esa-sceva/satcom-chunk-collection huggingface dataset:
### Existing Solutions and Approaches in Visually Impaired Persons' Assistance  
The development of assistance solutions for blind and severely visually impaired people implies several challenges. Thus, in order to efficiently guide the VIP, the assistance solution must be able to identify the location of the user. Secondly, the solution must be able to identify the relevant information that should be useful to the user, and thirdly, it should be able to deliver the information in an adequate manner [13].  
Generally, users' location can be established with the help of various wireless communication technologies, such as Bluetooth [14], or Wi-Fi, based on computer vision and inertial sensing [15], or with the help of the smartphone camera [16], while providing positioning errors between 0.4 to 1.5 m. To identify the information from the area, camera-based image navigation solutions are widely used [17; 18]. These solutions imply a camera and specialized software that is able to recognize objects from the scene. Recently, various other solutions, such as artificial intelligence and Computer Vision (CV) applications, are swiftly progressing [19; 20]. For relative positioning and distance measurement, as well as for obstacle detection, LIDAR, ultrasound and camera recognition systems are also used [21]. Next, once the relevant information is identified, it is transmitted to the user as audio information or by haptic means.  
In terms of user solutions, blind assistance solutions are generally integrated into various devices, but most often into smart glasses and smart canes, while also being developed as personal computer or smartphone-compatible applications. These technologies are designed to enhance daily lives of individuals with visual impairments by providing real-time assistance and information. These smart devices and applications offer features like persons and object recognition, navigation aids, and text-to-speech capabilities, allowing users to receive auditory or haptic feedback about their surroundings. Smart canes are frequently equipped with sensors and GPS for obstacle detection and navigation support [22], Computer and smartphone applications offer accessibility features such as voice assistants, screen readers, and GPS-based navigation. These integrated software solutions aim to improve mobility, independence, and overall quality of life for VIPs. The high prevalence of personal computers and smartphones has led to the development of multiple software applications that are meant to assist visually impaired persons. Some of these applications are briefly presented in Section 2.2.
# Channel Correlation-Based Adaptive Power Transmission for Free-Space Optical Communications
## 2 Theoretical Analysis
### Channel Model  
The quality of signal transmission is often challenged in the turbulence channel. At the receiver side, the signal power instability is faced. In particular, the transmission performance of the FSO communication system is directly affected, which is more significant in high-speed data transmission and long-distance communication. The effect of atmospheric turbulence on signal strength can be described by the Rytov variance \(\sigma_{R}^{2}\), and \(\sigma_{R}^{2}\) is given by  
\[\sigma_{R}^{2}=1.23C_{n}^{2}k^{7/6}L^{11/6}, \tag{1}\]  
where \(C_{n}^{2}\) is the Hufnagel-Valley model-based atmospheric refractive index structure constant, \(k\) is the wave number, and \(L\) is the link distance. The atmospheric refractive index structure constant \(C_{n}^{2}\) is given by  
\[\begin{array}{c}C_{n}^{2}(h)=5.94\times 10^{-53}\times(v/27)^{2}h^{10}\exp(-h/1000 )\\ +2.7\times 10^{-16}\exp(-h/1500)+C_{0}^{2}\exp(-h/100),\end{array} \tag{2}\]  
where \(v\) is the wind speed, \(h\) is the altitude, and \(C_{0}^{2}\) is the atmospheric refractive index structure constant at the surface. Moreover, the scintillation effect caused by atmospheric turbulence gradually increases as \(\sigma_{R}^{2}\) grows. The scintillation index \(\sigma_{I}^{2}\) is used to measure the magnitude of the scintillation effect and the degree of signal fluctuation [15; 16]. The large scintillation index \(\sigma_{I}^{2}\) is accompanied by great signal fluctuations, and \(\sigma_{I}^{2}\) is represented by  
\[\sigma_{I}^{2}=\left<I^{2}\right>/\left<I\right>^{2}-1, \tag{3}\]  
where \(I\) is the fluctuation intensity of the transmission signal and \(\left<\cdot\right>\) is the ensemble mean value. The lognormal distribution model is employed in this study. Figure 1 illustrates the probability density function (PDF) of the modeled turbulence channel with \(\sigma_{I}^{2}\) of 0.37 and 0.5, which coincides with the lognormal distribution, and the PDF is expressed by  
\[f(I)=\frac{1}{I\sqrt{2\pi\sigma^{2}}}\exp\left(-\frac{(\ln I-\mu)^{2}}{2\sigma ^{2}}\right), \tag{4}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of the lognormal distribution. A greater turbulence intensity usually implies a larger standard deviation in the PDF, which leads to a wider fluctuation range and more violent light intensity fluctuation.
