esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Phytochemical Analysis
#### 2.3.3 Total Flavonoid Content  
The total flavonoid content (TFC) for the polyphenol-rich extracts (GF-CE and GF-PE) was measured using the aluminum chloride reagent, as described by [PERSON] et al. [35]. The extracts (1.0 mL), previously diluted, were mixed with 0.7 mL of NaNO\({}_{2}\) 5% (\(m/v\)) and 10.0 mL of ethanol 30% (\(v/v\)) for 5 min. Then, 0.7 mL of AlCl\({}_{3}\) 10% (\(m/v\)) was added and mixed altogether. After 6 min of reaction, 5.0 mL of NaOH (1 M) was added. Finally, the solution was diluted to 25 mL using ethanol 30% (\(v/v\)). After standing for 10 min, the absorbance of the reaction mixture was measured at 430 nm with a spectrophotometer. Quercetin (dissolved in ethanol) was used as a standard for the calibration curve. The results were calculated according to Equation (2) and are expressed as milligrams equivalent of quercetin per gram dry weight extract (mg QE/g DW).  
\[\text{TFC (mg QE/g DE) = \frac{C_{quercetin}\times V}{m}} \tag{2}\]  
\(C_{quercetin}\) is the standard (quercetin) concentration established from the calibration curve; \(V\) is the volume of extract (mL); and \(m\) is the total extract weight (g).  
Phenolic Compound Identification and Quantification  
The analysis of the phenolic compounds in the polyphenol-rich extracts (GF-CE and GF-PE) was conducted using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) with an ExionLC TM AC system (Sciex, Framingham, MA, USA) for separation and SCIEX Triple Quad 5500+ (SCIEX, USA) equipped with electrospray ionization (ESI). Separation was achieved using a ZORBAX Eclipse Plus C\({}_{18}\) Column (4.6 mm \(\times\) 100 mm, 1.8 \(\upmu\)m) (Agilent, Santa Clara, CA, USA). The mobile phases consisted of (A) 0.1% aqueous formic acid and (B) 100% acetonitrile, with the following gradient elution conditions: 2% B from 0-1 min, 2-60% B from 1-21 min, 60% B from 21-25 min, and returning to 2% B from 25.01-28 min. The flow rate was set at 0.8 mL/min, with an injection volume of 3 \(\upmu\)L. The multiple reaction monitoring (MRM) analysis of the selected phenolic compounds was conducted in the negative ionization mode, with the following parameters: curtain gas at 25 psi, ion spray voltage at 4500 V, source temperature at 400 \({}^{\circ}\)C, ion source gases 1 and 2 at 55 psi with a declustering potential at 50, collision energy at 25, and collision energy spread at 10.
## 3 Methods
### Script Parsing and AR Display Software
#### 3.3.2 AR Registration Based on SLAM Map Reloading  
In ORB_SLAM2, AR registration scans an unknown scene with a camera, estimates the camera pose following the multi-view geometry principle, identifies the plane, and places a virtual model on the plane to achieve AR display. Based on this, the QDARV system provides an AR registration method based on SLAM map reloading. This method loads the scene map, uses the camera to scan the mapped scene, applies feature matching to estimate the camera pose, parses the SLAM-AR display content script, and places the virtual model to achieve AR registration.  
In the online positioning mode of ORB_SLAM2, the tracking thread, local mapping thread, loop detection thread, and AR display thread of the system are synchronized with the optimization task. The tracking thread extracts Oriented Fast and Rotated Brief (ORB) feature points from the image and estimates the camera pose between frames. The local mapping thread adopts the Bundle BA method to optimize key frames and map points. The loop detection thread detects whether the camera returns to the previously visited position and calls the g2o optimization library [39] to eliminate accumulated errors. The AR display thread realizes the AR display by detecting the plane on which the 3D model is placed in the real scene.  
