esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Description
### Nitric Oxide Infrared Radiative Emission  
In July 2001, NASA launched the TIMED satellite, which carried the SABER, a ten-channel broadband limb-scanning infrared radiometer, covering a spectral range of 1.27-17 \(\upmu\)m [44]. SABER scans once every 58 s to obtain vertical profiles of atmospheric constituents, such as NO, CO\({}_{2}\), O\({}_{3}\), H\({}_{2}\)O, NO, O\({}_{2}\), and temperature from the surface to an altitude of 180 km [23; 45; 46]. In one orbit, SABER covers latitudes from 53\({}^{\circ}\) in one hemisphere to 83\({}^{\circ}\) in the other, with an asymmetric hemispheric coverage [46; 47; 48]. SABER collects 24 h of data every 60-65 days [14; 44], covering 15 longitude bands each day, and measures radiance (W/m\({}^{2}\)/sr) within the tangent height range of approximately 20 to 400 km with a vertical resolution of 2 km [49]. It measures 10 emissions, including two primary coolants, NO 5.3 \(\upmu\)m and CO\({}_{2}\) 15 \(\upmu\)m [23; 36; 50], and measuring radiance. During the aurora period reported here, we used SABER version 2.0 data about NO 5.3 \(\upmu\)m during 18-23 November 2003.
## 4 Damage Detection Framework
### Mahalanobis Distance  
The Mahalanobis distance is a statistical tool used to measure and quantize the distance between a multidimensional point and a multivariate distribution. In this study, the Mahalanobis distance is used to calculate the error vector of the new feature vector fed into the autoencoder compared to the error vectors of the training data. From the error vectors of the training data, the sample mean vector and the sample covariance matrix are calculated and used to compute the Mahalanobis distance. The benefit of the Mahalanobis distance is its ability to contribute to the robustness of the framework, consider the EOCs present in the system, model it in the sample mean and sample covariance matrix, and hence enhance the performance of the damage detection framework.  
The Mahalanobis distance D equation is the following:  
\[\mathrm{D}=\sqrt{\left(x-\mu\right)^{T}\left[\Sigma\right]^{-1}\left(x-\mu \right)}, \tag{1}\]  
where x is the multidimensional error vector of the point, \(\mu\) is the sample mean vector, and \(\Sigma\) is the semi-positive sample covariance matrix of the multivariate distribution of the error vectors.  
This study utilized the Mahalanobis distance to evaluate the relationship between a feature vector and a collection of feature vectors within a multivariate distribution. The error vector represents the discrepancy between the reconstructed and original feature vectors, with its magnitude being less than that of the original. The sample mean and covariance matrix were calculated from the k-dimensional error vectors in the training dataset, illustrating that the damage detection framework functioned in an unsupervised manner.  
Damage detection is determined by comparing the Mahalanobis distance (D) to a predefined threshold (lim). If D is less than or equal to lim, the feature vector signifies healthy signals and an intact structure. In contrast, if D surpasses lim, it indicates potential damage and structural issues. This straightforward framework, as described in Equation (2), effectively assesses the structural health through the Mahalanobis distance metric.  
\[\mathrm{D}\ \leq lim\ \rightarrow\ \mathrm{Healthy} \tag{2}\] \[\mathrm{D}\ > lim\ \rightarrow\ \mathrm{Damaged}\]
###### Abstract  
Finite element (FE) modeling of the passive ligamentous spine is widely used to assess various biomechanical behaviors. Currently, FE models that incorporate the vertebrae, ligaments, and the personalized geometry of the bony spine may be used in conjunction with external loads from the muscles. However, while the muscles place a load (moment) on the spine and support it simultaneously, the effect of the passive support from the adjacent spinal muscles has not been considered. This study thus aims to investigate the effect of passive support from the psoas major, quadratus lumborum, and erector muscles on the range of motion (RoM) and intradiscal pressure (IDP) of the lumbar spine. Various L2-sacrum spinal models that differed only in their muscle properties were constructed and loaded with a pure moment (2.5-15.0 Nm) alone or combined with a compressive (440 or 1000 N) follower load. The RoM and IDP of the model that excluded the effect of muscles closely matched previous FE results under the corresponding load conditions. When the muscles (40-160 kPa) were included in the FE model, the RoM at L2 was reduced by up to 6.57% under a pure moment (10 Nm). The IDP was reduced by up to 6.45% under flexion and 6.84% under extension. It was also found that the erector muscles had a greater effect than the psoas major and quadratus muscles.  