# An Energy-Efficient and Secure Routing Protocol for Intrusion Avoidance in IoT-Based WSN
## 5 Network Model and Discussion  
In this research work, the implementation and evaluation phases were carried out in NS2 as a network simulator [52] along with the mannasim framework [53]. A varying number of nodes are distributed in random order with 10 malicious nodes. The malicious nodes broadcast false route reaction packets in order to be selected as next-hop for data forwarding. We considered a squared size sensor field by keeping a fixed number of nodes. Initially, nodes have 5 J of energy level and homogeneous in terms of various resources. Further, the transmission range was set to 20 m for each node. A constant bit rate (CBR) connection was established between sending and receiving nodes. To evaluate the energy consumption over the sensor field, the energy model [21] was used in this research work. The energy model evaluated the energy consumption (\(E_{t}\) and \(E_{r}\)) related to data transmission and receiving by using Equations (9) and (10). In this research study, Table 1 gives the default simulation parameters, where the units of m is meter, s is second, J is joule and nJ is nana joule.  
\[E_{t}=\left\{\begin{array}{ll}\left(E_{elect}+d^{2}\times E_{amp1}\right) \times k,&d\leq\ d_{t}\\ \left(E_{elect}+d^{4}\times E_{amp2}\right)\times k,&d>d_{t}\end{array}\right., \tag{9}\]  
\[E_{r}=E_{elect}\times k, \tag{10}\]  
where:  
\(k\) shows the number of data bits;  
\(d\) is the transmission distance;  
\(E_{elect}\) is the amount of consumed energy in data transmission;  
\(d_{t}\) is the distance threshold value.
## 2 Methodology
### Electric Field Responses  
In background model \(\sigma^{\mathrm{P}}\), the Fast Hankel transform is applied to calculate the primary field (background field) \(\mathrm{E}^{\mathrm{P}}\), as expressed in Equation (3):  
\[E^{\mathrm{P}}=\frac{Idl}{2\pi r^{3}\sigma^{\mathrm{P}}}\cdot\mathcal{G}\cdot \left[\left(3\cos^{2}\varphi-2\right)-e^{-ikr}(1+ikr)\right] \tag{3}\]  
where Idl is the dipole moment, \(\sigma^{\mathrm{P}}\) is the conductivity of half space, \(\mathrm{k}=\sqrt{\mathrm{i}\omega\mu\sigma}\) is the wave number, \(\omega\) is the angular frequency, \(\mathrm{r}\) is the distance from the site to the center point of the transmitting source (i.e., the transmitter and receiver distance), and \(\varphi\) indicates the included angle between the sites and the center of the transmitting source. \(\mathrm{G}\) is the factor related to the resistivity and thickness of the layers.  
For the secondary field (induced field) \(\mathrm{E}^{\mathrm{S}}\) in the anomaly model \(\sigma^{\mathrm{s}}\), the staggered grid finite difference method using the Yee grid [24] is applied. The secondary field \(\mathrm{E}^{\mathrm{S}}\) is defined at the midpoint of the grid element edge. The staggered grid finite difference method is depicted in Figure 1.  
The control Equation (2) is discretized in the \(X\), \(Y\), and \(Z\) directions. The electric field on each edge of the cells can be represented by the 12 electric field components around it. According to the rectangular grid, \(\mathrm{N_{x}}\cdot\left(\mathrm{N_{y}}-1\right)\cdot\left(\mathrm{N_{z}}-1 \right)+\mathrm{N_{y}}\cdot\left(\mathrm{N_{x}}-1\right)\cdot\left(\mathrm{N_{ z}}-1\right)+\mathrm{N_{z}}\cdot\left(\mathrm{N_{x}}-1\right)\cdot\left(\mathrm{N_{y}}-1\right)\), equations are formed. The linear algebraic Equation (4) is obtained by finite difference discretization of rectangular grids:  
\[K\cdot E^{s}=S \tag{4}\]  
In Equation (4), K is a large symmetric sparse coefficient matrix, S is the vector related to the boundary conditions and the primary field, and E\({}^{s}\) is the value of the secondary field to be solved. This study adopts the first boundary condition, also known as Dirichlet boundary condition. By employing the quasi-minimal residual method (QMR) to solve the system of Equation (4), the electric field components at each node are obtained.
