esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 6 Proposed Link Cost Estimation and Modified Route Selection Procedure
### Modified Route Selection Procedure
This aspect is beneficial in that it increases the redundancy of the system, avoids that a certain set of nodes be overused, and to the point of this paper, allows a node to proper measure the link costs of multiple nodes. Because our procedure uses unicast packets to improve the link cost estimation and selects routes in round-robin fashion, we shall refer to it as the **U-RR procedure**. With respect to existing models, the use of unicast transmissions in our U-RR procedure is similar to [22; 24; 25]; however, our U-RR procedure is tailored to ZigBee and its 3-bit link cost quantization. It is important to highlight that our U-RR procedure works as the ZigBee specified procedure when the cumulative route cost offered by the RREQ sender is different than the cost offered by the current next hop node. As such, the U-RR procedure is able to maintain the qualities of the existing ZigBee procedure, improving it only when there is a tie between next hop candidates, which is fairly common in ZigBee because of its 3-bit link cost quantization. An additional important point is that, in the existing ZigBee procedure, the choice between next hop candidates with the same cumulative cost is random: a node would choose as next hop, the node whose RREQ arrived first. In the U-RR procedure, the choice between next hop candidates with the same cumulative cost is no longer random, being guided by the number of past packets sent to each candidate.
# Counter-Terrorism Video Analysis Using Hash-Based Algorithms
## 6 Implementation and Performance Study
### Choice of Threshold  
To ensure that the system detects matching videos it is necessary to establish a suitable threshold; the maximum permitted distance between two frames to consider them matching frames. This may cause some false positives, but its probability is reduced by the frame extraction rate choice. Furthermore, they can be dismissed _a posteriori_ by a person. Videos with less than three matching frames should by default be checked by a person, as our tests have shown that videos with false positives often have just one matching frame, exceptionally two.  
We use a threshold of 4 units for our results (frames with up to 6.25% hash difference are considered matching), with a 3-5 f/s extraction rate. However, a threshold of 3 units (4.69% hash  
Figure 4: Schema of the process used to convert videos into hashes.  
Figure 5: Schema of the process used to convert videos into hashes.  
difference) did prevent all false positives at the expense of setting a higher frame rate for the correct detection of all matching videos, which caused the processing time to triple. The high frame rate was at 5-12 f/s, but this case is not described in the present text due to performance concerns.
## 4 Numerical Simulation
### Simulation of Related Working Conditions  
In the simulation of the shield-tunnel construction, two shield tunnels were excavated. On the right was the tunnel built first, and on the left was the tunnel built later. Each shield tunnel was 78 m in length and divided into 26 excavation sections; that is, each excavation section was 3 m in length. A soil pressure of 0.18 MPa and a grouting pressure of 0.15 MPa were considered in the process of simulating the tunnel excavation. In the practical construction, synchronous grouting of a segment was a complicated process, which was simulated using the \"presumption of equivalent replacement layer\" in the finite element model. The \"equivalent layer\" was made of a mixed material of soil and cement mortar. The values of the elastic modulus and Poisson's ratio referred to the cement soil, and the thickness was calculated as follows:  
\[\delta=\eta A \tag{2}\]  
where \(\delta\) is the thickness of the equivalent layer, \(A\) is the clearance of the shield tail construction, which is half of the difference between the outer diameter of the shield and the outer diameter of the segment, and \(\eta\) is the correction factor.  
The range of 0.7-2.0 for the coefficient \(\eta\) given by the Japanese shield regulations based on many shield excavation examples was adopted. Sandy soil and part of the hard rocks formed most of the soil layers of the excavation face in the project. \(\eta\) was considered to be 1. In that case, the thickness of the \"equivalent layer\" was taken as \(\delta=0.3\) m. The hardening process of the grout must be considered in shield tail grouting, which is achieved by reducing the grouting pressure and enhancing the strength of the \"equivalent layer.\" At the early stage of grouting, the strength of the cement mortar is low with a high grouting pressure. It can ensure that the \"equivalent layer\" can fill the clearance of the shield tail and reduce the stratum loss. As the cement mortar hardens, the strength of the \"equivalent layer\" reaches the final point, and the radial grouting pressure decreases and dissipates to 0 MPa. Based on previous experiences [34], this assumption can effectively simulate the synchronous grouting of segments in the construction of a shield tunnel.  
