esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Patient-Derived Xenograft (PDX) Models, Image Acquisition, and Microscopy  
In this study, \(n=5\) immunocompromised 8-week-old Nu/J male mice (Jackson Laboratories, Bar Harbor, ME, USA) were injected sub-hippocampally with patient-derived GSCs using a stereotaxic apparatus (Kopf, Tujunga, CA, USA). Each mouse was initially sedated with 4% isoflurane and maintained with 2% isoflurane. After leveling the skull, a hole was drilled with a #72 micro drill bit (Kyocera, Kyoto, Japan) at coordinates \(-2.0\,\mathrm{mm}\) AP and \(+1.5\,\mathrm{mm}\) ML relative to Bregma. A 75 RN Hamilton syringe was then lowered to a depth of \(-2.5\,\mathrm{mm}\) DV at a rate of \(0.5\,\mathrm{mm}\) per minute, and 500,000 primary GSCs resuspended in a total volume of \(3\,\mathrm{\SIUnitSymbolMicro L}\) were injected at a rate of \(0.5\,\mathrm{\SIUnitSymbolMicro L}\) per minute using a Stoolting Quintessential Stereotaxic Injector (Stoeling Co, Wood Dale, IL, USA). To reduce backflow, the syringe rested for 2 minutes post-injection, before withdrawal at a rate of \(0.5\,\mathrm{mm}\) per minute, and the cavity was immediately sealed with bone wax (Ethicon Inc., Raritan, NJ, USA).  
After allowing tumors to grow for two months, mice were sacrificed, and whole brains were removed and fixed in 10% phosphate-buffered formalin. Preserved brains were scanned with a modified QSI protocol employing a multi-shot EPI pulse sequence on a Bruker 7T (BioSpec, Billerica, MA, USA) scanner with a Cryoprobe, applying 512 gradient directions, a b-value of 1200 s/mm\({}^{2}\), an echo time of 16.7 ms, a voxel size of 0.16 \(\times\) 0.16 \(\times\) 0.16 mm, and 8 k-space segments. Tumor-associated tractography was generated based on manually drawn ROI in DSI Studio. One of the imaged mouse brains was then sectioned and immuno-stained for human nuclear antigen to highlight human cell nuclei in the xenograft brain tissue, and images converted to a binary map of only the stained cells using Amira Software (v2020.2, Thermo Scientific, Waltham, MA, USA). Tumor cells were then manually segmented to remove background, and a map of segmented tumor cells reconstructed in 3D for qualitative comparison with tractography.
# A Novel Optical-Based Methodology for Improving Nonlinear Fourier Transform
## 5 Applications of NFT and INFT on an Optical Communication System
In Figure 7, we show the Q-factor versus transmission distances for all three modulations. We also observe better performance when b-modulation is used, shown in the blue line, and similar performances for the other two modulations. We observe that in the first 400 km of transmission distance, b-modulation has a lower Q-factor with respect to the other two. After 500 km of transmission distance, b-modulation start to shows a better Q-factor for all of the distances that we have simulated.
## 4 Numerical Examples
### Simply Supported FG Plate  
In this section, a simply supported FG plate subjected to a uniformly distributed load \(q\) is analyzed and compared. The FG plate is comprised of aluminum (\(E_{m}=70~{}Gpa,v_{m}=0.3\)) and ceramic (\(E_{c}=151~{}Gpa,~{}v_{c}=0.3\)). The side length of the plate is \(L=1m\), and the thickness of the plate is \(h\). A quarter of the FG plate is modeled as a result of symmetry, as shown in Figure 19. The displacement boundary conditions for the symmetric and simply supported sides in local coordinates are also illustrated in Figure 19. The \(16\times 16\) regular mesh similar to Section 4.1 is employed for computation. Tables 10 and 11 list the normalized defection \(W_{0}\) of the FG plate for different aspect ratios \(h\)/\(L\) and different exponents n in Equation (15), where \(W_{0}=W_{c}/\frac{dL^{4}}{E_{m}h^{3}}\) and \(W_{c}\) is the central deflection of the plate. In [PERSON] et al. [45], the FG plate is solved by the meshless collocation method with multiquadric radial basis functions and a third-order shear deformation theory. The problem is also solved by [PERSON] and [PERSON] [70] using the C\({}^{0}\) isoparametric finite element with 13 degrees of freedom per node, and the power-law similar to Equations (15) and (16) is used to describe the through-the-thickness distribution of FG materials in the HSDT model. The results obtained by the present TrSDTPE, FfSDTPE, and FiSDTPE are in good agreement with the meshless solutions of [PERSON] et al. [45], which compute the effective elastic moduli by the rule of mixture.  
