esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Thematic Taxonomy of UWSNs
### Security
#### 6.4.2 Access Control  
In access control process, the restriction of data access is applied to secure front-end and back-end data, services and resources of UWSNs. The source of risk such as malicious node and unauthorized data vulnerability can be avoided or decreased by employing smart devices or adaptive method. There are two types of access methods; centralized and distributed techniques. All the inquiries of access control for centralized approach must go through a server to authorize connection. However, in distributed access control method, access control server designates an entity to allow access to UWSNs resources. The network services should always be available in the system to overcome any possibilities for communication errors in UWSNs.
## 3 Data and Methods
### Sensitivity Analysis  
The most sensitive parameter in the procedure must be identified for site-specific studies. This is because this parameter is useful for model calibration, validation and uncertainty analysis [74]. We conducted initial sensitivity analysis experiments in this study to obtain the most sensitive parameters and simplify the fitting processes. The model parameters listed in Tables 1 and S1 have been basically used in the various processes of \(\rm P\) cycle. Table 1 showed the most important parameters that are more closely related to GPP. Hence, we selected six key parameters associated with the \(\rm P\) cycle model (Table 1) and four meteorological factors (cloud cover, near-surface temperature, precipitation, vapor pressure; Table S2) as the important parameters and variables for sensitivity analysis. We have also conducted sensitivity analysis on total biomass and six parameters, and four meteorological factors for rpleaf as well.  
We calculated the sensitivity index (SI) to quantify the ratio between relative change in model output for a relative change in each parameter for each site using time series, while other parameters were kept constant [75]. Sensitivity is generally expressed as the ratio between a relative change in a model output and a relative change in a parameter. For each parameter, the SI at a given site was described as  
\[\text{SI}=\frac{\sum_{\text{j=1}}^{\text{n}}\left(\frac{(y_{\text{j}_{1}}-\overline {\text{y}}_{2})/y_{0}}{2\times\times\text{y}_{0}}\right)}{\text{n}} \tag{16}\]  
where n is the total number months of model simulation, j is the ordinal numeral of month, x\({}_{0}\) means the initial value of the input data, \(\Delta\)x is the difference between the initial and adjusted value of the input data, and y\({}_{0}\) is the model output after adjustment of the input data. The six model parameters were varied \(\pm\)50%; meteorological factors, cloud cover, precipitation and vapor pressure were varied \(\pm\)20%; and near-surface temperature was varied \(\pm\)2 \({}^{\circ}\)C. Then we calculated variations in GPP among different biomes, and according to the size of the GPP change, we determined the optimal values of the most sensitive parameter for each biome (Table S3).
## 3 Results and Discussion  
### Optimized Crystal Structure of MgBi\({}_{2}\)O\({}_{6}\)  
At ground state, MgBi\({}_{2}\)O\({}_{6}\) crystallizes in a tetragonal trirutile-type structure with \(P4_{2}/mnm\) space group. It can be seen from the crystal structure depicted in Figure 1 that BiO\({}_{6}\) and MgO\({}_{6}\) octahedrons connect with the sharing edges and stack along the \([001]\) direction by 2:1 ratio. In the \([110]\) and \([\overline{1}10]\) directions, BiO\({}_{6}\) and MgO\({}_{6}\) octahedrons are connected with each other by sharing O vertices. The optimized lattice constants listed in Table 1 are in good agreement with the experimental values [17]. The BiO\({}_{6}\) octahedrons are slightly distorted and the bond lengths of Bi-O(8\(j\)) are not completely identical.
