esa-sceva/satcom-chunk-collection huggingface dataset:
# Bacteria-Mediated Modulatory Strategies for Colorectal Cancer Treatment
## 2 Bacterotherapy in CRC Treatment
### Mechanisms Used in Bacterotherapy in CRC
#### 2.1.2 Bacterial Peptides

Table 1: Collection of bacterial peptides proposed for CRC therapy. _Arenamides_  
In addition, new types of cyclohexadepispeptides, namely Arenamides A-C, were found to be isolated from the marine bacterial species _Arenamides Salinispora arenicola_. In previous research, Arenamides A and B have been demonstrated to block TNF-induced activation, thus suppressing the pro-inflammatory NF-kB signaling pathway as well as nitric oxide and prostaglandin E2 production. Moreover, cytotoxic activity against human colon carcinoma cells (HCT-116) was observed [100]. _Ohmyungsamycin_  
Other bioactive metabolites showing cancer cytotoxic action are the cyclic depsipeptides Ohmyungsamycin A and B that are derived from a marine bacterial strain (SNJ042) belonging to _Streptomyces_ sp. and are both comprising unusual amino acid units such as N,N-dimethylvaline, \(\beta\)-hydroxyphenylalanine and N-methyl-4-methoxytyptophan [101]. In particular, Ohmyungsamycin A has been demonstrated to exhibit growth inhibiting effects on CRC cells (HCT-116), while it did not affect the growth of fibroblast cells (MRC-5), suggesting that it is able to selectively suppress the proliferation of human cancer cells [102]. Thereby the underlying mechanisms leading to its anti-cancer activity in CRC cells, namely modulation of the Skp-p27 axis leading to MCM4-induced cell cycle arrest in G0/G1 phase, finally causing apoptosis, have been revealed [102]. _Mixirins_  
Mixir is a cyclic acylpeptide that is derived from the marine _Bacillus_ species. All three types of mixirins, namely A, B and C, have been found to exhibit cytotoxicity against the HCT-116 human colon tumor cell line, but further studies on mixirin, specifically considering CRC, have not been carried out yet [103]. _Urukthapelstatin_  
The cyclic thiopeptide Urukthapelstatin A is secreted by _Mechercharimyces sapoprohorigenens_, a marine strain that belongs to the _Thermoactinomycetaceae_. Previous studies have demonstrated the anti-cancer activity of Urukthapelstatin A as they found growth inhibition colon cancer (HCT-116) cells among other types of cancer, when treated with Urukthapelstatin A, probably due to inhibition of DNA synthesis [104; 105]. However, further studies need to be made for validation. Altogether, the efficacy of bacterial toxins as well as non-ribosomal peptides, acting as cytotoxic agents in order to suppress cell proliferation, offer a great opportunity as selective anti-tumor agents because they can interfere with different cancer-promoting signaling pathways. Moreover, the cancer-inhibiting mechanisms have already been shown across different CRC cell lines in vitro and already in a few animal models (Tables 1 and 2).
## 1 Introduction
ran a fixed-size sliding window on the whole image and used ResNet-34 to determine whether each window contained defects [28]. Although algorithms for automatic detection of magnetic particle inspection images based on image processing have been continuously proposed, mature industrial applications are still rare. This paper mainly focuses on crack defect detection in MPI for bearing rings. As shown in Figure 1, there are many complex textures and false indications in the MPI image of the bearing ring. Figure 1a show an MPI image of the bearing outer ring containing crack defects, which are captured by the industrial camera in the bearing factory. We analyze the geometric characteristics of crack indications and common interference factors in Figure 1a by drawing their three-dimensional intensity distribution maps with MATLAB. As shown in Figure 1b, the crack indications generally represent yellow-green linear shapes due to the accumulation of fluorescent magnetic particles. As shown in Figure 1c, there are many randomly distributed magnetic particles caused by rough surfaces, which form the complex textured background in MPI images of bearing rings. The intensities of the crack indications and the randomly distributed magnetic particles are similar, which results in low contrast between the crack indications and background. In addition, some accumulations of fluorescent magnetic particles that also appear as linear shapes may be mistaken for cracks, and we usually call these false indications. Figure 1d show the false indications formed by magnetic particles gathered at the groove of the workpiece. Therefore, the accurate location of complete cracks in MPI images with complex texture backgrounds is challenging. Traditional methods based on hand-designed features of images and machine learning algorithms are useful for images with a simple background. However, these techniques cannot deal with noise, variable illumination, and backgrounds with complex textures. Although existing detection methods based on deep learning algorithms have improved the performance of feature expression, many computations in locating cracks are required, which cannot meet the real-time requirements of detection. To solve the problems mentioned, a bearing ring defect detection method combining visual characteristics and high-level features is proposed. Human vision can quickly locate the region of interest in complex backgrounds. For the magnetic particle inspection of bearing rings, the human eye can quickly find cracks according to the visual characteristics of cracks. The geometric characteristics and greyscale distribution characteristics of crack are introduced to represent the visual characteristics. The main contributions of the study include (1) presenting an image preprocessing algorithm based on the visual characteristics of crack indications to obtain crack defect candidate regions, which realize the rough location of cracks, and (2) introducing the improved MobileNetV3 model to extract high-level features of crack defects for further discrimination.
