esa-sceva/satcom-chunk-collection huggingface dataset:
# Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch
## 2 System Architecture
### Self-Learning Function  
The self-learning function mainly performs some services in the data plane, such as the L2-switch, which can be triggered flexibly by extended OpenFlow actions.  
At present, all of OpenFlow actions only support the modification of the header of a packet and forwarding, but some simple services, such as L2/L3, NAT and firewall functions, can't be implemented directly on the data plane, in which case the efficiency of the network sometimes is relatively low. By performing self-learning in an OpenFlow switch, packets can be processed on the data plane instead of being sent to the controller. This keeps the flexibility and benefit of SDN, because the controller can use the Openflow API, especially the action to call this service at an appropriate moment. In particular, a new rule generated by self-learning action is the same in spirit as the OpenFlow rules, so those rules can be inserted in the original flow tables with no difference.  
The self-learning function combines both advantages of OpenFlow pipeline processing packets and L2 function of the traditional switch. First, if a packet executes a self-learning action, the source mac and ImPort field will be extracted from the header of the packet. According to this extracted information, it will generate a new rule whose \"dst-mac\" match field is the same as the source mac and OutPort action is the same as the ImPort. Second, it checks whether the user space flow tables contain a rule as same as the new rule. If not, the new rule can be directly inserted in flow tables. Otherwise, the old rules need be updated according to the new rule.  
Self-learning function provides packet-processing service in the data plane, which can improve the network efficiency, so it can be applied in many SDN-based vehicle sensor network scenarios. If this function works, it decreases the number of packets sent to the controller and relieves the load of the controller. In some controller unavailable scenarios, the self-learning function can ensure that a switch processes packets and forwards them normally. It also ensures that SDN-based vehicle sensor networks work normally in agriculture scenarios.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## Appendix A  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Scenario I**} & \multicolumn{3}{c}{**Scenario II**} & \multicolumn{3}{c}{**Scenario III**} \\ \hline
**Parameters** & **Metrics** & **PSO** & **GA** & **PSO-GA** & **PSO** & **GA** & **PSO-GA** & **PSO** & **GA** & **PSO-GA** \\ \hline \multirow{6}{*}{Optimal Path Length} & Mean (km) & 2.446 & 2.457 & 2.451 & 9.511 & 9.458 & 9.412 & 11.398 & 11.631 & 11.208 \\  & Median (km) & 2.441 & 2.451 & 2.439 & 9.092 & 9.121 & 9.110 & 10.848 & 11.020 & 10.783 \\  & Min (km) & 2.424 & 2.420 & 2.428 & 8.989 & 9.044 & 8.973 & 9.687 & 10.692 & 10.579 \\  & Max (km) & 2.604 & 2.680 & 2.589 & 11.378 & 11.394 & 11.160 & 16.096 & 14.424 & 14.548 \\  & Std (m) & 31.198 & 35.874 & 40.497 & 721 & 692 & 608 & 1219 & 830 & 918 \\  & Skewness & 4.280 & 4.929 & 2.872 & 1.280 & 1.722 & 1.639 & 1.844 & 2.101 & 1.919 \\  & Kurtosis & 20.8757 & 31.004 & 9.706 & 3.074 & 4.343 & 4.358 & 6.502 & 6.990 & 6.013 \\ \hline \multirow{6}{*}{Reliable Path Length} & Mean (km) & 2.163 & 2.169 & 2.171 & 6.395 & 6.282 & 6.165 & 5.086 & 5.420 & 4.959 \\  & Median (km) & 2.157 & 2.166 & 2.157 & 5.955 & 5.925 & 5.987 & 5.209 & 5.138 & 5.219 \\  & Min (km) & 2.120 & 2.121 & 2.127 & 5.184 & 5.177 & 5.234 & 2.479 & 1.738 & 1.720 \\  & Max (km) & 2.367 & 2.443 & 2.349 & 8.984 & 8.898 & 