esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Link Budget
### Link Budget under Realistic Atmospheric Conditions
#### 4.2.5 Discussion on Probability of Good Detections for One Satellite Pass  
As mentioned before, for a given lidar system and a chosen orbit, there is a \(TOT_{max}\) below which the detections are always good (\(SNR_{GE}\geq 10\)). The atmospheric and the forest optical thicknesses are then complementary. A value of \(FOT\) is associated with each single lidar profile; if \(FOT\leq 2 TOT_{max}\) the probability of good detections depends on the probability distribution of \(\tau\), \(\mathbf{F}(\tau)\). This probability can be computed by:  
\[p=\mathbf{F}\left(TOT_{max}-\frac{FOT}{2}\right) \tag{12}\]  
For example, \(TOT_{max}\) has been found as equal to 2.44 when considering an emitted energy \(\sim\)100 mJ for a NIR lidar payload embedded onboard the ISS. If we want to detect forest with \(FOT\leq 3\), which correspond to 92% of the forest from our \(FOT\) distribution reference, this probability is \(p=\mathbf{F}\left(\tau=0.94\right)\sim\)0.73 (see the gray dash line in Figure 14a). With the same lidar onboard ICESat, we found \(TOT_{max}=1.90\) and then \(p=0.62\) and 0.59 for tropical and boreal forests, respectively. These \(TOT_{max}\) values are reported in Table 6 for each relevant spaceborne mission.
## 1 Introduction
Besides the fact that HA is the main inorganic fraction in the bone extracellular matrix, the primary constituent of the organic matrix is collagen; its primary function is mechanical support acting as a scaffold for bone cells [16]. Biodegradable synthetic polymers such as polyglycolide, poly-(lactide-co-glycolide)s, polyhydroxyalkanoates, polycaprolactone, polylactide (PLA) have been studied as candidates for implant scaffolds showing promising biomedical applications [17]. However, these pure biomedical polymers might not always mimic the mechanical properties of native tissues such as elasticity or modulus because of their intrinsic shortcomings. However they can be used to design structural integrity and mechanical support environment which is favourable for cell attachment, proliferation or differentiation [18]. Among the synthetic polymers, PLA is often repeatedly singled out as the matrix material for small screws and pins among the commercialized biosensor implants [19]. The process of designing the polymer-based composites with HA inclusion should consider how material will be sterilized and whether the sterilization will cause any significant changes in the physical, chemical, mechanical and biocompatibility properties of the final product that might generate deplorable responses in the human body or compromise its function [20]. An equally important aspect of composite characteristics, is its degradation pattern on the porosity and mechanical strength in timespan. Besides the large number of research undertaken for synthetic polymers such as PLA in regenerative medicine, only a few studies investigate how sterilization and degradation effect physicochemical propertiesof manufactured materials with different weight-average molar mass (\(M_{\mathrm{w}}\)) of PLA or HA morphology included [21; 22; 23; 24; 25; 26; 27; 28]. The aim of this work was to investigate the effect of sterilization and in vitro hydrolytic degradation on the structural and mechanical properties of porous composites prepared from two different \(M_{\mathrm{w}}\) PLA with the inclusion of HA hexagonal rods or whiskers with and without metal addition. To the best of our knowledge, there is no literature reports combining the effect of polylactide molecular properties with varied morphological inclusion of hydroxyapatite on the monitored change in mechanical, physical and chemical properties of composites when sterilized and degraded in time.