After loading the map, the system runs in the offline positioning mode and retains only the tracking thread and AR display thread to achieve pose estimation and AR registration for stopping the local mapping thread, loop detection thread, and optimization tasks. The system tracks the camera pose through relocation. First, the tracking thread obtains the image frame captured by the camera and finds candidate keyframes similar to the current frame in the keyframe library. Then, Bag of Words (BoW) is used for feature matching between the current frame and the keyframe. If the number of matching points is less than 15, the current candidate frame is discarded. Finally, the number of inliers obtained after BA optimization is taken to determine whether the relocation is successful. If the number of matching points between the candidate frame and the current frame is greater than 50, the relocation is considered successful. Furthermore, the Efficient Perspective-n-Point (EPNP) [40] algorithm is adopted to estimate the current camera pose. Meanwhile, the AR display thread obtains the model matrix and parameters of translation, rotation, and scaling of the 3D model through the parsed SLAM-AR display content information, and it renders the virtual model into the real scene through the OSG 3D rendering engine, thereby achieving augmented reality display of the planned scene.  
The AR registration method based on SLAM map reloading proposed in this study can realize re-display of AR scenes. The real-time pose estimation results have a small accumulated error. Moreover, only keeping the tracking thread and AR display thread can reduce the computational resources required for pose estimation tasks, which greatly improves the work efficiency of the system.
# Experimental Study of Imbibition Characteristics of Silica Sol in Coal-Measure Mudstone Matrix
## 6 Conclusions  
1. This paper reveals the silica sol grouting mechanism by two-phase displacement theory in dual-porosity media and confirms that grouting in a matrix system is dominantly controlled by the imbibition effect for silica sol.
2. Tests of key factors affecting imbibition show that the theoretical groutability ratio of silica sol is optimum compared with MP364 and P.O 425R. The imbibition potential of silica sol is quite significant, which is slightly lower than water. Moreover, silica sol has a low viscosity and a strong diffusivity.
3. The pressureless imbibition experiment shows that the silica sol absorption rate follows a power function. Colloid spontaneous migration features and phase characteristics were analyzed by the tests of time-varying spectrograms of \(T_{2}\).
4. The water absorption experiment, NMR imaging, and SEM imaging show the correctness of the pressureless imbibition experiment.
5. The silica sol groutability in coal-measure mudstone is extremely powerful. The paper lays a theoretical and experimental foundation for field grouting in the coal mine.  
The paper is financially supported by the National Natural Science Foundation of China (No. 51674244), the Natural Science Foundation of Jiangsu Province of China (No. BK20140198) and the Research Innovation Program for College Graduates of Jiangsu Province (No. KYLX16_0562).  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed analysis tools and checked the paper; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Systemic Evaluation of PSS in the Early Concept Phase Using Graph-Based Reasoning
## 3 State of the Art  
The issue of evaluating PSS concepts has been addressed from several research perspectives within the PSS community, indicating its importance and the difficulty of the challenge. Different approaches build on foundations from disciplines like systems engineering, multi-criteria decision making (MCDM), and risk assessment. A fundamental framework that underlies the continuous evaluation and selection of concepts in most complex engineering endeavors is provided by Value-Driven Design(VDD) [26; 27; 28]. VDD defines metrics for a system that serve as indicators for the level of goal achievement and guide the engineering process [26; 29]. The framework is used here to contextualize the different research streams within the state of the art. It employs two primary types of models: engineering models and value models. Engineering models can be simulation or mathematical models that utilize real data and assumptions as input and describe the physical nature of the system on a causal basis. This description serves as input for the value models. Value models aim to systematically balance metrics that are not inherently comparable (e.g., monetary, functional, and sustainability-related aspects) [27; 28]. They thereby reflect the strategic orientation of the organization in prioritizing different stakeholder interests within a measure of overall quality assessing system concepts [26; 30]. In order to comprehensively explore the state of the art of PSS concept evaluation under uncertainty, we reviewed systematic studies on PSS concept evaluation [17; 31; 32]. They point out that research on PSS concept evaluation is limited relative to the overall research on PSSs. The foundation of PSS concept evaluation approaches was further extended through snowballing in the domains of risk and uncertainty assessment within the PSS context. Subsequently, we conducted a review on the use of graph-based reasoning in the PSS domain and selected approaches relevant for PSS concept evaluation, as it is a key technique used in the solution approach.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 3 Connectivity Models for Vehicular Communications
