esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to save frequency resources, a new remote sensing satellite service can gradually adopt relay cooperation transmission to realize the same frequency and common channel transmission of multiple data. To solve the problem of mutual interference between messages, this study proposes a universal model of relay cooperative channel transmission, and the separation mechanism of heterogeneous signals for simultaneous unicast and multicast transmission is also studied. In this study, a signal space is used to reconstruct the degrees of freedom, and an orthogonalized spatial alignment direction is designed to obtain the equivalent parallel transmission channel. We also propose a constellation point remapping scheme under the optimal constraint of spatial separation of transmission signals. Furthermore, we merge constellation points to solve the problem of fuzzy mapping of physical layer network coding. The simulation results show that the co-channel transmission with interference suppression can be realized when the equivalent degrees of freedom of the signal intersection subspace is not less than \(d_{F}K(K-1)+d_{c}K\). The Euclidean distance between constellation points is increased by constructing orthogonal signal spatial alignment directions, which brings an additional BER performance gain of 2 dB. If the signal alignment direction and channel quality are jointly designed, the transmission quality can be further improved.  
multidirectional relay; signal alignment; degrees of freedom; high order modulation Article
# Strain Rate Effect on Artificially Cemented Clay with Fully Developed and Developing Structure
###### Abstract  
The rapid expansion of land reclamation necessitates a fundamental understanding of the strain rate effects on structured clays. While the rate effect has been widely studied in various soils, the interplay between bond structure and strain rate sensitivity remains unclear. This study investigates these mechanisms using artificially cemented kaolin (ACK) with controlled curing periods (2 and 30 days) to simulate naturally bonded clays. A series of undrained triaxial tests was conducted under low (100 kPa) and high (600 kPa) confining stresses, employing constant strain rates (0.01-5%/h) pre-peak and stepwise rate changes post-peak. The results reveal that the strain rate effects are governed by the bond structure maturity and drainage mechanisms. For the 30-day curing ACK, the pre-peak strength under low confining stress shows minimal rate sensitivity due to the rigid bond, while high confining stress induces a \"negative\" rate effect attributed to localised drainage along shear planes. The post-peak behaviour consistently exhibits a positive isotach-type rate effect (+3%/log-cycle) driven by viscous sliding. In contrast, the 2-day curing ACK displays negative rate effects pre-peak influenced by ongoing curing and post-peak strength reductions (\(-8\%/\log\)-cycle) linked to stick-slip dynamics. These findings establish a framework for predicting rate-dependent behaviour in structured clays, offering insights into land reclamation and subsequent construction work.  
structure state; cemented clay; strain rate effect Article
# Investigation of Rain-On-Snow Floods under Climate Change
###### Abstract  
Rain-on-snow (ROS) floods can cause economic damage and endanger human lives due to the compound effect of rainfall and snowmelt, especially under climate change. In this study, possible future changes of seasonality, magnitude and frequency characteristics of ROS floods were investigated for the selected catchments in Slovenia, Europe. For this purpose, five global/regional climate models (GCM/RCM) combinations were applied using the RCP4.5 climate scenario for the period 1981-2100. To determine ROS floods' characteristics in the future, a lumped conceptual hydrological model Genie Rural a 6 parametres Journalier (GR6)) with snow module CemaNeige was applied. The results indicate that the number of ROS floods could increase in the future. Moreover, also the magnitudes of extreme ROS floods could increase, while a slight decrease in the median values of ROS flood magnitudes was observed. The strength of seasonality for a high-altitude catchment could decrease in the future. A slight shift in the average ROS floods' timing could be expected. Furthermore, a catchment located in a temperate continental climate could have a different response to the climate change impact in comparison to a catchment located in a mountain climate with alpine characteristics. Additionally, differences among investigated climate models show a large variability.  
