esa-sceva/satcom-chunk-collection huggingface dataset:
# Plenty of Room at the Bottom: Nanotechnology as Solution to an Old Issue in Enhanced Oil Recovery
## 2 Nanofluids
### Effect of Nanoparticles on Reservoir and Fluid Properties
#### 2.2.3 Effect on Interfacial Tension  
Silica hydrophilic nanoparticles have also proven to have an effect on the IFT. Nanoparticles migrate to the interface, thus reducing the oil/brine contact. This works similarly to surfactants, but using a different principle [54; 75]. The mechanisms behind the adsorption of neutral particles are explained using the lattice-Boltzmann method, and then they were compared with those of surfactants [76]. Surfactants adsorb at the liquid/liquid interface due to their well-known amphiphilic properties, whilst neutral wetting nanoparticles adsorb due to the fact that maintaining a particle-fluid interface requires less energy. This behavior can be explained considering the free energy of the system. The reduction of the latter requires either the reduction of interfacial tension (achieved using surfactants), or the reduction of the area of contact, which is indeed the effect of adsorbed nanoparticles. In emulsions nanoparticles can also break up oil droplets (Figure 6), making it easier to migrate through the porous media [76].  
Figure 5: Interaction between hydrophobically associating polyacrylamide (HAPAM) polymer and silica nanoparticles as a function of the polymer concentration. Figure adapted from Ref. [8]. CAC: critical aggregation concentration.  
Different kinds of nanoparticles were investigated concerning their influence on the interfacial properties: polysilicon nanoparticles (PSNPs) [9,77,78], amphiphobic nanoparticles based on functionalized carbon nanotubes (CNTs) [79], oxides of different metals (e.g., Al, Zn, Mg, Fe, Zr, Ni, Ti) [6,9], and magnetic nanoparticles [80]. One of the reported advantages of the novel compound CoFe\({}_{2}\)O\({}_{4}\)-chitosan nanoparticles is their biodegradability, which makes them more environmentally friendly than traditional products (Figure 7).  
Figure 6: Oil droplet breakup in the presence of nanoparticles: the droplet without particles in a steady state condition (**a**); and snapshots of the droplet covered with particles at different times until the breakup (**b** to **g**). Figure adapted from Ref. [76].  
Figure 7: The effects of CoFe\({}_{2}\)O\({}_{4}\)–chitosan nanoparticles on interfacial tension (IFT). Figure reproduced from Ref. [80].
## 3 Synthesis Methods
As we can see, some authors used silver nanoparticles to improve the mechanical properties of PMMA without taking into account the antimicrobial effect or biocompatibility. This shows us the heterogeneity that exists in the development and evaluation of dental materials, such as those discussed in this review. The applications of the PMMA\(-\)AgNP composites reviewed here are focused mainly on bone cements and acrylic bases for dentures. The most used synthesis method is mixing an aqueous suspension of silver nanoparticles directly on the monomer before mixing the monomer-polymer system. Other synthesis methods, such as cluster beam deposition and pulsed laser deposition, were used to form PMMA\(-\)AgNP composites [3; 7]. Characterization methods show that the addition of silver nanoparticles modifies the mechanical properties of PMMA depending on the size and concentration used. The size of the silver nanoparticles reported in this review was between 2 and 200 nm, and a spherical shape was most commonly reported by authors. The size and shape depends on the synthesis method. The concentrations of AgNPs used on the materials tested were between 0.01% and 2% in aqueous solutions. An example of silver nanoparticles is shown in Figure 2.
