esa-sceva/satcom-chunk-collection huggingface dataset:
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 4 Experiment  
In our experiment, we trained our model using ocean current data, and we divided the dataset into training, testing, and validation sets. To demonstrate the generalization of the model, we fine-tuned it and made predictions on underwater vehicle drift trajectory data. We compared the performance of our QCNN-LSTM model with other selected models. This paper begins by introducing the selection and processing of the data, followed by an explanation of the evaluation metrics used in the experiment. Lastly, we discuss the reasons behind choosing these models as our comparative models. In the experiment,  
Figure 5: QCNN-LSTM model structure.  
we configured several hyperparameters. The learning_rate was set to 0.001 to ensure the stability of the model during training and avoid instability caused by overly large parameter updates. The batch_size was set to 1 to implement the training method of stochastic gradient descent, where only a single sample is used for parameter updates at each iteration. The number of epochs was set to 100 to ensure sufficient iterations over the dataset during training, allowing the model to better learn the features of the data and achieve improved performance.
## 4 Methodology
### Feature Modeling  
The features were extracted from the data described in Section 3, and all algorithms used in this article use the same modeling. The features used in the modeling are listed below:  
1. Starting point of a path (\(P_{i}\)), composed of \(X\), \(Y\), and \(Z\) coordinates;
2. End point of a path (\(P_{f}\)), also composed of the coordinates \(X\), \(Y\), and \(Z\);
3. Relative time at which the end point was recorded (\(T_{f}\)). The value of \(T_{f}\) is calculated by adding the number of points from \(P_{i}\) to \(P_{f}\) multiplied by the latency. It is important to mention that the time of \(P_{i}\) is a reference value, so it is always 0; thus it is not necessary to use it as a feature;
4. Relative time at which the intermediate point is to be predicted (\(T_{n}\)) with \(n\) being the indicator of chronological order of the point. For example, if you want to predict only one point between \(P_{i}\) e \(P_{f}\), then there will be an observation with \(T_{1}\). If we want to predict \(m\) intermediate points, then we will have an observation with \(T_{2}\), and another with \(T_{3}\) before finally reaching the last point to be predicted (\(T_{m}\)).  
The targets of each observation are the \(X\), \(Y\), and \(Z\) coordinates of each point located between \(P_{i}\) e \(P_{f}\). Furthermore, in order to build several examples, we used the concept of the sliding window [48] which is illustrated in Figure 2. We also used a parameter that indicates the number of intermediate points between \(P_{i}\) and \(P_{f}\), the \(d\). In Figure 2, the invariant \(Z\) axis is assumed for ease of visualization. Each gray circle represents a point (which have \(X\), \(Y\), and \(Z\) coordinates, and the relative time that was recorded) and intervals with \(d=2\). All windows, composed by \(P_{i}\),\(P_{f}\) and a _Target_, are size three, so we have \(0\leq n\leq 3\).  
Figure 2: _Cont._
## 3 Methodology
### Network Architecture
#### 3.3.1 Probabilistic Encoder  
The encoder transforms the input trajectory sequence into a distribution in latent space. It employs a bidirectional LSTM network to capture temporal dependencies in both directions:  
\[h=\text{BiLSTM}(X) \tag{4}\]  
A critical innovation in our encoder is the weather attention mechanism, which explicitly models the influence of weather conditions on trajectory patterns. For each time step \(t\) in the sequence, the weather attention computes:  
\[\alpha_{t}=\text{Attention}(h_{t},W_{t}) \tag{5}\]  
where \(W_{t}\) represents the weather characteristics (wind components) at time \(t\), and the attention function is implemented as a multi-head scaled dot product attention mechanism:This enables the model to dynamically adjust its focus based on weather patterns, giving more weight to weather factors when they significantly influence the trajectory, as illustrated in Figure 2.  
The final hidden state is then projected into the latent space to parameterize the posterior distribution:  
\[\mu=W_{\mu}h+b_{\mu} \tag{7}\]  
\[\log\sigma^{2}=W_{\sigma}h+b_{\sigma} \tag{8}\]  
During training, we sample from this distribution using the reparameterization trick:  
\[z=\mu+\sigma\odot\epsilon,\text{ where }\epsilon\sim\mathcal{N}(0,I) \tag{9}\]  
This probabilistic encoding captures the inherent uncertainty in trajectory prediction by representing not just a single future path, but a distribution over possible continuations. However, not all mathematically possible trajectories are physically realistic or conform to aviation standards. To address this challenge, we incorporate stability constraints in the decoder component.  
