esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Case Study
### Process Robustness Analysis
#### 6.3.3 Process Robustness Comparison of Three Substitution Strategies under RD Failure Mode  
As shown in Figure 13, (1) in the RD failure mode, as the number of node attacks increases, the RoS values under all three substitution strategies rapidly decline and finally decrease to 0, at which point all cloud orders fail to be processed. This indicates that the RD failure mode is more destructive to the robustness of the CMP than either the ID or IB modes. (2) The \"external\" curve is always located above the \"internal\" curve, and the \"inter&exter\" curve is nearly always located above the \"external\" curve. This indicates that the RoS value is largest under the internal-external integration replacement strategy, followed by the external substitution strategy, and then the internal substitution strategy. Further, under the three strategies, the numbers of node attacks required to make all cloud order processing fail (i.e., when the process robustness decreases to its lowest) are approximately 110 (\"internal\"), 140 (\"external\"), and 160 (\"inter&exter\"). Therefore, to attain robustness of the CMP under the RD failure mode: internal-external integration strategy > external substitution strategy > internal substitution strategy.  
Figure 12: Variation of RoS under IB failure mode.  
Figure 13: Variation of RoS under RD failure mode.
## 3 Results  
### Multi-Axis Forces and Moments  
In this study, the forces and moments generated in the four experimental groups with different CA designs were analyzed. Significant differences (\(p<0.001\)) were observed between the groups for all measured forces and moments (Table 2).  
The buccolingual force (Fx) values showed that all groups generated a buccal force. While Groups 2 and 4 exhibited similar Fx values (6.38 \(\pm\) 2.33 cN and 4.42 \(\pm\) 2.32 cN, respectively; \(p>0.05\)), Groups 3 and 1 had significantly higher Fx values (7.28 \(\pm\) 3.05 cN and 20.09 \(\pm\) 3.88 cN, respectively). The mesiodistal force (Fy) was the highest in Group 4 (5.67 \(\pm\) 1.19 cN), followed by Groups 2 and 1, and the lowest in Group 3 (1.20 \(\pm\) 0.90 cN) (\(p<0.001\)). All groups experienced an intrusive force (Fz), with decreasing forces recorded in Groups 1, 4, 3, and 2, in that order; no statistically significant difference was observed between Groups 2 and 3 (\(p>0.05\)).  
The angulation moment (Mx) values showed distal tipping moments in all groups, with a mean Mx of -7.66 \(\pm\) 1.64 (Nmm) in Group 1, and Groups 4, 3, and 2 showing a gradual decrease in the distal tipping tendency. The inclination moment (My) values were measured, and Group 1 showed the largest lingual inclination moment (\(-17.41\)\(\pm\) 1.88 Nmm), while Group 2 showed the smallest moment (\(-4.78\)\(\pm\) 2.14 Nmm). The rotation moment (Mz) values showed that all groups had mesial rotation moments, with a decreasing trend from Group 1 to Group 4; no significant difference was observed between Groups 2 and 3 (\(p>0.05\)).  
Thus, despite the different edentulous area designs of the CAs, all groups mainly showed buccal and distal bodily movements and intrusive forces, whereas the crowns showed distal and lingual tilting and mesial rotation moments.
## 3 Results  
Representative OCT images that were collected from untreated dentine at day 0, 10, and 15 are shown in Figure 1A-C. The emergence and progression of the erosive lesion induced by the demineralization challenge can be easily identified in the tomographic images. Figure 1D shows the numerical fits calculated from the OCT signals that were extracted from regions delimited by red polygons in each image. The magnitude of OCT signal is represented as a function of the optical penetration depth. OAC values that were calculated from each numerical fit are depicted in Figure 1E. As long as demineralization increases, the light is less attenuated and it penetrates deeper into the sample, so that the OCT signal decays more slowly and the optical attenuation coefficient decreases (Figure 1E).  