## 2 Experimental Section
### Otoneurological Testing
According to the technique proposed by the same group [34] to reach adequate numbers of head values for the lateral semi-circular canal,the operator delivered a range of velocities in a random order and direction to achieve at least 10 artefact-free impulses. By means of this technique, 50-60 impulses were delivered in each canal direction to ensure that the desired number of artefact-free responses was achieved. * **Static Posturography Testing (SPT):** The previously instructed subjects maintained an upright position on a standardized platform for static posturography (EDM Euroclinic(r)) during a 60 s recording period per condition, defined as eyes closed (CE) or opened (OE) while standing on either the stiff platform (SP) or a 6 cm heightened AIREX(r) Balance-pad plus foam carpet (FC, AIREX AG, 25% compression resistance, 20 kPa; apparent density 55 kg/m\({}^{3}\); tensile strength 260 kPa). The sampling frequency was set at 25 Hz [35; 36] and the centre of pressure (CoP) was supervised during the test. Classic posturographic parameter evaluations involving the trace length (length), the surface of the ellipse of confidence (surf), and the mean velocity (avspe) of body sways with relative SD (stdspe) were performed, as well as the fast Fourier transform (FFT) elaboration of oscillations on both the X (right-left) and Y (forward-backwards) planes [35; 36]. Time-domain oscillation signals (X and Y) were extracted by the original software manufacturer into.txt format and the FFT elaborations were gained through Matlab software. In order to obtain the FFT of X and Y oscillations, a core function (Appendix A) was implemented using as input the.txt file provided by the software manufacturer. A main script was written to acquire the FFT for each patient, related to four different conditions, by accessing to the above mentioned Matlab function. For each condition the script returned a file containing the normalized value of the FFT for X and Y oscillations. Spectral values (power spectra, PS) of body oscillations were quantified on an.xls file, for every frequency from 0.01 to 5.00 Hz [35; 36]. According to previous experiences [35; 36], the frequency spectrum was clustered into three groups: 0.01-0.70 Hz (low frequency interval); 0.70-1.00 Hz (middle frequency interval); 1.00-5.00 Hz (high frequency interval). Within each group, the spectral intensity was determined by adding the relative PS and the group mean PS (\(\pm\) SD) [35; 36].
## 4 Simulation Results of Fully Mechanized Mining with a Large Mining Height and the Analysis
### Characteristics of Coal Wall Failure Under Different Advancements of Mining  
Overburden migration is the root cause of coal wall spalling. After analysis of the characteristics of overburden caving through DEM numerical simulation, following a 50 m advancement of mining, roof fractures occurred. At this time, the roof was not caved, and cracks were generated near the coal wall because the tensile strength of coal reached the limit. This is the initial mining period, during which the coal wall was stable; as the advancement of mining reached 70 m, the roof began to break and was caved. Due to the strong strength and large thickness of the limestone roof in Huoxi Coalfield, the direct roof was a thin mudstone pseudo roof which easily formed a masonry-cantilever structure. The working face underwent hybrid cyclic pressure, with a caving interval of 20 m. The overburden migration is shown in Figure 8.  
Figure 7: Numerical simulation results under different conditions of dirt bands. In this figure, (**a**) without dirt band; (**b**) upper dirt band; (**c**) middle dirt band; (**d**) lower dirt band; (1) fractures diagram, (2) displacement diagram and (3) contact force chain diagram.  
Under the conditions of the original rock roof, the thick hard limestone had a long caving interval, and was prone to advanced breaking. With the torsion and the caving pressure of roof, the strength of the coal seam could no longer withstand the roof pressure. As a result, cracks and separation began to develop in the plastic zone, and were concentrated in the upper part. At the same time, wedge-shaped failure occurred in the failure zone. In case of the rotation and caving of key plates of the thick hard roof, the coal wall and roof generated concentrated stress. The roof failure zone was squeezed to break, and a slight falling collapse occurred with mining.  
As can be seen from Figure 8, spalling was related to the advanced breaking of roof, and was serious when the mining was advanced to 80 m and 100 m. When the mining was advanced to 80 m, initial caving pressure occurred in the roof. The depth of the failure boundary point was 4.13 m, and the height of the failure zone was the same as the mining height, accompanied by a roof subsidence of 3.65 m, as shown in Figure 7(a); when the mining was advanced to 100 m, there was cyclic pressure on the roof. The depth of the failure boundary point was 2.34 m, and the height of failure in the wedge-shaped failure zone was 3.69 m, with a roof sinking volume of 0.34 m, as shown in Figure 7(c).  
In summary, spalling on the working face is concentrated when there is rotation and caving of the roof. At the same time, the roof sinks in large quantities. When caving is estimated, the protection level and working resistance of the hydraulic support for the working face should be increased. Furthermore, mining should be appropriately accelerated to avoid the falling before the support and the consequent spalling, thereby improving the stability of surrounding rock during fully mechanized mining with a large mining height.
