esa-sceva/satcom-chunk-collection huggingface dataset:
# Ten Years of TerraSAR-X Operations
## 5 Radar Experiments with TerraSAR-X
### Topsr  
TOPSAR is a burst mode like ScanSAR that allows for the quasi-parallel acquisition of several sub-swathes and by this the acquisition of a much wider total ground swath at the expense of a reduced azimuth resolution. The TOPSAR mode overcomes limitations that are imposed by the ScanSAR--the most prominent is the scalloping effect--by steering the antenna along-track during the acquisition of a burst. In this way, all for the targets are illuminated with the complete azimuth antenna pattern, and thus, scalloping is circumvented and an azimuth dependency of signal to noise ratio (SNR) and distributed target ambiguity ratio (DTAR) is avoided, or at least considerably reduced. TOPSAR was demonstrated with TerraSAR-X in the year 2007 [26].  
Different TerraSAR-X images have been acquired over Toulouse, France, in order to perform a scalloping analysis in the TOPSAR and ScanSAR modes. Figure 7 presents one sub-swath from the TOPSAR and ScanSAR acquisitions that were processed _without_ the nominally applied scalloping correction in the processing. The azimuth resolution in the images is 16 m. About five range looks were processed, resulting in 16 m ground range resolution. The sub-swath size is approximately 90 km in azimuth and 25 km in slant range. No weighting has been applied in the burst image overlap area.  
Figure 5: Azimuth pattern steered to \(2.2^{\circ}\). Azimuth frequency corresponding to the direction towards the scene center (green) and ambiguous frequencies (red).  
The TOPSAR image sub-swath that is shown in Figure 7a was acquired with nine bursts. The commanded steering angle is in-between \(\pm 0.52^{\circ}\). Due to the low variation of the steering angle, the scalloping effect is hardly visible and it was quantified to be approximately 0.3 dB. It is not fully zero as electronic azimuth steering is implemented in TerraSAR-X, i.e., the element pattern is not steered and it causes a residual scalloping. The ScanSAR image was acquired with 33 bursts. The measured scalloping in the ScanSAR intensity profile is around 1.2 dB.  
Figure 6: (**Top**) TerraSAR-X staring spotlight (ST) experimental acquisition example over Oberpfaffenhofen, Germany with five azimuth looks, incidence angle 35.6\({}^{\circ}\), PRF 3786 Hz, scene extent 8.8 km \(\times\) 3.6 km in ground range and azimuth, respectively. The single look resolution is 1.00 m in ground range and 0.21 m in azimuth. (**Bottom**) Zoom into staring image (**left**) and HS image (**right**). The geometric resolution in both zooms is about 1 m in azimuth and ground range, i.e., the staring image is composed of five looks, while the HS sliding image comprises only one.
## 4 Power and Electromagnetic Trace Acquisition  
Design_ultra was produced in the IHP 250 nm technology for a maximum working frequency of 20 MHz (50 ns minimal clock cycle period) and bonded to a printed circuit board (PCB). In our experiments, we used 4 MHz as the working clock frequency. The die area was 2.7 mm\({}^{2}\), with dimensions of 2.49 mm \(\times\) 1.09 mm. Design_seq was implemented in VHDL and ported to a Spartan-6 FPGA. The ECSM design is also running at 4 MHz.  
Figure 2: General structure of our hardware accelerator.  
During a kP execution, we measured the current through the FPGA and ASIC using a current probe from the security test equipment from Riscure [52] and the electromagnetic emanation using an MFA-R-75 EM probe from Langer [53]. Samples captured using the oscilloscope were saved to a file. We refer to power measurement as power trace, denoted as PT, and electromagnetic measurements as electromagnetic trace, denoted as EMT. The traces were captured using a LeCroy Waverunner 610 Zi oscilloscope with a 2.5 GS/s sampling rate, i.e., with 625 measurement points per clock cycle. We used a 232-bit-long random number as the scalar k and the base point G of EC B-233 [13] as point P.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
###### Abstract  
Cybersecurity threats are becoming increasingly sophisticated, frequent, and diverse, posing a major risk to critical infrastructure, public trust, and digital economies. Traditional intrusion detection systems often struggle with detecting novel or rare attack types, particularly when data availability is limited or heterogeneous. The current study tries to address these challenges by proposing a unified, multimodal threat detection framework that leverages the combination of synthetic data generation through Generative Adversarial Networks (GANs), advanced ensemble learning, and transfer learning techniques. The research objective is to enhance detection accuracy and resilience against zero-day, botnet, and image-based malware attacks by integrating multiple data modalities, including structured network logs and malware binaries, within a scalable and flexible pipeline. The proposed system features a dual-branch architecture: one branch uses a CNN with transfer learning for image-based malware classification, and the other employs a soft-voting ensemble classifier for tabular intrusion detection, both trained on augmented datasets generated by GANs. Experimental results demonstrate significant improvements in detection performance and false positive reduction, especially when multimodal outputs are fused using the proposed confidence-weighted strategy. The findings highlight the framework's adaptability and practical applicability in real-world intrusion detection and response systems.  
