esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the results from ICrA, PCA, and SCA over sEMG data from elbow movements in the horizontal plane are obtained and compared. The three approaches found similar correlation pairs in consonance for the flexion and extension phases of the cyclic movements. ICrA and SCA produced matching results in terms of the number and type of the detected consonance dependencies. PCA confirmed the results obtained by ICrA and SCA and found two additional consonance pairs for the extension phases. Based on the obtained outcomes, two shorter optimized experimental protocols were proposed.  
The three methods are reliable and suitable for, separately or in combination, application in sEMG studies of upper limb movements in the horizontal plane. This investigation can be applied successfully in sports training and rehabilitation practice.  
Figure 3: Identical results obtained by ICrA, PCA, and SCA for twenty-eight (**a**) flexion phases and (**b**) extension phases.  
**Author Contributions:** Conceptualization, M.A. and S.A.; methodology, M.A., S.A. and R.R.; software. M.A.; validation. S.A. and M.A.; formal analysis, M.A. and S.A.; investigation, M.A., S.A. and R.R.; resources, S.A., M.A. and R.R.; data curation, M.A. and S.A.; writing--original draft preparation, M.A. and S.A.; writing--review and editing, M.A., S.A. and R.R.; visualization, S.A. and M.A.; supervision, S.A. and R.R.; project administration, S.A. and M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of the Institute of Biophysics and Biomedical Engineering. Bulgarian Academy of Sciences (202 ND/28 February 2022).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Experimental Section
### Construction of pCA24N-ortT-ydcY, pCA24N-ortT-ghoS, and pCA24N-ortT-UTR  
The putative antitoxin genes, _ydcY_ and _ghoS_ were PCR-amplified from _E. coli_ K12 BW25113 chromosomal DNA using primer pair _ydcY-SaI_-F/_ydcY-Hind_III-R and _ghoS-SaI_-F/_ghoS-Hind_III-R respectively. The forward primers were designed to include the synthetic RBS from pCA24N plasmid [60] (Table 3). The pCA24N-_ortT_-UTR plasmid was constructed by cloning the intervening DNA flanking _ortT_ from the BW25113 chromosome into pCA24N-_ortT_. To do so, the 217 bp upstream intergenic region (between _yncL_ and _ortT_), an inactivated _ortT_ (174 bp), and the 381 bp downstream intergenic region including _ydcY_ (a total of 772 bp, Figure S2D) was PCR amplified using UTR-F/R primer pair (Table 3). The chromosomal _ortT_ gene was inactivated by changing the start codon (_i.e._, ATG\(\rightarrow\)A\(\underline{\text{C}}\)G) via site-directed mutagenesis using the primer pair _ortT_-ACG-chrom-F/R (Table 3) so that the final plasmid (_i.e._, pCA24N-_ortT_-UTR) that was used in the growth experiment (Figure 3B) had only one copy of active _ortT_ like the other two plasmids (_i.e._, pCA24N-_ortT-ydcY_ and pCA24N-_ortT-ghoS_). PCR products were purified using a Promega PCR product purification kit (Promega, Madison, WI, USA). The purified PCR product and the vector pCA24N-_ortT_ were digested with _SaI_ and _Hind_III restriction enzymes (New England Biolabs, Beverly, MA, USA) and then ligated using T4 DNA ligase. The ligation mixture was transformed into BW25113 competent cells and the resulted plasmids were verified by sequencing.
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 1 Introduction
In Section 2, the principle of deceptive jamming detection and the geometric configuration among two cross-track antennas, jammers and jammed targets are provided. The detection scheme for the proposed method is presented in Section 3 and the basic steps of deceptive jamming extraction are further discussed in Section 4. In Section 5, experimental analysis of the proposed method is provided and conclusions are drawn in Section 6.
