esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
## 1 Introduction
### Purpose  
In this study, we aimed to improve the extraction accuracy of organ segmentation from PET/CT images using deep learning. We focused on the fact that although LDCT images contain abundant anatomical (morphological) information, the image contrast of soft tissue organs in the abdomen is significantly lower than that of diagnostic CT images. In addition, PET images offer higher image contrast for soft tissue organs based on FDG uptake (functional information); however, their spatial resolution is lower. To address these challenges, we proposed a hybrid organ segmentation method that focuses on morphological and functional information. We used two networks: one using only LDCT images and the other using both LDCT and PET images as inputs.  
Moreover, this study used fewer cases to train the networks than did existing studies. Therefore, we aimed to achieve highly accurate organ segmentation on a small number of datasets using a pre-trained model. In many studies using deep learning, ImageNet [15], which consists of natural images, has been used as a pre-training dataset, and good results have been reported [11]. However, within a study utilizing deep learning for medical images, findings indicated that employing a pre-trained dataset (RadImageNet) containing a substantial number of labeled medical images captured across various imaging modalities led to improved accuracy when compared to the use of ImageNet. [16]. Therefore, we applied RadImageNet as a pre-training dataset and compared its accuracy with that of ImageNet.
## 3 Results and Discussion
### Durability Properties
#### 3.4.2 Erosion Resistance  
The durability of earth blocks was also assessed via their resistance to water erosion, in accordance with New Zealand Standard NZS 4298 [47]. It should be noted that the resistance to erosion decreased with the increase in erosion depth according to the mentioned standard. Figure 20 depicts the erosion depth of the four samples tested for each formulation. As can be noticed in this figure, the water did not create any surface deterioration for all samples, which means that this method of stabilization of CEB is greatly efficient in its resistance to water erosion. All the tested bricks had an erosive depth inferior to 5 mm, which corresponds to an erodibility index lower than class 3, allowing us to classify the blocks as slightly erosive materials, and thus satisfying the requirement of NZS [47].  
This result shows also that the stabilization of earth blocks with cement increases its erosion resistance. Moreover, the higher the content of cement, the better the erosion resistance. Based on the visual perception throughout this experimental test, the good compactness and cohesion between the cement and soil with a low content of fibers seems to be the main reason for the good performance of this composite material in terms of the erosion resistance. The cement's hydration reaction with the matrix contributes to reducing the void network inside the mixture by binding earth particles to fibers and then protecting the block from the infiltration of water. These findings of the erosion test show that the composite developed in this study can satisfy the requirements of this building material. This is highly crucial, since the durability of CEBs is as important as their mechanical strength because of their masonry applications and the external loading that is applied during their service life.
## 3 Methods
### Synthetic Data Generation Techniques
#### 3.4.2 The Fast Gradient Sign Method  
A gradient-based method for producing adversarial instances is the Fast Gradient Sign Method (FGSM) [24], which can be adapted for the purpose of synthetic data generation in model extraction attacks. FGSM is an efficient and computationally inexpensive method that creates artificial samples by utilizing gradients that are part of the target model.  
Figure 3: The main workflow of training surrogate models using synthetic generation techniques. We split the MNIST dataset into two parts. In phase 1, we use split \(D_{t}\) to pre-train the surrogate models. In phase 2, the image is sampled uniformly from the split \(D_{r}\). Generate synthetic image data using the surrogate model. Submit the altered image to the victim model to obtain predictions. Use the perturbed image and its associated predictions to train the surrogate models.  
Given a target model \(f\), an input sample \(x\), and a loss function \(\mathcal{L}(\cdot,\cdot)\), By perturbing \(x\) in the orientation of the gradient of the loss function in relation to the input, the FGSM creates a synthetic sample \(x^{\prime}\):  
\[x^{\prime}=x+\epsilon\cdot\text{sign}(\
abla_{x}\mathcal{L}(f(x),y))\]  
where \(y\) is the ground truth label or target output for \(x\). The sign function applied element-wise to the gradient is \(\text{sign}(\cdot)\), and a tiny variable \(\epsilon\) regulates the size of the perturbation.  