## Applications
### Categories Based on Operational Strategies
#### 3.3.2 Cross-Device FL  
Cross-device FL involves a large number of devices (e.g., smartphones and IoT devices) with relatively small local datasets [45]. This type of FL is characterized by high variability in device availability, computational power, and network connectivity [56]. Cross-device FL must handle these challenges efficiently to enable training on a diverse and extensive network of devices.  
**Applications**  
* Mobile applications: Improving user experience on mobile apps by training models on user behavior data from many devices.
* IoT networks: Enhancing predictive maintenance models using data from various IoT sensors and devices.
## 6 Simulation Results
### Performance Results
#### 6.2.3 QPSK with SUMF Receiver  
In this section, we show that the QPSK modulation with SUMF could also be employed and outperform the BPSK in terms of maximum achievable spectral efficiency results. Indeed, there were cases, depending on the minimum \(C/N\) and power spreading, where the SE performance obtained with the QPSK modulation plus SUMF that were quite close or even better than those obtained with the BPSK modulation. Figure 16 shows such an example comparing the SE performance of the MASSIVE waveform with TF#25, which employed the QPSK modulation, with a waveform sharing the same features but for using the BPSK modulation.  
In such scenarios, two opposite cases can be defined, depending on the needs, in which a QPSK with SUMF still represents a viable solution compared to BPSK. The SE can be written as:  
\[\zeta=N_{\rm STT}\cdot\frac{r_{b}}{r_{c}}\left[\rm bit/chip\right] \tag{4}\]  
Figure 16: SE and PLR of QPSK with SUMF vs. BPSK with SUMF, minimum \(C/N=0\) dB and power spreading 3 dB.  
Figure 15: PLR trend with SIC, case TF#25.  
where \(N_{\rm STT}\) is the number of simultaneous transmitting terminals, \(r_{b}\) is the per-terminal bit rate and \(r_{c}\) the chip rate. Hence, if \(\zeta^{\rm(BPSK)}\simeq\zeta^{\rm(QPSK)}\), where \(\zeta^{\rm(BPSK)}\) and \(\zeta^{\rm(QPSK)}\) are the aggregated throughput values obtained with BPSK and QPSK plus SUMF, respectively, for the same chip rate and coding rate values, then the following cases hold.  
* Use Case #1: QPSK modulation can be chosen in order to double the data rate per-terminal, at the expense of reducing the number of simultaneous transmitting terminals by half.
* Use Case #2: BPSK modulation can be chosen in order to double the number of simultaneous transmitting terminals, at the expense of reducing the data rate per-terminal by half.
