esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble
### AI and Posterior Segment Diseases
#### 3.3.4 AI and Age-Related Macular Degeneration (ARMD)  
ARMD is a chronic and untreatable macular disease characterized by retinal pigment changes, drusen, choroidal neovascularization, hemorrhage, and geographic atrophy. ARMD is a main source of vision damage in individuals over 50. Due to the aging population and the gravity of ARMD, regular screening is needed.  
Automatic diagnosis of ARMD can, of course, reduce the workload of ophthalmologists and increase efficiency. In [47], a VGG16 CNN was used to analyze 11 central OCT images of normal and ARMD patients for the automatic diagnosis of ARMD. Data were analyzed for all images, and the mean likelihood of images containing OCT data and mean likelihood from all patient scans were computed, obtaining AUCs of 0.928, 0.938, and 0.975, respectively.  
DL was used by [PERSON] et al. to identify and evaluate ARMD. They implemented DL to categorize ARMD in accordance with the Age-Related Eye Disease Study (AREDS) Severity Scale and estimated the 5-year probability of development using soft and hard prediction, as well as regression maps. The average five-year overall risk estimate errors ranged from 3.4% to 5.8% in higher AREDS categories, with a larger average error [48].  
DL and CNNs were implemented by [PERSON] et al. for the detection and classification of ARMD into 13 classes by means of color fundus photographs [49]. They implemented a random forest ensemble model of six CNNs with different architectures to achieve 92% accuracy in 13-class classification.  
[PERSON] et al. utilized the DeepSeeNet network to categorize fundus color images into ARMD severity grades [50]. This network had greater precision compared to retinal experts for ARMD grading (0.671 vs. 0.599), with AUCs of 0.94, 0.93, and 0.97 for large drusen, pigmentary anomalies, and identification of late ARMD, respectively.
## 2 Overview  
WSN is a bridge element that combines the digital virtual world with the real world [50; 51]. A WSN is formed by collecting many sensors called nodes, which have limited computing, sensing, and communication functionaries. The sensor nodes are implemented in a geographical area for monitoring physical phenomena such as humidity, temperature, and vibrations [52; 53]. The nodes are small devices with essential components, such as the detection, processing, and communication of subsystems and a power supply unit, as shown in Figure 4.  
The node architecture shown in Figure 4 has four important units. Sensors and an ADC (analog to digital converter) constitute the sensing unit. The analog signals generated by sensors are converted to their digital form by the ADC and are then forwarded to the processing unit, which consists of a memory-equipped microprocessor or microcontroller. The processing unit is also responsible for controlling the sensor nodes intelligently. Data transmission and reception via a radio frequency (RF) channel are handled by the communication unit. It also connects the nodes to the rest of the network. The most vital component is the power unit containing a battery that supplies energy to all the components of the system. Taking into account the low cost of production and energy consumption, these units are integrated into a small module. There can be other sub-units of the node that are application dependents such as a power generator, location finding system, and a mobilizer.
## 3 Methods
### Valid Domains in Image Space and Statistics
#### 3.6.2 Valid Dynamic Co-Occurrence Ranges in Statistics  
The nonuniformities are over the signals regions of the images and affect their dynamic ranges and contrasts. The upper parts of the images dynamic ranges may correspond to artifacts and are compressed linearly in the proposed method. To achieve this, reference intensities \(\eta_{i,ref}\) for high cumulative percentages, 90%, of the dynamic ranges are computed. The dynamic ranges of the noise are delimited by the minimum signal intensities, \(\eta_{i,min}=0.1\times\eta_{i,ref}\). The dynamic ranges are preserved up to \(\eta_{i,app}=1.5\times\eta_{i,ref}\). Beyond these values the intensity ranges are compressed linearly up to \(\eta_{i,max}=3.0\times\eta_{i,ref}\) for ranges \([1.5\times\eta_{i}^{0.9},3.0\times\eta_{i}^{0.9}]\). The dynamic ranges are scaled linearly so that \(\eta_{i,max}\) become intensity 500, that is, \(\eta_{i,max}\mapsto 500\). This value is a tradeoff between intensity resolution and computational complexity for processing the statistics. After the intensity scaling, the direct computation of the statistics from the images would create spikes in the statistics in cases where a dynamic range is compressed and voids in cases where a dynamic range is expanded. To avoid these artifacts in the statistics, noise of a very small standard deviation, 0.5, is added to both images.  