The fundamental concept underlying FGSM is to traverse the trajectory that aligns with the gradient of the loss function about the input, to produce a synthetic sample \(x^{\prime}\) that maximizes its loss function \(\mathcal{L}(f(x^{\prime}),y)\). This perturbation is intended to generate samples that are informative and challenging for the target model, potentially leading to improved model extraction performance when used for training a replica model. FGSM is an efficient method as it requires only a single gradient computation per input sample. However, it may be susceptible to gradient masking or obfuscation techniques employed by the victim model for defense against model extraction attacks. To enhance the effectiveness of FGSM for model extraction, various extensions and modifications can be explored, such as iterative approaches (e.g., Projected Gradient Descent), incorporating additional constraints or regularization techniques, or combining FGSM with other synthetic data generation methods.
## 3 Materials and Methods
### Mask Detection Module
#### 3.3.1 Dataset  
There is no proper dataset available for mask detection and classification. The available datasets mostly have noisy data or are created artificially, which are not useful for creating a real-time mask detection system. We have not used the simulated face mask images dataset that does not represent real-world representation. Hence, that is why we used a large dataset consisting of 149,806 images in total gathered from different sources for our proposed model. For the module of the face mask detection and classification, we used three datasets, MAFA and Masked Face-Net, which are available on Github, and the third dataset from Bing. The Github (MaskedFace-Net) dataset [36] contains 133,783 images in total, in which 67,049 images belong to the category who wear the face mask properly, and 66,734 images belong to individuals who have not wear face mask properly. From the (MaskedFace-Net) dataset, we have only used 34,456 front face images for proper masked faces and 33,106 frontal images for improper masked faces. We used another large dataset from Github (FMLD) [37], and used the MAFA dataset [38], which contains 30,811 images in total with a different orientation and occlusion degree. We used Haar cascade classifier [32] to performs feature extraction by using the Haar Wavelet technique with 24 \(\times\) 24 window size to select only 12,000 frontal face images from the MAFA dataset and split the dataset as 9600 images for training and 2400 images for testing purpose. The Bing dataset contains 4039 images, in which we split our dataset by 3232 images for training and 807 for testing. We gathered face masks images from Bing using the bing-images library [39] available in Python, which can directly fetch images using URLs. Table 2 shows the overall dataset description. For the categorical classification that is shown in Figure 9, we combined the Github dataset [37] with Bing dataset.
## 3 Results
### Optimisation of the Biosensor Conditions
#### 3.3.3 Calibration Study  
A calibration study was performed with the glycerol biosensor in conjunction with chronoamperometry (Figure 7). For this purpose, a 100 \(\mu\)L aliquot of the standard glycerol solutions prepared in 0.1 M PBS pH 9.0 was deposited on top of the working/reference electrodes, and the concentration range of glycerol was 1 to 7.5 mM. Chronoamperometry was performed using an applied potential of 0.0 V vs. Ag/AgCl, preceded by an incubation time of 3 min. Figure 7A,B show the chronoamperomgrams obtained for the standard solutions.  
The current was measured at 30 s, 100 s, 200 s, 300 s, and 340 s for each concentration and plotted against the corresponding concentrations. The limits of detection (LOD) were calculated based on the standard error of the regression line (STE\({}_{\gamma\chi}\)) of the current response and the slope of the calibration curve (S) according to the formula: LOD = 3.3 \(\times\) (STE\({}_{\chi\chi}\)/S).  
Figure 6: Plot of amperometric responses versus glycerol concentration, obtained with an MB-SPCE in solutions containing glycerol, GLDH (10 U), and NAD\({}^{+}\) (5 mM) at an applied potential of 0.0 V vs. Ag/AgCl.  
Table 1 shows the linear regression equations, R\({}^{2}\) values, and the LOD for every current sampling time. Based on this data, we considered that a measurement time of 100 s would provide a reliable response for the analytical application described in the next section.
