esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Internal Data Markets
## 4 A Generalised Model for Secure Internal Data Markets
### Audit and Control  
While most internal markets do not have the personal and technical resources for fully monitoring all events taking place in the market, it is advisable to introduce measures that allow for the ex-post detection of certain attacks, especially data manipulation. Thus, the model introduces an \"Audit and Control\" (A&C) mechanism that stores at least the following information on each data set:  
* Hashes of the incoming data sets.
* Timestamp of the execution in order to be able to track back enrichments based on other data sets and enable reconstructing the respective versions.
- Version number from version control, if available.
- Hashes of input and output data sets.
- Meta data on the execution, such as number of records in the data sets and user names in the case of manual execution.
* Access rights to the data in the data store.
* Post-processing steps executed on data delivery through the brokering module, including the same information as that for the pre-processing steps, as well as the request information from the data consumer.  
From a technological perspective, there exist several methods for providing Audit and Control, e.g., blockchains, third-party controlled logging and other well-researched mechanisms for providing data and process integrity. While the integration of a transparent Audit and Control mechanism is quite straightforward for simple markets, where data are collected, stored and provided (perhaps including a fingerprint, watermark or just giving away an aggregated version), it is rather problematic to provide a transparent oversight in the case of more complex enrichment processes, especially in cases where classification based on neural networks is used, as the problem of explainability (or, rather, the lack of explainability) makes it currently impossible in most real-life use cases to determine what part of the data was actually used in the classification and is thus reflected in the delivered end result.
## 3 System Model and Problem Formulation
### Channel Model
The SNR can be calculated as follows:  
\[SNR=EIRP-k-PL-B+\frac{G}{T} \tag{9}\]  
where \(EIRP\) is the effective isotropic radiated power (in dBW), \(\frac{G}{T}\) is the antenna gain-to-noise-temperature (in dB/K), \(k\) is the Boltzmann constant, equal to \(-228.6\) dBW/K/Hz, \(PL\) is the path loss (in dB), and \(B\) is the channel bandwidth (in dBHz). Although the original unit of each component is different, the unit is unified by standardization in decibel operation. By adding and subtracting decibels, the formula unifies the transmit power, path loss, noise, and receive performance into SNR, which visually reflects the link quality. EIRP can be calculated as follows:  
\[EIRP=P_{T}-L_{C}+G_{T} \tag{10}\]  
where \(P_{T}\) is the antenna transmit power (in dBW), \(L_{C}\) is the cable loss (in dB), and \(G_{T}\) is the transmit antenna gain (in dBi). Antenna gain-to-noise-temperature \(\frac{G}{T}\) can be derived as follows:  
\[\frac{G}{T}=G_{R}-N_{f}-10 log_{10}(T_{0}+(T_{\alpha}-T_{0})10^{-0.1N_{f}}) \tag{11}\]  
According to the Shannon formula, the data rate (in bps) of the satellite-ground link can be expressed as follows:  
\[R_{GSL}=Blog_{2}(1+SNR) \tag{12}\]
# Symmetric-Key-Based Security for Multicast Communication in Wireless Sensor Networks +
## 3 System Model and Assumptions
### Attack Model  
The attackers may come from inside or outside the network. They can eavesdrop on the traffic, inject new messages, replay and change old messages, or spoof other identities.  
As already mentioned above, we assume the TTP as completely trustworthy and the NMM as honest but curious. We further assume that the NMM does not collaborate with malicious nodes. Moreover, we assume that the security related information at the nodes and at the NMM is stored in tamper resistant hardware, which is currently very common and available at a reasonable price.  
We do not discuss the mechanisms to detect mallebehavior of a node (e.g., by storing trust tables in each node) and we refer to the literature on intrusion detection mechanisms [21] and attestation mechanisms [22] to detect abnormal behavior of a compromised node.
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Multi-Source Data Deep Fusion Network  
The mileage and base information of a vehicle play a pivotal role in the formulation of maintenance projects. Based on maintenance project correlation representation, in order to thoroughly take advantage of their impact on this endeavor, we propose a multi-source data deep fusion network that combines maintenance project correlation representation result, mileage, and base information.
