esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Emergency Communication Networks
## 3 Review of Emergency Communication Networks
### Network Classification
#### 3.1.2 Ad Hoc Networks
From a technical perspective, the main challenges of VANET are as follows:  
* Signal attenuation: objects placed between two communication vehicles as obstacles are one of the challenges that affect VANET efficiency;
* Bandwidth limitation: VANET has no central coordinator to control the communication between nodes;
* Connectivity: due to the high mobility and rapid change in the topology, fragmentation often occurs in the network;
* Small effective diameter: due to the small effective network diameter of VANET, the communication connectivity between nodes is weak. * Fly Ad Hoc Network (FANET)  
FANET is a self-organizing wireless network that enables communication between flying nodes, such as UAVs, without a fixed network infrastructure, making it low cost, flexible, and easy to deploy. FANET can be regarded as an extended paradigm of MANET,  
Figure 4: Structure diagram of VANET. realizing communication between UAVs and between UAVs and control stations. FANET not only has the inherent characteristics of a wireless ad hoc network, such as multiple hops, self-organization, and centrality, but also has some functions related to UAVs, such as the rapid change in network topology caused by high-speed moving nodes, sparsity of network nodes and network heterogeneity, network node peers, and data relay capability. UAVs have potential advantages in wireless communications because they can transfer data from the air to one or more ground stations [82], so they can be widely used in disaster areas where communications are completely shut down due to an unexpected disaster. As shown in Figure 5a, there are four types of links between UAVs and different radio facilities (e.g., FANET base station, cellular base station, and satellite) in the ad hoc network. FANET can be composed of a control center, sensing system, satellite communication system, and UAV swarm, as shown in Figure 5b. The control center can be an air or ground command center to send control messages to the UAV swarm. The sensor system encompasses the ground and air, mainly by the radar system, which is responsible for collecting status information from the ground or UAV. The control center uses a mobile ad hoc network to issue instructions to the UAV swarm, and after the drone bees perform corresponding tasks, the working status and movement information is fed back to the control center through the sensor system. FANET can be used in a variety of environments, ranging from civilian to military, because of its robustness, adaptability, low operating cost, and ease of construction [83]. These applications include precise geographic positioning, search and rescue missions [84], intelligent transportation systems, target detection, disaster tracking and monitoring, and forest fire prevention.
###### Abstract  
This study computationally investigates the heat transfer characteristics in a double-pipe counter-flow heat-exchanger. A heated viscoelastic fluid occupies the inner core region, and the outer annulus is filled with a colder Newtonian-Fluid-Based Nanofluid (NFBN). A mathematical model is developed to study the conjugate heat transfer characteristics and heat exchange properties from the hot viscoelastic fluid to the colder NFBN. The mathematical modelling and formulation of the given problem comprises of a system of coupled nonlinear partial differential Equations (PDEs) governing the flow, heat transfer, and stress characteristics. The viscoelastic stress behaviour of the core fluid is modelled via the Giesekus constitutive equations. The mathematical complexity arising from the coupled system of transient and nonlinear PDEs makes them analytically intractable, and hence, a recourse to numerical and computational methodologies is unavoidable. A numerical methodology based on the finite volume methods (FVM) is employed. The FVM algorithms are computationally implemented on the OpenFOAM software platform. The dependence of the field variables, namely the velocity, temperature, pressure, and polymeric stresses on the embedded flow parameters, are explored in detail. In particular, the results illustrate that an increase in the nanoparticle volume-fraction, in the NFBN, leads to enhanced heat-exchange characteristics from the hot core fluid to the colder shell NFBN. Specifically, the results illustrate that the use of NFBN as the coolant fluid leads to enhanced cooling of the hot core-fluid as compared to using an ordinary (nanoparticle free) Newtonian coolant.  