## 3 Proposed LDPC Architecture
### Block Diagram
The reconfigured approach facilitated a seamless calculation of minimums, thereby enhancing the overall performance of the decoder, which is demonstrated in the results section. The MSU processing unit receives Z sequences of \(dc_{max}\) values. It covers Z check node units, with each unit processing 19 values to find the two minimums. Every CN unit input is regulated by a \"2:1 Mux\", which provides a maximum stored value to complete its 19 inputs for layers with weights smaller than \(dc_{max}\). The MSU performs parallel computations of the first and second minimums (Min1 and Min2). It also determines the index of the first minimum while indicating the original sign of each LLR and its product. Remarkably, this process involves only \((2\times dc_{max}-3)\) comparison blocks. When considering BG1 with a rate of \(2/3\) and the maximum codeword length, the SOLL approach requires only 35 comparison blocks for each CN unit. This method involves two types of comparisons: Initially, the (Min-Max Block) MMB evaluates and sorts two inputs, assigning the smaller and bigger values to the corresponding 'S' and 'B' outputs, respectively. Subsequently, the (Min Block) MB identifies the minimum value among the 'B' outputs of the MMBs. The MB has a single output, 'S', which represents the second minimum output after the comparison is complete, as shown in Figure 4. Once the MMBs complete the comparison operation, they identify the minimum output as the smallest value between the 'S' outputs of the MMBs. Additionally, each MMB indicates the position of the smaller values. If the left input is smaller, we assign the binary '0' to the 'L' output and we assign '1' if the right input is smaller. A tree of \"2:1 Mux\" is then employed to generate an address, using the 'L' outputs of the MMBs to identify the min1 indexed position. Figure 4: Redesign of the SOLL approximation in Simulink for 5G-NR scenarios. The green color denotes MMB blocks, while the yellow color signifies MB blocks. The Add_Unit chain summarizes the Subs_Unit output and the NCN_msgs, which are both stored in MSU_RAM. This produces the updated processed data, which is then shifted back and sent to the Writing network. The processed data are combined with the unprocessed to form the complete LLRs frame, which is then stored in Lyr_RAM. Finally, we feed this combined data into the \"2:1 Mux\" for the upcoming decoding iterations. The decoder architecture needs only two main memories. First, the MSU_RAM block comprises several sub-memories, each corresponding to a layer in the base matrix and its weight. In the base graph, the first four layers' weights are \(dc=19\), requiring 19 sub-memories each, while the fifth layer weighs 3, requiring 3 sub-memories. Each of the sub-memory contains Z memory locations. Second, the Lay-Mem block includes 35 memory banks of Z units to store the updated data after each layer process.
## 2 Experimental Work
### Engineering Properties Analysis
#### 2.5.5 Permanent Deformation Analysis  
Wheel tracker test was performed to determine the rutting of the asphalt mixtures under loading cycles. Test was performed according to BS EN 12697-22. Due to shortage of time and less quantity of materials, three slabs were prepared with 0%, 1% and 3% CNT content and the gradation used was NHA Class B. First, Marshall method of mix design was carried out to determine the optimum binder content of all three samples. Marshall method of mix design was performed as per ASTM D1559 using hammer weight to 4.5 lbs., hammer drop height of 18 inches and application of 75 blows on each side (for heavy traffic) of the specimen. OBC for unmodified sample was 4.38% while for 1% and 3% CNT-modified samples it was 4.42% and 4.47%, respectively. Researchers have experienced both increases and decreases in the optimum binder content requirements when utilizing  
Figure 7: Dimensions of stub (mm) used in BBS test and piston setup.  
nanomaterials [35; 36]. In this study, an increase in optimum binder content was experienced with the increase in CNT dosage. The same trend in OBC by adding nanomaterial was observed by [PERSON] and [PERSON] [37]. This increase in OBC could be attributed to large surface area of CNTs used in this study. This could also be due to the increase in viscosity of the asphalt binder with the addition of addition of the nanomaterial which made binder stiff. A higher viscosity leads to a thicker binder film of the modified bitumen in the mix, thus increasing the binder volume in the mix [38; 39].  
Mixing of asphalt was done at 158 \(\pm\) 5 \({}^{\circ}\)C and compaction of asphalt was done at 145 \(\pm\) 5 \({}^{\circ}\)C. A 300 mm \(\times\) 300 mm \(\times\) 50 mm slab was prepared and compacted with the help of cooper roller compact in four different phases with 2.5, 3.5, 4 and 4.5 bar pressure to maintain 5.5 \(\pm\) 0.5% air voids. The number of passes kept for the specimen was 10,000 and load applied during the test was 700 \(\pm\) 20 N, while 40 \({}^{\circ}\)C temperature was kept constant during test.  
All the above-mentioned tests were performed at Taxila Institute of Transportation Engineering, University of Engineering and Technology Taxila, Pakistan.
