esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries on the 3 GPP NR Physical Layer Specification
### Numerologies for eMBB and URLLC Traffic  
To satisfy the high reliability requirements of URLLC traffic, it is necessary to have multiple HARQ retransmissions or PDSCH repetition, which may be impossible to transmit as much as desired within a transmission time interval (TTI) of 1 ms. A TTI of 1 ms is equivalent to a subcarrier spacing (SCS) of 15 kHz, which is a default slot structure in LTE. Therefore, the 1 ms TTI should be shortened to enable multiple transmissions across multiple TTIs to meet the reliability requirements within 1 ms. In 5G NR numerology, the TTI is determined in terms of the parameter \(\mu\in[0,\cdots,4]\), which allows for scalable subcarrier spacing. More specifically, TTI can be shortened to \((1/2)^{\mu}\cdot 1\) ms while SCS is expanded as \(2^{\mu}\cdot 15\) kHz. In fact, the TTI for eMBB traffic remains 1 ms, i.e., \(\mu=0\), while that for URLLC traffic can be shortened, \(\mu=1,2,3,4\). Therefore, in the same CBW, two slot structures of different numerologies are allowed for the coexistence of eMBB and URLLC traffic.
## 3 Methods
### Numerical Modelling  
#### 3.3.1 Finite-Element Model  
For evaluating the seepage behaviors of underground caverns, we adopted a numerical procedure combining a Signorini-type variational inequality formulation proposed by [PERSON] et al. [29], which has been successfully applied to evaluate the seepage behaviors of underground caverns [36; 37; 38]. The partial differential equation and iterative formulation of the numerical approach are described in [PERSON] et al. [29], and the algorithm flow of the numerical procedure is found in [PERSON] et al. [36]. Moreover, the finite-element numerical program was written using the MATLAB software (2023b).  
Figure 8 displays a two-dimensional (2D) numerical model of an underground powerhouse with a refined triangular mesh. The left and right boundaries of the numerical model were taken as the groundwater divide and the lower reservoir, respectively, which can be assigned according to the initial groundwater level. Moreover, the top and bottom boundaries were taken to the ground surface and sea level, respectively. Four weathered belts, five faults, and seepage control structures (i.e., drainage holes, drainage tunnels, and waterproof curtain) were involved in the model, with 35,110 triangular elements and 18,541 nodes.  
In the numerical simulations, the following hydrogeological assumptions were made: (1) the rock mass and the waterproof curtain are homogeneous with isotropic permeability, (2) the flow is a steady state and satisfies [PERSON]'s law, (3) the pore water pressure on cavern boundaries is zero. Moreover, the boundary conditions were applied: (1) the constant  
\begin{table}
\begin{tabular}{c c c c} \hline
**SGR** & **Tunnel Rating** & **Class** &
\begin{tabular}{c} **Probable Conditions for Groundwater Inflow into** \\ **Tunnel (L/s/m)** \\ \end{tabular} \\ \hline
0–100 & No risk & I & 0–0.04 \\
100–300 & Low risk & II & 0.04–0.1 \\
300–500 & Moderate risk & III & 0.1–0.16 \\
500–700 & Risky & IV & 0.16–0.28 \\
700–1000 & High risk & V & \(Q>0.28\), inflow of groundwater and mud from \\  & & & crashed zones is probable \\
1000\(<\) & Critical & VI & Inflow of groundwater and mud is highly probable \\ \hline \end{tabular}
\end{table}
Table 1: SGR for groundwater inflow into tunnels.  
Figure 8: Two-dimensional finite-element model of underground powerhouse along the diversion tunnel #1 alignment considering different weathered zones and structures.  
head boundary at the left and right boundaries, (2) the no-flow boundary at the bottom of the model, and (3) the complementary condition of Signorini's type at the boundaries of underground powerhouse and drainage structures; see [PERSON] et al. [29].
###### Abstract
Furthermore, determining controller parameters is, to some extent, easier than in other procedures. For these reasons, IDA-based controllers for various power converters, such as rectifiers and inverters, have already been improved and presented here. Finite-control-set model-predictive control (FCS-MPC), on the other hand, has become a prominent technique for power converter control [7]. It is built on the premise of employing a discrete VSC model with accompanying filter to forecast future behavior for all potential control inputs, and then applying the one that minimizes a programmed cost function (CF) at each sampling time [9]. This technique has been widely and effectively applied to standalone converters due to its flexibility and intrinsically fast reaction. The online evaluation of all conceivable switch configurations can be performed over many periods, but the number of computations grows exponentially with each step. In this research, an improved algorithm is presented and compared to the enhanced passivity model. The system design overview for the 2L-VSI will be discussed in Section 2, while the improved passivity-based control will be discussed in Section 3 and the improved model predictive control will be discussed in Section 4. The simulation results and discussion are given in Section 5 and the experimental validations are given in Section 6, while the discussion is reported in Section 7 and conclusions in Section 8.