Figure 18: Model of symmetric edge cracks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(dlb\) & 0.2 & 0.3 & 0.4 & 0.5 & 0.6 \\ \hline TrSDTPE & 1.0966 & 0.9156 & 0.8201 & 0.7687 & 0.7317 \\ FfSDTPE & 1.0941 & 0.9140 & 0.8187 & 0.7690 & 0.7306 \\ FiSDTPE & 1.0995 & 0.9173 & 0.8218 & 0.7656 & 0.7328 \\ [PERSON] [68] & 1.1144 & 0.9225 & 0.8246 & 0.7697 & 0.7377 \\ Boduroglu [69] & 1.1140 & 0.9250 & 0.8268 & 0.7692 & 0.7351 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Normalized SIFs \(F_{1}\) for the symmetric edge cracks problem (\(b/h=10.0\)).
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 4 Application and Validation
### Experimental Analysis of Faulty Gear
#### 4.1.3 Vmd  
In this method, the collected signal is decomposed with VMD first, and then the Hilbert envelope spectra (HES) [24] of each component signal can be obtained. The result in Figure 6 shows that the motor rotation frequency \(f_{r}\) and its double frequency and triple frequency can be seen in each subfigure of Figure 6, and the frequency of 288 Hz, which is very close to \(f_{z}\), can be seen in Figure 6b,d. However, the features are still not clear enough.
## 2 Principle of Noise Reduction Based on Singular Value Decomposition (SVD)
### Improved Method Based on the Curvature Spectrum of Incremental Singular Entropy
The location where the difference between two adjacent peaks has a swinging decline for the first time is selected as the demarcation point. The steps of selecting of the effective singular values and de-noised signal reconstruction are as follows:  
Input: the noisy signal sequence \(x^{\prime}=\{x_{1}{}^{\prime},x_{2}{}^{\prime},x_{3}{}^{\prime},\ldots,x_{n}{ }^{\prime}\}\); Output: the de-noised signal; Initialize: dimension p which is determined by Equation (2);Step 1. Building the Hankel matrix \(H\) in dimension \(p\). Step 2. Decomposing the matrix \(H\) by SVD, the singular value sequence \(S=\text{diag}(\alpha_{1},\,\alpha_{2},\,\alpha_{3},\,\ldots,\,\alpha_{p})\);  
Step 3. Calculate \(\Delta E_{i}\) (\(i=1,\,2,\,3,\,\ldots,\,p\)) and \(CE_{i}\) (\(i=1,\,2,\,3,\,\ldots,\,p\)) according to Equations (9)-(13). Step 4. Find out all maximum values of peaks and calculate the difference \(\Delta CE_{i}\) (\(1\leq i\leq p-1\)) as follows:  
\[\Delta CE_{j}=CE_{j}\ \ -\ CE_{j+1}\ \ (j=1,\ \ 2,\ \ \ldots,\ \ \text{i}\ -\ 1) \tag{14}\]  
here we choose the non-negative values of \(\Delta CE_{j}\) to follow the direction of the declines about the maximum peaks:  
\[\Delta CE_{k}\ =\ (\Delta CE_{k}\geq 0\ |\ 1\leq k\leq j) \tag{15}\]  
Step 5. Choose the effective singular values according to the strategy described below:  
\(1=1\):\(k\), \(k\) is the length of \(\Delta CE_{k}\) [\(\Delta CE^{\prime}\)] = find [\((\Delta CE_{1-1}-\Delta CE_{k})>(\Delta CE_{1}-\Delta CE_{1+1})\)];  
\(L_{\text{max}}=\Delta CE^{\prime}\); the difference declines in an infinite large degree for the first time. Use \(L_{\text{max}}\) to determine the number of effective singular values \(r\); if (the envelope waveform of curvature peak is convex at \(r\)) (select the first \(r\) effective singular values). Otherwise (select the first \(r-1\) effective singular values);  
Step 6. Reconstruct the de-noised signal in terms of Equations (5)-(7).
