esa-sceva/satcom-chunk-collection huggingface dataset:
# Synergistic Effects of CuO and ZnO Nanoadditives on Friction and Wear in Automotive Base Oil +
## 2 Materials and Methods
### Scanning Electron Microscopy and Elemental Surface Analysis  
To gain insight into the tribological mechanisms at play, worn surface morphologies were examined using both optical microscopy and scanning electron microscopy (SEM). SEM imaging enabled high-resolution observation of surface features, including signs of abrasion, adhesion, and potential tribofilm development. Additionally, SEM analysis allowed the detection of residual nanoadditives on the wear scars, offering evidence of additive interaction with the surface.  
This study conducted SEM investigations on the disk specimens, focusing on the worn tracks. Imaging was performed at 1000\(\times\) magnification, using a 10 kV accelerating voltage, a working distance of approximately 10 mm, and in secondary electron (SE) mode to highlight topographical features. For each wear scar, three characteristic locations were selected for detailed analysis, based on the tribological relevance of contact pressure and relative sliding velocity (see Figure 5).  
* The first point was located at the dead center (DC) of the wear track, i.e., along the central axis in the direction of motion where the ball reverses direction. This point corresponds to the minimum relative velocity and maximum static contact duration.  
Figure 4: Disk wear volume evaluation using a Leica DCM 3D confocal microscope.  
Figure 5: Selected SEM/EDX analysis points along the wear track, representing distinct combinations of contact pressure and relative sliding speed.  
* The second point (WTHP) was at the geometrical center of the wear scar, along the central line of motion. The contact pressure and sliding velocity are at their highest, making this location critical for understanding dynamic wear processes.
* The third point (WTLP) was selected at the outer edge of the wear scar, where relative sliding velocity is maximal, but contact pressure is minimal.  
These three regions were selected because they allowed the combined assessment of the two most relevant factors influencing tribofilm formation--contact pressure and relative sliding speed--under otherwise constant conditions, thus providing insight into the wear mechanisms and how these parameters affect the role and effects of the nanoparticles in the tribological system. At each selected location, energy-dispersive X-ray spectroscopy (EDX) was used to perform two types of analyses:  
1. Elemental mapping, to visualize the spatial distribution of elements associated with tribofilms or transferred material;
2. Quantitative elemental composition, providing the atomic percentage of all detected elements, particularly those related to the additive nanoparticles (O, Cu, and Zn) and boundary layers (Fe, Cr, Si, O, and C).  
This comprehensive microstructural and compositional characterization allowed for the correlation of observed wear features with lubricant composition and further validated the additive behavior under boundary lubrication conditions.
## 3 Numerical Studies
### Benchmark iFEM Results
The minimum value of \(\varepsilon_{p}\) (corresponding to far-field strains) is similar in both plots of Figure 6. This further corroborated the conclusions derived from Figure 4, that the iFEM is accurate in reconstructing the undamaged far-field strains of the plate. The results for Damage Case-3 are shown in Figure 7. The inferences here are similar to those of Damage Case-1, except for a key feature, i.e., the strain field orientation. Both FE and iFEM results showed a greater \(\varepsilon_{p}\) distribution perpendicular to the crack front, i.e., oriented at 45\({}^{\circ}\) with respect to the horizontal axis. This indicated that information regarding damage orientation could also be obtained by analyzing the reconstructed strain field. Figure 4: Contour plots of \(\varepsilon_{p}\) for an undamaged plate: (**a**) FE results, and (**b**) benchmark iFEM results. The following conclusions could be drawn from the benchmark iFEM results. The iFEM procedure successfully reconstructed the far-field strains since the strain sensors are located far from the vicinity of the crack. Nevertheless, the presence and location of damage could be inferred from the iFEM results by contrasting the local strain peaks with the far-field strains (that acted as a healthy baseline state of the structure). It is noted that although a highly accurate reconstruction of the damaged strain field across the entire plate domain is preferred, it is not a prerequisite for the presented damage detection methodology. The current study aimed to maximize reconstruction accuracy and minimize the number of sensors used so that meaningful conclusions could be drawn from the reconstructed strain field for the purpose of damage detection. For the damage cases investigated, damage detection is successful regardless of the damage size and orientation. Although these benchmark results are promising, using such a large number of strain rosettes (440) may be  
Figure 5: Contour plots of \(\varepsilon_{p}\) for Damage Case-1: (**a**) FE results, and (**b**) benchmark iFEM results. Figure 6: Contour plots of \(\varepsilon_{p}\) for Damage Case-2: (**a**) FE results, and (**b**) benchmark iFEM results. Figure 7: Contour plots of \(\varepsilon_{p}\) for Damage Case-3: (**a**) FE results, and (**b**) benchmark iFEM results. generally impractical. This limitation could be overcome by using fiber optic strain sensors as discussed in the next section.