The working conditions simulated in this study involved bridge and culvert construction, shield tunnel excavation, and body reinforcement. Specifically, the pile foundation was simulated by beam elements, the raft was simulated by solid elements, the segment was simulated by slab elements, and the equivalent layer was simulated by solid elements. Regarding the foundation-reinforcement plan consisting of the MJS method and the double-pipe jet-grouting pile, reinforcement construction was implemented by activating the working conditions, which involved \"changing the original soil mass parameters to the reinforcement parameters.\" The structures of the bridges, the culverts, and the reinforcement bodies were simulated, as shown in Figure 16.  
Figure 16: Main structure model and solid simulation diagram.
## 1 Introduction
Reference [8] presented a parallel 2D elastic frequency-domain FWI algorithm based on a discontinuous forward problem [15] that was applied to a realistic synthetic onshore case study. They obtained a high-resolution P- and S- wave velocity of the complex onshore structure using a joint inversion of the surface and body waves recorded by a wide aperture acquisition geometry. Reference [16] studied the application of the FWI method in the time-domain on the problem of subsalt imaging with the modified Flooding Technique and showed the difference between the results of elastic and acoustic FWI methods. These differences reveal that the result of the acoustic FWI algorithm on elastic data for the subsalt imaging problem is not reliable. The application of the multi-parameter viscoelastic FWI using a frequency-domain on synthetic data example was proved by [17]. The low-order finite element discontinuous Galerkin method was used to solve the forward problem which can be a good option when studying the complex topographies and high-velocity contrasts, and the quasi-Newton L-BFGS optimization was implemented to estimate the inverse of the Hessian matrix in order to decrease the computational cost and improve the reconstruction of the velocities, density, and attenuation parameters. Reference [9] implemented the FWI-SIMAT algorithm to investigate the capability of the acoustic FWI in the reconstruction of the Marmousi velocity model both in the time and frequency domain. Reference [18] used a developed FWI method in which a two-stage sequential approach (SFWI) was tested on the field datasets recorded in the Black Sea and in the shallow-water area of a river delta in the Atlantic Ocean to obtain detailed subsurface images containing rock formations that might be potential gas deposits. Most applications of the FWI methods on complex structures have been performed in the frequency domain or ignored seismic wave attenuation. Ignoring the viscous effect of the propagation media provides an unrealistic reconstructed S- wave velocity model, especially in the study of the complex geologic media [19]. Reference [20] showed that taking key elements properly into account, FWI produces a reliable high-resolution near-surface model that could not be otherwise recovered through traditional methods. Although few attempts have been reported that incorporate FWI for land studies [18]; however, they were convincing in providing acceptable seismic image. Therefore, it is supposed that deriving a processing workflow modified for accurate imaging of seismic data from complex regions would be promising in resolving the problem of low SNR and strong lateral velocity changes due to complexity in wave propagation media.
## 2 Materials and Methods
### Fungal Identification  
For fungal identification, a dual approach encompassing both phenotypic and genotypic techniques was employed. Pure cultures underwent examination through microscopic and macroscopic analyses. The initial phenotypic classification utilized several mediums including PDA, Czapek yeast autolyaste agar (CYA, 30.0 g\(\cdot\)L\({}^{-1}\) sucrose, 15 g\(\cdot\)L\({}^{-1}\) agar, 5.0 g\(\cdot\)L\({}^{-1}\) yeast extract, 3.0 g\(\cdot\)L\({}^{-1}\) NaNO\({}_{3}\), 1.0 g\(\cdot\)L\({}^{-1}\) K\({}_{2}\)HPO\({}_{4}\), 0.5 g\(\cdot\)L\({}^{-1}\) KCl, 0.5 g\(\cdot\)L\({}^{-1}\) MgSO\({}_{4}\cdot\)7H\({}_{2}\)O, and 0.01 g\(\cdot\)L\({}^{-1}\) FeSO\({}_{4}\cdot\)7H\({}_{2}\)O), Czapek-Doxagar (1.2% agar, BioMaxima, Poland), and malt extract agar (MEA, BioMaxima, Lublin, Poland) in the case of _Penicillium_ spp., focusing on characteristics like colony hue, expansion, and the presence  
Figure 1: Geographic location of the Brestovská Cave in Slovakia (**A**). Subterranean passages of the cave (**B**) with marked underground stream (in blue) sampling points along the tourist path (indicated by a red line): site I outside the cave, and sites from II to VI within the cave. The cave entrance and exit are located next to study site I. Scale bars: A = 50 km; B = 50 m.  
