esa-sceva/satcom-chunk-collection huggingface dataset:
## Validation
### Bingham Fluid Driven by Pressure Gradients  
Byron-Bird [49] derived analytical solutions for the Bingham flow in a channel, driven by a pressure gradient \(P_{0}-P_{L}\). The channel was depicted as the length \(L\) and the width \(2B\). The no-slip boundary condition was applied to the surfaces of the channel (Figure 1).  
The yield surface is located at \(x=x_{0}\) where \(x_{0}=\frac{\tau_{0}L}{P_{0}-P_{L}}\). The velocities in the plug region \(v_{z}^{>}\), and in the liquefied region \(v_{z}^{<}\) are:  
\[v_{z}^{>}=\frac{(P_{0}-P_{L})B^{2}}{2\mu_{B}L}\bigg{[}1-\left(\frac{x_{0}}{B} \right)^{2}\bigg{]}-\frac{\tau_{0}B}{\mu_{B}}\bigg{[}1-\frac{x_{0}}{B}\bigg{]} \left(-x_{0}\leq x\leq x_{0}\right) \tag{13}\]  
\[v_{z}^{<}=\frac{(P_{0}-P_{L})B^{2}}{2\mu_{B}L}\bigg{[}1-\left(\frac{x}{B} \right)^{2}\bigg{]}-\frac{\tau_{0}B}{\mu_{B}}\bigg{[}1-\frac{x}{B}\bigg{]} \left(x_{0}<x\leq B\text{ and }-B\leq x<-x_{0}\right) \tag{14}\]  
if \(\tau_{0}=0.0\), \(x_{0}=0.0\), and the material is a Newtonian fluid.  
Four cases were proposed to validate the models with Equations (13) and (14). They are one Newtonian case and three Bingham cases with different parameters such as channel length \(L\), channel  
Figure 1: Flow in a channel driven by the pressure gradient, showing the plug region \(-x_{0}\leq x\leq x_{0}\) and the liquefied region \(x_{0}<x\leq B\) and \(-B\leq x<-x_{0}\), based on Byron-Bird [49].  
width \(2B\), one end's pressure \(P_{0}\), Bingham viscosity \(\mu_{B}\), and yield stress \(\tau_{0}\). Figure 2 shows good agreements between the theoretical and numerical results of the all runs. One of the important features of a Bingham fluid is the plug zone (Figure 2b-d), which cannot be seen in the Newtonian fluid (Figure 2a). Note that the velocity of a Bingham fluid is constant in the plug region. In this region, the rate of change of velocity (strain rate) is equal to zero. In the liquefied region, the strain rate is greater than zero and the stress-strain relation of the fluid is dependent on the plastic viscosity \(\mu_{B}\). These figures demonstrate that the present numerical model can accurately describe the rheological behavior of Bingham fluids.
## 5 Results of Experimental Test
### Configuration C  
Four holes were drilled into the 20 mm thick piston plate, with orifices located at the center of the plate, spaced evenly. Despite having the same total cross-sectional open area as Configuration B, Configuration C demonstrated different performance results.  
Figures 25 and 26 display the results of cyclic load testing and the load-displacement graphs for Configuration C. The figures reveal a similar pattern to the results for Configuration B. However, despite having the same total cross-sectional open area, Configuration C produced a higher load than Configuration B. This improvement is attributed to the placement of orifices at the center of the piston plate, which led to a more uniform distribution of viscous fluid flow compared to Configuration B.  
Figure 25: Cyclic load for 2 mm diameter for Configuration C. (**a**) Load generated by 10 mm movement; (**b**) load generated by 20 mm movement; (**c**) load generated by 30 mm movement; (**d**) load generated by 40 mm movement.  
Table 7 shows the damping force capacity of the viscous plane damper for Configuration C. The performance improved notably, especially with the 1 mm diameter orifices. The smaller size of these orifices enhanced fluid flow control through the holes, increasing the drop pressure and thereby boosting the damping force. This finding aligns with the study by [PERSON] et al. (2009), which demonstrated that smaller orifices lead to higher drop pressures, resulting in greater damping force inside the barrel and better reduction of building movement against external forces such as seismic activity and typhoons ([PERSON], 2016).  
