esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The rapid development of maritime digital communication leads to a growing demand for spectrum resources. Maritime cognitive radio receives more and more attention, as it can utilize the inefficient limited spectrum resources.  
Considering the current available research about the common cognitive radio and the characteristics of maritime cognitive radio, we established a centralized maritime cognitive radio communication model. The model sets up signal channels for the CBS to control the data transmission in the system, which embodies the characteristics of maritime wireless communications.  
Based on the model, we create a simplified queuing model with two queues with priority. We analyze the system model by using a two-dimensional Markov chain. A DSA algorithm, which achieves the channel allocation for SUs with different priorities, is proposed. The algorithm adjusts the channel allocation by the priority factor according to the system status. Simulation results show that, when different kinds of SUs have similar arrival rates, the improvement is positively related to the size of the fixed priority factor and negatively related to the arrival rates of \(SU_{q1}\). Additionally, the DSA algorithm, with an adaptive adjustment of the priority factor, can effectively improve system efficiency. The improvement of the algorithm is about 7%-26% and the specific improvement is negatively correlated with the SU arrival rate.  
This work was supported by the Chinese National Science Foundation under grants 61501078 and 61231006, and the Fundamental Research Funds for the Central Universities under grants 3132016208.  
[PERSON] designed the maritime cognitive radio communication system model, the dynamic spectrum allocation algorithm based on a queuing model, and organized the paper; [PERSON] was responsible for writing and editing the manuscript and conducted the simulation work; [PERSON] and [PERSON] supervised the work and contributed to the writing of the paper.  
The authors declare no conflict of interest.
## 5 Discussion
### Comparison of the Result of EMR-SF between Different Layered Interval Distances by LRBF  
In this section, the applicability of the layered interval distance of the grid point acquisition method provided in this paper will be discussed. The maximum aggregation distance and the layered interval distances of 55 m, 45 m, 35 m, 25 m, and 15 m are used as a comparison. The interval distance of the grid points of LRBF is set to 7 m. The error statistics for the LRBF with different layered interval distances are shown (Table 4).  
The accuracy of LRBF with different layered interval distances varies (Table 4). In general, the accuracy of LRBF gradually improves as the layered interval distance decreases, but it is not better as the layered interval distance decreases. When the layered interval distance is 15, the accuracy is lower than that when the layered interval distances are 35 m and 25 m. The EMR-SF constructed by LRBF with different layering interval distances is shown (Figure 19). When the layered interval distances are 55 and 15, the electromagnetic field constructed in the central region appears to be less reasonable, and the space field obviously appears to be discontinuous. This indicates that an interval distance that is too large or too small is not conducive to the construction of the EMR-SF used by LRBF. The EMR-SF is similar for 45-, 35-, and 25-layered interval distances; finally, they have similar accuracy in the interpolation error.  
The construction accuracy and visualization results of the EMR-SF show that the LRBF obtains a large error if the layered interval distance is too large or too small. A too-small layered interval distance will increase the number of layers, thereby increasing the amount of calculation and causing excessive error transmission. To obtain a better layered interval distance, from the view of the distribution pattern of sampling points, the maximum aggregation distance was calculated by using Ripley's K function to be 25 m. Taking this distance as the LRBF layer interval distance, and comparing the error with other values, it can be seen that this method of obtaining the layer interval distance based on the distribution pattern of sampling points improves the construction accuracy of EMR-SF by LRBF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layered Interval Distance (m)** & _RMSE_ & _MAE_ & _MAXE_ & _MINE_ \\ \hline
55 & 0.0453 & 0.0231 & 0.47 & \(0.9\times 10^{-7}\) \\
45 & 0.0398 & 0.0231 & 0.51 & \(0.5\times 10^{-4}\) \\
35 & 0.0373 & 0.0176 & 0.45 & \(0.3\times 10^{-5}\) \\
25 (maximum aggregation distance) & 0.0378 & 0.0179 & 0.45 & \(0.2\times 10^{-4}\) \\
15 & 0.0418 & 0.0195 & 0.43 & \(0.3\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different layered interval errors (\(\upsilon/m\)).