double-pipe counter-flow heat-exchanger; Newtonian-Fluid-Based Nanofluid (NFBN); non-isothermal viscoelastic fluid flow; Giesekus model; nanofluid variable-thermal-conductivity +
Footnote †: journal: Applied Sciences
## 2 Material and Methods
### Biochemical Markers' Assessment  
Based on the results from the FET test, embryos were exposed to 2 and 20 mg/L of FOS. Seven replicates (\(n\) = 7) of 15 embryos each were used per experimental condition and kept in Petri dishes at 27 \(\pm\) 1 \({}^{\circ}\)C. After 96 h of exposure, the embryos were frozen in liquid nitrogen and stored at \(-\)80 \({}^{\circ}\)C until further analyses. Samples were homogenized in ultrapure water, on ice, using an ultrasonic homogenizer (Sonifier 250, Branson sonicator). The homogenates were divided for the biochemical analyses: total glutathione (TG) content, catalase (CAT) activity, glutathione 5-transferase (GST) activity, cholinesterase (ChE) activity, protein quantification, and energy budgets. Phosphate buffer (0.2 M; pH 7.4) was added to the homogenate aliquots reserved to TG, CAT, GST, and CHE, which were then centrifuged (10,000\(\times\)\(g\); 20 min; 4 \({}^{\circ}\)C) to obtain the post-mitochondrial supernatant (PMS). A Labsystem Multiskan EX microplate reader was utilized for all the biochemical determinations. TG content was assessed based on the method of [PERSON] (1969) [28]. CAT activity was measured according to [PERSON] (1985) [29]. The measurement of GST activity was performed following the method of [PERSON] et al. (1974) [30]. The measurement of ChE activity was achieved following the protocol defined by [PERSON] et al. (1961) [31], adapted to a 96-well microplate by [PERSON] et al. (1996) [32]. The quantification of the protein was done following the Bradford method [33], adapted to a 96-well microplate, using bovine \(\gamma\)-globulin as a standard. The energy budgets (lipids', carbohydrates', and proteins' contents) were measured following [PERSON] and [PERSON] (1997) [34], with minor modifications for 96-well microplate reading [35].
## 2 Design Method of Pilot Oxygen Mask Based on Virtual Fit Analysis
### Information Required for Virtual Fit Analysis
#### 2.1.2 Boundary of Pilot Oxygen Mask  
As shown in Figure 1, the gourd-shaped part of the mask contacting the face is designed by forming a line connecting design landmarks on the mask using spline interpolation. The mask design landmarks are defined as points that form the mask boundary using a spline curve [15; 31]. The locations of the design landmarks are determined by considering the shape of facial features such as the root and sides of the nose and the sides and bottom of the lip for a natural fit of the mask boundary to the face. For example, as shown in Figure 1, the boundary of the existing MBU-20/P pilot oxygen mask proposed by [PERSON] et al. [15] is drawn by connecting eight design landmarks: a nasal root point (D1), two nasal side points (D2 and D3), two cheek points (D4 and D5), two chin side points (D6 and D7), and a bottom lip point (D8). Note that the mask boundary can be modified by adjusting the locations of the design landmarks or adding/removing design landmarks.
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 2 Materials and Methods
### New Model  
#### 2.4.1 K-Means Clustering  
K-means is a widely used and simple clustering algorithm. It divides the dataset into k clusters, aiming to minimize the distance between data points within each cluster so that data points in the same cluster are more similar to each other than to points in different clusters.  
Its principle and steps are as follows: first, the number of clusters k is set. Then, k data points are randomly selected as centroids. The data points are assigned to the nearest centroid's cluster by calculating the Euclidean distance. The mean of the data points within each cluster is then recalculated to serve as the new centroid. The process of reassigning points to clusters and recalculating centroids is repeated until the change in centroids isbelow a set threshold, at which point the iteration stops. Once the iteration ends, all data points are assigned to one of the k clusters, completing the clustering process.  
The K-means clustering algorithm operates in the original feature space, and its calculation is straightforward and efficient. It is suitable for large datasets and can perform clustering with relatively low computational cost. However, it also has some drawbacks. The value of k must be set in advance, and in practical applications, this value can be challenging to determine. Accurately setting k requires a good understanding of the dataset. In real-world applications, the value of k is usually determined by trying different values and comparing the clustering results.
## 3 Proposed Solution to Predict the AoA
### Beamforming and Precoding Strategy
#### 3.4.1 Beamforming Weight Computation Using Predicted AoA  
Let \(\hat{\theta}_{u}(t)\) denote the predicted or estimated AoA of user, \(u\), at time, \(t\), as provided by the DE-ACSW-R prediction pipeline (i.e., fused LR/SVR with clustering and jump detection). For a uniform linear array (ULA) of \(N\) antennas with inter-element spacing, the corresponding steering vector is as follows:  
\[a(\hat{\theta}_{u})=\left[1,\ e^{-j\frac{2\pi}{\lambda}d\ sin(\hat{\theta}_{u}) },\ldots,\ e^{-j\frac{2\pi}{\lambda}(N-1)d\ sin(\hat{\theta}_{u})}\right]^{T} \tag{28}\]  
This steering vector is normalised and used to compute the beamforming weights for user, u. In a matched filter formulation (i.e., maximum ratio transmission), the beamforming vector is as follows:  
\[w_{u}=\frac{a\big{(}\hat{\theta}_{u}\big{)}}{\big{\|}a\big{(}\hat{\theta}_{u} \big{)}\big{\|}} \tag{29}\]  
These weights are applied to the transmit signals to spatially focus the beam in the direction of the predicted AoA.
