esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In existing biometric authentication methods, the user must perform an authentication operation such as placing a finger in a scanner or facing a camera. With ear acoustic authentication, the acoustic characteristics of the ear canal can be used as biometric information. Therefore, a person wearing earphones does not need to perform any authentication operation. Existing studies which use the acoustic characteristics of the ear canal as biometric information only measure the characteristics of one ear. However, the acoustic characteristics of the human ear canal can be measured from both ears. Hence, we proposed a new method for acoustic authentication based on the ability to measure the acoustic characteristics of the ear canal from both ears. The acoustic characteristics of the ear canal of 52 subjects were measured. Comparing the acoustic characteristics of the left and right ear canals, a difference in the signal between the left and right ear was observed. To evaluate the authentication accuracy, we calculated the evaluation indices of biometric authentication, equal error rate (EER), and area under curve (AUC). The EER for bilateral ear acoustic authentication using signals from both ears was 0.39%, which was lower than that of a single ear. The AUC was 0.0016 higher for bilateral ear acoustic authentication. Therefore, the use of bilateral signals for ear acoustic authentication was proved to be effective in improving authentication accuracy.  
b 2022123167 17 February 202218 [PERSON]  
et al. investigated individual authentication using inaudible sound frequencies [13]. [PERSON] et al. evaluated the effectiveness of biometric authentication in everyday life using the differences in the acoustic characteristics of the ear canal [14].  
Although ear acoustic authentication has been studied as a wearable biometric authentication method, wearing earphones on both ears has not been studied. We hypothesized that the accuracy of ear acoustic authentication could be improved by measuring the acoustic characteristics of the ear canal from both ears, because the authentication information is doubled. In the field of sound-image localization, it has been established that differences in the shape of the listener's head, ear, and ear canal strongly influence the quality of sound localization [15; 16; 17]. This sound image localization technique requires earphones to be worn in both ears, and not just one. This is because the location of the sound is estimated from the sounds heard by both ears. The use of bilateral signals for authentication is inspired by this. In this paper, we construct a system that can measure the ear canal acoustic characteristics of both ears simultaneously. We visualize and analyze the characteristics to find out if there is a difference in the bilateral signal because if there is a difference, it may improve the accuracy of authentication. In addition, we calculate and evaluate the accuracy of the bilateral ear acoustic authentication using a 1:1 authentication system.
# Investigation of the Scattering Noise in Underwater Optical Wireless Communications
## 5 Simulation Results
Note, for the clear water and at the FEC limit of \(3\times 10^{-3}\) and considering the scattering noise, the received power is \(-20\) dBm for the \(52\) m link distance, which are comparable with [17,18]. For the coastal water, the scattering noise impact is more evident, as depicted in Figure 4b, where the BER is higher compared with the case with no scattering noise. In this case, the received power level at the FEC limit of \(3\times 10^{-3}\) is \(-19\) dBm. For the harbor water, the BER performance is also affected by the scattering noise and is marginally higher than the coastal water, see Figure 4c, with considerably reduced link distance. Considering the scattering noise, the received power is \(-19\) dBm at the FEC limit of \(3\times 10^{-3}\), which is almost the same as [18]. Note, at the FEC limit of \(3\times 10^{-3}\) and considering the scattering noise, the maximum link spans are \(20\) and \(4.6\) m for the coastal and harbor waters, respectively, which are marginally shorter than those reported in [16,17]. Higher scattering noise coefficient results in increased BER performance degradation. Figure 5 illustrates the variance of the scattering noise and compares it with shot noise as a function of the link distance. As shown, for dirtier water or the longer link distances, the variance plots are almost the same for both noise sources. This shows that in some conditions, scattering noise is comparable with shot noise and can not be neglected. In dirtier waters or over longer transmission link spans, more propagating photons will experience scattering, which will result in increased scattering noise coefficient \(\kappa\), see Figure 3. As shown in Figure 5, the scattering noise variance increases with the distance and becomes almost the same as shot noise variance based on (11). At longer transmission distances, most photons experience scattering (i.e., \(\kappa\approx 1\)), arriving at random times producing scattering noise the same as shot noise in the photodiode which all the absorbed photons in the photodiode produce carriers at random times. Thus scattering noise is equal to the shot noise under these conditions. Figure 4: The BER performance against the link distance: (**a**) clear, (**b**) coastal, and (**c**) harbor. Figure 5: The variance (shot noise and scattering noise) against the link distance for three types of water: (**a**) clear, (**b**) coastal and (**c**) harbor.
