esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reliability Engineering Approach for Managing Risks in CubeSats
## 5 Conclusions
Such socio-technical perspective could be particularly relevant for other project fields such as mission analysis and project management, that are not object-oriented. Both these areas may introduce bias on the project or induce failures, (e.g.,): a wrong budget cost estimation may imply a change in the used material, with effect on the mass budget or component properties; or discrepancies in the documentation may generate delays, pushing for excessive workload and production pressure in the operators, with potential critical consequence on the manufacturing process [44]. Following recent trends in the development of the theory of Resilience Engineering, and more generally, systemic risk and safety approaches, it would be possible to accounts for the dynamics of the system response, from a disruption to the after-shock scenario [45]. These methods will allow for interpreting the complexity of a system, overcoming the shortcomings arising from the reductionist approaches focused on decomposition and linear thinking. Conceptualization, F.C., R.P. and F.P. ; Data curation, F.I., A.M. and L.P.; Formal analysis, F.I., A.M. and L.P.; Investigation, P.M.; Methodology, R.P. ; Project administration, F.P. ; Resources, P.M. and F.P. ; Supervision, F.C. and F.P. ; Validation, P.M. and R.P. ; Visualization, A.M.; Writing--original draft, A.M., F.I., L.P. and R.P. ; Writing--review & editing, F.C., P.M., R.P. and F.P. This research received no external funding. The presented risk analysis methodology has been applied to the IKUNS (Italian-Kenyan University NanoSatellites) project for the preliminary design of University nano-satellites at Sapienza--University of Rome. The IKUNS project is managed by the Italian Space Agency (ASI), which the authors gratefully acknowledge. The authors declare no conflicts of interest.
## 5 Analysis of Influence Factors
### Influence of Stratum Conditions
#### 5.1.2 Influence of Cohesion  
As can be seen from Figure 22, when the cohesion decreases from 2.0 c to 0.5 c, the changing trend of horizontal displacement of the pile, settlement, and axial force are similar to the change of elastic modulus from 2.0 E to 0.5 E. The shape of the curve is basically unchanged. When the cohesion increased from 1.0 c to 2.0 c, the maximum values of horizontal displacement, settlement and axial force decreased by 10.8%, 16.7%, and 2.3%, respectively.  
It can be concluded that when the stratum conditions are poor, the spatial effect is more obvious and the elastic modulus has a greater influence than the cohesion. At the same time, the elastic modulus has a greater influence on the pit deformation and the internal force of the structure than the cohesion. It is suggested that the elastic modulus and cohesion of the soil should be increased to 1.5-2.0 times the initial value when the soil is reinforced by grouting.
## 1 Introduction
For instance, artificial neural networks have been employed to model the relationship between excitation and rocking response [16]. In [17], supervised machine learning methods were proposed to classify and predict rocking responses under various ground motion properties. While these models can efficiently predict rocking responses and overturning risks, their accuracy depends on large amounts of training data obtained from traditional numerical simulations or equivalent model analyses [18; 19], which are computationally intensive and time consuming. Therefore, there is a need to develop an efficient method for analyzing the rocking response of freestanding packages. Such a method could provide the necessary training data for formulating intelligent models, thereby improving computational efficiency and analytical insights. In this study, the freestanding package is simplified and modeled as a rigid block placed on a moving base. The possible motion states under various conditions are analyzed and determined, and the specific conditions leading to each motion state are explicitly identified. Furthermore, an efficient algorithm for analyzing rocking response is proposed, incorporating a time transformation method. Compared with the conventional fourth-order Runge-Kutta method with event detection, the proposed algorithm enables the accurate determination of package collision time instants while employing larger time intervals. This approach significantly improves computational efficiency in evaluatingrocking responses and overturning risks of freestanding packages while maintaining satisfactory analysis accuracy. The proposed algorithm enables the comprehensive analysis of rocking responses for freestanding packages across diverse parameters and excitation conditions. This computational approach generates extensive training datasets, which can subsequently be utilized to develop machine learning models for predicting package rocking behavior and assessing overturning risks.