The load produced by Configuration C with 1 mm diameter orifices was significantly higher than that of the 2 mm diameter orifices. On average, the load with 1 mm orifices was over 40% greater than with 2 mm orifices. This increase in load is attributed to the improved fluid flow dynamics within the box barrel due to piston plate movement, highlighting the importance of orifice size in affecting the damper's performance.  
Figure 26: Cyclic load for 1 mm diameter for Configuration C. (**a**) Load generated by 10 mm movement; (**b**) load generated by 20 mm movement; (**c**) load generated by 30 mm movement.
# QoS Aware and Fault Tolerance Based Software-Defined Vehicular Networks Using Cloud-Fog Computing
## 2 Literature Review
### SDVN-Based Models  
SDVN is based on SDN and VANET architectures. SDVN is not fully centralized, as it is partially centralized and has hierarchical architecture. In [5], the author proposed a topology-based routing protocol for SDVN. The proposed model is used for vehicle dynamic path selection for communication among other vehicles in real time. They used predicated topologies to maintain routing tables and paths. They used two communications models such as uni-cast for communication and geo-cast for data dissemination. The proposed SDVN model performed well in terms of communication and dissemination of information. Similarly, in [12], the authors proposed and placed dynamic controllers in the edges of SDVN architecture. The proposed model performed well in road traffic. The proposed model reduced network changes as compared with the available network models. In [13], the authors proposed multi-access edge computing (MEC) for vehicles. They used two communication models: information/messages receiving, while the other is for messages forwarding to the connected vehicles. Model one is used for information forwarding to V2V and V2I. The second model is used for updating information and forwarding information using the Open-Flow protocol. The proposed model consists of four layers and reduces the latency of communication messages, and improves the routing path. In [14], the author proposed a prototype-based SDVN architecture in which the author examined backbone real hardware that consists of Open-Flow switches. In [15], the proposed RTISAR model reduces delay and packet loss, and makes overall communication from source to destination. In [16], the author proposed an application layer for VANET vehicles to reduce communication delay and manage massive traffic in ruler and urban areas.
## 5 Experiments and Analysis
### Mixed Berths  
The mixed berth was simulated using real liquid terminal data to experiment with 10, 27 and 42 vessels, with 1000 iterations performed using both the NSGA-II-Dijkstra and INIIDA algorithm, respectively. The results are illustrated in Figure 10:  
Figure 10: Results of mixed berths.  
Figure 9: Optimal solution Gantt chart of small-scale berths.  
When the operation plan for the mixed berth involves 10 vessels, both algorithms can find the first level Pareto set. However, as the number of vessels in the operation plan increases, the first level Pareto set obtained by the INIIDA is significantly better than that obtained by the NSGA-II-Dijkstra algorithm. This demonstrates the superior effectiveness of the INIIDA in the calculation results.  
According to the actual conditions of terminal operation, a Gantt chart is used to represent the optimal solution for mixed berths with 10 vessels, as shown in the figure below. Figure 11 illustrates the number of berths, the number of oil transfer arms, the length of pipelines, and the number of oil storage tanks.
## 5 UK Salt Cavern Exergy Storage Capacity Estimates--Results and Commentary
For the operational cycle based on 108 kg/s fill rates and maximum withdrawal rates of 1.5 MPa/hr, stored exergy to work estimates range between \(\approx\)264-296 TWh (Figures 5c and 6c) and 119-151 TWh (Figures 7c and 8c) respectively, meeting between \(\approx\)88-98% and 40-50% of the UK electricity demand, respectively. Taking the estimates based on UGS cavern numbers for the two depth ranges and cavern sizes, stored exergy to work estimates could, using the most cycled operational mode (Figures 5c and 8c), provide between 13.3 and 13.7 TWh, respectively, meeting \(\approx\)4.5% of the UK electricity demand. The other halite basins provide similarly important additional exergy storage and exergy to work support, with for example, just 1% of all caverns of 100 m and greater in the 500-1500 m depth range in the basins providing a further \(\approx\)34-65 TWh of work and meeting \(\approx\)12-22% of the UK electricity requirements, depending on the mode of operation and cycle numbers (Figure 6a-c, S2, Tables S6-S8). Whilst 1% of caverns 100-150 m height in the same depth range might provide a further \(\approx\)9.53-18.1 TWh of work, meeting \(\approx\)3.2-6.0% of the UK electricity requirements (Figure 8a-c, S2, Tables S6-S8). Relative to UGS cavern numbers in the basins, the figures for 100 m plus caverns in the depth range 500-1500 m might provide an additional \(\approx\)12.1-23.2 TWh of work, meeting \(\approx\)4-7.7% of the UK electricity requirements (Figure 6a-c, S2, Tables S6-S8), whilst 100-150 m height caverns might provide \(\approx\)7.5-14.2 TWh of work, meeting \(\approx\)1.5-4.7% of UK electricity requirements (Figure 8a-c, S2, Tables S6-S8). These data illustrate the potential importance of CAES and salt cavern storage to UK electricity demand and supply.