# An Overview of Transcranial Magnetic Stimulation and Its Application in Multiple Sclerosis
## 3 TMS Measures
### Central Motor Conduction Time  
To calculate the central motor conduction time (CMCT), both cortical and peripheral measures of latency are required. The peripheral motor latency can be determined by delivering terminal magnetic stimulation at the level of the exiting motor nerve roots in the appropriate cervical or lumbar vertebral foramen to stimulate the nerve roots. Notably, the intensity of foraminal magnetic stimulation is recommended to be just above the suprathreshold to prevent stimulation of the nerve at a distal location. Subtracting the peripheral latency from the latency of the MEP evoked through TMS will produce the CMCT [12]. This removes the contribution of the peripheral nervous system from the overall latency; therefore, the CMCT is said to be a representation of the conduction time between the primary motor cortex and motor neurons in the spinal cord. It is important to consider the potential effects of physiological confounders such as age, height, and sex on CMCT. A paper by [PERSON] et al. [13] reported that most studies have found no/weak correlation between age and sex with CMCT. Regarding height, there appears to be no correlation between height and CMCT for the upper limbs; however, there exists a strong correlation between height and CMCT for the lower limbs [22; 23; 24]. Abnormal CMCT indicates potential dysfunction within the conducting pathway.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 4 Experimental Platform  
One of the aims of this work is to experimentally validate GCMR. Hence, we implemented GCMR in a prototype developed using the Quagga open source routing suite and executed it in the iLab.t Virtual Wall emulation testbed platform.
## 5 Discussions
### Technical Speculations for the New Generation of Nightlight Satellite Sensors
#### 5.2.3 Imaging Sensitivity Requirement  
Both DMSP/OLS and VIIRS/DNB needed to apply the single wide spectral band strategy to gather as much light energy as possible during a single scan to increase the sensitivity. How to increase the number of bands and sensitivity at the same time might be a very large challenge for next generation nightlight remote sensing satellite sensors.  
To face this challenge, ISS astronauts applied a specially designed tool to compensate the movement of the ISS to increase exposure time. However, such an imaging strategy obviously cannot take observations in a carpeting mode. That challenge is expected to be solved in the near future with the development of photo sensing materials and technology. On the other hand, the VIIRS DNB sensor is a temperature-controlled charge coupled device (CCD), provides global daily measurements in single nocturnal visible and near-infrared (NIR) band that is suitable for Earth system science and applications studies. One thing that must be pointed out is that VIIRS/DNB only has a mono-spectral band, which is still below the spectral band requirement for detecting all types of city lights with a diverse range of spectral characteristics. In addition, it takes 0.56 s for the instrument to scan across the width of the swath. During this time, the nadir point of the satellite moves 3.75 km (the satellite moves at ~6.7 km/s). In the future, if equipped with a multi-spectral sensor, how to maintain the same level of sensitivity with reduced light energy will be the primary problem to be solved.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 5 Application of _Trichoderma_ Fungi in Promoting Plant Growth
### Induction of Plant Resistance to biotic Stress
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of Pathogens** & \begin{tabular}{c} **Speces of** \\ _Trichoderma_ \\ \end{tabular} & **Crops** & \multicolumn{2}{c}{**Mechanisms of Action**} & **Reference** \\ \hline _Rhizoctonia solani_ & _Trichoderma tarzianum_ & bean & \(\bullet\) & Increasing the synthesis of & \\  & & & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & & & \(\bullet\) & integrity of plant cell membranes. & \\ \hline _Rhizoctonia solani_ & _Trichoderma atroviride_ & cucumber & \(\bullet\) & Increasing the accumulation of & \\  & & & & volatile organic compounds, & \\  & & & & salicylic acid derivatives, phenolic, & \\  & & & & and hydrogen peroxide in plant; & \\  & & & & \(\bullet\) & Increasing the activity of & \\  & & & & antioxidant enzymes of plants; & \\  & & & & \(\bullet\) & Reducing lipid peroxidation; & \\  & & & & \(\bullet\) & Increasing the deposition of lignin & \\  & & & & and callose in cucumber cells. & \\ \hline _Botrytis cinerea_ & \begin{tabular}{c} _Trichoderma_ \\ _longbrachiatum_ \\ \end{tabular} & cucumber & \(\bullet\) & Increasing the concentration of & \\  & & & & salicylic acid, jasmonic acid, & \\  & & & & and ethylene; & \\  & & & & \(\bullet\) & Increasing the synthesis of & \\  & & & & defense metabolites. & \\ \hline _Botrytis cinerea_ & \begin{tabular}{c} _Trichoderma asperellum_ \\ \end{tabular} & tomato & \(\bullet\) & Increasing the concentration of & \\  & & & & jasmonic acid and ethylene. & \\ \hline _Colletotrichum_ & \
\begin{tabular}{c} _Trichoderma asperellum_ \\ \end{tabular} & sorghum & \(\bullet\) & Increasing the activity of & \\  & & & & antioxidant enzymes of plants; & \\  & & & & Increasing the lignifications & \\  & & & in plant. & \\ \hline _Colletotrichum_ &
\begin{tabular}{c} _Trichoderma asperellum_ \\ \end{tabular} & chilli & \(\bullet\) & Increasing the activity of defense & \\  & & & and antioxidant enzymes of plants; & \\  & & & & Increasing the synthesis of & \\  & & & phenolic compounds; & \\  & & & & Reducing the accumulation of & reactive oxygen in plant. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mechanisms of _Trichoderma_ fungi in inducing plant resistance to biotic stress.