# Research on Corrosion Damage Evolution of Aluminum Alloy for Aviation
## 3 Results and Discussion
### Corrosion Rate
#### 3.3.1 Image Binarization  
For the acquisition of corrosion rate, the most important thing is to be able to accurately obtain the surface area of corrosion pits, but the corrosion morphology is often more complicated. Even under a high-power microscope, it is difficult to accurately lock the morphology of the pits. If it is equivalent to a regular figure or judged by the naked eye, it will inevitably lead to large errors due to subjectivity. In order to accurately extract the corrosion pits' shape on the corroded surface and obtain the dynamics of the corrosion rate, this paper proposes a method to obtain the corrosion rate based on image binarization. The specific implementation process is shown in Figure 11.  
Through the binarization processing of the corrosion morphology based on MATLAB R2014a software (MathWorks, Natick, MA, USA), the binarization images at different corrosion times were obtained, as shown in Figure 12. The scale bar is 1:1400. There are only two colors in the image, black and white, which represent the substrate and corrosion pits, respectively.  
After the image was binarized, the corrosion rate could be easily solved according to the pixel value represented by the pit and the aggregate size of the original image. In this paper, three test pieces were selected under each corrosion time, and a total of four sections were selected for each test piece. The results are shown in Figure 13.  
Figure 11: Flow chart of binarization processing of corrosion pits’ image.
# Broadband DOA Estimation by Exploiting DFT Extrapolation
## 2 DFT Extrapolation
### Application for Broadband DOA Estimation  
There are two differences between the method proposed in [11] and the ones used in this study. The algorithm used in this study estimates the DOA using the sampled signal. In other words, the sampled signal is a spatial series, not a time series, and the spectral domain is taken as the DOA domain in this study. The problem, however, is that the number of spatial series sampled with an antenna array is extremely small compared to the length of the sampled time series; the former is at best 30, but the latter is usually of the order of \(10^{2}\). Thus, the effect of noise is more significant when the number of samples in the series becomes smaller. Additionally, if \(\lambda\) is too large or the tolerance is too small in the iterative process with the Cauchy regularizer, the extrapolated signal becomes zero because the sparsity increases excessively due to the excessive iteration. We addressed this difference by heuristically adjusting the hyperparameters in the algorithm.
# Enhancing the SRAM PUF with an XOR Gate
## 2 Background  
### SRAM Technology  
SRAM is a type of volatile random-access memory that employs latching circuitry to retain information as long as it is powered. A typical CMOS SRAM cell comprises four to six transistors arranged in a specific configuration that forms a flip-flop circuit. The transistors are typically divided into two cross-coupled inverters. The cross-coupled inverters store and amplify the bit of data stored in the SRAM cell, as shown in Figure 1[7]. When an SRAM cell is powered on, it will wake in an initial state, '0' or '1'. The threshold voltage of the cross-coupled transistors determines the initial state of an SRAM cell. Due to small variations during the manufacturing process and asymmetries in the coupled transistors, the exact threshold voltage per device follows a random distribution. As a result, ideal SRAM cells should have an even random split of '0's and '1's when initially powered on, as both inverters have exactly the same threshold voltage.  
However, most SRAM cells have substantial threshold differences, leading to a heavily skewed initial state of a strong '0' or '1'. Conversely, a low threshold difference leaves a state susceptible to noise, where its initial state can be either a '0' or '1', as illustrated in Figure 2. When the objective is the design of PUFs, SRAM cells that exhibit small differences in threshold voltage are often referred to as flaky or fuzzy. These cells switch easily between the two states on start-up, leading to unpredictable behavior. In addition to the threshold voltage differences, there can be many other small parameters resulting from the fabrication process that can cause the cells to assume a strong '0' or '1' state or exhibit flaky behavior, making each device unique and unclonable.  
Figure 1: Six- transistor SRAM cell [7].
