esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Microstructure of Geopolymers  
#### 3.2.1 Phase Analysis  
Figure 4 shows the XRD diffractograms of the geopolymer materials prepared using an 8 M NaOH activator solution, different amounts of calcium hydroxide, and a curing time of 28 days. The typical diffraction peaks representing the phases of quartz (20 = 20.56\({}^{\circ}\), 26.6\({}^{\circ}\)), mullite (20 = 26.1\({}^{\circ}\), 31.2\({}^{\circ}\)), kaolinite (20 = 12.2\({}^{\circ}\), 24.8\({}^{\circ}\)), illite (20 = 38.5\({}^{\circ}\)), hematite (20 = 33.1\({}^{\circ}\), 53.8\({}^{\circ}\)), gibbsite (20 = 18.2\({}^{\circ}\), 20.2\({}^{\circ}\)), and sodalite (20 = 23.9\({}^{\circ}\)) in raw materials were also observed with relatively low intensity in the prepared geopolymer materials [15; 16; 31]. It is noticeable that the typical peaks at 20 = 24.2\({}^{\circ}\), 29.4\({}^{\circ}\) and 20 = 29.4\({}^{\circ}\), 31.1\({}^{\circ}\), 48.5\({}^{\circ}\), which are the characteristics of C-A-S-H [2; 35] and C-S-H [36; 37; 38], respectively, were found for the obtained geopolymers. The formation of C-A-S-H, which is the most stable gel type in geopolymer, is believed to play a crucial role in enhancing the compressive strength of the materials investigated in this study [35]. Moreover, the rise in intensity of the peak at 20 = 29.4\({}^{\circ}\), which was clearly observed, could confirm that a greater amount of C-S-H/C-A-S-H was formed in the geopolymer materials with an increasing amount of added calcium hydroxide. Combining the results of intensity increase at 20 = 48.5\({}^{\circ}\), which was clearly observed for the material prepared with 75 g of calcium hydroxide, it could be concluded that more C-S-H was formed with a higher amount of added calcium hydroxide. The  
Figure 3: Compressive strength of geopolymeric materials vs. the amount of calcium hydroxide at different concentrations of NaOH: 4 M (**a**), 6 M (**b**), 8 M (**c**), 10 M (**d**).  
presence of C-S-H and C-A-S-H phases confirmed by XRD analysis highly supports the results of compressive strength discussed in the previous section.
# Development of Inkjet Printable Formulations Based on Polyorganosilazane and Divinylbenzene
## 3 Results and Discussion
### Inkjet Printing  
First, printing experiments of the developed formulations were conducted by a PixDro LP50 inkjet printer. All formulated inks were printable. Figure 8a-c shows the printed single layer of the inks DVB15, DVB60, and DVB75 on a silicon wafer with a resolution of 450 \(\times\) 450 dpi.  
The temperature of the print head was 27 \({}^{\circ}\)C for the inks DVB60 and DVB75 and 40 \({}^{\circ}\)C for DVB15 due to higher viscosity. The relative humidity was 24% during the printing process. A higher amount of DVB reduced the viscosity of the inks, therefore, it was possible to print the layers at room temperature. Homogeneous closed single layers were successfully printed. There were little imperfections at the edges of the layers which were due to the non-ideal lab conditions, especially working in an ambient atmosphere. Although the inkjet printer was flooded with nitrogen, it was not fully inert and this can affect printing quality as OPSZ is sensitive to humidity. The humidity can affect the chemical structure of OPSZ due to hydrolysis and polycondensation reactions by forming silanol groups out of the Si-NH groups with water [55]. Figure 8a shows there were some holes in the layer of DVB15, which were due to failed nozzles. The big black points in the layers could have been caused by dust particles or impurities that may have been on the wafer and which cannot be prevented entirely.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **D-Band** & **G-Band** & **I\({}_{\text{D}}\)/I\({}_{\text{G}}\)** & **L\({}_{\text{a}}\)** \\  & **(cm\({}^{-1}\))** & **(cm\({}^{-1}\))** & **-** & **(nm)** \\ \hline DVB0 & 1338 & 1563 & 1.0 & 1.2 \\ DVB15 & 1329 & 1541 & 1.3 & 1.5 \\ DVB30 & 1320 & 1539 & 1.9 & 1.7 \\ DVB45 & 1330 & 1546 & 2.0 & 1.8 \\ DVB60 & 1327 & 1535 & 1.5 & 1.6 \\ DVB75 & 1322 & 1547 & 1.8 & 1.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Raman results fitted by [PERSON] of the SiCN ceramics with different amounts of DVB. The standard deviation of the ratio is 0.1.  
