esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
We introduce an innovative approach for secure communication in the Internet of Things (IoT) environment using a one-time pad (OTP) protocol. This protocol is augmented by incorporating a secure multiparty sum protocol to produce OTP keys from genuine random numbers obtained from the physical phenomena observed in each device. We have implemented our method using ZeroC-loc v.3.7, dependable middleware for distributed computing, demonstrating its practicality in various hybrid IoT scenarios, particularly in devices with limited processing capabilities. The security features of our protocol are evaluated under the Dolev-Yao threat model, providing a thorough assessment of its defense against potential cyber threats.  
one-time pad cryptography; Internet of Things; secure multiparty computation +
Footnote †: journal: Applied
## 2 Methodology  
### Multi-Sensing EMI Method  
In 1994, [PERSON] et al. [64] first proposed a one-dimensional EMI theoretical model. In this model, the PZT patch is considered a thin bar undergoing axial vibration in response to an applied alternating voltage. The altering voltage and output current of the PZT patch are respectively denoted as \(V\) and \(I\), respectively. As shown in Figure 1, the PZT patch is considered fixed at one end, and the other end is attached to structure represented by a single-degree-of-freedom (SDOF) spring-mass-damper system. The frequency-dependent electrical admittance of PZT patches coupled to the structure is given by:  
\[Y(w)=\frac{I}{V}=iw\bigg{(}\varepsilon 33(1-i\delta)-\frac{Z_{s}(w)}{Z_{s}(w)+Z _{a}(w)}d_{3x}^{2}Y\overset{-E}{33}\bigg{)}, \tag{1}\]  
where \(i\) is the imaginary unit; \(w\) represents the angular frequency of the excitation; \(a\) is the geometry constant of the PZT patch; \(\varepsilon\overset{-T}{33}\) is the dielectric constant of the PZT patch at zero stress; \(d_{3x}\) is the piezo-electric coupling constant in the arbitrary \(x\) direction at zero stress; \(Y\overset{-E}{22}\) is the complex Young's modulus of the PZT at the zero electric field; and \(Z_{s}(w)\) and \(Z_{a}(w)\) denote the mechanical impedance of the structure and the PZT patch, respectively. The mechanical property of the PZT patch remains unchanged during the monitoring process; hence, the frequency-related electrical admittance is directly related to the mechanical properties of the structure. The real part of the impedance was used in this paper, as it has been reported to be less sensitive to temperature changes compared to the imaginary part [65; 66].  
As shown in Figure 2, the multi-sensing EMI method is achieved by connecting several PZT patches serially and mounting them onto the different locations of a structure, and the impedance signatures can be obtained by a frequency sweep [67; 68]. In Figure 2, \(Mn\), \(Cn\), and \(Kn\) (\(n\) = 1,2,3... ) represent the mass, damping, and rigidity of different locations of the structure, respectively. It is worth mentioning that each PZT(\(n\) = 1,2,3... ) has different resonant frequencies, which ensures that multiple frequency peaks can be obtained when the PZT patches are connected in series [69]. The change of the physical characteristics of the structure in different regions will change the vibration state of the PZT patch at the corresponding position, which will be reflected by the change in the impedance signature at different frequency peaks.  
Figure 1: The electromechanical impedance model of a Lead Zirconate Titanate (PZT)-driven structural system.
## 4 Discussion
### Work Limitations and Future Research Directions  
We have based the experiments in this paper on a real-world data set concerning a particular video delivery operator. In this case, the hosting nodes of the corresponding proprietary CDN are deployed in the Italian territory. However, such a medium-scale deployment is not the unique possible configuration for a CDN. Consequently, as future work, we plan to obtain or generate data concerning high-scale topologies to assess the scalability of our algorithm to such scenarios.  
Further, this paper presents the assessment of the performance of various DRL-based algorithms. However, the authors of this work had access to real-world data set limited to a five-day trace. Consequently, the algorithms presented in this work were trained on a four-day trace, while the evaluation period consisted of a single day. Future research directions include assessing our agent's training and evaluation performance on data concerning more extended periods.  
Finally, in this work, we have used a VNF resource-provisioning algorithm that is greedy and reactive, as specified in Appendix A.1. A DRL-based resource-provisioning policy would instead act proactive and long-term convenient actions. Such a resource provisioning policy, combined with the SFC deployment policy presented in this work, would further optimize QoS and Costs. Thus, future work also includes the development of a multi-agent DRL framework for the joint optimization of both resource provisioning and SFC deployment tasks in the context of live-streaming in vCDN.  