## 4 Tests
### Ground Tests
In test scheme 1, the star-sensor and laser communication loads are respectively installed on the satellite structure plate. The maximum angle variation of the prism before and after the temperature pull-up test is 117.74\", which appears on the \(X\)-axis of the star sensor. In test scheme 2, the maximum angle variation is 10.72\" when the on-board equipment is turned on in high temperature conditions, which appears on the \(Z\)-axis of the star sensor. The comparison between scheme 1 and scheme 2 shows that the optimal design obviously reduces the influence of the thermal deformation of the satellite on the angle variation between the prism of the star-sensitive bracket and the laser communication load. Scheme 3 shows that under the condition of maximum temperature pull-up during the ground test, the maximum angle variation between prisms appears on the star-sensor's \(Y\)-axis, with a maximum value of 31.6\". When converted into radians, the result is 0.1532 mrad. Under such extremely strict temperature test conditions, the results are close to the requirement of 0.16 mrad assigned in Section 2, which further proves the effectiveness of the optimized design.
# The Effects of Methane Storage Capacity Using Upgraded Activated Carbon by KOH
## 2 Materials and Methods
### Adsorbed Natural Gas System Experiments  
The ANG system consisted of a compressor with an adjustable flow rate and a prototype storage cylinder with a volume of 50 mL (Diameter: 40 mm; Height: 40 mm) that was filled to maximum capacity. Using this system, the CH\({}_{4}\) storage capacity was evaluated as a function of pressure up to 20 MPa. In the charge period, the CH\({}_{4}\) gas was maintained at a constant pressure with a pressure regulator. The weight of the reactor filled with ACs was measured using a balance, and the CH\({}_{4}\) was sufficiently adsorbed in the reactor. The weight difference was the amount of CH\({}_{4}\) adsorbed. The desorption of CH\({}_{4}\) was then tested in atmosphere (101 kPa) and vacuum conditions (20 kPa) with a discharge flow rate of 0.5 L/min. After the desorption process, the weight of the reactor with ACs was measured again using the balance. The weight differences indicated the amount of CH\({}_{4}\) desorbed. In the ANG system, deliverability, which represents useable gas capacity, is one of the main factors in the application. It can be defined as the difference in the amount of CH\({}_{4}\) between adsorption at the target storage pressure and the residual after desorption. To evaluate delivery capacity, discharge flow rates were regulated with a flowrate of 0.1 and 0.5 L/min at 0.4 MPa, respectively. A CNG experiment in the absence of absorbents was also performed as a function of pressure up to 20 MPa for comparison.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & \multicolumn{4}{c}{**Ultimate Analysis (wt.\%)**} & \multicolumn{4}{c}{**Proximate Analysis (wt.\%)**} \\ \hline  & **Carbon** & **Hydrogen** & **Oxygen** & **Nitrogen** & **Sulfur** & **Volatile** & **Fixed Carbon** & **Ash** \\ \hline AC-WR & 71.60 & 1.60 & 17.20 & 0.80 & n.d. & 18.56 & 74.68 & 6.76 \\ AC-CR & 89.03 & 0.30 & 2.50 & 0.00 & n.d. & 5.14 & 92.73 & 2.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ultimate and proximate analysis of ACs (activated carbons) as precursors. AC-WR: the commercial ACs based on wood material, AC-CR: the commercial ACs based on coconut material, n.d.: not detected.