## 4 Results
### Comparison of the Results Obtained for the Six Experiments
This also held for Experiment 6, so this is an adequate option to perform a greater number of experiments in subsequent research, considering also that this setting presented good results in Experiments 1, 2 and 3, although in Experiment 3, two of the four nodes presented a low percentage of received packets for this setting. The difference in percentage of packets received between Experiments 4 and 5 (in the best settings), shows that by increasing the number of LoRa devices (from 1 to 3), the percentage of packets received decreases between 10% and 20%. The difference in percentage of packets received between Experiments 5 and 6 (in the best settings), shows that by increasing the maximum speed, the percentage of packets received decreases between 3% and 10%. The baseline setting (SF = 9, BW = 125, CR = 4/5) had very similar results to those presented by the best setting in each of the six experiments. This determines that the baseline setting, although it is not the best setting in all the experiments, had a good overall performance in all of them. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Best Settings**} & \multicolumn{3}{c}{**Best Settings**} & \multicolumn{3}{c}{**Best Settings**} & \multicolumn{3}{c}{**Best Settings Regarding \%**} \\  & \multicolumn{3}{c}{**Regarding RSSI in**} & \multicolumn{3}{c}{**Regarding RSSI in**} & \multicolumn{3}{c}{**Regarding RSSI in**} & \multicolumn{3}{c}{**of Received Packets in**} \\  & \multicolumn{3}{c}{**Exp. 1**} & & \multicolumn{3}{c}{**Exp. 2**} & & \multicolumn{3}{c}{**Exp. 3**} & & \multicolumn{3}{c}{**Exp. 3**} \\ \hline SF & 7 & 7 & 7 & 7 & 8 & 9 & 7 & 8 & 9 & 7 & 9 & 7 \\ \hline BW & 250 & 500 & 500 & 500 & 500 & 500 & 500 & 500 & 500 & 125 & 250 & 125 \\ \hline CR & 4/6 & 4/7 & 4/5 & 4/7 & 4/7 & 4/7 & 4/7 & 4/8 & 4/6 & 4/6 & 4/6 & 4/8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Best settings in Experiments 1, 2, and 3.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 2 Trajectory Planning Based on MINCO Trajectory Classes
### Trajectory Planning Based on MINCO Trajectories and Safe Corridors
#### 2.2.3 Unconstrained Processing
It is equivalent to representing a convex polyhedron with the origin \(v_{0}\) and the vectors between \(v_{0}\) and the other vertices as edges, denoted as \(\mathcal{P}^{\mathcal{H}}_{\omega}\):  
Figure 10.2  
All points \(q\) in the original polyhedron can be expressed in coordinates \(\omega\). The transformation between \(\mathcal{P}^{\mathcal{H}}_{\omega}\) and \(\mathcal{P}^{\mathcal{H}}\) is equivalent in (42). By using the square transformation \(\mathbf{\omega}=\left[\mathbf{x}\right]^{2}\), a simple polyhedron \(\mathcal{P}^{\mathcal{H}}_{\omega}\) can be converted into a unit ball \(\mathcal{B}^{n}\):  
Figure 10.3  
The treatment of the ball constraint has already been discussed, so after the transformation in (42), the square transformation \(\omega=[x]^{2}\), and the composite transformation in \(f_{B}\) in formula (39), the transformation that maps the constraint \(\mathcal{P}^{\mathcal{H}}\) to the whole space \(R^{n}\) can be obtained, denoted as \(f_{\mathcal{H}}\):  
Figure 10.4  
The processing of the convex polyhedral constraints is shown in Figure 10.5Furthermore, the new variables introduced for \(f_{\mathcal{H}}\) are denoted as \(\xi\). Then, any \(\xi\in R^{n}\) can correspond to a \(\mathbf{q}\in\mathcal{P}^{\mathcal{H}}\), and \(\xi\) in the entire space \(R^{n}\) can fill the entire convex polyhedra \(\mathcal{P}^{\mathcal{H}}\) by transformations. Denote \(g_{i}\) as the \(i\)th element of \(\partial/\partial\mathbf{q}\), i.e., \(\partial/\partial q_{i}\); then, the \(i\)th element of the gradient of the cost function with respect to the new variable \(\xi\) is:  
Figure 10.6  
The transformation process does not introduce new minima, nor does it eliminate existing minima. Thus, for the spatial safety constraints \(\mathbf{q}\) in \(\widetilde{\mathcal{F}}\), it can be unconstrained by proxy variables \(\xi\).