## 2 Materials and Methods
### Data Used
#### 2.2.1 Landsat-Series Satellite Data  
Surface reflectance datasets of Landsat-series satellite with 30 m spatial resolution were used for decadal LULC classification (e.g., 1990, 2000, 2010, and 2020) and vegetation cover loss analysis during the study periods. Based on the data availability, the LULC study was conducted from 1990-2020. Due to the unavailability of data and different spatial resolution, data before 1990 was not taken for the study. Landsat-5 Thematic Mapper (TM) was used for the years 1990, 2000, and 2010, whereas Landsat-8 Operational Land Imager (OLI) sensor was used for 2020. Landsat-7 Enhanced Thematic Mapper Plus (ETM+) was not considered due to poor data quality (i.e., scanline errors). Multi-date images (all available scenes) with 0% cloud cover were only taken to make a single composite image (using median operation) for particular study years to reduce the spectral inconsistencies due to seasonal impacts or other factors. The number of cloud-free scenes available for each year was: 5 scenes (5 January 1990, 21 January 1990, 6 February 1990, 11 April 1990, 23 December 1990) for 1990, 4 scenes (17 January 2000, 2 March 2000, 2 December 2000, 18 December 2000) for 2000, 2 scenes (2 April 2010, 14 December 2010) for 2010, and 4 scenes (24 January 2020, 23 November 2020, 4 December 2020, 25 December 2020) for 2020. The satellite scenes were corrected for atmospheric effects using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithms ([PERSON] et al., 2006) for Landsat-5 and Land Surface Reflectance Code (LaSRC) algorithms ([PERSON] et al., 2016) for Landsat-8. Dark Object Subtraction (DOS) was also performed using QGIS software (version 3.14) to remove the effects of atmospheric scattering and absorption from satellite imagery (black spots caused due to shadow).
## 2 Materials and Methods
### Identifying Graph Topologies from Observations of Stationary Graph Signals
#### 2.1.2 Size of the Feasible Set
If \(\operatorname{rank}(\mathbf{W}_{\mathcal{D}})=N-k\) and \(\operatorname{rank}(\mathbf{W}_{\mathcal{M}}\mathbf{U})=k\), then \(\mathcal{S}\) is a singleton. Proof. : Since \(\mathbf{\lambda}\in\ker(\mathbf{W}_{\mathcal{D}})\), there exists an \(\mathbf{\alpha}\in\mathbb{R}^{k}\) such that \(\mathbf{\lambda}=\mathbf{U}\alpha\). From the known entries of \(\operatorname{vec}(\mathbf{S})\) denoted by \(\mathbf{w}:=[\operatorname{vec}(\mathbf{S})]_{\mathcal{M}}\) we have \(\mathbf{W}_{\mathcal{M}}\mathbf{\lambda}=\mathbf{W}_{\mathcal{M}}\mathbf{U}\alpha -\mathbf{w}\). Thus, to uniquely identify \(\mathbf{\alpha}\) and equivalently \(\mathbf{\lambda}\) (and \(\mathbf{S}\)), it is sufficient to have \(\operatorname{rank}(\mathbf{W}_{\mathcal{M}}\mathbf{U})=k\). Proposition 1 further implies that \(\operatorname{rank}(\mathbf{W}_{\mathcal{M}})\geq k\) under the assumption that \((\mathbf{W}_{\mathcal{M}}\mathbf{U})=k\). This is due to the inequality \(\operatorname{rank}(\mathbf{W}_{\mathcal{M}}\mathbf{U})\leq\min\{ \operatorname{rank}(\mathbf{W}_{\mathcal{M}}),\operatorname{rank}(\mathbf{U})\}\). Observing \(k\) \"_sufficiently different_\" edges for unique recovery of \(\mathbf{S}\) is the intuition behind the rank constraint on \(\mathbf{W}_{\mathcal{M}}\). In real-world graphs, we have observed that \(k\) is typically much smaller than \(N\); see also Section 3 and ([6], Section 3). This would make it feasible to uniquely identify the graph, given only its eigenvectors and the status of \(k\) edges. However, in practice we may not know about the status of those many edges, or, the output covariance \(\mathbf{C}_{\mathbf{y}}\) may only be imperfectly estimated via sample covariance matrix \(\mathbf{\hat{C}}_{\mathbf{y}}\). This motivates searching for an optimal graph while accounting for the (finite sample size) approximation errors and the prescribed structural constraints, the subject dealt with next.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Clinical Decision-Making and Personalized Medicine
#### 2.4.3 OmicsNet 2.0: A Platform for Network Visualization and Multi-Omics Interaction Mapping  
Building on the web-based, user-friendly approach of OmicsAnalyst, OmicsNet 2.0 takes multi-omics integration a step further by focusing on network visualization and molecular interaction mapping. OmicsNet 2.0 is a web-based platform designed for network-based multi-omics data integration and visualization. The platform allows users to construct and analyze molecular interaction networks by integrating diverse omics data, including genomics, transcriptomics, proteomics, metabolomics, and microbiomics. OmicsNet 2.0 is particularly valuable in clinical research and personalized medicine due to its ability to map disease-associated molecular networks and facilitate biomarker discovery [31]. The strength of the platform lies in its ability to visualise complex molecular networks, allowing the understanding of the relationships between different molecular entities such as genes, proteins and metabolites. By integrating multi-omics data into biological networks, OmicsNet 2.0 helps the identification of critical pathways and molecular interactions associated with disease development and progression.  
