esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptation to CT Reconstruction Kernels by Enforcing Cross-Domain Feature Maps Consistency
## 6 Discussion  
Here, we summarize our results, discuss the most important limitations of our study, and suggest possible directions for future work.  
We have shown that the proposed F-consistency significantly improves the performance on the target domain compared to the baseline model. However, we do not train the oracle model, which indicates the upper bound for other methods in a domain adaptation task. The oracle model should be trained via cross-validation on the target domain. In our case, the target domain contains only nine images, which leads either to lower results due to the small size of the training set or high dispersion of the results. Therefore, we compare the adaptation methods only with the baseline model and between each other.  
In Section 5.1, We show our model to achieve the highest results in terms of the consistency score. Contrary, the authors of [32] observing a tendency of models to converge to trivial solutions using consistency loss. They assume that the models learn to distinguish domains for which they are penalized; thus, the models yield trivial but perfectly consistent predictions. Although we run the same setup with [32], we do not observe trivial predictions for our method. The latter is demonstrated through the whole Section 5. Our intuition here is that the inner structure of domains and the semantic content of images in our case are more diverse, preventing the model from overfitting under a specific domain.  
We highlight that adversarial and consistency-based methods depend on a diverse unlabeled pool of data; see Section 5.2. On the other hand, FBPAug does not require additional data since it augments the images from source dataset. One could think of this method as enforcing the consistency between the original and augmented image predictions using ground truth as a proxy. Simultaneously, we show that the models operating with the earlier layers perform better. Therefore, we could train F-Consistency on the pairs of original and augmented with FBPAug images to achieve even better results. We leave the latter idea for future work.  
Figure 4: Trade-off between the segmentation quality and consistency scores induced by the regularization parameter \(\alpha\) (Section 2.4). The blue line corresponds to Dice Scores calculated on the COVID-train dataset. The orange line corresponds to the consistency scores calculated on the Paired-private dataset. The shaded areas correspond to the standard deviation along the Y-axis.
## 4 Cryptographic Analysis
### Waveform Plotting  
Waveform plotting represents the amplitude of the audio signal distributed in time [25]. Figure 4 shows the wave amplitude of the plain audio files (Figure 4a,c,e) and the wave amplitude of the same files after encryption (Figure 4b,d,f). The compared waves shown in Figure 4 are completely different, as can be seen, which shows the good properties of the proposed scheme for audio encryption. Waveform plotting was performed using MATLAB (R2021b, MathWorks, Natick, MA, USA).
# The Emerging Roles of RNA Modifications in Glioblastoma
###### Abstract  
Glioblastoma (GBM) is a grade IV glioma that is the most malignant brain tumor type. Currently, there are no effective and sufficient therapeutic strategies for its treatment because its pathological mechanism is not fully characterized. With the fast development of the Next Generation Sequencing (NGS) technology, more than 170 kinds of covalent ribonucletic acid (RNA) modifications are found to be extensively present in almost all living organisms and all kinds of RNAs, including ribosomal RNAs (rRNAs), transfer RNAs (tRNAs) and messenger RNAs (mRNAs). RNA modifications are also emerging as important modulators in the regulation of biological processes and pathological progression, and study of the epi-transcriptome has been a new area for researchers to explore their connections with the initiation and progression of cancers. Recently, RNA modifications, especially m\({}^{6}\)A, and their RNA-modifying proteins (RMPs) such as methyltransferase like 3 (METTL3) and \(\alpha\)-ketoglutate-dependent dioxygenase alkB homolog 5 (ALKBH5), have also emerged as important epigenetic mechanisms for the aggressiveness and malignancy of GBM, especially the pluripotency of glioma stem-like cells (GSCs). Although the current study is just the tip of an iceberg, these new evidences will provide new insights for possible GBM treatments. In this review, we summarize the recent studies about RNA modifications, such as N\({}^{6}\)-methyladenosine (m\({}^{6}\)A), N\({}^{6}\), 2\({}^{\prime}\)O-dimethyladenosine (m\({}^{6}\)A\({}_{\text{m}}\)), 5-methylcytosine (m\({}^{5}\)C), N\({}^{1}\)-methyladenosine (m\({}^{1}\)A), inosine (I) and pseudouridine (\(\psi\)) as well as the corresponding RMPs including the writers, erasers and readers that participate in the tumorigenesis and development of GBM, so as to provide some clues for GBM treatment.  
