esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
## 2 Theory
### Optimum Threshold Value  
There are many techniques to analyze an image. Among them, the simplest and easiest method is binarization of an image using a threshold value. This is used in many pre-processing steps of image processing, such as separating the background from an object in an image, extracting only pixels with a brightness value above a certain level, or simplifying the overall information of the image. The Otsu algorithm is the most representative method for calculating the threshold value of an image.  
The Otsu algorithm is a technology which calculates the optimal threshold value that can classify images into two classes by using a histogram based on a grayscale level. In the binary image, [0, k] is classified as 'class 0', and [k, 1] is classified as 'class 1' based on the threshold value k. Through this process, binary images can be acquired based on a threshold value. In general, converting a 2D thermal image to a binary image can clearly characterize the characteristics of the defects [30; 31; 32].  
In order to classify into two binary images, it is necessary to calculate an optimal threshold value. If it is an M \(\times\) N image with L intensity levels such as 0, 1, 2,..., L \(-\), pixels with intensity values within [0, k] are classified as class 1, and intensity values within [k + 1, L + 1] are classified as class 2. The probability that a pixel is classified into class 1 or 2 is as follows [33; 34]:  
\[\mathrm{P_{1}(k)}=\sum_{i=0}^{k}\mathrm{P_{i}} \tag{1}\]  
\[\mathrm{P_{2}(k)}=1-\mathrm{P_{1}(k)} \tag{2}\]  
The average intensity values of pixels classified into classes 1 and 2 are as follows:  
\[\mathrm{m_{1}(k)}=\frac{1}{\mathrm{P_{1}(k)}}\sum_{i=0}^{k}\mathrm{iP_{i}} \tag{3}\]  
\[\mathrm{m_{2}(k)}=\frac{1}{\mathrm{P_{2}(k)}}\sum_{i=k+1}^{L-1}\mathrm{iP_{i}} \tag{4}\]  
Figure 1: Flowchart of the overall research process of this study.  
There are mean intensity values up to the k level, which of all images is  
\[\text{m}_{\text{G}}=\text{P}_{1}\text{m}_{1}+\text{P}_{2}\text{m}_{2} \tag{5}\]  
In order to calculate the optimal threshold value, the Otsu algorithm should allow the concept of between-class variance. The equation of between-class variance is as follows:  
\[\sigma_{\text{b}}^{2}=\frac{\left(\text{m}_{\text{G}}\text{P}_{1}-\text{m} \right)^{2}}{\text{P}_{1}(1-\text{P}_{1})} \tag{6}\]  
Calculating the optimal k value is a simple principle, although the optimal k value can be calculated only by substituting all k values in the intensity range [0, L \(-\) 1]. The k value was calculated using MATLAB software, and the principle of the Otsu algorithm is to classify the image based on the k value obtained in this way.
# Risk-Based Access Control Model: A Systematic Literature Review
## 4 Methodology  
The risk-based access control model has several advantages in terms of flexibility and ability to provide an effective security model for dynamic systems. This systematic literature review is conducted to examine and investigate current research regarding risk-based access control models and explain the findings of the conducted review. A systematic literature review is mainly conducted as a way to specify, evaluate, and interpret all available research related to specific research questions, certain subjects, or phenomenon of interest [21].  
Figure 2: Main elements of a risk-based access control model.  
Conducting a systematic literature review passed through five stages, as depicted in Figure 3. The first stage aims to formulate the research questions that the current review paper attempts to answer and then decide the criteria to include or exclude articles in the second stage to make sure that the selected articles are the best and most appropriate regarding the review objectives. The third stage is the main stage that discusses at length which different databases will be searched to locate relevant articles. The fourth stage analyzes the results and then in the fifth stage, the results of each research question will be discussed.  
This approach/methodology was adopted to make the reader fully aware of the stages of conducting this systematic literature review. The methodology was started by defining the research questions to have a specific target while reviewing multiple publications. Then, inclusion and exclusion criteria were presented to show how the retrieved publication are filtered to reach the target of the study. Data sources where publications were retrieved are also presented to show the digital libraries utilized to collect these publications. In addition, the selection of relevant articles is discussed. The adopted methodology has several advantages in which it aims to describe the full process utilized to reach the target of the study to make the reader fully aware of all the procedures conducted by the researcher. In addition, this approach was adopted in several previous systematic literature reviews. On the other hand, this approach produces some drawbacks in which it limits the scope of the review/study, which may not give all the information about a certain topic to the reader.
