esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning for Computational Mode Decomposition in Optical Fibers
## 2 Methodology
### Specified Mode Combinations (SMC) Data Design for the Training Process  
For the training process of the network, data must be generated from which the network can extract characteristic properties about the problem at hand. For the presented problem, one ensemble of the training dataset consists of an intensity distribution and the label vector with the corresponding mode weights. Each change of an element of the mode weights also leads to a change in the intensity distribution. Therefore, the aim is to make the training data as representative as possible. A trivial possibility is to specify a certain number of random combinations of mode weights and to determine random intensity distributions from them. With this procedure however, the required size of the dataset will become immensely large with increasing complexity or increasing number of modes in order to be able to make an accurate prediction. For this reason, a new approach was chosen in order to ensure the most accurate prediction of the network as well as a manageable size of the training dataset. For this purpose, all combinations of mode field distributions are synthetically created and varied with two predefined step widths--one for amplitude \(s_{amp}\) and one for the phase \(s_{phase}\) of the respective mode. This means that the total amount of generated training data is dependent on both step sizes. The approximate amount of data \(M\) can be computed by  
\[M\approx\left(\frac{1}{s_{amp}}+1\right)^{N}\cdot\left(\frac{1}{s_{phase}}+1 \right)^{N-1}. \tag{8}\]  
Additionally, duplicate ensembles of the dataset are eliminated, which is why the final value of M is reduced. Eventually, a defined vector with \(N\) magnitude and \(N-1\) relative phase distances is generated for each ensemble of the training dataset. In addition, care is taken to ensure that the Euclidean standard of the amplitude weights remains constant at \(1\), which is why the amplitude values are normalized. Finally, both the amplitude and phase values are additive, superimposed with white noise, the level of which is as large as half the respective step size. This procedure is performed in order to generate amplitude and phase distributions that are as realistic as possible. The resulting label vector for the training dataset is then generated by computing the cosine of the phase value (Equation (5)). The method of training data design is referred to in the following as Specified Mode Combinations (SMC).
## 4 Research Methods
### System Workflow  
The system implements a structured workflow that coordinates the interaction between customers, mobile agents, and GIS components to deliver personalized recommenda  
Figure 5: UML diagram.  
tions. Figure 6 illustrates the systematic process flow from the initial customer interaction to final feedback collection.  
The workflow follows a decision-based path that begins with customer interaction and branches based on location data availability. This process encompasses several key phases:  
1. Initial Customer Interaction: The system engages with the user through the interface, initiating the recommendation process;
2. Location Verification: The system checks for customer location data availability, creating two possible paths: (1) when a location is available, the system collects and processes precise location data; and (2) when a location is unavailable, the system defaults to standard location parameters;
3. Mobile Agent Deployment: Based on the available data, relevant mobile agents are activated to gather and process information;
4. Recommendation Generation: The system synthesizes collected data to create personalized product suggestions;
5. Feedback Collection: Customer responses are gathered to continuously improve the recommendation engine.  
This systematic approach ensures efficient resource utilization while maintaining service quality. The workflow's branching structure allows the system to provide relevant recommendations regardless of location data availability, ensuring consistent service delivery across various usage scenarios. The integration of mobile agents at the deployment stage enables dynamic data collection and processing, while GIS components provide crucial spatial context for enhanced recommendation accuracy when location data are available.  
The final feedback collection phase creates a continuous improvement loop, where customer responses help refine and optimize the recommendation algorithm for future interactions. This creates a self-improving system that becomes more accurate and effective over time.  
Figure 6: A flow chart of the proposed system.