m\({}^{6}\)A; RNA modification; glioblastoma; epi-transcriptome; RNA processing 2020; 12, 736; doi:10.3390/cancers12030736
## 4 Metal Ion Leaching and Solute Transport
### Landfill Capping Layers
\begin{table}
\begin{tabular}{c c c} \hline
**Model or Platform/Type** & **Advantages** & **Drawbacks** \\ \hline ADAPT & DRAINMOD-NII and ADAPT demonstrate the same performance as regards soil water N leachate & \\ \hline ANFS & Combines ANN and fuzzy inference system advantages. & Long training time before implementation \\ \hline ANIMO & SWAP combined with ANIMO results in a more realistic simulation of & \\ \hline ANIMO & & \\ \hline \multirow{4}{*}{ANN} & AI reduces the time needed for data sampling and enhances identification ability of the nonlinear patterns of input and output, is more reliable compared to the other classical statistical methods, and demonstrates high accuracy in groundwater level management. Deep learning or & \\ \cline{1-1}  & unsupervised algorithms are more accurate. ANN models incorporate the & \\ \cline{1-1}  & most popular algorithm due to their high accuracy, implementation & \\ \cline{1-1}  & easiness, and input parameters flexibility. & \\ \hline ANSWERS2000 & Simulate surface transport of both dissolved and particulate phosphorus. & \\ \hline APSIM & Validated extensively. A specific simulation module is developed for sugarcane. & \\ \hline AquMod & Unconfined aquifers/run quickly and efficiently to simulate groundwater & \\ \cline{1-1}  & levels for contrasting aquifer types. & \\ \hline \multirow{4}{*}{Aq Yielded/Aq Yielded-N} & Simplicity, few inputs requirement, prediction with limited data, sufficient & \\ \cline{1-1}  & estimation, equal accuracy compared to STCS, no posts or diseases are & \\ \cline{1-1}  & under consideration. The yield and soil water content for irrigated crops are & \\ \cline{1-1}  & equally well predicted. Model entails microbial transformations of & \\ \cline{1-1}  & N and C. & \\ \hline \multirow{2}{*}{Bome-BGC} & Numerous studies across variant biome types were implemented & \\ \cline{1-1}  & worldwide. It underwent validation in Tibet. & \\ \hline BRANN & Effective to improve model network generalization by controlling and & \\ \cline{1-1}  & penalizing large weights of model parameters. & \\ \hline CAMEL & & Lack of published validation with field data until 2020. \\ \hline \multirow{2}{*}{CENTURY} & Wide appliance range over agroforestry and land-use systems e.g., tropical & \\ \cline{1-1}  & and temperate forests, grasslands, croplands, and agroforestry systems. It is & \\ \cline{1-1}  & highly adaptable. & \\ \hline \end{tabular}
\end{table} 
Table 3: Model advantages and drawbacks according to the relevant literature as a result of their implementation.
## 4 Experimental Setup
### Experiments Results
#### 4.1.1 The Computational Complexity of SA-MPCMOEA  
When using the Kriging model, the time complexity of the algorithm is mainly determined by the surrogate model training. The model training of Kriging requires the calculation of the inverse of the covariance matrix, and the time complexity is \(O(N^{3})\), where \(N\) is the population size. The independent modeling of each objective function increases the total training cost to \(O(MN^{3})\) (\(M\) is the dimension of the objective function). In each iteration, the surrogate prediction of the offspring individuals requires \(O(MN)\) time (traversing \(M\) models, and the single prediction complexity of each model is \(O(N)\)). The overall complexity is \(O(MN^{3}+TMN)\), where \(T\) is the number of iterations. As shown in Table 1, SA-MPCMOEA is compared with other algorithms on CF test problems and LIR
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 1 Introduction
The experimental analysis allowed us to characterize the improvement yield by the MF operation in practical cases and to identify clearly the additional benefits and potential limitations of the MF detection schemes in the considered applications. The paper is organized as follows: in Section 2, the main blocks of the DVB-T-based PR basic processing scheme are briefly illustrated. The MF detection schemes employed are described in Section 3, together with the required modifications that make them suitable for the DVB-T-based PR case. Section 4 reports the results of a comparative analysis of the theoretical performance obtained by simulations. The test campaigns and the corresponding experimental results obtained for the aerial surveillance application are illustrated in Section 5, together with an extensive analysis of the detection performance improvement provided by the MF schemes compared to the SF approaches. Section 6 is dedicated to an in-depth discussion of the outcomes obtained in experimental tests performed in a maritime scenario. Finally, our conclusions are drawn in Section 7.