# Reducing System Load of Effective Video Using a Network Model
## 2 Related Work
### 360\({}^{\circ}\) Video  
In general, video is recorded in a single direction or in the direction of a viewpoint, so only the recorded video or the played view can be outputted. However, in the case of 360\({}^{\circ}\) or VR-AR, all areas may be affected or only specific areas depending on the recording device used. The above method can produce contents using multiple cameras. It can also create and express camera objects by using a program. Typical programs are Unity, Unreal Engine, and 3D Capture. Otherwise, 360\({}^{\circ}\) videos can be created, as shown in Figure 2.  
This method can be easily manufactured using a camera equipped with a 180\({}^{\circ}\) fisheye lens. This is a feature of the 360\({}^{\circ}\) camera [4]. Stitching that uses multiple cameras to shoot  
Figure 1: User screen for FoV.  
Figure 2: Stitching and warping for 360-degree videos.  
an object in multiple directions and then turns each image into a single image represents the final image in a spherical shape, so this image must be converted into a standard 2D image. In order to represent a spherical image as a 2D image, a process called warping is performed, and distortion occurs in the process. The 2D images derived from \(360^{\circ}\) images by means of this process are displayed as inverse warping [5; 6].  
Figure 3 shows a \(360^{\circ}\) image (a) and an inverse warping image (b). It is difficult to see the entire \(360^{\circ}\) image on one screen. Warping of the 3D image is performed in order to produce a 2D image which is easier to see. It is very difficult to remap \(360^{\circ}\) images to a 3D form in the way described above. However, this can be easily done with a program. Video games that use \(360^{\circ}\) video require a high FPS (Frames Per Second). Therefore, we propose a method that can adjust FPS using the minimum amount of video.
## 3 Results and Discussion
### Calibration Model for Quantitative Estimation
#### 3.3.2 MgO-Al\({}_{2}\)O\({}_{3}\) (Ma)-CaS Samples  
Table 5 shows estimated values for the coefficient of determination and mean absolute error (MAE) relative stability for samples of MA-CaS. The ratio of the intensities for Raman shifts at 412 cm\({}^{-1}\) for MA, and at 157 cm\({}^{-1}\) for CaS, showed a better coefficient of determination (R\({}^{2}\)), and the lowest MAE value. Figure 11 also demonstrates from the relative stability analysis that a relative intensity ratio between 157 cm\({}^{-1}\) and 412 cm\({}^{-1}\) performed better than the other peaks used in this study. A linear regression constructed between a relative intensity of 412/157 cm\({}^{-1}\) and a phase fraction for MA in the MA-CaS system is presented in Figure 12. In this study, the most intense Raman peaks at 416 cm\({}^{-1}\) for  
Figure 10: Raman spectroscopy estimation for the phase content (wt. %) for CaS as a function of the phase content of CaS in CaS-CA sample.  
Figure 9: Evaluation of relative stabilities (Si) of the calibration variable candidates and mean absolute error values obtained for training and validation sets for CaS–CA.  
MA and 157 cm\({}^{-1}\) for CaS were the most suitable peaks for Raman spectroscopy quantitative analysis for samples containing only MA-CaS.
## 3 Results and Discussion
### Equilibrium Studies  
Data obtained from the equilibrium studies have been analysed using two-parameter ([PERSON], [PERSON], [PERSON]) and three-parameter ([PERSON], [PERSON], Sips) models.  