8.618 & 7.649 & 6.821 & 7.160 \\  & Std (m) & 41.460 & 45.610 & 53.338 & 1030 & 939 & 678 & 1193 & 993 & 952 \\  & Skewness & 3.988 & 4.433 & 2.273 & 1.232 & 1.681 & 2.225 & \(-0.352\) & \(-1.327\) & \(-1.007\) \\  & Kurtosis & 19.269 & 27.085 & 9.148 & 3.102 & 4.364 & 8.132 & 3.179 & 6.275 & 5.220 \\ \hline \multirow{6}{*}{Ratio of Path Reliability} & Mean (\%) & 88.391 & 88.266 & 88.545 & 66.869 & 66.106 & 65.381 & 45.110 & 44.740 & 44.786 \\  & Median (\%) & 88.270 & 88.227 & 88.380 & 65.386 & 64.767 & 65.562 & 48.582 & 47.067 & 48.787 \\  & Min (\%) & 87.213 & 86.922 & 87.434 & 57.568 & 58.681 & 58.327 & 18.905 & 12.939 & 13.111 \\  & Max (\%) & 90.875 & 91.142 & 90.754 & 78.965 & 78.096 & 77.715 & 61.262 & 58.022 & 62.250 \\  & Std & 0.621 & 0.601 & 0.732 & 5.522 & 4.752 & 3.715 & 10.834 & 8.973 & 9.653 \\  & Skewness & 1.970 & 2.032 & 1.900 & 0.806 & 1.262 & 1.306 & \(-1.000\) & \(-2.131\) & \(-1.452\) \\  & Kurtosis & 9.713 & 11.891 & 6.383 & 2.740 & 3.789 & 6.044 & 3.408 & 8.032 & 4.872 \\ \hline \multirow{6}{*}{Execution Time} & Mean (s) & 17.844 & 18.246 & 12.115 & 42.667 & 41.090 & 22.103 & 66.991 & 68.837 & 32.940 \\ \cline{1-1} \cline{2
## 3 Results and Discussion
### Total Phenolic  
In terms of mg/g gallic acid equivalents (GAE), the influence of SDBD plasma on the total phenolic content of palm varieties was evaluated. As seen in Figure 2A, the total phenol content of all treated date cultivars increased when palm dates were treated with plasma generated from SDBD. The total phenolic content of untreated samples varied from 79.60 \(\pm\) 6.36 to 500.33 \(\pm\) 13.50, whereas treated samples ranged from 112.71 \(\pm\) 3.16 to 653 \(\pm\) 17.47. According to the findings, the total phenol content of treated and untreated samples differed significantly (_p_ < 0.05). The plasma-treated _Shalbi_ cultivars had the smallest rise in total phenolic, which was 1.02 times that of untreated ones. On the other hand, the treated _Ajwa_ variety showed the greatest rise in total phenolic, with a 1.65-fold increase over the untreated type.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Treatment Time**} & \multicolumn{3}{c}{_A. Niger_ **Spore Log (CFU/g)**} \\ \cline{2-4}  & **After 15 Days of** & **After 16 Days of** & **After 17 Days of** \\  & **Inclusion** & **Inclusion** & **Inclusion** \\ \hline
0.00 & 4.33 \(\pm\) 0.09 & 4.47 \(\pm\) 0.03 & 4.50 \(\pm\) 0.01 \\
1.00 & 2.50 \(\pm\) 0.29 & 2.77 \(\pm\) 0.23 & 2.80 \(\pm\) 0.06 \\
1.50 & 2.20 \(\pm\) 0.82 & 2.43 \(\pm\) 0.85 & 2.63 \(\pm\) 0.81 \\
2.00 & 1.83 \(\pm\) 0.22 & 2.03 \(\pm\) 0.18 & 2.37 \(\pm\) 0.19 \\
2.50 & 0.87 \(\pm\) 0.57 & 0.93 \(\pm\) 0.63 & 1.03 \(\pm\) 0.73 \\
3.00 & 0.33 \(\pm\) 0.03 & 0.33 \(\pm\) 0.03 & 0.33 \(\pm\) 0.03 \\ \hline \end{tabular}
\end{table}
Table 1: The effect of SDBD plasma on _A. niger_ spore log (CFU/g) at different treatment time; \(p\) value = 0.00.  
The impact of non-thermal plasma on mandarin peel increased dramatically, although the same conditions of non-thermal plasma had no effect on mandarin flesh [54]. Previous research has shown that non-thermal plasma treatment significantly increased the total phenolic content of strawberries [55], fresh cut pitaya fruits [56], bananas ([PERSON] et al., 2022), and blueberries [57]. However, [58] have hypothesized that phenols accumulate because they function as antioxidants that prevent the damage due to free radicals. Non-thermal plasma treatment might damage the polysaccharide cell walls of plants, leading to an increase in polyphenol extraction [59,60]. After non-plasma treatment, the bond between phenolics and the cell membrane may break down, leading to the release of phenolics [57,61].