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 5 Limitation and Threat to Validity  
In this SLR, various ML techniques and fraud types were identified. We develop our protocols to promote external and internal validity as much as possible while answering the RQs. However, there are still some limitations and validity threats that can be encountered and mentioned here.  
1. This SLR is only limited to conference and journal papers that discuss machine learning (ML) in the context of detecting financial fraud. By using our search approach in the early stages of the review, several non-relevant research papers were identified and excluded from this review. This ensures that the selected research papers satisfied the criteria for the study. However, it is believed that using more sources, such as additional source books, would have further enhanced this review.
2. Although major databases were taken into consideration when exploring the research articles, there may be other digital libraries with relevant studies that were overlooked. We compared search terms and keywords to a well-known list of research studies to mitigate this limitation. However, some synonyms may be overlooked when searching for the keywords. The SLR protocol has been revised to address this problem by ensuring no essential terms are left out.
3. We restricted our search to only English-language articles. Thus, this results in linguistic bias because some related papers in this field of study may exist in other languages. However, fortunately, all the gathered papers in this study were written in English. As such, we have no language bias.
## Materials and Methods
### AMSPEC II Spectral Data  
The vegetation canopy spectral data was obtained from the AMSPEC II spectral observation system which was installed at a flux tower height of 36 m. This was a multi-angle spectral observation system consisting of a UniSpec-DC Spectral Analysis System (PP Systems, Amesbury, MA, USA), upstream fiber, downstream fiber, an automatic tilt rotation, a data transmission system (PTU-D46-17P70T), and a data storage system under the tower. The data storage system continuously archived the multi-angle observations of vegetation canopy spectral reflectivity.  
The upstream fiber was used to measure the downward radiation of the sun while the downstream fiber was used to automatically observe the spectral reflectance of the vegetation canopy. The spectral  
Figure 1: Location of the study site flux tower at the Dinghushan National Nature Reserve, Guangdong Province, China.  
range of the spectrometer was 300-1100 nm with a spectral resolution of 3.3 nm. The range of viewing zenith angle was set as 42\({}^{\circ}\) to 62\({}^{\circ}\) and the viewing azimuth angle was set from 10\({}^{\circ}\) to 160\({}^{\circ}\) and 190\({}^{\circ}\) to 340\({}^{\circ}\) with an angle step of 10\({}^{\circ}\). Data was archived at 15-min intervals over 24 h each day. In order to analyze the LUE calculated by the EC flux data, the spectral observation data per half-hour was selected from 9:00-17:00 every day during April 2014, to March 2015. First, the spectrometer data was retrieved. Then, the whiteboard correction parameters and the value of the central wavelength were used to control the quality of data. The whiteboard was a standard diffuse reflection reference for measurement. After the outliers were removed, the canopy reflectance at the wavelengths of 531 and 570 nm were extracted from the archived data. The PRI value was calculated by Equation (1):  
\[PRI=\frac{R_{531}-R_{570}}{R_{531}+R_{570}} \tag{1}\]  
where \(R_{531}\) and \(R_{570}\) represent the reflectivity of the vegetation canopy at 531 and 570 nm, respectively.