Figure 2 shows the averaged values and standard errors of OAC values that were obtained for each group before the erosion cycle (day 0), immediately after receiving treatment (day 10), and at the end of demineralization challenge (day 15). OCT signal attenuation is quantity sensitive to dentine demineralization, and therefore it can be used as a marker for detecting demineralization [16]. A decrease in the OAC values after receiving treatment indicates that demineralization is progressing, i.e., the treatment modality was not able to induce chemical and morphological changes to improve the resistance of dentine to acid challenge. On the other hand, no alterations in the OAC values after treatment indicate that demineralization is not progressing, therefore suggesting the protective effects that are induced by treatment [11].  
A decrease in the OAC values calculated from OCT images that were collected from control, fluoride, L1, L3, FL1, and FL3 groups were evidenced at the end of the experiment (day 15), indicating no protective effects against the progression of erosive lesions in the specimens from these groups. No significant statistical differences were obtained comparing the OAC values at day 10 and 15 from  
Figure 1: Representative Optical Coherence Tomography (OCT) images collected from untreated dentine at day 0 (**A**), 10 (**B**), and 15 (**C**). Numerical fits calculated from OCT signals extracted from regions delimited by red polygons in each image are shown in **D**. OAC values calculated from each numerical fit are depicted in **E**.  
Figure 2: Averaged values of optical attenuation coefficient calculated from OCT images that were collected before the erosion cycle (day 0), immediately after receiving treatment (day 10) and at the end of demineralization challenge (day 15). Bars denote standard error. Distinct letters indicate statistically significant differences (p < 0.05), according to Tukey’s test.  
samples of L2 and FL2 groups, suggesting that treatment was effective in avoiding the progression of erosion lesions.
# Design and Lateral Stability Analysis of an Attitude Adjustment Tractor for Moving on Side Slopes
## 2 Design Concept
### A Wheel Tractor with Attitude Adjustment Function
#### 2.2.2 Front Axle  
For a tractor to adjust its attitude, the front axle should have the ability to coordinate with the rear axle. As shown in Figure 3, a new front axle for the hillside tractor is proposed.  
The tractor can passively adapt to the ground with the attitude adjustment mechanism while ensuring all four wheels keep in contact with the ground, as shown in Figure 3. With this new attitude adjustment mechanism, the angle between the front wheels and the  
Figure 3: Front axle: (**a**) CAD model; (**b**) Front view model; (**c**) Top view model; (**d**) Attitude adjustment; (**e**) Principle schematic.  
ground can be passively changed with the ground. Thus, the front wheels can be vertical when the tractor adjusts its attitude on side slopes. As shown in Figure 3d,e, the attitude adjustment mechanism is composed of two symmetrical four-bar linkages. The freedom of this attitude adjustment mechanism in the front wheel is:  
\[F=3\times n-\sum_{i=1}^{n}iP_{i}=3\times 5-2\times 7=1, \tag{1}\]  
where \(F\) is the DOF, \(n\) is the number of the active components, \(i\) is the constraint number in the \(i\) level, and \(P_{i}\) is the number of kinematic pairs in the \(1\) level. This front axle consists of \(5\) active components and \(7\) kinematic pairs.  
Thus, the DOF (degree and freedom) of this front axle is \(1\). It means if one rotation angle is determined, the others can be achieved. With this degree, all four wheels can keep contact with the ground while encountering small obstacles. As the adjustment mechanism is composed of two symmetrical four-bar linkages, it can be designed to adjust the lateral slopes with larger angles.  
With these features of the front axle, this tractor can keep all four wheels in contact with the ground. The front axle also can cooperate with the active rear axle to actively adjust the tractor's attitude.
## 2 Antenna Design and Optimization
### Parametric Analysis
#### 2.2.1 Dual-Band Response
By systematically optimizing (WH_cut) along with other parameters, the design achieves a strong configuration that effectively accommodates dual-band operation. These optimized values of the affected parameters ensure that the AR remains below 3 dB. Achieving a low AR is vital for promoting effective CP, which is important for various communication applications.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 2 Background
### Attack Surface
#### 2.3.2 Attack Surface on the Software Layer  
Attack Surface on the Software Layer corresponds to the software part of the bootloader and firmware in the device composition. It mainly includes the following five aspects: unsafe bootloader, unsafe operating system, leakage of sensitive information in firmware, unsafe application service, and incorrect configuration strategy:  
1. **Unsafe bootloader.** It is often easy to ignore the point of attack because the bootloader is a piece of code that is loaded from the chip after the device running. Its function is to initialize the device and load the firmware. Thus, it has a high risk when problems arise. For example, _checkm8_[32], the Boot ROM exploit, has widely been proclaimed as the most important single exploit ever released for iPhone, iPad, Apple TV, and Apple Watch devices.
