esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Biological Applications of EUV/SXR Microscopes Based on a Double Stream Gas-Puff Target Source  
### A 2D Full Field Imaging of Biological Samples  
#### 4.1.1 EUV Bioimaging  
In this section, few biological samples, imaged with the EUV microscope, are commented on and discussed. All of the images were acquired at a 10 Hz source repetition rate, the detector temperature was \(-20\)\({}^{\circ}\)C and the readout rate was \(f_{\mathrm{s}}\) = 50 kHz. The presented images were acquired after 200 EUV pulses and a EUV exposure of \(\sim\)20 s.  
CT 26 Fibroblast Cells from Mus musculus Colon Carcinoma  
The first image, presented in Figure 13, shows CT 26 fibroblast cells from _Mus musculus_ mouse colon carcinoma (strain BALB/c). The samples were placed on top of a 30 nm Si\({}_{3}\)N\({}_{4}\) membrane (from Silson Ltd., Warwickshire, England). The sample in Figure 13a was prepared with 30% hexamethyldisilazane (HDMS) in absolute ethanol (EOH), while the sample in Figure 13b was prepared on top of a similar membrane, but with gradual dehydration in EtOH series (final concentration 70%), without any fixation procedure. The insets in Figure 13a,b, show small external features of the cells with a size varying from 60 to 120 nm. In the green line profile traced through the feature in the inset of Figure 13b (120 nm in size), the KE test (Figure 13c) was performed [71]. The slope, rising from 0 to 95%, indicates a Rayleigh (full-pitch) resolution of 66 nm, which corresponds to a 33 nm half-pitch spatial resolution. However, it should be taken into account that the feature that was imaged is not fully opaque and transmits some EUV radiation, so the conditions for the estimation of the KE resolution test were not totally fulfilled.  
Diatoms  
Figure 14 shows a sample of diatoms, originally stored in a 60% EtOH solution, and later deposited on top of a 30 nm Si\({}_{3}\)N\({}_{4}\) membrane. The sample was imaged with the optical microscope (a), and with the EUV microscope (b). EUV radiation allows resolving of external features, barely visible in the visible wavelength range, approaching the (half-pitch) spatial resolution of the microscope. However, as can be seen, the EUV radiation (b) is absorbed by the sample and it made the sample more opaque,  
Figure 13: (**a,b**) EUV images of CT 26 fibroblast cells. Small features approaching the microscope resolution are shown in the zoomed insets. The zoom in (**b**) depicts the line profile through the smallest visible feature (indicated with a green line), showing a 0–95% slope rise of 66 nm. (**c**) Shows the KE test calculated over the green line of the (**b**) inset. Figure modified, based on [71; 76].  
hiding the portion of the sample where the diatoms were mainly agglomerated. The insert shows a portion of the sample in which it was possible to resolve small features, of the order of \(\sim\)135 nm.