## 4 Results and Discussions
### Simulation Results with Number of Subcarriers Varied  
Figure 6 shows that when the number of subcarriers is 64, the bit error rate for the DFT-LS-WIENER technique at 15 dB SNR is 0.02285, which is significantly less than the L.S., LS-DFT, MMSE, MMSE-DFT techniques.
# A Novel DFA on AES: Based on Two-Byte Fault Model with Discontiguous Rows
## 2 Description of AES
### Encryption Process  
The round operations and key scheduling make up the AES encryption process. The following three stages of operation are performed on the round operations:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Encryption Rounds (R)** & **Key Schedule Rounds** \\ \hline AES-128 & 10 & 10 \\ AES-192 & 12 & 8 \\ AES-256 & 14 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the three types of AES encryption.  
Figure 1: Use of a 4*4 matrix to represent the state, the key, and the ciphertext. (**a**) The state; (**b**) the key; (**c**) the ciphertext.  
1. SubByte (\(SB\)) Layer.
2. Diffusion Layer: it consists of two sublayers of ShiftRow (\(SR\)) and MixColumn (\(MC\)).
3. AddRoundKey (\(ARK\)) Layer.  
The last round of AES-128, AES-192, and AES-256 encryption does not contain \(MC\). The encryption process of AES is represented in Figure 2. The 128 bits of input data are calculated through the R round to get the 128 bits of output data, and each round will have a corresponding round-key involved.  
The round-key is acquired through the key schedule. The same function 'T', consisting of RotWord, SubByte, and XOR with Rcon[\(i\)] will be applied to each round-key in the last column of the key scheduling.  
(a) RotWord: A cyclic rotation operation, such as a four-byte input (X1, X2, X3, X4) through the RotWord function will produce a four-byte output (X2, X3, X4, X1).  
(b) SubByte: A byte substitution operation in which each byte entered is mapped to another byte by a nonlinear substitution S box, similar to the S-byte of transformations in the round transform.  
(c) Rcon[\(i\)]: A 32-bit round constant word set defined as Rcon[\(i\)] = (\(RC[i\)], \(\{00\}\), \(\{00\}\), \(\{00\}\)), \(\{00\}\)), where \(RC[i\) is represented as the number on \(GF(2^{8})\). The specific values are shown in Table 2.  
The final two rounds of the AES-128 key schedule are depicted in Figure 3. It is necessary to know the round-key of any round to recover the AES-128 key, usually to recover the last round of keys K\({}^{10}\) {\(w(40)\), \(w(41)\), \(w(42)\), \(w(43)\)) (see the green part of Figure 3). Figure 3 only shows the process of the last two round-keys of AES-128, for all this, it contains all information of the initial key, therefore, the initial key can be recovered according to the key schedule.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(i\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline RC[\(i\)] & 01 & 02 & 04 & 08 & 10 & 20 & 40 & 80 & 1B & 36 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The value of each round of constants.  
Figure 3: AES–128 key schedule process.  
Figure 2: The encryption process of AES.
# Proof of Optimum (PoO): Consensus Model Based on Fairness and Efficiency in Blockchain
## 5 Application and Findings
### Consensus Nodes  
Consensus nodes, also called miners, can be defined as the actual suppliers of computing power to the network. Miners compete to solve TSP tasks issued by data nodes and are rewarded as a result. Nodes use genetic algorithms to solve TSP tasks. The behavior of the consensus nodes is described in Algorithm 1.  