## 3 Controller Design  
Due to its simplicity in development and installation and its capability to function effectively even in the presence of system uncertainty, the PID controller continues to be one of the most commonly used controllers in recent years. The PID transfer function is shown in Equation (2) below.  
\[C(s)=\frac{U(s)}{E(s)}=K_{p}+\frac{K_{i}}{S}+K_{d}S \tag{2}\]  
where \(K_{p}\) represents proportional gain, \(K_{i}\) represents integral gain, and \(K_{d}\) represents derivative gain. While PID controllers offer several advantages, they may not always achieve the desired performance, especially in nonlinear systems.  
This study proposes three strategies for controlling the frequency of stand-alone synchronous generators: a PLC-PID controller, a fuzzy PID controller, and a PSO-fuzzy PID controller. These strategies are validated through both simulations and real-time experiments.  
In the classical (PLC-PID) control scheme, the control structure is implemented directly in PLC using TIA Portal instructions. In the second scheme, Fuzzy PID and PSO-Fuzzy PID in MATLAB/Simulink are used with PLC to implement frequency control. Communication between PLC and MATLAB is conducted via the OPC server, with the PLC acting as a monitoring and measurement unit. Figure 6 shows the PLC-PID control structure, while Figure 7 illustrates Fuzzy PID and PSO-Fuzzy PID control structures.  
Figure 6: PLC-PID control scheme.
## 3 Materials and Methods
### ppGpp Analysis  
ppGpp was extracted with formic acid [68]. At 36 h (exponential growth phase) and 72 h (stationary phase) of incubation, bacterial cells were collected by centrifugation and suspended in 1 mL of 0.9% saline. Then 100 \(\upmu\)L of 11 M formic acid was added to the suspension. The sample was vigorously mixed and incubated on ice for 30 min. These samples were centrifuged at 10,000\(\times\)\(g\) for 10 min at 4 \({}^{\circ}\)C. The supernatant was filtered through 0.2 \(\upmu\)m filters and stored at \(-\)20 \({}^{\circ}\)C until use in HPLC analysis.  
To assay ppGpp, 100 \(\upmu\)L of the extract was subjected to 1100 Series HPLC (Agilent, Santa Clara, CA, USA) by using a ZORBAX SB-C18 column (4.6 \(\times\) 250 mm, 5 \(\upmu\)m, Agilent) at a flow rate of 1.0 mL/min. The mobile phase (pH 6.0) consisted of 125 mM KH\({}_{2}\)PO\({}_{4}\), 10 mM tetrabutyl ammonium dihydrogen phosphate, 60 mL/L methanol, and 1 g/L KOH [68]. The eluted nucleotides were monitored at 254 nm and identified by comparison with the retention time of 100 \(\upmu\)M ppGpp standards (TriLink Biosciences, San Diego, CA, USA). The ppGpp standard was eluted at 71 min under the current conditions.
# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
## 6 Conclusions and Future Works  
In this paper, we address the MECS sleep control problem in a multi-access edge computing system. To increase the energy efficiency of an MEC system while bounding the queue length of each MECS within a given value, we comprehensively exploit the spatio-temporal correlation structure in the task arrival rate distribution among the MECSs by using the STGCN model. By using only one STGCN model, we predict the task arrival rate for each MECS at the same time. In addition, we reduce the time to find an optimal sleep control vector given a system load state. We evaluate the performance of the proposed method via extensive simulation studies. The results verify that compared to a conventional method based on the LSTM model, the proposed method increases the energy efficiency of a system and decreases the average queue length.  
Figure 8: Comparison of queue length difference distributions. (**a**) Box plots for \(\delta_{i}^{q,LC}(t)\); (**b**) Box plots for \(\delta_{i}^{q,SG}(t)\).  
Figure 9: End-to-end inference time distribution comparison. (**a**) LSTM-GA; (**b**) Proposed method.  
As our future works, we will investigate methods to deal with the prediction error involved in the task arrival rate prediction. Any predictor introduces error inevitably. We will scrutinize the trend of errors that the STGCN predictor causes and analyze the tradeoff between energy consumption and queue length according to the type of errors. Then, we will study ways to improve the performance by controlling errors adaptively according to the operation state of an MEC system.  