## 1 Introduction
[15] identified catechin, myricetin, quercetin, naringenin, and p-coumaric acid in the methanolic extract of leaves and confirmed the antioxidant potential in TEAC, FRAP, and ORAC assays [15]. [PERSON] et al. [16] showed the presence of flavonoids and antioxidant activity assessed using the DPPH and ABTS techniques of extracts from dried _B. pendula_ leaves [16]. [PERSON] et al. [17] compared the antioxidantpotential and total phenols, phenolic acids, and flavonoids in aqueous and ethanolic extracts of _B. pendula_ leaves. The extract prepared using ethanol (80%) was characterized by a higher content of flavonoids (including rutin and quercetin) and phenols, whereas the aqueous extract was a more valuable source of phenolic acids (including protocatechuic, ellagic, and chlorogenic acids). Antioxidant activity was assessed by the DPPH technique. There was no significant difference between the two extracts, but the ethanolic extract was a better scavenger of ABTS radicals. Aqueous extracts proved to be a better chelator of Fe\({}^{3+}\) ions, while a higher potential as assessed by TAA and FRAP methods was found for the ethanolic extract [17]. Not only are _B. pendula_ leaves a valuable source of antioxidants, including polyphenic compounds, _B. pendula_ raw materials are also rich in antioxidants in the outer bark [18], pollen [9], sap [10], buds, aglets, bines, and bark [15]. Despite the increasing use of alcoholic plant extracts as active ingredients in cosmetic preparations, there is still a lack of antiseptic preparations with antioxidant potential and reduced ethanol content. One of the key elements in preventing the development of infections is the use of disinfectants containing biocidal substances capable of removing microorganisms or inhibiting their growth on both skin and abiotic surfaces [19]. Despite the lifting of the epidemiological emergency, disinfectants are still used on a large scale. In promoting the EU's environmental objectives, it seems important to look for modern solutions and alternatives to the disinfectants currently in use [20]. Extracts obtained from the leaves of plants (due to their content of phenolic compounds) are characterized by their high antioxidant potential [21]. The high polyphenol content is a key parameter responsible for the effective biological activity of these preparations [22]. The purpose of this study was to obtain a novel biocidal preparation based on birch leaves with high antioxidant potential and with a reduced ethanol content. The new antiseptic had a mean 60 s kill time of more than 99.9 against _Staphylococcus aureus_ ATCC 6538 (at a concentration of 30 g/100 mL) and _Pseudomonas aeruginosa_ ATCC 15442 (at a concentration of 40 g/100 mL), while ethanol reduced these microorganisms at a concentration of 80 g/100 mL [23].
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 5 Conclusions and Outlook  
Thanks to the exponential increase in computational power for high resolution, intensive data processing and statistical analysis, a novel point of view regarding ground clutter has emerged in recent years: Its spatio-temporal characteristics are being investigated with the aim of monitoring the quality (and stability) of radar hardware. However, since most of ground distributed targets consist of a large number of randomly distributed scatterers, the corresponding backscattered signal is characterized by an extreme spatio-temporal variability in magnitude. So far, monitoring techniques have been based on the statistical parameters of the location (e.g., 90 percentile) of the reflectivity of a large number of radar bins that are above a given high reflectivity threshold (e.g., 55 dBZ) for more than a given percentage of time (e.g., 95%).  
In this note, we present an analysis that deals with the spectral (1 st and 2 nd Doppler moments) and polarimetric (differential reflectivity, copolar correlation coefficient and differential phase shift) information that characterizes an individual radar bin (\"Bright Scatterer\") which contains a dominant point target with deterministic backscattering properties in a background comprised of a random distribution of scatterers. The power backscattered by this BS is very large: It is around 80 dBZ (at 18 km range), which corresponds to a power of approximately \(-19\) dBm, as received by the Lema radar. The particular Doppler and polarimetric signatures of the BS clearly emerge, so that, thanks to the statistical analysis presented in Section 3 of this note, empirical confidence intervals can be established for monitoring the reproducibility of weather radar observables (see Section 4). On the one hand, the check was successful in most of the analyzed clear sky days. On the other hand, the analysis of a heavy rain day shows 3 warnings: it can be argued that the backscattering properties of the BS in heavy rain conditions are remarkably different from those obtained for clear skies. Another field of future investigation is the study of the second-order properties of the various time series (\(ZH\), \(ZV\), \(Z^{dr}\), \(\rho^{HV}\), considered as random processes), merging several clear sky days; the study will be based on spectral analysis in order to reveal the diurnal (or shorter) components, scaling behavior over temporal scales and their auto-correlation (or power spectrum).  
The author would like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for the stimulating discussions. He would also like to thank the anonymous reviewers for their helpful and valuable suggestions.  
The author declares no conflict of interest.