The printed layers were dielectric after pyrolysis. The reason for that is that a thin single layer is insufficient to induce a percolation threshold of the free carbon and generate electrical conductivity.
## 4 Proposed Methodology
### Custom Convolutional Neural Network  
CNN is a category of deep-learning system which detects and extracts features from images automatically [31]. It has acquired significance in medical image analysis, as it has in many other fields as a result of its higher performance. The layers of a standard CNN include convolution layer, dropout layer, activation function, fully connected layer, and pooling layer [32]. The image pixels need to be processed and are given as an input to the CNN. The original input pixels are subjected to detecting feature vectors, also termed as filters, in the convolution layer in order to extract a collection of features [33]. CNN's primary function, convolution, allows automated feature extraction [34]. During the step of pooling, a dimensionality reduction process is conducted by applying filters to an input vector [33]. The reduction technique is carried out by taking the minimum, maximum, or median of the values in the filtering window, which is strung across the initial input vector [19].  
In neural network models, overfitting problems can arise, especially when the manifold training samples is insufficient. With a view to address this issue, a dropout operation was used, which increased the network's capacity to alter distinct environments by arbitrarily deactivating a fraction of its neurons during training. The fully connected layer helps the process go on to the categorization stage. The output matrix is flattened before being sent on to the classifier after the feature extraction and pooling procedures. The proposed algorithm is shown in Figure 2.  
The dataset has two fundamental aspects. The first component aspect is a metadata file that contains specific data for cancer lesion images. The skin lesion's location, the patient's age and gender, the lesion's diagnosis, and the skin lesion directory are all included in the metadata file. The second and primary section of the collection is comprised of visual files.  
The objective of this study is to categorize skin lesions only based on digital images. Thus, the data file was reorganized to simply include the lesion type and the image file directory. Each lesion's textual labelling was transformed into digital values between 0 and 6. Each subtype labelling codes are shown in Table 2.  
The original dermoscopic images are of 600 \(\times\) 400 pixels resolution and are saved in the RGB format. It was observed that the processing burden increases proportionally with picture size. Hence, image size reduction increases processing speed. Therefore, all samples in the collection are downsized to 24 \(\times\) 24 pixels. Since the colour is a distinguishing factor in diagnosing the kind of lesion, the original colours of the photographs were maintained. The sharpening filters are implemented to enhance the contrast of every applied image.  
Figure 1: Comparison of sample images with their respective ESRGAN enhanced images.
## 5 Results and Discussion
### Stage II
#### 5.3.2 More Demanding LiDAR Configuration
Figure 8: The impact of attenuation on SNR and MCS for configuration 1 (Auto CSI, TRS, and SSB) from Stage II under more demanding LiDAR configuration. To better understand this phenomenon, we examined the downlink retransmission rate, defined as the ratio of retransmitted downlink transport blocks to transmitted downlink blocks without retransmissions, expressed as a percentage. Table 12 presents these values for each packet size and attenuation level. Table 12 shows that for 16 B packets at 0 dB attenuation, the retransmission rate remains noticeably high (4.03%), which aligns with the elevated RTT observed for these smallest packets and constitutes a significant outlier compared to larger packet sizes under the same conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Attenuation (dB)** & **Packet Size (B)** & **Downlink** \\  & & & **End-to-End Latency (ms/SD)** \\ \hline  & & 16 & 9.54/0.01 \\  & 0 & 512 & 9.59/0.01 \\  & & 1436 & 9.64/0.01 \\ \cline{2-4} (1) Auto CSI, TRS, SSB & & 16 & 9.70/0.02 \\  & 10 & 512 & 9.78/0.03 \\  & & 1436 & 9.89/0.04 \\ \cline{2-4}  & 20 & 16 & 9.89/0.04 \\  & 20 & 512 & 9.84/0.02 \\  & & 1436 & 9.93/0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Downlink end-to-end latency depending on attenuation and packet size, measured using Configuration (1) Auto CSI, TRS, and SSB in the more demanding LiDAR scenario. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Additional** & **Packet Size (B)** & **Average RTT** \\  & **Attenuation (dB)** & & **Latency (ms/SD)** \\ \hline  & & 16 & 59.02/28.95 \\  & 0 & 512 & 35.06/19.38 \\  & & 1436 & 36.40/16.50 \\ \cline{2-4}  & & 16 & 32.22/8.52 \\ (1) Auto CSI, TRS, SSB & 10 & 512 & 33.36/9.82 \\  & & 1436 & 35.43/8.98 \\ \cline{2-4}  & & 16 & 146.79/7.45 \\  & 20 & 512 & 144.39/10.67 \\  & & 1436 & 163.78/59.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Average RTT latency (ms) and standard deviation for Configuration 1, grouped by packet size and attenuation level, in the more demanding LiDAR scenario. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **Attenuation (dB)** & **Packet Size (B)** & **DL Retrans. [\%]** \\ \hline  & & 16 & 4.03 \\  & 0 & 512 & 3.08 \\  & & 1436 & 2.59 \\ \cline{2-4}  & & 16 & 3.49 \\ (1) Auto CSI, TRS, SSB & 10 & 512 & 2.98 \\  & & 1436 & 3.72 \\ \cline{2-4}  & 20 & 16 & 43.12 \\  & 20 & 512 & 41.98 \\  & & 1436 & 42.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Downlink retransmission rate (%) for different attenuation levels and packet sizes, measured using Configuration (1) Auto CSI, TRS, and SSB in the more demanding LiDAR scenario.