## 4 Effect of Seismic Parameters on Vertical DRFs
### Effect of the Site Conditions on the Vertical DRFs  
Figure 10 illustrates the mean DRF curves of distinct soil classes (i.e., A, B, C, and D) at damping ratios (\(\xi\)) equal to 1%, 3%, 10%, 15%, 20%, 30%, and 40%. The results presented in the figure show that the amplitude of and trend in the DRFs are relatively similar for site classes A and D, as also observed by [PERSON] (2019). In addition, the same tendency for soil classes B and C is also observed in the DFRa values. It can be also observed from the figures that the DRFa and DRFd curves follow a fairly similar path, and the DRFs are very close to unity for periods lower than 0.02 s (T \(<\) 0.02 s) and for all soil classes. On the other hand, the DRFd and DRFv curves are almost identical for periods longer than 1.0 s (T \(>\) 1.0 s) and tend to decrease, almost reaching unity with an increasing natural period. From very short periods to 0.02 s, the DRFv values become narrow and very close to unity for all the soil classes.  
A comparison of the DRF values for distinct soil classes is plotted in Figure 11 for different damping ratios. It can be said that the DRF values have no specific order and follow no systematic path for distinct soil classes. For example, the DRFs calculated from the earthquakes recorded in soil A are slightly greater than those for the ground motions recorded in soil type D, and the DRFs calculated from the earthquakes recorded in soil type B are slightly greater than those for the ground motions recorded in soil type C. It is noticeable that the DRFa values are very close to each other for the different soil classes and at damping ratios of 1% and 3% according to Figure 11a-d. However, the differences become more evident for periods longer than 1.0 s (T > 1.0 s) at almost all the damping ratios (especially for \(\xi\) > 1%). Based on these results, it can be highlighted that soil type has no apparent effect on the DRFa at low damping ratios, and the effect of soil type seems to be evident for periods longer than 1.0 s at high damping ratios. Except for small differences at shorter periods (T < 0.02 s), the trends in the DRFd and DRFv curves are almost identical, especially compared to that of the DRFa. In general, the results indicate that the soil type has no apparent effect on the DRFd or the DRFv. This conclusion is also consistent with the results reported by various researchers in the literature [28, 29, 32, 33, 50].  
Figure 11: _Cont._  
Figure 10: The variation in the median DRFs for different damping ratios by site conditions; (**a–d**) DRFa, (**e–h**) DRFd, and (**i–l**) DRFv.
## 2 Materials and Methods
### Measurement of Volumetric Strain  
Following each wetting-drying (WD) cycle, volume measurements were conducted on the soil samples to analyse their volumetric behaviour over successive cycles. Measurements were performed using a high-precision electronic Vernier calliper with an accuracy of \(\pm 0.005\) mm. For each specimen, the height (h\({}_{1}\), h\({}_{2}\), and h\({}_{3}\)) and diameter (d\({}_{1}\), d\({}_{2}\), and d\({}_{3}\)) were recorded at three equally spaced points along the specimen's surface to ensure representative data.  
Measurements were performed with utmost care and minimal handling to minimise disturbance, particularly in samples with reduced structural integrity due to moisture after saturation. The average values of the height and diameter measurements were used to compute the specimen volume and the corresponding volumetric strain (\(\epsilon_{\
u}\)) for each WD cycle.  