The intensity ranges of \([\eta_{i}^{min},\eta_{i}^{max}]=[0.1\eta_{i,ref},3\eta_{i,ref}]\) are assumed to correspond to the valid signal regions of the _ROIs_, \(I_{ROI,i}=1\). The regions in the images ROIs with intensities in ranges \([0,\eta_{i}^{min}]\) and intensities beyond \(\eta_{i}^{max}\) are invalid, \(I_{ROI,i}=0\).  
The \(C_{ii}\) are computed over \(([\eta_{i,min},\eta_{i,max}]^{2})\). The same ranges are used for the deconvolution of the \(C_{ii}\) to give the \(R_{i}^{s}\). The deconvolution of \(C_{ij}\) for \(R_{i}^{b}\) considers \([\eta_{i,min},\eta_{i,max}]\) for \(i\) and the range starting from zero \([0,\eta_{j,max}]\) for image \(j\). Beyond the valid ranges, the entries of \(R_{i}\) are set to unity, \(R_{i}^{s}=R_{i}^{b}=1\).  
The estimates of the initial restoration fields \(\widehat{b^{-1}}_{i,inc}\) in Equation (12) involve window \(\mathcal{N}_{\rho}\) in image space over the valid part of the ROI of image \(i\) and is set to the neutral value of unity outside. The resulting estimates are spatially smoothed with a Gaussian to give \(W_{i}\) with a window depending on \(\sigma_{\rm s}\) in Equation (13). The smoothing windows of the nonuniformity with Equation (13) consider a spatial multiplicative field with unit weight in the corresponding valid region \(I_{ROI,i}=1\) and with value much less than unity in the remaining ROI. As a result the spatial Gaussian smoothing gives smoothly unity farther from the valid region and towards the invalid region of the image with \(I_{ROI,i}(\mathbf{x})=0\).
# Theoretical Study: High Harmonic Generation by Light Transients
## 2 Method
### Pulse Synthesis  
Three realistic spectra [18] centered in the VIS at 540 nm, the NIR at 880 nm and the MIR at 2.1 um were used for the pulse synthesis. Before they were combined, each individual spectrum was normalized to its energy content and then multiplied by a factor between 0 and 1 to define the energy ratio between them. The delay between the pulses was set by adding a linear term in the spectral phase to the individual spectra. Here, it was ensured that they still overlapped, thus forming a single pulse. Afterwards, the spectra were combined and the overall CEP of the resulting waveform was altered by adding a zero order phase. The pulse in time-domain was calculated by the fast Fourier transform of the combined spectrum. To ensure a constant peak intensity of 15\(\times\)10\({}^{14}\) W/cm\({}^{2}\), each waveform was normalized to the maximum of its electric field envelope.  
Figure 3: Detailed setup of the temporal synthesis of the VIS, NIR and MIR pulses. The amplified spectra of all three OPCPA channels centered at 0.55 μm, 1 μm, and 2 μm are first sent through a delay line to achieve a temporal overlap between the synthesizer’s three arms. Thereafter, the temporally synchronized pulses are compressed to their Fourier transform limit in each arm separately using a set of broadband dielectric chirped mirrors. After passing through a pair of glass wedges to fine tune the relative delay between each arm the three compressed pulses are spatially combined in two broadband dielectric beam combiners. The generated light transients are evaluated using attosecond streaking [29] or electro-optic sampling [30]. By adjusting the relative spectral amplitude of each arm (\(A_{1}\), \(A_{2}\), \(A_{3}\)) and their relative phase (\(\tau_{1}\), \(\tau_{2}\), \(\tau_{3}\)), a variety of transients can be generated. As examples, Panels (**a–c**) show three differently synthesized transients. The figure is adopted from [17].
