esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 3 Experiment and Results
### Result
#### 3.2.4 Kappa Coefficient Effect Size Test  
To demonstrate that the experimental results in the two data sets are not chance events, the Kappa coefficients of the models in the two data sets are therefore tested for effect sizes. The results are shown in Table 5. Cohen's d reflects the degree of difference between two aggregates after they are affected by something, and the larger the effect size, the greater the degree of difference. Generally, 0.2 \(\leq\) d \(<\) 0.5 is called a small effect, 0.5 \(\leq\) d \(<\) 0.8 is called a medium effect, and d \(\geq\) 0.8 is called a large effect. As can be seen in Table 3, the [PERSON]'s d value for the Kappa coefficient in both data sets is 0.795, which is a medium effect and very close to the large effect. This also indicates that the experimental results in both data sets are not chance events and are statistically significant.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Method** & **Category** & **Precision** & **Recall** & **F1-Score** & **Kappa** & **MIoU** & **OA** \\ \hline \multirow{8}{*}{SETRnet} & Impervious surfaces & **79.79\%** & 78.52\% & 79.15\% & & & \\  & Building & 82.60\% & **91.60\%** & **86.87\%** & & & \\  & Low vegetation & 75.70\% & 75.81\% & 75.76\% & & & \\  & Tree & 78.69\% & 75.79\% & 77.22\% & 72.07\% & 59.97\% & 78.57\% \\  & Car & 81.73\% & 70.11\% & 75.48\% & & & \\  & Clutter/background & 57.67\% & **43.70\%** & **49.72\%** & & & \\ \hline \multirow{8}{*}{SwinUnet} & Impervious surfaces & 77.50\% & 90.51\% & **83.50\%** & & & \\  & Building & 89.98\% & 82.71\% & 86.19\% & & & \\  & Low vegetation & 79.34\% & **82.38\%** & **80.83\%** & & & \\  & Tree & 85.91\% & **78.41\%** & **81.99\%** & **76.47\%** & **63.62\%** & **85.01\%** \\  & Car & **86.52\%** & 72.43\% & 78.85\% & & & \\  & Clutter/background & **73.67\%** & 35.99\% & 48.36\% & & & \\ \hline \multirow{8}{*}{TransUnet} & Impervious surfaces & 61.12\% & **91.52\%** & 73.36\% & & & \\  & Building & **90.13\%** & 68.86\% & 78.07\% & & & \\ \cline{1-1}  & Low vegetation & **83.90\%** & 72.67\% & 77.88\% & & & \\ \cline{1-1}  & Tree & **86.33\%** & 71.98\% & 78.50\% & 68.18\% & 58.00\% & 75.76\% \\ \cline{1-1}  & Car & 83.33\% & **75.43\%** & **79.19\%** & & & \\ \cline{1-1}  & Clutter/background & 57.52\% & 42.10\% & 48.62\% & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation table of classification results in the Potsdam data set. The bolded numbers are the models with the best performance in terms of accuracy.
# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 2 Materials and Methods  
### Overview  
This section is dedicated to the description of an overview of AMU-Net followed by our proposed encoder, decoder, and novel foreground loss function. Figure 1 illustrates the overall architecture of the proposed AMU-Net model, which follows a U-Net-like encoder-decoder structure to enhance semantic segmentation performance. The model integrates multiscale feature processing and foreground saliency enhancement to improve segmentation accuracy. The Contextual Dynamic Encoder (CDE) is designed to effectively process semantic features from medical images. Within the CDE, the dark blue blocks represent the Modulated Predictive Coding Module (MPCM), which enhances feature representation through hierarchical predictive coding, while the orange blocks represent the Dynamic Multi-Scale Attention (DMSA) module, which improves feature refinement by capturing features at different scales.  
The decoder consists of an Adaptive Feature Decoder Block (AFDB), which integrates an Adaptive Fusion Attention Block (AFAB) and a weighted sum operation to refine feature representation. Additionally, a Spectral Feature Fusion Head (SFH) is used to enhance the fusion of spectral features in the final segmentation output. To further improve performance, the model introduces an auxiliary-agreement loss, which helps refine predictions by leveraging agreement-based learning mechanisms.
