esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Details  
### Plasma Surface Treatment  
A glass slide is used as a basic substrate throughout all experiments. After being sonicated in deionized water for 20 min, the glass slide is treated by O\({}_{2}\) (Oxygen, Praxair, Inc., Danbury, CT, USA) and Ar (Argon, Praxair, Inc., Danbury, CT, USA) plasma to remove surface contaminants. Then the surface energy of the substrate is modified by plasma enhanced chemical vapor deposition (PECVD) process, in order to produce hydrophilic and hydrophobic surfaces with various wettabilities. During the plasma surface treatment, a fluorocarbon film is deposited on the surface by C\({}_{4}\)F\({}_{8}\) (octafluorocyclobutane, Praxair, Inc., Danbury, CT, USA) plasma for a low-energy (i.e., low wettability) hydrophobic surface coating. A high-energy (i.e., high wettability) hydrophilic substrate is obtained by treating the surface with C\({}_{4}\)F\({}_{8}\) plasma and O\({}_{2}\) plasma in a sequential manner. The surface energy of the substrates is characterized by ejecting ink droplets onto the differently plasma-treated substrates and measuring the dried diameter of the droplets. As the desirable diameter is 30 to 90 \(\upmu\)m, various plasma surface treatments are tested at room temperature. The plasma treatment conditions giving the wanted surface wettabilities are listed in Table 1. In the table, each treatment condition is named by the dried diameter of the droplets combined with the first spell of a word \"surface\". For example, the \(S\) 30 is the plasma treatment condition to produce the droplet diameter of about 30 \(\upmu\)m on a substrate. Additionally, in order to investigate the effect of the combination of plasma gases on surface energy, \(S\) 70 substrates are prepared by two different plasma processes: \(S\) 70\({}^{1}\) (by a single C\({}_{4}\)F\({}_{8}\) plasma process) and \(S\) 70\({}^{2}\) (by a sequential plasma process with C\({}_{4}\)F\({}_{8}\) and O\({}_{2}\)).
## Device Implementation  
### Textile Pressure Sensing Matrix  
A textile pressure sensing matrix was designed using a multilayer textile structure. This multi-pressure sensing structure is inspired by previous studies on textile-based pressure sensors [24] and is sketched in Figure 9a. A continuous layer of piezoresistive textile (stretchable fabric manufactured by Eeonyx ([[http://eeonyx.com/](http://eeonyx.com/)]([http://eeonyx.com/](http://eeonyx.com/)), US), surface resistivity of \(10^{5}\)\(\Omega\)/m\({}^{2}\), see Figure 9b) is placed between two layers of fabric having custom-designed highly conductive stripes (stretchy zebra fabric produced by Eeonyx, where the width of conductive stripes is 24 mm and the insulating stripes is 6 mm). As shown in Figure 9a, the top and bottom conductive patterns are perpendicular. Each intersection between a row (top layer) and a column (bottom layer) represents a sensing element (taxel). On applying a force on a taxel, the conductive layer gets compressed and the density of the conductive particles increases, causing the resistance to decrease. The number of taxels (i.e., sensing areas) is given by the product of the number of rows and the number of columns.  
Figure 10 presents the pressure sensing matrix with its physical model, implementation, and graphical interface. The acquisition system, based on the Arduino DUE board, was designed to sequentially select all the taxels of the matrix and to measure their electrical resistance--rows were selected by sequentially providing the 3.3 V to each strip using the Digital Ports (D in Figure 10) of the microcontrollers, while the four columns were identified through four different analog ports  
Figure 9: (**a**) Multi-pressure sensing structure; (**b**) piezoresistive fabric; and (**c**) zebra-patterned conductive fabric.  
(A in Figure 10). The change in resistance, related to the applied force, was converted to voltage by a voltage-resistance divider, with the fixed resistance of 10 k\(\Omega\), and the voltage signal was digitized by an analog-to-digital converter (12-bit ADC) integrated with the Arduino DUE board. A firmware designed with Arduino IDE was uploaded on the Arduino board, and the digital values of the matrix were transmitted through the serial port. An appropriate interface, designed with processing, was used to interpret the magnitude of pressure through a matrix system represented by a heat map that replicated the sensor matrix structure.
# Misconfiguration in Firewalls and Network Access Controls: Literature Review
## 3 Misconfigurations
### Generalization  
Rule generalization is a misconfiguration that is essentially the opposite of shadowing. Generalization is defined by [9] as when a rule with a more narrow scope has higher priority than a second rule with a scope that encompasses the first. If the rules were reversed, you would have shadowing. An important distinction in generalization is that the second rule must have a larger scope than the first. Using a similar example to that from redundancy, the first rule allows port 22, while the second rule blocks 20 to 30. The different action means that instead of redundancy, the second rule is a generalization of the first.  