Conceptualization and methodology, J.F.C.M., L.R.C., R.S. and A.S.R.; software, J.F.C.M. and R.S.; investigation, validation and formal analysis J.F.C.M., R.S. and L.R.C.; resources and data curation, J.F.C.M., L.R.C. and R.S.; writing--original draft preparation, J.F.C.M.; writing--review and editing, J.F.C.M., R.S., R.P.C.C., L.R.C., A.S.R. and M.M.; visualization, J.F.C.M.; supervision and project administration, L.R.C., A.S.R. and M.M; funding acquisition, R.P.C.C. and M.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by _ELIS Innovation Hub_ within a collaboration with _Vodafone_ and partly funded by ANID--Millennium Science Initiative Program--NCN17_129. R.C.C was funded by ANID Fondecyt Postdoctorado 2021 # 3210305.  
Not applicable, the study does not report any data.  
The authors wish to thank [PERSON], [PERSON] and [PERSON] for their valuable insights.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy
## 3 Proposed Method
### Initial Biases Estimation  
Once the sensing axes of each sensor are determined and the noise parameters are aligned, an initial estimate of the bias is carried out by means of a ZUPT filter applied on the measurements of averaged accelerations and angular rates provided by the redundant configuration (Figure 4). To make this stage work with success, the cubic IMU is kept stationary for about two minutes and its outputs are iteratively processed through the ZUPT filter.  
ZUPT filter is based on the Kalman Filter that evaluates in the prediction stage the navigation parameters (position, velocity, and attitude) obtained as an integration of acceleration and angular rate data.  
Successively, the predicted position and velocity are compared in the correction stage with those unchanging of the stationary condition. Any difference between the considered values only arises because of the biases that can be thus estimated and given as output from the ZUPT.
## 6 Simulation Results
### Analysis of the Used Security Key Sensitivity and Space Size  
A security scheme is considered strong if (i) it is highly sensitive to any minor variation in its security keys [56] and (ii) the size of the latter exceeds \(2^{100}\)[54], which is the minimum recommended key size to avoid brute force attacks. Our scheme satisfies requirement (i) because previous experiments in this work demonstrated this criterion (see Figure 9). It remains satisfying requirement (ii) for validating the highest security level offered by our scheme.  
The global security key used in our scheme is  
\[KEY=\{KEY1,KEY2,KEY3,KEY4\}=\{\{a1,b1,a2,b2\},\{\alpha,\beta,\delta,\varphi,x_ {0},y_{0}\},\{\alpha^{*},\beta^{*},\beta^{*},x^{*}_{0},y^{*}_{0}\},\{\alpha^{ **},\beta^{**},\beta^{**},x^{**}_{0},y^{**}_{0}\}\}.\]  
It is worth mentioning that the real-type elements of KEY1 are not very sensitive to a slight variation. Therefore, we only considered the components of KEY2, KEY3, and KEY4 in the calculation of the security KEY size. If we consider that floating-point arithmetic can return inaccurate results from 15 digits after the decimal point, then the size of the security KEY used in our system is about \(\left(10^{15}\right)^{18}=10^{270}\). This size is larger than the recommended minimum size for security keys [57]. As a result, our security system can successfully resist brute force attacks.
# Biosynthetic Pathways of Hormones in Plants
###### Abstract  
Phytohormones exhibit a wide range of chemical structures, though they primarily originate from three key metabolic precursors: amino acids, isoprenoids, and lipids. Specific amino acids, such as tryptophan, methionine, phenylalanine, and arginine, contribute to the production of various phytohormones, including auxins, melatonin, ethylene, salicylic acid, and polyamines. Isoprenoids are the foundation of five phytohormone categories: cytokins, brassinosteroids, gibberellins, abscisic acid, and strigolactones. Furthermore, lipids, i.e., \(\alpha\)-inolenic acid, function as a precursor for jasmonic acid. The biosynthesis routes of these different plant hormones are intricately complex. Understanding of these processes can greatly enhance our knowledge of how these hormones regulate plant growth, development, and physiology. This review focuses on detailing the biosynthetic pathways of phytohormones.  
abscic acid; auxins; biosynthesis; brassinosteroids; cytokins; ethylene; gibberellins; jasmonic acid; melatonin; polyamines; salicylic acid; strigolactones +
Footnote †: journal: Metabolites
## 3 Suichang Gold Mine Shrinkage Method to Filling Method Case  
### Overview of the Mine  
Suichang Gold Mine is located in Zhilington Village, Lianzhu Township, Suichang County, Lishui City, Zhejiang Province. The ore body occurrence elevation is +140 m\(\sim\)+460 m, the ore body dip angle is 45-55\({}^{\circ}\), the horizontal thickness of the ore body is 2 m\(\sim\)20 m, and the average thickness is 4 m. The mine adopts the combined development of adi (located at +420 m level)-blind shaft, and the mining method is the shrinkage method.  