# Band Gap Properties in Metamaterial Beam with Spatially Varying Interval Uncertainties
## 3 Results and Discussion
### Metamaterial Beam with Interval Density Field and Interval Resonator  
Since there may be uncertainties in the basic beam, it is obvious that there may be uncertainties in the spring-mass resonator. The spring stiffness and mass in each resonator can be assumed to be an independent interval variable and expressed as follows:  
\[m_{i}^{I}=m(1+\Delta m_{i}[\varepsilon_{i}]),k_{i}^{I}=k(1+\Delta k_{i}[ \varepsilon_{i}]),(i=1,2,\cdots N_{u}) \tag{28}\]  
The values of \(m\) and \(k\) are shown in Section 3.1. \(\Delta m_{i}\) and \(\Delta k_{i}\) denote the dimensionless deviation amplitude of \(m_{i}^{I}\) and \(k_{i}^{I}\), respectively. Figure 9a shows the IVTA results considering both the uncertainties in the density of the basic beam (\(C_{B}=0.05,l_{B}=0.8L_{\text{beam}}\)) and the uncertainties in the resonators (\(\Delta m_{i}=\Delta k_{i}=0.02,(i=1,2,\cdots N_{u})\)). It should be noted that due to the extremely low computational efficiency, the vertex method was not used in the numerical examples presented in this subsection. When the interval parameters of the resonators were added into the calculation, the beginning frequency was also influenced by uncertainties. The safe band gap range predicted by the MCM was from 29.7639 Hz to 67.2779 Hz, reducing the band gap width by 7.41% compared to the deterministic band gap. Furthermore, the safe band gap range obtained by the IPFEM was from 29.9966 Hz to 69.3091 Hz. Both the beginning frequency and the ending frequency were higher than those obtained by the MCM, and the relative errors were 0.78% and 3.02%, respectively. Another IVTA could be implemented by keeping interval field parameters \(C_{B}\) and \(l_{B}\) unchanged and increasing \(\Delta m_{i}\) and \(\Delta k_{i}\) to 0.04. The calculation results are presented in Figure 9b. In Figure 9b, the safe band gap calculated by the MCM ranged from 29.8576 Hz to 66.7813 Hz, reducing the band gap width by 8.87%. Meanwhile, the safe band gap calculated by the IPFEM was from 30.0621 Hz to 69.1674 Hz. Similarly, the beginning frequency and ending frequency were higher than those obtained by the MCM, and the relative errors were 0.68% and 3.57%, respectively. That is to say, the IPFEM could not provide a conservative solution for the safe band gap, but its calculation precision was acceptable.  
Figure 8: Relative error of the ending frequency of the safe band gap according to IPFEM for different interval field parameters. (**a**) Different \(C_{B}\). (**b**) Different \(l_{B}\).
# Vegetation Removal on 3D Point Cloud Reconstruction of Cut-Slopes Using U-Net
## 2 Cut-Slope of Doam Dam  
The cut-slope used in this study was a rocky cut-slope located in Doam Dam at the north-east of South Korea, as shown in Figure 1a. Images taken with UAV at different times (winter and summer) were used to reconstruct 3D point clouds of the cut slope. A DJI Phantom 3 Professional UAV was used to take \(4000\times 3000\) pixels images as shown in Figure 1c. The UAV was manually driven and the camera position and orientation of each image is shown as a pyramid in Figure 2. The images were processed by an open-source photogrammetry software COLMAP [25] to reconstruct a 3D point cloud of the cut-slope, as shown in Figure 1b. In the reconstruction, the \"automatic reconstruction\" command of COLMAP was used with the default setting.  
The photogrammetry procedure consists of structure from motion (SfM) and multi-view stereo (MVS) as illustrated in Figure 3. Structure from motion estimates the camera position and orientation of each image (i.e., the structure) by triangulation of the feature points appearing on multiple images. Structure from motion also determines the 3D coordinates of the feature points, known as a sparse point cloud. As a sparse point cloud is usually not dense enough to represent the surface of a slope, multi-view stereo procedure is performed to generate a dense point cloud by triangulation on each pixel of two adjacent images to determine the corresponding 3D coordinates in the world coordinates.  
As shown in Figure 1c, there was no clear distinction between the rock and vegetation, which made vegetation removal challenging on both the 2D images and 3D point cloud.  
Figure 1: Doam Dam: (**a**) location, (**b**) 3D point cloud, and (**c**) typical image taken by UAV.  
Figure 3: Photogrammetry procedure.  
Figure 2: Camera positions and orientations of the drone survey shown as pyramids.