# Comparison of Temperature Dependent Carrier Transport in FinFET and Gate-All-Around Nanowire FET
## 1 Introduction  
For several years, fin field effect transistors (FinFETs) have been used industry to continue CMOS down-scaling. However, as FinFETs also have been look forward to being further scaled, critical issues such as drain-induced barrier lowering, threshold voltage (\(V_{\textit{TH}}\)) roll-off and parasitic resistance occur, etc. [1]. In order to alleviate these issues, the gate-all-around nanowire field effect transistors (GAA NW-FEsT) have been recently proposed as a promising device to replace FinFETs, due to their superior gate control [2, 3]. The better electrostatic gate control provided by the surrounding gate enables a more aggressive gate length scaling than FinFETs [4, 5, 6]. However, advanced fabrication technologies remain challenging, e.g., the GAA structure formation [7] and doping process [8] to restrain a naturally formed parasitic channel based on bulk FinFETs. Such device fabrication directly affects key device parameters (\(V_{\textit{TH}}\), subthreshold swing (\(SS\)) and effective mobility (\(\mu_{\textit{eff}}\)), etc.). Furthermore, the electrical characteristics of semiconductors such as band gap [9], carrier density [10], mobility [11], velocity saturation [12], \(V_{\textit{TH}}\)[13], and leakage current [14] depend strongly on the temperature resulting in a change of circuit performance as a function of the operating temperature. Therefore, it is important to explore the possible degradation at a high temperature which might lead to performance degradationin a hot temperature corner. It is helpful to understand and to model the temperature dependence of GAA devices to enable the design of circuits in this technology.  
In this study, we investigated the high temperature characteristics of vertical 2-stacked n-channel metal oxide semiconductor (NMOS) GAA NW-FETs and FinFETs in order to understand the potential differences between the two devices.
# A New Method for Detecting Onset and Offset for Singing in Real-Time and Offline Environments
## 2 Materials and Methods
### State of-the-Art Onset Detection Algorithms
#### 2.2.4 Essentia  
This offline onset tracking method was used with its default values for the window and hop sizes, 1024 and 512, respectively, for a Hann window [26]. Therefore, the duration of each frame was roughly 11.61 ms. There are two approaches to this library. The first method, Essentia Onset HFC (EssHFC), uses a high-frequency content detection function [31]. The high frequency is calculated by multiplying the magnitude of each frame position (frequency) with the summation of the magnitudes of the spectral frame. The discrete spectrum of \(N\) unique points is formulated in Equation (1).  
\[\text{HFC}=\sum_{i=0}^{N-1}i|X(i)|, \tag{1}\]  
The second method, Essentia Onset Complex (EssCplx), uses a complex domain spectral difference function to identify significant changes in magnitude and phase [32]. This algorithm tries to identify significant energy changes on note onsets or the deviation of phase values within the phase spectrum caused due to pitch changes.  
Finally, it should be mentioned that all these algorithms/libraries calculate only onsets and do not compute offsets or identify transitions.
## 2 Materials and Methods
### Data
#### 2.2.4 MODIS Land Cover Data  
The MODIS Land Cover Type Product (MCD12C1, 0.05\({}^{\circ}\)) supplies an annual global land cover type dataset (2001-2020). Taking 2012 as the representative year over 2007-2018, we categorized vegetation types based on the IGBP classification scheme and focused on six vegetation types with larger area proportions: mixed forests (MF), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), woody savannas (WS), savanna (S), and open shrublands (OS).