Figure 1: Overall architecture of the proposed stochastic trajectory prediction model, featuring a bidirectional LSTM encoder with weather attention mechanism, a latent variable representation of trajectory distribution, and a decoder with Lyapunov stability constraints.
# Discrete Element Analysis of Grouting Reinforcement and Slurry Diffusion in Overburden Strata
###### Abstract  
Research on the grouting reinforcement mechanism of overburden is constrained by the concealed and heterogeneous nature of geotechnical media, posing dual challenges in theoretical analysis and process visualization. Based on discrete element numerical simulations and laboratory tests, an analytical model for grouting reinforcement in overburden layers is developed, revealing the influence of grouting pressure on slurry diffusion shape and distance. The results indicate the following: (1) Contact parameters of overburden and cement particles were obtained through laboratory tests. A grouting model for the overburden layer was established using the discrete element method. After optimizing particle coarsening and the contact model, the simulation more accurately represented slurry diffusion characteristics such as compaction, splitting, and permeability. (2) By monitoring porosity and coordination number distributions near grouting holes before and after injection using circular measurement, the discrete element simulation clearly visualizes the slurry reinforcement range. The reinforcement mechanism is attributed to the combined effects of pore structure compaction (reduced porosity) and cementation within the overburden (increased coordination number). (3) Based on slurry diffusion results, a functional relationship between slurry diffusion radius and grouting pressure is established. Error analysis shows that the modified formula improves the goodness of fit by 34-39% compared to the classical formula (Maag, cylindrical diffusion). The discrete element analysis method proposed in this study elucidates the mechanical mechanisms of overburden grouting reinforcement at the particle scale and provides theoretical support for visual evaluation of concealed structures and optimization of grouting design.  
overburden; grouting; discrete element methods; slurry diffusion form; slurry diffusion formula +
Footnote †: journal: Applied sciences
## 5 Conclusions  
In summary, the response curves of Pn and WUE of the cotton leaves under different configuration modes at different growth period of a juibue-cotton intercropping system showed an increased trend with the increase in light radiation intensity, with a decrease observed after the inflection point. By contrast, Ci decreased rapidly and then stabilized, whereas Gs and Tr increased linearly. Based on all the factors analyzed, it was concluded that in the juibue-cotton intercropping system, the PAR intercepted by the cotton in the four-row configuration (CM2 treatment) was the highest, the light-response curve was the best, the fitted LSP and Pmax were larger, the utilization rate of strong light was higher, the dry matter distribution and LER were greater compared to the other configuration modes (CM1 and CM3 treatments). Moreover, the canopy structure of CM2 was more reasonable, and the photosynthetic material accumulation was greater than that of the other intercropping treatments. The CM2 mode, which achieves a balance between light resource utilization and economic benefits, provides an effective way to achieve high cotton yields in a juibue-cotton intercropping system in Xinjiang.  
Conceptualization, G.C. and S.W.; methodology, T.L., Y.L., and P.W.; software, T.L. and P.W.; validation, G.C.; formal analysis, S.W.; investigation, T.L., and L.L.; resources, G.C. and S.W.; data curation, T.L., R.K., and W.F.; writing--original draft preparation, Y.L., G.C., W.Y., and Y.Z.; writing--review and editing, G.C., Z.F., and Q.W.; visualization, Y.Z.; supervision, S.W.; project administration, G.C.; funding acquisition, G.C. and S.W.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (grant number 32060449,31601272), and the Regional Innovation Guidance Project of Xinjiang Production and Construction Corps (grant number 2021 BB012).  
Not applicable.  
The data that support the findings of this study are available from the corresponding authors upon reasonable request.  