# A 6 DOF Virtual Environment Space Docking Operation with Human Supervision
## 3 Adaptive Control
Assuming a process model is given by Equation (5), the magnitude, phase and derivative of the phase of \(P(s)\) are computed as \[ \begin{split}|P(j\omega_{gc})|=K\cdot\frac{\sqrt{\omega_{gc}^{2}+\omega_{ 0}^{2}}\sqrt{\omega_{gc}^{2}+\omega_{1}^{2}}+\sqrt{\omega_{gc}^{2}+\omega_{1}^ {2}}}{\sqrt{\omega_{gc}^{2}+\rho_{0}^{2}}\sqrt{\omega_{gc}^{2}+\rho_{1}^{2}} +\rho_{2}^{2}\sqrt{\omega_{gc}^{2}+\rho_{2}^{2}}}\\ \angle P(j\omega_{gc})=\tan^{-1}\left(\frac{\omega_{gc}}{\omega_{ 0}}\right)+\tan^{-1}\left(\frac{\omega_{gc}}{\omega_{1}}\right)+\tan^{-1}\left( \frac{\omega_{gc}}{\omega_{1}}\right)-\tan^{-1}\left(\frac{\omega_{gc}}{\rho_ {0}}\right)-\tan^{-1}\left(\frac{\omega_{gc}}{\rho_{1}}\right)\\ -\tan^{-1}\left(\frac{\omega_{gc}}{\rho_{2}}\right)-\tan^{-1} \left(\frac{\omega_{gc}}{\rho_{2}}\right)\end{split}  \tag{13}\]  
\[ \begin{split}\frac{d\angle P}{d\omega_{gc}}=\frac{\omega_{0}}{ \omega_{0}^{2}+\omega_{0}^{2}}+\frac{\omega_{1}}{\omega_{0}^{2}+\omega_{1}^{2 }}+\frac{\omega_{1}^{2}}{\omega_{0}^{2}+\omega_{1}^{2}}-\frac{\rho_{0}}{\omega _{0}^{2}+\rho_{0}^{2}}-\frac{P_{1}}{\omega_{0}^{2}+\rho_{1}^{2}}-\frac{\rho_ {2}}{\omega_{0}^{2}+\rho_{2}^{2}}-\frac{P_{2}^{2}}{\omega_{0}^{2}+\rho_{2}^{2} }\end{split} \]  
To mimic a space rendezvous, the relative mass on each leg of the 6 DOF platform can be varied at times during the simulation. In practice, the relative mass can be estimated continuously from the measured position and velocity of the platform. However, a continuous adaptation of controller parameters is not necessary and significantly increases the computational burden of the deployed system operation. Instead, using the standard performance index, a tolerance interval of \(\pm 10\%\) is introduced in the overshoot (OS\(\%<20\%\)) and settling time (_Ts_\(<0.1\)s) values. The relations in (13) are easily computed online from previous samples' reflected mass estimation. However, an additional condition of the violation of the tolerance interval is implemented to prevent the sluggish and unnecessary adaptation of controller parameters and consequently discretization and deployment on real-life emulator. The latter steps are performed solely when the threshold of tolerance interval is exceeded. Then, replacing (11) and (12) in (8)-(10) leads to  
\[K_{p}\sqrt{1+2K_{i}\omega_{gc}^{-\mu}\cos\frac{\pi\mu}{2}+K_{i}^{2}\omega_{ gc}^{-2\mu}}=\frac{1}{|P(j\omega_{gc})|} \tag{14}\]  
\[\frac{K_{i}\omega_{gc}^{-\mu}\sin\frac{\pi\mu}{2}}{1+K_{i}\omega_{gc}^{-\mu} \cos\frac{\pi\mu}{2}}=\tan(\pi-PM+\angle P(j\omega_{gc}))\]  
\[\frac{\mu K_{i}\omega_{gc}^{-\mu-1}\sin\frac{\pi\mu}{2}}{1+2K_{i}\omega_{gc}^{- \mu}\cos\frac{\pi\mu}{2}+K_{i}^{2}\omega_{gc}^{-2\mu}}+\frac{d\angle P(j\omega )}{d\omega}|_{\omega=\omega_{gc}}=0\]  
With respect to the digital implementation of this controller, two more parameters are practically relevant: the degree of approximation and the user-specified \(\omega\)-range in which the desired behavior should be obtained, and tuning rules are given in [21] (includes the Matlab function code provided in the paper).