# Structural Evaluation of Cable Bolts under Static Loading
## 3 Materials and Methods
### Model Geometry  
To create the model, each individual geometric component was constructed. The double shear test comprises 2 key components, the box and the bolt. [PERSON] et al. (2017) modelled a double shear test which consisted of 300 mm cubic end blocks with a central block of 450 mm (L) \(\times\) 300 mm (W) \(\times\) 300 mm (H) and a 1050 mm cable bolt with no gap between the blocks. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2018) [5] followed a similar construction for the double shear test, but incorporated dynamic loading to further build upon the model. Both studies were developed from a relatively similar model and hence, the following model was similarly constructed.  
As the various types of bolts that were to be modelled are complex in geometry and possess a unique layout, the bolts were simplified to be a single cylindrical smooth bolt. Although this does not accurately reflect the actual design of the bolt type, this was a reasonable simplification to avoid excessive modelling complexities. Each bolt was of 1600 mm length with a diameter of 18, 20, 23, 25, 28 or 31 mm. Figure 1 illustrates the 28 mm bolt.  
[PERSON] et al. (2017) [2] differentiated between the geometry of the end and middle blocks; the blocks here are considered as having the same geometry. The blocks are representative of the individual rock layers within the geological rock profile and remain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Jennmar Bolt Type**} & **Bolt Diameter** & **Typical Yield** & **Typical Ultimate Tensile** \\  & **(mm)** & **Strength (kN)** & **Strength (kN)** \\ \hline
17.8 Yield Lok Bolt & 18 & 147 & 196 \\ J-Tech® 20 mm Bolt & 20 & 170 & 200 \\ Yield Lok® Bolt 23 mm & 23 & 245 & 328 \\ J-Tech® 25 mm & 24 & 245 & 294 \\
63T Sumo Cable Bolt & 28 & 560 & 630 \\
70T 12 Wire Sumo Cable Bolt & 31 & 640 & 705 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Jennmar bolt properties.  
Figure 1: The 28 mm bolt.  
fixed at the ends, allowing only the middle block to move in the vertical, \(y\), plane. The blocks were 500 mm (L) \(\times\) 500 mm (_W_) \(\times\) 500 mm (H). A hole with a diameter equal to the bolt under investigation was extruded through the centre of the cube in the ZY plane. Figure 2 illustrates the concrete box with a 28 mm extruded hole.  
To aid ABAQUS in developing an efficient mesh, localised subdivisions were introduced by creating partitions around areas of concern. Focal areas where partitions were created included contact points, and areas where loading would be applied. ABAQUS develops a mesh by seeding the part, followed by auto-meshing. This develops a generic meshing model which cannot specifically refine meshing around boundary locations. This is further discussed in mesh generation.
# Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data
## 3 Results and Discussion
### Future Applications  
As more data are gathered, several avenues exist for extensions or alternatives of the methods proposed in this paper. Notably, with a larger number of images, the use of deep learning for image segmentation, especially convolutional neural networks, would likely become a promising option to explore. Even with moderate sample sizes, the use of deep generative models such as generative adversarial networks (GANs) [34] may provide a mechanism to augment the true dataset with near-realistic images that would enhance model training. GANs have already shown promise in several areas of medical imaging [35; 36; 37; 38], and thus would be a natural choice for generation of realistic-lookingmurine US images. Other data augmentation strategies that have been proposed for medical image generation specifically, such as ASNG [39], could be explored as well.  
Transfer learning [40] may be a viable option that could be applied even without additional real or generated data, incorporating a pre-trained network from another application domain. Finally, if a network were developed that could segment murine cardiac images with very high accuracy, it is likely that such a model would be useful in clinical applications, even if downstream transfer learning is only used to fine-tune models on human cardiac images.  