Methodology, J.P.; Software, M.K.; Validation, M.K.; Writing--original draft, J.P.; Supervision, J.P. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Research Foundation of Republic of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1F1A1065371).  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.
## 4 Discussion
### Analysis of the Influence of Orientation (North/South)  
Orientation plays a substantial role in daylight and circadian performance. For north-facing rooms, both the \(DA\) and \(CSA\) values show greater stability across various configurations with bright coatings, except in scenarios with dark finishes and lower WWRs, where values diminish significantly. For instance, \(DA_{300}\) values remain relatively stable across all WWRs, but \(DA_{500}\) values exhibit marked differences between centered and upper window positions, especially for larger WWRs where centered configurations tend to maintain higher average values.  
South-facing orientations generally yield higher \(CSA1\) values with narrower dispersion, particularly in rooms with bright coatings and larger WWRs. However, the presence of a window lintel reduces \(CSA\) levels near the window by approximately 20% for bright coatings and up to 30% for dark coatings. For optimal performance in south orientations, centered configurations with WWRs of 35% to 45% are recommended to maximize both the \(DA\) and \(CSA\) values, while upper window positions may be less effective due to the greater variability in daylight distribution.
## 2 Materials and Methods
### Modeling Setup
#### 2.2.3 Numerical Model Illustration
To ensure the model's computational process convergence, constant head boundary conditions (BC-I) were set at the hydrological post locations along the rivers, simulating a direct hydraulic relationship between the surface water and groundwater. Flow rates along these boundaries were calculated during the groundwater flow balance component calculation and were considered the groundwater recharge/discharge rates to/from the river. Annual rainfall percolation to groundwater was defined as recharge distributed over an area (mm). Considering the area's groundwater depth and relatively high summer temperatures, the initial value of areal recharge was set at 10% of the total annual atmospheric precipitation and was subsequently adjusted during model identification. The model's evapotranspiration (ET) BC was set to the entire model area, given that the evapotranspiration potential and the groundwater relationship were set as linear. Since MODFLOW is not functionally designed to calculate vadose (unsaturated) zone water flow, the evapotranspiration value calculated in the model reflects the flow rate coming from the groundwater level surface into the unsaturated zone without considering its direct movement. Evapotranspiration was calculated for each time step, depending on the groundwater level depth and the potential surface evapotranspiration corresponding to open water surface evaporation. Evapotranspiration decreases with groundwater level depth, up to a critical groundwater evapotranspiration (Hcr) depth of 3.0 m where the evapotranspiration value becomes negligibly small, which is dictated by the unsaturated zone prevailing lithological composition. In accordance with the State Commission for Mineral Reserves of the Republic of Kazakhstan (GKZ RK) [27; 29], a long-term average water surface evaporation of 900 mm/year was set, which reflects the central part of western Kazakhstan [24; 27; 29]. The annual groundwater pumping for a specific post was quantified using the \"Pumping well\" MODFLOW tool for model cells representing a given aquifer segment or single water well. The pumping rates changed yearly according to the annual values from 2007 to 2022. The model time step was specified in days from the beginning of the model calculation (start time--0 days = 1 January 2007). Intermediate time steps were set automatically depending on the boundary conditions, time of change, and observation periods (stress periods) within a calculation period. Since the stress periods for the model were specified in average annual values, the calculation results were written to the model output file with a timestep of one year. The total model calculation duration was 23,000 days (to 2070), which is the forecast prediction range.
## 2 Materials and Methods
### RNA Isolation and MlongTPS29 Cloning  
The _M. longifolia_ used to extract RNA was introduced from the Botanical Garden Berlin-Dahlem in Germany with the accession number of ES-0-B-0180887 and then cultivated at the Germplasm Nursery in the Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing, Jiangsu Province. Total RNA of _M. longifolia_ leaves was extracted using a FastPure Plant Total RNA Isolation Kit (Vazyme, Nanjing, China) according to the manufacturer's instructions. After quality and concentration detection, 1 \(\upmu\)g of total RNA was used to synthesize the first strand cDNA with a HiScript II 1 st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China). To identify the candidate limonene synthase in _M. longifolia_ genome sequence, limonene synthases of _M. spicata_ (AAC37366.1) and _M. piperita_ (ABW86881.1) were used as queries to BLAST in _M. longifolia_ TPSs. Polymerase chain reaction (PCR) was performed to amplify _MlongTPS29_ with a gene-specific forward primer (5'-ATGCTTTCAAAGTGTTTAGTG-3') and reverse primer (5'-TCATGCAAAAAGGCTCGAAT-3'). The amplified fragments were purified using the TaKaRa MiniBEST Agarose Gel DNA Extraction Kit Ver.4.0 (Takara, Dalian, China) and then cloned into the pClone007 Blunt Simple Vector (Tsingke, Beijing, China). The positive clones were screened and sequenced for confirmation.