## 3 The Proposed Method
### A Bounding Formula after Removing the Redundant Failure Modes  
Assume that there are \(r(r<m)\) redundant failure modes in the system. These redundant failure modes can be removed due to the fact that their failure surfaces have no contribution to that of the system. After removing the redundant failure modes, the remaining failure modes in the system can be renumbered in ascending order according to their original number. A novel bounding formula can then be obtained as follows:  
\[\frac{A_{1}+\sum\limits_{j=2}^{m-r}\max\left[\left(A_{j}-\sum\limits_{k=1}^{j- 1}A_{jk}\right),0\right]}{A_{\mathrm{w}}}\leq\frac{A_{\mathrm{s}}}{A_{ \mathrm{w}}}\leq\frac{\sum\limits_{j=1}^{m-r}A_{j}-\sum\limits_{j=2}^{m-r} \max\left(A_{jk}\right)}{A_{\mathrm{w}}} \tag{21}\]  
The above bounding formula also encounters the ordering dependency problem and, hence, needs \((m-r)!\) possible ordering alternatives to obtain the narrowest reliability bounds. In general, the proposed method requires significantly fewer alternatives than the NRBM. For example, for a system with 10 failure modes, the NRBM has 3,628,800 alternatives while the proposed method has 40,320 alternatives when 2 redundant failure modes are involved. On the other hand, the proposed method incurs a slightly greater computational cost for the identification of redundant failure modes. In a nutshell, the proposed method normally has a higher efficiency than the NRBM.  
Figure 4: Two different cases of a subsystem with three failure modes: (**a**) Redundant failure mode general case. (**b**) Redundant failure mode critical case.  
In terms of the accuracy, the proposed bounding formula can also provide efficient reliability bounds to estimate the system's non-probabilistic failure degree. This estimation is not inferior to that of the NRBM, as demonstrated in Section 4.
## 3 Experimental Results: Measuring Key Customer Transaction Metrics Based on POS and RFID Data
The distribution of time spent in sections, calculated using POS and RFID data, can be visualized as a histogram showing the percentage of customers across various time intervals. On the x-axis, intervals like 0-2 min, 2-4 min, etc., represent the duration of time customers spend in each section, while the y-axis indicates the proportion of the total customer sample within each interval. Typically, there is an initial spike in the 0-2-min range, reflecting quick purchases, followed by a significant proportion of customers in the 8-14-min range, indicating standard browsing behavior. Fewer customers fall into the 20-40-min range, often engaging in detailed comparisons or waiting for assistance, with a small percentage spending 46+ min, possibly due to consultations with shop assistants or bulk purchases. Identifying peak intervals helps optimize staffing and promotional activities, while the tails of the distribution provide insights into improving store navigation and customer engagement, ultimately aiding in resource allocation and enhancing the shopping experience. Figure 11: Customer trajectory (with stop points—red dots, and movement—blue lines) during her shopping trip (example of customer #5110203). Figure 12: Distribution of time spent in sections, calculated based on POS and RFID data as a percentage for the entire sample of customers (x-axis—intervals of time values, y-axis—proportion of customers in the sample). In order to carry out such calculations, the processing of two data streams was carried out according to the scheme described above, i.e., the two streams are connected using a key identifier, which is the client number. If we visualize all the routes of all the clients inside the sales area for the entire period of the experiment (4 weeks), we will obtain the image in Figure 6:0 From this image, it is clear that the sections on the perimeter of the store are highly trafficked, while the inner sections are not very much walked in, excluding section B6.
## 4 Available Capacity Modeling Based on Terminal Voltage Drop
### Performance Analysis of the Terminal Voltage Drop During Rest  
To clarify the relationship between the terminal voltage drop during rest and the degree of degradation of the battery, Figure 7 showed, in each cycle test, when the No. 1 battery was charged to fully charged state under the operation conditions in cycle test, the terminal voltage drop after 5, 10, 15, 20, 25 and 30 min of rest. Figure 8 shows the changes in the terminal voltage drop when the No.1 battery was charged to the fully charged state in the 1 st, 80 th,..., 560 th cycle tests.  
Figures 7 and 8 showed that:  
(1) The increasing number of battery life cycles, the change in the terminal voltage drop is increasing for the same rest time.  
(2) For the same cycle life test, with the increase in resting time, the change in the terminal voltage drop is decreases. For example, the voltage drop of the first 5 minutes is bigger than the voltage drop of the second 5 min.  