## 2 Materials and Methods
### Model Development
#### 2.1.1 Tumor Compartment
\[\frac{dA_{I}}{dt} = s_{A}+\rho\frac{D_{A}+D_{N}}{k_{D}+(D_{A}+D_{N})}-\lambda_{A}D_{ N}A_{I}-\delta_{AI}A_{I}, \tag{5}\] \[\frac{dA_{M}}{dt} = \lambda_{A}D_{N}A_{I}-\mu_{A}A_{M}-\delta_{AM}A_{M}. \tag{6}\]  
**T Cells:** In our formulation, we consider three T cell populations: CD8\({}^{+}\) T cells or cytotoxic T lymphocytes (CTLs); CD4\({}^{+}\) T cells or helper T cells (Th cells); and regulatory T cells (Tregs). Briefly, mature APCs migrate to lymphoid organs where they activate resident naive T cell populations [23], which subsequently migrate back to the tumor site [24]. This process is explained further when we discuss cellular dynamics within the Lymphoid Compartment. Within the tumor, activated CTLs (\(T8_{A}\)), Th cells (\(T4_{A}\)) and Tregs (\(T_{\text{reg}}\)) localize from their respective lymphoid populations at rates \(\mu_{i}\), \(i=T8,T4,TR\). Activated T cells are assumed to have the same average life-span, and undergo natural death at a rate \(\delta_{T}\). In addition, Tregs induce contact mediated apoptosis of activated CTLs and Th cells [25], at an assumed rate \(\delta_{R}\). Finally, activated Th cells transform into Tregs in the presence of the immunosuppressive cytokine TGF-\(\beta\)[26], whose tumoral concentration is represented by the variable \(G\). This transformation rate is taken to be a Michaelis-Menten-like function with maximum transition rate \(\lambda_{R}\) and half saturation constant \(k_{G}\). Taken together, these assumptions yield the following equations governing the dynamics of activated T cells within the tumor. In the equations below, the superscript \(L\) refers to the number of that cellular species in the Lymphoid Compartment. \[\frac{dT8_{A}}{dt} = \mu_{T8}T8_{A}^{L}-\delta_{T}T8_{A}-\delta_{R}T8_{A}T_{\text{reg}}, \tag{7}\] \[\frac{dT4_{A}}{dt} = \mu_{T4}T4_{A}^{L}-\delta_{T}T4_{A}-\delta_{R}T4_{A}T_{\text{reg}} -\lambda_{R}T4_{A}\frac{G}{k_{G}+G}\,,\] (8) \[\frac{dT_{\text{reg}}}{dt} = \mu_{T8}T_{\text{reg}}^{L}-\delta_{T}T_{\text{reg}}+\lambda_{R}T4 _{A}\frac{G}{k_{G}+G}. \tag{9}\]  
**TGF-\(\beta\):** In our model, TGF-\(\beta\) (\(G\)) is taken to be a representative immunosuppressive cytokine. TGF-\(\beta\) is expressed by Treg cells at a rate \(\alpha_{G}\), and by tumor cells at a rate \(\alpha_{GT}\)[26]. It undergoes natural degradation at a rate \(\delta_{G}\). These assumptions lead to the following equation governing TGF-\(\beta\) dynamics. \[\frac{dN}{dt} = \epsilon_{N}\alpha_{N}N-\omega_{N}\delta_{NA}N-\delta_{NN}N-\delta_{ K}N\frac{T8_{A}}{k_{T}+T8_{A}}, \tag{1}\]0
# Double-Sided Freeform Lens for Light Collimation of Light Emitting Diodes
## 3 Lens Design and Simulation with UV-LED
Figure 6a shows a two-dimensional view of the ray-tracing results between the finite LED light source, double freeform lens, and detector plane. As shown, not all of the rays collimated and incident on the detector plane. Thus, while the LED used in the simulations had a total emitting power of 821 mW, the total light power received on the detector plane was just 400.3 mW. In other words, the double freeform collimating lens had an energy efficiency of 48.8%, where the losses were due mainly to the collecting angle of the lens and the Fresnel losses at the air/lens interfaces. To better understand the light collimating effect of the double freeform lens, for each ray reaching the detector plane, the collimated angle \(\gamma\), that is, the angle between the incident ray and the \(+z\) axis, was determined, together with the associated light energy of the ray following refraction by lens surfaces \(S_{1}\) and \(S_{2}\). A statistical analysis was then performed to determine the variation of the total light intensity with the collimating angle \(\gamma\). As shown in Figure 6b, around one half (50%) of the collimated UV-LED light intensity was provided by rays with a collimating angle of less than 1.56\({}^{\circ}\), while around 90% of the intensity was contributed by rays with a collimating angle of less than 3.62\({}^{\circ}\). Notably, this  
Figure 5: Double freeform lens designs with different values of \(\xi\) (**left**) and estimated intensity distribution of collimated beam on projected plane (**right**). performance was better than that of a conventional mercury lamp and fly's eye homogenizer system, in which the typical collimating angle is around 1.5\({}^{\circ}\) at a 50% light intensity level. In other words, the proposed double freeform lens and UV-LED have good potential to replace the mercury light sources used in conventional lithography applications within the industry.