# Study of Implantation Defects in CVD Graphene by Optical and Electrical Methods
## 4 Discussion and Conclusions
We used semi-insulating 6H-SiC (0001), so for a low implantation range substrate does not influence the measured electrical properties. For the highest implantation fluences the electrical conductivity of graphene decreases to zero (due to mobility decreasing to zero). If implantation by He\({}^{+}\) or N\({}^{+}\) ions would convert damaged semi-isolated SiC to partly conductive layer it would be possible to measure a conductivity of the graphene/SiC system for the highest implantation fluence. However, this is not the case, the system of graphene/SiC does not conduct for the highest fluences of He\({}^{+}\) or N\({}^{+}\) implanted fluences as well. Doping of SiC by nitrogen ion implantation usually requires elevated temperature of the implanted sample during ion implantation process and subsequent annealing at high temperature as was presented in [31; 32]. In our experiments, ion implantation was performed at room temperature and no annealing was applied after ion implantation. At these conditions SiC most probably was not doped by implanted nitrogen ions and did not affect electrical measurements. In conclusion, our results proved that ion beam bombardment generates defects which act as neutral scattering centers in the graphene diminishing the carrier's mobility. Defect sizes estimated with both experimental methods were different, however, of the same order of magnitude. The differences of defect sizes may be due to the different physical mechanism probed by the applied methods. However, the obtained defect sizes in both methods were essentially larger than for simple graphene vacancies. Thus, the most likely, defects generated in implantation process are predominantly reconstructed divacances or larger vacancy complexes. Conceptualization, P.C., G.G. and J.M.B. ; methodology, P.C., G.G. and J.M.B. ; validation, J.M.B., P.C. and G.G. ; formal analysis, G.G., P.C. and J.M.B. ; investigation, P.C. and G.G. ; writing--original draft preparation, G.G., J.M.B. and P.C. ; writing--review and editing, J.M.B., G.G. and P.C. This research was funded by European Union Horizon 2020 research and innovation programme under grant agreement No. 785219 and by National Centre for Research and Development (grant PBS 3/B6/24/2015). Sample of graphene was grown in the Department of Epitaxy and Characterization of IEMT. The authors declare no conflict of interest.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 4 High-Order QAM and APSK Modulation and Demodulation
### APSK Soft Demodulation  
Due to the different constellation structures of APSK and QAM, the real and imaginary parts of APSK are in a nonlinear relationship. Unlike QAM, the modulation symbols of APSK cannot be divided into \(I\), \(Q\) branch components. When making LLR decisions, the number of bits in the APSK modulation symbols cannot be divided into real and imaginary bits. Instead, the \(LLR\) of a certain bit position (0 or 1) of all modulation symbols in the constellation diagram must be accumulated. Let \(b_{k,i}\) be the \(i\)-th bit of the \(k\)-th APSK mapping symbol, and its LLR definition can be expressed as:  
\[LLR(b_{k,i})=\log\left(\frac{\sum_{s=S_{i}^{(0)}}\exp\left(-\frac{h^{2}|y_{k}-s |^{2}}{2\sigma^{2}}\right)}{\sum_{s=S_{i}^{(1)}}\exp\left(-\frac{h^{2}|y_{k}-s |^{2}}{2\sigma^{2}}\right)}\right) \tag{33}\]  
where \(k=1,2,\ldots,M^{\prime}\), and \(M^{\prime}\) is the number of modulation symbols in APSK; \(i=1,2,\ldots,N^{\prime}\), and \(N^{\prime}\) is the number of bits contained in each APSK modulation symbol; \(S_{i,j}^{(0)}\) and \(S_{i,j}^{(1)}\) are the sets of all modulation symbols on the constellation diagram in which the \(i\)-th bit is 0 or 1, respectively; \(y_{k}\) is the \(k\)-th received symbol; \(\sigma^{2}\) represents the variance of the impulse noise, and \(h\) represents the channel gain.  
(33) can be simplified by replacing the numerator and denominator sums with their maximum sum as follows [26]:  
\[LLR(b_{k,i})=\frac{1}{2\sigma^{2}}\left(\min_{s\in S_{i}^{(1)}}\left(h^{2}|y_{k }-s|^{2}\right)-\min_{s\in S_{i}^{(0)}}\left(h^{2}|y_{k}-s|^{2}\right)\right) \tag{34}\]  
Finally, the LLRs of each bit can be used to make decisions and demodulate the correct bit decisions for the \(N^{\prime}\) bits of the received modulation symbol \(y\) by:  
\[\begin{cases}b_{k,i}=0,\;if\;LLR(b_{k,i})\geq 0\\ b_{k,i}=1,\;if\;LLR(b_{k,i})<0\end{cases} \tag{35}\]  
Figure 5: 16 APSK Constellation Mapping Diagram.  
Therefore, for the 16 APSK modulation symbol containing 4 bits, the LLRs of the 4 bits in the received symbol \(y\) after being affected by noise can be expressed as:  
\[LLR(b_{k,i})_{(i=1,2,3,4)}=\frac{1}{2\sigma^{2}}\left(\min_{s\in S_{i}^{(1)}} \left(h^{2}|y_{k}-s|^{2}\right)-\min_{s\in S_{i}^{(0)}}\left(h^{2}|y_{k}-s|^{2} \right)\right) \tag{36}\]  
According to the decision criterion in (35), the LLRs of the 4 bits can be used to make decisions and demodulate the 4-bit values in the 16 APSK modulation symbol.