## 4 Results and Discussion
### Front and Rear Distance Monitoring Test  
With reference to the 50 m safe distance standard specified in Section 2.1, in the range of 50 m, we used the monocular cameras (A, B) and the binocular camera (a\({}_{1}\), a\({}_{2}\), b\({}_{1}\), b\({}_{2}\)) to perform monocular and binocular ranging tests on 20 groups of different distances. The calculated distances (\(Cd\)) are recorded, and the maximum running time of the algorithm in the experiment is 0.13 s (the minimum FPS for image reasoning and calculation in the experiment is 7.7 f/s). The distance in the experiment is calculated by monocular and binocular ranging algorithms, while the real distance (\(Rd\)) is measured by the laser range finder. The test arrangement is shown in Figure 14. The error (\(E\)) calculation is based on Equation (7), and the results are shown in Figure 15.  
\[E=\frac{|Cd-Rd|}{Rd}\times 100\% \tag{7}\]  
As shown in Figure 15, the measurement error of the front and rear distance of the system is controlled within 5% in the range of 50 m, which meets the requirements of error in actual driving. Compared with monocular ranging, binocular ranging shows higher accuracy. In addition, the accuracy of monocular and binocular ranging is proportional to the target distance.  
Figure 14: Camera arrangement.  
Figure 13: The performance of binocular camera calibration errors.
## 3 Design Optimization
### Performance Metrics & Design Criteria
#### 3.3.9 Manufacturing-Process Limitations and Costs  
From a mere fluidic and mathematical point of view, elementary spatial needs may be captured rather well by \(\overline{R}\) (11), \(\bar{R}\) (12) and \(\overline{A}\) (13). Yet, in practice, the degree of freedom in patterning structures \(\Gamma\) and their packing density empowering fluidic multiplexing is further influenced by materials and their processing technologies along prototyping, pilot series and (commercial) mass fabrication, the latter being typically based on tool-based polymer replication and pick-and-place assembly schemes [79].  
Figure 8: Variation of retention rate \(\Omega\) vs. (**a**) actual atmospheric pressure \(p_{0}\) and (**b**) with the fractional deviation \(\chi=p_{0}^{\prime}/p_{0}-1\) of the pressure of the enclosed gas pocket \(p_{0}^{\prime}\) from \(p_{0}\) at the point of isolation from atmosphere (Figure 1a). Note that variations in \(\chi\) of more than a few permille have a considerable impact on \(\Omega\) (5), (6) and (16). The dotted lines indicate the standard deviations \(\Delta\Omega\) obtained at \(p_{0}=p_{\text{std}}\) (**a**) and for \(\chi=0\), (**b**) using the standard parameter values (see Appendix A.1).  
This repertoire of fabrication techniques commonly involves different dimensional tolerances, and often imposes considerably varying types and magnitudes of design restrictions, e.g., on minimum feature sizes, aspect ratios, wall thicknesses, draft angles (for demolding), the homogeneity of packing density and the distribution of hydrodynamic resistances (for the flow of the mostly centrally, pressure-injected hot melt), curvature of concave or convex edges on tools and their replicates, and sufficient interstitial and peripheral surface area for pressure-proof bonding.  
Furthermore, not all area untouched by \(\Gamma\) may be available for placing other structures, e.g., as it may be too small or occluded in the center of a neighboring \(\Gamma\) While these restrictions are hard to exactly quantify, they might, more or less, be captured by a reserving a \"fringe\" of a width corresponding the minimum wall thickness or bonding surface around the outer contours of \(\Gamma\).  
Thus, in addition to the fluidic aspects mostly addressed in this work, future endeavors should be directed toward factoring in the design optimization toward criteria associated with manufacture and assembly, and their effect on the technological challenge and related cost of development and production [81].
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
## 3 SIMO DLCSK System Model
### Chaotic Signals  
We consider discrete-time chaotic signals generated by the Chebyshev map and the Logistic map for \"0\" and \"1\", respectively. The chaotic Chebyshev map generated by the second order Chebyshev polynomial function is used, which can be written as \(\tilde{x}_{t+1}=1-\tilde{x}_{t}^{2}\)[68]. Another chaotic map is the Logistic map, which is generated using a recursive function, \(\tilde{x}_{t+1}=\rho\tilde{x}_{t}(1-\tilde{x}_{t})\)[69]. The parameter \(\rho\) is called the bifurcation parameter, and the values of interest for \(\rho\) are in the interval \([1,4]\). For \(3.57\leq\rho\leq 4\), the generated sequence is non-periodic and non-converging [70].  
