esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Simulation Analysis
### The Impact of Different Replication Times P on the System  
The length of the information slot in the RSST-SR-DCSK system is denoted as \(\beta(\beta=P\times R)\), where \(P\) is the number of times the reference signal is copied, and \(R\) is the length of the reference signal. When changing \(P\), the length of the information slot in the system also varies, with the reference slot being relatively short. Simulation tests are conducted to assess the impact of the replication count \(P\) on the system.  
Figure 11 illustrates the selection of simulation parameters for the RSST-SR-DCSK system. Second-order Chebyshev mapping is used with an initial value of 0.4. The effect of varying values of \(P\) on the system's bit error rate is examined for two conditions: \(R=64\) and \(R=256\). Based on graphs (a) and (b), it can be observed that when \(P\) is 2, the system's BER is approximately 1 dB higher compared to the case when \(P\) is 4.  
Figure 11: Effect of different \(P\) on RSST-SR-DCSK system error performance. (**a**) R = 64; (**b**) R = 256.  
Figure 10: Comparison correlation between RSST-SR-DCSK and SR-DCSK. (**a**) RSST-SR-DCSK Correlation Chart; (**b**) SR-DCSK Correlation Chart.
# Bearing Behavior of Cold-Formed Thick-Walled Steel Plates with a Single Bolt
## 4 Discussion
### Comparison of Bearing Resistance  
Table 6 lists the measured and predicted bearing resistance of all the specimens. The comparisons of the bearing resistance of the measurements and predictions are presented in Figure 13.  
It can be seen that the bearing resistance predicted by the modified ANSI/AISC 360-22 agrees best with that of the test results, with an average difference and a standard deviation of 7.8% and 6.2%, respectively. The predictions of the EN1993-1-8 [7] are excessively conservative compared to the measurements, and the average difference and standard deviation are 31.0% and 10.8%, respectively.  
Figure 12: Proportion of specimens with different failure modes [6,7].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\(e_{1}/d_{0}\)} & \multirow{2}{*}{\(e_{2}/d_{0}\)} & \multicolumn{6}{c}{**Failure Modes**} \\ \cline{3-6}  & & & & \multicolumn{3}{c}{**Prediction**} \\ \cline{5-6}  & & & & **ANSI** & **ANSI-Modified** & **EC** \\ \hline D6.0(10.0)-1.0-3.0 & 1.0 & 3.0 & S & S & S & S \\ D6.0(10.0)-1.2-3.0 & 1.2 & 3.0 & S & S & S & S \\ D6.0(10.0)-1.5-3.0 & 1.5 & 3.0 & S & S & S & S \\ D6.0(10.0)-2.0-3.0 & 2.0 & 3.0 & S & S & S & S \\ D6.0(10.0)-2.5-3.0 & 2.5 & 3.0 & S & B & S & S \\ D6.0(10.0)-1.5-1.0 & 1.5 & 1.0 & N & N & N & M \\ D6.0(10.0)-1.5-1.2 & 1.5 & 1.2 & N & N & N & M \\ D6.0(10.0)-1.5-1.5 & 1.5 & 1.5 & S & S & S & S \\ D6.0(10.0)-1.5-2.0 & 1.5 & 2.0 & S & S & S & S \\ \hline \hline \end{tabular}  
* \(S\) represents shear-out failure; N represents net-section fracture; and M represents mixed failure.  
\end{table}
Table 5: Comparison of failure modes of test results and predictions.  
For the specimens that failed by shear-out, the measured bearing resistance is generally lower than the predictions when the end distance of the specimens is less than 2.5\(d_{0}\). The ANSI/AISC 360-22 [6] and the modified ANSI/AISC 360-22 overestimate the bearing resistance by 17.6% and 14.7%, respectively, when the end distance of the specimens is equal to 2.5\(d_{0}\). This can be explained by the reduced area of the shear planes due to the fracture that occurred, as was mentioned in Section 3.2. In this case, a reduction factor of 0.85 is recommended, based on our test results.  
From the comparison above, attention should be drawn to the calculation formula proposed on the basis of the test results of the hot-rolled steel specimens with a shear-out failure potentially overestimating the bearing resistance of the cold-formed steel connections because of the reduced area of the shear planes caused by a fracture. The existing test data are very limited, and further investigation is needed to confirm the proposed reduction coefficient.