# Future Perspectives for Gamma-ray Burst Detection from Space
## 2 Einstein Probe
### Expected Performance  
The angular resolution of the WXT is about 5 arcmin (FWHM) for the central focal spot, and the effective area is in the range of 2-3 cm\({}^{2}\). The grasp parameter of the WXT is shown in Figure 2. In a 1000 s exposure, a sensitivity of approximately (2-3) \(\times\) 10\({}^{-11}\) ergs\({}^{-1}\)cm\({}^{-2}\) in the 0.5-4 keV band can be achieved at the 5-\(\sigma\) level. Such sensitivity and spatial resolution improve by one order of magnitude or more upon the previous and current wide-field X-ray monitors.  
Operating in the 0.3-10 keV energy range, the FXT has a narrow field of view (60 arcmin in diameter) and an effective area of about 300 cm\({}^{2}\) at 1 keV (for one unit). The spatial resolution (PSF) is about 23 arcsec in half-power diameter (HPD), which gives a source localization precision of 5-15 arcsec (90% c.l.) depending on the source intensity. The FXT is responsible for the quick follow-up observations (within 5 min) of the triggered sources from the WXT, and will also observe other targets of interest as target of opportunity (ToO) observations.  
Once a transient is detected with the WXT, the spacecraft will slew to point the FXT to the target for quick follow-up observations. Meanwhile, the alert information of the transient will be down-linked quickly to the ground segment and made public to trigger follow-up observations. Quick command up-link for time-critical ToO observations is also possible.  
Figure 1: Preliminary configuration of the EP payloads’ alignment, with twelve WXT modules surrounding two FXTs.  
With the unprecedented capability of the WXT and FXT, the EP is expected to characterize the cosmic high-energy transients over wide time scales and at high cadences, revealing new insights into a diverse set of systems including dormant black holes, neutron stars, supernova shock breakouts, active galactic nuclei, X-ray binaries, gamma-ray bursts, stellar coronal activity, and electromagnetic-wave sources and gravitational-wave events. In particular, EP will provide valuable data for the prompt emission of GRBs in the soft X-ray band, which has not been common in previous GRB detections. Meanwhile, EP will also monitor the variability of several types of X-ray sources in large samples all over the sky.
## 4 Experiment
### Evaluation Metrics
#### 4.2.2 Spectral Similarity  
To quantify the spectral fidelity of shadow compensation methods, three metrics are employed: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Spectral Angle Mapper (SAM [42]). MAE measures the average absolute deviation between the compensated and the original image pixel values. RMSE computes the standard deviation between the compensated image and the original image. SAM assesses spectral similarity by computing the angle between the spectral vectors of the compensated image and the original image. For materials in shadowed conditions, we choose the same in non-shadowed conditions to calculate metrics.
## 5 Simulation
### Example 2  
The time-switching topology and dynamic leaders are simulated in this case. The followers dynamics are assumed to be the same with Case 1. The extended algorithm Equations (5), (6), and (30) will be implemented in the MASs. The communication topology is assumed to switch over three graphs, i.e., \(S_{\mathcal{G}}=\{\mathcal{G}_{1},\mathcal{G}_{2},\mathcal{G}_{3}\}\), which are presented in Figure 5. An extra time-varying signal \(\sigma(k)\) is introduced to indicate the switching process. The definition of \(\sigma(k)\) is given as follows:  
\[\sigma(k)=\left\{\begin{array}{lcl}1&\quad\text{if}&\mathcal{G}(k)=\mathcal{ G}_{1}\\ 2&\quad\text{if}&\mathcal{G}(k)=\mathcal{G}_{2}\\ 3&\quad\text{if}&\mathcal{G}(k)=\mathcal{G}_{3}\end{array}\right.\]  
The dynamic trajectories of the leaders are selected as  
\[Agent\ 5:w_{5}(k) =(0.5+0.001k)sin(\tau k/150)\] \[Agent\ 6:w_{6}(k) =(0.5+0.001k)sin(\tau k/150)-1\] \[Agent\ 7:w_{7}(k) =0.3 sin(\tau k/100)-0.5\]  
From the description, one can see the amplitude of leader 5 and 6 increases gradually over time.  
