esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Experiment
### Analysis of Denoising Effects under Different \(T_{2}^{*}\)  
\(T_{2}^{*}\) is one of the most important parameters of the MRS signal. The following denoising simulation experiments were performed under different \(T_{2}^{*}\) values. On the basis of the simulation data in Section 3.1, the random amplitude was \(150\) nV, the power frequency amplitude was in range of \([150\sim 180]\) nV, and the variation range of \(T_{2}^{*}\) was \([50\sim 500]\) ms. For each group, the algorithm was executed \(20\) times under the same \(T_{2}^{*}\). The noise reduction results are shown in Table 1.  
Figure 2: Denoising results of the MP algorithm when SNR = \(-20.05\) dB: (**a**) noisy data and reconstructed power-line harmonics by MP and HMC; (**c**) is the corresponding spectrum. (**b**) After removing the harmonics, the MRS signal is extracted by MP and HMC; (**d**) is the corresponding spectrum.  
Table 1 shows that when the noisy MRS data under different \(T_{2}^{*}\) values were denoised by the MP algorithm, the SNR was improved by up to 35.55 dB. The uncertainty of \(E_{0}\) gradually decreased as \(T_{2}^{*}\) increased, and the relative uncertainty of \(T_{2}^{*}\) decreased with the increase in \(T_{2}^{*}\).
###### Abstract  
This study investigated a method for improving the quality of images with a low number of excitations (NEXs) based on deep learning using T\({}_{2}\)-weighted magnetic resonance imaging (MRI) of the heads of normal Wistar rats to achieve higher image quality and a shorter acquisition time. A 7T MRI was used to acquire T\({}_{2}\)-weighted images of the whole brain with NEXs = 2, 4, 8, and 12. As a preprocessing step, non-rigid registration of the acquired low NEX images (NEXs = 2, 4, 8) and NEXs = 12 images was performed. A residual dense network (RDN) was used for training. A low NEX image was used as the input to the RDN, and the NEX12 image was used as the correct image. For quantitative evaluation, we measured the signal-to-noise ratio (SNR), peak SNR, and structural similarity index measure of the original image and the image obtained by RDN. The NEX2 results are presented as an example. The SNR of the cortex was 10.4 for NEX2, whereas the SNR of the image reconstructed with RDN for NEX2 was 32.1. (The SNR NEX12 was 19.6) In addition, the PSNR in NEX2 was significantly increased to 35.4 \(\pm\) 2.0 compared to the input image and to 37.6 \(\pm\) 2.9 compared to the reconstructed image (\(p\) = 0.05). The SSIM in NEX2 was 0.78 \(\pm\) 0.05 compared to the input image and 0.91 \(\pm\) 0.05 compared to the reconstructed image (\(p\) = 0.0003). Furthermore, NEX2 succeeded in reducing the shooting time by 83%. Therefore, in preclinical 7T MRI, supervised learning between the NEXs using RDNs can potentially improve the image quality of low NEX images and shorten the acquisition time.  
p 2023 13 6567 10.3390/app13116567 10.3390/app13116567 10.3390/app13116567 10.3390/app13116567 10.3390/app13116567 10.3391/app13116567 10.  
methods using deep learning. However, most of these studies were conducted using clinical MRI (1.5T, 3T), and there have been few studies on image quality improvement using deep learning in preclinical high-filed MRI.  
In recent years, the residual dense network (RDN) [9] has attracted considerable attention as a network for deep learning that introduces a residual dense block (RDB) and extracts rich features via densely connected convolutional layers. Furthermore, it allows for a direct connection from the previous RDB state to all layers of the current RDB, resulting in a contiguous memory mechanism. It is also equipped with processes such as local and global feature fusion, allowing for the proper learning of global hierarchical features. In previous studies, high image-quality improvement accuracy has been achieved using natural images. In addition, since previous studies that adapted RDN for CT images of coronary arteries have shown good results in SNR, CNR, and visual evaluation, we thought that preclinical 7T MRI would show similarly good results. Therefore, we decided to use RDN in this study. Additionally, we did not find any examples of the use of RDN for image quality improvement in preclinical 7T MRI.  
This study aimed to investigate whether it is possible to improve image quality and reduce imaging time using deep learning in preclinical 7T MRI.