## 6 Safety Proof
** Similar to [35], the given scheme LR-SP-IBBE obtains continuous leakage resilience by refreshing the private key periodically. The update algorithm inputs \(SK_{ID,k}\) and public parameters \(PP\) and achieves one update secret key \(SK_{ID,k+1}\). In the update procedure, an additional value is added to the random value of the original exponent of one private key. Since the newly added value is randomly selected from \(Z_{N}\), the distribution of this new private key is the same as that of the original one. After the private key update algorithm ran, we obtained one fresh private key. Thus, the proposed scheme had continuous leakage resilience. \(\square\)  
**Theorem 3. **_The relative leakage rate of the symmetric key of LR-SP-IBBE \(is\tau=\left|\left|\left|\left.\log_{2}^{N}\right|\right.\right.\right.\approx 1\)_  
**Proof. ** When the symmetric key is not disclosed,\(\widetilde{H}_{\infty}(\mathcal{A}|VD)=log_{2}^{N}\), where \(\mathcal{A}\) represents the adversary and \(VD\) represents the joint distribution of all variables. The adversary \(\mathcal{A}\) can obtain the \(l\) bits of information for the symmetric key by a side-channel attack. This indicates that the variable _Leak_ is \(l\) bits in length. By means of conclusion 1, \(\widetilde{H}_{\infty}(\mathcal{A}|(VD,Leak))\geq\widetilde{H}_{\infty}( \mathcal{A}|View)-l\geq log_{2}^{N}-l\). In this way, if the \((log_{2}^{N}-l,\varepsilon)\) extractor is selected, \(StD((Ext(K,r),r),(E,r))\leq\varepsilon\), where \(E\) represents a uniform distribution. Thus, when \(log_{2}^{N}-l\) is close to zero, the leakage is close to \(log_{2}^{N}\). Then, \(CM=Ext(K,\ r)\oplus M\) and uniform distribution are indistinguishable. Therefore, the relative leakage rate is: \(\tau=\left|\left|Leak\right|/\left(\log_{2}^{N}\right)\approx\ (\log_{2}^{N})/(\log_{2}^{N})\approx 1\). \(\square\)
## 3 Experiments and Analyses
### Experiment Setup
#### 3.1.1 Edge Intensity  
The quality of the fused image is measured by the local edge intensity \(L\) in image \(I\)[38]. It folds a Gaussian kernel \(G\) with the image \(I\) to get a smoothed image. Then it obtains the edge intensity image by subtracting the smoothed image from the original image. The spectrum of the edge intensities depends on the width of the Gaussian kernel \(G\).  
\[\mathbf{L}=max(0,\mathbf{I}-\mathbf{G}*\mathbf{I}) \tag{11}\]  
Figure 3: Four Source Image Pairs of Different Scenes for Multi-focus Fusion Experiments, (a)–(d) are source image pairs of hot-air balloon, leopard, lab, bottle respectively.  
The fused image \(H\) is calculated by image \(L_{j},j=1,...,n\) and the weighted average of local edge intensities.  