A major attribute of OmicsNet 2.0 is its user-friendly web interface, which simplifies the construction and visualization of networks for users without advanced bioinformatics experience. The platform also offers tools for functional enrichment analysis and pathway exploration, making it particularly relevant for those seeking to understand the functional implications of molecular interactions identified through network analysis. While the web interface is easy to use, handling large datasets and complex network analysis may still require significant computational resources, which could limit its use in resource-constrained environments. Also, despite being powerful for network-based insights, OmicsNet 2.0's focus on network visualization may not be appropriate for all multi-omics research scenarios, where non-network-based approaches are preferred.
## 3 Method
### Preprocessing  
In order to reduce the influence of the atmosphere on the electromagnetic wave, Sen2 Cor was used to conduct atmospheric correction for Sentinel-2A/B Level-1C products to get Level-2A products, then multi-spectral images with a spatial resolution of 10 m were obtained by using SNAP to resampling them and format conversion. At the same time, multispectral images (30 m resolution) and panchromatic images (15 m resolution) after radiation calibration and atmospheric correction of Landsat-8/9 were fused to obtain multispectral images with 15 m resolution. Finally, adopt the same boundary vector to cropped images to obtain the time series optical images.  
Used SARscape, the Sentinel-1A image from 4 February 2022, was taken as the master image and the remaining images as the slave images. The dual polarization coherence maps between all image pairs were calculated. After geocoding, image cropping, and  
Figure 5: Research workflow.  
resampling, the SAR coherence maps (resolution of 5 m) were obtained, which is consistent with the optical images range.
## 2 Swab Surge Modeling
### Model Accuracy Evaluation
#### 2.3.1 Mean Square Error (MSE)  
The mean square error (MSE) assesses the average squared difference between the observed and predicted values. When a model has no error, the MSE equals zero. As modelerror increases, its value increases. The mean square error is also known as the mean square deviation (MSD), which is not explained by the regression model.  
\[\text{MSE}=\frac{1}{\text{N}}{\sum_{i=1}^{\text{N}}}\Big{(}\text{y}_{\text{i}}^ {\text{predicted}}-\text{y}_{\text{i}}^{\text{Actual}}\Big{)}^{2} \tag{13}\]  
Both ordinary linear regression and ANN ML algorithms use the loss, which is the sum of the difference between observed and predicted values to optimize the regression coefficients.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## 2 SIFT Algorithm
### Direction Matching  
The next step is the keypoint orientation assignment, and the purpose is to achieve the SIFT features of rotation invariance. For scaling smoothed image \(I_{\text{L}}\), which means that the image \(I_{\text{L}}\) has been performed by keypoint elimination, we compute the central derivative of \(I_{\text{L}}\) at every keypoints. Further scale and orientation at every keypoint \((x,y)\) can be calculated by Equation (4):  
\[\left\{\begin{array}{l}\theta(x,y)=\arctan 2\{\left[I_{\text{L}}(x,y+1)-I_{ \text{L}}(x,y-1)\right]/\left[I_{\text{L}}(x+1,y)-I_{\text{L}}(x-1,y)\right] \right\}\\ g(x,y)=\sqrt{\left[I_{\text{L}}(x+1,y)-I_{\text{L}}(x-1,y)\right]^{2}+\left[I_ {\text{L}}(x,y+1)-I_{\text{L}}(x,y-1)\right]^{2}}\end{array}\right., \tag{4}\]  
where \(\theta(x,y)\) is the direction of the gradient and \(g(x,y)\) is the gradient modulus value.  
After obtaining the gradient direction and amplitude, the gradient direction of the keypoints can be determined by a gradient direction histogram. The maximum/peak value of the histogram represents the direction of gradient at the neighbor of this keypoint. The peak value of histogram is set as the main direction of this keypoint, and when there is another peak with the value of the main peak's 80%, this direction is expected to be recorded as the auxiliary direction of this keypoint to improve the robustness. The direction matching has been completed with the location, orientation, and scale.