of distinctive morphological features such as spores, following established diagnostic keys and monographs [39; 40; 41].  
Species confirmation involved sequencing the internal transcribed spacer (ITS) regions of fungal rDNA. DNA extraction from PDA-cultivated fungal colonies was performed using the Bead-Beat Micro AX Gravity kit (A&A Biotechnology, Gdansk, Polska), with ITS regions amplified through PCR using specific primers (ITS1, 5\({}^{\prime}\)-TCCGTAGGTGAACCTGCGG-3\({}^{\prime}\) and ITS4, 5\({}^{\prime}\)-TCCCTCGCTTATTGATATGC-3\({}^{\prime}\)) [62]. PCR was performed using a T100 Thermal Cycler (Bio-Rad, Berkeley, CA, USA), according to our previous protocol [63]. The PCR products underwent agarose gel electrophoresis on a 1.2% agarose gel, purification (Clean-Up kit, A&A Biotechnology), and sequencing (Macrogen Europe, Amsterdam, The Netherlands).
# Vortex Breakdown Control by the Plasma Swirl Injector
## 4 Conclusions  
In this study, the concept of using the plasma swirler to control vortex breakdown was proposed and tested. It was observed in the experiments that, by varying the strength of the plasma actuation, the vortex breakdown region could be effectively controlled. The plasma swirler, as a nonintrusive, no-moving-part method of controlling vortex breakdown, was demonstrated experimentally. Flowfield measurement demonstrated that the plasma actuation was both effective and efficient in controlling the development of vortex breakdown. Experimental results showed that the 15 kV actuation was strong enough to trigger the onset of vortex breakdown. The overall shape of the bubble did not change significantly when the strength of actuation increased from 18 kV to 21 kV. The interior structures of the bubble were nearly the same, containing one major cell or vortex ring. Without involving moving parts or mass adding, the plasma actuation offers great flexibility in flow and combustion control. It offers a choice for fundamental research of vortex breakdown phenomena, where the actuation strength and frequency can be varied and controlled easily. Further research is needed to enhance the plasma flow control authority to put this method into engineering applications.  
Conceptualization, G.L.; validation, X.J., W.D. and J.Y.; formal analysis, C.L.; data curation, Y.M.; writing--original draft preparation, G.L.; writing--review and editing, X.J.; supervision, G.X. All authors have read and agreed to the published version of the manuscript.  
Figure 4: Profiles of the mean axial velocity.  
Figure 5: Contours of mean axial velocity magnitude and streamlinesin the XZ plane with bulk velocity of 5.7 m/s.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The research was supported by the National Natural Science Foundation of China (Grant No. 51676188) and the National Youth Natural Science Foundation of China (Grant No. 51706224), which are gratefully acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Result and Performance Assessment of Models
### Taylor Diagram  
A Taylor diagram is the graphical representation of how closely the pattern (or patterns) match the observation quantified in terms of the correlation, root mean square error, and amplitude of their variations (standard deviations). This diagram evaluates the aspects of different complex models and performs a comparative analysis of these models with the reference data (self-observed data) [44]. From Figure 2, it can be seen that while predicting SOS-LSSVM and MARS did not deviate much from the observed or actual values and the models overlap the reference value, EmNN has slightly deviated from the reference, as can be seen in the figure. It has lower correlation than the other two and a high standard deviation and RMSE.
# Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
## 4 Experimental Result
### Synthesis Image  
We compare the performance of the proposed SVD-CSC and other methods with a synthesized image. Figure 11 shows the results of image tests using an image with uniformly dark colors and the same degrees of illumination. From Figure 11b, image enhancement results shown in Figure 11c-g were obtained using sparse coding, single image CSC, single image SVD-CSC, large data CSC, and large data SVD-CSC methods, respectively. In Figure 12, we compare the methods using the S-CIELAB color metric [28], which includes a spatial processing step, and is useful and efficient for measuring color reproduction errors in digital images. We show the S-CIELAB errors between Figure 11a and the sparse coding results in Figure 11c, between Figure 11a and the single image CSC results in Figure 11d, between Figure 11a and the single image SVD-CSC results in Figure 11e, between Figure 11a and the large data CSC results in Figure 11f, and between Figure 11a and the large data SVD-CSC results in Figure 11g. Figure 12a,c,e,g,i show a green color when the S-CIELAB error exceeds 30 units. Figure 12b,d,f,h,j show the histogram distribution of the S-CIELAB error. The S-CIELAB error histogram distributions give the numbers of pixels per error unit. In this test, the S-CIELAB error values for the proposed method (SVD-CSC) indicate that 8.1% of the image exceeded 30 units, whereas 22.9%, 7.9%, 6.4%, and 13.8% of the image exceeded 30 units with sparse coding, single image CSC, single image SVD-CSC, and large data CSC respectively. Quantitatively, the error of single image SVD-CSC is the least, but the dictionary created in this method cannot be used in general. On the other hand, in the case of the large data SVD-CSC, the learned dictionary can be used robustly in general, with performance close to that of the single image SVD-CSC.  
Figure 11: Comparison of various methods using S-CIELAB color metric: (**a**) Original image; (**b**) Shadow version of the original image; (**c**) Image enhancement by sparse coding; (**d**) Image enhancement by single image CSC; (**e**) Image enhancement by single image SVD-CSC; (**f**) Image enhancement by large data CSC; (**g**) Image enhancement by large data SVD-CSC.
## 8 Multi-Segmental Channel Estimation
### MSE Lower Bound Analysis
Because we assume all data and pilot symbols for transmission are known to the channel estimator in the later iterations, this means that the matrix \(\mathbf{B}\) can be calculated and \(\mathbb{E}(\mathbf{BB}^{H})\) accordingly. The overall MSE lower bound is given by:  
\[\text{MSE}_{overall}=\frac{1}{N_{s}L}\text{tr}\{(\mathbf{I}_{N_{s}}-\mathbf{\Psi }\mathbf{\Psi}^{H})\mathbf{R}+\frac{1}{N_{s}L}\text{tr}\{\mathbb{E}(\mathbf{zz}^ {H})\mathbb{E}(\mathbf{BB}^{H})\} \tag{71}\]  
Note that the prior assumption that channels are constant within the segment is not used in the MSE analysis, so the overall MSE is a lower bound for MSCE.
# Dual-Gratings Imaging Spectrometer
## 2 Dual-Gratings Imaging Spectrometer
### Limitations of Dual-Grating Imaging Spectrometers  
The dual-grating imaging spectrometer can acquire two bands that are far apart. However, compared with the traditional single-grating imaging spectrometer, the current dual-gratings imaging spectrometer has the following shortcomings:  
1. Cost increase. In order to prevent overlapping of spectra, the dual grating imaging spectrometer needs to add two filters and one grating, which increases the cost.
2. The low energy utilization rate. This shortcoming is composed of two reasons. The first is because the double gratings bisects the aperture into an image, resulting in only half of the total energy in each band. The second reason is because in the imaging process, it needs to pass through the filter twice, which reduces the energy utilization rate.
3. Stray light is more complicated. Due to the lower energy utilization rate of dual gratings, the stray light in the system is more than that of traditional single gratings.  