## 3 Results
### Steady-State Rolling  
Equivalent plastic strain (EPS) was used to detect the steady-state rolling. The rolling in the large scale implicit transient model (i.e., control model) reached steady state in a range of distance between 40 mm and 470 mm along the rolling direction, while this range was between 20 mm and 60 mm for the short transient models. In the steady-state rolling range, the EPS remained constant or fluctuated around a constant value, as shown in Figure 5.  
The EPS predicted by the short implicit transient model demonstrated slightly larger fluctuation than other short models, and the fluctuation became negligible in the distance range from 40 mm to 50 mm. Based on the EPS distributions, the inspection plane of the short models was selected with a location at 48 mm from the rolling start position. The Eulerian model reached steady state after 7 s rolling, and then the analysis halted, which was equivalent to a rolling distance of 58 mm.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Number of Elements**} & **Wall Clock Time with 4** & \multirow{2}{*}{**Total Time Saving, \%**} \\  & & **CPUs Used (Seconds)** & \\ \hline
3D large scale implicit transient & & & \\ model (control model) & 63,011 & 64,002 & N/A \\ \hline
3D short implicit transient model & 9036 & 2238 & 96.50\% \\ \hline
3D short explicit transient model & 9036 & 2355 & 96.32\% \\ \hline
3D Eulerian steady-state model & 27,600 & 2613  & 95.91\% \\ \hline
2D short implicit transient model & & & \\ with analytic rigid roller & & & \\ \hline
2D short implicit transient model & & & \\ with elastic roller & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of computational efficiency between different models.  
Figure 5: Equivalent plastic strain obtained along centreline on the top of the WAAM wall.  
In the developed efficient models, the rolling load was maintained by applying a constant roller displacement (1.674 mm) in the vertical direction. The applied displacement was equivalent to a rolling load of 50 kN (25 kN for the half model due to symmetry). The rolling load was verified by examining the reaction force on the pivot of the roller (Figure 6) and by comparing contact pressure during rolling on the surface of the WAAM wall (Figure 7). Similar distribution and magnitude of contact pressure was obtained in all the models.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 5 Physical Layer Performance Evaluation  
The research community has given great attention to LoRa and LoRaWAN technological aspects. Numerous studies research the coverage aspects, LoRa resilience to self-interference and other type of interference. This section covers the physical layer performance evaluation studies. It includes studies related to coverage performance and interference impact on communication.
# Perioperative Nutritional Support: A Review of Current Literature
## 6 Perioperative Nutritional Support in Non-Onological Pathology
### Spine and Hip Surgery  
Low albumin, transferrin and lymphocyte levels have been associated with increased surgical wound infections, post-surgical complications, increased length of hospital stay, and 30-day readmission and mortality after spinal surgery. However, few studies have evaluated the role of nutrition in these patients [156]. In a clinical trial, the use of nutritional support with protein and carbohydrate powders before and after surgery was associated with a shorter hospital stay, a lower incidence of hydroelectrolyte disturbances and an increase in albumin levels on postoperative day 3 [157]. As in other surgeries, there are recommendations on perioperative nutrition made by the ERAS Society [156]. Liquids and solids are allowed up to 2 and 6 h, respectively, before surgery, with carbohydrate loading to attenuate insulin resistance and the catabolic state produced by surgery.  
As for hip fracture surgery, it is more frequent in elderly patients, with a higher prevalence of malnutrition or high risk of malnutrition [158]. Mortality can reach up to 30% one year after fracture, and is higher in malnourished patients [158; 159]. It has been shown that nutritional status tends to deteriorate further during hospitalization of these patients, especially if there is cognitive impairment [158]. Despite all this, perioperative nutritional support in hip fracture patients is still under debate. There are some studies that have shown benefits in terms of mortality [160], quality of life [161], or surgical infections [162]. However, other studies have not demonstrated clinical benefits of this support and have found that the tolerance of these patients to the nasogastric tube was low [163; 164; 165]. Therefore, more studies are needed in this regard.