spine; muscle; finite element analysis; range of motion; intradiscal pressure Article
## 2 Background Information
### Numerical Simulation of Hydrodynamic Noise  
Numerical simulation is one of the main tools used to study hydrodynamic noise. Currently, numerical prediction of flow noise is usually performed using CFD methods, with direct numerical simulation (DNS), large eddy simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) methods being the most commonly used. All numerical simulation methods detailed in this study are based on the Eulerian framework, which describes the continuous field variables of a fluid on a fixed spatial grid. The Eulerian method has significant advantages in revealing the macroscopic characteristics of the flow field and the noise generation mechanism, which is especially suitable for high-Reynolds-number flows and complex noise problems; compared with the Lagrangian method, which mainly tracks the motion of the fluid particles, the Eulerian description is more effective in capturing the large-scale turbulent structure and its effect on the sound field.
## 3 System Model and Problem Formulation
### The Drone Energy Consumption Model  
We calculate the energy consumption of the drone during horizontal flight, lifting, and levitation, with the energy consumption during levitation being related to wind speed. The power consumed by the drone when flying horizontally with speed \(v\) can be calculated in three parts: the power required to overcome the drag of the rotor blade profile, the fuselage that hinders the forward motion of the aircraft, and the power required to lift the payload [17]. Adding these three terms together, we obtain  
\[P_{h}(v)=N_{R}P_{b}(1+\frac{3v^{2}}{v_{tip}^{2}})+\frac{1}{2}C_{D}A\rho(H)v^{3}+W( \sqrt{\frac{W^{2}}{4N_{R}^{2}\rho^{2}(H)A_{r}^{2}}+\frac{v^{4}}{4}}-\frac{v^{2} }{2})^{\frac{1}{2}}, \tag{4}\]  
where \(W\) is the weight of the drone, and \(N_{R}\) and \(v_{tip}\) are the number of drone rotors and the tip speed of the rotor, respectively. \(C_{D}\) is the drag coefficient; \(A_{f}\) and\(A_{r}\) are the fuselage area and the rotor disc area. \(P_{b}=\frac{\Delta}{8}\rho(H)sA_{r}v_{tip}^{3}\), \(\Delta\) represents the profile drag coefficient, \(\rho\) is the air density function, and \(\rho(H)=(1-2.2558\cdot 10^{-5}H)^{4.2577}\).  
The drone power consumed in a vertical climb with speed \(v_{c}\) is  
\[P_{v}(v_{c})=\frac{W}{2}(v_{c}+\sqrt{v_{c}^{2}+\frac{2W}{N_{R}\rho(H)A_{r}}})+ N_{R}P_{b}. \tag{5}\]  
When hovering, a horizontal speed, \(v_{hov}\), is needed to counteract the wind speed. The hovering power consumption is provided as \(P_{h}(v_{hov})\), which is in accordance with Equation (4).
## 4 Trajectory Planning Model  
### Joint Trajectory Parameterization  
The trajectory of the robot can be regarded as a set of discrete configuration sequences with time as the independent variable. To ensure the stability and reliability of the robot human system during operation, the discrete sequence should be as continuous and smooth as possible. In this paper, the motion state of the WMDAR system only discusses 14 degrees of freedom, since the waist and head degrees of freedom are not considered. If a group of interpolation nodes are found between the start and end of the robot, and are connected with each other by smooth curves, the continuous smooth trajectory planning of the robot under constraints can be realized.  
It is assumed that the trajectory of the robot joint \(i\) can be described by a sequence composed of a group of \(m+1\) nodes, and the sequence is \(\{q_{0},q_{1},\cdots,q_{m}\}\). The time interval between adjacent nodes is \(\{T_{1},T_{2},\cdots,T_{m}\}\). The total time of motion is \(T=\sum_{i=1}^{m}T_{i}\). The five-degree B-spline curve is used to parametrically describe the joint trajectory to keep the acceleration curve continuous and smooth. The B-spline equation is:  
\[p(\mu)=\sum_{j=0}^{n}d_{j}N_{j,k}(\mu) \tag{24}\]  
where \(n\) represents the number of splines, \(j\) is the control point serial number, and there are \(n+k\) control points in total. \(\mathbf{d}_{j}\) are the control points, and \(\mathbf{N}_{j,k}(t)\) is a k-degree canonical B-spline basis function. Where \(\mu\in[\mu_{i},\mu_{i+1}]\) is a normalized time vector (\(0\leq\mu\leq 1\)), \(\mathbf{p}(\mu)\) is the position of the joint at time \(t\), and \(k\) represents the number of B-splines. In this paper, \(k=5\).  