2. **Unsafe operating system.** Due to the short development cycle and lightweight requirements of the IoT device, the kernel of the operating system is tailored, and the version is usually not up-to-date, which causes various buffer overflow problems such as privilege escalation. In addition, devices use various sensors and communication modules including a large number of drivers in the kernel. For example, the Marvell WiFi chip driver was found multiple vulnerabilities such as CVE-2019-14901, CVE-2019-14897, and CVE-2019-14896 [33]. They cause stack-based or heap-based buffer overflow in the kernel. This is also an important part of the attack surface.
3. **Leakage of sensitive information in firmware.** Local storage of IoT devices generally uses a lightweight storage solution. Developers often ignore security and use plain text or simply encrypting data, which can easily lead to the leakage of sensitive information.
4. **Unsafe application service.** Application services development lacks security standards. Simple and unsafe application code is compiled and used directly to speed up product development. Therefore, it is easy to introduce unknown vulnerabilities. IoT security researchers have discovered a large number of application vulnerabilities developed by manufacturers, including backdoors that are unknown for some reason.
5. **Incorrect configuration strategy.** Services such as ssh, telnet are enabled for easy management of IoT products. There will be configuration problems. Weak authentication policies are configured by default, which allows attackers to easily obtain the shell of device. For example, Telestar Digital GmbH IoT radio devices could be exploited by remote attackers to hijack devices by telnet servers without authentication [34]. The vulnerabilities have been tracked as CVE-2019-13473 [35] and CVE-2019-13474 [36].
# Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
## 4 Results and Discussions
### Case 4: Bolund Island Field Experiment (239\({}^{\circ}\) Wind Direction)
In addition, the modification also compensates for the shortcomings of the standard model in simulating the turbulence on the leeward slope, i.e., the standard models are unable to simulate the non-conservation between the turbulence generation and the dissipation on the leeward slope.
# A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
## 4 Numerical Results
### Impact of \(M_{2}\) and \(\alpha\) on Outage Probability  
Figure 4 describes the average outage probability of the proposed model with varying the number of users in Phase II for different path loss values. It can be seen that the system achieves lower average outage probability as the number of users in Phase II deceases. This phenomenon is experienced because the allocation of the power coefficients in the NOMA system is based on the user's channel condition or desired rate. Therefore, increasing the number of users results in power allocation coefficients that are relatively close to each other making it difficult for SIC to perfectly decode each user's data.
## 3 Results and Discussion
### Preliminary Test in Na Half Cells
During the first discharge, an irreversible capacity related to the SEI formation [34] is observed, namely 335 and 270 mA h g\({}^{-1}\) for the EMIFSI and N\({}_{1114}\)FSI cells, respectively, which corresponds to a coulombic efficiency value of 27 (EMIFSI) and 39 (N\({}_{1114}\)FSI) %. The HC electrode is found to deliver an initial reversible capacity of 126 and 174 mA h g\({}^{-1}\) in EMIFSI and N\({}_{1114}\)FSI electrolytes, respectively. These results are comparable with those obtained in organic standard electrolytes (i.e., 1 M NaTFSI in propylene carbonate with 3 wt.% fluoroethylene carbonate as an additive [18]). The first charge-discharge voltage-capacity curve of the NMO electrodes is plotted in Figure 6B, which shows five plateaus (in both electrolyte formulations) corresponding to different phase transitions during the reversible Na\({}^{+}\) intercalation process. This finding is in good agreement with the voltammetry results (Figure 5). An initial charge capacity around 190 mA h g\({}^{-1}\) is observed for both IL electrolytes, corresponding to over 91% of the theoretical value of 208 mA h g\({}^{-1}\) (i.e., 0.85 Na equivalent), as determined by [PERSON] and colleagues in monoclinic \(\alpha\)-NaMnO\({}^{19}\). However, a larger reversible capacity exceeding 160 mA h g\({}^{-1}\) (vs. about 140 mA h g\({}^{-1}\) in 0.1 NaTFSI-0.9 EMIFSI) is delivered in 0.1 NaTFSI-0.9N\({}_{1114}\)FSI electrolytes, leading to higher initial coulombic efficiency (i.e., 83 (N\({}_{1114}\)FSI) vs. 73 (EMIFSI) %). These values are higher than those recorded in organic electrolyte solutions [18; 34; 35; 36; 19], making the EMIFSI and N\({}_{1114}\)FSI electrolyte formulations appealing for the realization of safer and more reliable sodium-ion battery systems.