## 3 Results and Discussion
### Thermal Curing  
ATR-FTIR analysis was performed to evaluate curing of the composite samples. The spectrum of uncured Sylgard + curing agent showed a characteristic peak at 2160 cm\({}^{-1}\), which is attributable to the stretching vibration of Si-H groups, related to the curing agent [28; 31]. Figure 1 shows the pristine Sylgard (Figure 1a) and the formulation CuS5 (Figure 1b) as an example of filled formulation, before and after the thermal treatment. In all the cured formulations, the peak at 2160 cm\({}^{-1}\) completely disappeared after thermal treatment, confirming that the proposed thermal curing process (150 \({}^{\circ}\)C, 60 min) fulfilled the cross-linking of the elastomeric matrix despite the presence of fillers (all the ATR-FTIR analysis are reported in the Supplementary Materials Figures S1-S5).  
Figure 1: ART-FTIR spectra; (**a**) spectra of Sylgard pristine formulation (Entry 1; Table 1) before (orange curve) and after (blue curve) thermal curing at 150 \({}^{\circ}\)C. zoom of the Si-H peak at 2160 cm\({}^{-1}\) in the insert of the graph; (**b**) spectra filled Sylgard formulation containing spheroidal Cu (Entry 2, 3, 4; Table 1), before (blue curve) and after (red curves) thermal curing at 150 \({}^{\circ}\)C. Zoom of the Si-H peak at 2160 cm\({}^{-1}\) in the insert of the graph.  
Curing reaction was also followed by DSC analysis. The thermogram in the scan mode for pristine Sylgard (Figure 2a) exhibited a curing peak centered at 96 \({}^{\circ}\)C, meaning that the curing rate is maximum around this temperature. From this thermogram in the scan mode, the cure is complete in the first run since the second heating did not present any residual peak. To mimic the curing condition of the composite in the oven, isothermal analyses were performed at different temperatures as shown in Figure 2b. It is evident the increase of reactivity by increasing the temperature. The addition of the filler did not influence the curing kinetics since the position of the peak was almost unchanged for all tested compositions as shown in Figure 3 and in Table 2. The DSC result is in agreement with the ATR-FTIR analysis and both confirmed that the addition of fillers in the formulation did not influence the curing process. A previous study on epoxy matrix [32], showed the influence of the filler on the curing, this was not seen in our study since all the composite cured properly in the selected temperature and timing. The influence of the filler and matrix can explain the different behavior with respect to the previous study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entry** & **Acronym** & \
\begin{tabular}{c} **Time Curing Peak** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **Tg** \\ **(\({}^{\circ}\)C)** \\ \end{tabular} \\ \hline
1 & Sylgard & 9 & \(-121\) \\  
& \(-119\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time-curing peak and TG values of silicone composites.
# Antivirus Evasion Methods in Modern Operating Systems
## 1 Introduction  
In the present day, the internet is widely accessible to people of all ages and backgrounds, allowing them to use it for communication, entertainment, and information acquisition. However, hackers also have access to the internet and can use malicious software to compromise user privacy and important files.  
To protect against such attacks, users often install antivirus software that aims to reduce the risk of device infection. Downloading files from unknown sources, even well-known programs like web browsers, can lead to infection. Therefore, it is essential to download content from trusted and verified sources. If a suspicious file is downloaded, the antivirus software will typically display an appropriate message and move the infected file to quarantine, where it can be cleaned or deleted. User trust in antivirus software is essential, and regular updates of the signature database and the tool itself are important.  
Hackers use various bypassing and evasion techniques to gain access to devices. Antivirus software developers continuously develop new security tactics and update signature databases to combat emerging threats. However, the increasing number of new viruses implies that current protection technologies may not always be sufficient [1].  