## 1 Introduction
For remote operations, these systems can provide operators with realistic tactile feedback, enabling a true tactile experience even over long distances [41,42]. This capability is particularly valuable in fields such as healthcare and military operations, where real-time tactile feedback during remote surgery or other operations enhances control and precision [37,43,44]. In disaster relief, robots equipped with artificial tactile systems can carry out precise operations in complex environments, aiding rescuers in executing hazardous tasks. With tactile feedback, these robots can detect their surroundings, identify and clear obstacles, and even assist in rescuing individuals trapped during emergencies [30,45, 46, 47, 48]. As technology advances, future tactile systems are anticipated to become more sensitive [17,18], adaptable [49,50], and capable of engaging effectively with humans in increasingly complex environments [51, 52, 53]. The potential applications of this technology in diverse fields promise to enhance societal intelligence and elevate the quality of life. Through ongoing research and innovation, artificial tactile systems will progress toward their ultimate objective--simulating human touch to provide precise solutions for human needs. This paper introduces the design of an array-based, multi-channel acquisition system utilizing flexible sensors. The system incorporates high-sensitivity, flexible sensors made from PET fabric, which detect varying pressures with minimal sensitivity to frequency fluctuations. Compared with other devices described in the supplementary document (Table S1), our sensors demonstrate a significant advantage in low-pressure detection. Moreover, they feature a simple structure and are made from easily accessible materials. The sensor demonstrates rapid responses in load and unload pressure tests, with response and recovery times of 119 ms and 68 ms, respectively, while sustaining a reliable performance over 1000 testing cycles. A signal acquisition circuit was developed for the hand-shaped sensor array, and a real-time signal acquisition hardware system was constructed using the STM32F104C876 microcontroller. Utilizing a trained convolutional neural network (CNN) model, the system accurately identifies objects of various shapes and sizes, achieving an impressive accuracy of 99.60% on the test set. Moreover, in a test conducted by a group consisting of five men and three women, the classification accuracy on the test set reached 93.75%. Additionally, it effectively distinguishes four stages of a ball-throwing motion: empty hand, holding, throwing, and catching. Furthermore, this result is also applicable to other repetitive throws.
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 5 Experiment
### Algorithm Comparison
#### 5.2.2 Comparison on Number of Solved Instances  
u-toksa, as an exact algorithm, does not participate in the experiments of this section. This section compares the performance differences between the DNTS algorithm and the HAYWOOD algorithm in processing random instances, both of which are heuristic algorithms. For this purpose, we generated test instances different from those used in the previous experiments and ensured that each instance had a stable extension. The instances were divided into four groups based on the number of arguments, with 50 instances per group, totaling 200 instances for the experiment, aiming to assess the solving efficiency of each algorithm for instances of various sizes. To ensure the reliability of the experimental results, each group of instances was tested three times. In Table 7, best represents the maximum number of instances solved, worst represents the minimum number of instances solved, and average represents the average number of instances solved in three runs.  
Based on the experimental results in Table 7, it can be observed that the HAYWOOD and DNTS algorithms are able to successfully solve basically all the instances in the instances generated with 1000 and 2000 arguments. However, the number of solutions of both algorithms decrease as the instance size increases. Nevertheless, the DNTS algorithm still outperforms HAYWOOD in terms of the number of solutions, and this advantage becomes more and more significant as the instance size increases.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Silver-Doped Zinc Oxide Films  
The p-type silver (Ag)-doped ZnO films were synthesized on glass slides under various deposition cycles [175]. The film thickness (0.34 to 0.49 \(\upmu\)m) absorbance value increased (due to the accumulation of a new layer on the substrate), but the band gap reduced (2.91 to 2.49 eV) with the increasing deposition cycle (from two to four cycles). In the SEM analysis, the films prepared under four cycles showed smooth surfaces with smaller grains, completely covered on the surface of the substrate.
# Emerging Trends in Pharmacotoxicological and Forensic Sample Treatment Procedures
## 3 Main Solid Matrices for Forensic Purposes
### Hair  
Hair analysis is widely used in pharmacotoxicology to monitor the consumption of substances and to extend control to previous periods. The use of this matrix was then widespread in postmortem toxicology. Over the years, many studies have been carried out to detect several typologies of drugs in hair compared with their presence in other conventional matrices, e.g., blood and urine.  
To prepare hair samples for LC-MS/MS analysis to quantify some newly designed stimulant drugs, [PERSON] et al. used two extractions in series. Collected samples were washed and decontaminated with water and acetone and then cut. For the first extraction they used a solution of water:acetonitrile:methanol was (50:25:25, _v:v:v_), conducted at 40 \({}^{\circ}\)C for 18 h with agitation (900 rpm). The upper phase obtained was stored, and the lower phase led to the second extraction similarly. After this, both supernatants (by first and second extraction) were mixed and purified using a filter composed of regenerated cellulose [78]. [PERSON] et al., in their paper, proposed a method to extract and determine any presence of steroids in hair samples collected postmortem. In this case, the treatment of the sample involves several steps:  
1. cleaning of hairs with dichloromethane
2. addition of methanol and sonication for 60 min.