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 2 Materials and Methods
### Notation  
In the proposed method, four features are extracted from low-resolution images, and the set of features is called the feature map. We used the gradient map proposed by [PERSON] et al. [13] as the feature map. Let \(x_{i}^{high}\in\mathbb{R}^{p^{2}\times 1}\) be a patch of size \(p\times p\) extracted from a high-resolution image, and \(x_{i}^{low}\in\mathbb{R}^{4p^{2}\times 1}\) be a set of four patches of size \(p\times p\) extracted from the feature map. The \(i\)th training data patch \(x_{i}\in\mathbb{R}^{5p^{2}\times 1}\) is defined as \(x_{i}=\left[\begin{array}{c}x_{i}^{high}\\ x_{low}^{low}\end{array}\right]\). \(X=\left\{x_{1},\cdots,x_{N_{t}}\right\}\in\mathbb{R}^{5p^{2}\times N_{t}}\), \(X^{high}=\left\{x_{1}^{high},\ \cdots,\ x_{N_{t}}^{high}\right\}\in\mathbb{R}^{p^{2} \times N_{t}}\), and \(X^{low}=\left\{x_{1}^{low},\ \cdots,\ x_{N_{t}}^{low}\right\}\in\mathbb{R}^{4p^{2} \times N_{t}}\) represent the set of patches of the training data, high-resolution training data, and low-resolution training data, respectively, where \(N_{t}\) is the number of training data. \(\overline{x}_{i}^{high}\) indicates the mean value of the intensity values of the \(i\)th training data patch \(x_{i}^{high}\). \(D=\left[\begin{array}{c}D^{high}\\ D^{low}\end{array}\right]=\left\{d_{1},\cdots,d_{N_{d}}\right\}\), \(D\in\mathbb{R}^{5p^{2}\times N_{d}}\) is called a dictionary and \(d_{i}=\left[\begin{array}{c}d_{i}^{high}\\ d_{i}^{low}\end{array}\right]\) is the \(i\)th atom of the dictionary where \(N_{d}\) is the number of atoms. \(d_{i}^{high}\in\mathbb{R}^{p^{2}\times 1}\) is a high-resolution dictionary atom of size \(p\times p\), and \(d_{i}^{low}\in\mathbb{R}^{4p^{2}\times 1}\) is a low-resolution dictionary atom of size \(p\times p\). All the atoms are arranged in raster scan order. The high-resolution dictionary \(D^{high}\in\mathbb{R}^{p^{2}\times N_{d}}\) and the low-resolution dictionary \(D^{low}\in\mathbb{R}^{4p^{2}\times N_{d}}\) are defined as \(D^{high}=\left\{d_{1}^{high},\ \cdots,\ d_{N_{t}}^{high}\right\}\) and \(D^{low}=\left\{d_{1}^{low},\ \cdots,\ d_{N_{t}}^{low}\right\}\), respectively. \(Y^{low}\in\mathbb{R}^{4p^{2}\times N_{patch}}\) indicates the feature map of the low-resolution input image to be reconstructed, where \(N_{patch}\) is the number of patches of the input image. \(I_{sr}\in\mathbb{R}^{p^{2}\times N_{patch}}\) represents the reconstructed high-resolution image. \(\overline{I}_{sr}\in\mathbb{R}^{p^{2}\times N_{patch}}\) represents the mean value of the intensity values of the reconstructed high-resolution image patch. The sparse representations are denoted as \(\alpha\in\mathbb{R}^{N_{t}\times N_{patch}}\) and \(\beta\in\mathbb{R}^{N_{d}\times N_{t}}\), and \(\lambda\) represents the sparsity regularization parameter.
## 4 Results and Discussion
### Structural Equation Modeling (SEM)
Anonymous self-reported surveys provide participants with the opportunity to relate their past involvement in events that were against the traffic rules and regulations. Therefore, in this study, the use of self-reported perceptions, behavior, and positive affect of the motorcyclists while riding on the road is applicable. Based on the results obtained, increasing awareness of traffic rules and traffic safety could improve the practice of road safety, and this could be provided in schools and universities, households, and workplaces. This mainly involves the civilized values of society [28]. Educational awareness initiatives could reduce the engagement of motorcyclists' personal riding behavior, such as frequently changing lane to overtake the vehicle in front (estimate value = 0.456), exceeding speed limit even feeling unsafe (estimate value = 0.434), and riding fast on curve (estimate value = 0.406). Enforcement of traffic rules and regulations is suggested to complement the educational initiatives in order to effectively reduce the number of traffic violations [27]. Thus, this could effectively counter the significant risky riding behavior of the motorcyclists, such as racing with other vehicles (estimate value = 0.347) and using phones while riding (estimate value = 0.297). Concurrently, findings from this study also show that age and experience are of little significance for any of the three latent variables--risk perception, risky affect, and risky behavior. This finding may be distorted by the age of the majority of the sample, with most respondents being between the ages of 21 and 25. Therefore, in a future study, a higher percentage of riders with higher age and experience should be included to obtain more consistent findings.