This article discusses popular bypassing techniques and tools that should be detectable by most antivirus programs but also shows that complex attack chains that combine evasion techniques can bypass modern and commonly-used antiviruses. The paper is structured into nine sections: introduction, related work, security mechanisms, evasion techniques, testing environment, results of research on bypassing antiviruses, discussion, conclusions, and further research directions.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 2 Vehicular Named Data Networking (VNDN)  
The Information-Centric Network (ICN) project called Named Data Networking (NDN) is a new architecture designed to upgrade traditional TCP and IP networks to content-based networks. The primary purpose of the modification of the network is to make it easier for data packets in a communication network to refer to their content names instead of their destination IP addresses [26]. Recently, most research has focused on integrating vehicular communication with the NDN architecture, known as Vehicular Named Data Networking (VNDN). In other words, VNDN is an application class of VANETs and NDN in which a group of vehicles is connected through the On-Board Units (OBUs) and obtains access to a centralized data network. An OBU allows vehicles to process, store, and communicate with each other. In addition, OBUs can be connected wirelessly with or without Road Side Units (RSUs), enabling them to form ad hoc, dispersed, and self organized networks. The major objectives of VANET using the NDN architecture are the rigorous exchange of information and the effort to increase road safety while improving the driving experience. In VNDN, vehicles can communicate and exchange interest and data packets via V2V and V2 RSUs [27]. If a consumer vehicle needs any content, it generates a request (interest) with the name of the content and broadcasts it on the network. The intermediate vehicles rebroadcast the interest until it reaches the data producer. Once the interest packet reaches a node/vehicle that possesses the requested data, i.e., the name of the interest is the same as the name of the data or a prefix of the name of the data, the data are sent to the requested consumer vehicle. Since there are no source and destination addresses in a VNDN packet, the intermediate vehicles use the name and prefix in the Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB) to exchange interest and data packets [28]. Figure 1 demonstrates the general method used in the VNDN-based architecture, where the third layer makes use of a content name instead of an IP address.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 1 Introduction
Proposing optimizing methods for network training with respect to the practical problems of terminal heterogeneous resources. The Reward Dynamic Alignment (RDA) method is proposed to obtain the correct empirical data, initial episodes early termination method and asynchronous multi-agents joint training method to improve the speed and utility of network training. The rest of the paper is organized as follows. Section 2 presents research related to the task scheduling algorithm. Section 3 introduces the multi-application scheduling scenario and defines the average application execution time minimization problem. Section 4 proposes the multi-application scheduling algorithm-MASA and presents RDA, early termination of the initial episodes and asynchronous multi-agents joint training optimization methods. Simulation results of the proposed algorithm are discussed in Section 5 to validate its performance. Finally, the paper is summarized in Section 6.
# Region-of-Interest Optimization for Deep-Learning-Based Breast Cancer Detection in Mammograms
## 4 Discussion
The accurate determination of ablation margins helps in ensuring that the entire tumor and a sufficient margin of healthy tissue surrounding it have been effectively treated. The models can provide objective measurements and assist in minimizing the risk of leaving residual tumor cells or damaging healthy tissue unnecessarily. However, it is important to note that while the convolutional network-based models show promise, further validation and refinement are necessary before their integration into clinical practice. Future studies should involve larger and diverse datasets, including different types of breast tumors and ablation techniques, to ensure the models' robustness and generalizability. Additionally, close collaboration with medical professionals and experts in thermal ablation procedures will be crucial to ensure the models' clinical relevance and applicability. Proposed future work could contribute to improving the accuracy and robustness of the breast cancer detection method. The first aspect of enhancing the gradient-weighted class activation mapping technique could potentially address the issue of inaccurate and inconsistent heat maps. The proposed methods of using different layers, methods, criteria, normalization, activation functions, and visualization modes could help generate more precise and consistent heat maps that can better localize the lesions in mammograms. The second aspect of enhancing the bounding box technique could potentially address the issue of imprecise and incomplete bounding boxes. The proposed methods of using different algorithms, shapes, and techniques to detect the contours, represent the bounding boxes, and handle overlapping or touching bounding boxes could help produce more accurate and complete bounding boxes that reflect the exact shape and boundary of the lesions. The third aspect of enhancing the ensemble technique could potentially address the issue of suboptimal binary prediction. Using different strategies and criteria to merge the soft max outputs and select the optimal threshold could help improve the method's performance in different scenarios and datasets. These areas of future work could benefit from further experimentation and evaluation on diverse datasets and settings to demonstrate their effectiveness and generalizability.