## 1 Introduction
The evidence from prospective and retrospective case series, as well as randomized controlled clinical trials, supports the potential for achievingand sustaining successful outcomes over extended periods. Depending on the clinicians, the preference leans towards surgical interventions. The potential challenge in non-surgically managing peri-implantitis and improving the condition therapeutically has arisen. Various mechanical instrumentations have been implemented for the management of peri-implantitis [22]. However, there is no universally accepted gold standard among them [23]. Air abrasion is reported to effectively remove the bacterial biofilm from the implant surface and peri-implant tissues, which is a primary etiological factor in peri-implantitis [24]. Air abrasion helps in cleaning the micro-roughened surfaces of the implant, which are prone to biofilm accumulation [25]. The use of air abrasion showed that the plaque index showed improvement post-treatment. The technique using air abrasion is less invasive compared to mechanical debridement with curettes or ultrasonic scalers, causing less damage to the implant surface and surrounding tissues [26]. Thus, air abrasion therapy may be more beneficial as it minimizes the damages to the surface and helps in maintaining the roughness necessary while removing the biofilm. Therefore, this study aims to conduct a meta-analysis to evaluate the differences in managing the inflammatory response around dental implants between air abrasion and mechanical decontamination. The null hypothesis posits that there will be no significant difference in treating the inflammatory response around dental implants between air abrasion and other mechanical decontamination techniques.
# Analysis of Pressure Effect on Three-Dimensional Flame Surface Density Estimation
## 4 Modeling and Approximations of \(\langle\cos\phi\rangle_{s}\)
### Isotropic Normal Vector  
Isotropic normal vector assumption was made by [PERSON] et al. [17; 27] to evaluate the scalar dissipation rates and 3D flame surface density from low-dimension measurements. When combined with the assumption that the distributions of \(\phi\) and \(\theta\) are independent with each other, the following relations are derived,  
\[p(\theta)=\frac{1}{2\pi};\quad p(\phi)=\frac{\cos\phi}{2}\]  
Then, \(\langle\cos\phi\rangle_{s}\) will be  
\[\langle\cos\phi\rangle_{s}=\int_{-\pi/2}^{\pi/2}\cos\phi p(\phi)d\phi=\frac{ \pi}{4} \tag{11}\]  
The above deviation implies that if the flame orientation angle \(\phi\) at all positions within the flame brush follows the probability density function of \((\cos\phi)/2\), then  
\[\Sigma=\frac{4}{\pi}\Sigma^{\text{2D}} \tag{12}\]  
However, for more practical scenarios, such as Bunsen flames or V-shape flames, the isotropy of the wrinkled flame highly depends on the position and the combustion condition, indicated by the turbulence intensity \(u^{\prime}\). Thus, the \(p(\phi)\) may differ at each position.
## 2 Materials and Methods
### Observational Data  
Observed data are used in this study with the purpose of validating and correcting simulated data. The E-OBS Version 17.0 dataset (EOBS hereafter) was developed by the European Climate Assessment and Dataset (ECA&D) and consists of a land-only daily gridded dataset for Europe for the 1950-2006 period [36, 37]. Data for the 1986-2005 period with a rotated pole grid with a horizontal resolution of 0.22\({}^{\circ}\) were extracted.  
All simulated and EOBS data were interpolated to a regular grid with a horizontal resolution of 0.2\({}^{\circ}\), which allows the comparison between them. Daily maximum (Tmax) and minimum temperatures (Tmin) were extracted for all simulations and for E-OBS, for the grid-point nearest Lisbon.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Config** & **GCM** & **Initialization** & **RCM** &
\begin{tabular}{c} **Downscaling** \\ **Realisation** \\ \end{tabular} \\ \hline mod1 & CNRM-CERFACS-CNRM-CM5 & r1i1p1 & CLMcom-CCLM4-8.17 & v1 \\ mod2 & CNRM-CERFACS-CNRM-CM5 & r1i1p1 & SMHI-RCA4 & v1 \\ mod3 & ICHEC-EC-EARTH & r12i1p1 & CLMcom-CCLM4-8.17 & v1 \\ mod4 & ICHEC-EC-EARTH & r12i1p1 & DMI-HIRHAM5 & v1 \\ mod5 & ICHEC-EC-EARTH & r12i1p1 & KNMI-RACMO22E & v1 \\ mod6 & ICHEC-EC-EARTH & r12i1p1 & SMIH-RCA4 & v1 \\ mod7 & ICHEC-EC-EARTH & r1i1p1 & KNMI-RACMO22E & v1 \\ mod8 & IPSI-IPSL-C4 SA-MR & r1i1p1 & IPSL-INERIS-WRF331F & v1 \\ mod9 & IPSL-IPSL-CM5A-MR & r1i1p1 & SMHI-RCA4 & v1 \\ mod10 & MPI-M-MPI-ESM-LR & r1i1p1 & CLMcom-CCLM4-8.17 & v1 \\ mod11 & MPI-M-MPI-ESM-LR & r1i1p1 & MPI-CSC-REMO2009 & v1 \\ mod12 & MPI-M-MPI-ESM-LR & r1i1p1 & SMIH-RCA4 & v1a \\ mod14 & NCC-NorESM1-M & r1i1p1 & DMH-HIRHAM5 & v2 \\ mod15 & NCC-NorESM1-M & r1i1p1 & SMIHI-RCA4 & v1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Forcing Global Model (GCM) and Regional Model (RCM).  