(3) During the whole rest duration, at the beginning of the rest, the voltage drop is the fastest and with the increase in the number of battery life
## 5 Performance Analysis and Results
### Encryption Quality Metrics
#### 5.2.4 Key Space Analysis  
The total number of unique keys that can be utilized in the encryption process is calculated by the key space. If the length of each initial value or control parameter is set to 16 decimals, the secret keys for the proposed encryption consist of eight initial values (\(x_{0}^{1}\)) valid in the range of [0, 1] and four control parameters (\(r^{1}\),\(r^{2}\),...,\(r^{4}\)) valid in the range of 0.1 to 20. The key space determines the total number of distinct keys that can be used in the encryption process. The secret keys for the suggested encryption consist of eight starting values (\(x_{0}^{1}\)) valid in the range of [0, 1] and four control parameters (\(r^{1}\),\(r^{2}\),...,\(r^{4}\)) valid in the range of 0.1 to 20 if the length of each initial value or control parameter is set to 16 decimals. It is possible to determine the entire complexity (total key space) as follows: \(10^{15}\times 10^{15}\times 10^{15}\times 10^{15}\times 10^{15}=10^{4 \times 15}\)=\(10^{60}\). The key space of an image of size 256 \(\times\) 256 is 256 \(\times\) 256 \(\times\) 28 = 4\(\times 10^{5}\). In addition, the multi-parameter \(\overline{a}\) and \(\overline{b}\) has a key space for \(\overline{a}=10^{15\times 256}\) and for \(\overline{b}=10^{15\times 256}\), so the total multi-parameter key space for the MP-FrFT-OFDM is equal to \(10^{7680}\). Finally, the total key space of the proposed cryptosystem can be calculated as \(10^{60}\times 4\times 10^{5}\times 10^{7680}=4\times 10^{7745}=2^{2200}\). The findings showed that the key space of our approach is very vast, preventing all sorts of brute force assaults. The key space of the proposed algorithm is more than \(2^{100}\). The findings and analyses of the important space analysis are presented in Figure 10.
## 2 Materials and Methods
### MTT Assay  
To evaluate the effect of ZnO NPs on cell proliferation in vitro, a Healthy L929 fibroblast cell line (ATCC, USA) was used. The cells were essentially grown in DMEM medium containing 10% fetal bovine serum [2]. Cells were plated into 96-well dishes with 100 mL of DMEM and kept at 37 \({}^{\circ}\)C in a humidified 5% CO\({}_{2}\) incubator. To investigate the effect of ZnONP concentration and exposure period on cell viability, different concentrations of green-produced ZnO NPs (7.5, 15, 30, 60, and 120 \(\upmu\)g/mL) were added to each well and mixed with distilled water before being incubated for 24, 48, and 72 h. The diluent was used in the same quantity in the control cells (distilled water). Following the exposure period, the media in each well was changed to a new medium (100 mL) containing 5 mg/mL of MTT solution at 24, 48, and 72 h. After being incubated for four hours in the dark, the formazan crystal of the MTT reduction was dissolved in DMSO, and the absorbance was then determined using a microtiter plate ELISA reader. ZnONP effect was assessed using the proportion of reduced MTT dye control absorbance at 570 nm. Each experiment was performed three times. The viability of the cells was determined according to their capacity to transform the yellow dye MTT into a blue formazan crystal. The following formula was used to determine the percentage of cell viability.  
\[\%\text{cellular viability}=(\text{OD specimen/OD control})\times 100\]
# Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains +
## 5 Experiments  
We have implemented one variant of our covert channel in a hypothetical system that uses one-time passwords based on hash chains with cryptographic hash functions MD5 (\(m=128\)) and SHA-3 (\(m=256\)). The implementation was done in C, and we used existing open-source implementations for the cryptographic hash functions. For MD5, we used [PERSON]'s 1999 free implementation that is based on the RFC 1321 specification [4] but does not use the reference implementation given there. For SHA-3 we used [PERSON]'s 2015 code from brainhub.org. As the system is not doing any real application, we implemented client and server, i.e., overt sender and receiver, together with CS and CR in one thread on the same computer, so that they transfer passwords through memory instead of an insecure network. In addition, client and server do no work besides computation of passwords. Hence, this implementation can be considered an extreme test with respect to stealthiness. The covert sender and receiver are placed directly with the client (sender) and server (receiver).  