The nodes obtain the TSPs they will solve and start producing solutions at the same time. Merely finding a good solution is not enough to create a block. At the same time, the extent to which genetic algorithm genes are gathered around the solution and how quickly the solution is found are taken into consideration. Miners look for solutions to TSPs using a genetic algorithm. The consensus node that finds the solution calculates the winning coefficient according to Equation (2) and sends it to the other consensus nodes. Nodes sort and choose the node with the highest winning coefficient among themselves. The winning node announces that it has won and broadcasts the block. Then, the node spreads the block it created to other nodes. It declares that it has created the block itself. Figure 3 shows the structure of the sample PoO block data. The data structure of the block consists of a header and body, similar to that of a regular blockchain. The block header contains the winner ID, the TSP, the previous block hash, and the Merkle tree root. The number of seconds in which TSP solution is found in the block body includes the solution density and the solution. There are verified transactions in the transactions section, while the Merkle tree root is used to prevent forgery by obtaining data blocks from a peer-to-peer network in an original way. A Merkle tree is a hash-based formation using cryptography [1]. This is why it is also known as a hash tree.  
Figure 3: PoO block structure with normal block.
## 3 Simulation of Underwater Electric Field Communication
### CSMA/CA Channel Backoff Algorithm Design and Simulation Analysis
#### 4.2.1 Software Environment Setup  
In this study, the Raspberry Pi served as the main control system for AUV multi-node communication, providing the operation of the CSMA/CA backoff protocol and the transmission and reception of communication information. First, the operating system needed to be installed on the Raspberry Pi, and the CSMA/CA backoff algorithm and data transmission and reception modules needed to be developed. The Raspberry Pi officially provides the Raspberry operating system, which is developed based on Linux and specifically designed for the Raspberry Pi, offering enhanced security and greater versatility.
# CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
## 4 Results  
### Dataset  
High-resolution remote sensing image data possess characteristics such as high spatial resolution and detailed terrain features, often making them sensitive to cloud and fog presence. Hence, for the experimental dataset, this study selected the GF1_WHU dataset [40]. Acquired from May 2013 to August 2016, the dataset comprises 108 scenes of GF-1 Wide Field of View (WFV) 2A-level images along with their corresponding reference masks. These reference masks are manually drawn through visual inspection, resulting in more precise delineation of cloud and cloud shadow boundaries, which facilitates better model fitting. Example graph of the dataset is shown in Figure 6. To reduce computational complexity, this study employed RGB channel image thumbnails. Given the focus on cloud detection, the dataset was further processed to remove cloud shadow annotations. Ultimately, it was resized to 777 images of dimensions 512\(\times\)512 for both training and testing. Among them, 538 images were allocated for training, while an additional 239 images were set aside for testing.  
Figure 5: An example of the bright channel and dark channel. (**a**) Input image. (**b**) Bright channel. (**c**) Dark channel. (**d**) Ground Truth.  
Figure 6: This figure illustrates representative instances from the dataset, showcasing images with varying cloud cover across different scenes.
## 3 Results
### Experiment  
In the first step of the experiments, the test of laser pulse generation with RZ format coding was performed. Figure 6 presents a 16-bit information frame in which 6 bits of \"1\" are written. The amplitude fluctuations of the pulses were observed depending on the quantity of \"1\"-bit. For a higher number of \"1\", these fluctuations were decreased, and a significant reduction in pulse amplitude was also noticed.  
One bit of \"1\" was placed in the frame to determine the maximum pulse frequency for RZ format coding. The maximum bit frequency equals 155 MHz. However, in this case, both laser pulse rise time and receiver signal bandwidth are the main limiting factors of the modulation rate. It can be observed on the shapes of the registered laser pulses with different time duration--for the shortest pulses, the pulse amplitude becomes twice lower when the pulse is reduced from 20 ns down to 5 ns.  
Further, a data transmission test of NRZ format coding was also carried out. The laser was driven using PRBS (pseudo-random bit sequence) signal of 12-bits frame with five bits of \"1\" (001101000110) using the same generator. For the developed OWC system, the maximum frequency for which the eye diagram was still recorded was 70 MHz (Figure 7a). An observed bath tube diagram with a bit error rate (BER) of \(10^{-9}\) was also presented in Figure 7b. For the frequencies above 78 MHz, the eye diagram was closed from time to time; therefore, for the described OWC configuration with \"on-shelf\" ICL, the maximum modulation frequency for NRZ format coding was defined at 70 MHz.  