The authors sincerely thank the anonymous reviewers who made valuable comments on this paper.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results and Discussion
### Biomass Production, Sugars and Nitrogen Consumption
In detail, _Morchella_ species consumed almost the whole quantity of initial FAN, up to 95.0% in sucrose substrates, whereas slightly lower reduction rates were observed in molasses substrates (85.0-94.1%; M-20, 68.8-85.8%; M-25). Similar results have been also reported by [PERSON] et al. [55] in glucose-based cultures of _Monascus purpureus_. The lower FAN consumption rates in molasses media are probably due to their higher initial nitrogen concentration (expressed in glycine equivalents). In addition, nitrogen is the second most important nutrient in fungal fermentations, as it is essential for cellular metabolism. Macrofungi choose initially to consume carbon sources to control their energy needs (which were in larger amounts in molasses nutrients) and then the nitrogen sources.
## 5 Conclusions  
We have proposed a mobile cognitive BTS with spectrum sensing capabilities that takes advantage of the flexibility of SDR technology to deploy added-value services such as terminal detection in case of emergencies. The cognitive BTS provides communication services inside moving vehicles, avoiding interference in real-time. We have developed and tested the most critical parts of our proposal, demonstrating their correct behavior and the validity of our approach.  
As future work, we plan to incorporate new services based on intelligent spectrum sensing, which are not available in conventional cellular networks.
## 3 Materials and Methods: Thermodynamic Model and Simulation
### The Steam Rankine Cycle and ORC Power Plant  
3.5.1. The Steam Rankine Cycle (SRC)  
The energetic balance equation applied for the turbines in the SRC is:  
\[\dot{m}_{wf,SRC}h_{17}=\dot{W}_{SRC,T}+\dot{m}_{wf,SRC}h_{18} \tag{59}\]  
The net power output of the SRC can be calculated as:  
\[\dot{W}_{net,SRC}=\dot{W}_{SRC, Turbine}-\dot{W}_{P_{100}} \tag{60}\]  
Its energy and exergy efficiencies are:  
\[\eta_{en,SRC}=\frac{\dot{W}_{elec,RT}}{\dot{m}_{12}\ (h_{12}-h_{13})} \tag{61}\]  
\[\eta_{ex,SRC}=\frac{\dot{W}_{elec,RT}}{\dot{m}_{12}\ (ex_{12}-ex_{13})} \tag{62}\]  
3.5.2. The Organic Rankine Cycle (ORC)  
The energy and exergy efficiencies of the ORC are given by:  
\[\eta_{en,ORC}=\frac{\dot{W}_{elec,ORC, Turbine}}{\dot{m}_{14}\ (h_{14}-h_{15})} \tag{63}\]  
\[\eta_{ex,ORC}=\frac{\dot{W}_{elec,ORC, Turbine}}{\dot{m}_{14}\ (ex_{14}-ex_{15})} \tag{64}\]  
The exergy destruction rates of the main components are calculated as shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Exergy Destruction Rate** \\ \hline SOFC & \(Ex_{air}+\dot{Ex}_{ammonia}+\dot{Ex}_{8}-\dot{Ex}_{6}-\dot{W}_{s}=\dot{Ex}_{ des}\) \\ \hline Afterburner & \(\dot{Ex}_{7}-\dot{Ex}_{9}=\dot{Ex}_{des}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Exergy destruction equations of the main components.  
The overall energy and exergy efficiencies for the entire integrated system are expressed as follows [11; 36]:  
Energy efficiency:  
\[\eta_{en,overall}=\frac{\dot{W}_{dec,total}}{\dot{m}_{ammonia}\ LHV_{ammonia}} \tag{76}\]  
In these expressions, \(\dot{W}_{dec,total}\) denotes the net power production and consumption of the system:  
\[\begin{array}{l}\dot{W}_{dec,total}=\dot{W}_{dec,SOFC}+\dot{W}_{Gasturbine}+ \dot{W}_{SRC,\ turbine}+\dot{W}_{KC,\ turbine}+\dot{W}_{ORC,\ turbine}\\ -\dot{W}_{Air\ comp}-\dot{W}_{SRC,\ pump}-\dot{W}_{KC,\ pump}-\dot{W}_{ORC,\ pump}\end{array} \tag{77}\]  
\(LHV_{ammonia}\) represents the lower heating value of ammonia (kJ/kg).  