3. centrifugation and exsiccation of the first supernatant that was successively diluted with buffer (first aliquot)
4. in the remained sample on the bottom was added 1 mL of NaOH and shacked (second aliquot).  
Both aliquots were subjected to liquid-liquid extraction with ethyl acetate, and the obtained extract was dried and restored in dichloromethane to be further subjected to a solid phase extraction using the ISOLUTE C18 column [79]. Numerous studies and works in literature treat the hair as a forensic sample to investigate a wide range of substances (e.g., antidepressants, benzodiazepines, barbituric derivates, cocaine, amphetamine, derivates of opioids, and many drugs of abuse). Among these, we can note the typical attention of all the authors regarding the fundamental operations in the treatment preceding this type of specimen analysis. Is commonly highlighted the importance of washing and decontamination of the sample, cutting the segments of interest based on the clinical case of the victim, homogenization, and choice of the best extraction techniques, frequently using liquid-liquid extraction, in some cases followed by an additional solid phase extraction [80; 81; 82; 83; 84]. Recently, the technique of micro-segmentation of hair has been increasingly used and consists of cutting a fragment of 4 mm which corresponds to the average daily hair growth, to identify the use of a particular substance on a specific day [85,86].
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 5 Physical Network Optimization
### Penalty Function  
The definition of a penalty function is to make the model trying best to achieve the highest efficiency at certain circumstance. In other word, the decision will be made by the MS from the perspective of releasing the overhead of core network.  
In order to make the difference between the slices of different maps more obvious (e.x. satellite, cellular or low altitude drone network), we define the penalty function target-that is, we define the penalty function separately for the three types of networks.  
* Penalty Function of Satellite Network \[\mathbf{PW}_{1}(t)=-log(G^{\prime}\big{(}\mathbf{DA},\ \mathbf{R}^{\prime},\mathbf{L}_{\mathbf{c}},\mathbf{L}_{\mathbf{u}},\mathbf{P} _{kb}(t)\ )\big{)}\\ =-log(\mathbf{DA}+\lambda_{11}*\mathbf{R}^{\prime}+\lambda_{12}* \mathbf{L}_{\mathbf{c}}+\lambda_{13}*\mathbf{L}_{\mathbf{u}}+\lambda_{14}* \mathbf{P}_{kb}(t))\] (41)
* Penalty Function of Cellular Network \[\mathbf{PW}_{2}(t)=-log(G^{\prime}\big{(}\mathbf{DA},\ \mathbf{R}^{\prime},\mathbf{L}_{\mathbf{c}},\mathbf{P}_{kb}(t)\ )\big{)}\\ =-log(\mathbf{DA}+\lambda_{21}*\mathbf{R}^{\prime}+\lambda_{22}* \mathbf{L}_{\mathbf{c}}+\lambda_{23}*\mathbf{P}_{kb}(t))\] (42)
* Penalty Function of UAV Network \[\mathbf{PW}_{3}(t)=-log(G^{\prime}\big{(}\mathbf{DA},\ \mathbf{R}^{\prime},\mathbf{L}_{\mathbf{u}},\mathbf{P}_{kb}(t)\ )\big{)}\\ =-log(\mathbf{DA}+\lambda_{31}*\mathbf{R}^{\prime}+\lambda_{32}* \mathbf{L}_{\mathbf{u}}+\lambda_{33}*\mathbf{P}_{kb}(t))\] (43)  
If we combine these three cases into one formula, we get the following more general case.  
\[\mathbf{PW}(t)=-log(G^{\prime}\big{(}\mathbf{T},\ \mathbf{R}^{ \prime},\mathbf{S}(\mathbf{t}),\mathbf{P}_{kb}(t)\ )\big{)}\\ =-log(\mathbf{DA}+\lambda_{1}*\mathbf{R}^{\prime}+c_{1}*\lambda_ {2}*\mathbf{L}_{\mathbf{c}}+c_{2}*\lambda_{3}*\mathbf{L}_{\mathbf{u}}+\lambda_ {4}*\mathbf{P}_{kb}(t)) \tag{44}\]  
Here, the parameter \(\mathbf{DA}\) and \(\mathbf{P}\) has been determined by the previous sections, while \(\lambda_{i}\in[0,1]\) is a parameter aimed to describe the sensitiveness of the system. Moreover, \(C_{i}\in\{0,1\}\) is the network label of the system, which is used to indicate the difference in the penalty function corresponding to different physical networks (i.e., cellular network or satellite network).  
