data and Methodologies  
### Data  
ESMs data. Four ESMs (BNU-ESM, IPSL-CM5A-LR, MPI-ESM-LR, and NorESM1-ME) were used to assess climate change impacts on NPP and NEP in China. The selection was based on the availability of monthly NPP and NEP covering the period of enhanced global warming targets for the representative concentration pathway (RCP) 4.5 and RCP8.5 scenarios, and the approximate classification of vegetation function types to eliminate the effect of land cover as far as possible and to be capable of intercomparison. The RCP4.5 and RCP8.5 describe greenhouse gas concentration trajectories and are the latest generation of scenarios for driving climate models since the Fifth Assessment Report (AR5) of IPCC. They are the products of an innovative collaboration between integrated assessment models, which provide an important approach to study future climate change and its potential effects [35]. In consideration of the transient response of ecosystems to climate change and dynamic simulation, we used RCP4.5 and RCP8.5 scenarios to assess the impact of 1.5 \({}^{\circ}\)C and 2 \({}^{\circ}\)C warming above the pre-industrial level on terrestrial ecosystems in China. The monthly mean NPP, NEP from 1961-2099 for each ESM were obtained from the program for climate model diagnosis and intercomparison ([[https://esgf-node.llnl.gov/projects/cmip5/](https://esgf-node.llnl.gov/projects/cmip5/)]([https://esgf-node.llnl.gov/projects/cmip5/](https://esgf-node.llnl.gov/projects/cmip5/))). The key features of the ESMs and CEVSA2 model are summarized in Table 1.  
Input data for the CEVSA2. The CEVSA2 model is driven by climatic data at the 10-day time step, including temperature, precipitation, cloud cover, and relative humidity. The data sets were obtained from RegCM 4.0 model driven by BCC_CSM1.1, the latest version of the regional climatic system model developed by the National Climate Center of China [41]. All of these climate data were downscaled to a spatial resolution of 0.1 degrees by 0.1 degrees using the ANUSPLIN software with version 4.3 [42]. Soil types and soil texture were obtained from the 1:14,000,000 digital map of soil texture in China [43]. The vegetation distribution map was reclassified and re-sampled to match with other datasets based on the Global Land Cover 2000 database (GLC2000, European Commission Joint Research Centre, 2003, [[https://forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php](https://forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php)]([https://forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php](https://forobs.jrc.ec.europa.eu/products/glc2000/glc2000.php))). All these spatially explicit datasets are at a resolution of 0.1 degrees. Historical and future atmospheric CO\({}_{2}\) concentrations were downloaded from the Data Distribution Centre of IPCC ([[http://www.ipcc-data.org](http://www.ipcc-data.org)]([http://www.ipcc-data.org](http://www.ipcc-data.org))).
# An Empirical Study on Software Defect Prediction Using CodeBERT Model
## 7 Discussion
### The Relationship between Prediction Pattern and Buggy Rate  
The empirical results of RQ2 indicate that CodeBERT-PK and CodeBERT-PS outperform CodeBERT-PT significantly. The key difference between them is the extra information from the keywords and sentences.  
We investigate the relationship between keywords used in CodeBERT-PK and bug distribution, as shown in Table 7. We first calculate the occurrences of keywords and exclude some unrelated cases that may confuse the statistics, e.g., \"debug\", \"prefix\", and \"postfix\". We then count the number of samples that includes the keyword at least once and check whether it is a buggy sample or a clean sample. The buggy rate include the keywords is also calculated accordingly. The results show that the buggy rate of samples that include keywords is higher than the average buggy rate of 35.1%. The keyword-based prediction pattern highlights the relationship between the keywords and the prediction results, implying that source code that includes the keywords is more likely to be buggy. This may be the reason why CodeBERT-PK could outperform CodeBERT-PT in software defect prediction. The fact that the prediction performance of CodeBERT-PS outperforms or is similar to CodeBERT-PK indicates that the pre-trained CodeBERT model is capable of understanding natural language sentences that are strongly linked with the source code, which is proven in previous studies on the CodeBERT model. Specifically, the experimental settings of the CodeBERT-PS model could help in establishing implicit links among the purpose space (software defect prediction tasks), code semantic space (structural and semantics of the source code), and naturals text space (function names, variable names, etc.)
# Ship's Digital Twin--A Review of Modelling Challenges and Applications
## 4 Applications
### Remote Control and Monitoring  
Paper [24] presented a DT of a scaled ship model that uses a DT to control the model remotely by adjusting the set points through a DT interface. There were four possible applications of the DT proposed in [24], ranging from monitoring functionality, identifying the ability of the DP system to maintain position, predicting ship behaviour in future operations, and simulating various parameters not considered during exploitation. The tests of the concept were performed in the Numerical Offshore Tank at the University of Sao Paulo, where measured variables were directly used for 3D visualisation of a DT's movement on operator screen.  
One of the few applications of the DT concept in real-time was presented in [14], proposing a simulation and remote-control centre concept utilising real-time data transfer from a ship and ship crane system to an onshore simulation and control centre for the purpose of remote control, monitoring, and crew assistance.