## Materials and Methods
### Instrumentation and Measurements  
Raman and SERS spectra were recorded with a fiber coupled commercial Raman setup from WITec GmbH, Ulm, Germany. For excitation, a diode laser (Fandango, Cobolt, Kassel, Germany) with an emission wavelength at 514 nm was used. The same objective (20\(\times\), 0.4 N.A., Zeiss, Jena, Germany) was applied to focus the laser beam on the surface of the sample and to collect the backscattered photons. For recording the reference Raman spectrum of PZA 20 mW and for POA 5 mW laser power was incident on the sample, respectively. For all SERS measurements, 40 mW laser power was applied. For detection, a thermoelectrically cooled CCD (1024 \(\times\) 127) camera and a 600 l/mm grating with a spectral resolution of \(\sim\)5 cm\({}^{-1}\) were used.  
For Raman measurements on powder, a small amount of the sample was placed on a conventional microscope glass slide. Spectra were acquired with a 1 s integration time and 10 accumulations. SERS spectra in cuvettes (BRAND(r) 96-well PCR plate from Sigma Aldrich, Darmstadt, Germany) were measured after mixing equal amounts of colloidal h- AgNPs (100 uL) with the sample solution (100 uL) and adding additional high purity water or an aggregation agent (20 uL). For aggregation NaNO\({}_{3}\) was used at the concentration of 0.5 M. Each spectrum is the result of 10 accumulations of spectra recorded with an integration time of 1 s.  
LoC-SERS measurements were carried out by mounting a droplet based microfluidic chip (Figure S1) on the microscope table of the Raman setup. For injecting the solutions into the chip a computer controlled neMESYS pump system from Cetoni GmbH, Gera, Germany was employed and glass syringes purchased from ILS GmbH, Stutzerbach, Germany with 250, 500 and 2500 uL volumes were used. The syringes were connected to the chip with teflon capillaries (inner diameter of 0.5 mm; Wicom GmbH, Heppenheim, Germany). If not otherwise stated, the flow rates were set as follows: 20 nL/s for mineral oil, AgNO\({}_{3}\)/NH\({}_{3}\) and N\({}_{2}\)H\({}_{4}\), each. For the sodium citrate solution: 5 nL/s; and 40 nL/s in total for the two ports providing the sample and the aggregation agent (NaNO\({}_{3}\)). The controlled aggregation of metallic nanoparticles during the SERS analysis is beneficial to create hot spots. Due to the very well-controlled environment generated in the microfluidic chip the application of agglomeration agents can be very helpful to achieve high signal intensity in the SERS spectra. The spectra were continuously recorded during the dynamic flow with a 0.5 s integration time. The number of spectra for each measurement, if not otherwise stated, was 1200.