###### Abstract  
Introduction  
In orthodontics, determining the parameters of tooth size and dental arch and conducting Bolton analysis is crucial for diagnosis, treatment planning, and patient outcomes. This study evaluates the accuracy and reliability of measuring dental-arch dimensions on digital models created using the CEREC Primescan intraoral scanner, compared to measurements taken from plaster models. **Methods:** The study included two types of dental models ( plaster and intraoral scan) from sixty-three subjects. Impressions were taken to create plaster models, and the subjects' mouths were scanned with the CEREC Primescan system (Dentsply Sirona, Charlotte, NC) to create digital models. Intra-arch measurements included tooth heights and widths, overjet, and overbite. The arch width and depth were examined at the first permanent upper or lower molar. The paired \(t\)-test and Bland-Altman plot were used to determine the accuracy, while intra-rater and inter-rater correlation coefficient values were calculated to assess the reliability of measurements from the intraoral scan compared to those from the plaster model. **Results:** For tooth heights, there was a statistically significant difference in only one measurement (tooth 34) between the plaster and digital models, with an average difference of 0.01 mm. For tooth widths, there was a statistically significant difference in only one measurement (tooth 15) with an average difference of 0.03 mm. The Bland-Altman plots of almost all of measurements of tooth heights and widths showed that differences between the two models were within the limits of agreement. The inter- and intra-rater correlation coefficient values for measurements on the digital model were found to be statistically insignificant. **Conclusion:** Measuring dental dimensions on digital models obtained through the Primescan intraoral digital system yielded similar results to those obtained from plaster models and showed excellent reliability, indicating its potential application in clinical practice.  
3D scanning; accuracy; digital models; intraoral digital; orthodontics; Primescan; reliability; reproducibility 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2010, pp. 155-160. IEEE.
###### Abstract  
This study presents the design and fabrication of light retroreflectors utilizing surface plasmon resonance (SPR) in parallel-superimposed bi-grating structures. The bi-gratings were inscribed onto a thin azobenzene molecular glass film via photolithography and subsequently coated with a thin gold layer to support SPR. The two superimposed gratings operate in tandem, with one grating coupling incident light into the SPR mode and the other coupling it back out toward the light source, thereby achieving retroreflection. Monochromatic retroreflection is demonstrated for a target wavelength (785 nm) at angles from 5\({}^{\circ}\) to 10\({}^{\circ}\), while multi-wavelength retroreflection is achieved for red, orange, and green wavelengths at corresponding angles. The findings highlight the potential of these bi-gratings for applications in optical sensing, communication, and advanced photonic systems, where compact, tunable, and angularly responsive designs are essential.  
SPR; retroreflector; tunable; bi-grating Article
## 2 Materials and Methods
### Experimental Setup and Test Procedures
The second test is conducted by exciting the specimens with an electrodynamic shaker (TMS-K2007E01, The Modal Shop Inc., Cincinnati, OH, USA) along the Z direction at the input nodes _in1_ and _in2_ (Figure 3). The excitation force and the acceleration at nodes _in1_ and _in2_ are measured by an IPE piezoelectric impedance head (PCB 288D01, PCB Piezotronics Inc., Depew, NY, USA). The input nodes _in1_ and _in2_ are excited in distinct runs (i.e., not simultaneously), so that only a reference node at a time is considered in the analyses. The results obtained by using the _in2_ reference point basically match those obtained with excitation applied at node _in1_. Hence, only the results concerning the _in1_-case are reported in this paper, whereas the _in2_-case is neglected hereafter. For a better graphical representation of the results, nodes _C2_, _C3_, and _C4_ are kept visible in the modal geometry also in shaker tests, although no data are associated with them. Two different excitation profiles are adopted, namely burst random and chirp signals, and two excitation levels are set for each one (also to investigate possible non-linear dynamic response of the CFRP panels). All the experimental campaigns are performed by using an LMS SCADAS SCM-05 frontend and LMS Test.Lab software package (Siemens Digital Industries Software, Plano, TX, USA), with the following acquisition setup parameters:  
* Sampling frequency: \(F_{Sh}\) = 512 Hz (impact hammer tests), \(F_{Ss}\) = 2048 Hz (shaker tests);
* Bandwidth: \(BW_{h}\) = 0.5-256 Hz (impact hammer tests), \(BW_{s}\) = 5-1024 Hz (shaker tests);
* Acquisition duration: \(T\) = 8 s (spectral frequency resolution: \(\Delta f\) = 0.125 Hz);
* Number of averages: \(N_{av}\) = 10;  
Figure 3: Geometry of the modal model: _C2_–_C4_ are active in the impact tests only; _in1_ and _in2_ are active in the shaker tests only. * Shaker excitation levels: \(L_{R}\) = 0.2 V, 1 V (Random tests), \(L_{C}\) = 0.1 V, 0.5 V (Chirp tests). The frequency bandwidth in impact tests is lower than in shaker tests due to practical constraints: the excitation is in fact provided by an impact hammer featuring a plastic covered tip (to preserve the thin panels integrity), and the corresponding force spectrum proves almost flat up to 300 Hz only.