## 6 Open Challenges, Future Prospects and Research Directions
### Exploring Hybrid Systems
#### 6.4.4 Spatial Diversity
These innovative designs will facilitate the creation of lightweight transceivers that can be deployed in a diverse array of applications, from small-scale IoT devices to large-scale urban infrastructure [510]. Cost-Effective Solutions: Reducing the cost of these technologies will be crucial for enabling the widespread adoption of advanced spatial diversity systems. By streamlining manufacturing processes and exploring new materials that offer improved performance at lower costs, researchers can ensure that these technologies are accessible for various applications, including broadband access in underserved areas. Additionally, new materials with superior optical properties can improve the efficiency and reliability of FSO transceivers. Extensive Field Trials and Standardized Protocols  
Extensive field trials are essential not only for the widespread adoption of advanced FSO systems but also for validating the performance of advanced spatial diversity systems and their associated new technologies and algorithms in real-world environments. Thisprocess will provide essential data for further refinement to enhance system performance. In this regard, the trials will provide valuable insights into system behavior under various atmospheric conditions and help refine algorithms and protocols for optimal performance. By conducting tests in diverse settings--rural, urban, and coastal--researchers can assess the adaptability and reliability of FSO systems across different scenarios.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 4 TSN Standards for Automotive Communications
### TSN Support for Automated Driving
#### 4.6.3 Redundancy  
Automated Driving systems include delay-sensitive real-time applications (e.g., machine vision) that cannot tolerate delays introduced by the retransmissions of lost frames.  
The TSN standard IEEE 802.1 CB - Frame Replication and Elimination for Reliability improves on reliability by enabling the duplication and transmission of the frames over disjoint paths and the elimination of replicas at the MAC layer.
# Design and Construction of an LED-Based Excitation Source for Lock-In Thermography
## 3 Experimental Specifications
### Radiant Intensity Distribution  
To measure the radiant intensity in the far field, a robot goniophotometer (opsira robogonio mrg-6) in combination with an optical powermeter (Thorlabs PM100D with power sensor S121C) is used. For the experiments, the LED output power is set to 90% of the maximum power. The C0-C180 and C90-C270 planes of the angular distribution of the radiant intensity with and without focusing optic are shown in the top row of Figure 7. The intensity distribution shows a radial symmetry, i.e., no significant differences between the two C-planes are visible.  
Without focusing optic, the maximum of the radiant intensity distribution is 24 W/sr. This is less than expected from the summation of the typical maximum intensity of 900 mW/sr per LED according to the datasheet [24], i.e., a maximum intensity of \(36\cdot 0.9\cdot 900\,\text{mW/sr}=29.16\,\text{W/sr}\) is expected for the total LED board. The observed difference of more than 5 W is caused by the use of LEDs belonging to a lower grade batch with a maximum radiant intensity of 780 mW/sr only. This leads to a maximum intensity of \(36\cdot 0.9\cdot 780\,\text{mW/sr}=25.27\,\text{W/sr}\), which is very close to the measured value of 24 W/sr. In addition to the fact  
Figure 6: Thermographic image of the excitation source without additional optics after 90-min operation with \(I=0.9\,\)A. The LEDs at points 1 and 2 are analyzed for their thermal resistance.  
that LEDs of lower grade are used, shadowing effects are present at angles larger than 25\({}^{\circ}\) (Figure 7a top). This also contributes to the slightly reduced experimental value. The integrated optical power of the board amounts to 32 W.  
In contrast to the LED source without optic, the source with focusing optic shows a significantly narrower distribution with a maximum of the radiant intensity of 72 W/sr (Figure 7b top) and a total optical power of 24 W. The decrease in total optical power of 8 W is attributed to losses by the optic. The bottom row of Figure 7 shows the irradiance on an area of 1.0 m \(\times\) 0.8 m at a distance of 0.5 m. The maximum irradiance generated by the source without additional optic is 96 W/m\({}^{2}\) whereas the addition of the optic increases this value to 275 W/m\({}^{2}\).