All the initial values and the parameters are the same with Case 1 while \(\lambda=0.5\). From Figure 5, the in-degree matrices are \(\mathcal{D}^{1}=diag(2,1,3,2,0,0)\), \(\mathcal{D}^{2}=diag(3,1,2,2,0,0)\), and \(\mathcal{D}^{3}=diag(2,1,2,3,0,0)\) for graphs \(\mathcal{G}_{1}\), \(\mathcal{G}_{2}\), and \(\mathcal{G}_{3}\), respectively. Thus, it is easy to check \(\rho_{i}=0.3<1/\max_{l=1,2,\ldots,\mathcal{G}}d_{i}^{l}=\frac{1}{3}\) also satisfies the convergence condition in Theorem 2.  
Figure 6 depicts the changes of indicative signal \(\sigma(k)\) from \(k=1\) to \(k=50\), where one can observe that the communication network randomly switches among three graphs in different time instants. Figures 7-9 show the outputs, inputs and estimated PPD of the follower agents, respectively. In Figure 7, the green curves present the envelopes of the leaders' trajectory, i.e., these two curves are the upper and lower boundaries of the convex hull. It can be found all followers are driven into the space within these two curves, which verifies the result in Theorem 2. Furthermore, the trajectories in Figures 7-9 are not smooth, which is caused by the quick switch of the communication topologies and the dynamic changes of the leaders.  
Figure 4: PPD estimations in Example 1 (\(\lambda=2\)).  
Figure 8: Inputs of agents in Example 2.  
Figure 6: Topology switching over time.  
Figure 7: Outputs of agents in Example 2.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Infrastructure
#### 4.1.1 Internet of Things (IoT) Infrastructure
The system enables the users to easily play videos of events involving a car with a given license plate during a precise time interval. However, the system will have a high initial investment cost due to its high processing power requirements. Researchers also investigated the use of fog computing to improve ITS-based services. Fog computing places computing devices near end-user devices to process data locally, thereby minimizing latency compared to cloud-based processing [51]. [PERSON] et al. [52] discussed a novel joint design of a publish/subscribe (pub/sub) communication model based on vehicular fog computing (VFC) architecture. It utilizes fog nodes as the data platform for message aggregation and employs a message dissemination approach based on the communication model. Considers the interplay between the pub/sub system and fog computing, combining these two research fields to support efficient and reliable communication in the Internet of Vehicles (IoV). Middleware in traffic surveillance has demonstrated its significant impact on real-time data processing, event-driven decision making, and system scalability in Smart Traffic Infrastructure. After reviewing the literature, Apache Kafka became the authors' favorite because it is well-suited for handling high-throughput, real-time event streaming, which is critical for vehicle tracking and traffic monitoring systems. We also found that machine learning (ML) is an integral part of the Smart Traffic surveillance system, helping in image detection, identification, or converting to text for easy transmission. However, we also found that challenges such as high computational demands, reliance on continuous data streams, and potential processing bottlenecks must be addressed to ensure system efficiency and scalability.
# Improving the Efficiency of Modern Warehouses Using Smart Battery Placement
## 4 Experimental Results
If we consider the simplified version of Equation (3), where only the distance traveled is taken into account, this would result in approximately 50% less energy and time consumed. Given that a shorter path to the battery is less likely to contain turns, it is safe to assume that based on Equation (3), the actual energy gains could be even higher. Different robots have distinct energy consumption patterns. Some robots might consume more energy when making turns due to the mechanics involved in such actions. In contrast, other robots, especially those designed for high-efficiency straight-path travels, might consume more energy when idling, waiting for obstructions to clear, or for the path ahead to become available. Therefore, calculating precise numbers to identify the exact energy gains of the proposed algorithm would most likely require the particular energy requirements of each robot. In more intricate or demanding environments, the energy consumption model may warrant further refinement. For instances where granular precision becomes pivotal, additional factors, such as variations in terrain, robot acceleration patterns, or even ambient temperature fluctuations, could be incorporated. Such enhancements can provide an even more nuanced understanding of energy expenditure, ensuring the model's robustness and adaptability to diverse operational scenarios. \begin{table}
\begin{tabular}{c c c c} \hline
**Trial No.** & **Avg. Distance (Proposed)** & **Avg. Distance (Random)** & **Improvement (\%)** \\ \hline
2 & 2.4 & 4.7 & 49 \\
3 & 2.7 & 5.1 & 47 \\
4 & 2.6 & 4.9 & 47 \\
5 & 2.7 & 4.6 & 41 \\
6 & 2.5 & 5.0 & 50 \\
7 & 2.6 & 4.7 & 45 \\
8 & 2.8 & 5.2 & 46 \\
9 & 2.7 & 5.0 & 46 \\
10 & 2.6 & 4.9 & 47 \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental results for environments (a), (b), and (c). The unit of measurement used in the experiments was the size of one cell. For example, if the distance of a cell to the battery or charging station location was 2, this means that the robot had to move the equivalent distance of two cells to reach it.