It is worth mentioning that in the process of training, the system dynamically adjusts the value of sensitive factors according to the current network state and other information. This is an optimization problem with only four finite variables, so it can be quickly solved by existing optimization algorithms or grid search methods.
## 3 Results and Discussion  
### Crystal and Microstructure  
Figure 3 shows the results of the crystal structure and microstructural analysis of induction-hardened AISI 52100 bearing steel. It was confirmed that some retained austenite with a face-centered cubic structure and a black-colored phase spherical in shape were finely distributed in the martensitic matrix. As a result of the SEM microscopy and compositional analysis of the AISI 52100 steel (Figure 3c), it was observed that black, round-shaped carbides with a diameter of about 1 \(\upmu\)m were uniformly distributed in the matrix as a (Fe-Cr)\({}_{3}\)C phase, as confirmed by XRD and optical microscopy. It is known that these carbides in AISI 52100 bearing steel improve wear resistance and corrosion resistance.  
Figure 4 shows the results of XRD analysis before and after quenching heat treatment on the TiC-reinforced AISI 1020 matrix composite. Before the quenching heat treatment, the \(\upalpha\)-Fe (ferrite) matrix and the TiC phase were confirmed, and a second phase, such as including other impurities or unwanted chemical reactants, was not observed. The composite specimen was maintained at 890 \({}^{\circ}\)C for 1 h and then water quenched, and had two main phases. After quenching heat treatment, a broadening of the \(\upalpha\)-Fe peak was observed, which is attributed to the martensitic (BCT lattice structure) phase transformation during the quenching heat treatment, resulting in a change in lattice spacing and residual stress. The inducement of dislocation formation may have occurred due to the difference in thermal expansion coefficients between the matrix and the reinforcement.  
Figure 4: XRD analysis of (**a**) heat-treated TiC/AISI 1020 composite and (**b**) as-cast TiC/AISI 1020.  
Figure 3: (**a**) XRD, (**b**) OM, and (**c**) SEM analysis results of AISI 52100 bearing steel.  
Figure 5 shows the microstructural observation results of the TiC/ AISI 1020 matrix composite using an SEM. As can be seen from Figure 5, it was confirmed that spherical TiC particles with a size of 1-5 \(\upmu\)m were uniformly distributed in the AISI 1020 alloy matrix. From the XRD analysis and the microstructural analysis results, a metal matrix composite with uniform distribution of TiC reinforcement in AISI 1020 was successfully manufactured by liquid pressing infiltration without impurities or unwanted phases. The TiC volume fraction in the TiC/ AISI 1020 composite calculated through image analysis was about 60%, which matched the TiC volume fraction to the TiC preform before infiltration.
# Influence of Rock Slide Geometry on Stability Behavior during Reservoir Impounding
###### Abstract  
Assessing the stability behavior of deep-seated rock slides in the surroundings of large dam reservoirs requires an understanding of the geometry, the kinematics, the groundwater situation, and the rock mass and shear zone properties. This study focuses on the influence of rock slide geometry on stability evolution during initial reservoir impounding. Therefore, nine different rock slide models, mainly taken from published case studies with a well-explored geometry, were analyzed. Based on the assumption that the rock slides are close to limit equilibrium in a no-reservoir scenario, reservoir impounding causes a reduction in the factor of safety (FoS). The results show a large impact of the water level for rotational slides where the majority of the rock mass is located at the lower part of the slope. This results in a maximum reduction in the FoS of up to 12%. In contrast to this, translational rock slides are less affected by reservoir impounding. The stability analysis shows that the change in FoS is strongly controlled by the kinematics of the rock slide and the geometry near the foot of the slope. Consequently, a comprehensive in situ investigation of the geometry and kinematics is necessary in order to reliably assess the influence of initial reservoir impounding.  
deep-seated rock slides; large dam reservoirs; first-time impounding; slope stability analysis +
Footnote †: journal: Applied Physics Letters