Generalization is quite similar to correlation. The authors of [9] define generalization as \"Suppose the first rule matches the packets that also match the second rule while performing different actions, the rules are then considered generalized\". Earlier we gave the example of a Venn diagram to describe correlation. Generalization is like having a smaller circle inside a bigger circle. It can be thought of as \"poking holes\" inside of another rule.  
The impact that generalization has is similar to that of correlation. One rule is partially covering another rule. This can cause issues depending on what the the covered rule was blocking or allowing. Just like correlation, this could potentially lead to some good traffic getting blocked or some bad traffic being allowed. This misconfiguration is, however, the most likely to be intentional. As [12] puts it, \"Generalization is considered only an anomaly warning because inserting a specific rule makes an exception of the general rule, and thus confirming this action by the administrator is important\". In their tool that detects and corrects firewall misconfigurations, they do not automate fixes for generalization. Oftentimes, network administrators will create a broad rule then have exceptions for certain allowed traffic. In fact, an implicit deny all is a general rule to every single allow rule; thus any rule inside of a firewall that allows traffic is generalized by the implicit deny all. For these reasons, generalization is not always harmful to access control policy so long as its presence is deliberate.
## 4 Discussion
### Technical Recommendations and Limitations  
Even though the ngTMA(r)-method appears to be very efficient and accurate, some technical difficulties and limitations remain. In the following several sources of errors are addressed, and recommendations on avoiding them during the process of creating a TMA block using the ngTMA(r)-method are given.  
Recommendations/considerations to avoid core loss:  
1. In this study, animal organs were resected and prepared especially for the execution of a TMA procedure. Hence, idealised conditions were created to embed organs and tissues into the paraffin in a good angulation and with a proper block thickness. If donor blocks are acquired from patient tissue samples and previous histopathological examinations were performed, it is important to ensure that the remaining tissue thickness is sufficient. The result of a decreased block thickness may be a higher amount of core loss due to reduced friction between tissue block and the used punching tool. Furthermore, proper block thickness ensures a good yield of TMA slides for further immunohistochemistry staining.
2. The organs used in this study were from healthy animals. No tumour tissue or inflammatory tissue was included. Therefore, a higher core loss when using samples from squamous cell carcinoma of the mouth or other oral malignant or inflammatory lesions is likely.  
When a sample is collected, immediate and thorough fixation is paramount to reduce any degradation process and optimise tissue preservation and conservation.
4. In the case of skin tissue, we recommend using samples which are not too thick to shift the ratio of tissue to the surrounding paraffin. This may decrease the tissue-specific effect of skin, leading to a lower amount of core loss.
5. Different specific melting temperatures of the punched tissue and the surrounding paraffin may lead to deformation of tissue cores due to expansion and contraction. Paraffin blocks enriched with polymers are recommended [10]. Recommendations concerning slide processing:
1. Mismatching of the H&E slide and the picture taken from the donor block can lead to inaccurate digital aligning (Figure 2). This may happen due to shrinkage in the water bath and distortion during the slicing and processing of the H&E slide. If possible, the last acquired slide from the donor block should be used, due to it having highest concordance to the remaining tissue.
2. Difficulties may arise during the slicing of the multi-tissue TMA blocks. Due to differing adjacent types of tissues, they may behave in an inhomogeneous way, if cut with the microtome. Therefore, it is advisable to use only one tissue type on each TMA if possible.
3. Unilateral contraction due to temperature differences in the sectioning process may result in a shifted ratio of the different areas of the punched core.
4. There should be empty paraffin on the sides of the TMA block to ensure enough space for the microtome to gain momentum to provide a clean cut when reaching embedded tissue cores. We recommend at least 2 mm.
5. Coated slides should be used to increase friction and minimise distortion effects.
###### Abstract  
Steganography is the art and practice of communication using hidden messages. The least significant bits (LSB) based method is the well-known type of steganography in the spatial domain. Usually, achieving the larger embedding capacity in LSB-based methods requires a large number of LSB bits modification which indirectly reduces the visual quality of stego-image and increases the risk of steganalysis detection attacks. In this study, we propose a novel steganography method with data mapping strategy which can reduce the number of bits modification per pixel. In the proposed method, four secret data bits are mapped with the four most significant bits of a cover pixel. Furthermore, the only two LSBs of a pixel are modified to indicate the mapping strategy. Experimental results show that the proposed method is able to achieve 3.48% larger embedding capacity while enhancing the visual quality (i.e., peak signal to noise ratio (PSNR) 3.73 dB) and reducing the modification of 0.76 bits per pixel. Moreover, the proposed method provides security against basic Regular and Singular groups (RS) steganalysis and histogram steganalysis detection attacks.  