# Efficacy of Instruments for Professional Oral Hygiene on Dental Implants: A Systematic Review  
[PERSON]  
1 Department of Surgical Sciences (DISC), Division of Prosthetic Dentistry, University of Genoa, Largo R. Benzi 10, 16132 Genoa, Italy; [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (F.M.)  
[PERSON]  
1 Department of Surgical Sciences (DISC), Division of Prosthetic Dentistry, University of Genoa, Largo R. Benzi 10, 16132 Genoa, Italy; [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (F.M.)  
[PERSON]  
1 Department of Surgical Sciences (DISC), Division of Prosthetic Dentistry, University of Genoa, Largo R. Benzi 10, 16132 Genoa, Italy; [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (F.M.)  
[PERSON]  
1 Department of Surgical Sciences (DISC), Division of Prosthetic Dentistry, University of Genoa, Largo R. Benzi 10, 16132 Genoa, Italy; [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (F.M.)  
[PERSON]  
1 Department of Surgical Sciences (DISC), Division of Prosthetic Dentistry, University of Genoa, Largo R. Benzi 10, 16132 Genoa, Italy; [EMAIL_ADDRESS] (M.M.); [EMAIL_ADDRESS] (F.M.)  
Footnote 25: email: {L.D.G.}; jacopoopo@tiscalAn air polishing treatment can be performed which uses a jet of air, water and microparticles, the most common being sodium bicarbonate, glycine and erythroid, in order to remove stains on the surface of the teeth [5; 6; 7].  
Manual instruments such as curettes are also available, made by different possible materials (metal alloy or plastic materials). The dentist or the dental hygienist can use metal curettes to deeply remove tartar and carefully clean the root of natural teeth, while their use has been reported to damage the surface of implant prosthodontic components. For this reason, plastic instruments might be preferred when instrumenting dental implants. However, they are more fragile and their bulky design makes it difficult to reach all the surfaces to be cleaned.  
The dental implant is a predictable replacement for natural teeth and it requires constant maintenance and monitoring for long-term success [8]. Appropriate instruments should be used in patients with prosthetic restorations supported by implants in order to accurately clean them without damaging their surfaces. Metallic tools could scratch the titanium surface, creating a rougher surface that might favour microbic adhesion and plaque build-up. It is widely accepted that local debridement of implants should be performed with titanium instruments or instruments that are softer than titanium [9; 10]. Tools such as plastic curettes or air polishing might be preferrable [11]. However, their efficacy and effectiveness has been questioned and plastic remnants could remain after instrumentation [12]. In addition, alterations of the implant surface could be caused by air polishing with large-sized powders with higher cleaning capacity [13].  
The aim of the present systematic review is to investigate and summarize data available in the literature on the efficacy of instruments for professional oral hygiene of dental implants described in the literature in the last 10 years in patients not affected by perimplantitis.
## 5 Analysis of Results and Discussion
### Impact Load-Time History  
The ductility features imparted into the CDP and DP models enable simulating the behavior of ECC materials under dynamic loading close to experimental findings, as shown in Figure 21, with the CDP model exhibiting superior performance. The tolerance in the applied load estimated using the DP model was about four times that of the CDP model compared to actual experimental data. For instance, as displayed in Figure 22, the accumulated impact load applied on the ECC2-0, ECC2-0.5, ECC2-1, and ECC2-1.5 estimated using the CDP model was 11.6%, 3.5%, 1.0%, and 2.2% lower than the corresponding experimental data, respectively, while that estimated by the DP model was 39.6%, 31.7%, 25.2%, and 26.9% lower than the experimental results, respectively. The difference in numerical accuracy can be attributed to the fact that the CDP model is constructed based on the two main failure mechanisms of concrete: tensile cracking and compressive crushing. The evolution of yield surfaces in concrete is controlled by two hardening variables, which causes failure under tensile and compressive stresses simultaneously. While the DP model is based on the same criterion, only one of the two main failure mechanisms can be assigned for the material during the simulation, which affects its overall performance.  
Figure 21: Typical impact load-time history curves for composite specimens at different impact levels: (**a**) 1 st hit, (**b**) 2 nd hit, and (**c**) 3 rd hit.