# Identification of Ratholes in Desert Steppe Based on UAV Hyperspectral Remote Sensing
## 4 Result
### Non-Rathole Area Characterization  
Remote sensing vegetation indices are effective, real-time, empirical measures of surface vegetation conditions, which can indicate the force of vegetation growth and have better sensitivity than single bands used to detect biomass, while the combination of visible and near-infrared bands can effectively obtain various vegetation indices [19]. With the development of technology, there is an extremely strong demand for vegetation indices for applications in agriculture, vegetation, and ecology. Examples include monitoring of vegetation distribution and growth, yield estimation, and extraction of various biological parameters [20]. Common vegetation indices are shown in Table 2.  
The normalized difference vegetation index (NDVI) has the advantages of a wide spatial coverage, high monitoring sensitivity, simple calculation, and effective elimination of radiation interference, which can truly and effectively reflect the vegetation growth state. Moreover, it can be used to calculate the vegetation cover, and is also the most widely used vegetation index for ecological monitoring [27; 28]. The normalized vegetation index is more applicable to desert steppes than the other vegetation indices mentioned above because of the sparse vegetation and low vegetation cover. The NDVI was calculated for the non-rathole area, and the results are shown in Figure 7. The results show that the minimum CV value (cursor value) of each type of sample was \(-0.019260\), and the maximum value was \(2.195495\). To avoid errors, [\(-0.05\), \(2.2\)] was used as the threshold range. According to the statistics for the number of image elements and the threshold range after the operation (Table 3), the threshold range of ratholes was [\(1.126505\), \(2.195495\)], the threshold range of bare soil was [\(-0.019260\), \(0.306244\)], and the threshold range of vegetation was [\(0.306244\), \(0.631748\)].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**V vegetation Index** & **Calculation Formula** & **References** \\ \hline Normalized difference & \(NDVI=\frac{(NIR-R)}{(NIR+R)}\) & [21; 22] \\ vegetation index & \(EVI=2.5\frac{(NIR-R)}{(NIR+6\times R-75\times R+1)}\) & [23] \\ Ratio vegetation index & \(RVI=\frac{NIR}{R}\) & [24] \\ Difference vegetation index & \(DVI=(NIR-R)\) & [25] \\ Soil-adjusted vegetation index & \(SAVI=(1+L)\frac{(NIR-R)}{(NIR+R+L)}\) & [26] \\ \hline \hline \end{tabular} _NIR_ stands for near-infrared band; \(R\) stands for red band; \(B\) stands for blue band; \(L\) is the soil conditioning factor, and the value range is 0–1.  
\end{table}
Table 2: Several common types of vegetation indices.  
Figure 6: Rathole.
## 5 Conclusions  
This study showed that biofloc could be formed externally (ex-situ biofloc) using probiotics _B. megaterium_ and _B. cereus_, diatom microalgae _C. calcitrans_, and consortium of nitrifying bacteria, with the ratio of 1:16:9 as a starter. Overall, the ex-situ biofloc supplementation in shrimp grow-out culture can increase the shrimp culture performance (shrimp growth, survival and total biomass), and provide inorganic substances such as trace minerals (calcium, manganese, zinc and iron) to support shrimp growth performance in combination with a major source of energetic nutrients (proteins and lipids) from the commercial pellet. The optimal feed formulation for the shrimp grow-out phase was the 5% reduction of the commercial pellet in supplement with 0.3% ex-situ biofloc (95%C+BF). Shrimp fed with 95%C+BF showed a significant increase of growth-related genes (e.g., _fatty acid synthase_, _triacylglycerol lipase_, _carnitine palmitoyltransferase_, _NADH dehydrogenase_, _ATPase_ and _cytochrome b oxidase_) compared to the control (100%C). Together, this study sheds light on the potential modes of action of ex-situ biofloc to maintain water culture quality, enhance shrimp growth performance and survival, and enable shrimp nutritional value. All these beneficial effects of ex-situ biofloc as a feed supplement in an intensive shrimp culture at low salinity conditions can be feasible for the shrimp farming industry, especially in-land culture, leading to a reduction of the cost of shrimp production and to sustaining the quality of shrimp products in the future.  