## 1 Introduction
### Static Beam Shaping to Optimize Laser Beam Fusion Cutting  
The application-oriented research focuses on optimizations in the case of LBFC primarily increasing cutting speed or cut edge quality. A cut edge is of high quality by an absence of burr, together with a homogenous smooth surface without striations [5, 6, 7, 8]. Researchers investigated various methods to improve the cutting result, such as controlling the intensity distribution [9, 10, 11], spot size [7, 12, 13], spot shape [10, 14], and focal position [7, 15, 16, 17, 18]. All these methods have in common that they belong to static beam shaping. This means that the properties of the laser beam are modified before the cutting process takes place [19, 20]. However, achieving a high cutting speed demands different process conditions than realizing a high quality cut edge [1; 2; 3; 4]. In summary, the achieved process improvements of the static beam shaping methods do not allow a high cutting speed while maintaining high quality for thick metal plates.
# Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents
## 4 HybridSumm: A Hybrid Summarization Model for Maritime Judgment Documents
### RoBERTa-DCNN-GR: An Extractive Summarization Model
#### 4.1.2 Vector Representations of Sentences  
To obtain vector representations of sentences in maritime judgment documents, the raw text must be converted into the standard input format of RoBERTa-wwm-ext, which involves four processing stages (as shown in Figure 3):  
1. Text serialization. Following BERT's sentence vector generation paradigm, we prepend the [CLS] token and append the [SEP] token to each sentence. The final-layer hidden state of the [CLS] token is extracted as the sentence vector. Subsequently, the text is tokenized into subwords using RoBERTa-wwm-ext's WordPiece tokenizer, mapped to token ID sequences via its pre-trained vocabulary, and processed with padding/truncation to a fixed length.
2. Token embeddings. Each subword token is converted into a vector representation through a pre-trained 768-dimensional embedding matrix. These vectors are then ordered according to the original token sequence to form the input representation.
3. Segment embeddings. Segment embeddings are primarily designed to distinguish between multiple sentences in the input text. However, they are scarcely used in RoBERTa-wwm-ext due to pre-training optimizations like dynamic masking. Following the model's default configuration, all inputs employ all-zero segment embeddings.
4. Position embeddings. Position embeddings encode sequential information of tokens. These embeddings share the same dimensionality as token embeddings (768 dimensions). We employ learnable absolute positional embeddings, where each position index is associated with a unique trainable vector. During training, these embeddings are optimized through backpropagation to better capture positional dependencies between tokens, thereby enhancing contextual semantic modeling.  
After generating token embeddings, segment embeddings, and position embeddings through preprocessing, these embeddings are fused via element-wise summation to form  
Figure 3: Input representation.  
the input representation for RoBERTa-wwm-ext. To derive sentence vector representations, we adopt a strategy distinct from BERT: performing global average pooling over all output token vectors (including [CLS] and [SEP]), as mathematically formalized in Equation (1):  
\[\mathbf{h}_{sentence}=\frac{1}{n}\sum_{i=1}^{n}\mathbf{h}_{i} \tag{1}\]  
where \(\mathbf{h}_{i}\) represents the hidden state of the \(i\)-th token, and \(n\) is the sequence length.