In the process of using the shrinkage method in Suichang Gold Mine, due to the high ore grade, in order to improve the recovery rate, the shrinkage method first adopts concrete replacement pillars, and then mining rooms, which leads to the complexity of the mining process and low efficiency. At the same time, the current use of concrete pillars construction of manual labor intensity is extremely high, with extremely low efficiency, and the concrete replacement ore pillar cost is extremely high (see Figure 6). The use of pneumatic rock drills and electric rakes in production has resulted in low mining efficiency, which is only able to reach a production capacity of 30 t/d. The primary dilution rate of mining is as high as 40%, while there is a secondary dilution rate of up to 5%.  
Due to the long-term use of the shrinkage method to recover ore, a large number of goafs have been formed underground. In the vicinity of the mine, there are a large number of residential areas and a National Mine Park (see Figure 7). The development of these goafs over time may lead to surface subsidence, posing a great safety hazard. In addition, the shrinkage method does not use the large amount of tailings generated after beneficiation for backfilling in the goat. As shown in Figure 7, the tailings pond of the Suichang Gold Mine is close to the design elevation, and the tailings pond is a major source of danger, according to China's \"Opinions on Further Strengthening Work Safety\"; once the tailings pond reaches the design elevation, the mine must be closed and production stopped, which  
Figure 6: Construction diagram of pillar replacement.  
means that the mine is facing the dilemma of suspending production. Therefore, Suichang Gold Mine plans to use tailings to form a backfill, treat the original goat and the existing goat mined by the shrinkage method, and transform the existing mining method into an upward horizontal layered filling method.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 4 Experiment
### Experimental Results  
#### 4.3.1 Performance Evaluation of Methods  
To validate the reliability of the combined dielectric parameter and hyperspectral data detection method, we conducted comparative experiments with traditional single-view detection methods on the apple core browning disease dataset and performed an Analysis of Variance (ANOVA) on the performance of the classifiers selected for each method. Tables 1-4, respectively, show the final average and standard deviation results of the experiments using different classifiers for the hyperspectral detection method, dielectric detection method, combined detection method (without CCA), and combined detection method (with CCA). It can be observed that when using SVM (polynomial) as the classifier, the hyperspectral detection method (sensitivity: 0.8781 \(\pm\) 0.0467, specificity: 0.8839 \(\pm\) 0.0458, accuracy: 0.8819 \(\pm\) 0.0554) and the dielectric detection method (sensitivity:  
The combined detection method (with CCA), compared to the combined detection method (without CCA), incorporated CCA feature dimension reduction. After dimension reduction, the number of hyperspectral features and dielectric signal features was reduced to 35 each. The concatenated 70-dimensional data were then fed into the classifier. Table 1 shows that using SVM (RBF) as the classifier yielded the best results (sensitivity: 0.9998 \(\pm\) 0.0020, specificity: 0.9970 \(\pm\) 0.0076, accuracy: 0.9970 \(\pm\) 0.0077) for the combined detection method (with CCA). Furthermore, the combined detection method (with CCA) showed improved detection performance compared with the combined detection method (without CCA), indicating that CCA maximized the classifier performance without losing important information. The enhancement in detection performance with CCA is attributed to its ability to sift through a vast array of hyperspectral and dielectric property data, extracting key feature information that captured the internal quality changes in apples [41].
## 5 Results
### Spatial Distribution of Carbon Reduction Benefits of BS-FMMS
#### 5.3.1 All-Day Period  
Comparing workdays and non-workdays, it is found that the carbon emission reduction benefits of BS-FMMS show similar spatial characteristics. The carbon emission reduction of BS-FMMS in the metro station areas in the south of Baoan district, the west of Nanshan district, the central part of Longhua district and the south of Futian district is relatively high, while the carbon emission reductions of BS-FMMS in the metro station areas in the north of Baoan district (see Figure 6a,b), the northeastern part of Longgang district and the western part of Luohu district is relatively low. There are 31 metro station areas with higher carbon emission reductions on non-workdays than on workdays, mostly in Nanshan district, Futian district and Luohu district (see Figure 6c).  