# Knowledge Distillation-Based GPS Spoofing Detection for Small UAV
## 6 Evaluation
### GPS Spoofing Detection  
We compared our proposed method with a gated recurrent unit (GRU) [40] and a recurrent neural network (RNN) [41]. In Algorithm 1, we compared the GRU, RNN, and LSTM in terms of the performance of GPS detection. With the benign flight data, we trained the LSTM, GRU, and RNN to predict the GPS data with a time sequence. According to the training result, we set \(Th_{p}=1200\) as the detection baseline for a spoofing attack, which also considers reducing the false-positive situations. The configuration of the LSTM, GRU, and RNN is shown in Table 1. To prevent overfitting and underfitting, we set the epoch at 3000.  
We evaluated the performance of the LSTM, GRU, and RNN in the GPS spoofing dataset, as shown in Figures 3-5. In the dataset, a GPS spoofing attack at the time stamp  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **LSTM** & **GRU** & **RNN** \\ \hline Input Size & 6 & 6 & 6 \\ Hidden Size & 12 & 12 & 12 \\ Number of Layers & 12 & 12 & 12 \\ Number of Classes & 1 & 1 & 1 \\ Output Size & 3 & 3 & 3 \\ Learning Rate & 0.001 & 0.001 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of Networks.  
Figure 3 shows the LSTM-based GPS spoofing detection. Figure 3 shows that the LSTM-based approach has good stability for the GPS location prediction, which could help the small UAV to fly in a stable status. Also, we can see the detection point (timestamp: 348732228) is close to the spoofing attacking point, which is critical to rescuing the UAV and getting rid of the spoofing attack. Figure 3 shows that the LSTM-based approach can detect the spoofing attack successfully.  
Figure 4 shows the GRU-based GPS spoofing detection. Figure 4 shows that the GRU-based approach has good stability for the GPS location prediction before the spoofing attack is triggered. Compared with the LSTM-based approach, we can see that the detection point (timestamp: 361729095) is a little bit later than in the LSTM-based approach. However, the GRU approach can also detect the spoofing attack successfully. When the spoofing attack is enacted, the GRU-based detection has fluctuations in the location prediction, which could cause the flight shake of the small UAV even though the small UAV is stable generally.  
Figure 5 shows the RNN-based GPS spoofing detection. Figure 4 shows that the RNN-based approach has good stability for the GPS location prediction before the spoofing attack is triggered. We can see that the detection point (timestamp: 365732590) is a little bit later than in the LSTM-based and GRU-based approaches. However, the GRU approach can also detect the spoofing attack successfully. When the spoofing attack is enacted, the RNN-based detection has sharp fluctuations in location prediction, which could cause serious effects on the safe flight of the small UAV and increase the risk of a crash.  
From the figures, we can see that LSTM-based detection can generate stable GPS prediction points for navigation. GRU-based GPS detection begins to generate fluctuating positions when the attacks are enacted.
# Matched-Filter Thermography
## 4 Matched Filtering in Thermography
### Thermal Coherence Tomography (TCT)  
Thermal Coherence Tomography (TCT) is very similar in nature to TWR. The key differentiator of the two techniques is in the pulse compression methodology. That is, TWR incorporates a wideband LFM scheme for pulse compression while TCT uses a narrowband phase modulation scheme known as binary phase coding (BPC) [50]. The BPC signal consists of a single frequency carrier and a binary coded envelope. The narrowband signal is formed either by multiplying these components in the time-domain or alternatively by convolving their spectra in the frequency-domain (Figure 7).  
Figure 6: TWR imaging of transparent sample with iso-depth absorbers of different absorption coefficients using (**a**) Cross-correlation (CC) amplitude (unit: a.u.); (**b**) CC peak delay time (unit: millisecond); (**c**) CC phase (unit: degree) and their mean horizontal profiles; (**d–f**), respectively. Chirp parameters: 0.01–1 Hz in 6 s.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture
### Routing Network
#### 4.3.2 Interleaver  
The \(W_{v}=30\) MULX output blocks from the SCND are cyclically shifted by \(W_{v}=30\) parallel barrel shifters in the interleaver module based on shift values stored in ROM2-shift. Before being read by the SVND, a group of \(\eta_{v}=56\) messages of the sub-matrix must be converted from a row-centric to a column-centric representation based on the corresponding shift value in BGM \(H_{\rm 1D}\) by a barrel shifter. Each barrel shifter has \(Z_{c}=56\) inputs and outputs because the shift value can be any integer between 0 and \(Z_{c}=56\). The hardware description language (HDL) used for the BSs integrated with the proposed flexible decoder architecture is customised for the supported BCMs during the design phase. The choice of multiplexer input for every BGM is influenced by the expansion factor \(Z_{c}=56\) value linked to the corresponding BGM based on the cyclic shift decomposition algorithm mentioned in [28].