Keywords:rain-on-snow floods; climate change; hydrological modelling; peak discharges; lumped model; compound events +
Footnote †: journal: Applied Sciences
# Fine-Tuning BERT-Based Pre-Trained Models for Arabic Dependency Parsing
## 6 Evaluation and Discussion  
Several experiments have been carried out to evaluate the dependency parsing for Arabic as a sequence labeling problem by fine-tuning the BERT-based pre-trained models. First, the three encoding methods presented in Section 2.2 were evaluated to measure their parsing accuracy on MSA and CA treebanks. Then, in Section 2.3, comparison studies were performed on nine different BERT-based Arabic models. Finally, three fine-tuning techniques were compared using the optimal selections from the first two experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PADT** & **CATiB** & **ArPoT** \\ \hline Sentences & 7664 & 2591 & 2400 * \\ Max\_L & 398 & 609 & 68 \\ Avg\_L & 36.85 & 65.28 & 14.77 \\ Max\_DL & 397 & 479 & 46 \\ Avg\_DL & 4.07 & 4.21 & 2.07 \\ TTR & 9.34 & 9.5 & 28.26 \\ \hline \hline \end{tabular}  
* After sentence alignment in [19] for 2685 verses.  
\end{table}
Table 5: Number of sentences (Sentences), maximum sentence length (Max_L), average sentence length (Avg_L), maximum dependencies length (Max_DL), average dependencies length (Avg_DL), and TTR type-token ratio (TTR).
# Reduced Graphene Oxide-Metalloporphyrin Sensors for Human Breath Screening
## 4 Discussion
Three ML algorithms showed varied predictive performances suggesting the detection and discrimination capabilities of VOC sensors are affected by classification analysis, which should be carefully investigated for VOC sensor implementation. With the bootstrapped samples, SVM showed great performance distinguishing isopropanol and carbon monoxide with an F1-score of 1. However, considerable variation was observed, and the performance is clearly higher than what is expected due to the insufficient sample size and overfitting, despite using the necessary techniques (cross-validation and bootstrapping) to avoid it as much as possible [67]. More sample collection and the addition of sensing elements with different gas-sensing properties could improve the discrimination capabilities of the models [68]. We also derived an ML-based chronic kidney disease classifier to discriminate healthy vs. unhealthy groups. The results show that an SVM classifier with linear kernel could successfully distinguish healthy vs. unhealthy individuals with a 91.7% accuracy. Future work will build on the presented preliminary results to test our RGO-metalloporphyrin sensors with real human breath. Figure 9: (a) RGO thin film samples were noncovalently functionalized by metalloporphyrin molecules (here, CoTPP), (b) RGO-metalloporphyrin sensors with different metalloporphyrins showed varying sensing responses. **Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app112311290/s1](https://www.mdpi.com/article/10.3390/app112311290/s1)]([https://www.mdpi.com/article/10.3390/app112311290/s1](https://www.mdpi.com/article/10.3390/app112311290/s1)): Figure S1: Dimensions of the interdigitated electrode; Table S1: PCA weights; Figure S2: F1-score results of bootstrapped samples for **(a)** acetone, **(b)** isopropanol, **(c)** ammonia, and **(d)** carbon monoxide classification; Table S2: \(R_{norm}\) (%) of four different RGO-metalloporphyrin films at different ammonia concentrations. **Author Contributions:** B.M.L. : methodology, sensor development, experimental validation, data analysis (sensing property characterization, ML algorithms), writing--original draft preparation, writing--review and editing, visualization. A.E. : data analysis (ML algorithms), writing--review and editing. I.T. : project administration, conceptualization, writing--review and editing. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the NSF-NIFA/NSF AI Institute for Next Generation Food Systems, United States Department of Agriculture (USDA), National Institute of Food and Agriculture (NIFA) award number 2020-67021-32855 and NSF award 1743101 to I.T. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available within the article or Supplementary Materials. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### EASY Concept Design Details: System Functionality for Different Users
#### 4.4.3.1 Connections and Accessibility  
As a web-based mapping application EASY would be designed for a minimum connection speed of 256 kbps to be adequately responsive to end-users due to the size of the graphical outputs of mapping applications. However, with broadband internet becoming more mainstream even in rural and remote areas it is anticipated that the system would be scaleable and be able to benefit from increased bandwidth when available.