## 6 Conclusions, Future Works and Limitation
### Conclusions  
This paper presented a comprehensive survey on the design of the IRS as a promising technology for 5G wireless networks and provides a detailed overview on several IRS applications in wireless communication. The paper built upon the existing knowledge that demonstrated the importance of IRS in the design of a wireless communication network. Highlighting the IRS as a key technology in achieving a smart radio environment demonstrated the potential of an IRS to aid in the joint optimization of the transmitter, receiver and propagation channel. Particularly, this survey provided a detailed discussion on the IRS technology covering its system architecture, internal architecture, performance limits and deployment strategies and also highlighted the potential scenarios that can benefit from a smart radio environment. Furthermore, key limitations, open research opportunities and future directions have also been discussed.  
**Author Contributions:** The conceptualization, methodology and original manuscript draft preparation was done by F.C.O., Q.Z.A. and F.A.K. The validation, supervision, manuscript editing, manuscript review and funding acquisition was done by Q.Z.A., W.B.A., F.C., S.A.R.Z. and T.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
###### Abstract  
In order to combat the difficulty of fault feature extraction and fault recognition in the field of bearing fault diagnosis, a bearing fault diagnosis method based on improved variational mode decomposition (VMD) and parallel hybrid neural network is proposed, which combines reweighted kurtosis (RK) with variable mode decomposition (VMD) and uses reweighted kurtosis as the evaluation index to select the decomposition times of variational mode decomposition, while removing part of the interference in the fault signal and retaining its impact characteristics. Afterwards, the processed fault data set is brought into a parallel hybrid neural network model with a global average pooling layer (GAP) for feature extraction, feature fusion, and fault classification. The parallel hybrid neural network model can extract fault signal features more comprehensively and improve the accuracy of fault diagnosis, while the global average pooling layer can speed up the training and testing. Experiments on the Xian Jiao Tong University (XJTU) and Case Western Reserve University (CWRU) bearing public data sets show that the diagnosis accuracy reaches 99.72% and 99.73%, respectively, indicating that the method has good fault diagnosis accuracy and better diagnosis performance compared with other models.  
g +
Footnote †: journal: applied sciences
## 3 Results
### Clinical Outcomes Variables
#### 3.4.1 Plaque Index (PII)  
A greater overall PII reduction was noted in the test group as compared to the control group, after 3 (0.28 vs. 0.13, respectively) and 6 weeks (0.33 vs. 0.14, respectively), with statistically significant intergroup differences at week 3 (2.43 vs. 2.65, respectively, \(p\) = 0.028) and at week 6 (2.38 vs. 2.64, respectively, \(p\) = 0.011) (Table 4). The same was true for the lingual and proximal areas. Additionally, intragroup analyses in the test group showed statistically significant PII decreases between baseline and week 3, and baseline and week 6 (Table 4), including buccal, lingual and proximal areas.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & **Test Group** & **Control Group** & **Mean** & **95\% CI Bounds** & \(p\) \\ \cline{2-7}  & **Mean (SD)** & **Mean (SD)** & **Difference** & **Lower** & **Upper** & \\ \hline