## 4 Conclusions  
In this study, a linear cutting test was performed using the clearance and rake angles as the main variables to analyze the effect of the shape of the cutting tool of the pre-cutting machine. This study can be used as a reference to determine the cutting tool of the pre-cutting machine. The conclusions are as follows:  
1. The cutting force and clearance angle have an inverse relationship, and this relation is sharp as the angle increases from 0\({}^{\circ}\) to 5\({}^{\circ}\). This sharp reduction occurs because of a dramatic decrease in the friction surface between the cutting tool and rock. Furthermore, when the angle exceeded 10\({}^{\circ}\), it appeared to converge to a certain value.
2. Similar to the cutting force, the clearance angle and cutting volume are inversely related; however, unlike the cutting force, the cutting volume decreases linearly as the angle increases. The specific energy decreased significantly from 0\({}^{\circ}\) to 5\({}^{\circ}\) and seemed to converge to a constant value after 10\({}^{\circ}\), similar to the cutting force.
3. The relationship between the rake angle and cutting force was direct and indirect before and after 5\({}^{\circ}\), respectively. When the rake angle was increased from 0\({}^{\circ}\) to 5\({}^{\circ}\), the cutting force decreased slightly. However, it increased subsequently owing to the increase in the friction surface of the cutting tool.  
Figure 19: Relationship between the specific energy and rake angle at depths (\(p\)) of: (a) 3 mm, (b) 6 mm, and (c) 9 mm.  
As the rake angle increases, the cutting volume also increases, similar to the cutting force. Even when the rake angle increased from 0\({}^{\circ}\) to 5\({}^{\circ}\), the cutting volume decreased slightly, similar to the cutting force. Subsequently, the volume increased with the angle. The specific energy also showed an increasing trend with increasing angles.
* Finally, the effect of the shape of the cutting tool appears to be mainly due to the friction between the cutting tool and rock. Among the shape variables of the cutting tool, the clearance angle has a greater effect on the cutting force, cutting volume, and specific energy. In addition, the linear relationship between the cutting force and cutting volume was confirmed.  
[PERSON]-e.K. proposed the concept of the research and developed the study. [PERSON] and [PERSON] contributed to the review of the final manuscript and made recommendations for paper revision. [PERSON] conducted the experiments and provided suggestions for the experiment. [PERSON]-k.Y. supervised the study and provided important suggestions. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea grant funded by the Korean Government (NRF-2019R1A2C2003636).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Application to Real Urine Analysis  
The applicability of the validated method was further assessed by analyzing spiked real urine samples at different concentration levels in the range of 0.1-4 \(\mathrm{\SIUnitSymbolMicro m}\)\(\mathrm{L}^{-1}\). For all spiked levels, the average recoveries were 87.3-114.9% and the RSD% was lower than 8.9%, demonstrating the absence of matrix effects due to the good recovery of the target analytes. All things considered, the herein developed SI-HLIME protocol can be successfully used for the quantitative determination of the target analytes.  
Figure 4: Representative HPLC-PDA chromatograms of a sample spiked with (**A**) ISTD and (**B**) ISTD and mixture of PIR and MEL at a concentration level of 0.1 \(\mathrm{\SIUnitSymbolMicro m}\)\(\mathrm{L}^{-1}\).