# Effective Optical Scattering Range Determination Based on Ray Tracing
## 2 Theoretical Background  
The surface roughness that produces scattering which limits the performance of the imaging is our primary concern. The scattering depends on the wavelength, polarization and the distribution of the incident beam. The surface topography also influences the scattering distribution and in Section 2.1 the definition of roughness is introduced and Section 2.2, the relevant concept of classic optical scattering is given. Finally, the scattering transfer behavior in a typical system is demonstrated which is the center of this paper.
## 3 Results and Discussion
### FT-IR Analysis  
FT-IR method is useful for the rapid detection of cells containing PHA [52]. The ester carbonyl group stretching vibration (C=O) in PHA occurs at bands between 1724 to 1745 cm\({}^{-1}\). Bands at 1150-1300 cm\({}^{-1}\) and 2800-3100 cm\({}^{-1}\) correspond to C-O-C groups and C-H bonds, respectively.  
Figure 4: Confocal microscopy images of (**A**) co-culture grown on agarose; (**B**) co-culture on xylan; (**C**) _Sde_ 2-40 on agarose; and (**D**) _Sde_ 2-40 on xylan. The images in the panel indicate (**1**) stained cells; (**2**) differential interference contrast (DIC) image; and (**3**) merged image. Arrows indicate the _B. cereus_ cells. _B. cereus_ cells showing fluorescence are indicated by arrow in co-culture.  
FT-IR spectra on lyophilized bacteria in this study are illustrated in Figure 5. The characteristic PHB transmittance bands at 1724-1745 cm\({}^{-1}\) and 1282 cm\({}^{-1}\) were observed [52]. Bands at 1600 and 1700 cm\({}^{-1}\) for amide I and band at 1544 cm\({}^{-1}\) for amide II (polypeptides structures) of cellular proteins were detected, as reported earlier by [PERSON] et al. [53].
## 4 Experimental Results
### Mechanical Characteristics Measurement
The maximum carrying load of the ILUM-D is around twice that of the ILUM-S, which shows that the double-stator structure can effectively improve the output performance of the motor under the same preload and optimal frequency and voltage driving conditions. Figure 11: The plot of the output characteristics versus the amplitude of the applied voltage: (**a**) horizontal thrust force; (**b**) horizontal velocity. Figure 12: Output velocity versus the carrying load. Furthermore, the step response characteristics of the motors were investigated. In the resolution measurements, a pulse sawtooth wave voltage signal with a pulse interval of 500 ms was used, as shown in Figure 13a, for more intuitive processing and summarization of the data. The displacement plots versus the time are shown in Figure 13b, from which the output displacement in each of the 10 pulse cycles is compared. The maximum displacement in a single pulse cycle is representative of the resolution that the motor can achieve. In this way, the displacement resolution of 26 nm for the ILUM-S and of 27 nm for the ILUM-D can be achieved under a pulse sawtooth wave signal when the amplitude of the applied voltage is 300 V\({}_{\text{P-P}}\). The comparisons between the proposed ILUM-D and the previous linear inertial motor or linear actuator are shown in Table 3. The ultrasonic motors [17; 24] are capable of reaching a relatively higher velocity (20 mm/s, 10 mm/s) because they operate under a higher frequency (152 kHz, 30-70 kHz). The piezoelectric motor [25] is able to produce a relatively higher velocity (16 mm/s) and thrust force (3.3 N) using two multilayer actuators in a stator with a two-phase signal applied. The higher drive voltage occurs because the PZT used in the prototype is a single-layer structure. Furthermore, the proposed ILUM-D achieves the maximum thrust of 1.645 N with a resolution of 27 nm. Compared to the mentioned ultrasonic motors, the ILUM-D has significant advantages in maximum thrust force and resolution.