We decided to use a gridded dataset, such as E-OBS, since these were developed to validate models. E-OBS represents, as model data, the area average whereas station data are local.
# Effects of Long-Term Fertilization on Phosphorus Form and Availability in Black Soil
## 3 Results
### Correlation Analysis Among Soil Phosphorus Forms Under Long-Term Fertilization  
The various forms of phosphorus in the soil under long-term fertilization were closely related to each other, with higher correlations representing greater impacts (Figure 5). The soil TP was significantly and positively correlated with each type of phosphorus: AP, H\({}_{2}\)O-Pi, NaHCO\({}_{3}\)-Pi, NaHCO\({}_{3}\)-Po, NaOH-Pi, NaOH-Po, HCl-Pi, and HCl-Po. The AP was significantly and positively correlated with H\({}_{2}\)O-Pi, NaHCO\({}_{3}\)-Pi, NaHCO\({}_{3}\)-Po, NaOH-Pi, NaOH-Po, HCl-Pi, HCl-Po, and orthophosphate. Pyrophosphate and orthophosphate were significantly correlated, which indicated a certain correlation between most of the forms of phosphorus, which had a greater influence on each other. H\({}_{2}\)O-Pi, NaHCO\({}_{3}\)-Pi, and NaHCO\({}_{3}\)-Po were significantly correlated, with correlation coefficients of up to 0.97 and 0.99. Additionally, significant positive correlations were also found between NaHCO\({}_{3}\)-Pi and the other forms of phosphorus, which indicated a transformational relationship between the reactive inorganic phosphorus and the other forms of phosphorus. This indicated a transformational relationship between active inorganic phosphorus and the other forms of phosphorus. A highly significant positive correlation was furthermore found between orthophosphate and moderately and mildly active phosphorus.  
Figure 4: \({}^{31}\)P NMR spectra of soil under long-term fertilization (displacement: \(\delta\), \(\times 10^{-6}\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Treatment** & \multicolumn{2}{c}{**Inorganic Phosphorus**} & \multicolumn{2}{c}{**Organic Phosphorus**} \\ \hline  & **Orthophosphate** & **Pyrophosphate** & **Orthophosphate Monoesters** & **Orthophosphate Diesters** \\ \hline CK & 12.13 & 0.47 & 19.80 & 4.34 \\ NPK & 39.95 & 1.12 & 25.63 & 2.11 \\ M1 NPK & 95.21 & 0.59 & 20.28 & 1.56 \\ M2 NPK & 106.58 & 0.38 & 20.95 & 1.39 \\
1.5M1 NPK & 118.11 & 0.67 & 22.27 & 1.37 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Phosphorus-containing compounds in soil NaOH-EDTA extracts under long-term fertilizer application (mg\(\cdot\)kg\({}^{-1}\)).
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 3 The Four-Layer IoT Cyber Risk Management Framework
### IoT Cyber Infrastructure Layer
#### 3.2.1 Employees/Internal Users  
The employees/internal users provide the behavioral element of the system. The focus of this element is on factors affecting user satisfaction, attitude, intention, and actual behavior on the use of cybersecurity technologies and compliance with security and privacy policies.  