###### Abstract  
In this paper, a joint method combining Hough transform and reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) is presented to detect time-varying interferences with crossed frequency for a Global Navigation Satellite System (GNSS) receiver with a single antenna. The proposed method can prevent the cross-term interference and detect the time-varying interferences with crossed frequency which cannot be achieved by the classical time-frequency (TF) analysis with the peak detection method. The actual performance of the developed method has been evaluated by experiments with conditions where the real BeiDou system (BDS) B1I signals are corrupted by the simulated chirp interferences. The results of experiments show that the introduced method is effectively able to detect chirp interferences with crossed frequency and provide the same root mean square errors (RMSE) of the parameter estimation for chirp one and the improved initial frequency estimation for chirp two compared with the Hough transform of Wigner-Ville distribution (WVD) when the jamming to noise ratio (JNR) equals or surpasses 4 dB.  
GNSS; time-varying interference; single antenna; RSPWVD; Hough transform +
Footnote †: journal: Sensors
# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 3 Validation on Synthetic GPR Data
### Ablation Experiments  
We designed the CSA module for our model to validate the effectiveness of this module in reconstructing void defects. We modified our proposed model to create two variant models: one retaining only channel attention (removing spatial attention) and another retaining only spatial attention (removing channel attention). We conducted comparative experiments between these two variant models and our proposed model with the CSA module.  
In the experiments, we selected GPR images of void defects with rebar clutter interference from the test dataset as input for the trained models. We compared the suppression effects of different models, with the outcomes displayed in Figure 6.  
As shown in the figure, the model with the CSA module, which simultaneously applies channel and spatial attention, more comprehensively enhances useful features and suppresses noise. The suppression results show more layers and details of the void, with more prominent and continuous waveforms. When only the channel attention module is added, the lack of spatial attention may lead to insufficient capture and suppression of clutter features in the spatial distribution. This results in less accurate reconstruction of void details, with some non-void clutter being slightly mis-reconstructed. When only the spatial attention module is added, the lack of feature selection and weighting across channels may result in an inability to fully utilize information from different channels to suppress clutter, making the void signals appear slightly blurred. Overall, the model with the CSA module demonstrates the best performance. The other variant models show some deficiencies in reconstructing void defects, especially in correctly reconstructing the weak void waveforms under rebar clutter. This validates the superior performance of the CSA module in removing rebar clutter while maintaining void defect integrity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **RMSE** & **PSNR (dB)** & **SSIM** \\ \hline Ours & 5.0145 & 27.3702 & 0.8761 \\ UNIT & 5.2327 & 25.7002 & 0.8496 \\ CycleGAN & 5.1943 & 25.7281 & 0.8374 \\ DualGAN & 5.4676 & 23.0580 & 0.8259 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of models evaluation metrics.  
The suppression effect of the model was evaluated using the same RMSE, PSNR, and SSIM metrics. The results are shown in Table 3. It is evident that the model incorporating both channel attention and spatial attention modules outperforms the other two variant models, further validating the necessity of these attention mechanisms for improving the model's performance in rebar clutter suppression. Additionally, all models outperform the scenario without any attention modules. This demonstrates the superiority of the CSA module in improving performance.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 2 Theory
### Collisional Plasmas  
For experiments in which the collisions of the electrons with neutrals cannot be neglected, the position and width of a resonance peak are used to separate the contributions of the electron  
Figure 3: The change in the (**a**) real and (**b**) imaginary part of the relative permittivity of a plasma as a function of the electron density for four collision frequencies. The imaginary part of the permittivity is zero when the electrons do not collide and for this reason there is no change in the imaginary part of the permittivity for \(\
u_{\rm m}=0\) Hz.  
density and collision frequency to the real and imaginary part of the permittivity. Atmospheric-pressure plasmas are typically significantly smaller than the cavity and a completely filled cavity can therefore not be assumed. In this case the volume occupied by the plasma needs to be determined by, for example, camera images.  