## 6 Conclusions  
Based on the geological background of the northern slope in the Fushun open-pit mine, the stability and failure mode of an anti-dip interbedded rock slope were simulated by PFC 2D. The influence of different factors, such as interlayer joints, the dip angle of the rock layers, and the slope angle, on the stability of a rock slope were investigated in this study. The main conclusions are listed as follows:  
Figure 19: Contact force chain of a slope with an 80\({}^{\circ}\) slope angle when the time step was 30,000.  
For the interbedded anti-dip rock slope numerical model with an irregular slope surface and joints, the pre-existing joints in the rock stratum promote the shear dislocation of particles, resulting in crack initiation. The failure mode is shear-slip instability failure.
* The existence of joints induces the generation of cracks in the rock slope, increases the crack density, and accelerates the process of slope failure but does not change its failure mode.
* The inclination angle of the rock stratum affects the instability state of the interbedded anti-dip rock slope. When the dip angle of the rock stratum is small, shear-slip instability failure occurs. The gravitational moment increases with an increasing dip angle, which leads to toppling-slip compound failure along the slip zone.
* Shear-slip failure occurs in an interbedded anti-dip rock slope with a small slope angle. However, the failure mode of an anti-dip interbedded rock slope with a large slope angle is a flexural toppling-slip compound failure. The overturning and toppling characteristics of a slope with a large slope angle are obvious.
* These simulation results can contribute to a better understanding of the failure mechanisms of interbedded anti-dip rock slopes under different geological conditions and provide a reference for disaster prevention.  
Methodology, Z.Y., H.J. and F.M.; Validation, Z.X.; Formal analysis, Z.X.; Investigation, J.H.; Resources, H.J.; Writing--original draft, M.L.; Writing--review & editing, Z.Y.; Supervision, F.M.; Funding acquisition, F.M. All authors have read and agreed to the published version of the manuscript.  
We gratefully acknowledge financial support from the Taishan Scholars Program (2019 KJG002, 2019 RKB01083) and the Natural Science Foundation of Shandong Province (ZR2022 QD004, ZR2023 QE074).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 2 Materials and Methods
### Deep Leaning Applied  
After slide scanning and image preprocessing have finished, higher level tasks should be carried out to understand the information present in the image. In research with diatoms, some of the most interesting high level tasks deal with answering the questions [8]:* \"Where and how many diatoms are in the image?\" (i.e., detection)
* \"To what taxon does each diatom belong?\" (i.e., classification)  
The automation of the mentioned tasks is truly worthwhile because detection is time-consuming, and classification is carried out by scarce diatomists able to distinguish specimens' taxa with subtle invisible differences to the untrained eye [58; 59]. Automatic detection and classification have been the subject of study for many decades, being addressed by diverse machine learning algorithms. However, the more recent trend is to apply deep neural networks (DNNs) to these topics considering their promising results and the unnecessary handcrafted selection of features required by other approaches.  
In the next sections, it will be explained how DNNs have been applied to the detection and classification of diatoms in our system.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 3 The Proposed Method
### InceptionDMK Module  
Edge images primarily contain contour and structural information. Inspired by the Inception [39] architecture, our InceptionDMK module effectively captures fine directional features, such as horizontal and vertical edges, through depthwise separable convolutions with square and strip-shaped kernels. This design is particularly suited for enhancing edge-detail channels. The module integrates the strengths of depthwise separable convolutions and multi-scale feature extraction through four parallel branches, as shown in Figure 2.  
* Bypass branch (_x_pa_): This branch directly bypasses convolution operations, preserving the original input features.
* Local spatial feature branch (_x_ltw_): Employs a \(3\times 3\) square kernel depthwise separable convolution to extract local spatial features from the image.
* Horizontal feature branch (_x_w_): Utilizes a \(1\times 7\) horizontal strip-shaped kernel to capture directional features along the horizontal axis.
* Vertical feature branch (_x_h_): Uses a \(7\times 1\) vertical strip-shaped kernel to extract vertical directional features.  
The outputs of these branches are concatenated along the channel dimension, ensuring that the output tensor matches the input tensor in channel count and spatial dimensions. This design efficiently combines multi-scale and multi-directional features. The enhanced InceptionDMK module retains computational efficiency while significantly improving the extraction of multi-scale and multi-directional features. This allows the network to enhance edge details more effectively while achieving stronger feature representation capabilities.