## 3 Spaceborne GNSS-R Raw IF Data and Processing
### Raw IF Data Processing
#### 3.2.2 Open-Loop Tracking Model Computation  
The main inputs for the bistatic geometry computation are the positions and velocities of the receiver and GNSS transmitter. The position the receiver \(\mathbf{R}(t_{\rm M})\) is extracted from the mission science data product, and the positions of the GNSS satellites \(\mathbf{T}(t_{\rm M})\) are interpolated from the IGS Multi-GNSS Experiment (MGEX) products [66]. The coarse positions of the specular points are computed first by using the World Geodetic System 1984 (WGS84) ellipsoid as the reference surface, and the mean surface elevation along the SP track \(h_{\rm elv}\) is interpolated from the combined GMTED-DTU model. Then, an approximated Earth ellipsoid is placed at a height corresponding to the mean surface elevation \(\overline{h_{\rm elv}}\), and the specular point positions \(\mathbf{S}(t_{\rm M})\) are recomputed on this resized ellipsoid. It is noted that the propagation time of the direct and reflected signals \(\delta t^{\rm d}\) and \(\delta t^{\rm r}\) are also taken into account in the computation of the specular point positions. With the positions of the transmitter, receiver and specular point, the bistatic delay \(\delta\rho^{\rm dr}\), defined as the range delay difference between the direct and reflected signal, can by computed by  
\[\begin{split}\delta\rho^{\rm dr}_{\rm OL}(t_{\rm M})=& |\mathbf{R}(t_{\rm M})-\mathbf{S}(t_{\rm M})|+|\mathbf{T}(t_{\rm M}- \delta t^{\rm r})-\mathbf{S}(t_{\rm M})|\\ &-\left|\mathbf{T}(t_{\rm M}-\delta t^{\rm d})-\mathbf{R}(t_{\rm M })\right|\end{split} \tag{2}\]  
With the bistatic delay and the direct signal parameters from Section 3.2.1, the code phase (\(\Phi^{\rm r}_{\rm code}\)), carrier phase (\(\phi^{\rm r}_{\rm carrier}\)) and carrier frequency (\(f^{\rm r}_{\rm carrier}\)) of the reflected signals can be predicted by  
\[\begin{split}\Phi^{\rm r}_{\rm code}(t)=\Phi^{\rm d}_{\rm code}(t )-\delta\tau^{\rm dr}_{\rm OL}(t)\cdot f_{\rm C}\\ \phi^{\rm r}_{\rm carrier}(t)=\phi^{\rm d}_{\rm carrier}(t)-\delta \tau^{\rm dr}_{\rm OL}(t)\cdot f_{\rm RF}\\ f^{\rm r}_{\rm carrier}(t)=f^{\rm d}_{\rm carrier}(t)-\frac{\partial }{\partial t}\delta\tau^{\rm dr}_{\rm OL}(t)\cdot f_{\rm RF}\end{split} \tag{3}\]  
in which \(\delta\tau^{\rm dr}_{\rm OL}(t)=\delta\rho^{\rm dr}_{\rm OL}(t)/c\) is the time delay between the direct and the reflected signal fitted to a 3 rd degree polynomial function, \(f_{\rm RF}\) and \(f_{\rm C}\) are the carrier frequency and the chip rate of the transmitted GNSS signal, e.g., \(f_{\rm RF}=1575.42\) MHz and \(f_{\rm C}=1.023\) MHz for the GPS L1 C/A code signal.
## 1 Introduction
### Conclusions of the Literature Review  
The presented state-of-the-art showed that several researchers sought the detection of any vital element like water stress, nitrogen levels, and vegetation indexes that could lead to an improvement of production and quality in crops, which translated to an increase in profitability. However, most of the research did not integrate different means of detecting CLR to have more insights and better accuracy in predicting this disease. Furthermore, the determination of the infection percentage of the crop through visual inspection is a tedious task, which is also laborious, time consuming, and subject to human error and inconsistency [46]. For this reason, the objective of this research is to evaluate to what extent it is possible to diagnose the CLR development stage in the Colombian Caturra variety (the most susceptible to the disease) through a technological integration system that involves Remote Sensing (RS), Wireless Sensor Networks (WSN), and Deep Learning (DL). Adequate management of CLR could preserve the quality and selling price of the final product, reduce production costs by rationing control costs, and protect productivity. The present research aims to facilitate the management of the most dangerous disease in the Colombian Caturra variety's coffee production to strengthen the profitability of the rural inhabitants.  
The present work provides empirical evidence of a novel diagnostic method for the classification of the development stage of CLR in coffee crops, by means of a technological integration of image data (RS), WSN, and DL. This contribution allows coffee growers to detect CLR disease automatically, thus optimizing the production and maintenance of their crops and replacing the task of manual inspection. Through this method, the performance evaluation is done, and the results are presented to conclude to what extent it is possible to diagnose CLR disease. Thus, this information can be useful for coffee growers to determine if the integration of RS, WSN, and DL in our method could positively impact their profitability.