Keywords: steganography; mapping data hiding; information hiding; spatial domain; least significant bits; most significant bits +
Footnote †: journal: Accepted: 19 September 2018; Published: 9 November 2018
# EHANet: An Effective Hierarchical Aggregation Network for Face Parsing
## 5 Results and Discussion
### Efficiency and Accuracy  
We carry on the experiments to demonstrate the potential of our segmentation architecture in terms of accuracy and efficiency trade-off. Since our goal is to design an efficient and universal framework, the impact of different input resolutions and different backbones on model performance needs to be given quantitatively. As shown in Table 5, we note that deeper network tends to perform better (ResNet18 \(\rightarrow\) 73.00%, ResNet101 \(\rightarrow\) 75.35%). However, the weakness is that the amount of floating-point calculations increases exponentially (3.1 G vs. 11.5 G). From another perspective, the increased input brings rich spatial details. Although it can improve the accuracy of the model, it brings high latency. Moreover, as the image dimension gradually rises from 256 to 640, the performance gain becomes inconspicuous (e.g., an increase of 2.36% from 256 to 384 but only an increase of 0.53% from 512 to 640), indicating that the model tends to be saturated.  
Regardless of different backbones or different resolutions, our EHANet consistently brings consistent positive gains in terms of mIoU, which suggests the scalability of our proposed method. In particular, the fastest setting of our method runs at a speed of 313 FPS at mIoU 73.00%. The whole comparative experiment provides a reference for how to choose the appropriate model in real-world environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Input Size** & **FLOPs** & **FPS** & **\#Params** & **mIoU (\%)** \\ \hline EHANet + ResNet18 & 256 \(\times\) 256 & **3.1**G & **313** & **11.8**M & 73.00 \\ EHANet + ResNet18 & 384 \(\times\) 384 & 7.1G & 143 & - & 75.36 \\ EHANet + ResNet18 & 512 \(\times\) 512 & 12.5G & 83 & - & 76.51 \\ EHANet + ResNet18 & 640 \(\times\) 640 & 19.6G & 55 & - & **77.04** \\ \hline EHANet + ResNet34 & 256 \(\times\) 256 & 5.6G & 294 & 21.9M & 74.77 \\ EHANet + ResNet50 & 256 \(\times\) 256 & 6.7G & 204 & 25.0M & 75.21 \\ EHANet + ResNet101 & 256 \(\times\) 256 & 11.5G & 167 & 43.9M & 75.35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison of models at different resolutions and different backbones. \(M=10^{6}\), \(G=10^{9}\). “-” represents the same statistics as above.  
Figure 7: Face parsing results comparison against FPN-ResNet18 [8] baseline, where significantly improved regions are marked with white dashed boxes. To highlight the details, we merge the input and output color maps according to 1:1. Our method performs better on both boundary details and fine-grained segmentation.
# Promising Cr-Doped ZnO Nanorods for Photocatalytic Degradation Facing Pollution
## 2 Materials and Methods
### Photocatalytic Reaction during the Photodegradation Process of the Methyl Orange  
In order to perform the photocatalytic reaction, 165 mL from 10 ppm aqueous dye molecule methyl orange (MO, Fluka Analytical) was mixed with 3.3 mg from each sample of the synthesized Cr-doped ZnO nanorods. To ensure the fine dispersion of catalysis (Cr-doped ZnO nanorod powder), the mixed solution was stirred for 10 min followed by 5 min sonication to reach adsorption-desorption equilibrium (see Table 1). Then, a 12 (UV-Vis) lamp (253.7 nm wavelength) enclosed in silica tube was immersed in the solution of the photocatalytic reaction. We isolated the photocatalytic reaction unit using a suitable cover to avoid all hazardous UV radiation. The mixed solutions were continuously agitated during the UV-illumination. About 3 mL from the solution was extracted each 10 min and then filtered with a 0.2 \(\upmu\)m pore size of PTFE-membrane syringe filter to remove the freed catalyst powder. The absorption of the filtered solutions was examined, as per our previous work [11], via a UV-vis spectrophotometer at the maximum characteristic peak of MO (\(\approx\)464 nm wavelength). Figure 1 represents a schematic graph of the photocatalytic experiment.
## 5 Application Analysis of the Prediction Model  
To verify the calibration effect of the neural network model on the layer velocity of the measurement box of the integrated measurement and control gate, the flow measurement device was used to conduct gate flow measurement tests again under seven working conditions. The variation range of flow is 400-1500 m\({}^{3}\)/h, and the variation range of gate opening change is 100-400 mm. The 10 sets of data were continuously obtained for each working condition, and a total of 70 sets of data were obtained as prediction samples. The flow of the measurement box was predicted using a trained PSO-BP neural network model. The PSO-BP model predicted the flow, the theoretically calculated flow from the flow velocity of each layer in the measurement box, and the measured flow were compared, and their relative errors with the measured flow rate were calculated, as shown in Figure 8. There are a total of 10 layers of flow velocity inside the measurement box. Based on the size of the measurement box, the flow calculation formula inside the measurement box can be described as:  
\[Q_{c}=[0.025(v_{1}+v_{10})+(0.025+0.5/14)(v_{2}+v_{9})+0.5/7\times(v_{3}+v_{4}+ v_{5}+v_{6}+v_{7}+v_{8})]\times 0.6 \tag{10}\]  
where \(Q_{c}\) is the theoretically calculated flow from the flow velocity of each layer in the measurement box and \(v_{1}\)-\(v_{10}\) are the flow velocities of the first to tenth layers in the measurement box, respectively.  