###### Abstract  
There is increasing interdisciplinary interest in phytoplankton community dynamics as the growing environmental problems of water quality (particularly eutrophication) and climate change demand attention. This has led to a pressing need for improved biophysical and causal understanding of Phytoplankton Functional Type (PFT) optical signals, in order for satellite radiometry to be used to detect ecologically relevant phytoplankton assemblage changes. Biophysically and biogeochemically consistent phytoplankton Inherent Optical Property (IOP) models play an important role in achieving this understanding, as the optical effects of phytoplankton assemblage changes can be examined systematically in relation to the bulk optical water-leaving signal. The Equivalent Algal Populations (EAP) model is used here to investigate the source and magnitude of size- and pigment-driven PFT signals in the water-leaving reflectance, as well as the potential to detect these using satellite radiometry. This model places emphasis on the determination of biophysically consistent phytoplankton IOPs, with both absorption and scattering determined by mathematically cogent relationships to the particle complex refractive indices. All IOPs are integrated over an entire size distribution. A distinctive attribute is the model's comprehensive handling of the spectral and angular character of phytoplankton scattering. Selected case studies and sensitivity analyses reveal that phytoplankton spectral scattering is most useful and the least ambiguous driver of the PFT signal. Key findings are that there is the most sensitivity in phytoplankton backscatter (\(b_{bb}\)) in the 1-6 \(\upmu\)m size range; the backscattering-driven signal in the 520 to 570 nm region is the critical PFT identifier at marginal biomass, and that, while PFT information does appear at blue wavelengths, absorption-driven signals are compromised by ambiguity due to biomass and non-algal absorption. Low signal in the red, due primarily to absorption by water, inhibits PFT detection here. The study highlights the need to quantitatively understand the constraints imposed by phytoplankton biomass and the IOP budget on the assemblage-related signal. A proportional phytoplankton contribution of approximately 40% to the total \(b_{b}\) appears to a reasonable minimum threshold in terms of yielding a detectable optical change in \(R_{ps}\). We hope these findings will provide considerable insight into the next generation of PFT algorithms.  
Keywords: phytoplankton; PFT; ocean colour; satellite radiometry; radiative transfer; optical modelling +
Footnote †: journal: Applied Soft. 2018, 8, 2681; doi:10.3390/app8122681
# Optimization of the Drying Temperature for High Quality Dried Melissa officinalis
## 5 Conclusions  
The storage stability and quality of dried MAPs are primarily influenced by drying temperature, along with other parameters. This study investigated the impact of different drying temperatures (20, 35, 42, and 49 \({}^{\circ}\)C) on the quality characteristic and essential oil composition of _M. officinalis_. Additionally, the environmental impact of the drying process was evaluated to determine the optimal drying temperature for this herb. The results show that higher drying temperatures resulted in shorter drying times accompanied by increased energy consumption and higher carbon footprint per hour of operation. Drying at room temperature (20 \({}^{\circ}\)C) and high oven temperature (49 \({}^{\circ}\)C) resulted in a darker-colored product (i.e., a higher browning index). _M. officinalis_ dried at room temperature (20 \({}^{\circ}\)C) presented higher EO yield, whereas drying at high temperatures (42 and 49 \({}^{\circ}\)C) reduced EO yield. Furthermore, geranial and neral levels were significantly reduced at both lower (20 and 35 \({}^{\circ}\)C) and higher (49 \({}^{\circ}\)C) drying temperatures. In contrast, EO from plants dried at 42 \({}^{\circ}\)C contained the highest levels of geranial and neral. Based on these observations, the optimal drying temperature suggested for _M. officinalis_ is 42 \({}^{\circ}\)C, as it balances product quality, EO composition, and overall drying efficiency while minimizing negative environmental impacts. However, producers prioritizing EO yield may prefer 20 \({}^{\circ}\)C despite longer drying times.  