To better define the basis function of the spline curve, a node vector is introduced, \(t=[t_{0},t_{1},\cdots,t_{n-1},t_{n}]\). Each curve is divided into \(n\) segments in joint space. The position \(q_{i}\) of the end point of each curve is known. The continuity conditions are used for intermediate nodes.  
\[p_{i}(1)=p_{i+1}(0) \tag{25}\]  
The \(n-1\) equations can be obtained by substituting Equation (25) into Equation (24). Six constraints are introduced to keep the curve continuous and smooth.  
\[\left\{\begin{aligned} p_{1}(0)&=\theta_{o},p_{n}(1)= \theta_{f}\\ p_{1}{}^{(1)}(0)&=\mathbf{v}_{o},p_{n}{}^{(1)}(1)=\mathbf{v}_{f}\\ p_{1}{}^{(2)}(0)&=a_{o},p_{n}{}^{(2)}(1)=a_{f}\end{aligned}\right. \tag{26}\]  
The derivative vectors of each order can be obtained according to the De Boor recursive formula.  
\[p^{\prime}(\mu)=\sum\limits_{j=i-k+r}^{i}d_{j}^{r}N_{j,k-r}(\mu) \tag{27}\]  
where, \(d_{j}^{i}=\left\{\begin{aligned} d_{j},\;l=0\\ (k+1-l)\frac{d_{j}^{i-1}-d_{j-1}^{i-1}}{l_{j+k+l-l-l}-u_{j}},l=1,2,\cdots,r ;\;j=i-k+l,\cdots i\end{aligned}\right.\).  
The \(n+5\) equations can be obtained by synthesizing Formulas (24)-(26), and then \(n+5\) control points can be obtained. Finally, the B-spline equation is introduced to obtain the curve with a time interval as the parameter.
## 4 Simulation of Low-Frequency Communication in Plasma Sheath
### Propagation Characteristics of Low-Frequency EM Waves in Plasma Sheath
It can be seen from Figure 9 that the transmission increases at low frequencies (0.1-30 GHz), and the reflection decreases (0.1-50 GHz) with the addition of collision frequency before and after time \(t\). In brief, as the HPM constantly irradiates the plasma sheath throughout this precise period, the collision frequency grows independently after time \(t\) and before the start of the chemical reaction, while other EM parameters remain unaltered. The low-frequency EM waves' capacity to travel through the plasma sheath can be improved in this way. Figure 8: The change of dielectric loss tangent value of the plasma with time at stagnation line \(x=-0.17\) m.
# A Computational Study on Magnetic Nanoparticles Hyperthermia of Ellipsoidal Tumors
## 2 Materials and Methods
### Mesh and Timestep Sensitivity Analysis  
A mesh sensitivity analysis was carried out to determine the size of the mesh. The computational sample meshes are shown in Table 3. The mesh sensitivity was performed on an oblate spheroidal tumor with AR = 8. The quantity for which the analysis was performed is the tumor temperature at a distance 2 mm above the tumor geometric center that lies on the _y_-axis (see Figure 2) after 30 min of treatment. The simulation results in Table 3 show that increasing the mesh size and the temperature on the above-mentioned location generally increases. However, a closer look at the values shows that from mesh 3 to mesh 4 the temperature values change only on the third decimal, which means that temperature change between these two meshes is about 0.01%. Since this change is very small, mesh 3 is selected for the numerical simulations. Furthermore, the timestep in the present work is set to 1 s. Simulation runs with a smaller time step were also performed, namely 0.1 s, which resulted in no significant difference (<0.001%) in the solution.  
Moreover, the treatment temperature behavior of the computational model is verified with the closed-form transient solution proposed by [PERSON] et al. [67] for a tumor with AR = 1 (perfect sphere). In their work the solution is given in a dimensionless form (Equations (16) and (17) in [67]). Our computational results are in excellent agreement with the closed-form solution, as shown in Figure 4.