\[\mathbf{H}(x,y)=\sum_{j=1}^{N}w_{j}(x,y)\mathbf{L}_{j}(x,y), \tag{12}\] \[w_{j}(x,y)=\frac{L_{j}(x,y)}{\sum_{k=1}^{N}L_{k}(x,y)} \tag{13}\]
## 4 Discussion
Consistent with prior PPOP studies, pre-existing pain, anxiety, and depression were risk factors for persistent ocular symptoms six months after surgery [51]. Gabapentin and pregabalin have also been studied in the treatment of acute pain after photorefractive keratectomy (PRK). Two studies found no difference in subjective pain ratings immediately after PRK between gabapentin (300 mg three times daily) and oxycodone/acetaminophen (5 mg/325 mg) [52] or placebo [53]. Two studies, on the other hand, found that \(\alpha 2\delta\) ligands reduced acute postoperative pain during the first 72 h after surgery [54; 55]. Our study targeted a different aspect of pain, namely prevention of chronic ocular symptoms (dryness, pain), after LASIK. The chief limitation of our study was its small sample size. However, given our confidence interval of \(-53\%\) to \(5\%\) for the difference in frequency of DE symptoms between the groups, we have good cause to rule out a reduction greater than \(5\%\) in persistent DE symptoms in the pregabalin compared to control. This suggests that pregabalin does not have a clinically substantial benefit on the frequency of DE symptoms at the current dose and the current population studied. As such, future studies with a larger population of individuals at higher risk of DE symptoms after LASIK (those with pre-existing pain, depression, and/or anxiety) are needed to re-examine this question. Furthermore, our groups were not matched at baseline despite randomization. However, these baseline differences were not significant predictors of DE symptoms at six months, and as such, this occurrence does not seem to have confounded the study. Furthermore, we did not image cornea nerves, and thus, do not have data on differences in nerve regrowth between the groups after surgery. While many studies found that that \(\alpha 28\) ligands suppress ectopic discharge activity from injured nerve sites [56; 57; 58; 59], few have studied how this blockade affects nerve healing. One study on sciatic crush injury in rats found that low dose pregabalin (\(30\) mg/kg) over four weeks promoted nerve regeneration and functional recovery, while high doses (\(60\) mg/kg) did not [59]. On the other hand, another study of pregabalin \(10\) mg/kg over three weeks did not find an effect on nerve regeneration [60]. A third study found that pregabalin-collagen therapy after injury aided in nerve regeneration by inhibiting IL-10 release, a pro-inflammatory cytokine that negatively impacts the healing processes [61]. Our current study does not provide information on the effects of pregabalin on corneal nerve regeneration and as such, future studies are needed that focus on these connections, such as with the use of confocal microscopy. Of note, we focused on LASIK in this study given an epidemiological link between LASIK and DE symptoms; however, newer surgical techniques, like small incision lenticule extraction (SMILE), are now available and may lower the overall incidence of post-refractive DE symptoms in the future [62].
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 2 E-Nose System
### Gas Sensing Mechanism
N-type MOSs, such as ZnO [62; 63], In\({}_{2}\)O\({}_{3}\)[64; 65], Fe\({}_{3}\)O\({}_{2}\)[62; 66], TiO\({}_{2}\)[67; 68], WO\({}_{3}\)[69; 70], and SnO\({}_{2}\)[71; 72], exhibit electrons as their majority carriers, whereas p-type semiconductors, including NiO [73], Co\({}_{3}\)O\({}_{4}\)[74], Cr\({}_{2}\)O\({}_{3}\)[75], Mn\({}_{3}\)O\({}_{4}\)[76], and CuO [77], have holes as their majority carriers. In the field of gas sensing, n-type MOX gas sensors have been extensively studied and well documented. However, p-type MOX gas sensors have received comparatively less attention, and the research in this area is still in its early stages. Figure 3 illustrates the research landscape for p-type and n-type MOX gas sensors, gathered from Web of Knowledge on 1 March 2024, utilizing keywords like \"gas sensor\" and \"ZnO\". Out of all of the articles related to MOX gas sensors, only a mere 13% pertain to p-type MOX gas sensors. The process of adsorbing atmospheric oxygen onto the surface of metal oxides is uniform for both n-type and p-type semiconducting metal oxides. However, the resulting effects are notably distinct. In the case of n-type semiconductors, this ionosorption of oxygen results in a depleted surface area, leading to high resistivity. Conversely, for p-type semiconductors, the ionosorption process leads to the formation of an accumulation layer, resulting in a highly conductive surface region. However, despite significant advancements, MOX sensors continue to face challenges such as susceptibility to poisoning, drift, low selectivity, low repeatability, and prolonged response and recovery times. These issues hinder their reliability and applicability in diverse settings, impeding their utility for real-time monitoring and precise gas identification. Addressing these challenges is essential for advancing MOX sensor technology and broadening its practical utility in various fields. Figure 3: An analysis of research activities concerning n-type and p-type oxide semiconductor gas sensors derived from an internet search on Web of Knowledge, conducted on 15 March 2024.