# Time-Dependence of the Mechanical Behavior of Loess after Dry-Wet Cycles
## 5 Conclusions  
To assist in the understanding of the time-dependent behaviors and the long-term stability of loess slopes after dry-wet cycles, the mechanical properties of intact loess samples were investigated through triaxial creep tests. The following conclusions can be drawn according to the obtained experimental results:  
1. With the same number of dry-wet cycles, the strain-time curve of the loess samples shows a similar trend, where the strain eventually reaches a certain value with an increase of time when a small load is applied, whereas the creep curve of the stable phase increases linearly with time when the loess specimen is subjected to a higher loading. As the number of dry-wet cycles increases, the maximum value of the deviatoric stress corresponding to the creep failure gradually decreases, indicating that the deterioration of triaxial compressive strength is attributed to the dry-wet cycles.
2. The long-term strength of loess samples with different dry-wet cycles was obtained by using the Isochronous Curve Method, and the long-term strength of loess samples and the number of dry-wet cycles showed an exponential decreasing relationship.
3. The creep damage mechanism of loess samples due to dry-wet cycles can be explained as follows: the dry-wet cycle causes repeated migration and loss of soluble salts between soil particles in the loess sample, which results in damage of the sample's microstructure. Consequently, the development and expansion of joint fractures within the loess samples were also aggravated. The combined effects of microstructural damage and expansion of joint fractures causes a greater deterioration in the creep properties of loess soils.  
Conceptualization, X.W. and T.G.; methodology, K.L.; software, X.W. and K.L.; writing--original draft preparation, K.L.; writing--review and editing, X.W. and T.G.; visualization, X.W.; supervision, J.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 41902268) and the Opening Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology) (No. SKLGP2020K016).  
Not applicable.  
We thanks the editors, and the two anonymous reviewers for their constructive comments, which has helped us improve the quality of the manuscript.  
The authors declare no conflict of interest.
# SOPRENE: Assessment of the Spanish Armada's Predictive Maintenance Tool for Naval Assets
## 4 Unified Architecture
### Behavioural Prediction  
The main objective is to estimate the state of a piece of equipment (such as the engine or the propeller) in a future instant of time. In this manner, in order to carry out a prediction from an instant of time \(t_{i}^{s}\) for a future instant that is distant from _horizon_ units of time relative to the use of the engine (\(t_{i+horizon}^{s}\)), the system will use the information collected by the sensors during the previous _window_ units of time to \(t_{i}^{s}-(t_{i-window}^{s})\). The user can customize the window size and prediction horizon, although by default, the horizon is used twice as the window size.  
Both the _horizon_ and the _window_ are relative to the time scope \(s\) selected (and, therefore, to the data aggregation performed). In this sense, despite the large data pools available during the preprocessing stage, the datasets resulting after the aggregation process are proportionally reduced. Thus, several predictions techniques have been considered for properly dealing with the precision requirements and the data load: on the one hand, regularized linear regression methods such as L1 (lasso) and L2 (ridge) have been used to deal with the slow degradative failures and the smaller dataset after the aggregation. Both techniques use the Spark MLlib library to provide distributed training and execution. On the other hand, Long Short-Term Memory (LSTM) based networks [36] have also been developed to make use of the massive data to forecast sudden and close-in-time events, although its the execution is not distributed.  
The architecture keeps several aspects of predictive modeling open to maintain its flexibility. The final user is free to use the regression model, amount of training data, prediction and historic window length, among others, of his choice. This is performed in this manner because different equipment require different approaches. For example, using a LSTM network to model a collection of signals with data grouped by month and historic data along a couple of years will most likely overfit and, in that case, a lineal regression model will probably perform better.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Id** & **Signal** & **Nominal Value** & **Threshold Value** & **Tendency** \\ \hline \multirow{3}{*}{31} & Variable 1 & 1.3 & 2 & \(\uparrow\) \\  & Variable 2 & 5.4 & 5 & \(\downarrow\) \\  & Variable 3 & 55 & 60 & \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of failure mode identified by the failure mode, effects and criticality analysis (FMECA). This failure mode has an Id 31 and comprises three signals. Values shown do not correspond to any real failure mode.