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 2 Basic Models of Video Watermarking
### Watermark Extraction  
Watermark extraction is the inverse process of watermark embedding. The position of watermark embedding is determined first, and then the watermark is extracted from the video data combining the secret key with the watermark extraction algorithm. Finally, the extracted watermark is decoded to obtain the original watermark information. The basic model of watermark extraction is shown in Figure 6.  
The watermark detection process is used to detect whether there is watermark information in video data, which is a probability judgment process based on statistical principles. According to whether the original video is needed to participate during the process of watermark extraction, the watermark algorithms can be divided into two categories: blind detection algorithms and non-blind detection algorithms [17]. Blind detection algorithms that do not require original videos are generally used.
## 2 Optimization Model of Massive MIMO System
### Objective Function Establishment  
In the wireless communication network, the utility function is often used to measure the user's satisfaction with the service, and the best result of bandwidth allocation can be obtained by solving the optimal value of the utility function. Introducing the logarithmic utility function in [23], the utility function of the \(i\)-th user is shown in (2):  
\[\mathsf{u}(x_{i})=s_{i}\times\ln(1+\frac{x_{i}}{x_{\max_{i}}}) \tag{2}\]  
Among them, \(\mathsf{u}(x_{i})\) represents the utility of the \(i\)-th user, \(x_{i}\) represents the number of time slots allocated to user \(i\), \(s_{i}\) represents the service priority of user \(i\), and \(x_{\max_{i}}\) represents the number of time slots requested by user \(i\).  
First, consider the user's business priorities \(s_{i}\). The traditional utility function shown in (2) ignores the user's channel state information and the number of unsatisfied time slots of the user in the previous scheduling cycle and cannot fully express the user's characteristics. Therefore, this paper introduces the aforementioned dynamic priority \(f_{i}\).  
Then, considering the minimum guaranteed time slot, if the needs of low dynamic priority users are not met for a long time, it is easy to cause the user to be starved to death. Therefore, in order to ensure the basic communication of users, it is necessary to assign the minimum guaranteed time slot to each user first and then allocate bandwidth according to the principle of maximizing the total utility function. The improved utility function is shown in (3):  
\[\mathsf{u}(x_{i})=f_{i}\times\ln(1+\frac{x_{i}-x_{\min_{i}}}{x_{\max_{i}}-x_ {\min_{i}}})+\mathsf{u}(x_{\min_{i}}) \tag{3}\]  
Among them, \(\mathsf{u}(x_{\min_{i}})\) represents the utility value generated by allocating the minimum guaranteed time slot \(x_{\min_{i}}\) for user \(i\), which is used to compensate for the neglected utility value due to the priority allocation of the minimum guaranteed time slot for each service, expressed as:  
\[\mathsf{u}(x_{\min_{i}})=\ln(1+\frac{x_{\min_{i}}}{x_{\max_{i}}}) \tag{4}\]The total utility function of the system is expressed as:  
\[\max\sum_{i\in R}\mathrm{u}(x_{i})=\max\sum_{i\in R}\big{[}f_{i}\times\ln(1+ \frac{x_{i}-x_{\min_{i}}}{x_{\max_{i}}-x_{\min_{i}}})+\ln(1+\frac{x_{\min_{i}}}{x _{\max_{i}}})\big{]} \tag{5}\]  
\[\mathrm{s.t}\ x_{\min_{i}}\leq x_{i}\leq x_{\max_{i}}\]  
\[\sum_{i\in R}x_{i}\leq x_{all}\]  
Among them, \(x_{\min_{i}}\) and \(x_{\max_{i}}\), respectively, represent the minimum number of time slots to ensure the normal communication of user \(i\) and the number of time slots requested by user \(i\), and \(x_{all}\) represents the total number of time slots that can be allocated by the system.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 3 Materials and Methods
### ETC Gantry Data Completion Method Based on Improved Tensor Decomposition
#### 3.2.2 Laplacian Matrix for Sparsity Control
The \((i,j)\)th matrix element represents the similarity between the \(i\)th and \(j\)th entities. The similarity degrees for vehicles, gantries, and transit data are tailored according to parameters like car model, geographic proximity, and interval headway time. Direct comparisons among entities are facilitated by numerical differentials, with vehicle model specifics calibrated by the conversion factor detailed in Table 1.  