# Sphingolipidoses and Retinal Involvement: A Comprehensive Review
## 3 Ophthalmological Examination: A Key Tool in the Diagnosis and Management of Sphingolipidoses
### Adaptive Optics (AO)  
AO is an advanced imaging technique designed to achieve high-resolution visualization of microscopic retinal structures in vivo. This technology enhances the clarity of images by correcting optical aberrations, thereby allowing precise examination of blood vessels, nerve fibers, ganglion cells, and photoreceptors [43]. AO systems optimize optical performance, achieving lateral resolutions as fine as 2 microns [43,44].  
AO can be particularly beneficial in sphingolipidoses, especially FD, for evaluating vessel diameter, course, and paravascular deposits [45]. More generally, integrating AO with SD-OCT could significantly enhance the detailed analysis of retinal ultrastructure [46], whereas its combination with OCTA would facilitate a more comprehensive three-dimensional assessment of the retinal circulation [47]. This synergy could also support the development of a more refined differential diagnosis algorithm for retinal vascular pathologies.  
The primary limitation of AO lies in its restricted availability in routine ophthalmological practice, attributable to its high cost and operational complexity [46].
## 6 Future Directions
### Ubiquitous Global Coverage  
One of the most exciting points is that 6G networks offer an integrated coverage area with space-air-underwater terrestrial networks. It is expected to enable communication from anywhere at any time with its wide coverage area. In this way, it will facilitate communication, especially in rural areas and disaster areas, where communication is difficult. Communication, regardless of place and time, also supports sustainable development goals by reducing many inequalities. It is better understood how important and essential digital technologies are, especially in cases of global epidemics and pandemics. Every individual's access to communication networks from anywhere contributes to social equality. In addition, the digitalization of daily work is very promising in terms of sustainability by not causing a pause in the functioning of society in pandemic situations. Factors such as channel estimation and modeling, delay, and the Doppler effect at the point of seamless communication of multiple networks with each other are some of the challenges. The utilization of unmanned aerial vehicles as airborne base stations in integrated network management has been shown to increase coverage [135]. New approaches, techniques, and architectures are needed to optimize the integration of these areas.
## 3 Results and Discussion
### GC Analysis
#### 3.2.2 Semi-Volatile Compounds by GC-MS
Triphenyl phosphate is used as a plasticizer for coatings and lacquers, as well as a flame retardant and solvent [44; 45], while di(ethylene glycol) dibenzoate is a plasticizer for polyvinyl chloride acetate and a component of adhesives [46]. Some degradation products from antioxidants used as additives were identified in the extracts, such as 1,3-di-tert-butylbenzene and 2,4-di-tert-butylphenol, which were described as degradation from Igrafos 168 and Igranox 1076 [47] or 7,9-di-tert-butyl-1-oxaspiro(4,5)deca-6,9-diene-2,8-dione, which is a degradation product of Igranox 1010 [44]. In sample CM2, compounds related with the acrylic resins were detected, such as isobornyl methacrylate and methyl methacrylate. Poly(methyl methacrylate) is a kind of acrylic resin, which is essentially produced by polymerizing a methyl methacrylate monomer [48]. However, in samples CM1 and CM3, where the outer coatings were identified as epoxy resins by FTIR-ATR, some compounds related with epoxy resins were identified. For example, the epoxy hardener hexamethylenetetramine [49] or the common monomer bisphenol A (BPA). 1-(benzyloxy)naphthylalene and methyl dehydroabietate were found in sample CM1. The first is an UV-active sensitiizer, a substance that acts as a kind of solvent for the reactants of the coloring process [50], while methyl dehydroabietate is a component of varnishes and printing inks used as a tackifier for the enhancement of adhesive performance [44; 46]. Octocrylene, detected in sample CM3, is used as UV light absorber to prevent degradation of polymers. Squalene, identified in all the samples, has oxygen-scavenging capacity to extent the shelf life of oxygen-sensitive products [45]. Methyl benzoate and benzoic acid, identified in sample CM2, were described as photolytic decomposition products of the UV photoinitiator 2-hydroxy-2-methylpropiophenone, so they can be considered as NIAS. 2,4,6-trimethylbenzaldehyde, methyl 2,4,6-trimethylbenzene, and 2,4,6-trimethylbenzoic acid were also degradation products of the photointiation diphenyl(2,4,6-trimethylbenzoyl)phosphine oxide [29]. 1-dodecene is an olefin monomer [51], detected in sample CM2. 1-dodecene is included in the regulation (EU) 10/2011 with a specific migration limit (SML) of 0.05 mg/kg [21]. Some fatty acids were detected, such as: tetradecanoic acid, a lubricant and processing aid, included in the regulation (EU) 10/2011 [21]; pentadecanoic acid, an adhesive and potential resulting compound from the thermal oxidation of polyethylene [52]; and nonanoic acid used in lacquers, plastics, and as plasticizer [53]. Fatty acids are also used in the preparation of alkylds, which are oil-modified polyesters [54; 55].