# Visual-Inertial Navigation System Based on Virtual Inertial Sensors
## 3 Virtual Inertial Navigation System Based on Deep Learning
### Performance Verification of Zero-Velocity Update of VIMU  
In this study, we initially evaluated the accuracy of the gait classification. Table 1 shows that the DeepConvGRU model achieves a gait classification rate of approximately 97.6%. Considering the fault tolerance capability of the DenseNet-GRU hybrid model, the achieved accuracy in the experiment is sufficient for the subsequent development of virtual inertial sensors.  
We additionally gathered torso and foot inertial sensor data to validate three common gaits of the quadruped robot: walking, running, and uphill/downhill walking. To compare the constructed virtual inertial sensors using the DenseNet-GRU hybrid network model with the actual foot inertial sensor data, we refrained from presenting the actual fitting curve. This decision was made based on the observation that during the motion process, the amplitude of the motion information significantly exceeds the fitting error's magnitude, rendering the difference between the two negligible and indistinguishable. Table 2 shows the fitting output error of the virtual inertial sensor. From the results, it can be seen that the fitting output error of the virtual inertial sensor is relatively small when the quadruped robot moves relatively vigorously on slopes such as uphill and downhill, meeting the system accuracy requirements.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Walking** & **Running** & **Uphill** & **Downhill** & **Classification Rate (\%)** \\ \hline Walking & 343 & 2 & 2 & 1 & 98.0\% \\ \hline Running & 4 & 341 & 1 & 1 & 97.4\% \\ \hline Uphill & 1 & 3 & 340 & 1 & 97.1\% \\ \hline Downhill & 2 & 1 & 1 & 342 & 97.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy of DeepConvGRU model gait types.  
Figure 5: Flowchart of Zero-Velocity Update System.  
Where a and w, respectively, represent accelerometers and gyroscopes, M is the output deviation of the IMU, and S is the output noise of the IMU.  
Under the same conditions, the ZUPT method is applied to process the fitting output results of the VIMU. Table 3 presents the error of the ZUPT output of the VIMU, which shows that the ZUPT output error of the VIMU is relatively small during the typical motion of the quadruped robot, for which improvement enhances the system's accuracy for positioning.  
Based on the comparison results, it can be seen that the fitting curves of the VIMU have similar identification accuracy and a small curve error to the actual foot inertial sensor in the case of relatively intense movements such as uphill and downhill of the quadruped robot, as in the previous study, which meets the positioning accuracy requirements of the inertial navigation system [12].
## 5 Proposed Model
### Connectivity-Aware Network Construction  
Initially, the entities in the infrastructure layers are properly constructed to overcome the connectivity issues of huge communication traffic in sparsely connected networks. The entities in the infrastructure layer are composed of WSN nodes, local gateway, and sink nodes. The proposed work constructs the network based on the local gateway coverage in the form of 2D hexagonal grids. Within every local gateway coverage, there are several WSN nodes placed randomly. Note that, the proposed WSN nodes are mobile in nature. The sink nodes are placed at the edge of the infrastructure layer to resiliently communicate with the edge-assisted SDN switches for updating the flow rules. On the whole, the network construction overcomes the connectivity and coverage whole issues, respectively, in the SDWSN environment.  