**Author Contributions:** Conceptualization, M.L.S. and U.U.; methodology, M.L.S. and U.U.; microbial resource and animal experiment, M.L.S., H., and G.S.; nutritional analyses, A.P. and U.U.; molecular analysis, S.A. and W.R.; data analyses, S.A., U.U. and M.L.S.; writing--original draft preparation, S.A., U.U., M.L.S.; writing--review and editing, W.R. and N.K.; visualization, S.A. and U.U.; supervision, W.R. and N.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the International Foundation for Science, Sweden, grant number J-3-B-6003-1 under the IFS collaborative grant and the publication cost was supported by BIOTEC fellow's research grant, National Center for Genetic Engineering and Biotechnology, BIOTEC, Thailand, grant number P16-52214.  
**Institutional Review Board Statement:** Experimental protocols followed the guidelines of the Animal Care and Use Committee of Institut Teknologi Bandung, Indonesia.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** We gradually appreciate to [PERSON] and [PERSON] from National Center for Genetic Engineering and Biotechnology, Thailand for their valuable mentorship. We also thank to [PERSON] for his assistance in nutritional analysis.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Simulation Experiment and Result Analysis
### Correlation Coefficient Analysis
The Fast IVA algorithm used in this paper not only eliminates the tedious permutation process among the separated sub-signals and reduces the computational complexity of the algorithm but also has better separation performance.
## 3 Current Status of the Circuit Component in the MMW Regime
### Recent Works of SoC in MMW Regime  
Silicon-based technologies add more appeal to the MMW IC designers owing to the trend of higher-level integration. Nevertheless, high substrate loss of silicon is more severe than that of in GaAs technologies. Also, the conductive substrate of silicon-based technologies leads to the leakage signals from the local oscillator or mixed-mode circuits. This has to be taken into layout considerations. N-well and guard ring, which are available in silicon-based technologies, can be utilized to isolate the leakage signals between circuits [198]. In addition, low efficiency of the on-chip antennas and low output power of power amplifier (PA) are still challenging with silicon-based technologies in MMW regime. However, beam-forming architecture like phased array and system in packaging (SIP) are promising for high integration of antennas and output power PA in silicon-based technologies.
## 7 Robustness
### Extraction Failure Modes  
Across 30,000 test images, the worst BER observed was 0.18. A BER \(>\) 0.5 would require simultaneous (i) extremely low image entropy (\(H<\) 2.1), (ii) 4:2:0 chroma subsampling and (iii) destructive post-processing such as QF \(<\) 20 plus 50% random cropping. Such corner cases are rare in evidential practice. If encountered, two defensive options are available without altering the chain-of-custody procedure: (a) resample watermark blocks from a different PRNG epoch or (b) embed a 16-bit BCH code, which raises the payload to 144 bits but restores BER \(<\) 0.05 under the same stress.
###### Abstract  
Ultra-high-altitude unmanned aerial vehicles have created a high demand for the performance of propellers under low Reynolds numbers, while the efficiency of such propellers by the existing design framework has reached a bottleneck. This paper explores the possibility of extending the Gurney flap on low Reynolds number propellers to achieve efficiency breakthrough. An iterative optimization strategy for propellers with Gurney flaps is established, in which cross-sectional airfoils can be continuously optimized under updated Reynolds numbers and lift coefficients. A computational fluid dynamics (CFD) simulation based on the \(\gamma\)-\(Re_{\theta}\) model was used as an aerodynamic analysis method. Propellers with and without Gurney flaps were optimized successively. Optimal results were analyzed using the CFD method. Results showed that an optimal propeller with a Gurney flap can achieve an efficiency of 82.0% in cruising conditions, which is 1.8% higher than an optimal propeller without a Gurney flap. Compared with the latter, the consumed power of the optimal propeller with a Gurney flap can be reduced by 2.2% with the same advance speed. Furthermore, the variation of the improvement by the Gurney flap propeller, along with its Reynolds number, was studied. A wind tunnel test indicates that the performance of the propellers obtained by the CFD method are in good agreement with the test results.  