As pointed out in our research, the classification of all the tested models for the description of adsorption equilibrium isotherms of ammonium onto agricultural soils can be described as follows:  
* for silt loam: [PERSON] (\(R^{2}\) = 0.89) = [PERSON] (\(R^{2}\) = 0.89) \(>\) [PERSON] (\(R^{2}\) = 0.88) \(>\) [PERSON] (\(R^{2}\) = 0.81) \(>\) [PERSON] (\(R^{2}\) = 0.72) \(>\) [PERSON] (\(R^{2}\) = 0.66),
* for loam: [PERSON] (\(R^{2}\) = 0.94) \(>\)[PERSON] (\(R^{2}\) = 0.83) \(>\) [PERSON] (\(R^{2}\) = 0.79) \(>\) [PERSON] (\(R^{2}\) = 0.72) \(>\) [PERSON] (\(R^{2}\) = 0.63) \(>\) [PERSON] (\(R^{2}\) = 0.59),
* for sand: [PERSON] (\(R^{2}\) = 0.88) \(>\) [PERSON] (\(R^{2}\) = 0.65) \(>\) [PERSON] (\(R^{2}\) = 0.63) \(>\) [PERSON] (\(R^{2}\) = 0.56) \(>\) [PERSON] (\(R^{2}\) = 0.43) \(>\) [PERSON] (\(R^{2}\) = 0.39).  
The n value of the Freundlich isotherm indicates the degree of nonlinearity between the solution concentration and adsorption [62]. Briefly, if \(n\) = 1, then adsorption is linear; if \(n\) < 1, then adsorption is a chemical process; and if \(n\) > 1, then adsorption is a physical process. Adsorption is considered as satisfactory when the Freundlich constant \(n\) attains values within the range 1-10. In this study, the \(n\) value was found to be 4.17, 6.41, and 6.59 for silt loam, loam, and sand, respectively (Table 6), which indicates the physical character of ammonium adsorption onto agricultural soil.  
Based on the Langmuir isotherm, the efficiency of the adsorption process can be predicted by the dimensionless equilibrium parameter \(R_{L}\), called the separation factor, which is expressed by the following equation [63]:  
\[R_{L}=\frac{1}{1+K_{L}\cdot C_{0}} \tag{3}\]  
where: \(K_{L}\)--Langmuir constant, [L/mg], \(C_{0}\)--initial concentration of ammonium in the solution, [mg/L].  
Figure 8: [PERSON] (**a**), [PERSON] (**b**), [PERSON] (**c**), [PERSON] (**d**), [PERSON] (**e**), and [PERSON] (**f**) isotherm models and standard deviation error bars for ammonium adsorption onto loam, silt loam, and sand, respectively.  
Furthermore, the studies of many other researchers [28; 39; 65; 66; 67] demonstrated that models with two parameters always less accurately describe the sorption processes than those with more than two parameters. This was also confirmed by the results obtained in our studies (Table 7).
# Efficacy of Herbal Extracts-Based Nano-Formulations in Extending Guava Fruit Shelf-Life
## 3 Results  
Guava is a nutrient rich fruit with wide applications, but is limited by its short shelf life. The present investigation was carried out on guava (_Psidium guajava_ L.) fruits of variety Hisar Surkha (shelf life 4-5 days) at turning (T) stage. Herbal extracts of ajwain (_Trachyspernum annuli_) seeds and giloy (_Tinosporia cordiphola_) leaves were prepared using hot extraction and Soxhlet extraction methods, respectively. These extracts were used for synthesis of nanoformulations. Three nanoformulations, viz., Alginate: CaCl\({}_{2}\) (T1), Alginate: CaCl\({}_{2}\): ajwain extract (1:1) (T2), and Alginate: CaCl\({}_{2}\): giloy (1:1) (T3) were synthesized using the ionic gelation method. Mean particle size was determined using particle size analyzer (Metrohm instrument). T1 NFs had a mean size of 176.0 nm and polydispersity index (PDI) 0.173 (Figure 1). T2 nanoformulations had a mean size of 42.06 nm and PDI 0.465 (Figure 2) and T3 had a mean size of 216.8 nm and PDI 0.079 (Figure 3).  
Figure 1: PSA spectrum of T1 NFs.  