## 2 Experimental Principle and Setup
The distance between the PC1 and M3 is 0.8 m, corresponding to a round-trip time about 5.3 ns. Without voltage of the PC1, the reflected seed from the M3 will pass through the PBS2 and FR1, then enter into the dump. While with a quarter-wavelength voltage of the PC1, the pulse sequence with a specified range of repetition rate can be selected with polarization changed to \(s\)-polarization after double-passing through PC1 and then output by the PBS2. The switching time of the two Pockels cells in our setup is adjustable from 25 to 200 ns with a rising and falling time about 5 ns. The switch time of the PC1 is set to be 20 ns. With the polarization changed to \(p\)-polarization by HWP3, the selected pulses enter into the regenerative amplifier. The second optical isolator formed by a Brewster angle polarizer P1, HWP4, and FR2 is placed in front of the regenerative amplifier which is used to output the regenerative amplified pulse. The laser pulse with \(p\)-polarization is injected into the regenerative amplifier by the P2. The length of the whole cavity is 1.8 m, corresponding to a cavity round-trip time of 12 ns. The gain medium is 0.5 at. % doped Nd:YVO\({}_{4}\) with a size of 3 \(\times\) 3 \(\times\) 20 mm\({}^{3}\) and double-end-wedged cut at 2\({}^{\circ}\). Both sides of the Nd:YVO\({}_{4}\) have anti-reflective (AR) coating at the 1064 nm and 888 nm wavelength. A 50 W, 888 nm fiber-coupled laser diode with a numerical aperture of 0.22 and a diameter of 400 \(\upmu\)m is used as the pump source. The coupling ratio of the pump beam is 1:3 into the Nd:YVO\({}_{4}\) crystal. M4 and M8 are two concave mirrors with R = \(-\)2000 mm; M4 and M7 are two 1064 nm high-reflection (HR) convex mirrors with R = 1500 mm in which M4 is also 888 nm AR coated. Reflected by an end mirror M4, the seed beam double passes the quarter-wave plate (HWP) and reflects through P2 with \(s\)-polarization. Then the PC2 inside the cavity is switched on with a quarter-wavelength voltage and the seed laser continues to make round trips in the regenerative cavity with \(s\)-polarization until the PC2 is switched off. The regenerative amplified pulses are reflected by P1 with \(s\)-polarization, and then enter into the traveling-wave amplifier. To further amplify the output power, one stage traveling-wave Nd:YVO\({}_{4}\) amplifier is adopted. A Nd:YVO\({}_{4}\) crystal with the dimensions of 4 \(\times\) 4 \(\times\) 30 mm\({}^{3}\) is selected as gain medium with 0.5 at. % Nd\({}^{3+}\)-doped and at a 2\({}^{\circ}\) angle. The pumping source of the traveling-wave amplifier has the same parameters as the regenerative amplifier with a coupling ratio of 1:4. L is a concave lens with f = \(-\)360 mm used to compensate the thermal lens effect of the Nd:YVO\({}_{4}\) crystal. M10 and M11 are two 888 nm AR & 1064 nm HR coated 45\({}^{\circ}\) plane mirror. The single-pass amplified pulse is output from the M11.
## 5 Methodology
### Methods
#### 5.2.1 Synchronization  
To synchronize the received data and detect the beginning of the packet, we added a start frame delimiter (SFD) with a duration of three symbol periods.An SFD must differ from any symbol being transmitted, ensuring easy detectability. It comprised two periods of fast transitions and one period of no signal. The particular shape of the SFD, was designed for easy detection at the receiver end. In Figure 7, we show the start frame delimiter at the transmitter and how these data were received at the event camera.  
It can be noted that, due to various factors, the received signal may not capture all transitions from the transmitted signal. Nonetheless, it deviates sufficiently from the shape of n-pulse symbols, making it distinguishable. For detecting the SFD, the demodulator counts the number of transitions from positive events, followed by negative events throughout 2 symbol periods. If the demodulator registers a count exceeding the potential number of maximum transitions for n-pulse-modulated symbols, it effectively detects the start of a frame.