# Indoor Localization Using Semi-Supervised Manifold Alignment with Dimension Expansion
## 3 Algorithm Description
### Target Localization  
For each location query, we transform the newly-collected RSS measurement \(\mathbf{r}=(\text{rss}_{1}^{\text{new}},\text{rss}_{2}^{\text{new}},\)\(...,\text{rss}_{m}^{\text{new}})\) into \(\mathbf{P}_{y}^{\text{T}}\mathbf{r}^{\text{T}}\), where \(\text{rss}_{k}^{\text{new}}(1\leq k\leq m)\) is the newly-collected RSS value from the \(k\)-th AP. After that, we locate the target at the location, which is closest to \(\text{rss}_{k}^{\text{new}}(1\leq k\leq m)\) in manifold \(\mathbb{N}=\{\mathbf{P}_{x}^{\text{T}}\mathbf{H}^{\text{T}}\}\), such that:  
\[\arg\min_{\mathbf{h}_{i}}\left\{||\mathbf{P}_{x}^{\text{T}}\mathbf{h}_{i}^{ \text{T}}-\mathbf{P}_{y}^{\text{T}}\mathbf{r}^{\text{T}}||^{2}\right\} \tag{14}\]
# Real-Time Detection of Overloads on the Plasma-Facing Components of Wendelstein 7-X
###### Abstract  
Wendelstein 7-X (W7-X) is the leading experiment on the path of demonstrating that stellarators are a feasible concept for a future power plant. One of its major goals is to prove quasi-steady-state operation in a reactor-relevant parameter regime. The surveillance and protection of the water-cooled plasma-facing components (PFCs) against overheating is fundamental to guarantee a safe steady-state high-heat-flux operation. The system has to detect thermal events in real-time and timely interrupt operation if it detects a critical event. The fast reaction times required to prevent damage to the device make it imperative to automate fully the image analysis algorithms. During the past operational phases, W7-X was equipped with inertially cooled test divertor units and the system still required manual supervision. With the experience gained, we have designed a new real-time PFC protection system based on image processing techniques. It uses a precise registration of the entire field of view against the CAD model to determine the temperature limits and thermal properties of the different PFCs. Instead of reacting when the temperature limits are breached in certain regions of interest, the system predicts when an overload will occur based on a heat flux estimation, triggering the interlock system in advance to compensate for the system delay. To conclude, we present our research roadmap towards a feedback control system of thermal loads to prevent unnecessary plasma interruptions in long high-performance plasmas.  
Wendelstein 7-X; plasma facing components protection; imaging diagnostics; thermography; hot spot detection; thermal events detection
# Analytic Expressions for Radar Sea Clutter WSSUS Scattering Functions +
## 2 Results  
### Mathematical Background  
For a monostatic radar of arbitrary polarization, the baseband backscattered signal \(y(t)\) will be modeled as the response of an LTV system \(h(\tau,t)\) to a transmitted signal \(x(t)\):  
\[y(t)=\int h(\tau,t)x(t-\tau)d\tau+w(t), \tag{1}\]  
where \(\tau\) represents downrange delay (i.e., \"fast time\"), \(t\) is absolute time (\"slow time\"), and \(w(t)\) is thermal noise. In the literature, \(h(\tau,t)\) is commonly referred to as the time-varying impulse response. In this work, \(h\) will include the transceiver, antenna, atmospheric propagation, and backscattering environment, so \(x(t)\) is essentially the output of the transmit D/A, and \(y(t)\) is the input to the receive A/D, prior to matched filtering. Polarization considerations are contained in the antenna pattern and surface scattering radar cross-section (RCS). Variations in \(h(\tau,t)\) with respect to \(t\) represent channel-induced Doppler shifts. In the case where the entire scenario is static, \(h(\tau,t)=h(\tau)\) (i.e., \(h\) is LTI), and the integral in Equation (1) reduces to a convolution. In applying the LTV model to a radar system, we use the following assumptions:  
* The LTV impulse response \(h(\tau,t)\) is only valid over a single coherent processing interval (CPI) of \(N\) transmitted pulses* Over a single CPI, the range to target(s) is roughly constant
* Relative motion produces a Doppler shift on the carrier only and does not introduce time dilation of the pulse. The conditions for this assumption to be valid are that \(BT<0.1c/t\), where \(BT\) is the time-bandwidth product of the waveform and \(\dot{r}\) is the maximum range rate [47] (p. 382).  
We will model \(h(\tau,t)\) as a wide-sense stationary uncorrelated scattering (WSSUS) process characterized by a set of related correlation functions and show how these are a function of sensor geometry, system parameters, and environmental conditions. WSSUS processes, which are a subset of all random LTV systems, are the most widely used model in channel characterization problems [3; 13]. In the following sections, we will illustrate the properties of WSSUS processes and validate the core assumptions for the radar sea clutter problem.