**n** & 29 & 26 & & & & \\ \hline
**All sites** & & & & & & \\ \hline Baseline & 2.72 (0.40) * & 2.78 (0.36) & \(-\)0.069 & \(-\)0.27 & 0.14 & 0.514 \\ \hline
3 weeks & 2.43 (0.36) & 2.65 (0.33) & \(-\)0.215 & \(-\)0.40 & \(-\)0.02 & **0.028** \\ \hline
6 weeks & 2.38 (0.43) ** & 2.64 (0.28) & \(-\)0.265 & \(-\)0.46 & \(-\)0.06 & **0.011** \\ \hline Change BL-3w & 0.28 (0.37) & 0.13 (0.39) & 0.146 & \(-\)0.06 & 0.35 & 0.167 \\ \hline Change BL-6w & 0.33 (0.44) & 0.14 (0.40) & 0.195 & \(-\)0.03 & 0.42 & 0.095 \\ \hline
**Buccal** & & & & & & \\ \hline Baseline & 2.70 (0.52) * & 2.68 (0.45) & 0.013 & \(-\)0.25 & 0.28 & 0.921 \\ \hline
3 weeks & 2.44 (0.47) & 2.52 (0.43) & \(-\)0.078 & \(-\)0.32 & 0.16 & 0.530 \\ \hline
6 weeks & 2.39 (0.49) ** & 2.53 (0.38) & \(-\)0.142 & \(-\)0.38 & 0.09 & 0.243 \\ \hline Change BL-3w & 0.25 (0.43) & 0.16 (0.50) & 0.091 & \(-\)0.16 & 0.34 & 0.477 \\ \hline Change BL-6w & 0.30 (0.55) & 0.15 (0.46) & 0.154 & \(-\)0.12 & 0.43 & 0.270 \\ \hline \end{tabular}
\end{table}
Table 4: Plaque index (PII) analyzed by means of repeated measures ANOVA and Student’s \(t\)-test.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline  & & **Test Group** & & **Control Group** & \(p\) \\ \hline  & **n** & **Mean (SD)** & **Median (IQR)** & **n** & **Mean (SD)** & **Median (IQR)** & \\ \hline
**Removes dental plaque** & 30 & 2.98 (0.63) & 3.00 (0.63) & 29 & 3.34 (0.58) & 3.00 (1.00) & **0.017** \\ \hline
**Stronger teeth feeling** & 30 & 3.03 (0.49) & 3.00 (0.13) & 30 & 3.15 (0.60) & 3.00 (0.50) & 0.321 \\ \hline
**Stronger gum feeling** & 30 & 3.18 (0.56) & 3.00 (0.50) & 29 & 3.10 (0.71) & 3.00 (0.50) & 0.911 \\ \hline
**Gum relief feeling** & 30 & 3.18 (0.51) & 3.00 (0.50) & 30 & 3.30 (0.61) & 3.00 (1.00) & 0.437 \\ \hline
**Overall rating** & 30 & 3.05 (0.59) & 3.00 (0.50) & 30 & 3.33 (0.46) & 3.00 (1.00) & 0.077 \\ \hline
**Buy the product** & 30 & 3.05 (0.68) & 3.00 (0.63) & 30 & 3.21 (0.50) & 3.00 (0.50) & 0.371 \\ \hline
**Recommend the product** & 30 & 3.15 (0.61) & 3.00 (0.63) & 30 & 3.20 (0.50) & 3.00 (0.50) & 0.760 \\ \hline
**Product evaluation** & 19 & 7.65 (1.10) & 8.00 (1.00) & 18 & 8.00 (1.28) & 8.00 (2.00) & 0.558 \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._
# Real-Time Prediction of Operating Parameter of TBM during Tunneling
## 2 Methodology
### Algorithm for Real-Time Model Construction  
The prediction model based on ARIMAX should be optimized automatically in every WF step to implement the WF. In accordance with this need, we develop an algorithm for model construction without the help of any human judgment (Figure 2). In the first stage, the tuning space of hyperparameters p, d, q, and r pertaining to ARIMAX must be specified. Giving a dataset to the algorithm, the \(i\)-th (\(i=1,\,2,\,\ldots,N\)) hyperparameter set chosen from grid-search is used for model training. This process is repeated until N candidate models are built. Among N models, only the one with the lowest Akaike information criterion (AIC) [22] is finally selected as the predictive model.  