The elements of the similarity matrix, \(W\), along each column are aggregated to yield \(n\) values, and these \(n\) values are then positioned on the main diagonal to construct the degree matrix, \(D\), as an \(n\times n\) diagonal matrix, leaving the nondiagonal entries at zero. The derived Laplacian matrices, \(L_{(c)},L_{(c)},\text{ and }L_{(v)}\), are symmetric and positive semi-definite, each with its minimum eigenvalue being zero. This characteristic is instrumental for the execution of incremental approximate decomposition of the tensor block, simplifying the computational process involved in the analysis of the ETC gantry data.
## 4 Materials and Methods
### Transgene Sequences Validation  
_C. sativa_ tetraketide synthase (_Cs_TKS) and olivetolic acid cyclase (_C_SOAC) amino acid sequences were obtained from _C. sativa_ through Genbank, under the respective accession numbers of ACD76855.1 and JN679224.1 (Appendix A, Table A1). The amino acid sequence was then converted into a DNA sequence with respect to _P. tricornutum_'s codon usage table, which is accessible through NCBI Genbank, as well as the HIVE laboratory's codon usage table (CUT) [41]. Several cassettes containing the genes of interest were designed; in this article, we describe three of them. (1) _Pt_OA1 was a construction where TKS and OAC were linked by a self-cleavable sequence from the _Thosea asigna_ virus (T2A) [45] without tags, to ensure a similar enzymatic site exposition as that seen in _C. sativa_. (2) _Pt_OA2 is a construction where TKS and OAC were tagged, respectively, in the C-terminal with 6 xHis and c-Myc tags for possible protein detection or purification and were linked by a T2A sequence. (3) _Pt_OA3 is a construction designed to present TKS coupled with a reporter gene fluorescent protein eYFP on the N-terminal fused by (GlyGlyGlyGlySer)\({}_{3}\) to OAC, which is tagged with c-Myc on the C-terminal. Each inserted construction was designed to be expressed under the strong constitutive promoter 40 SRPS8 and the FcPA terminator in a pPGE30 backbone vector [61] that contained the zeocin resistance gene _ShBle_ as a selection marker. The plasmid pPGE30 contains a centromeric yeast fragment, allowing it to remain extrachromosomal [38]. All gene sequences were codon-optimized for _P. tricornutum_'s optimal codon usage, with a GC content ranging from 48 to 55%. A representation of each plasmid design is detailed in Figure 1. The primers used for the gene amplification and DNA fragment assembly are listed in Appendix A, Table A1. The fragments and transgene sequences that have been optimized for higher expression in _P. tricornutum_ are detailed in Appendix A, Table A2. The sequencing results of the plasmid DNA obtained from _P. tricornutum_ clones described in this study are detailed in Appendix A, Table A3.
## 3 Antimicrobial Peptides (AMPs)
### Mechanisms of Action
#### 3.1.2 AMPs Affecting Intracellular Physiology  
In addition to membrane targeting AMPs, there is growing evidence that certain AMPs directly interact with essential intracellular mechanisms, such as principle macromolecular biogenesis, cell wall biosynthesis, and cell division [14] after they have entered the cytoplasm through membrane disturbance, forming transient pores or receptor-mediated processes [130]. For instance, buforin II uses a proline hinge as a cell-penetrating promoter and then inhibits DNA and RNA synthesis in _E. coli_[131]. The 13-amino acid indolicidin translocates by membrane permeation, then stabilizes duplex DNA, and, consequently, halts DNA replication and transcription. Its synthetic 13-residue variant CP10A interacts with the membrane and shows inhibitory ability against DNA, RNA, and protein synthesis in treated _S. aureus_[132]. Microcin J25 isolated from _E. coli_ fecal strain AY25 can kill _Salmonella_ by  
Figure 6: Various schematic models of antimicrobial peptides (AMPs) induced membrane disturbance. Models (**A–C**) are derived from in vitro membrane lipid model studies [123; 124; 125], and models (**D–F**) are based on single cell live-imaging studies [126; 127; 128]. See the manuscript text for a detailed explanation of each model.  