The network construction is initiated by the SN, which tends to place the LBS at the center of every 2D hexagonal grid. The distance between the SN and LBS is denoted as dis, which can be formulated as:  
\[\mathrm{D}=\sum\
olimits_{\mathrm{j}=1}^{\mathrm{m}}\mathrm{disj} \tag{3}\]  
In Equation (3), \(\mathrm{j}=1\), \(2\), \(\ldots\), \(\mathrm{m}\), where \(\mathrm{m}\geq 1\). The WSN sensor nodes are placed among the SN and LBS at a certain distance. The angle between the WSN nodes and LBS can be formulated as:  
\[\theta_{\mathrm{j}}=\tan-1\frac{(\mathrm{x}\mathrm{j}+1-\mathrm{x}\mathrm{j}) }{(\mathrm{y}\mathrm{j}+1-\mathrm{y}\mathrm{j})} \tag{4}\]  
After that, the WSN nodes weights can be computed as follows:  
\[\mathrm{wf}=\beta\mathrm{Nei}^{\mathrm{cntt}}\mathrm{Res}^{\mathrm{ene}} \tag{5}\]  
where \(\beta\) denotes the relative constant, \(\mathrm{Nei}^{\mathrm{cntt}}\) denotes the count of neighbor, and \(\mathrm{Res}^{\mathrm{ene}}\) denotes the lingering energy. The WSN node that has amplified wf is selected as a best intermediate node and is placed in close proximity to the LBS. In a similar manner to other WSNs, once the capacity of the hexagonal grid gets overloaded, the hexagonal grid start constructing continuously until the WSN and LBS are effectively placed with better connectivity.
## 3 Proposed SE Framework with DWT Features  
This section introduces a novel SE framework that exploits the short-time DWT data as one of the sources to create the encoding features for an encoder-decoder SE architecture such as Conv-TasNet and DPTNet, introduced in the previous section. This new framework is partially inspired by the cross-domain TCN (CD-TCN) [31], in which the time-domain and frequency-domain features are employed together for the following mask-estimation process, as depicted in Figure 7. We propose the use of wavelet-domain features as a substitute for frequency-domain features, hoping to bring about further SE improvement.  
In the presented method, we would use the one-level and two-level DWT individually to create wavelet-domain features to see the corresponding behavior in an SE framework. The related details and variants are provided in the following two sub-sections. It is noteworthy that we can use DWT with a level greater than 2, and a higher-level DWT can generate more sub-bands with finer frequency-resolution features at lower frequencies. However, because of the factor-2 sampling procedure in DWT, the amount of data points at the lowest two DWT sub-bands reduces exponentially as the DWT level is increased.  
In general, vowels have their energy primarily concentrated in the frequency range of 250-2000 Hz, while voiced consonants have their energy in the range of 250-4000 Hz. Comparatively, unvoiced consonants have varying strength with their energy concentrated in the range of 2000-8000 Hz. Since the majority of speech corpora have a sampling rate of 16 kHz, which covers the frequency range of 0-8 kHz, the sub-bands obtained through one-level DWT are located approximately at (0 kHz, 4 kHz) and (4 kHz, 8 kHz), while the  
Figure 4: A schematic diagram of one-level DWT.  
Figure 5: A schematic diagram of L-level DWT.  
Figure 6: A schematic diagram of three-level DWT.  
sub-bands obtained through two-level DWT are located roughly at (0 kHz, 2 kHz), (2 kHz, 4 kHz), and (4 kHz, 8 kHz). The DWT features analyzed in this study, therefore, contain significant acoustic speech information. However, if a higher sampling rate is used, such as 44.1 kHz for music sampling, the high-pass DWT sub-band may not contain crucial speech components and should be disregarded during integration.
## 3 Results and Analysis
### Analysis of the Strain-Stress Relationship of Confined Compaction
#### 3.1.2 Strain-Stress Curve-Fitting Analysis  
The strain-stress curve-fitting equation can well describe the deformation law of crushed gangue under confined compaction. The commonly used fitting forms are the multi-parameter exponential function and logarithmic function, as shown in Equations (2) and (3).  
\[\sigma=a\cdot e^{bx} \tag{2}\]  
\[\epsilon=a-b\ln(\sigma+c) \tag{3}\]  
Although the above two fitting forms have a high degree of fitting with the experimental data, they still have certain shortcomings. Firstly, according to the functional form of the two equations, when the strain value is 0, the stress value calculated by the fitting equation is not 0. Secondly, when the stress increases infinitely, the strain value will also increase infinitely, which is obviously not consistent with the actual situation.  