# Hamming Code Strategy for Medical Image Sharing
## 3 Hamming Codes
### Using (15,11) Hamming Code
Each shadow looks as if it is a result of the overlapping of the two original images. From one shadow it is obvious that we could not reconstruct the corresponding original image, let alone the other original. Table 2 confirms that using MSBs gives a better result than using LSBs. Since we use 7 out of 8 bits in a pixel in (15,11) Hamming algorithms, the difference in PSNRs is subtle. Compared with (7, 4) Hamming algorithms, using (15,11) Hamming yields higher PSNRs in both LSBs and MSBs cases. Visually we can see that the result in (7,4) Hamming with MSBs is the best choice for our scheme. To further demonstrate the visual effects provided by our schemes, the comparisons in specific features that have much difference between the original images and the corresponding shadows are depicted in Table 3, where the size of images is \(512\times 512\). Among which, the former image is computed tomography and the other belongs to other modality. The more different the image is, the better the result achieves. As can be seen from Table 3, some of the outlines have blurred, especially in the experiments of pairs 2 and 4, where the shadow images are produced by using MSBs. It means that those images are badly damaged so that they would not be used in diagnosis. It is concluded that the proposed scheme does well in protecting the confidentiality of a patient's health. The computation cost of performing the proposed algorithms is low due to the efficiency of computing Hamming code. Implementing Hamming code on the graphics processing unit (GPU) [30] can speed up the process 99 times compared with the normal sequential approach, and ensure a fast response to those applications that are time-sensitive. Thus, our proposed scheme can be implemented in such an efficient way and we can offer instant medical images for doctors, which is valuable considering the fact that doctors are known to be very busy when making rounds and visiting patients in the inpatient department. To prove this point better, the execution time of creating shadows and reconstructing original images corresponding to experiments in Table 1, are listed in Table 4. It can be observed from Table 4 that the average execution time of creating shadows and recovering the original image are around 0.6857s and 0.7447s, respectively. It is so fast that is suitable to respond to time-sensitive applications. It should be noted that all images used in Table 4 are 8-bit depth grey-scale images (each one is a single 2D slice) with the size of \(512\times 512\). \begin{table}
\begin{tabular}{c c} \hline \hline
**Original Medical Images** & **Shadow Images Produced by Using (7,4) Hamming Code (MSBs)** \\ \hline Pair 1 & Pair 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons of specific features between original images and shadow images using the proposed scheme.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 2 Related Work
### Vision Transformer for MIS  
In recent years, Transformer-based network architectures have proven to be highly effective in advancing the field of MIS. These architectures have attracted considerable attention for their capability to surmount the constraints traditionally associated with CNNs in capturing global contextual information. Among the purely Transformer-based approaches to MIS, TransDeepLab was introduced by [PERSON] et al. [18]. This method, designed specifically for skin damage segmentation, incorporates various windowing strategies within the DeepLab framework. Similarly, [PERSON] et al. [19] presented the MissFormer network, which employs a multi-scale global information fusion mechanism to substantially improve segmentation performance. Additionally, Swin-Unet, developed by [PERSON] et al. [17],utilizes the Swin Transformer as a feature extractor. This model enhances its capability by incorporating a dynamic window mechanism, thus addressing the inherent limitations of the visual Transformer (ViT) in managing local inter-window interactions. Moreover, DS-TransUNet, proposed by [PERSON] et al. [34], features a dual-branch Swin Transformer encoder to extract multi-scale features. It includes an innovative Transformer interactive fusion (TIF) module designed to effectively integrate multi-scale features from the encoder. This is achieved by fostering long-range dependencies between features of varying scales through a self-attention mechanism. In the realm of 3D medical image processing, the UNETR network introduced by [PERSON] et al. [35] integrates a Transformer architecture with a U-shaped network design. This approach capitalizes on the Transformer encoder's ability to serialize representations of 3D volumetric data, thereby significantly enhancing the capture of global multi-scale features. Despite these significant advancements, the application of Transformers in modeling long-range dependencies continues to face challenges, particularly in local feature extraction, where CNNs still hold a comparative advantage, especially in tasks involving MIS.