# Waveguide Manufacturing Technologies for Next-Generation Millimeter-Wave Antennas
## 2 High-Precision Subtractive Machining
Several simulations were carried out prior to manufacturing to assess the greatest drill diameter that could be used without producing a significant alteration of the device's behavior. It was found that a drill diameter of 1.5 mm did not produce a significant alteration in the device's performance. Finally, given the highly complex geometry of this device, EDM was used to build the mode converter in Figure 3. It should be noted that building all its internal cavities using conventional milling would have been impossible in practice. Moreover, it was necessary to divide the mode converter into two parts that were manufactured separately. In this way, EDM was able to adequately access the different waveguide sections. Figure 3 depicts these two parts, along with a representation of the internal cavities of the mode converter. Figure 2: Top view of the comparator network. The rounding of the corners produced by the drill is included in the drawing, as well as the holes for inserting the screws and alignment pins. The main dimensions of the component, in millimeters, are annotated. As is shown in Figure 4, the measured performance of the final antenna matched, with great accuracy, the values predicted by the simulation. Hence, it can be concluded that, if executed carefully, subtractive manufacturing techniques constitute an accurate and reliable approach to manufacturing waveguide devices in the millimeter wave band. The prototype in [33] has also been manufactured using high-precision subtractive machining. The main performance indicators, compared with the results obtained by [22], are summarized in Table 1. However, it is worth noting that both designs operate in different frequency bands, and for this reason a direct comparison was not evident. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & [22] & [33] \\ \hline \(f_{0}\) (GHz) & 35 & 93 \\ Sum Gain (dBi) & 24 & 20 \\ Diff Gain (dBi) & 22 & 18 \\ Sum Cxp (dB) & \(-21\) & N/A \\ Diff Cxp (dB) & \(-21\) & N/A \\ BW (\%) \# 20 dB & 2.3 & 3.0 \\ Isolation (dB) & 33 & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of triple-radiation pattern monopulse antennas by high-precision subtractive machining. Figure 3: Representation of the mode converter: (**a**) the two waveguide parts that form the device, with the holes for screws and alignments pins visible; (**b**) depiction of the internal air cavities.
## 3 Overview of Matana
### MATANA Design Flow  
Figure 1 presents the design flow to integrate MATANA into a target system. MATANA follows a white box approach, where a precise description of the microarchitecture is required as well as a description of the target attack that must be detected. The design flow is split into four main steps:  
1. From the description of the microarchitecture of the target and the description of the attack, selection of the relevant internal signals that are modified when the target attack is performed, and design of the associated detection algorithm. More details on the methodology are provided in Section 3.3.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Implemented Protection against**} & **Monitored** & **Reconfigurability** & **Methodology** \\ \cline{2-5}  & **Microarch Attacks** & **Control-Flow Attacks** & **Signals Type** & **Provided** \\ \hline HPC-based [10; 11; 12; 13; 14] & unspecific & no & event & limited & no \\ CC-Hunter [15] & CTCs detection & no & event & limited & no \\ PMUe [16] & no & ROP detection & event & no & no \\ [PERSON] et al. [19] & no & DIFT & instruction & high & no \\ [PERSON] et al. [20] & no & ROP detection & instruction & high & no \\ Raksha [21] & no & DIFT & instruction + tag & limited & no \\ FlexCore [22] & no & DIFT & instruction & high & no \\ Harmoni [23] & no & DIFT & instruction + tag & limited & no \\ LBA [24] & no & DIFT & instruction & no & no \\ PHMon [25] & no & DIFT & instruction & limited & no \\ REHAD [26] & CSCAs detection & no & instruction & high & no \\ LiD-CAT [29] & CSCAs detection & no & cache internal & no & no \\ MATANA & unspecific & unspecific & any & high & yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different attack detection systems based on the analysis of microarchitectural signals. _Reconfigurability_ is classified _high_ if some reconfigurable fabric is used, _limited_ if some configuration bits that change the hardware’s behavior exist, and _no_ if the reconfigurability is not possible or not discussed. Note: CTC = Covert Timing Channel, CSCA = Cache Side-Channel Attack, ROP = Return-Oriented Programming, DIFT = Dynamic Information Flow Tracking.  
2. Patch of the target microarchitecture to output the signals (identified in step 1) towards the Detection Module. This Detection Module must also be connected to the system bus to communicate with software and connected to the processor interrupt system. More details are provided in Section 4.
3. Patch of the system kernel to allow communications from/to the Detection Module and the handling of interrupt requests.
4. Integration of the patched software and hardware into the final target system.
# Smart Healthcare Applications over 5G Networks: A Systematic Review
## 3 Results and Discussion
### Healthcare Benefits  
After analyzing each of the clinical applications proposed by the works included in the review, we were able to identify that each application presents different healthcare benefits, categorized into three large groups: constant and immediate attention with intelligent systems, specialized medical care in remote areas, and immediate assistance in emergencies.  