# FPGA Implementation of Mutual Authentication Protocol for Medication Security System
## 5 Conclusions  
Sensor networks are used in several domains that handle sensitive information. In this paper, the main objective in the security design of the described scenario was data authentication, whereby a WMSN should be able to verify whether sensor data originated from legitimate sensors. A medication safety mutual authentication protocol with the Padgen algorithm was proposed to improve the drawbacks of the authentication scheme in WSN. Through the Padgen algorithm, the message can be cover-coded and security can be enhanced during wireless data transmission. FPGA hardware verification of the proposed architecture was also demonstrated; the proposed architecture has a more efficient area and lower power than the traditional authentication scheme. For the integration of the proposed protocol in an electronic system, the interfaces of the integrated system components need to be further studied to realize wireless data transmission across system integration.  
Conceptualization, Y.-J.H.; methodology, [PERSON]-C.L. and [PERSON]-[PERSON]; software, [PERSON]-K.H. and C.-L.P.; validation, W.-C.L. and [PERSON]-[PERSON]; formal analysis, P.-K.H.; investigation, C.-L.P. and [PERSON]-[PERSON]; data curation, [PERSON]-K.H. and W.-C.L.; writing--original draft preparation, W.-C.L. and [PERSON]-J.H.; writing--review and editing, Y.-J.H.; visualization, P.-K.H.; supervision, Y.-J.H.; project administration, Y.-J.H.; funding acquisition, Y.-J.H. All authors read and agreed to the published version of the manuscript.  
Figure 7: FPGA Hardware verification of the functionality of the proposed protocol. (**a**) When Sw = 2°b00, outputs UserID; (**b**) When Sw = 2°b01, outputs CCPWD; (**c**) When Sw = 2°b10, outputs CCMsgM \(\parallel\) CRC; (**d**) When Sw = 2°b11, outputs CCMsgL \(\parallel\) CRC.  
**Funding:** This work was supported in part by the EDA healthcare under grant EDDHM109002, ISU-110-IUC-10 and the Ministry of Science and Technology of Taiwan under grant MOST 110-2221-E-214-025.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Materials Characterization  
The samples' micro-morphologies and structures were determined by a Hitachi SU8010-Scanning electron microscope (SEM, TESCAN, Brno, Czech Republic) and a D8 Advance X-ray diffractometer (XRD, Bede Scientific Ltd., Durham, UK). The elements' compositions, chemical states, molecular structures, and other aspects of the samples were tested by Escalab 250 Xi- X-ray photoelectron spectroscopy (XPS, VG ESCALAB MKII, VG Scientific, Cambridge, UK).
# Experimental Study on the Dynamic Characteristics of Fractured Coal Under Cumulative Impact
## 1 Introduction
[40] performed triaxial dynamic loading tests on coal specimens across low, medium and high strain rate regimes, revealing that dynamic compressive strength demonstrates progressive enhancement with increasing loading rates, axial stress levels and confining pressure magnitudes. While significant progress has been made in coal-rock dynamics research, the existing studies predominantly focus on intact specimens. Although the literature [41] conducted dynamic impact experiments on coal samples with prefabricated fractures, it did not account for the effects of cumulative impact disturbance. Practically, fractured coal undergoing cumulative impacts follows a \"damage accumulation-fracture coalescence-performance degradation\" evolutionary path, critically impacting coal seam stability. However, there are few studies on the dynamic characteristics of fractured coal under the action of cumulative impact disturbances. Therefore, this necessitates comprehensive investigation into the dynamic responses of fractured coal subjected to cumulative impact disturbances. On the basis of the research above, this study utilizes prefabricated fractured coal samples as the research subject, subjecting them to cyclic impact loading while monitoringwave velocity evolution characteristics. Then, the split-Hopkinson pressure bar (SHPB) device is used to carry out the dynamic impact on the fractured coal rock under the action of cumulative impact, followed by sieve analysis of fragmented particles. The research systematically evaluates cumulative impact effects on three critical aspects: dynamic compressive strength, fractal fragmentation characteristics and energy dissipation mechanisms of fractured coal-rock masses. The findings provide theoretical and technical references for mechanism exploration of coal-rock dynamic disasters and prevention strategies in deep mining operations.