In comparison, the carbon reduction benefit of BS-FMMS on the workday is more obvious and the distribution of carbon emission reduction is wide (see Figure 6d), and the top three metro station areas with the largest carbon emission reduction are Bihaiwan station (316.82 kg), Nanshan station (302.74 kg), and Gushu station (249.11 kg), with an average carbon emission reduction of 53.71 kg per metro station area.  
However, the carbon emission reduction benefit of BS-FMMS on non-workdays is relatively limited and spans a small range (see Figure 6d), and the top three metro station areas with the largest carbon emission reductions are Pengzhou station (186.03 kg), Nanshan station (185.29 kg), and Longhua station (184.58 kg), with an average carbon emission reduction of 39.37 kg per metro station area.  
Figure 5: Temporal distribution of carbon reduction benefits of BS-FMMS on workdays and non-workdays. (**a**) represents the workday and (**b**) represents the non-workday.
## 3 Results and Discussion
### Study Region Analysis
#### 3.2.1 Precipitation and Runoff Trends  
Using the linear regression approach, we find the trend in P and (P-Ru) to be positive and statistically significant at the 95% confidence level, but not the trend in Ru (\(p=0.55\)). The use of the Kendall-Sen slope estimator leads to similar results. The regression analysis shows precipitation has increased annually by about 0.67 mm\(\cdot\)yr\({}^{-1}\) while (P-Ru) has increased annually by 0.55 mm\(\cdot\)yr\({}^{-1}\) (Table 2). This corresponds to a (P-Ru) increase from an average of 529 mm\(\cdot\)yr\({}^{-1}\) in the first decade of the 20 th century to 593 mm\(\cdot\)yr\({}^{-1}\) in the first decade of the 21 st century, an increase of about 11.4%. Combined,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**P-Ru**} & \multicolumn{2}{c}{**dS/dt(gw)**} & \multicolumn{2}{c}{**NDVI**} \\ \cline{2-7}  & Linear & Kendall & Linear & Kendall & Linear & Kendall \\  & slope & slope & slope & slope & slope \\ \cline{2-7}  & [\(p\)-value] & [\(p\)-value] & [\(p\)-value] & [\(p\)-value] & [\(p\)-value] & [\(p\)-value] \\ \hline \multirow{2}{*}{HU04} & 0.80 & 0.81 & \(-\)0.06 & \(-\)0.03 & 0.04 & 0.04 \\  & [1.33(10)\({}^{-12}\)] & [6.90(10)\({}^{-11}\)] & [0.7129] & [0.8096] & [2.97(10)\({}^{-13}\)] & [4.87(10)\({}^{-10}\)] \\ \hline \multirow{2}{*}{HU05} & 0.83 & 0.56 & 0.07 & 0.04 & 0.04 & 0.04 \\  & [1.00(10)\({}^{-5}\)] & [1.40(10)\({}^{-3}\)] & [0.7223] & [0.7990] & [9.98(10)\({}^{-10}\)] & [3.02(10)\({}^{-9}\)] \\ \hline \multirow{2}{*}{HU07} & \(-\)0.10 & \(-\)0.10 & 0.10 & 0.18 & 0.04 & 0.04 \\  & [0.3618] & [0.3250] & [0.6892] & [0.4614] & [7.51(10)\({}^{-11}\)] & [7.93(10)\({}^{-9}\)] \\ \hline \multirow{2}{*}{HU09} & 0.25 & 0.22 & \(-\)0.61 & \(-\)0.37 & 0.06 & 0.05 \\  & [0.0107] & [0.0180] & [0.0094] & [0.0702] & [1.00(10)\({}^{-5}\)] & [7.81(10)\({}^{-2}\)] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._these results robustly indicate that the increase in (P-Ru) is mainly due to increase in P and that this increase has not been entirely directed to Ru, thus leaving some \"excess water\" unaccounted for.