In this study, we evaluate the effect of the changes in \(\rho\) on the classifier's performance. The parameter changes can be intentional (to achieve higher accuracy), or unintentional (due to environmental conditions) [71]. When \(\rho<3.57\), the generated signals by Logistic map show a periodic behavior and a classifier can easily separate it from a chaotic signal, (e.g., a signal generated by the Chebyshev map). There is a trade-off between the classification accuracy and the security. Since a periodic waveform can bring negative effect on the security, we select \(\rho~{}>~{}3.6\) for a case that definitely guarantees the chaotic  
Figure 1: The SIMO DLCSK digital communication system.  
dynamics. The chaotic maps have zero mean and unit variance, i.e., \(E\big{(}\hat{x}_{t}\big{)}=E\big{(}\hat{x}_{t}\big{)}=0\) and \(E\big{(}\hat{x}_{t}^{2}\big{)}=E\big{(}\hat{x}_{t}^{2}\big{)}=1\), where \(E(\cdot)\) denotes the probabilistic expectation operator.
## 3 Optical Modeling Results
### Surface-Textured CIGS Solar Cells
Figure 8 shows the calculated ADETAM-applied reflectance spectra of the surface-textured CIGS solar cell at various incidence angles of sunlight when the RMS surface roughness is \(\sigma_{\text{RMS}}=25\), \(50\), \(75\), and \(100\) nm. Because we assume that unpolarized sunlight has the equal contribution of s and p polarizations, the total reflectance spectrum at each incidence angle are obtained by the average over the reflectance spectrum of s-polarized light and that of p-polarized light. For comparison, the reflectance spectra of the planar CIGS solar cell are also plotted at each incidence angle of sunlight. In Figure 8, the spectral responses of the reflectance in the surface-textured CIGS solar cell are lower than those in the planar CIGS solar cell for all the incidence angles, which verifies the enhancement of light absorption in the surface-textured CIGS solar cell. As the RMS surface roughness increases, the total reflectance becomes lower, which agrees with the experimental results in the literature [33]. In Figure 8, as the RMS surface roughness of the surface-textured CIGS solar cell increases, the reflectance of the surface-textured CIGS solar cell decreases and converges to a certain value, which results from the incoherent multiple optical reflections inside the planar cover glass layer, as shown in Figure 6b. The scattering effect of the surface-textured structure cannot affect the incoherent multiple reflection caused by the front encapsulation layers, which will limit the absorption enhancement of the surface-textured CIGS solar cell. Because we assume two front encapsulation layers to be transparent, the reflectance caused by the incoherent multiple optical reflections can be calculated by [34]  
\[R_{inc}=\frac{R_{g-a}+R_{g-\textit{EVA}}-2R_{g-a}R_{g-\textit{EVA}}}{1-R_{g-a} R_{g-\textit{EVA}}}. \tag{18}\]  
Here, \(R_{g-a}\) is the reflectance from the cover glass to the air and \(R_{g-\textit{EVA}}\) is the reflectance from the cover glass to the EVA. The values of \(R_{g-a}\) and \(R_{g-\textit{EVA}}\) are determined by the  
Figure 7: Spectral 2D coherent calculation results of the optical absorption in the surface-textured CIGS solar cell at the wavelength of \(400\) nm when the equispaced phase thicknesses in the phase-shift layers are zero (\(b_{1}=b_{2}=0\)). The RMS surface roughness is set to be \(\sigma_{\text{RMS}}=100\) nm. The \(400\) nm input plane wave with the surface power density of \(5.57\) W/m\({}^{2}\) has the incidence angle of \(45^{\circ}\) for both (**a**) s and (**b**) polarizations. To clarify the spectral optical absorption profile near the ZnS/CIGS interface, all the calculation results are shown near the ZnS/CIGS interface. Fresnel equation, which depends on the refraction angle and light polarization [15]. The black dotted lines in Figure 8 show the calculated values of the incidence-angle-dependent reflectance of the incoherent multiple optical reflections, which are well matched with the minimum reflectance values of the surface-textured CIGS solar cell with \(\sigma_{\text{RMS}}=100\) nm.
# Fully Open-Source Meeting Minutes Generation Tool
## 2 Related Work in Literature
### Audio-to-Text  
Capturing information from audio is achieved by converting the audio signal into text data [10]. Various approaches for _audio-to-text_ conversion exist, but transformer-based models have recently gained attention for their strong performance and ability to maintain long-term dependencies in audio data.  