## Appendix A Frequency-Dependent Constitutive Equation for the Dissipative Structure
### Construction of the Radiation Impedance Operator  
The solution \(\{\psi^{\mathrm{sol}}(\mathbf{x},\omega),\mathbf{x}\in\Omega_{E}\}\) of Equations (102)-(103) can be calculated using the following integral equation:  
\[\psi^{\mathrm{sol}}(\mathbf{x},\omega)=\int_{\Gamma_{E}}\{G(\mathbf{x}- \mathbf{y})\,v(\mathbf{y})-\psi_{\Gamma_{E}}^{\mathrm{sol}}(\mathbf{y},\omega )\,\frac{\partial G(\mathbf{x}-\mathbf{y})}{\partial\mathbf{n_{y}}}\}\,ds_{ \mathbf{y}}\,. \tag{106}\]  
For all \(\mathbf{x}\) fixed in \(\Omega_{E}\), the linear integral operators \(\mathbf{R}_{\mathrm{S}}(\mathbf{x},\omega/c_{\mathrm{E}})\) and \(\mathbf{R}_{\mathrm{D}}(\mathbf{x},\omega/c_{\mathrm{E}})\) are defined by  
\[\mathbf{R}_{\mathrm{S}}(\mathbf{x},\omega/c_{\mathrm{E}})\,v=\int_{\Gamma_{E}} G(\mathbf{x}-\mathbf{y})\,v(\mathbf{y})\,ds_{\mathbf{y}}\,, \tag{107}\]  
\[\mathbf{R}_{\mathrm{D}}(\mathbf{x},\omega/c_{\mathrm{E}})\,\psi_{\Gamma_{E}}= \int_{\Gamma_{E}}\psi_{\Gamma_{E}}(\mathbf{y})\,\frac{\partial G(\mathbf{x}- \mathbf{y})}{\partial\mathbf{n_{y}}}\,ds_{\mathbf{y}}\,. \tag{108}\]  
Using Equation (102), Equation (106) can be rewritten as  
\[\psi^{\mathrm{sol}}(\mathbf{x},\omega)=\{\mathbf{R}_{\mathrm{S}}(\mathbf{x}, \omega/c_{\mathrm{E}})-\mathbf{R}_{\mathrm{D}}(\mathbf{x},\omega/c_{\mathrm{E} })\,\mathbf{B}_{\Gamma_{E}}(\omega/c_{\mathrm{E}})\}\,v\,. \tag{109}\]  
From Equation (103), it can be deduced that, for all \(\mathbf{x}\) in \(\Omega_{E}\),  
\[\mathbf{R}(\mathbf{x},\omega/c_{\mathrm{E}})=\mathbf{R}_{\mathrm{S}}(\mathbf{x },\omega/c_{\mathrm{E}})-\mathbf{R}_{\mathrm{D}}(\mathbf{x},\omega/c_{\mathrm{E }})\,\mathbf{B}_{\Gamma_{E}}(\omega/c_{\mathrm{E}})\,, \tag{110}\]  
and the radiation impedance operator \(\mathbf{Z}_{\mathrm{rad}}(\mathbf{x},\omega)\) is derived from Equations (104) and (105),  
\[\mathbf{Z}_{\mathrm{rad}}(\mathbf{x},\omega)=-i\,\omega\,\rho_{\mathrm{E}}\, \{\mathbf{R}_{\mathrm{S}}(\mathbf{x},\omega/c_{\mathrm{E}})-\mathbf{R}_{ \mathrm{D}}(\mathbf{x},\omega/c_{\mathrm{E}})\,\mathbf{B}_{\Gamma_{E}}(\omega/c_ {\mathrm{E}})\}\,. \tag{111}\]