Exergy efficiency:  
\[\eta_{ex,overall}=\frac{\dot{W}_{dec,total}}{\dot{m}_{ammonia}\ ex_{ammonia}} \tag{78}\]
# Assessing the Efficacy of Seawater Batteries Using NASICON Solid Electrolyte
## 3 Results
### Analyzing the Structure of NASICON Membranes (2% and 6% Excess Na)
of the elements Na, Zr, Si, P and O, and the calculated atomic ratios of Na, Si and O were 13.54:9.96:59.22 for Na\({}_{3.06}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\) and 14.24:9.78:58.99 for Na\({}_{3.18}\)Zr\({}_{2}\)Si\({}_{2}\)PO\({}_{12}\), which are close to the theoretical atomic ratio (15:10:60). The homogeneous distribution of elements was observed in EDX mappings through EDX analysis. The content of Zr and P for the two samples is approximately constant, based on EDS analysis. It has been established that the total molar ratios of Zr and P are almost identical to the total theoretical molar ratios (15%). Studies have shown that during conventional sintering (at about 1000 \({}^{\circ}\)C), there is a saturated vapor pressure of the elements Na and P above the sample that prevents further vaporization and also maintains the stability of the solid electrolyte NZSP [18]. The membrane samples were examined using BET and BJH analysis to examine their surface and micropores. Figure 3 provides access to the results. To determine the specific surface area, one must use the static (volumetric) manometric method to determine the amount of gas N\({}_{2}\) that has adsorbed or de-sorbed from the surface of porous solids. Both adsorption and desorption isotherms show a greater presence of a mesoporous structure  
Figure 2: EDX examination of powders NAS-2% and NAS-6% that were calcined at 1100 \({}^{\circ}\)C (Atomic %). in the hysteresis loop. Hysteresis arises due to differences in interface geometry between adsorption and desorption. The NASICON 2% powder indicated an area of 7.17 m\({}^{2}\) g\({}^{-1}\) and a volume of pores of 0.024 cm\({}^{3}\) g\({}^{-1}\), while the powder with an excess of 6% indicated an area of 6.61 m\({}^{2}\) g\({}^{-1}\) and a volume of pores of 0.093 cm\({}^{3}\) g\({}^{-1}\). The measurement of nitrogen adsorption/desorption isotherms was carried out at 77 K. According to the obtained curves; it was found that there is a hysteresis loop in adsorption-desorption isotherms, which implies that there is a mesoporous structure mainly present.
## 2 Materials and Methods
### Statistical Analyses and Model Evaluation  
We firstly calculated the daily values of EC-based GPP and then aggregated to the eight-day and annually values for seasonal and yearly GPP validation. To evaluate the performance of the MOD17A2H GPP model, we compared the modeled GPP with the flux tower-estimated GPP both in 8-day and annual time steps. We extracted the eight-day composite MODIS C6 GPP product (MOD17A2H) and the other MODIS products (e.g., MOD15A2H and MOD09A1 products) from the pixels centered on the flux towers, and compared the MODIS GPP product with the EC-based GPP observations.  
The model performance (i.e., differences between simulated and tower-based GPP) were quantified by using the coefficient of determination (R\({}^{2}\)), root mean squared error (RMSE), and relative RMSE (rRMSE):  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(Y_{sim,t}-Y_{obs,t})^{2}} \tag{4}\]  
\[\text{rRMSE}=\frac{1}{\overline{Y_{obs,t}}}\times\sqrt{\frac{1}{n}\sum_{i=1} ^{n}(Y_{sim,t}-Y_{obs,t})^{2}}\times 100 \tag{5}\]  
where, \(Y_{\text{sim}}\) and \(Y_{\text{obs}}\) represent the simulated and observed GPP data, respectively, and \(n\) is the total number of samples. All the statistical analyses and results presentation are performed in Matlab R2016b software (Mathworks, Natick, MA, USA).
## 6 Computational Complexity  
In this work, as shown in Algorithm 4, we have reduced the computational complexity in two main parts. The first one is based on using the rSVD for constructing the projection basis, and the other one is based on applying the DEIM procedure to efficiently select a small subset of the most relevant components in the least-squares problem used in the reconstruction process.  