Several transformer-based models, such as _Wav2 Vec 2.0_ and _HuBERT_, focus on self-supervised learning and are effective for low-resource languages [11; 12]. The _massively multilingual speech (MMS)_ model utilizes self-supervised learning to support thousands of languages, offering broad linguistic coverage. However, its extensive coverage comes at the cost of increased computational time [13]. _Whisper_ excels in multilingual and noisy environments, as it is trained on weakly annotated and noisy data [14], although it suffers from occasional hallucinations, where incorrect words or sentences are generated.  
Given _Whisper_'s strengths in handling multilingual and noisy environments, it appears to be a strong candidate for our system. However, further experiments comparing these models are needed to evaluate their suitability against other options and to validate their performance.
# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
###### Abstract  
With the population aging globally, cataracts have become one of the main causes of vision impairment. Early diagnosis and treatment of cataracts are crucial for preventing blindness. However, the use of deep learning models for assisting in the diagnosis of cataracts is limited due to reasons such as scarce data labeling, small sample size, uneven distribution, and poor generalization ability in the field. Therefore, this paper proposes a hybrid deep learning network for assisting in the diagnosis of cataract fundus images, attempting to solve the above problems and limitations. The network is based on the idea of transfer learning for feature extraction of fundus images, and introduces the Squeeze-and-Excitation (SE) module and prototype network for feature enhancement and classification, improving the model's generalization ability for new disease samples. Finally, this paper verifies the role of each part of the model through ablation experiments, especially the significant contribution of the SE_block module and the prototype network classifier in enhancing the model's performance. The experimental results show that the proposed model achieves excellent performance in the task of cataract fundus image recognition, with an accuracy of 0.9875, AUC value of 0.9984, and F1 score of 0.9855. The establishment of this hybrid model not only provides an effective tool for the auxiliary diagnosis of cataracts but also provides a new perspective and method for the application of deep learning in the field of ophthalmic disease recognition.  
cataract; ancillary care; transfer learning; Squeeze and Excitation; prototype network +
Footnote †: journal: applied sciences
## 3 Materials and Methods
### Feature Selection by Genetic Algorithm  
Feature selection is defined as picking the most reliable and discriminative features that reduce the high dimension of feature space to a minimum. In this work, at the posterior stage of feature extraction, the curse of the dimensionality problem may appear, mostly when the number of features is immense. In our case, 429 features are extracted, which may affect not only the cost of computation in terms of training time for the model but also the final accuracy results for the classifiers. Moreover, over-fitting may be found as well due to the subset of redundant and irrelevant features [45]. So, it is critical to get rid of that subset, if it exists. To this end, we use a genetic algorithm (GA) scheme which proves to be successful, as indicated by the experimental results.  
GAs consider that each individual (chromosome) in the population is described by a vector of features (genes) drawn from the entire feature set. The vector is randomly composed such that it contains a random combination of features. The fitness function is then evaluated to find the most relevant features among those that exist. The end result is used to produce successive generations. Two individuals are chosen as parents, with the criteria based on each of their fitness function values. While there are various selection systems, the roulette wheel is used in this study.  
A chromosome with high fitness value is considered a candidate for the next generation. In this work, initially, a chromosome of 429 genes is created for each dataset, where the HRF comprises 45 chromosomes, with another 288 chromosomes for Dataset_2. Specifically, each chromosome is represented by a string of 1s and 0s, with each bit pointing to a gene. For example, a chromosome with 10 genes could be 1100101011. This means that genes 1, 2, 5, 7, 9, and 10 are included and genes 3, 4, 6, and 8 are excluded. A weighted random selection is employed in the initial population where the probability of a candidate chromosome being selected is based on its accuracy function response. So, chromosomes with high probability will have a greater chance of selection. Two parent chromosomes are selected and mated. In this process, two new chromosomes (children) inherit half of each parent's chromosome characteristics in a crossover operation. The new chromosome may be further processed using mutation where the state of some genes may be changed from 1 to 0 or the opposite. The GA effectively reduces the feature space where, out of 429 features, we end up with 105 features which are the most relevant and informative.