### System Model  
Consider a high dynamic VANET, where \(N\) number of vehicles are randomly distributed over a unidirectional multi-lane highway. All the vehicles are supposed to be intelligent using onboard units (OBUs). For communication purposes, the OBU of each vehicle has radio equipment, i.e., global position system (GPS) and IEEE 802.11p, respectively, for location tracking and communication. Additionally, RSUs are also installed with an equal distance to cover the whole highway, which is shown in Figure 1. The purpose of an RSU is to extend the connectivity of vehicles and the coverage of V2X communication. The speed and arrival rate of vehicles follows normal distribution [3] and poison distribution [11], respectively. The inter-arrival time and spacing between vehicles follow exponential distribution [11]. Link reliability [3] and connectivity [11] are the two essential metrics to measure the performance of a highly dynamic networks. The probability density function (pdf) of the vehicle's velocity is given by the following:  
\[f_{v}(v)=\frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{(v-u)^{2}}{2v^{2}}} \tag{1}\]  
where \(u\) and \(\sigma\) represent the mean and standard deviation of vehicle speed, respectively.
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 2 Swarm Intelligence
### Firefly Algorithm (FA)  
FA [57] is a nature-inspired optimization algorithm based on the flashing behavior of fireflies. Fireflies use their bioluminescent flashes to attract mates and prey, and this behavior can be modeled to solve optimization problems. In FA, each firefly represents a potential solution, and its attractiveness is determined by its brightness, which is associated with the quality of the solution [20]. Fireflies move towards brighter and more attractive fireflies, leading the swarm to converge on optimal solutions. FA has been applied to various optimization problems and is particularly useful in multi-robot systems for path planning, task allocation, and formation control.  
1. Its brightness determines a firefly's attractiveness \(\beta\) and decreases with distance. The attractiveness \(\beta_{ij}\) of firefly \(i\) to firefly \(j\) is given by: \[\beta_{ij}=\beta_{0}e^{-\gamma r_{ij}^{2}}\] (10) where: \[\bullet\quad\beta_{0}\text{ is the initial attractiveness}.\] \[\bullet\quad\gamma\text{ is the light absorption coefficient}.\] \[\bullet\quad r_{ij}\text{ is the distance between firefly }i\text{ and firefly }j.\]
2. The distance \(r_{ij}\) between firefly \(i\) at position \(\mathbf{x}_{i}\) and firefly \(j\) at position \(\mathbf{x}_{j}\) is calculated using the Euclidean distance: \[r_{ij}=\|\mathbf{x}_{i}-\mathbf{x}_{j}\|\] (11)
3. Firefly \(i\) moves towards a brighter firefly \(j\) according to: \[\mathbf{x}_{i}=\mathbf{x}_{i}+\beta_{ij}(\mathbf{x}_{j}-\mathbf{x}_{i})+ \alpha(\text{rand}-0.5)\] (12) where: \[\bullet\quad\alpha\text{ is a randomization parameter}.\] \[\bullet\quad\text{rand is a uniformly distributed random number in [0,1].}\]
4. The brightness \(I\) of a firefly is associated with the objective function value. For a minimization problem, the brightness \(I_{i}\) of firefly \(i\) can be inversely related to the objective function \(f(\mathbf{x}_{i})\): \[I_{i}\propto\frac{1}{f(\mathbf{x}_{i})}\] (13)  
Consider a scenario where a team of robots must navigate a cluttered environment to reach their respective goals. Each robot uses the Firefly Algorithm to find the optimal path while avoiding obstacles. Each robot (firefly) starts with a random initial position and evaluates its initial path's quality (brightness). Robots calculate the attractiveness of other robots' paths based on their brightness and the distance between them based on Equations (10) and (11). Then, robots move towards more attractive paths discovered by other robots. This can be formulated according to Equation (2). Finally, following Equation (13), robots update their paths and recalculate the brightness based on the new path quality. The robots refine their paths through iterations, converging on optimal paths that avoid obstacles and minimize travel time. The FA's decentralized nature ensures robustness and adaptability in dynamic environments.