Promoting cybersecurity awareness and best practices is critical for the success of cybersecurity management [72]. According to a study conducted by Shred-it, more than 85% of senior executives believe that employee negligence is one of their biggest information security risks [73]. An empirical study shows that one of the reasons that cyberattacks occur at a rapid rate in the IoT systems is the poor compliance to information security policies that arises from behavioral issues and the lack of security awareness [74]. Another empirical study shows that there is a low level of IoT cybersecurity awareness by employees and proposes a cybersecurity e-brochure playbook as a possible solution [75].
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 5 Discussions and Future Work
Severity of the damages changes within the area of one storm, even in a relatively small area, e.g., the one in this study, 100 km \(\times\) 100 km, and depends on many factors, e.g., the structure of the growing stock, the soil properties, the terrain elevation variation and the small scale spatial variation of growing stock. Forests next to an open area or a young forest are more vulnerable to damages than the forests surrounded by mature forests. Furthermore, in addition to the changes in the growing stock, many other factors affect the changes of backscatter and also in a short time interval, e.g., changes in the moisture of the tree canopies and soil. The possibility to frequently acquire SAR data is thus important. It should be recognized that further work is needed for a near-real-time operational monitoring system. In the continuation work, the accuracy of the estimates will be improved by further method development and additionally using interferometric SAR data as well as meteorological data. The use of other geo-referenced data, such as land-use data, forest age and soil data and forest data from the surrounding areas, may improve the classification accuracy because the windstorm damages occur often on the borders of open areas, newly constructed roads and power-lines as well as next to young forests or forest regeneration areas. Potential of interferometric SAR coherence, possibly combined with backscatter intensity information, should be studied using Sentinel-1 multitemporal imagery, even though temporal decorrelation can limit its utility [62]. Further, time series of bistatic TanDEM-X scenes can be used for mapping forest change, due to high sensitivity to the vertical structure of the forests [63; 64]. For the latter, the limiting factor is data availability over large areas with small latency. Coming and existing satellite missions and constellations with frequent and tailored data acquisition increase the availability of data. It is also important that data providers adopt a systemic data acquisition strategy similar to Sentinel-1 and ALOS/ALOS-2 missions in connection with the hazard monitoring, particularly windstorm detection. A background mission with at least seasonal global coverage can be suitable.
# Optimization of Additive and Current Conditions for Void-Free Filled Through-Silicon Via
## 2 Materials and Methods  
TSV samples with blind vias AR 1:2-1:3 (about 60 \(\upmu\)m in diameter, about 120 \(\upmu\)m/150 \(\upmu\)m/180 \(\upmu\)m in depth) on Si substrate were prepared using the Bosch process of inductively coupled plasma reactive ion etching (ICP-RIE) for the TSV filling experiment [10; 11]. Si wafer of 1 \(\times\) 1 cm\({}^{2}\) size was used for small scale deposition. A nickel phosphorus (NiP) layer was formed as the diffusion barrier and seed layer through electroless plating. After forming the NiP layer, pretreatment was conducted using a vacuum desiccator to remove bubbles in the hole of the TSV after putting the TSV substrate in deionized water (DI). After pretreatment, copper plating was performed.  
Table 1 shows the composition of the Cu plating bath. The virgin makeup solution (VMS) for copper plating consists of 0.79 M of CuSO\({}_{4}\cdot\)5H\({}_{2}\)O, 1.02 M of H\({}_{2}\)SO\({}_{4}\), and 1.4 mM of Cl\({}^{-}\). The additives PEG, SPS, and JGB were used as a suppressor, an accelerator, and a leveler, respectively.  
The current conditions were conducted on DC 1 Ampere per Square Deci-metre (ASD) to obtain the additive conditions, and on-reverse conditions were used for void-free filling. Electrochemical analysis was performed on the influence of additives in three electrode cells using the EC-Lab electrochemistry software. Figure 2 shows a schematic diagram of the cyclic voltammetry (CV) analysis setup.  
For the CV analysis, a platinum (Pt) disk electrode with a diameter of 1.6 mm was used as the working electrode, and a Pt wire and Ag/AgCl electrode were used as the counter electrode and reference electrode, respectively. The cross-section of the TSV was observed using an optical microscope (OM) and a scanning electron microscope (SEM) in order to investigate the plating tendency inside the TSV according to various plating conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Virgin Makeup Solution (VMS)** & **Additive** & \\ \hline CuSO\({}_{4}\cdot\)5H\({}_{2}\)O & 0.79 M & poly(ethylene glycol) (PEG) & 100–500 ppm \\ H\({}_{2}\)SO\({}_{4}\) & 1.02 M & bis-(3-sodiumsulfopropyl disulfide) (SPS) & 1–5 ppm \\ Cl\({}^{-}\) & 1.4 mM & Janus Green B (JGB) & 10–50 ppm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of Cu bath.  