threat detection; cybersecurity; synthetic data generation; generative adversarial networks (GANs); multimodal fusion; deep learning; transfer learning Article
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 4 Method
### Channels
#### 4.2.2 Non-Line-of-Sight (NLOS)  
The NLOS channel builds upon the AWGN channel. In addition to the added noise on the receiver side, Rayleigh fading based on [PERSON]'s model is added [16]. Additionally, a 3 GPP compliant V2X-based attenuation is considered, using an arbitrary distance of 10 m between transmitter and receiver, describing the path loss \(PL\) in Equation (4), using the distance in the three-dimensional space \(d_{3D}\) and a carrier frequency \(f_{c}\) of 5.9 GHz [17].  
\[PL=36.85+30\log_{10}(d_{3D})+18.9\log_{10}(f_{c}) \tag{4}\]
## 3 Model Calibration and Optimization
### Genetic Algorithm Optimization Framework
#### 3.2.2 Parameter Sensitivity Analysis  
To ensure the robustness of the genetic algorithm parameters, a comprehensive sensitivity analysis was conducted. The analysis involved systematically varying each parameter while keeping others constant to assess their impact on the optimization outcomes. The results of parameter sensitivity analysis are shown in the Figure 6, and the results are summarized below.  
**Population Size**: Sensitivity analysis showed that population sizes smaller than 10 led to premature convergence and reduced diversity, while sizes larger than 10 did not significantly improve solution quality but increased computational time. A population size of 10 was found to be optimal, balancing diversity and computational efficiency.  
**Number of Generations**: A number of generations fewer than 1000 resulted in insufficient exploration of the solution space, while a number of more than 1000 did not yield significant improvements in fitness values. The chosen 1000 generations provided a good balance between convergence and computational resources.  
**Mutation Rate**: A mutation rate below 0.05 led to premature convergence, while rates above 0.15 caused excessive disruption and reduced the stability of the optimization process. The adaptive mutation rate range of 0.05-0.15 was optimal, allowing for sufficient exploration without compromising convergence.  
The sensitivity analysis confirmed that the selected parameters (population size = 10, generations = 1000, mutation rate = 0.05-0.15) provided the best tradeoff between convergence speed, solution quality, and computational efficiency. This ensures that the genetic algorithm operates within a parameter space that maximizes its ability to find robust and optimal solutions.
## 2 Materials and Methods  
Patients were recruited at the Department of Endocrinology and Neuroendocrine Tumors, Medical University of Silesia in Katowice, Poland. The main inclusion criteria for the study group were a diagnosis of neuroendocrine tumors, age \(\geq\) 18, and signed consent to participate in the study. The main inclusion criteria for the control group were healthy volunteers, age \(\geq\) 18, signed consent to participate in the study, and no history of any malignancy at the time of blood draw. Peripheral blood from patients was collected into S-Monovette tubes with a clotting activator (Sarstedt, Germany). To obtain serum, blood was centrifuged for 15 min at 3500 rpm. Serum was stored at -80 \({}^{\circ}\)C for further analysis. The serum concentrations of IGF-1R, ERK2, and EGFR were assessed using ELISA, following the procedures described by the manufacturers. All ELISA tests were performed at the Department of Medical and Molecular Biology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia. The analytical procedure adhered to the manufacturer's guidelines outlined in the technological manuals provided with the kits. To determine the concentrations of the tested samples, a standard curve was prepared using the standards supplied in the kit. All standards and serum samples were run in duplicates; additionally, serum samples were diluted as specified by the instructions (IGF-1R 2x, ERK2 none, EGFR 50x). For all ELISA tests, plates were read by a Bio-Tek \(\mu\)Quant Universal Microplate Spectrophotometer (Bio-Tek, Winooski, VT, USA) using 450 nm as the primary wavelength. Data analysis software KCJunior (Bio-Tek, Winooski, VT, USA) was used. The absorbance was transformed into a concentration of pg/mL for all three proteins. The following tests were used: RayBiotech Life, Inc., Peachtree Corners, GA, USA (Catalog No. ELH-IGF1R) for IGF-1R; Cloud-Clone Corp., Katy, TX, USA (Catalog No. SEA930 Hu) for ERK2; and Wuhan Fine Biotech Co., Ltd., Wuhan, China (Catalog No. EH0010) for EGFR. The minimum detectable dose is 6.00 pg/mL for IGF-1R, 15.99 pg/mL for ERK2, and 9.375 pg/mL for EGFR.