The volumetric strain was calculated using the equation below.  
\[\epsilon_{\
u}=\frac{V_{N}-V_{0}}{V_{0}}\times\mathbf{100} \tag{1}\]  
where \(V_{0}\) represents the initial volume of the compacted specimen, and \(V_{\mathrm{N}}\) denotes the volume after N WD cycles. A positive \(\epsilon_{\
u}\) value indicates swelling, while a negative value corresponds to shrinkage.
# Thermal and Thermomechanical Analysis of Amorphous Metals: A Compact Review
## 2 Thermal Characterization of Amorphous Metals
### A Brief Overview of Conventional Technology
#### 2.1.2 Possibilities of DSC for Metallic Glasses  
[PERSON] et al. studied the influence of composition on the properties of Pd\(\gamma\)Cu\({}_{6\text{-}\text{x-y}}\)Au\({}_{\text{x}}\)Ag\({}_{\text{y}}\)Si\({}_{10}\)P glassy ribbons using conventional DSC at heating and cooling rates of 0.33 K s\({}^{-1}\), and they found that there exists an optimal ratio for the addition of Ag and Au glass formers, which stands at 2% and 1%, respectively [47]. It is also possible to calculate the crystallization activation energy in order to gain insight into the kinetic processes [48]. [PERSON] et al. investigated the kinetics of Mg-based glasses [49], while [PERSON] et al. researched the crystallization kinetics of Cu\({}_{60}\)Zr\({}_{25}\)Ti\({}_{15}\) and (Cu\({}_{60}\)Zr\({}_{25}\)Ti\({}_{15}\))\({}_{95}\)Ni\({}_{5}\) bulk metallic glasses using the Arrhenius and Avrami models to calculate activation energy and kinetic parameters, respectively [50].
# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 5 Discussion
### Practical Implications  
The approach described in this work brings numerous advantages in real-world scenarios. The adoption of Kedro and the pipeline-based workflow allows the replication of the results and the extraction of new ones using the same identical piece of code. In fact, each node that creates the pipeline can be used multiple times on different data to output the same data structure. This means that the system is scalable, as new errors might be mapped and new models can be trained just by adding a new entry to the data to extract.  
Similarly, new entries can be quickly analysed, updating the initial statistical analysis and leading to more insightful details of the general behaviour of the data. With the additional knowledge, models could be updated all at once, retrieving on-screen training/testing performance, and having the models deployed on edge machines.  
Having an EWS active and running with these automatic \"updates\" provides the core value of this work:  
* Each model warns the operator in advance about possible paper jams, so that immediate recovery procedures can start;* Constantly updated models with improved discrimination capability can enlarge the forecast horizon;
* The possibility to extend the developed pipeline to different machine parts to monitor new zones;
* Lowers the downtime of the system and improves the production as models are fed with new data.  
Moreover, the adoption of Kedro, which allows the automatic deployment of the trained neural networks and their usage on real-time clients (like those close to the machine), brings improvements on many levels:  
* Allows fast retraining using the same approach on new data;
* Allows the automatic deployment and updating of new models directly on the production machines.  
Finally, the findings of this study demonstrate that legacy machines, despite not being originally designed with advanced monitoring capabilities, can be enhanced through the integration of sensors and AI models without requiring a complete replacement of existing infrastructure. This approach presents a cost-effective opportunity for manufacturing companies seeking to modernise their production systems with advanced predictive technologies, reducing technological upgrade costs while improving operational efficiency.
## 3 Results  
Table 3 presents the impact of the three irrigation scheduling methods on marketable yield, yield quality indicator, and water productivity for yield quality. FarMan scheduling demonstrated the highest marketable yield. IrriMan and PlaSoMan had comparable yields, but PlaSoMan resulted in a 10% yield loss compared to FarMan, while IrriMan caused a loss of about 7.5%.  
Moreover, PlaSoMan showed the highest values of YQ and \(\mathrm{WP_{YQ}}\). Both these parameters showed a significant interaction year x irrigation scheduling method (Figure 3A,B). Only PlaSoMan showed the highest value for both the YQ and WP in both years, even if they were different in terms of rainfall, while, in the second wetter year, the IrriMan scheduling showed the lowest YQ values (Figure 3A) and FarMan the lowest \(\mathrm{WP_{YQ}}\) values (Figure 3B).  