Figure 1: Example of real-time prediction procedure for walk forward (WF) in project A-1.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 1 Introduction
Overall, passive microwave remote sensing-based FT detection algorithms can be used to provide enhanced information on the FT state on a large spatial scale with a rapid revisit interval of mostly 2-3 days, especially with the support of L-band spaceborne missions. However, there is also a weakness of these series of products in that the spatial resolution is limited to only the level of tens of kilometers, which hinders the further exploration of the primary contributions to the FT signal, including the soil, snow, and vegetation states [20]. In comparison, active microwaves are not yet widely used but have potential and advantages for FT state monitoring. Active microwave sensors, including synthetic aperture radars (SAR) and microwave scatterometers, can accurately obtain the backscattering coefficient of the surface, which has a strong correlation with the physical properties of the soil, such as the dielectric constant that is proportional to the soil water content. With the continuous development of SAR, such as the C-band Sentinel-1 mission, which continues delivering high-resolution data, SAR has shown great potential in geographic information detection and its value in the process of monitoring freezing and thawing conditions. This study attempts to explore FT state monitoring possibilities by combining the advantages of existing robust FT retrieval algorithms and C-band SAR data over the region of Eastern Nunavik (Quebec, Canada). In Nunavik, permafrost landscapes are transforming, mainly triggered by increasing summer air temperatures. Recent and future global warming will result in widespread changes, also referred to as degradation of permafrost, including, e.g., thermokarst lakes, greening, and topographic deformation. In this work, the FT state is estimated from the Sentinel-1 database using a seasonal threshold approach and evaluated through comparison with temporally overlapping in situ measurements of soil temperature from different depths within the top 10 cm of soil. This work is expected to test the functionality of the usage of the discriminant algorithm with C-band data for the monitoring of the FT state over the transforming permafrost landscape. Furthermore, the differences in the sensitivity of Sentinel-1 in areas with different surrounding environments, as well as at different surface soil depths, are explored in higher detail, the influence of related variables such as land cover are discussed, and the algorithm is refined, including an optimization process of the threshold.
# Vehicular Visible Light Communication for Intersection Management +
## 3 V-VLC Vehicular Communication
### Mesh Cellular Hybrid Structure and Multi-Vehicle Cooperative Localization
The response includes both the infrastructure and the vehicle identifications and the \"confirmed vehicle\" message. Once the response is received (message distance in Figure 4b), the vehicle is required to follow the provided occupancy trajectories (footprint regions, see Figure 2). If a request has any potential risk of collision with all other vehicles that have already been  
Figure 4: (**a**) Graphical representation of the simultaneous localization as a function of node density, mobility and transmission range. (**b**) Design of the state representation in the west arm of the intersection, with cells length. approved to cross the intersection, the control manager only sends back to the vehicle (V2I) the \"response\" after the risk of conflict is exceeded. A route is a typical path that a vehicle follows as it approaches (request and message distances) and traverses the intersection. From a digital map (Figure 4), we automatically extract a set of attributes that characterize an intersection: the poses, \(q_{i}\) (\(x\), \(y\), \(t\)), the route and the traffic rules (stop, give way). Three types of information \(q_{i}\) (\(t\)), \(q_{i}\) (\(t\), \(t^{\prime}\)) and \(q_{ij}\) (\(t\)) compose the basic elements of a pose graph for multi-vehicle cooperative localization [29], \(t\) and \(t^{\prime}\) are the request and cross times. An indirect V2V relative pose estimation method is proposed in Figure 4b. Here, when there are two neighboring vehicles, the geometric relationship between them can be indirectly inferred via a chain of geometric relationships among both vehicles' positions and local maps. For two neighboring vehicles, both having self-localization ability (streetlamps I2V), we can perform local Simultaneous Localization and Mapping. The follower vehicle can be localized by itself, as in single-vehicle localization, \(q_{i}\) (\(t\)), and can also be localized by combining the localization result of the vehicle leader and the relative localization estimate between the two vehicles, \(q_{ij}\) (\(t\)). For a vehicle with several neighboring vehicles, it uses the indirect V2V relative pose estimation method to estimate the relative pose of each neighboring vehicle one by one. Here, each streetlamp (I2V transmitter) sends a message, received and processed by a SiC receiver, at the vehicle's rooftop. By means of the headlights (transmitters), the information is resent to the leader vehicle (V2V) [25,31] or a \"request\" message to go forward or turn right or to turn left is sent directly to the receiver (V2I), at the traffic light. For crossroad coordination, a local controller located at the traffic light analyses the potential conflict points and sends a \"response\" to the approaching vehicles. The use of both navigation and lane control signs to communicate lane restrictions is demanding. Downstream from the request distance, lane restrictions should be obeyed.