## 3 Methodology
### Wrapper Method  
The wrapper method is an approach that aims to find the optimal feature subset, tailored to a specific algorithm and domain. However, its dependency on multiple iterations of machine learning algorithms results in considerable time and cost implications, which is a notable drawback. Additionally, this method introduces complexities in implementation ([PERSON] et al., 2020).  
Recursive Feature Elimination. As described by [PERSON] (2017), this method involves training the model repeatedly and removing the least important features at each step, ultimately retaining only the essential features ([PERSON] et al., 2020).  
Permutation Importance. Introduced by [PERSON] (2001), this measure theoretically elucidates the impact of correlation on feature rankings ([PERSON] et al., 2020).  
SHAP. Proposed by [PERSON] and [PERSON] (2017), this technique expands usability by allowing feature inclusion based on the independence between features, founded on the SHAP theory ([PERSON] et al., 2020). The SHAP framework objectively distributes benefits by considering each feature's marginal contribution.  
Boruta. As detailed by [PERSON] and [PERSON] (2010), the Boruta algorithm is a wrapper feature selection method utilizing random forest ([PERSON] and [PERSON], 2010). It evaluates candidate features alongside shadow features to determine all significant features related to the outcome.  
BorutaSHAP. Building on the Boruta algorithm, BorutaSHAP, as described by [PERSON] (2020), uses randomly shuffled shadow features. It improves upon Boruta by integrating Shapley values and an optimized Shap TreeExplainer, tailored specifically for tree-based models ([PERSON] and [PERSON], 2010).
# Optimized Dissolved Oxygen Fuzzy Control for Recombinant _Escherichia coli_ Cultivations
## 3 Material and Methods
### Fuzzy Controller Implementation in SUIPERSYS_HCDC Program and E. coli Cultivation Test
#### 3.4.3 E. coli Strain and Culture Media  
Recombinant fragment of Pneumococcal surface protein (PspA) gene from clade 4 (PspA4 PRO) was cloned into pET37b+ vector and inserted into _Escherichia coli_ BL2(1 DE3) [54]. The clone was kindly provided by Dr. [PERSON] from Butantan Institute (Sao Paulo, Brazil).  
Batch cultivations were carried out in a chemically defined medium (HDF-High Density Fermentation medium) for microorganism cultivations adapted from [55; 56; 52]. The mineral salts (NH\({}_{4}\))\({}_{2}\)HPO4 (5.30 g/L), KH\({}_{2}\)PO\({}_{4}\) (17.73 g/L), citric acid (2.27 g/L), and trace elements from stock solutions of CoCl\({}_{2}\).6H\({}_{2}\)O (3.33 mg/L), CuCl\({}_{2}\).2H2O (2.00 mg/L), MnCl\({}_{2}\).4H\({}_{2}\)O (20 mg/L), H\({}_{3}\)BO\({}_{3}\) (4.00 mg/L), Na\({}_{2}\)MoO\({}_{4}\).2H\({}_{2}\)O (2.80 mg/L), Zn(CH\({}_{3}\)COOH)2.H\({}_{2}\)O (33.80 mg/L), EDTA (18.80 mg/L) were mixed and solubilized in distilled water. The pH was adjusted to 6.3 (to avoid salt precipitation), and the solution was autoclaved at 120 \({}^{\circ}\)C for 15 min. Glycerol (60.00 g/L) and MgSO\({}_{4}\).7H\({}_{2}\)O (1.60 g/L) solutions were also adjusted to pH 6.3 and autoclaved separately. For the medium assembly, the three solutions were mixed in a laminar flow cabinet. Kanamycin (50.00 mg/L) and Thiamine (45.00 mg/L) were diluted in milli-Q water, sterilized by membrane filtration (0.22 \(\upmu\)m pore diameter)and added to the cooled medium. Polypropylene glycol (PPG) (1.00 mL/L) was added as antifoam [55].