# Method to Remove Tilt-to-Length Coupling Caused by Interference of Flat-Top Beam and Gaussian Beam
## 3 Theoretical Analysis
### Discussion  
To increase the credibility of the conclusion, we respectively set the aperture stop size \(R=0.5\) mm, 1 mm, 1.5 mm to recalculate path length signals for the same situations in Figure 4a,b. The results are shown in Figure 5a,b. Therefore we can conclude that in the case of the diffraction model shown in Figure 1, the TTL coupling noise can be eliminated if the detector used is a large single-element photo-diode (SEPD) (i.e., a QPD with gap size equal to 0) and \(z_{0}=0\) (even if the Gaussian beam is non-diffracted). This provides a different approach to use of an imaging system [11] which is based on [PERSON]'s principle of imaging the point of rotation onto the detector to eliminate the TTL noise.  
Figure 4: (**a**) The flat-top beam is placed with a transverse offset \(d_{offset}\) and a tilt angle \(\alpha\) around the pivot \(O\) on the detector. The dummy stop only works for flat-top beam and its center is always at the starting point of flat-top beam. There is no geometric path difference between the two beams in this case. (**b**) The upper part is the numerically computed \(LPS_{LPF}\) results for interference between the flat-top beam and the Gaussian beam on the detector with an initial offset between the two beam centers. The lower part is analytically computed the geometric optical path difference \(LPS_{OPD}\).  
In reality, the balance that eliminates the TTL noise can be broken by other non-ideal factors, such as different beam parameters, beam misalignment, incomplete detection or wavefront aberrations. As an example, Figure 6 shows the numerical simulations of the coupling for the same situation in Figure 3a but with slightly different waist positions for the Gaussian beam.
## 2 Methodological and Operational Framework for CDTs
### Deploying the CDT Operational Model in Connected Agile Supply Networks  
#### 3.2.1 Overall  
The usage of CDTs in connected factories and supply chains is very important. Through CDTs, a manufacturing company can virtualize its assets and have a better monitoring of their performance both at factory and inter-factory level. They can also improve Production Planning and Predictive Maintenance [14], monitor virtual production lines by connecting all involved stakeholders [15] and optimize Packaging, Materials and Logistics [16]. Similar to the concept that a factory is a network of interconnected CDTs, a vision for agile and connected supply chains is illustrated in Figure 3 and includes the following:  
* A dynamic, living system of \"cognitive digital twins\" representing all assets, operations and actors involved: factory, logistics service provider (LSP), trucks, warehouses, etc. Each entity participating in the supply chain can be modelled as a CDT. Deploying the enablers described above, each CDT can share only the necessary information with other CDTs and agree on governance aspects and rules applicable in all communications and decision-making process.
* Interconnected CDTs at intra- and inter-factory (supply chain) level. This implies the definition of the information/events to be shared under a common security/privacy and data integrity framework.
* Different levels of cognition. Depending on the level of autonomy (defined in the governance framework for each of the CDTs) different cognition capabilities will apply. Those will vary from basic understanding to autonomous decision making and actuation.
# Remote Control Device to Drive the Arm Gestures of an Assistant Humanoid Robot
## 5 Experimental Evaluation
### Measurement of Target Joint Positions  
Joint trajectories are the evolution of the angular values of each joint over time, and they define the performance of the imitation [39]. The target joint positions established in the remote control device have been monitored to assess the information submitted to the mobile robot.  
Figure 9 shows the evolution of the joint angles in the right and left arms measured in the remote controller during a dynamic gesticulation example. The joint positions have been sampled at a frequency of 50 Hz and do not show discontinuities. The joints are numbered as in Figure 2: joints 1, 2 and 3 are in the shoulder, and joint 4 is in the elbow. In the gesturing example represented, the arms of the remote control device made three inward circles in the air, with the arms raised (almost parallel to the ground) and the elbow slightly bent. This circular profile can be appreciated in Figure 9, where each ascending curve corresponds to the start of a new turn. The comparison of the joints' angle profiles in the right and left arm registering the same symmetric gesture shows similarities between both arms, but they are not equal because the operator is focused on performing a natural gesticulation rather than generating a perfectly symmetric movement. The small differences and irregularities between the movements of the two arms is what makes the robot movement feel more natural and familiar.