## 2 Overview of WiFi-Direct and Cyber Attacks
### Cyber Attacks
#### 2.2.6 Jamming Attack  
This attack always targets D2D systems and is performed over wireless channel due to its broadcast nature. An active adversary who is also mobile user aims at generating a noise signal to overload the channel and perturb group communication between legitimate D2D users, which includesa loss of the exchanged messages lost [14]. In performing this attack, the jammer tries to reduce the signal-to-noise ratio by transmitting multiple radio signals.
# Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth
## 4 Results  
### Prediction of Margin Lines  
Table 3 shows the overall accuracy of the five folds reported in terms of the chamfer distance L1 norm (CD). CD was reported for the raw prediction of the margin line point cloud before removing outliers and for the spline fitted on the point cloud after removing outliers. The percentage of outliers is also presented in the table. According to the Shapiro-Wilk test, the results for the percentage of outliers and CD are not normally distributed. Therefore, we reported the median values in the table. In addition to the five individual folds, we reported the results obtained by their combination (i.e., best of five folds) as shown in the table. This combined result was obtained by identifying the model with the least percentage of outliers for each case, and selecting it to provide the point cloud of the margin line for that case.  
As shown in Table 3, the individual models trained with the five folds yielded different results for CD of the raw predictions ranging from 0.121 to 0.134, and the splines ranging from 0.139 to 0.151. The CD values for splines were higher than raw predictions of point clouds for all models. It was also observed that the model with the minimum CD for raw prediction (i.e., fold 2), scored also the least for spline CD. The model with the minimum percentage of outliers (i.e., fold 4) did not provide the least CD neither for raw predictions nor for splines. It is also observed that the combined model (i.e., best of five folds) scored the minimum CD for splines compared with all individual folds. This is also true for the percentage of outliers, except for fold 4 which had also no outliers.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** & **Best of 5** \\  & & & & & & **Folds** \\ \hline Raw prediction CD [mm] & 0.134 & 0.121 & 0.132 & 0.129 & 0.134 & 0.126 \\ Spline CD [mm] & 0.151 & 0.139 & 0.149 & 0.147 & 0.146 & 0.137 \\ Raw prediction HD [mm] & 0.327 & 0.268 & 0.301 & 0.277 & 0.304 & 0.260 \\ Spline HD [mm] & 0.276 & 0.233 & 0.266 & 0.249 & 0.263 & 0.242 \\ Percentage of outliers [\%] & 0.130 & 0.130 & 0.065 & 0 & 0.065 & 0 \\ Percentage of confident predictions [\%] & 68.66\% & 73.88\% & 76.12\% & 79.85\% & 73.13\% & 88.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Median values of: (1) CD and HD of raw point cloud predictions and splines; (2) the percentage of outliers and confident predictions for five folds and combined model.  
Figure 6: Illustration of the post-processing procedures to remove outliers.
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 3 Evolution of Industrial Maintenance
### Predictive Maintenance Tools and Materials
#### 3.2.3 Big Data  
Collecting, processing, and analyzing real-time Big Data from cyber-physical systems is a strategic phase for the intelligent transformation of the maintenance function, especially with respect to failure prediction, planning, and risk management [34]. This is done through the planning and optimization of interventions using artificial intelligence tools and techniques such as machine learning and deep learning or through statistical models and approaches based on the data and information collected by the different sensors [10]. The characterization of massive data or Big Data is usually done according to three \"V's\", the V's of Volume, Variety and Velocity, to which other complementary \"V's\" are added, such as Value and Veracity/Validity (See Figure 6).
# Finite Element Simulation of HELICOIL(r) Inserts
## 6 Simulation Model for the HELICOIL(r) Insert
### FE Model  
For the simulation, solid models were created. These models include two bodies: A screw (H1, **a**; see Table 2) and a specimen (\(\mathsf{c}=15\) [mm]). The screw was modelled as a cylinder with a radius of 2.5 [mm], and the effect of the threads on the solution was neglected. The sample was modelled as a cube with dimensions \(20\times 20\) [mm] and height 15 [mm], having a cylindrical hole with a radius of 2.5 [mm]. The model was simplified by using two planes of symmetry. A load was applied by a Rigid Contact Body (RCB), mounted on top of the screw. Node N2604 was used as point T2 (\(\mathbf{b}\approx 4\cdot 0.5=2.0\) [mm], where 0.5 [mm] is the vertical dimension of an element and 4 is the number of elements). The FE model included 4728 nodes and 3750 elements (8 nodes, Hex, type 7; see [3]). The mesh, with a basic description of the geometry of the model, is shown in Figure 8. The screw is marked in red, while the area of the sample with the HELICOIL(r) insert is marked in blue. The point \(T2\) lies at the interface of grey and blue colours.  