## 2 Materials and Methods  
For structural optimization in the conceptual and preliminary design, mainly two different types of optimizations are used: topology and shape optimization. Only shape and size optimization are applied [3]. Accordingly, engineers often perform shape and size optimizations in preliminary and detailed design stages. Both approaches rely on optimizing possible design parameters of the structure, mostly with heuristics or metaheuristics. Shape variation uses two main techniques: Mesh- and CAD-based [3; 6] processes. CAD-based processes can be realized with the combination of CAD programs for the geometry and pre-processors for the mesh-generation and solver-deck setup or with CAD programs with integrated pre-processor, such as SFE CONCEPT [4; 22; 23]. In particular, SFE CONCEPT offers the ability to generate structures quickly and pre-process Finite Element (FE) decks easily to follow Finite Element Analysis (FEA). Hence, the SFE CONCEPT tool takes over the design and pre-processing to optimize structural components [18; 24; 25]. A vector of design variables (DVs) \(x\) describes the geometry variably. The upper \(x^{upper}\) and lower \(x^{lower}\) design variable boundaries define the geometrical limits of optimized parts. The response of the calculated FE simulation leads to the response function, which can be subdivided into the objective and constraint function, such as mass, intrusion, or stiffness [15]. [PERSON] and [PERSON] [9] formulate the general form of an optimization problem as:  
\[\begin{array}{rl}\min\limits_{x}&f(x)\\ &g(x)\ \leq\ 0\\ \text{{subject to}}&h(x)=0\\ &x^{lower}\leq x\leq x^{upper}\end{array} \tag{1}\]  
Determining the design variables \(x\) that minimize the objective function \(f\) is the target of the optimization problem. The vectors \(g\) and \(h\) represent the inequality and equality constraints. Each objective and constraint function value depends on the design variables \(x\). Feasible design points fulfill all constraints, while the design points which fail to comply with the constraints are infeasible. If the optimization problem has no constraints, it is called an unconstrained problem, while the contrary is called a constrained problem. If the problem has more than one objective function, it is called a multi-objective optimization. By the reformulation of the problem with \(m\) objective functions, the following condition results:  
\[\begin{array}{rl}\min\limits_{x}&f_{1}(x),\ldots,f_{m}(x)\\ &subject\ to\ g(x)\ \leq\ 0\end{array} \tag{2}\]
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 3 Signaling Schemes
### Selection of Parameters for PAS
The leftmost part of the curve where \(\mathbb{H}(X)\) goes to \(R\) belongs to the uncoded signaling case, i.e., \(R_{\text{c}}=1\), where \(R\) is attained by shaping the constellation such that \(\mathbb{H}(X)=R\). Here \(\Delta\text{SNR}\) is infinite since without coding, reliable communication is only possible over a noiseless channel. The minimum \(\Delta\text{SNR}\) in Figure 5 is obtained with \(\mathbb{H}(X)=2.745\), which corresponds to \(R_{\text{c}}=0.835\) from (14). In IEEE DVB-S2 [77] and 802.11 [78], the code rate that is closest to 0.835 is \(5/6\approx 0.833\). Accordingly, the best performance is expected to be provided with rate-\(5/6\) FEC code, with an SNR gain over uniform that amounts according to this analysis to 0.83 dB. This will be confirmed by the numerical simulations presented in Section 5.3. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Formula** & **Value per 1-D** & **Value per 216-D** \\  & **(per \(n\)-Sequence)** & **(Example 2)** & **(Example 2)** \\ \hline Data on amp. & \(n\mathbb{H}(A)\) & 1.75 & 378 \\ Data on sign & \(n\gamma\) & 0.50 & 108 \\ \hline Shap. redundancy & \(n(m-1-\mathbb{H}(A))\) & 0.25 & 54 \\ Cod. redundancy & \(n(\mathbb{H}(A)+1-R)\) & 0.50 & 108 \\ \hline Redundancy & \(n(m-R)\) & 0.75 & 162 \\ Data, \(nR\) & \(n(\mathbb{H}(A)+\gamma)\) & 2.25 & 486 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Content of an amplitude sequence as in Figure 4 based on Example 2.