## 2 Materials and Methods
### The Volatile Compounds  
Volatile compounds were isolated, identified, and semi-quantified in cherry tomatoes using HS-SPME (headspace solid-phase micro-extraction). Briefly, 15 mL of tomato juice (obtained from 10 tomatoes) were placed in 50 mL SPME-vials together with 1.5 g of NaCl and 10 uL of benzyl acetate (internal standard of 1000 mg L\({}^{-1}\)). Samples were equilibrated for 15 min at 40 \({}^{\circ}\)C. Then, a DVB/CAR/PDMS fiber of 1 cm long (Supelco, Bellefonte, PA, USA) was then introduced into the sample headspace at 40 \({}^{\circ}\)C for 40 min, with constant stirring (600 rpm) on a magnetic stirrer (IKA C-MAG HS 4, IKA-Werke GmbH & Co. KG, Staufen, Germany). Desorption of volatile compounds from the fiber coating was done at the chromatograph injection port for 3 min. A gas chromatograph (GC-MS), Shimadzu GC-17A (Shimadzu Corporation, Kyoto, Japan), coupled with a Shimadzu mass spectrometer detector (GC-MS QP-5050A) was used for the volatile analysis. The separation was carried out on a fused silica capillary column SLB-5 ms (30 m (length) \(\times\) 0.25 mm (inner diameter) \(\times\) 0.25 um (film thickness)) made of 5% diphenyl and 95% dimethylsiloxane (Supelco Analytical). Helium was used as gas at a flow rate of 1.0 mL min\({}^{-1}\). The splitless mode was selected, and the oven program was (i) 80 \({}^{\circ}\)C; (ii) rate of 3.0 \({}^{\circ}\)C min\({}^{-1}\) until 170 \({}^{\circ}\)C; (iii) rate of 25 \({}^{\circ}\)C min\({}^{-1}\) until 300 \({}^{\circ}\)C and held for 5.8 min. The injector and detector temperatures were 250 and 300 \({}^{\circ}\)C, respectively. Identification of most volatile compounds was performed using 3 different analytical methods: (i) retention indexes, (ii) retention times of standards, and (iii) comparison of mass spectra using both standards Wiley's Spectral Library Collection). Analyses were conducted in four replicates and results were expressed as mg L\({}^{-1}\).
## 4 Results and Discussion
### Recognition of Different Spectral Data
#### 4.2.2 LIBS Data of _Tegillarca granosa_ Pollluted by Heavy Metals
In addition, compared with infrared spectral data, LIBS data have a higher dimension and there are many characteristic spectral lines of a certain atom, making LIBS data relatively complicated. RF often needs to build a large number of trees to accomplish this task. As a result, the recognition effect of RF is not ideal. Though LIBS data are susceptible to noise and are often affected by problems such as base-line drift [21; 22], the LRSC algorithm still had good accuracy, with an average accuracy of 81% over 50 times. It can also be  
Figure 5: Residual between the estimated prediction samples based on different classes of training samples and the actual prediction samples. (In each graph, each column represents a class of prediction samples; A, B, C, D, and E denote the prediction samples that belong to the first through fifth classes, respectively; Symbols 1  
seen that the average recognition rate of PLSDA was only 46%, and the corresponding variance was also increased due to the complexity of the LIBS data and the defects of the optimal parameter selection. Without preprocessing, such as extracting the characteristic spectral lines, the LRSC algorithm has no greater effect in directly recognizing the LIBS data than it does with the infrared spectral data. Nevertheless, horizontally compared with other classification algorithms, the LRSC algorithm still has certain recognition advantages and has stability that other algorithms lack, for it does not depend on parameter optimization. It is obviously more difficult to recognize the full spectral data with larger dimensions, more noise, and more complex spectral lines than to recognize the infrared spectral data. Combined with other data reconstruction methods similar to the spectral changes, the present LRSC algorithm is a new idea in the field of spectral data classification.