Considering these limitations, the current structure should be used in fields that are abundant in energy, insensitive to cost, and have ultra-spectral imaging requirements for multiple bands with large intervals. Therefore, this article suggests the solar application field.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 5 Analysis of Escalator
### Performance Analysis
#### 5.3.2 Bandwidth and Buffering Capability  
In Escalator, The bandwidth of the node is determined by the size of the convergecast slotframe. In a network with \(N\) nodes, the size of the convergecast slotframe \(L(S_{Conv})\) should be greater than \(N\times 2\). The node then transmits \(r-d\) packets to the sink while \(S_{Conv}\) repeats \(r\) times. The bandwidth Escalator provides to each node in network \(G\) is computed as follows:  
\[B(G)=\frac{1}{L(S_{Conv})\times Dt}\times\frac{r-d}{r}\quad\textit{ packets}/second, \tag{13}\]  
where \(Dt\) is the duration of the timeslot. As \(N\) increases, \(L(S_{Conv})\) increases, and bandwidth \(B(G)\) per node decreases. Conversely, when \(N\) decreases, \(B(G)\) increases.  
We analyze the amount of buffer used to transmit convergecast traffic at each node. A node receives and transmits a packet using consecutive links. Thus, each node uses one buffer to transmit convergecast traffic. If a packet is delayed due to a collision, it will remain in the buffer until the next cycle of the convergecast slotframe. Each node has a delay of packet transmission due to collision of up to \(\lceil\frac{L(S_{Conv})}{L(S_{Base})}\rceil\) times for each cycle of the convergecast slotframe. Therefore, the minimum buffer amount for each node of network \(G\) for the convergecast traffic transmission in Escalator is calculated as follows:  
\[Q(G)=1+\lceil\frac{L(S_{Conv})}{L(S_{Base})}\rceil\textit{ packets}. \tag{14}\]
## 3 Results
Note that, similar to our study, LSTM was also tested by [56], and the author also found reasonably good performance. Figure 3 below shows an overview of the comparison of the forecasting models' performance for all three items. The SVR is not performing in an optimized way, which is also common in the literature [57]. Another point, which literature also supports, is that in instances where the coefficient of variation (cV) has a low value, conventional forecasting models, such as SES, are often adequate [58]. In the context of data sets with a high cV value, literature posits that machine learning models are highly capable of predicting with high accuracy, even though there is a presence of high volatility [59]. We refer to Appendix C.1 for the parameter settings for both forecasting and inventory models. Previously, we compared the performance of data-driven and classical approaches, and we found that the classical approach can still be implemented, at least in forecasting. But  
Figure 3: Actual quantity versus forecasts quantity. if we look at inventory management, data-driven solutions like Q-learning are a possible solution for replenishment. We refer to Tables 3 and 4 for the detail results. Definitely, in terms of the total cost, the method outperforms the EOQ and EOQ is good when we consider the holding cost and ordering cost, but in our case, due to the high ordering cost compared to the low inventory holding cost, we obtain the desirable solution under the Q-learning method. The results in Table 3 demonstrate that the EOQ method orders more frequently, compared to the number of orders for Q-learning and DQN, and generally performs much worse. Although DQN is another possible data-driven approach, it is also not performing well in the context of our study. The rationale behind this is that, compared to Q-learning, DQN requires a much larger data set to be able to train the agent properly. This study only has four years of data, so it might be considered a bottleneck for DQN's potential performance [60]. In addition to non-deteriorating items, we also compute the replenishment decision for deteriorating items, and the results are presented in Table 4 and Figure 4. We refer to Appendix A for the detail calculation of profit function. As expected, the cost must be increased and the results demonstrate that fact. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Technique** & **Holding Cost** & **Ordering Cost** & **Variable Order** & **Total Profit** & **Total Demand** & **Total Procerned** \\ \hline EOQ & 120,998 & **8000** & **1,140,551** & 9,877,461 & 363,233 & 363,233 \\ \hline Q-Learning & **83,026** & 17,000 & 1,162,031 & **9,998,166** & 363,233 & 364,175 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results for constant-deteriorating item with time-varying demand Item-3.