The efficacy of synthesized and characterized NFs (T1, T2 and T3) was checked by examining their antimicrobial activity and DPPH radical scavenging activity. Antimicrobial activity was determined using the disc agar diffusion method where three bacterial inoculations were used, viz., _Escherichia coli_ (_E. coli_), _Pseudomonas aeruginosa_ (_P. aeruginosa_) and _Bacillus cereus_ (_B. cereus_). Figure 4 shows the antibacterial activity of different extracts and NFs against three bacterial strains at two concentrations. In each plate, two holes were made marked as L and R and 100 \(\mu\)L and 150 \(\mu\)L concentration of extracts and NFs was added to the respective holes. Then, plates were incubated at 37 \({}^{\circ}\)C and formation of inhibition zones was observed. The size of different inhibition zones formed at different concentrations is shown in Table 1. Larger inhibition zone formation was observed at higher concentrations (150 \(\mu\)L) (R hole). The zone of inhibition ranged from 6 \(\pm\) 0.42 mm to 14 \(\pm\) 0.92 mm. Ajwain extract, T1, T2 and T3 NFs showed antimicrobial activity against all the three bacterial strains, viz., _E. coli_, _P. aeruginosa_ and _B. cereus_ whereas silo extract showed activity against _E. coli_ and _B. cereus_ but not against _P. aeruginosa_. The zone of inhibition was larger at 150 \(\mu\)L concentration as against 100 \(\mu\)L concentration in all the plates. T1 NFs showed good antimicrobial activity, which was further enhanced in T2, and T3 NFs. T2 showed maximum activity against _E. coli_, _P. aeruginosa_ whereas T3 showed maximum activity against _E. coli_ and _B. cereus_.  
Figure 3: PSA spectrum of T3 NFs.  
Figure 2: PSA spectrum of T2 NFs.
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 5 Validation Experiments
### Multi-Point Sound Sources  
We evaluated whether the proposed AMBIQUAL algorithm can accurately predict localization accuracy of multiple point sound sources which were used earlier in MUSHRA subjective tests (see Section 4.6). Aggregated AMBIQUAL localization accuracy scores by encoding schemes with 95% confidence intervals are shown in Figure 12d.  
AMBIQUAL localization accuracy scores were compared with subjective MUSHRA scores. Scatter of AMBIQUAL vs. MUSHRA results is presented in Figure 12f (per encoding scheme) and Figure 12h (per sample type). Like before, results show good correlation between objective AMBIQUAL and subjective MUSHRA quality scores (Pearson corr = 0.864, Spearman corr = 0.883).
# Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm
## 2 Materials and Methods
### Analysis of Turning Motion of DDMR  
Figure 1 shows the analysis of DDMR's turning motion. The rectangular coordinate system \(XOY\) is established, the coordinates of DDMR are set as \((x,y)\), the linear velocity is \(v\), and the angular velocity is \(\omega\). The width of DDMR is \(B\), the turning center is \(ICR\), and the turning radius is \(R\). Because DDMR has nonholonomic constraints [29], its motion can be described only through linear velocity \(v\) and angular velocity \(\omega\)[30].  
The controller makes the DDMR reach the expected motion state by controlling the velocities of the drive motors on both sides, the linear velocities of the drive wheels on both sides can be expressed as follows:  
\[\left\{\begin{array}{l}v_{in}=(R+B/2)\omega\\ v_{out}=(R-B/2)\omega\end{array}\right. \tag{1}\]  
where \(v_{in}\) and \(v_{out}\) are, respectively, the inner linear velocity and outer linear velocity of DDMR. From Equation (1), the linear velocity of the center of mass can be expressed as follows:  
\[\mathrm{v}=\omega R=(v_{in}+v_{out})/2 \tag{2}\]  
Based on Equations (1) and (2), the turning radius \(R\) and angular velocity \(\omega\) of DDMR can be expressed as follows:  
\[\left\{\begin{array}{l}\omega=(v_{out}-v_{in})/B\\ R=[B(v_{out}+v_{in})]/[2(v_{out}-v_{in})]\end{array}\right. \tag{3}\]  
According to Equation (3), when \(v_{out}\)=\(-v_{in}\), \(R=0\), DDMR can realize in place turning. Assuming that the angular velocity of DDMR is constant in the turning process, the turning angle \(\theta\) is proportional to the turning time \(\Delta t\). Many studies [31] have lower driving time by reducing the number of turns, but this is not comprehensive. The direct factor affecting the turning time is the total turning angle, and there is no direct functional correspondence between it and the number of turns. Since the DDMR realizes turning through the speed difference of the drive motors arranged on both sides of the body, when the total turning angle is too large, it will aggravate the wear of the drive motors, conveyor belts, and other components, and even reduce the service life. Secondly, the turning of the DDMR is a relatively slow process, which includes three decomposition actions: deceleration, turning, and acceleration, and consumes more time than that of the straight motion. Therefore, reducing the total turning angle can effectively shorten the time required for the DDMR to drive along this path. So the total turning angle should be used as the evaluation index to reduce the turning time.