In the first part, for a given complete data matrix \(\mathbf{Y}\in\mathbb{R}^{n\times n_{s}}\), computing a projection basis of rank \(k\) for \(\mathbf{Y}\) by using SVD can be performed efficiently via a rank-revealing QR factorization and then manipulating the factors to obtain the final SVD. The cost of this approach is typically \(\mathcal{O}(nn_{s}k)\) floating-point operations (flops) [26; 46]. The rSVD can approximate SVD with computational cost that is reduced to \(\mathcal{O}(nn_{s}\log(k)+(n+n_{s})k^{2})\) flops. This computational cost can be reduced further when using a random matrix \(\Omega\) that has some internal structure to accelerate the computation of \(\mathbf{Y}\Omega\) in Step 3 of Algorithm 2.  
In the second part of reducing the computational cost for solving the least-squares problem, we incorporated the DEIM procedure to reduce the size of the problem in the reconstruction process. In general, the well-known approaches for solving least-squares problems are based on using normal equations, QR factorization and SVD [47; 48; 49]. The last two approaches are efficient for large-scaled problems [48; 49]. For the coefficient matrix \(\mathbf{V}_{c}\) of size \(n_{c}\)-by-\(k\) with \(n_{c}\gg k\), the computational cost of solving the least-squares problem in (1) using the QR factorization or the SVD approaches is in the of order \(\mathcal{O}(n_{c}n_{s}^{2})\) flops. Therefore, when the DEIM algorithm is used to reduce the size of the least-squares problem to a coefficient matrix of size \(m\times k\) with \(m\ll n_{c}\) as shown in (9), the computational cost is reduced to \(\mathcal{O}(mn_{s}^{2})\) flops. Note that the complexity of performing the DEIM procedure is \(\mathcal{O}(m^{3})\), which is negligible for \(m<n_{s}\) and \(m\ll n_{c}\) in most practical cases when compared to the overall complexity of the reconstruction process.  
The next section investigates the effectiveness of the proposed techniques described in Algorithm 4 through different cases of numerical tests. The reduction in computational cost explained above is also reflected in these reconstruction results.
# Prediction of Belt Drive Faults in Case of Predictive Maintenance in Industry 4.0 Platform
## 3 Research Area
### Analysis of the Results  
Analysis of the obtained results was carried out after collecting the data from the acceleration sensors. For this purpose, a fast Fourier transform was carried out for both the signal from the system without damage and the system in which the damage occurred in the pulley, drive belt, or both at the same time. First, the analysis was performed for the failed pulley (Figure 3).  
For both the sensor placed horizontally and the one placed vertically, the maximum magnitude of the signal from the failed system occurred for the initial signal frequencies. The analyzed damage affected to a greater extent the sensor placed vertically which suggests that the damaged component vibrates in the horizontal plane. The signal energy from the sensor placed horizontally identified the defective pulley to a lesser extent, which was noticeable as overlapping of signal bars coming from the defective system and the one operating correctly.  
Figure 3: Fault pulley analysis.  
In the next step, the case where a drive belt failure was introduced was analyzed (Figure 4).  
As in the previous case, for the damaged system, the highest energy (magnitude) was characterized by the initial bars of the signal, noting that the maximum value for the horizontally placed sensor was 3\(\times\) greater than the value obtained for the vertically placed sensor. Limiting the analyzed bandwidth to 2 kHz, the influence of belt drive damage was visible using two ways of sensor placement.  
Finally, an identical analysis was performed for a system in which failure was introduced to the pulley and the belt drive simultaneously (Figure 5).  
Figure 4: Fault belt drive analysis.  
Figure 5: Fault belt drive and pulley analysis.  
The fast Fourier transform performed for the case under study confirmed the folding of the two previous signals. The horizontal sensor showed the greater influence of the belt transmission fault, while the vertical sensor showed the influence of both faults through the maximum magnitudes at frequencies below 1 kHz.  
Based on the analysis, it should be concluded that the introduction of defects into the system in the form of belt transmission or pulley damage manifested itself in the form of vibrations, which could be measured using acceleration sensors located horizontally or vertically on the device. The influence of the introduced damages on the response of the sensor was visible, which was the basis for proposing an algorithm enabling the detection of system damages by analyzing the signal coming from the mentioned sensors.