The covert channel transfers a null-terminated character string. We use \(v=64\) symbols with the following coding. Lower case letters are converted into upper case letters. ASCII characters 1 to 31 and from 95 onward are converted to blanks (ASCII character 32). ASCII characters 32 to 94, comprising upper case letters, digits and some punctuation marks, are converted to symbols 0 to 62, and ASCII character 0 (termination symbol) is converted to symbol 63. Please note that for the purpose of embedding into the binary hash value, we denote by symbol \(i\) a bit string of length \(m\) where only bit \(i\) is set to 1.  
If encryption is used, then we employ a stream cipher that produces key stream symbols in the range 0 to 62, and encrypt by adding symbol and key symbol modulo 63, while termination symbol 63 remains unaltered. As the stream cipher, we employ the same cryptographic hash function as used in the OTP application. The seed is the cryptographic key, and we repeatedly hash. The bytes from the produced hash values are taken as modulo 63 and used as key stream symbols.  
All experiments were performed on a Dell Latitude 5500 notebook with an Intel Core i5-8265U CPU at 1.6 GHz and 8 Gibibytes of main memory, running a Windows 10 operating system. We used gcc version 9.2.0 as the compiler, without activating optimizations.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Axis** & **Scanning Method** & **Mean \(\pm\) SD (\(\upmu\)m)** & **F** & \(\mathbf{p}\)**-Value** \\ \hline \multirow{4}{*}{X} & Intraoral scan & 85.67 \(\pm\) 56.01 \({}^{\text{a}}\) & & \\  & Impression CBCT scan & 72.41 \(\pm\) 40.67 \({}^{\text{ab}}\) & & \\  & Cast CBCT scan & 57.10 \(\pm\) 42.63 \({}^{\text{bc}}\) & & \\  & Extraoral scan & 52.00 \(\pm\) 12.36 \({}^{\text{c}}\) & & \\  & Intraoral scan & 51.32 \(\pm\) 28.56 \({}^{\text{a}}\) & & \\  & Impression CBCT scan & 40.97 \(\pm\) 16.72 \({}^{\text{b}}\) & & \\  & Cast CBCT scan & 31.04 \(\pm\) 18.61 \({}^{\text{c}}\) & 48.490 & 0.000 \\  & Extraoral scan & 15.60 \(\pm\) 9.17 \({}^{\text{d}}\) & & \\  & Intraoral scan & 93.13 \(\pm\) 50.63 \({}^{\text{a}}\) & & \\ Z & Impression CBCT scan & 86.41 \(\pm\) 41.97 \({}^{\text{a}}\) & & \\  & Cast CBCT scan & 122.9 \(\pm\) 33.25 \({}^{\text{b}}\) & 11.808 & 0.000 \\  & Extraoral scan & 90.45 \(\pm\) 45.93 \({}^{\text{a}}\) & & \\  & Intraoral scan & 76.71 \(\pm\) 49.50 \({}^{\text{a}}\) & & \\ All & Impression CBCT scan & 66.59 \(\pm\) 39.80 \({}^{\text{a}}\) & 11.855 & 0.000 \\  & Cast CBCT scan & 70.35 \(\pm\) 50.80 \({}^{\text{a}}\) & & \\  & Extraoral scan & 52.68 \(\pm\) 41.39 \({}^{\text{b}}\) & & \\ \hline \end{tabular} The results of the one-way analysis of variance (ANOVA) test. The \({}^{\text{a}}\), \({}^{\text{b}}\), \({}^{\text{c}}\), and d refer to the groups resulted from the Turkey’s post hoc test. Intraoral scan, digitized model of true model via intraoral scanner; impression CBCT scan, digitized model from impression of true model via CBCT scan; cast CBCT scan, digitized model from cast replica of true model via CBCT scan; extraoral scan, digitized model from cast replica of true model via extraoral scanner.  
\end{table} 
Table 2: Mean value of the deviation in each scanning method according to the axis. Figure 4: The accuracy of digitized model is described in a target figure, and the center of the target is determined as the true model value. The digitized model is more accurate when the mean deviation value is close to the target center. The model is also more consistent in linear dimension when the marked range is narrow. Each section of the target represents a different axis. The upper left section represents the X-axis, the upper right represents Y-axis, the lower left represents the Z-axis and lower right represents the total. The mean deviation value is marked with a circle, and the standard deviation is marked as a range.