Another aspect of this work that could have a more direct impact on clinical translation is the use of a structured grid to sample cardiac kinematics (i.e., four-slices across and six rotations around the LV). While recent studies into 3D speckle-tracking echocardiography have shown promise in characterizing clinical data [41], contours of the left-ventricular boundaries are often unstructured and tracking speckle-patterns is susceptible to error propagation if there is subpar image quality or notable image artifacts. Using an approach similar to the one presented here could allow for the problem of boundary predictions to be simplified and lead to more robust results when speckle-tracking is insufficient, which is a common obstacle associated with murine 4 DUS data analysis.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 2 Materials and Methods
### The Dual-Polarization, Doppler Radar Located on Monte Lema at an Altitude of 1625 m
#### 2.2.4 Differential (Horizontal/Vertical) Radar Reflectivity
In the quantization process based on 255 values the resolution is \(15.751/255\cong 0.062\) dB. All the values outside the above-defined range of 15.751 dB are left- or right-saturated. Since \(ZV_{dBZ}\) is also saved in the MeteoSwiss archive (in addition to \(Z^{dr}\) and \(Z_{dBZ}\)), the user can retrieve the original, non-saturated value by simply subtracting \(ZV_{dBZ}\) from \(ZH_{dBZ}\); obviously, in this case, the derived value only has a resolution of 0.5 dB, as can be seen from Equation (2).
## 4 Validation of the Proposed Calculation Method  
The No. 9 double-slip turnout for 60 kg/m rail, which has experienced derailment at the guardrail, was taken as an example to verify the reasonableness of the proposed calculation method. The number of design drawing for the double-slip turnout is SC450, which has a lead curve radius of 220 m, gage widening of 15 mm, angle of attack of 1\({}^{\circ}\)20\({}^{\prime}\)10\({}^{\prime\prime}\) for the tip of the switch rail and distance of 1751 mm from the tip of the switch rail to the end of the buffer segment of the guardrail. The end of the gage-widening segment extends to the guardrail, whose distance to the end of the buffer segment is 999 mm.  
The proposed calculation was used to calculate the guardrail interval limit for the acute-angle crossing of the double-slip turnout, as shown in Figure 10. The calculated limit was 1365.82 mm. However, the existing calculation method used by the railway maintenance department sets the limit to 1370 mm. Therefore, normal vehicle passage cannot be guaranteed; there is wear and impact at the opening section of the guardrail, which eventually leads to further derailment.  
twisting angle is relatively large. Therefore, the proposed method was used to calculate the interval limit for the switch rail nonworking edge (i.e., distance from the nonworking edge of the switch rail to the working edge of the opposite rail) when a vehicle passes through the double switch area (Figure 11). The limit was calculated to be 1365.6 mm. Then, the minimum flangeway of the switch rail (i.e., minimum distance between the nonworking edge of the switch rail and the working edge of the stock rail) can be calculated using Equation (6).  
\[\eta_{\min}=G-C_{\max}, \tag{6}\]  
where \(\eta_{\min}\) is the minimum flangeway of the switch rail, \(G\) is the track gage (i.e., 1450 mm), and \(C_{\max}\) is the interval limit for the switch rail nonworking edge (i.e., 1365.6 mm). Thus, \(\eta_{\min}\) is 84.4 mm.  
According to the design requirements of the SC450 turnout, the minimum flangeway of the switch rail in the double switch area is set to 80 mm by the railway maintenance department. Thus, it also cannot ensure normal vehicle passage. The nonworking edge of the switch rail will also have wheel-track impact. Field tests showed that the nonworking edge of the switch rail for this turnout experiences wheel-rail impact and wear. Additionally, the flangeway of the switch rail corresponding to the starting position of wear traces (i.e., where the wheel started to contact the nonworking edge of the switch rail) happened to be 84 mm, which is consistent with the calculation results herein. Therefore, the feasibility of the proposed calculation method was verified.  
Figure 11: Interval limit for the switch rail nonworking edge required in the double-switch area of No. 9 double-slip turnout for the 60 kg/m rail (Design Drawing No. SC450) that experienced derailment.