## 3 Thermographic Techniques for Skin Cancer Diagnosis
### Dynamic Infrared Thermography
#### 3.2.4 Thermal Wave Imaging  
The feasibility of the frequency-modulated thermal wave imaging (FMTWI) technique used for the detection and distinction of skin cancer stages was theoretically proved for the first time by [PERSON] et al. [45] in 2005. The FMTWI technique foresees a controlled heating of the skin surface through a frequency-modulated heat flux of the desired magnitude and bandwidth, as shown in Figure 4. As a consequence, a highly diffused thermal wave propagates within the tissue, leading to time-varying temperature distributions. During the active heating, the temperature of the irradiated surface was recorded by an IR camera [45]. This method can be applied to thermally probe skin lesions at different depths using a single experimental test. Additionally, it requires a low peak power of the incident heat flux, and it is faster than other imaging techniques requiring periodically amplitude-modulated thermal excitation, such as LIT. The results showed that the FMTWI technique is suitable for the detection and differentiation of melanomas with large volumes rather than melanomas at the earliest development stages (with small volumes). Finally, by extracting the phase information from the simulated thermograms, the authors reported that the phase images have the advantage of allowing different development stages of melanoma to be clearly detected, compared to raw thermograms recorded by simply applying dynamic IR thermography.
## 3 Results
### Kaempfer Increases Early Apoptosis in HeLa Cells  
To ascertain whether G2/M cell cycle arrest is accompanied by apoptosis induction by kaempfer, Annexin V/PI double staining was employed. Treated HeLa cells at 20, 30 and 50 uM for 48 h exhibited an increase in the proportion of early apoptotic cells with an insignificant change in the proportion of post apoptotic cells. In contrast, the percentage of live cells showed a decreasing trend. Early apoptotic cell population was found to be increased from 0.35% to 4.74%, 6.49% and 8.26% at 30, 40 and 50 uM in 24 h, whereas in 48 h treatment, it raised to 18.1%, 24% and 25.2%, respectively. Therefore, Annexin/PI staining clearly established the apoptosis-inducing property of kaempfer in HeLa cells (Figure 2C,D).  
Kaempfer Modulates Expression of Various Genes Involved in Cell Cycle Regulation and Signalling Pathways  
Overexpression of MAPK and AKT/mTOR leads to uncontrolled cell proliferation and survival, consequently causing transformation of cells. Kaempfer treatment (30 and 50 uM for 48 h) limits cancer cell proliferation by downregulating MAPK and AKT/mTOR pathways at the transcript level. Expression of various MAPK pathway genes was considerably downregulated, such as MAPK1, MAK14, MAP2K1, MAP2K3, MAP2K5, MAP2K6, MYC and ELK 1. Further, the transcript-level expression of PI3K/AKT/mTOR genes, such as AKT2, MTOR, PIK3C2B, PIK3 CA, PIK3 CB, PIK3 CD, was also found to be significantly downregulated. However, expression of various tumor suppressor genes, such as PTPRR, ATM, ATR, FOXO 1, and FOXO 3, was upregulated.  
Inhibition of G2-M progression through cell cycle checkpoints was further validated at the molecular level by analysing the expression of cell cycle regulator genes. Kaempfer treatment (50 uM) demonstrated a decreased expression of CCNB1, CCNB2, CCNE2, CDK2, CDKN2A, CDKN2B, and CDK4, which explains the G2-M arrest induced by kaempfer in comparison to control cells, whereas the expression of TP53, a TSG was significantly upregulated (Figure 3A).
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 2 System Model
### Transmission
In Precoder 1, the vectors \(\mathbf{\tilde{x}}_{k}\) are precoded by the matrices \(\mathbf{W}_{k}\in\mathbb{C}^{N_{s}^{\prime}\times N_{r}^{\prime}}\), where \(N_{s}^{\prime}\leq N_{s}\) is the number of reflecting mirrors intended for users under MUI and \(N_{r}^{\prime}\leq N_{r}\) is the number of receive antennas in this subset. Users who are not under MUI, are concatenated in the vector \(\mathbf{x}_{2}\) and then are taken to Precoder 2. The input vector to Precoder 2 is  
\[\mathbf{x}=(\mathbf{x}_{1}^{T},\mathbf{x}_{2}^{T})^{T}. \tag{7}\]  
The Precoder 2 is designed to pre-cancel the interference in the first hop (from the BS to the RIS). It guarantees that the signals with appropriate phases and amplitudes are reflected by the RIS. The output signal \(\mathbf{t}_{\mathbf{x}}\in\mathbb{C}^{N_{l}\times 1}\) of Precoder 2 can be written as  
\[\mathbf{t}_{\mathbf{x}}=\mathbf{F}\mathbf{x}, \tag{8}\]  
where \(\mathbf{F}\in\mathbb{C}^{N_{l}\times N_{s}}\) is a precoding matrix. Table 1 shows the first 16 out of 256 DQSM signals.