low Reynolds number propeller; Gurney flap; aerodynamic optimization; wind tunnel test; ultra-high-altitude unmanned aerial vehicle Article
## 3 Dependability  
Dependability is the ability of a system to deliver a service that can justifiably be trusted; in other words, it should avoid frequent and severe service failures [24]. Though crucial in importance, dependability is often overlooked in favor of other research directions. Priority has been given to coordinating computing activities between distributed nodes in order to achieve higher performance, or security mechanisms that help in protecting users and their data. As previously mentioned, dependability is a compound metric and can be discussed through four important indicators: reliability, availability, safety, and security. Although performance and security are important, and as such most of the works focus on them, the other three requirements of dependable systems should not be underestimated [25; 26]. Moreover, there are many facets of dependability, for instance, confidentiality and integrity [27]. However, some of the concepts converge into the four aspects discussed throughout this article. Therefore, for brevity we limit the discussion to the topics of reliability, availability, safety, and security, as described below.
## 2 Named Data Networking: Nuts and Bolts
### Mobility Support in NDN  
The packet forwarding process in mobile networks can be challenging due to the constantly changing network topology [21]. However, the NDN paradigm overcomes this issue by breaking the point-to-point nature of IP networks and enabling ubiquitous data retrieval. Unlike IP networks, NDN allows mobile nodes to retrieve data from anywhere in the network, instead of delivering data to a specific mobile node [22]. Furthermore, NDN's implementation allows it to operate over any transport protocol, which is a significant advantage. Additionally, the location-independent data retrieval enabled by NDN's data-centric security is another key advantage to mobility support.  
In NDN, mobile consumers are not identified by addresses, which avoids the overhead associated with address management and connection sessions that TCP/IP requires. Instead, when a consumer requests content, the _Interest_ packet creates reverse path entries in the PIT as it travels to the data producer. This enables a mobility-friendly forwarding process, as _Data_ packets can follow the reverse path to reach the consumer. If the consumer moves to a new location, resending the _Interest_ packet is sufficient to update the reverse route to the new location [23].  
The simplest way of retrieving the content of a mobile producer consists of broadcasting _Interest_ packets throughout the network until they reach the intended mobile producer or the network cache holding the content. However, this approach consumes significant network resources, making it unsuitable for large networks. Another approach involves using a DNS-like node to map produced data names to the actual location of a mobile producer [24]. A consumer sends a query to the _rendezvous_ point to ask for the position of the producer. The _rendezvous_ point can also act as a relay, which forwards requests to the mobile producer's position and sends back data to the requesting consumer. Data depots  
Figure 3: Example of packet forwarding in vehicular NDN.  
are another approach that leverages the built-in caching capabilities of NDN, allowing consumers to send _Interest_ packets to a data depot instead of directly retrieving content from mobile producers [25]. The data depot can then send the requested data if it holds them or fetch them using one of the other approaches. To retrieve data associated with a specific region, a geographic-based forwarding strategy is used where any mobile node in that region can act as a producer. For example, a consumer can request traffic information for a particular road by sending an _Interest_ packet to the relevant region using geographic-based routing. When a node in that region receives the request, it either sends the requested data (if it already has them in its cache) or generates them before sending them back to the consumer.
# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 3 Experimental Results
### Statistical Significance Comparison  
To further validate the advantages of the proposed BSRSF metric against other blind metrics, we compare the statistical significance of the BSRSF metric and other blind metrics. In this study, we perform F-tests on the SROCC scores derived by these metrics. F-tests are carried out at the 5% significance level. The experimental results on SIQAD are listed in Table 4, where \"1\" shows that the row method outperforms the column method in terms of statistical significance, \"\(-\)1\" shows that the contrary meaning and \"0\" shows that the row and column methods are not distinguishable in terms of statistical significance. From Table 4, we can observe that all comparison results of the BSRSF method to other compared methods are marked with \"1\". Thus, the BSRSF method completely statistically exceeds all compared blind methods.