The atmospheric-pressure plasmas investigated in references [18; 19] were assumed to have no spatial dependency within the plasma volume. With this assumption, the collision frequency of the electrons \(\
u_{\rm m}\) is given by [18; 23],  
\[\
u_{\rm m}=\pi f_{1}\frac{\Delta(1/Q)}{\Delta f/f_{1}}, \tag{15}\]  
while the electron density \(n_{\rm e}\) can be obtained from [18; 23],  
\[n_{\rm e}=\frac{2\epsilon_{0}m_{\rm e}}{e^{2}}\frac{\
u_{\rm m}^{2}+4\pi^{2}f_ {1}^{2}}{\mathcal{V}}\frac{\Delta f}{f_{1}}. \tag{16}\]  
Here, \(\mathcal{V}\) is the ratio between the effective (electric-field-squared weighted) plasma volume and the effective (electric-field-squared weighted) cavity volume,  
\[\mathcal{V}=\frac{\iiint_{\mathcal{V}_{\rm p}}|\mathbf{E}_{1}|^{2}d^{3} \mathbf{r}}{\iiint_{\mathcal{V}_{\rm cav}}|\mathbf{E}_{1}|^{2}d^{3}\mathbf{r}}. \tag{17}\]  
In contrast to in the derivation of Equation (13), the influence of the change in resonance frequency on the permittivity of the plasma is not taken into account in Equations (15) and (16). The influence of the assumption of no spatial dependency on the results of investigations of plasma with a spatial profile in the electron dynamics is investigated in Section 3.3.  
A disadvantage of probing a plasma with an electron collision frequency that is much higher than the probing frequencies is that the relative shift in resonance frequency \(\Delta f/f_{1}\) is much smaller than would be the case for a non-collisional plasma with the same electron density. For a \(f_{\rm res}\) of 3.5 GHz, the \(\Delta f/f_{1}\) is \(\sim\)2100 times smaller for a plasma with a \(\
u_{\rm m}\) of 1 THz compared to that with a \(\
u_{\rm m}=0\) Hz. Furthermore, \(\mathcal{V}\ll 1\) for atmospheric-pressure plasmas, which reduces \(\Delta f/f_{1}\) even more. These atmospheric-pressure plasmas typically have a higher electron density which compensates (partially) for the two mechanisms described above. However, for the atmospheric-pressure discharges investigated in our articles [18; 19]\(\Delta f/f_{1}\) has been \(\sim\)4 orders of magnitude smaller than that for typical low-pressure plasmas.
## 3 Methodology
### DESTformer Architecture
#### 3.2.2 Model Inputs  
The input to the encoder is the past sequence \(X\) of length \(T_{x}\). Consistent with Auto-former, the sequence \(X\) is decomposed into seasonal component \(X_{S}\) and trend component \(X_{T}\) through the frequency domain transform. The latter half of the seasonal component sequence is concatenated with a zero vector of length \(T_{y}\) along the time dimension as the input to the seasonal component in the decoder. The latter half of the trend component sequence is concatenated with a sequence mean vector of length \(T_{y}\) along the time dimension as the input to the trend component in the decoder. The input to the encoder is represented as \(X_{en}\), the seasonal component input to the decoder is denoted as \(X_{del}\), and the trend component input to the decoder is represented as \(X_{del}\). Mathematically, we have  
\[\begin{split}& X_{ens},X_{ent}=FreDecomp(X_{en\frac{T_{x}}{2}:T_{x}}),\\ & X_{des}=Concat(X_{ens},X_{0}),\\ & X_{del}=Concat(X_{ent},X_{Mean}).\end{split} \tag{2}\]
## 6 Hardware and Software for Data Protection
### Types of IDS
#### 6.3.2 Signature-Based Detection  
Signature detection is normally detected by their misuse and this method uses recognised patterns of unauthorized behaviour to predict and detect subsequent similar attempts. These patterns are called signatures [37]. For host-based intrusion, detection, and prevention, one example is \"three failed logins.\" For network intrusion, detection and prevention, a signature can be as simple as a specific pattern that matches a portion of a network packet. For instance, packet content signatures and header content signatures can indicate unauthorized actions. The occurrence of a signature might not actually be attempted unauthorized access, it could just be an honest mistake. These systems are not unlike virus detection systems, they can detect many or all known attack patterns, but they are of little use for as yet unknown attack methods. The main advantage of the misuse detection paradigm is that it can accurately and efficiently detect instances of known attacks. The main disadvantage of misuse detection methods is that it lacks the ability to detect the newly invented attacks. Signature databases must be constantly updated, and IDS must be able to compare and match activities against large collections of attack signatures [37]. The next sub-section goes on to look at anomaly detection called Anomaly-Based Detection.