## 3 Colloidal Silica Transport Through Soil Stratum
### Transport Mechanisms of CS in Liquefiable Sand
#### 3.2.2 CS Viscosity  
With the increase of viscosity of the used colloid, the hydraulic conductivity of the porous media decreases synchronously. According to the Kozeny-Carman equation, the hydraulic conductivity of the porous media is inversely proportional to the viscosity of the permeant, as expressed by the following equation:  
\[k=\frac{\kappa\gamma}{\mu} \tag{1}\]  
where \(k\) is the hydraulic conductivity, \(\kappa\) is the intrinsic permeability, \(\gamma\) is the permeant density and \(\mu\) is the permeant viscosity [45]. With a double increase of permeant viscosity during the transport, there is a double decrease in the hydraulic conductivity of the porous media, and there is a double decrease of flow rate under the same hydraulic gradient when substituting Equation (1) into [PERSON]'s law. In summary, although CS can be transported though porous media (loose sand) with deposition and release, the viscosity of the permeant must be controlled to be as low as possible to facilitate the transportation of colloids.  
Several permeation tests with different pH and ionic strengths have been performed using the column shown in Figure 11 to study CS transport mechanisms [45]. In these tests, the column was filled with Nevada sand by the pluviation method. It was indicated that the smoothest CS flow could be obtained when the pH was 6.5 and the ionic strength was 0.1 N. When pH was lowered to 6.2 or ionic strength was increased to 0.15 N, the CS gelled prematurely, and it was difficult to reach 100% coverage of the entire column. It should be noted that pH and ionic strength influences the viscosity, thus influencing the transport of CS. The flow rate of CS remarkably decreases as a result of the increase of viscosity in the source CS, which is quantitatively shown in Figure 12. Therefore, viscosity is the dominating factor that controls the flow rate and scope of CS in the column.
## 2 Localization Solution  
This section introduces a novel and cost-effective localization solution that uses ultrasound technology to achieve precise positioning without complex synchronization mechanisms. The proposed system utilizes ultrasound beacons and receivers, coupled with an innovative time difference measurement approach, to determine a robot's position within a predefined area. The following subsections will detail the background of ultrasound technology in localization, the core principles of the proposed system, its base operation, the analysis of the localization strategy, and the mathematical principles underlying the approach.  
Ultrasound is a commonly used technology for indoor positioning and beacon systems, which is notable due to its favourable cost/performance ratio, wide availability, and robustness in challenging environments. Various approaches to ultrasonic localization exist, demonstrating the technology's versatility and reliability in numerous industrial and commercial applications. For example, in [18], a relative positioning system is demonstrated using three ultrasonic sensors to obtain Time-of-Flight (ToF) data and using the time differences between the signals to calculate the robot coordinates. Another application in which ultrasound is widely used is obstacle detection and avoidance. In [19], a small autonomous mobile robot is presented equipped with a microcontroller and four ultrasonic sensors to avoid real-time obstacles, using a linear recursive Kalman filter to improve the response of the system and ensure smoother movement. These examples illustrate the range of applications for ultrasound technology, from presence detection to parking systems.
# Microbially Induced Desaturation and Carbonate Precipitation through Denitrification: A Review
## 5 Challenges in Denitrification-Based MICP/MIDP Biotechnology
The generation and distribution of nitrogen gas and carbonates are highly influenced by the movement and transport of fluid, which are affected by the solution's chemistry and the existing microbial community [6]. As for groundwater, the chemical content of the injected solution is diluted along the flow direction. In the case of soil, CaCO3 precipitates quickly form around the injection site, and the carbonate buildup blocks the further transport of the solution and occludes the neighboring pores as well. Thus, in both groundwater and soil, spatial heterogeneity is a common issue. Therefore, more efforts are needed to improve the current approaches to create a homogeneous distribution of both microbes and the available precursors in groundwater and soil. Among the different MICP pathways, MICP through denitrification seems promising as it is not a rapid process, does not rely on presence of oxygen and external alkalinity. \begin{table}
\begin{tabular}{c c} \hline \hline
**Challenges in In-Situ Applications** & **Strategies to Mitigate Those Challenges** \\ \hline \multirow{3}{*}{Generation of harmful intermediates} & Avoid by ensuring the completeness of reactions (i.e., proper substrate concentration) \\  & Use for other applications (nitric can be utilized as a commercial anodic rebar corrosion inhibitor) \\  & Treat the harmful intermediates on site or collection after the application is done \\ \hline \multirow{3}{*}{Environmental factors} & Stimulation of inactive cells in the field by providing appropriate nutritional conditions \\  & Incorporation of a functional isolate or a non-axenic microbial community into the application field to \\  & enumerate the number of functional microorganisms \\  & Combined ureolysis and denitrification process \\ \hline \multirow{3}{*}{CaCO3 precipitation rate} & Proper substrate concentration \\  & Applying an optimized substrate regime and residence time \\  & Isolate and select more appropriate strains \\  & Adding iron nanoparticles \\ \hline \multirow{3}{*}{Controlling of gas generation} & Control the generation, distribution, and persistence of the gas \\  & Applying an optimized substrate regime and residence time \\  & Proper substrate concentration \\ \hline \multirow{3}{*}{Obtaining homogeneous treatment} & Uniform distribution of microorganisms and solution chemistry \\  & Applying an optimized substrate regime and spatial distribution \\ \hline \multicolumn{2}{c}{Monitoring} & Mathematical model \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Challenges faced in the real application of MICP and MIDP through denitrification, and the strategies to mitigate those challenges. The above-mentioned challenges limit the usage of MICP technologies in real applications. These challenges need to be overcome before the method is upscaled from the laboratory-scale to field-scale applications.