# Fate of Soil Carbon Transported by Erosional Processes
## 4 Soil Erosion and the Global Carbon Budget
### Grazing Systems and Gaseous Emissions by Hydric Erosion  
Similar to mechanical tillage, overgrazing can also accelerate hydric erosion and influence gaseous emissions. In addition to reducing the plant cover, overgrazing may aggravate risks of hydric erosion by altering soil bulk density, water infiltration rate, surface runoff and transport of POC and DOC and the attendant emission of GHGs into the atmosphere. Based on an experiment involving some sandy loam Aerosols in South Africa, [PERSON] and [PERSON] [36] assessed the effects of three levels of plant covers (100%, 25-50% and 0-5%) on loss of SOC under simulated rainfall. Plant C input into the soil profile and SOC stocks (g C/m\({}^{2}\).yr) in 0-0.02 m layer, respectively, were 1950 \(\pm\) 180 and 300 \(\pm\) 16.2 at 100% cover. In comparison, soil C input by plants decreased by 38.5 \(\pm\) 3.5% at 25-50% and by 75.4 \(\pm\) 6.9% at 0-5% cover. The losses of SOC by water erosion were 0.75 g C/m\({}^{2}\) at 100% cover and increased by 66% at 25-50% cover (3.76 \(\pm\) 1.8 g C/m\({}^{2}\)) to 213% at 0-5% cover (7.08 \(\pm\) 2.9 g C/m\({}^{2}\)). Furthermore, these losses of SOC were mostly in POC form, which being a labile fraction can be easily mineralized with the attendant emission of GHGs.
# An Improved Lightweight Model for Defect Detection on Paths in Images
## 3 Results and Analysis
### Interpretability Experiment  
Deep learning models are sometimes referred to as \"black-box\" models because, despite their exceptional performance on a variety of tasks, it is challenging to understand how they make decisions and reason. Developing an intuitive knowledge of deep learning models' performance requires a thorough understanding of their interpretability. For validation purposes, we chose the ACD-YOLOv8 and the YOLOv8s for this investigation.  
We conducted a detailed evaluation of the two models' performance by analyzing their confusion matrices, as shown in Figure 7. The results reveal that both models demonstrate  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Params** & **Size** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]-0.95** & **F1** & **FPS** \\  & **(M)** & **(m)** & **(\%)** & **(\%)** & **(\%)** & **F1** & **FPS** \\ \hline YOLOv8s & 11.1 & 22.5 & 86.5 & 56.5 & 0.82 & 357 \\ v8s + DyHead & 9.3 & 18.9 & 87.1 & 58.2 & 0.830 & 196 \\ \hline \hline \end{tabular} Note: Table 7 verifies the impact of integrating the DyHead attention mechanism. The results demonstrate a clear enhancement in detection performance over the baseline YOLOv8s.  
\end{table}
Table 7: DyHead module experiment verification.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**CCFM** & **ADown** & **DyHead** & **Params (M)** & **Size (MB)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]-0.95** & **F1** & **FPS** \\ \hline - & - & - & 11.1 & 22.5 & 86.5 & 56.5 & 0.820 & **357** \\ \(\surd\) & & & 7.3 & 14.8 & 85.0 & 56.3 & 0.812 & **357** \\ \(\surd\) & \(\surd\) & & 9.5 & 19.2 & 86.3 & 57.0 & 0.82 & 313 \\ \(\surd\) & \(\surd\) & & **5.9** & **12.1** & 86.5 & 56.7 & 0.818 & 323 \\ \(\surd\) & & \(\surd\) & 17.2 & 14.8 & 87.2 & 57.6 & 0.824 & 128 \\ \(\surd\) & \(\surd\) & \(\surd\) & 7.4 & 15.1 & 86.8 & 57.5 & 0.826 & 185 \\ \(\surd\) & \(\surd\) & \(\surd\) & 7.4 & 15.1 & **87.4** & **58.1** & **0.830** & 196 \\ \hline \hline \end{tabular} Note: Table 8 presents an ablation study that dissects the individual and combined contributions of the proposed modules: CCFM, ADown, and DyHead. Bold indicates the best result and underline indicates the second-best result.  