# Line-Detection Based on the Sum of Gradient Angle Differences
## 3 Proposed Line-Detection Method
### Definition of SGAD  
First, the gradient angle at each pixel is calculated as follows:  
\[\theta_{i}=\tan^{-1}\left(\frac{g_{r_{i}}}{g_{c_{i}}}\right), \tag{9}\]  
where \(g_{r_{i}}\) and \(g_{c_{i}}\) are the gradients of the smoothed intensities in (7) at pixel \(i\) in the row and column directions, respectively, as follows:  
\[g_{r_{i}}=\frac{\partial\mathbf{I_{s}}}{\partial r}(i)\text{ and }g_{c_{i}}=\frac{ \partial\mathbf{I_{s}}}{\partial c}(i), \tag{10}\]  
where \(r\) and \(c\) are row and column coordinates, respectively.  
Then, the difference between the gradient angles of two pixels is calculated by finding the minimum positive angle between the gradient vectors at the two pixels as follows:  
\[\delta_{i,j}=\min\left(\text{abs}\left(\theta_{i}-\theta_{j}\right),2\pi- \text{abs}\left(\theta_{i}-\theta_{j}\right)\right). \tag{11}\]  
The difference in angles is calculated for four pairs of gradient vectors, as shown in Figure 3. The angle differences of the four pairs are summed to generate the SGAD measure, as follows:  
\[\text{SGAD}=\delta_{1,8}+\delta_{2,7}+\delta_{3,6}+\delta_{4,5}. \tag{12}\]
# End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control
## 4 Proposed Method
### Reward Function  
The design of the reward function is essential for an RL problem, and can influence the convergence of the algorithm. At each time instant, the RL-agent is encouraged to select the action that can provide the largest reward. To some extent, the reward function is supposed to give a positive response when the RL-agent makes a good decision. On the contrary, it gives a negative response to actions that mislead the RL-agent or violate the rules. Taking the landing of the fixed-wing UAV as an example, the UAV should decrease its height along the glide slope towards the centerline of the runway within a certain region. Therefore, the basic rules are not crossing the position border, including the height constrained by the glide slope and the lateral range confined by the geographic fence. Additionally, the  
Figure 4: The network architectures: (**a**) the architecture of the actor-network; (**b**) the architecture of the critic-network.  
attitude of the UAV should also be confined within a certain range to achieve stable and safe flight. For example, the pitch angle may be related to the maximum allowed attack angle to prevent stalling. These border-crossing conditions are judged to be an abnormal part of the reward and they are considered by giving a negative penalization value to inform the RL-agent of the constraints of the mission. Additionally, it is also necessary to encourage the RL-agent when it makes efforts to get closer to the goal of landing. It is hard to explicitly judge whether the current action is beneficial to the final success of landing. Here, these positive rewards are regarded as the normal part, and their values are determined according to the current pose of the UAV and its desired landing pose. Specifically, the desired lateral distance, height and yaw angle of the UAV when it lands right in the centerline of the runway are expected to be 0. In order to distinct from the negative reward, the bias term is added when calculating the positive reward to increase its baseline.  
From the above, both the normal and abnormal parts are considered in the design of the reward function, as shown in Equation (1):  
\[r=\left\{\begin{array}{ll}-w_{1}\|\Delta x\|-w_{2}\cdot|y|-w_{3}\cdot|z|-w_{4 }|\gamma|+b,&\text{normal}\\ -m,&\text{abnormal}\ (\text{ cross }\text{ the position border})\\ -n,&\text{abnormal}\ (\text{cross the attitude border})\end{array}\right. \tag{1}\]  
where \(\Delta x\) is the distance error along the \(x\)-axis, \(y,z\) are the lateral and vertical coordinates of the UAV, \(\gamma\) is its yaw angle, \(w_{1},w_{2},w_{3},w_{4},b,m,n\) are constant parameters. \(\|\bullet\|\) represents the square error, and \(|\bullet|\) represents the absolute value. The normal part describes the approaching degree to the terminal landing state. The abnormal parts respectively indicate the constraints condition for the position and the attitude of the UAV.