For simulation of the interface between the screw and the specimen (i.e., HELICOIL(r) insert and threads), a cohesive contact was used. In MSC Marc, the implementation was performed within the glued contact capability, where the relative motion between the two bodies is small and a slight penetration may occur between them. The stiffness of the contact was defined by the contact stress, as a function of displacement. The behaviour can be defined for normal and tangential directions in an independent manner. In our example, we used two planes of symmetry, such that only the behaviour in the tangent direction needed to be identified. Due to the use of cohesive contact, the segment-to-segment contact  
Figure 7: Simulation model design of HELICOIL® inserts.  
procedure without augmentation was used. A detailed description can be found in [3]. The values that define the stiffness in the normal direction are shown in Table 4. These values did not change during the identification process. The table defining the stiffness in the tangential direction is presented in Section 6.4.
# Temperature Stable, High-Quality Factor Li2 TiO3-Li4 NbO4F Microwave Dielectric Ceramics
## 2 Experimental Procedure  
(1-x)Li\({}_{2}\)TiO\({}_{3}\)-xLi\({}_{4}\)NbO\({}_{4}\)F ceramics were prepared by a conventional solid-state route. TiO\({}_{2}\) (99.9%, Sinopharm, China), Li\({}_{2}\)CO\({}_{3}\) (99%, Sinopharm, China), N\({}_{2}\)O\({}_{5}\) (99.9%, Zibo Weijie, China), and LiF (98%, Sinopharm, China) powders were used as starting materials. Stoichiometric Li\({}_{2}\)CO\({}_{3}\) and TiO\({}_{2}\) were mixed according to the formula of Li\({}_{2}\)TiO\({}_{3}\) and milled with ZrO\({}_{2}\) balls in ethanol for 6 h. Then, the mixtures were dried and calcined at 800 \({}^{\circ}\)C for 2 h in air. At the same time, stoichiometric Li\({}_{2}\)CO\({}_{3}\), N\({}_{2}\)O\({}_{5}\), and LiF were mixed, milled, dried, and calcined at 700 \({}^{\circ}\)C for 2 h in air in another furnace. The obtained Li\({}_{2}\)TiO\({}_{3}\) and Li\({}_{4}\)NbO\({}_{4}\)F powders were weighed according to the designed molar ratios, mixed with 30 mL ethanol, and milled by balls for 8 h, dried, and sieved. Subsequently, the powders were sieved through 60 mesh. Then, the powder (the particle size at a scale of 4-10 \(\mu\)m) was granulated with 5 wt% PVA as binder and uniaxially pressed into cylindrical disks under a pressure of 100 MPa. These samples were buried by mixed powder with the same composition and sintered at 1000-1125 \({}^{\circ}\)C for 2 h at a heating rate of 3 \({}^{\circ}\)C/min.  
The bulk densities of the sintered ceramics were measured by Archimedes method. The crystal structure was analyzed using X-ray diffraction (XRD) with Cu K\({}_{\alpha}\) radiation (D8-Advanced, Bruker, Germany). The microstructures were observed by a scanning electron microscope (SEM) (JSM 6510 LV, JEOL Japan). Microwave dielectric properties were measured using a network analyzer (E5071C, Agilent, USA) with TE\({}_{018}\) resonant mode. The temperature coefficient of the resonant frequency (\(\tau_{\rm f}\)) was calculated with the following formula:  
\[\tau_{\rm f}=\frac{(f_{80}-f_{25})}{f_{25}(80-25)}\times 10^{6} \tag{1}\]  
where \(f_{80}\) and \(f_{25}\) were the resonant frequencies at 80 \({}^{\circ}\)C and 25 \({}^{\circ}\)C, respectively.
## 3 Communication and Channel Modeling  
In this section, we present a brief study on channel modeling and the impact of environment and RIS-assisted communication on channel performance. Through channel modeling, we highlight critical points that should be considered in the UAV navigation for UAV-enabled, RIS-assisted communication.