###### Abstract  
Climate change has been unprecedented in the past decades or even thousands of years, which has had an adverse impact on the mechanical properties of concrete structures. Many researchers have begun to study new concrete materials. Graphene nanoplatelet (GNP) is an attractive nanomaterial that can change the crystal structure of concrete and improve durability. The aim of the present study was to investigate the effect of GNP (0.05%wt) on the carbonation depth of concrete under simulated changing climate conditions (varying temperature, relative humidity, and carbon dioxide (CO\({}_{2}\)) concentration), and compare it with ordinary concrete. When the concentration of CO\({}_{2}\) is variable, the carbonation depth of graphene concrete is 10% to 20% lower than that of ordinary concrete. When the temperature is lower than 33 \({}^{\circ}\)C, the carbonation depth of graphene concrete is less than that of the control sample; however, above 33 \({}^{\circ}\)C, the thermal conductivity of GNP increases the carbonation reaction rate of concrete. When the humidity is a variable, the carbonation depth of graphene concrete is less than 15% to 30% of ordinary concrete, and when the humidity is higher than 78%, the difference in the carbonation depth between the ordinary concrete and the graphene concrete decreases gradually. The overall results indicated that GNP has a favorable effect on anti-carbonation performance under changing climate conditions.  
climate change; concrete; graphene nanoplatelet (GNP); carbonation depth +
Footnote †: journal: Applied Sciences
# Decomposition Methods for Machine Learning with Small, Incomplete or Noisy Datasets
### Mathematical Notation and Definitions  
Vectors and matrices are denoted using boldface lower- and upper-case letters, respectively. For example \(\mathbf{x}\in\mathds{R}^{I}\) and \(\mathbf{A}\in\mathds{R}^{I\times J}\) represent a vector and a matrix, respectively. Columns of a matrix \(\mathbf{A}\in\mathds{R}^{I\times J}\) are referred as vectors \(\mathbf{a}_{j}\in\mathds{R}^{I}\).  
A tensor is a multidimensional array generalizing vectors and matrices to higher number of dimensions. For example, a tensor \(\mathbf{X}\in\mathds{R}^{I\times J\times K}\) is a 3D array of real numbers whose elements \((i,j,k)\) are referred to as \(x_{ijk}\). The individual dimensions of a tensor are referred to as modes (1 st mode, 2 nd mode, and so on). By generalization of matrix multiplication, a tensor can be multiplied by a matrix in a specific mode, only if their size matches. Given a tensor \(\mathbf{X}\in\mathds{R}^{I_{1}\times I_{2}\cdots\times I_{N}}\) and a matrix \(\mathbf{A}\in\mathds{R}^{I\times I_{n}}\), the mode-\(n\) product \(\mathbf{Y}=\mathbf{X}\times_{n}\mathbf{A}\in\mathds{R}^{I_{1}\times\cdots \times I_{n-1}\times I\times I_{n+1}\cdots\times I_{N}}\) is defined by: \(y_{i_{1}\cdots i_{n-1}|\tilde{y}_{i_{n}+1}\cdots i_{N}}=\sum_{i_{n}=1}^{I_{n}} x_{i_{1}\cdots i_{n-1}i_{n}}\tilde{y}_{i_{n}}\), with \(i_{k}=1,2,...,I_{k}\) (\(k\
eq n\)) and \(j=1,2,...,J\).  
The \(\ell_{0}\)-norm \(\|\mathbf{x}\|_{0}\) of a vector \(\mathbf{x}\in\mathds{R}^{N}\) is defined as the number of non-zero entries of the vector. When the number of non-zero entries is much less than the dimension of the vector, i.e., \(\|\mathbf{x}\|_{0}\ll N\), the vector is sparse.  
Given two matrices: \(\mathbf{A}\in\mathds{R}^{I_{1}\times I_{1}}\) and \(\mathbf{B}\in\mathds{R}^{I_{2}\times I_{2}}\), the Kronecker product is defined as follows:  
\[\mathbf{A}\otimes\mathbf{B}=\begin{bmatrix}a_{1,1}\mathbf{B}&a_{1,2}\mathbf{B }&\ldots&a_{1,I_{1}}\mathbf{B}\\ a_{2,1}\mathbf{B}&a_{2,2}\mathbf{B}&\ldots&a_{2,I_{1}}\mathbf{B}\\ \vdots&\vdots&\ddots&\vdots\\ a_{I_{1},1}\mathbf{B}&a_{I_{1},2}\mathbf{B}&\ldots&a_{I_{1},J_{1}}\mathbf{B} \end{bmatrix}. \tag{1}\]