\end{table}
Table 8: Comparison of ablation experiments.  
notable limitations, including high false negative rates and elevated false positive rates. Both models perform well in detecting D10 (transverse fractures) and D40 (potholes), according to a thorough investigation. At 80% and 82% accuracy rates, respectively, the initial YOLOv8s model did not do well in identifying D20 (mesh cracks) or Repair (road repairs).  
In comparison, the ACD-YOLOv8 introduced in this study achieved a notable enhancement in recognition accuracy for D20 and Repair, with improvements of 6% and 4% respectively, as shown in Figure 7.
## 4 Optimization Problem Modeling
### Optimization Constraints
#### 4.4.1 Transmission Link Constraints  
This study assumes that, for any time \(t\), each IoT device can link to only one relay satellite, which is expressed as follows:  
\[\sum_{k=1}^{K}z_{m,t^{\prime},k,t}^{i}\leq 1,\quad\forall m,t^{\prime}<t \tag{12}\]Similarly, at any given \(t\), each observation satellite is restricted to a connection with a single relay satellite, which is expressed as follows:  
\[\sum_{k=1}^{K}\gamma^{o}_{n,k,t}\leq 1,\quad\forall n,t \tag{13}\]  
For the relay satellites, at any moment \(t\), data transfer is limited to transmitting from one observation satellite. This is denoted by the following:  
\[\sum_{k^{\prime}=1,k^{\prime}\
eq k}^{K}\gamma^{r}_{n,k,k^{\prime},t}\leq 1, \quad\forall n,k,t \tag{14}\]  
Further, at any point in time \(t\), each relay satellite can only facilitate the transmission of data from one observation satellite within a single relay transfer:  
\[\sum_{n=1}^{N}\gamma^{r}_{n,k,k^{\prime},t}\leq 1,\quad\forall k,k^{\prime} \
eq k,t \tag{15}\]  
Finally, each relay satellite is also constrained to transmitting data from a single observation satellite to the ground station. This is defined by the following:  
\[\sum_{n=1}^{N}\gamma^{g}_{n,k,t}\leq 1,\quad\forall k,t \tag{16}\]
## 4 Performance Validation
### Comparative Analysis of Context Switching  
Context switching saves a process's current state and restores another process's saved state. It is a costly operation that incurs a significant overhead on the operating system, as it involves saving and restoring the processor's state, memory, and other resources. When the number of processes is high, the number of context switches increases, leading to a higher overhead on the operating system. In this case, the operating system's performance can be affected as the scheduler spends more time between processes and less time executing them. However, as the number of processes decreases, the number of context switches will also decrease significantly. This leads to less overhead on the operating system and, therefore, to better performance. However, it is important to note that it also has its downsides, as a low number of processes running simultaneously could lead to less efficient use of system resources.  
The operating system will have less CPU and memory utilization, potentially leading to less efficient use of the system's resources and a slower response time for the user. Traditional variants of Round Robin scheduling algorithms interrupt and reassign processes, even when only one process is in the ready queue. The operating system allocates a fixed TQ to process. After the quantum expires, the process is interrupted and assigned the same TQ to continue and complete its execution. This can lead to additional unnecessary context switches, as shown in Figure 4, which can add significant overhead to the operating system.  
On the other hand, our newly proposed RRAPS algorithm solves this problem by equating the time quantum with the remaining burst time of the process. This means that when there is only a single process in the ready queue, the time quantum will equal the time the process needs to complete its execution, thus avoiding any additional unnecessary context switches. This eliminates the over-scheduling problem and reduces the operating system's overhead. The new algorithm can be considered more efficient than the traditional variants of the Round Robin scheduling algorithm, as it adjusts the time quantum based on the process's requirements and eliminates unne  
Figure 4: Comparison of context switching between algorithms for 100 processes.