Figure 1: Two types of 3D lamination process: (**a**) wire bonding, (**b**) through-silicon via (TSV).
## 3 Laboratory Study
### Testing
#### 3.3.4 Environmental Scanning Electron Microscope (ESEM) Test  
The intricate behavior of bitumen is influenced by both its chemical composition and microstructure. This fact motivates researchers to investigate bitumen structures at the micro and nano scales, using various technologies to establish the relationship between microstructure and physical properties.  
Environmental Scanning Electron Microscopy (ESEM) is a type of electron microscopy that enables the imaging of oily materials, such as bitumen, in their natural state. This technique eliminates the need for bitumen conductive coating processes and ultra-high vacuum typically required for conventional Scanning Electron Microscopy (SEM) [24]. ESEM can be used to investigate the morphology and dispersion of modifiers within the bitumen matrix, aiding in a better understanding of its behavior. In this study, the protocol developed by [PERSON] et al. (2017) was followed [58]. The bitumen samples were softened at 150 \({}^{\circ}\)C for 30 min. Approximately 0.5 to 1 g of bitumen was poured into 8 mm diameter and 2 mm height stainless steel molds using a spatula. The sample then was flattened by placing the mold on a hotplate to ensure a flat surface. Observations were conducted at room temperature using a FEI Quanta 250 FEG ESEM. The test parameters were set up with an acceleration voltage of 20 keV, a chamber pressure of 0.8 mbar in low vacuum mode, and a magnification of \(\times\)1000 in secondary electron (SE) mode.
# Mixed-Integer-Based Path and Morphing Planning for a Tensegrity Drone
## 2 State of the Art
### Deformation-Enabled Path Planning  
In the previous work, a method for simultaneously planning a path and sequence of deformations for a tensegrity drone, cast as a sequence of semidefinite programs (SDP), was introduced [12]. If one of many possible paths needs to be chosen, the program will need to take the form of a mixed-integer optimization. A major practical limitation of the proposed approach was the assumption that the shape of the tensegrity structure (and its deformation) can be represented as a positive-definite matrix, allowing the planning if a sequence of deformations as a sequence of covering ellipsoids, which can then be mapped back to actual configurations. Here this assumption is lifted; the algorithm instead works directly with tensegrity configurations. The algorithm proposed in this paper can directly plan a path while choosing a sequence of configurations, as a single mixed-integer quadratic problem. Quadratic problems are known to be easier to solve numerically than SDPs. In both cases, the introduction of integer variables requires an iterative solving procedure based on branch and bound methods, which are in general computationally hard. However, if choosing one path out of multiple available ones is required, the ability to cast the problem as mixed-integer quadratic rather than a mixed-integer semidefinite gives a clear advantage. The limitations of the method are demonstrated via numerical studies in the later parts of the paper.
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 6 Discussion  
To reduce the energy consumption of the WSN for microgrid status detection, this paper considers the two stages of sensor acquisition and transmission, but most of the current work only considers the transmission stage [28; 29]. Although the energy consumption of the acquisition stage is less than that of the transmission stage, such neglect does not achieve the minimum energy loss in reality. In this paper, when formulating the objective function of the optimization problem, the transmission energy consumption is taken into consideration. Finally, the sensor association with the best energy is obtained by solving the problem. In addition, for topology control, most of the current articles either select the CHs first and then associate the CMs, or select the CHs based on a result of clustering [30; 31; 32]. Few works try to obtain the optimal topology control strategy through multiple iterations. Considering that the initial clustering is generally not completely reasonable, and the CHs selected on this basis will not be energy-optimal, which is the necessity of multiple iterations. In this paper, the sensor association and the CHs selection are iterated many times until the CHs and sensor association no longer change. At this time, they are relatively considered to be optimal. In summary, this paper has made certain improvements based on existing work and theoretically can well improve the energy utilization efficiency of the WSN in the microgrid. The simulation results also show the effectiveness of the ATSAO proposed in this paper. Of course, since the main work of this article is to design the acquisition time and control sensor associations based on optimization problems, relatively few factors are considered in the selection of CHs. The limitations of this part will be improved in our future work.