###### Abstract  
Currently, the frame structures with special-shaped concrete-filled steel tubular columns have been widely used in super high-rise buildings. Those structural members can be used to improve architectural space. To investigate the seismic behavior of spatial composite frames that were constructed by connecting steel beams to L-shaped concrete-filled steel tubular (CFST) columns, a finite element analysis (FEA) model using commercial finite element software ABAQUS was proposed to simulate the behavior of the composite spatial frames under a static axial load on columns and a fully-reversed lateral cyclic load applied to frames in this paper. Several nonlinear factors, including geometry and material properties, were taken into account in this FEA model. Four spatial specimens were designed, and the corresponding experiments were conducted to verify the proposed FEA model. Each testing specimen was two-story structure consisting of eight single span steel beams and four L-shaped CFST columns. The test results showed that the proposed FEA model in this paper could evaluate the behavior of the composite spatial frames accurately. Based on the results of the nonlinear analysis, the stress developing progress of columns is investigated. The load transferring mechanism and failure mechanism are also determined. The results are discussed and conclusions about the behavior of those spatial frame structures are presented.  
L-shaped; concrete-filled steel tubular column; frame; finite element model; seismic behavior +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Biochar Characterization  
#### 3.1.1 Fourier Transform Infrared Spectroscopy (FTIR)  
The changes of biochar surface functionalities with pyrolysis temperature are illustrated in Figure 1. In general, the FTIR analysis shows that the functional groups of biochar samples decrease with increased pyrolysis temperature. This is due to the rapid removal of volatile compounds as a result of the accelerated and intensified degree of carbonization at higher pyrolysis temperatures [18; 19; 20]. The FTIR spectra of biochar samples shows a broad peak at 3394 cm\({}^{-1}\) which corresponds to the O\(-\)H bond stretching of the alcoholic and phenolic hydroxyl groups. The intensity of this peak was found to be very strong at lower pyrolysis temperature; however, it becomes negligible at higher temperature. The characteristic C-H stretching vibration of alkyl structure of aliphatic group can be seen at 2927 cm\({}^{-1}\). The peak at 1693 cm\({}^{-1}\) represents the stretching of carbonyl bonds (C=O) of the carboxylic groups or conjugated ketone. The stretching vibrations of the aliphatic -C=C- appears at 1597 cm\({}^{-1}\)[21].  
It can be seen from Figure 1 that the intensity of all these peaks decreases with increased pyrolysis temperature, as compared to the FTIR spectra of the date palm feedstock (see Figure S1 in the Supplementary Materials), and becomes negligible for the biochar produced at 700 \({}^{\circ}\)C. It is well known fact that the breakage of hydroxyl groups of aliphatic chain of biomass takes place between 120 \({}^{\circ}\)C and 200 \({}^{\circ}\)C. The cracking of aliphatic methyl, methylene, methoxyl groups, and reformation of other functional groups such as carbonyl and carboxyl occurs at \(\sim\)400 \({}^{\circ}\)C. However, stable heteroaromatic and aromatic compounds will not be affected at this temperature. At higher temperatures, i.e., more than 600 \({}^{\circ}\)C, almost no aliphatic functional groups will be present in the biochar. These aliphatic structures are known to reform into aromatic structures, resulting in the increased presence of phenolic and ether groups. Moreover, at high temperatures many C=C bond breakages take place due to availability of adequate energy. Therefore, at higher temperature, due to extensive carbonization, formation of graphite-like structures of the biochar occurs which shows less intense peaks [22]. It was also reported that the biochar prepared at higher temperatures exhibited lower (O/C) and (H/C)ratios, corresponding to the absence or limited presence of functional groups on surface and high percentages of carbon content [23].