## 3 Results
Figure 4: Box plots showing the distribution of the DVCnorm for the re-optimized plans with the selected replanning strategies. Abbreviations: ASC = aperture shape controller, DVC = dose volume constraint, MCS = modulation complexity score, mGPR = measured gamma passing rate, MRO = manual re-optimization, MU = monitor units, pGPR = predicted gamma passing rate. Figure 3: Box plots showing the distribution of the MCSs, pGPRs, and mGPRs for the arcs of the clinical plans and re-optimized plans with the eight replanning strategies used for the first three patients. ASC = aperture shape controller, DVC = dose volume constraint, MCS = modulation complexity score, mGPR = measured gamma passing rate, MRO = manual re-optimization, MU = monitor unit, pGPR = predicted gamma passing rate. Figure 5 shows the box plots of the MCS, pGPR, and mGPR for all of the analyzed strategies. The re-optimization strategies produced a significant decrease in the complexity of the arcs, as indicated by the increase in MCS from a median value of 0.17 [inter quartile range (IQR) = 0.05] to 0.21 [IQR = 0.07], 0.19 [IQR = 0.07] and 0.20 [IQR = 0.09] for, respectively, the MUlimit, ASC_L, and ASC_H. In particular, a larger variability was observed in the latter approach. The reduced complexity was reflected in an improved deliverability. The pGPR increased from a median of 93.3% [IQR = 5.1%] to 94.4% [IQR = 5.3%] (MUlimit), 95.0% [IQR = 4.9%] (ASC_L), and 95.1% [IQR = 2.6%] (ASC_H), while the median mGPR increased from 99.3% [IQR = 1.6%] to 99.7% [IQR = 0.8%] (MUlimit), 99.5% [IQR = 0.7%] (ASC_L), and 99.8% [IQR = 0.6%] (ASC_H). For both the pGPR and mGPR, the increase was significant when using the ASC setting. Notably, the pGPR was susceptible to larger variations due to the intrinsic uncertainty in the ML model's predictions.
# Temporal and Spatial Diversity of Renewable Groundwater Resources in the River Valley
## 4 Results
### Groundwater Footprint  
The assessment of renewable groundwater resources made it possible to calculate the groundwater footprint (GF), quantifying groundwater demand by both man and the environment. Groundwater footprint was defined as GF = A[C/(R-E)], where A, C, R and E were defined as surface area, groundwater abstraction, infiltration recharge rate and outflow, respectively [8].  
The groundwater footprint GF for the Vistula valley in the study area was calculated on 80.38 km\({}^{2}\) for the average conditions presented in variant 1. The groundwater footprint index GF/A, determining the pressure on groundwater, is 0.14, classifying the aquifer as not threatened by excessive groundwater use, like those reported by [PERSON] et al. (2012) in Europe (GF \(<\) 1). The groundwater use was low in the study area but water loss is possible on evapotranspiration. The groundwater footprint in this case was 931.84 km\({}^{2}\) and the GF/A ratio was 1.6 (Table 5; Figure 11). The GF and GF/A indexes were also calculated for variant 2 and 3. The results indicate 36% smaller GF during highest groundwater level and lower renewable resources (variant 2) than variant 1, but GF/A index including evaporation still was over 1. The result of GF during groundwater drought (variant 3) was significantly higher than previous variants: over 84% higher than variant 1 and the GF/A index, including evaporation, reached 2.96.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Symbol**} & \multicolumn{2}{c}{**Variant 1**} & **Variant 2** & **Variant 3** \\ \cline{3-6}  & & **(1999–2013)** & **(Year 2011)** & **(Year 2003)** \\ \hline Area [km\({}^{2}\)] & Remarks & A & 581.8 & 581.8 & 581.8 \\ Groundwater intakes [m\({}^{3}\)/d] & & C & 4779.7 & 4779.7 & 4779.7 \\ Recherge [m\({}^{3}\)/d] & Modelling research in & R & 79622.9 & 118841.3 & 45362.28 \\ Run off [m\({}^{3}\)/d] & Modelling research in & E & 45028.3 & 24583.4 & 36559.64 \\ Groundwater evaporation [m\({}^{3}\)/d] & Modelling research in & GE & 50629 & 104492.4 & 21278.72 \\
**Groundwater footprint** & only groundwater intakes & GF & GF1 = 80.38 & GF2 = 29.50 & GF3 = 315.91 \\
**Groundwater footprint** & groundwater intakes & GF & GF1 = 931.84 & GF2 = 674.47 & GF3 = 1722.30 \\
**[km\({}^{2}\)]** - Figure 11 & groundwater evaporation & GF/A & 0.14 & 0.05 & 0.54 \\ Groundwater stress & groundwater intakes & GF/A & 1.6 & 1.16 & 2.96 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Groundwater footprint, groundwater stress in variant 1–3.