# Numerical Investigation of the Hydrodynamic Characteristics of 3-Fin Surfboard Configurations
## 2 Materials and Methods
### Mesh Generation  
The domain uses tetrahedral meshing with inflation layers along the fin wall. The k-\(\omega\) turbulence model requires the first layer height to be within the viscous sub-layer of the flow. Y+ represents the non-dimensional distance from the wall to the first node layer. This distance is required to be Y+ \(\approx\) 1-5 for the first layer to be within the viscous sublayer and ensure the most accurate solution at the wall [14]. Y+ was computed from the mesh for a first layer height of 3.2 \(\times\) 10\({}^{-6}\) m or 0.0032 mm, with 20 layers at a growth rate of 1.2. The calculated Y+ on the fin surface is shown in Figure 4; the maximum value on the fin wall is 1.37 and the minimum is 0.025. The area of red in the contour originates from the surfboard wall, not the fin wall itself.  
Finally, a mesh-sensitivity study was conducted using the grid resolution method to determine the most applicable element size for each of the bodies of influence surrounding the fins. The study comprised five grid densities (Table 2), each decreasing the overall element size for each body by half across the domain, resulting in a grid refinement ratio (\(r\)) of 2. The mesh resolutions were all simulated with an inlet velocity of 7 m/s at an AOA 15\({}^{\circ}\). The difference in \(C_{L}\) between M2 and M1 is approximately 0.15%; however, the computational expense increases by 60%.
## 4 Discussion
### Strengths and Limitations  
This systematic review has several strengths. It included all studies published to date on the topic since 1975. Extensive search strategies were employed with detailed, careful and critical assessment to identify risk of bias in compliance with the PRISMA guide lines. Moreover, meta-regressions were conducted, both uni- and multivariate. However, the review is limited mainly by the methodological deficiencies of included studies. All eight publications described in this review are retrospective case series and are therefore vulnerable to bias. The absence of randomization and a control group means that any changes observed are the result of an uncontrolled pre-post analysis instead of a comparative analysis with control subjects who received an alternative treatment such as current standard conservative care. The small sample size of each study poses a further difficulty in generalizing the findings. Only one study performed an a priori power analysis of the minimum sample size required [41]. The other limitation of this review is the heterogeneity of study design and outcome measures of the studies included. Several surgical procedures in the included studies were carried out at different levels of metatarsal bones by surgeons with different levels of experience; hence, there may be potential bias. Further, the different patients' outcomes scores used by the authors may have contributed to the observed significant heterogeneity among studies, impairing comparability and assessment of the merits of each technique. Further, only studies published in English were included; however, our review did not lose much data from studies published in other languages, as only one was in German [55]. Finally, another limit of this review is the large time frame taken into consideration. As already mentioned, the difference over time regarding the clinical management of diabetic patients has played a fundamental role in the improvement of PDFU outcomes. Better results found in most recent articles can be explained by looking at the improvement of the management of diabetic patients in a multidisciplinary way. In the future, it would be interesting to investigate the specific role of the healing of PDFUs in terms of lengthening the prognosis quo ad vitam of diabetic patients and its economic impact on National Health Services.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\(\beta\)-CD & \(\beta\)-cyclodextrintrin  
\(\beta\)-CD-MOFs & \(\beta\)-cyclodextrintrin metal organic frameworks  
\(\varepsilon\)-PL & \(\varepsilon\)-polylysine  
ABTS & 2,2\({}^{\prime}\)-Azino-bis(3-ethylbenzothazoline-6-sulfonate)  
CEO & Clove essential oil  
CMC & Carboxymethyl chitosan  
DPPH & 1,1-diphenyl-2-picrylhydrazil  
DTNB & 5,5\({}^{\prime}\)-dithiobis(2-nitribenzoic acid  
EO & Essential oil  
EOs & Essential oils  
FAC & Fractional antioxidant capacity  
FDA & Food and Drug Administration  
FFA & Free fatty acids  
FRAP & Ferric-reducing antioxidant power  
GAE & Gallic acid equivalent  
GRAS & Generally recognised as safe  
MAE & Microwave-assisted extraction  
MDA & Malondialdehydedehyde  
MetMb & Metmyoglobin  
ORAC & Oxygen radical absorbance capacity  
PCL & Polycaprolactone  
PG & Propy gallate  
PS/EVOH/PE & Polystyrene/ethylene-vinyl alcohol/polyethylene  
PV & Peroxide value  
ROS & Reactive oxygen species  
TBA & Thiobarbituric acid  
TBARS & Thiobarbituric acid reactive substances  
TPC & Total phenolic content  
TVN & Total volatile nitrogen  
TVB-N & Total volatile basic nitrogen  
UAE & Ultrasound-assisted extraction  
VC & Vitamin C