binding and obstructing the second channel of RNA polymerase [133] and inhibiting the cell division process of other _E. coli_ cells [134]. eNAP-2 generated by equine neutrophils can eradicate _E. coli_ by non-covalently binding to the bacterial serine proteases subtilisin A and proteinase K [135]. Nisin, which is the most investigated lantibiotic, is commonly used against Gram-positive pathogens like _S. aureus_ and _Bacillus_ spores, and targets lipid II, inhibiting cell wall synthesis [136].
## 5 Performance Evaluation
### Case Study: Wi-Fi Power Savings by LDWuR
#### 5.1.2 Latency  
The latency comparison of our WuR with existing WuRs is shown in Figure 9b. Our latencies depend on the expected transmission times of our wake-up radio and IEEE 802.11-compliant data radio that  
Figure 8: Power consumption is a function of beacon interval. (**a**) Power consumption _versus_ beacon interval; (**b**) improvement on power consumption _versus_ beacon interval.  
Figure 7: Power consumption is a function of packet rate. (**a**) Power consumption _versus_ packet rate; (**b**) improvement on power consumption _versus_ packet rate.  
are computed by their PERs, respectively. In the simulation, we apply the empirical performance of our WuR from previous work [24]. We adopt a receiver sensitivity of \(-89\,\mathrm{dBm}\) with a PER of 8% at 1 Mbps BPSK modulation of the D-Link DI-624 for IEEE 802.11-compliant data radio [32]. Based on those PERs, we calculate their expected transmission times to estimate their latencies. Two radios induce more expected transmission time and increase latency. As our MAC protocol adopts our wake-up radio and IEEE 802.11-compliant data radio, our latency is longer than the conventional MAC protocol. In terms of the hardware performances of our WuR, they are described in previous work [24]. We adopt our WuR as a basic module with directional and omnidirectional antennas for static and dynamic strategies, respectively.
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
## Experiments  
### Compressive Strength and Elastic Modulus Tests  
The compressive strength and elastic modulus of RPC samples after 28 days were determined experimentally. According to Chinese standard test methods for mechanical properties of concrete [36], 100 \(\times\) 100 \(\times\) 300 mm prisms were loaded uniaxially. The tests were conducted at a loading rate of approximately 0.5 MPa/s. Three samples for the compressive strength test and an additional three samples for the elastic modulus test were tested at each steel fibre content.
## 5 Computer Simulations
### The Optimal Relay Selection Mechanism  
Figure 4 plots the spectral efficiency as a function of the relay node, where an attempt is made to explain the process of selecting the optimal relay from the given number of active relay stations. To generate simulation results, the number of antennas deployed at communicating nodes is set as \(N_{t}=N_{rel}=100,\ N_{r}=25\) for the transmission of \(N_{s}=4\) data streams. It is further assumed that 20 active relay nodes are available between the source and destination for the selection of the best relay station. The obtained results in Figure 4 show that spectral efficiency varies with the relay node, as each relay station provides a different value of end-to-end SNR. Moreover, the variation in spectral efficiency depends on the condition of the relay-assisted transmission path from the source to the destination. It is important to mention that the proposed relay selection mechanism considers end-to-end SNR maximization, which is characterized by the suitable choice of analog RF beamforming matrix and the corresponding digital baseband processing component at the source and each relay node and destination, whenever the hybrid transceiver design is concerned. Since SNR is directly related to capacity expression, a relay selection strategy using end-to-end SNR maximization leads to the maximum achievable rate. It is obvious from Figure 4 that the optimal relay is the one that provides the best channel for the transmission of a signal from source to destination.  
Figure 4: Spectral efficiency vs. relay nodes in a mm-Wave propagation environment with UPA such that \(N_{cl}=4,\ N_{ray}=10\) and \(10^{\circ}\) spread angle. System parameters are set as \(N_{t}=N_{rel}=100,\ N_{r}=25\), \(N_{s}=4\) with the assumption that the number of RF-chains is equal to the number of data streams \(N_{t}^{RF}=N_{r}^{RF}=N_{rel}^{RF}=N_{s}\). There are 20 active relay nodes between the source and the destination for the selection of the best relay.