## 4 Platforms for UWSNs  
Over the years, research communities in UWSNs have developed various new designs and experiments in line with the technological advancements. According to [16], innovative solutions for evaluation, testing, and validation in UWSNs consist of three categories: software-based simulation, hardware-based simulation, and experimental field study. In comparing the three techniques, real-field experimental research, or testbeds, requires higher operating costs and presents difficulty in the data collection process and observations due to the unreliable environments of UWSNs. However, researchers typically need a combination of the techniques to verify the results of their experiment. Besides, mathematical models are used to support the verification and validation process. Figure 3 demonstrates the development of testbed platforms in UWSNs and their respective vital characteristics from 1995 to 2020. Generally, as categorized in [17], testbed platforms can be considered either long-term experimental, short-term experimental, or lab level.
## 5 Using a Lempel-Ziv Self-Index
### Homogeneous Lempel-Ziv Phrases
To see this, apply Lemma 6, replacing \(1\) by \(ed(A,B)-1\) and using \(A^{\prime\prime}\) and \(B^{\prime\prime}\). We conclude that we must have \(|A^{\prime\prime}|>|A|-q\lceil(1+\epsilon)/s\rceil\). On the other hand, by our initial hypotheses, we conclude that \(|A^{\prime\prime}|=|A|-|A^{\prime}|\leq|A|-q\lceil(1+\epsilon)/s\rceil\). Therefore we have reached an absurd condition and the lemma is proved. [PERSON] studied the structure of the set of \(O\) strings such that \(ed(P,O)=k\), denoted as \(U_{k}(P)\)[51], showing that \(|U_{k}(P)|\leq(12/5)(m+1)^{k}(\sigma+1)^{k}\). We will now count the number of strings for which the errors are spread homogeneously. We define as \(U_{k,q,s}(P)\) the set of \(O\) strings such that \(ed(P,O)=k\), and for any substring \(O^{\prime}\) of \(O\), with \(|O^{\prime}|=q\), and substring \(P^{\prime}\) of \(P\) we have that \(ed(O^{\prime},P^{\prime})\geq s\). **Lemma 8**: \(|U_{k,q,s}|\leq(2\sigma+1)^{k}(2k+1)[q(2+k/s)+k-m-1]^{k}\)_._  
**Proof** From the previous lemmas we conclude that, for the strings in \(U_{k,q,s}\) the spacing from an error to the next varies from \(|O|-q\lceil(k-1+\epsilon)/s\rceil\) to \(q\lceil(1+\epsilon)/s\rceil\). Therefore, counting the number of strings in \(U_{k,q,s}\) is a matter of choosing these spacings and the type of error to use. The total number of error types is \(2\sigma+1\). One \(\sigma\) counts insertions, the other counts substitutions, and the \(1\) counts the deletions. The number of spacings available is given by the following expression:  
\[q\lceil(1+\epsilon)/s\rceil-(|O|-q\lceil(k-1+\epsilon)/s\rceil)-1\leq q(2+k /s)-|O|-1.\]  
Therefore, the number of strings in \(U_{k,q,s}\) can be bounded as follows:  
\[|U_{k,q,s}| \leq (2\sigma+1)^{k}\sum_{|O|=m-k}^{m+k}[q(2+k/s)-|O|-1]^{k}\] \[= (2\sigma+1)^{k}[q(2+k/s)+k-m-1]^{k}(2k+1).\]  
This bound is somewhat loose. In fact, the \((2\sigma+1)\) and \([q(2+k/s)+k-m-1]\) never occur together. For example when \([q(2+k/s)+k-m-1]\) occurs, the other factor is \(1\). We have therefore shown that, by choosing \(q\) and \(s\) properly, \(U_{k,q,s}\) is much smaller than \(U_{k}\). Consider for example the optimal case where \(ms=qk\). Then \(U_{k,q,s}=O((2\sigma+1)^{k}(k-1+2(m.s/k))^{k}(2k+1))\). This shows that the dependence on \(m\) can be reduced to \(ms/k\), which is considerably smaller, especially when we take \(s=1\). To obtain this optimal result we choose the \(q\) and \(s\) parameters in the same way as in the hybrid index, by using \(q=\lfloor m/h\rfloor\) and \(s=\lfloor k/h\rfloor+1\) for an appropriate parameter \(h\). This parameter plays the role of the \(j\) parameter in the classical hybrid index [52].