Figure 6: A frame of pulses with 6 bits of “1”.  
Figure 7: (**a**) Eye diagram and (**b**) bathtube of NRZ format coding laser pulses at the maximal frequency of 70 MHz.
## 2 Materials and Methods
### Quantification of Oxidative Stress Markers and Antioxidant Responses in A. sydowii  
The mycelium was collected in a cell strainer and macerated with liquid nitrogen using a mortar and pestle. Cell lysis was achieved by adding 200 \(\upmu\)L of lysis buffer (0.3 M Tris pH 6.8, SDS 2% and glycerol 4%) to 100 mg of frozen macerated mycelium, with 0.3 g of 0.5 mm diameter glass beads. Samples were vortexed four times on lapses of 1 min and allowed to rest for another minute on ice. The supernatant was recovered by centrifugation and stored at \(-80\)\({}^{\circ}\)C until analysis.  
Hydrogen peroxide was quantified as total peroxides in cell extracts using the BIOXYTECH(r) H2O2-560(tm) Assay kit (Cat. 21024, OXIS International Inc., Portland, OR, USA). Briefly, this colorimetric assay is based on the oxidation of ferrous ions, which then bind to the xylenol orange dye to yield a colored complex. Sorbitol in the reaction enhances the oxidation of ferrous ions, which increases the assay sensitivity. The indicator dye production was evaluated by absorbance at 560 nm.  
Protein Advanced Oxidation Product (PAOP) level was determined by a modified [PERSON]'s method [56] using chloramine-T (_N_-chloro-p-toluene-sulfonamide) as standard. Chloramines were determined by production of triiodide ion from the oxidation of potassium iodide in solution at 340 nm.  
Lipid peroxidation was evaluated by the quantification of malondialdehydedehyde (MDA) and 4-hydroxyalkenals (4-HDA), two common degradation products of lipid peroxidation. MDA and 4-HDA were determined by reaction with _N_-methyl-2-phenylindole to form a chromophoric cyanine that can be quantified spectrophotometrically at 586 nm [57]. MDA concentration was calculated using a standard curve. To further determine cell susceptibility to lipid peroxidation, cell extracts were incubated with 2 mM copper sulphate at 37 \({}^{\circ}\)C for 24 h [58]. At the end of the incubation period, MDA and diene conjugate levels were also measured.  
Superoxide dismutase (SOD) activity was determined according to the [PERSON] method [59] based on the ability of the enzyme to inhibit the autoxidation of pyrogallol. The rate of autoxidation is obtained from the increase in absorbance at 420 nm, in the absence of superoxide dismutase. The levels of reduced glutathione (GSH) present in the sample were determined as described previously by [60]. Briefly, the GSH reacts with 5,5\({}^{\prime}\)-Dithiobis-(2-nitrobenzoic acid) dye (DTNB dye) to yield a colored compound that absorbs light at 412 nm. Concentration of GSH was determined by comparing the samples with a standard curve.
## 2 Flight Test Bed, Measurement System and Equipment, and Test Conditions
### Flight Test Bed and Measurement System Onboard
The accuracy of the pitot-rake measurement was approximately 3.5% which was calculated as a maximum error level obtained as a ratio of 1\(\sigma\) over an average velocity value measured for 1-min duration for all probes. The measurement system onboard the aircraft consisted of transducers for measuring pitot pressure, static pressure and static temperature, and the data recording system. Each measured signal was recorded by a commercially available laptop computer at a sampling frequency of 10 Hz. The air-data system (ADS) was equipped in the vicinity of the aircraft forebody as shown in Figure 1a for sensing air-data such as Mach number, pressure, temperature, angle of attack, and angle of sideslip. The accuracy of sensing air-data was, for example, 0.4% for measuring Mach number.