As for the water use efficiency calculated on a marketable yield basis (IWUE), the lowest values were obtained for the FarMan scheduling, which did not show significant differences between the two years. The irrigation scheduling based on the Irriframe model and the new PaSoMan system showed higher IWUE values, especially in 2016 (Figure 4A). Finally, the blue water requirement (BWR) was lower under IrriMan and PlaSoMan, especially in 2016 (Figure 4B).  
Moreover, the PlaSoMan showed also the highest values of all the qualitative parameters considered, and FarMan the lowest for soluble solids content, color index, lycopene and \(\beta\)-carotene (Table 4). On the contrary, titratable acidity and pH did not show significant differences among the three irrigation methods investigated (Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \multicolumn{3}{c}{**Irrigation Scheduling Method 1 **} \\ \hline  & **FarMan** & **IriMan** & **PlaSoMan** \\ \hline  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effects of the irrigation scheduling method on marketable yield, yield quality indicator (YQ), and water productivity for yield quality (\(\mathrm{WP_{YQ}}\)).
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 4 Challenges and Design Requirements
### Fault Tolerance  
Network failures, mote power dropout, and stochastic delay in the WHNCS are unpredictable. Failure of one or more motes could result in the collapse of the entire process. Such characteristics have led to the development of various techniques, such as energy aware routing for low-energy ad hoc sensor networks (EAR-LEAHSN), the energy-aware QoS routing protocol (EQoSR), the multi-level route-aware clustering algorithm (MRLC), and the distributed clustering-based multipath algorithm (DCM), to provide standby redundant paths/devices to support multi-hopping [122; 123]. Using these techniques in real time comes with complications, such as increased energy consumption due to the multiple copies of data transmission to sink node, greater bandwidth utilization for reliable data, continuous alternate path-finding, and complex data reconstruction processes [124].
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 2 Numerical Modeling  
### Geometry Definition  
A Distributed Mode Absorber (DMA) consists of a vibrating panel and an enclosed air volume behind the panel. In the scope of this study, the panel is modeled without the frame to observe only the vibrating region. It is created as a surface object (2D shell) starting from the origin with the dimensions in the \(x\) and \(y\) directions. The volume for the back cavity filled with air is created, starting from the panel area extruding from the depth in the negative \(z\) direction. The already existing housings from previous works regarding Distributed Mode Absorbers (DMAs) [7; 9] were used. The front panels of the presented prototypes are made from High Pressure Laminate (HPL) material with a thickness of \(h_{0}=3\) mm. Therefore, the model dimensions need to match the dimensions of these. The front panel is \(L_{x}=0.5\) m long and \(L_{y}=0.4\) m wide. With the same area, the air volume is created with a depth of \(h_{\text{BC}}=0.12\) m. The DMA geometry is shown in Figure 1.
# A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List
###### Abstract  
Compared with High-Efficiency Video Coding (HEVC), Versatile Video Coding (VVC) has more flexible division and higher compression efficiency, but it also has higher computational complexity. In order to reduce the coding complexity, a fast algorithm based on the most probable partition pattern list (MPPPL)and pixel content similarity is proposed. Firstly, the MPPPL is constructed by using the average texture complexity difference of the sub-coding unit under different partition modes. Then, the sub-block pixel mean difference is used to decide the best partition mode or shorten the MPPPL. Finally, the selection rules of the reference lines in the intra prediction process are counted and the unnecessary reference lines are skipped by using the pixel content similarity. The experimental results show that compared with VTM-13.0, the proposed algorithm can save 52.26% of the encoding time, and the BDBR (Bjontegarrd delta bit rate) only increases by 1.23%.  
versatile video coding; fast algorithm; coding unit partition; intra prediction +
Footnote †: journal: Applied  
doi: Article
# Dynamic Network Formation for FSO Satellite Communication
## 1 Introduction
### Our Contribution  
The main novelty of this work lies in the semi-optimal Degree Bounded Spanning Tree (DBST) heuristic--this method provides a solution to the bounded degree spanning tree problem in a dynamic network that can also be used as a reasonable solution in real-time. To the best of our knowledge, this is the first work that uses the ant-colony methodology in order to optimize the interlinks of real-world large satellite constellations. Moreover, based on simulated experiments with real-world satellite data, both the correctness and the efficiency of the algorithm were verified.