## 3 Challenges in Immersive Video Quality Assessment  
In this paper, we propose to use structural similarity [11] as a foundation and combine it with the ideas constituting the advantages of IV-PSNR [4]. The usage of a much better base metric (structural similarity instead of MSE--mean squared error [4]) allowed us to increase the correlation with MOS (when compared to IV-PSNR) and resulted in the formulation of IV-SSIM--the structural similarity metric designed for immersive video.  
Similarly to IV-PSNR, the proposed IV-SSIM metric does not analyze the temporal consistency of the video, measuring each frame of video independently. However, previous research on the quality of synthesized (rendered) video [37] demonstrated that temporal distortions in immersive video are primarily introduced by changing the viewport rather than by temporal inconsistencies. Therefore, for immersive video applications, it is much more important to properly assess the rendering-induced distortions than the temporal consistency of the content itself.  
The most significant source of distortion in immersive video is the process of virtual view rendering (synthesis). The rendering introduces two distinct types of error: corresponding pixel shift error and global color offset error.
## 2 Materials and Methods
### Measurement of Chlorophyll A Concentration  
Twenty milliliters of liquid was taken from each sample that cultured _Nannochloropsis_ sp. and a filter membrane (0.45 \(\upmu\)m) was used to filter it. After filtration, the filter membrane was taken out and put into a 50 mL liquid storage bottle after being cut into pieces. Next, 3600 \(\upmu\)L of 95% ethanol was added to the liquid storage bottle and then 400 \(\upmu\)L of distilled water was added. After being mixed evenly, it was heated in an oil bath (constant temperature heating magnetic stirrer, DF-101S, LICHEN-Bx Instrument Technology Co., Ltd., Shanghai, China) for 15 min at 75 \({}^{\circ}\)C and cooled naturally to room temperature. When there was no obvious green on the filter membrane, chlorophyll a was considered to have been extracted completely. A Disposable syringe and a 0.22 \(\upmu\)m filter membrane were used to transfer the filtrate from the liquid storage bottle to a cuvette. An ultraviolet spectrophotometer (U-T3, Yipu Instrument Manufacturing Co., Ltd., Shanghai, China) was used to measure and record the absorbance under wavelengths of 649, 665, and 750 nm. According to Formula (1), concentration of chlorophyll a was calculated.  
Chlorophyll a (mg/L) = 5.24 \(\times\) (A\({}_{665}-\) A\({}_{750}\)) + 22.4 \(\times\) (A\({}_{649}-\) A\({}_{750}\))
# Document Image Shadow Removal Based on Illumination Correction Method
## 4 Experiments  
### Evaluation Metrics  
Some generally used evaluation metrics [22; 25; 26; 27] are selected to quantitatively evaluate the effect of our shadow removal method.  
First, the root mean square error (RMSE) in RGB color space is selected as a quantitative evaluation metric to evaluate shadow removal accuracy by comparison with the corresponding non-shadow image, which is defined as follows:  
\[RMSE(\mathbf{\tilde{O}},\mathbf{O})=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\big{(} \tilde{O}_{i},O_{i}\big{)}^{2}} \tag{14}\]  
The second evaluation metric is peak-signal-to-noise ratio (PSNR), which is defined as follows:  
\[PSNR(\mathbf{\tilde{O}},\mathbf{O})=20\text{log}_{10}\bigg{(}\frac{255}{RMSE( \mathbf{\tilde{O}},\mathbf{O})}\bigg{)} \tag{15}\]  
The third evaluation metric is structural similarity (SSIM), which is defined as follows:  
\[SSIM(\mathbf{\tilde{O}},\mathbf{O})=\frac{(2u_{o}u_{o}+c_{1})(2\sigma_{0o}+c_ {2})}{(u_{o}^{2}+u_{o}^{2}+c_{1})(\sigma_{0}^{2}+\sigma_{0}^{2}+c_{2})} \tag{16}\]where \(u_{\delta}\) and \(u_{o}\) are the mean of pixel values, ; \(\sigma_{\delta}\) and \(\sigma_{o}\) are the variance of pixel values; \(\sigma_{0o}\) is the covariance; \(c_{1}\) and \(c_{2}\) are small constants used to avoid denominators of zero.  
These metrics take into account the color accuracy, signal-to-noise ratio, and structural similarity of the document images. Through them, we can have a more comprehensive understanding of the performance of shadow removal methods in different aspects.