## 5 Findings from Prediction Applications
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Application (EEG Time Series)**} & \multicolumn{3}{c}{**ANN-ALO System Including ALO with Different Adjustment**} \\ \cline{2-6}  & **ALO-A\({}_{1}\)** & **ALO-A\({}_{2}\)** & **ALO-A\({}_{3}\)** & **ALO-A\({}_{4}\)** & **ALO-A\({}_{5}\)** \\ \hline A1 & 0.0453 & 0.0430 & 0.0335 & **0.0333** & 0.0348 \\ A2 & 0.0372 & 0.0381 & 0.0366 & 0.0359 & **0.0356** \\ A3 & 0.0398 & 0.0408 & 0.0393 & **0.0376** & 0.0392 \\ A4 & 0.0355 & 0.0333 & 0.0330 & **0.0328** & 0.0333 \\ A5 & 0.0605 & 0.0623 & 0.0616 & **0.0579** & 0.0596 \\ A6 & 0.0363 & 0.0386 & **0.0348** & 0.0360 & 0.0362 \\ A7 & 0.0796 & 0.0904 & 0.0802 & **0.0793** & 0.0796 \\ A8 & 0.1161 & 0.1052 & 0.0946 & **0.0914** & 0.0931 \\ A9 & 0.0673 & 0.0667 & 0.0557 & 0.0544 & **0.0441** \\ A10 & 0.1013 & 0.0847 & 0.0952 & **0.0835** & 0.0849 \\ \hline \hline \end{tabular} Best MSE values are in bold style.  
\end{table} 
Table 6: Mean squared error (MSE) values calculated for each EEG time series (Figure 5), via different ANN–ALO adjustments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Application (EEG Time Series)**} & \multicolumn{3}{c}{**ANN–ALO System Including ALO with Different Adjustment**} \\ \cline{2-6}  & **ALO-A\({}_{1}\)** & **ALO-A\({}_{2}\)** & **ALO-A\({}_{3}\)** & **ALO-A\({}_{4}\)** & **ALO-A\({}_{5}\)** \\ \hline A1 & 12.4531 & 12.2219 & 11.2195 & **11.0653** & 12.1029 \\ A2 & 13.8490 & 14.5119 & 13.3933 & 12.9048 & **12.6544** \\ A3 & 15.8722 & 16.6799 & 15.4337 & **14.1190** & 15.3290 \\ A4 & 12.6193 & 11.0907 & 10.8994 & **10.7726** & 11.1167 \\ A5 & 16.3980 & 17.8881 & 16.2110 & **14.3851** & 15.2962 \\ A6 & 13.1566 & 14.8791 & **12.1109** & 12.9671 & 13.0991 \\ A7 & 15.6777 & 16.3109 & 16.1981 & **15.4309** & 15.7193 \\ A8 & 20.4559 & 20.3984 & 19.8566 & **17.0994** & 17.7556 \\ A9 & 22.3983 & 21.7839 & 20.9061 & 19.6770 & **19.4211** \\ A10 & 22.7159 & 19.9941 & 20.4185 & **18.9134** & 20.1498 \\ \hline \hline \end{tabular} Best MAE values are in bold style.  
\end{table} 
Table 5: Mean absolute error (MAE) values calculated for each EEG time series (Figure 5) via different ANN–ALO adjustments. Figure 7: Predictions for both training and test data regarding the EEG time series: **A1–A5** (the white line represents original data, and the red dots are for predictions. Some visible and remarkable periods including errors are represented by blue squares). Figure 8: Predictions for both training and test data regarding the EEG time series: **A6–A10** (the white line shows the original data, and the red dots are for predictions. Some visible and remarkable periods, including errors, are represented by blue squares).