Therefore, in order to better describe the strain-stress laws of the three stages of the confined compaction of crushed gangue, different function forms are adopted to numerically fit the strain-stress curves according to the characteristics of the strain-stress curves in each stage.  
The multi-parameter logarithmic function is used for fitting in the rapid compaction stage and the crushing compaction stage, and the Chapman-Richards function is used for fitting in the stable compaction stage. The specific fitting form is shown in Equation (4).  
\[\begin{cases}\epsilon_{1}=a_{1}\ln(b_{1}\sigma+1)&\text{Rapid compaction stage}\\ \epsilon_{2}=a_{2}-b_{2}\ln(\sigma+c_{2})&\text{Crushing compaction stage}\\ \epsilon_{3}=a_{3}\Big{(}1-e^{-b_{3}\sigma}\Big{)}^{c_{3}}&\text{Stable compaction stage}\end{cases} \tag{4}\]  
where \(a_{1}\), \(b_{1}\), \(a_{2}\), \(b_{2}\), \(c_{2}\), \(a_{3}\), \(b_{3}\), \(c_{3}\) are the fitting parameters.  
The strain-stress fitting curves under different water content conditions are shown in Figure 6. It can be seen that the piecewise function form of Equation (4) can well express the strain-stress relationship of the crushed gangue under different water content conditions, with the R-squared values all above 0.99. There is a high correlation between the fitting equation and the experimental data.
# CFD Simulation of Solid Suspension for a Liquid-Solid Industrial Stirred Reactor
## 4 Conclusions  
This paper presents the results of the CFD analysis of stirring a liquid-solid suspension in an industrial reactor. The numerical analysis of the flow inside the reactor was conducted using expert software Fluent 16 with the Eulerian multiphase model for modeling the two-phase flow and the RNG \(k\)-\(\varepsilon\) model for turbulence. The sliding-mesh approach was used for modeling the rotation of the stirring mechanism.  
The aim of this paper was to assess the capability of the impeller to generate a hydrodynamic field that would keep the entire solid phase off the bottom of the reactor with the desired uniform distribution in the bulk of the liquid phase. Two cases were analyzed: one where the impeller starts to operate when the solid phase is initially completely settled on the bottom of the reactor and one where the impeller starts to operate when the solid phase is initially homogenously distributed in the entire volume of liquid. To analyze the performance of the impeller, the distribution of the volume fraction of the solid phase was plotted on a vertical plane, on the entire 3D domain and on a histogram after 90 s of flow time. The hydrodynamic performances of the impeller were also analyzed with the help of the velocity vector distribution on one horizontal and one vertical plane and with the help of the velocity components axial, radial and tangential plotted on a vertical plane through the middle of the reactor.  
From the analysis of these entire data, for both investigated cases, it resulted that the original two-blade impeller was not able to generate a hydrodynamic field that could keep  
Figure 9: Tangential velocity distribution of the liquid phase: **(a)** flow started from the solid phase fully sedimented; **(b)** flow started from the solid phase dispersed in the entire volume of liquid.  
the entire solid phase in the suspension. At the end of the flow time, a part of the solid phase was sedimented on the bottom of the reactor. The volume fraction of the solid phase sedimented was larger for the first case investigated, proving that the impeller is not able to lift the already sedimented solid phase. Even when the simulation is started with a homogeneous solid suspension, unacceptable sedimentation still occurs.  
Another conclusion of the research is that the power required by the stirring system is not significantly changed when operating with a liquid-solid suspension.  
As a result, a proper stirring of the liquid-solid suspension requires a new design for the impeller able to generate a suitable flow that prevents the sedimentation, which hinders the chemical reaction.  
Investigation, A.S. and A.B.; Methodology, A.S and R.S.-R.; Supervision, R.S.-R.; Writing--review & editing, A.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grant number 50/2017.  
Not applicable.  
The authors declare no conflict of interest.