## 1 Introduction
In this regard, polymers are considered to be one of the best candidates as a flexible substrate for wearable applications [1]. For this reason, bending impacts on polymer-based flexible antennas, which include deviations from the central frequency, Transmission Line (TL) mismatch, and the reductions in signal strength were the focus for analysis and assessment in this study. The remainder of the paper is organized as follows: Section 2 provides a tutorial on the flexible antenna structure, bending capabilities, and impacts on antenna parameters. The steps used to design flexible antennas are described in Section 3, including material selection, property examination, design and fabrication, and measurement and testing of flexible polymer-based antennas. Radiation characteristics and bending analysis are presented in Section 4, and the conclusions are presented in Section 5.
# A Reinforcement-Learning-Based Distributed Resource Selection Algorithm for Massive IoT
## 5 Proposal: TOW Dynamics-Based Channel Selection Algorithm and Its IoT Prototype Implementation
More details of the processes of the algorithm running on the cognitive-IoT prototype are summarized in Algorithm 1. Figure 4: Flowchart of the proposed scheme. Figure 5: Cognitive-IoT prototype. ```
1:Initialize \(Q_{k}(0),\ R_{k}(0),\ N_{k}(0)\). 2:while wake time \(t\ =\ 1,2,3...\) is not expired do
3: Initiate channel access parameters, i.e., NB, CW, BE
4: Select channel \(k^{*}=argmaxX_{k}(t)(\forall k\in K)\)
5:while\(NB<macMax\CSAMBackoffs\)do
6: Delay for random \((2^{BE}-1)\)
7: Perform clear channel assessment (CCA)
8:if Channel is assessed as idle then
9: Transmit the data frame
10:\(N_{k^{*}}(t)++\)
11:if the data frame is transmitted, and the corresponding ACK frame is received then
12: Data transmission succeeded
13: Set \(\Delta Q_{k^{*}}(t)=+1\)
14: Increment \(R_{k^{*}}(t)\) by one (\(R_{k^{*}}(t)++\))
15:else
16: Data transmission failed
17: Set \(\Delta Q_{k^{*}}(t)=-\omega(t)\)
18:endif
19: Update \(Q_{k(t)}\) given by Equation (12)
20: Update \(X_{k}(t+1)\) given by Equation (3)
21: Update \(p_{k^{*}}(t+1)\) given by Equation (11)
22: Update \(\omega(t+1)\) given by Equation (10)
23:elseif Channel is assessed as busy then
24: Update \(NB=NB+1\) and \(BE=min(BE+1,macMaxBE)\)
25: Continue
26:endif
27:endwhile
28: Enter sleep mode for 1000 ms
29:\(t=t+1\)
30:endwhile
```  
**Algorithm 1** TOW dynamics-based channel selection algorithm for the cognitive-IoT prototype \(m\in M\). The cognitive-IoT prototype was developed on a well-used SBC, Lazurite 920 MHz [19]. Lazurite is equipped with the IEEE 802.15.4g radio-compatible transceiver supporting a 50-kbps data rate in the 920-MHz band. An ultra-low power 16-bit microcontroller (ML620Q504H) and 64 KB ROM are used in Lazurite. In addition, Lazurite's operating voltage range is 1.8 V\(\sim\)5.5 V, while the operating current range is 7 uA\(\sim\)mA The photographs of the cognitive-IoT prototype are shown in Figure 5.