## 1 Introduction  
In the past several decades, the surface plasmonics of self-assembled metallic nanostructures has been utilized for various biomedical applications such as biosensors, plasmonic filters, diagnostic devices, and photothermal-induced cancer therapy [1, 2]. To precisely and rapidly detect biomolecules, micro/nano-scale structures composed of noble metals, e.g., gold (Au) and silver (Ag), play a critical role in the field of plasmonics. Surface-enhanced Raman spectroscopy (SERS) demonstrates that metal nanostructures can control the surface optical properties [3]. An ideal SERS substrate should have high uniformity and field enhancement. Such substrates can be prepared on a wafer scale, and fluctuations of SERS signals on such highly uniform, high-performance plasmonic metasurfaces can be demonstrated in label-free microscopy [4]. However, it is still difficult to structure nanomaterials due to the limitations of controlling the single nanoparticle without well-confined conditions such as atomic force microscopic (AFM) manipulation [5]. Furthermore, the fabrication cost of SERS substrates needs to be reduced via proper manufacturing techniques [6].  
Among the conventional self-assembly methods for nanomaterials, the micro-encapsulation method in immiscible solution has been considered a promising technique due to its simplicity and cost-effectiveness [7, 8]. For example, many researchers have tried to implement micro-encapsulationto enclose solid or fluid materials inside a microscale membrane made of two-dimensional (2D) soluble layers, which would allow for the control of dosing frequency and prevent degradation of the pharmaceutical materials. Thanks to the benefits of micro-encapsulation, this approach to exploit the SERS effect has also been investigated [9; 10; 11]. Although these studies proposed multifunctional micro-encapsulation platforms for pharmaceutical analysis or biosensor development, there still remains an unexplored area in investigating and manipulating one-dimensional (1D) metal nanowire-based encapsulation for the SERS effect. From the perspective of material properties, 1D nanomaterials have great mechanical flexibility and high thermal or electrical conductivity [12; 13]. If SERS application can be adequately explored using these nanomaterials and micro-encapsulation methods, a newly proposed approach is expected, which could be helpful in future biomedical engineering applications [14; 15].  
In the present study, a formation method for micro-ring materials using micro-encapsulation in immiscible solution was introduced. In order to fabricate the ring structure, metallic nanowires and emulsion liquid droplets were used. In addition, a pinched flow fractionation (PFF) microchannel was experimentally investigated to uniformly and continuously separate the Ag nanowire micro-ring structure in different sizes. Printable ink containing Ag nanowire micro-ring structures was produced through a classification process. Lastly, a printing pattern for the ring-based ink was proposed and the ring patterns were characterized.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 4 Simulation Experiment and Theoretical Analysis
### Analysis of Security
#### 4.3.1 Histogram Analysis of Encrypted Image  
The histogram of the encrypted image should be uniformly distributed, which is obviously different from the histogram of the original image. In mathematical calculation, the variance of encrypted image is much smaller than that of original image. If \(hist_{i}(i=0,1,\cdots,255)\) represents the histogram of an image, the equation for calculating the variance is:  
Figure 7: PSNR performance comparison.  
\[S=\frac{1}{256}\sum_{i=0}^{255}{(\textit{hist}_{i}-\frac{1}{256}\sum_{i=0}^{255} \textit{hist}_{i})} \tag{24}\]  
The histograms and variances of the images in Figure 6a,b,f,g,k,l,p,q are shown in Figure 8, in which the histogram of the encrypted image is evenly distributed.  
Figure 8: The histograms and variances of the images in Figure 6a,b,f,g,k,l,p,q.
# Impact of Partial Replacement of Cement with a Blend of Marble and Granite Waste Powder on Mortar
## 3 Results and Discussion
### Effects of MGWP on Durability Properties of Mortar  
#### 3.3.1 Sulfate Attack Resistance  
The compressive strength loss of all mortar samples due to exposure to a sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)) solution was evaluated to determine their ability to withstand sulfate attack. The results are presented in Figure 10, which shows the loss in compressive strength of the mortar specimens at various curing periods. It is apparent that the compressive strength loss of the mortar containing 10% MGWP is lower than that of the other mixes at all curing ages, indicating its superior ability to resist sulfate attack. This enhancement in resistance could be primarily attributed to the presence of GWP, which is classified as a type N pozzolan and acts as a micro-filler, resulting in higher density and chemical attack resistance [37,38].  
Figure 10: Sulfate attack strength loss in different curing ages.  
Figure 9: Ultrasonic pulse velocity of mortar samples with varying percentages of MGWP at different curing ages.