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 3 Results
### Model Performance with Different Sets of Input Features
#### 3.2.4 Climate Data  
We further trained all six machine learning models using only climate-related variables. We report the R\({}^{2}\) and RMSE of the models in Figure 8. The derived R\({}^{2}\) ranges from 0.572 to 0.842. The R\({}^{2}\) and RMSE of GWRFR are 0.842 and 0.967 MT/ha, respectively. The results also illustrate that GWRFR outperforms other models such as MLR, PLSR, SVR, DTR, and RFR by 0.03-0.27 in terms of R\({}^{2}\). Again, MLR and PLSR have poor performance in terms of R\({}^{2}\) (0.572 and 0.566, respectively) and RMSE (1.589 and 1.599 MT/ha, respectively). GWRFR improves the yield prediction by 0.03 in terms of R\({}^{2}\) and 0.093 MT/ha in terms of the RMSE when compared with its non-spatial version, RFR (R\({}^{2}\) = 0.809 and RMSE = 1.061 MT/ha).  
Figure 7: The scatterplots of the actual yield and the predicted yield derived from (**a**) MLR, (**b**) PLSR, (**c**) SVR, (**d**) DTR, (**e**) RFR, and (**f**) GWRFR using only GPP data.  
The scatterplots of the observed and predicted yield values are also presented in Figure 8. As compared to previous feature sets, more cases of underestimations and overestimations can be observed now in the scatterplots; however, GWRFR can still perform consistently better than other models.
## 5 Discussion
### Component Evaluation  
As shown in Table 4, by comparing the experimental results of the three datasets provided by the ISPRS benchmark with the terrestrial truth model. The data TUB1 detected 10 rooms with 98.7% correctness and 99.2% completeness of the 2D floor planes. The data TUB2 detected seven rooms with 98.1% correctness and 95.2% completeness of the 2D floor plan. The data UOM detected seven rooms with 98.3% correctness and 93.6% completeness of the 2D floor plan. Table 5 gives the results of the door detection and room connectivity assessment, and the correctness and completeness of the room door detection was greater than 86% in all rooms.  
For the remaining four groups of indoor environments without real surface data, the generated models are compared with the point cloud to analyze and evaluate the navigation element extraction results. As shown in Table 6, all four groups of experimental room segmentation results are the same as the correct results, and the correctness and completeness of the results of door detection is greater than 80%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Real Room Number** & \begin{tabular}{c} **Detected Real Room** \\ **Number** \\ \end{tabular} & \
\begin{tabular}{c} **Correctness** \\ **on 2D Floor Plane** \\ \end{tabular} &
\begin{tabular}{c} **Completeness** \\ **on 2D Floor Plane** \\ \end{tabular} \\ \hline TUB1 & 10 & 11 & 0.987 & 0.992 \\ TUB2 & 10 & 9 & 0.981 & 0.952 \\ UOM & 7 & 7 & 0.983 & 0.936 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Room reconstruction evaluation results of benchmark dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **TUB1** & **Office1** & **Room** \\ \hline Proposed segmentation method & & & \\ Other segmentation method & & & \\ Other segmentation method & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Result comparison of room segmentation methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data** & **Real Room Number** & \begin{tabular}{c} **Detected Door** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Correctly Detected** \\ **Door Number** \\ \end{tabular} & \
\begin{tabular}{c} **Correctness** \\ **on Door** \\ \end{tabular} &
\begin{tabular}{c} **Completeness** \\ **on Door** \\ \end{tabular} \\ \hline TUB1 & 23 & 22 & 20 & 0.91 & 0.87 \\ TUB2 & 28 & 29 & 27 & 0.93 & 0.96 \\ UOM & 14 & 13 & 12 & 0.92 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Opening for benchmark data.