## 2 Methodology
### Generating Explanatory Examples
#### 2.1.2 Pix2 Pix  
The Pix2 Pix architecture has been adapted to generate realistic images of polyps from binary masks with background contours. The generator is based on the U-Net architecture [22], which includes residual blocks, attention blocks, and dilated convolutions to increase modelling capacity and stability during training. Dilated convolutions expand the receptive field without losing spatial resolution, capturing more contextual information  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Public Database Source** & **Number of Images** \\ \hline CVC-ClinicDB [16] & 612 \\ \hline CVC-300 [17] & 60 \\ \hline CVC-ColonDB [17] & 300 \\ \hline ETIS-LaribPolypDB [18] & 196 \\ \hline KVASIR-SEG [19] & 1000 \\ \hline PolypGen [20] & 1537 \\ \hline Total & 3705 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the dataset used for Pix2 Pix model training.  
Figure 2: Mask creation process for Pix2 Pix input. The process starts with an original colonoscopy image (far left), from which background details are extracted using the Canny edge detection method. These edges are then combined with the polyp’s binary segmentation mask. The resulting image (far right) serves as the input for the Pix2 Pix model, providing both the target polyp shape and the surrounding visual context.  
from the images. Attention blocks ensure the generator focuses on the most relevant regions, improving the image quality.  
Four loss functions were used in the generator for training: adversarial, L1, L2, and perceptual [23; 24; 25; 26]. Adversarial loss helps the generator create images that the discriminator cannot differentiate from the real thing. In contrast, the L1 and L2 losses adjust details and refine the characteristics of the images. Based on a pre-trained VGG19 network, which was trained on the ImageNet dataset [27], perceptual loss ensures that the high-level characteristics of the generated images are similar to those of the real images.  
The training configuration included the Adam optimiser, with learning rates \(2\times 10^{-4}\) for the generator and \(1\times 10^{-4}\) for the discriminator [28]. Early stopping and dynamic adjustment of learning rates [29; 30] were also used to avoid overfitting. The label smoothing technique was applied to smooth the real and false labels, stabilising the discriminator training and promoting better model convergence.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
###### Abstract  
Recently, there have been many cases in which direct current (DC) facilities have been placed in alternating current (AC) systems for various reasons. In particular, in Korea, studies are being conducted to install a back-to-back (BTB) voltage-sourced converter (VSC) high-voltage direct current (HVDC) to solve the fault current problem of the meshed system, and discussions on how to operate it have been made accordingly. It is possible to provide grid services such as minimizing grid loss by changing the HVDC operating point, but it also may violate reliability standards without proper HVDC operation according to the system condition. Especially, unlike the AC system, DC may adversely affect the AC system because the operating point does not change even after a disturbance has occurred, so strategies to change the operating point after the contingency are required. In this paper, a method for finding the operating point of embedded HVDC that minimizes losses within the range of compliance with the reliability criterion is proposed. We use the Power Transfer Distribution Factor (PTDF) to reduce the number of buses to be monitored during HVDC control, reduce unnecessary checks, and determine the setpoints for the active/reactive power of the HVDC through system total loss minimization (STLM) control to search for the minimum loss point using Powell's direct set. We also propose an algorithm to search for the operating point that minimizes the loss automatically and solves the overload occurring in an emergency through security-constrained loss minimization (SCLM) control. To verify the feasibility of the algorithm, we conducted a case study using an actual Korean power system and verified the effect of systematic loss reduction and overload relief in a contingency. The simulations are conducted by a commercial power system analysis tool, Power System Simulator for Engineering (PSS/E).  
b ack-to-back HVDC; embedded HVDC; HVDC operation point; Powell's direct set method; system loss minimization