It can be seen that the PSO-BP model's predicted flow is in good agreement with the measured flow. The relative error of the PSO-BP model is less than 10%, and there are no abnormal points with large relative errors. The theoretically calculated flow is generally consistent with the measured flow, but there are a few outliers with large deviation values, the maximum relative error reaches 50%. This may be due to the measurement error caused by the inability of individual probes in the measurement box to accurately obtain the path flow velocity during the transition of the water flow pattern. The outliers should be eliminated or corrected in the background when performing flow calculations for the entire measurement box.
# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
## 2 Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
#### 2.1.3 Non-Maximum Suppression  
Non-maximum suppression is an important step in edge detection, finding the local maximum of the pixel point, and setting the non-maximum point to 0, so that a large part of the non-edge points can be eliminated. The gradient directions are limited to four directions of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\), and the modulus \(M_{s}\) (\(x,y\)) of the gradient of each point in the gradient magnitude image is compared with two pixels along the gradient direction. If the gradient magnitude of the pixel position is smaller than the gradient magnitude of two adjacent pixels along the gradient direction, it means that the point is not a local maximum and needs to be suppressed. Let \(M_{s}\) (\(x,y\)) = 0; if the gradient magnitude of the pixel position is greater than the gradient magnitudes of two adjacent pixels along the gradient direction, it means that this point is a local maximum and is reserved as an edge point.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 2 Materials and Methods
### Verification Techniques
#### 2.3.4 Model Tuning
Instead, \(R_{mod}\) is tuned heuristically such that the modeled behavior matches the empirical data at key points: to match the white noise component at lag \(\tau=1\); to match the rolloff at lags \(\tau=5\) and \(\tau=30\); and to match the endpoint behavior at lag \(\tau=100\).
# Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class
###### Abstract
Due to this fact, artificial intelligence with computer-aided diagnosis systems is beginning to play a significant role in imaging techniques and the timely detection of breast cancer. Research has been carried out around the world on different computer-aided diagnosis (CAD) systems to detect benign and malignant masses and microcalcifications to increase the timely detection of cancer. The works analyze mammograms from various databases and images, such as full field digital mammogram (FFDM), histopathology, ultrasound, and magnetic resonance imaging (MRI). The authors include machine learning (ML) or deep learning (DL) techniques to obtain classifiers such as support vector machines (SVM), k-nearest neighbor (KNN), linear discriminant analysis (LDA), and stacked autoencoders (SAE) to create deep networks [14]. Likewise, transfer learning is applied to train convolutional neural networks (CNN) and combined with ML techniques such as SVM [15; 16; 17]. In order to make comparisons or change CNN attributes such as weights and learning rates, the architectures have been trained several times with different optimizers such as stochastic gradient descent (SGD) [18], stochastic gradient descent with momentum (SGDM), adaptive moment estimation (Adam) [19], and RMSprop. An example is the work of [PERSON] et al. in which they apply the previously mentioned optimizers in eight different CNNs with a breast ultrasound database, testing the performance of their classifiers, including a third class to refer to normal breast tissue and even creating their model of a shallow CNN with which they claim to have 100% on all performance metrics. The area of deep convolutional neural networks (DCNN) has been explored by several studies, including shallow DCNN [20], adding optimizers, and ML combinations such as SVM and extreme learning machine (ELM) [21], in turn, these deep networks have been implemented together with the fully complex valued relaxation network (FCRN) [22] and in works with faster region convolutional neural networks (Faster R-CNN) [23]. Since deep learning methods have achieved the most advanced performance in different medical applications, they have been studied to improve further and take the leading step in medical diagnostics. However, there is still room for improvement [18; 24]. In this study, we analyze the performance metrics of 12 classifiers based on AlexNet [25], GoogLeNet [26], and Resnet50 [27]. These CNNs are trained with balanced and unbalanced databases to classify benign and malign masses and microcalcifications. Moreover, we added a fifth class to classify normal tissue in order to analyze which of the CNN best categorizes the lesions. We aim to obtain a classifier to apply it in a CAD system that can help in the early detection of breast cancer, reducing the mortality rate and unnecessary examinations and expenses.