# Biosurfactants' Potential Role in Combating COVID-19 and Similar Future Microbial Threats
## 2 Approaches That Can Use Biosurfactants in Outbreak Prevention and Management
### Environmental Approaches
#### 2.3.2 Vector Control  
Infectious disease transmission can be from person-to-person or through a biological intermediate organism called a vector. Vectors to most important infectious diseases are insects which are commonly found in our environment. Important viral diseases such as dengue, chikungunya and Zika have all been causes of serious outbreaks in many regions worldwide. Their vector, _Aedes_ sp. mosquito, is in contact with over half of the world's population [74]. Protozoans are thought to have an unlimited pandemic potential; they are the only species which vector infectious diseases that have caused the extinction of a mammalian species. This was observed with _Trypanosoma lewisi_, a vector-borne disease that made the Christmas Island rat (_Rattus maclari_) in Australia extinct around the early 20 th century [75]. Furthermore, the malaria-causing protozoan _Plasmodium_ was also thought to have killed 50% of all humans that existed on earth [74]. Trypaniasis and other vector-borne pathogens can be confined to a geographical region because of the limitation of movement of the vector pathogen and the inability to survive in various earth climatic regions [74]. Nevertheless, their impact on human health cannot be overlooked.  
Biosurfactant-containing biopesticides are biodegradable, show lower resistance in insect populations, have higher selectivity and biological safety compared to non-target species, in addition to being highly effective at lower concentrations [76]. Biosurfactant lipoproteins have been used as bioactive components in biopesticide formulations containing _Bacillus thuringiensis_. These formulations are lethal to the pupal and larval stages of insect vectors, with mosquitocidal activity against _Aedes aegypti_, the vector of dengue fever [77], _Anopheles stephensi_, the primary mosquito vector of malaria in India [78], and _Culex quiniquefusciatus_, a vector for arboviruses and avian malaria [79]. ZnO nanoparticles synthesized with _B. licheniformis_ EPS showed toxicity against the larvae of malaria and Zika virus vectors _Anopheles stephensi_ and _Aedes aegypti_, with high biocompatibility and non-toxicity demonstrated on hemolysis potential tests [80]. Insect control through outdoor and indoor spraying is an important control approach in disease control programs in regions affected by vector-borne diseases. Conventional pesticides such as DDT have issues of environmental toxicity and developing insect resistance. Biosurfactants could be the solution to more effective and environmentally friendly biopesticides use.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 2 System Model and Problem Formulation
### Potential AP Locations  
The user locations next to a building are also potential locations for deploying the APs of the D-mMIMO. The set of all potential AP locations in the region is denoted as \(\mathcal{L}\). When installing an AP at a given location, we assume that the AP is installed vertically. The broadside of the AP's array is set with respect to the location. For simplicity, we consider eight possible broadside directions, as illustrated in Figure 4a. The direction for mounting an AP in each potential location is selected in a way that the broadside is perpendicular to the building as much as possible, as illustrated in Figure 4b.
## 4 Thermal Balance Verification and Uncertainty Analysis of the Test Setup  
### Thermal Balance Verification  
The thermal balance of the experimental system is verified to ensure the reliability of the experimental data. The verification experiment is conducted under conditions of an evaporation temperature of 0 \({}^{\circ}\)C, a condensation temperature of 60 \({}^{\circ}\)C, and at variable compressor speeds. The refrigerant's superheat is set to 10 \({}^{\circ}\)C, and the subcooling is set to 5 \({}^{\circ}\)C. The cooling capacity on the measured refrigerant side is calculated using the enthalpy difference and flow rate, and the electric heating power on the secondary refrigerant side is obtained through power meter one. The verification results, as shown in Figure 3, indicate that the deviation in cooling capacity on both sides is within the \(\pm\)5% error range, demonstrating that the experimental system has a very low heat leakage rate. Therefore, the experimental data measured by this test setup are reliable.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 3 SAR-Based Studies and Methods to Detect Snow
### Spatial and Temporal Scale of Snow Cover Studies
Hence, we further investigated how many observations were utilized on average for each year and study, and illustrated these findings in Figure 5c. Based on Figure 5b,c, it can be found that more than half of the published studies investigated snow cover for one year with an average of two observations within this year to account for the dynamics of the snowpack (see Figure 5b,c). These sparse observations indicate that current studies are still immature compared to the conventional optical-based SCE monitoring approach.