**Author Contributions:** Conceptualization, P.X. and A.C.; methodology, P.X. and A.C.; software, P.X.; validation, P.X. and A.C.; formal analysis, P.X. and A.C.; investigation, P.X. and A.C.; resources, P.X. and A.C.; data curation, P.X. and A.C.; writing--original draft preparation, P.X. and A.C.; writing--review and editing, P.X. and A.C.; visualization, P.X. and A.C.; supervision, P.X. and A.C.; project administration, A.C.; funding acquisition, A.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project \"Opti-AromaQ\" EXCELLENCE/0421/0299, which is co-financed by the European Union and the Republic of Cyprus through the Research and Innovation Foundation.  
Not applicable.  
The authors declare data availability only upon request.  
The authors declare no conflicts of interest.
## 3 Results
### Distinct Profile of Volatile Organic Compounds among Three Sphalgeneticla Species  
_S. calendulacea_ contained the most diverse VOCs profile, with 72 total compounds detected, eighteen comprising > 1% relative abundance (Table 1). The hybrid and _S. trilobata_ contained 60 and 73 VOCs, respectively, with 18 and 13 at > 1% relative abundance (Table 1). While all groups were dominated by terpenes and aromatics, _S. calendulacea_ also had more esters and alcohols, suggesting niche differentiation in their VOC profiles. The native _S. calendulacea_ exhibited a distinct VOC profile, including styrene, p-xylene, cis-muurola-4(14),5-diene, and benzyl benzoate, with \(\beta\)-cubebeene as the most abundant compound (14.39%; Table 1). The hybrid was characterized by copaene, \(\beta\)-cis-ocimene, 1,4-cadinadiene, \(\alpha\)-muurolene, and \(\alpha\)-cadinene, with (E)-germacrene D predominant (24.59%; Table 1). While sharing some VOCs with _S. calendulacea_, such as \(\beta\)-cubebeene, the hybrid's profile was intermediate yet distinct, with unique compounds and different relative quantities. _S. trilobata_ displayed the greatest differentiation, containing several unique VOCs including p-cymene, (1S)-(--)-\(\alpha\)-pinene, \(\beta\)-ocimene, cis-muurola-3,5-diene, and phenylethyll alcohol. Its major VOC was \(\alpha\)-phellandrene (20.37%; Table 1). The presence of multiple unique VOCs and differences in the relative abundances of shared compounds demonstrates that _S. trilobata_'s volatile signature was the most divergent among the three groups. Fold change analysis quantified VOC differences among _S. calendulacea_, _S. trilobata_ and their hybrid. _S. calendulacea_ upregulated primarily hydrocarbons and aldehydes compared to the hybrid, with 2-thujene showing the greatest increase (3.41-fold; Figure 4A). Conversely, (E)-germacrene D was downregulated 5.64-fold in _S. calendulacea_ relative to the hybrid. Comparing the hybrid and _S. trilobata_ revealed distinct differences, with phenylethyl alcohol and D-limonene upregulated 4.16-fold in _S. trilobata_, while terpen and aromatics were downregulated (Figure 4B). _S. trilobata_ had fewer upregulated VOCs compared to _S. calendulacea_ but showed an increase in phenylethyl alcohol (3.42-fold), D-limonene (2.35-fold) and humulene (1.36-fold), while \(\beta\)-cubebeene decreased 2.82-fold (Figure 4C). \(\beta\)-cubebeene's abundance in _S. calendulacea_ indicates its volatile signature was most differentiated from _S. trilobata_. Figure 4: Comparison of differences between SC and SH (**A**), ST and SH (**B**), SC and ST (**C**) volatile compounds based on fold change. SC: _S. calendulacea_; SH: the hybrids; ST: _S. trilobata_. Red and blue bar indicates upregulation and downregulation respectively.