## 3 Methodology
### Reduction in Accumulated Error  
In this last stage, if we find a satellite keypoint in the current image, we use (11) to apply the coordinates correction with the offset between the keypoint in pixel coordinates and the center of the image (Figure 15). Then, we transform this offset to a latitude/longitude offset with GDAL.  
\[\left[\begin{array}{c}\text{Latitude}_{corr}\\ \text{Longitude}_{corr}\end{array}\right]=\left[\begin{array}{c}\text{ Latitude}_{(kpt)}\\ \text{Longitude}_{(kpt)}\end{array}\right]-\varphi\bigg{(}sR_{\psi}\bigg{[} \begin{array}{c}x-c_{x}\\ y-c_{y}\end{array}\bigg{]}\bigg{)} \tag{11}\]  
where Latitude\({}_{(kpt)}\) and Longitude\({}_{(kpt)}\) are the keypoint coordinates in the satellite image. \(x\) and \(y\) are the keypoint coordinates in the image. \(c_{x}\) and \(c_{y}\) are the image center. \(\psi\) is the  
Figure 14: Quad-tree scheme. It divides a space into four quadrants or regions, recursively breaking down each quadrant into smaller sub-quadrants. This process continues until a specified level of detail is reached or the region contains no more than a certain number of data points [67].  
Figure 15: Offset from the keypoint to the image center. The red dots represent the matched keypoint, and the yellow dotted arrow is the offset to the the center of the image, which is marked with the red cross.  
yaw orientation, \(R_{\psi}\) is the rotation matrix, \(s\) the scale, and \(\varphi(\cdot)\) is the transformation from UTM to latitude/longitude.  
Figure 16 is utilized to provide a concise overview of this algorithm. There is a preprocessing step where we obtain the keypoints from the satellite image. If, during the flight, we find one of these keypoints, we correct our geolocalization, otherwise, we estimate the coordinates with the visual odometry phase.
## 4 Conclusions  
One of the most important aspects to be considered in the biorefinery is finding green solutions in the extraction techniques and at the same time preserving the bioactivity of the extracts avoiding their degradation during extraction processes.  
Extraction in an autoclave produced a high number of compounds in the modified chestnut and beech, with a greater quantity of alkyl compounds only in the heat-treated wood of both wood species. Derivatives of fatty acids have been extracted from both thermo-modified wood species, in particular octadecanoic acid, but chestnut and beech showed a different response to the heat process.  
Beech did not show substantial changes in the compounds due to less degradation after the heating process. Among others, squalene and ar-tumerone represented the main bioactive compounds.  
In recent years, the recovery of bioactive compounds from plant biomass has increased. Beech wood is recently used mainly as firewood, so it is interesting to underline the recovery of this wood and its biologically active compounds before burning material.  
In contrast, sweet chestnut showed a greater degradation after modification, with a reduction in compounds in the modified samples compared to the unmodified ones. The main biologically active compounds detected only in the unmodified chestnut samples were apocynin and ar-tumerone.  
The contrasting results observed in our study can be attributed to the species used and their different microstructural characteristics.  
In both species, the importance of the Solid-Phase Micro Extraction technique (SPME), complementary to Gas Chromatographic-Mass Spectrometric analyses (GC-MS), is evident to obtain a greater overview of the available biologically active volatile and non-volatile compounds with potential use in the biological, pharmaceutical and chemical fields.  
The application of natural wood extractives could be an important opportunity for the economic development of mountain areas due to the enormous quantity of biomass that can be used as a biorefinery. Additionally, further investigation should be conducted on such extracts to determine their potential biological activities and evaluate their applications as measured dietary supplements, nutraceuticals and natural health-promoting compounds.  
Conceptualization, writing, and original draft preparation by M.M. and L.T.; methodology, M.M., V.L.G. and T.L.; literature review, M.M. and L.T.; supervision, M.D.; funding acquisition, L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData are contained within the article.  
Conflicts of InterestThe authors declare no conflict of interest.
# How Deeply to Fine-Tune a Convolutional Neural Network: A Case Study Using a Histopathology Dataset
## 3 Methodology
### Datasets Used  
CNN algorithms, especially for the state-of-the-art architectures that are very deep, are very data-hungry and need hundreds of thousands of images to be accurately trained.  
Two datasets were used in this study. One very large dataset--the ImageNet dataset, also called the original dataset--was used to train the CNN. The second dataset, the histopathology dataset, was the target dataset that we wanted to classify and that we used to fine-tune the weights that the ImageNet dataset learned. A brief description of both datasets follows.