In Figure 9, it can be seen that the majority of clinical applications provide benefits related to constant and immediate care through intelligent systems (52.27%, \(n\) = 23) that allow specialized healthcare to be provided through remote monitoring and tracking. Table 3 shows papers included in each classification.  
Figure 8: Data transmission in smart ambulances.  
Remote diagnostic and surgical systems make it possible to minimize patient attendance at hospital centers through the use of digital platforms. This facilitates safe and specialized care, including major surgical interventions, through precise and non-invasive procedures. It is observed that the applications related to this description can be included within the category of specialized medical care in remote locations, and it comprises 40.91% (\(n=18\)) of the total.  
Finally, with a much lower percentage than the previous types (6.82%, \(n=3\)), the applications offer immediate assistance in emergency situations. In this case, the benefit consists of providing emergency treatments and first aid to safeguard the patient's life until he or she can be hospitalized.  
RQ2: What Is the Type of Technology Used, and What Technical Requirements Does an Intelligent Health System Demand through the Use of 5G?  
To answer this question, we have generated several subsections that address different specific topics related to the technology type used and technical requirements. In terms of the technology used, Section 3.2.1 analyzes the articles and presents a classification according to the type of device used in development. Then, in Section 3.2.2 another classification is presented based on the communication protocols used by the systems. Along with the communication protocols, the analysis of short- and long-range network technologies is proposed and is analyzed in Section 3.2.3, providing a broader vision of the diversity of technological solutions. Finally, in Section 3.2.4, the technical requirements proposed by the authors for the correct operation of the systems are analyzed.
## 3 Analysis of Failure Characteristics of a Buried Waterstop
### Finite Element Analysis of Force and Deformation of Waterstop
#### 3.2.3 Deformation Stress Characteristics of the Waterstop  
Taking operating condition 1 as an example, Figure 11 shows the von Mises stress distribution when the tensile, compressive, and uneven settlement deformation of the lining reaches the maximum. The stress on the waterstop is mainly concentrated between the first rib on the left and right sides during tensile deformation, and the stress on the waterstop during compression deformation and settlement deformation is mainly concentrated in the middle hole of the waterstop, whilst the farther side of the waterstop faces almost no force.  
Figure 11: Condition 1 von Mises stress cloud diagram (unit: MPa). (**a**) 1-1 stress cloud diagram. (**b**) 1-2 stress cloud diagram. (**c**) 1-3 stress cloud diagram.  
Figure 10: Damage cloud diagram of concrete stiffness in working condition 1. (**a**) 1-1 concrete compression damage. (**b**) 1-1 concrete tensile damage. (**c**) 1-2 concrete compression damage. (**d**) 1-2 concrete tensile damage. (**e**) 1-3 concrete compression damage. (**f**) 1-3 concrete tensile damage.  
The bond damage between the waterstop and the concrete when the deformation of the lining reaches the maximum is shown in Figure 12, where CSDMG is the bond stiffness damage value. Tensile deformation and settlement deformation inflict greater damage on the bond between the waterstop and the concrete, and the bond failure area is between the second ribs on both sides of the waterstop. However, the damage to the bond by compression deformation is small, and only the bond near the middle hole fails.  
It can be seen that: (1) due to the failure of the bonding part after deformation, the short-term resistance of the waterstop belt to seepage is only provided by the deformation contact pressure; (2) deformation will not completely destroy the bond between the waterstop belt and concrete, and joint deformation is uncertainty, so the waterproofing ability of the waterstop belt depends on its resistance to water seepage in the long term; (3) after deformation, the effective seepage path of the waterstop becomes shorter and the reliability of the waterstop as a barrier to leakage decreases. Once the joint is restored from a large deformation to a small deformation, compared with the joint without deformation, the waterproof is more likely to fail.
# Dynamic Ring Exploration with \((H,s)\) View +
## 6 Lower Bound of Exploration Time  
A lower bound of the exploration time for any \(S\) is given in this section. The following theorem holds.  
**Theorem 4**: _The exploration time of \(1\)-interval connected rings by a single agent with the \((H,S)\) view is lower-bounded by \(\Omega(n^{2}/H)\)._  
We first show that, provided that \(A\) is at the right or left extremity, say \(v_{i}\), of \(V^{t}\) at the \(t\)-th step where \(|V^{t}|\leq n-2H+1\), it takes at least \(|V^{t}|+H-1\) steps for \(A\) to explore \(H\) nodes from the circumstance under the following link scheduling: \(e_{i+H-1}\) (resp., \(e_{i-H}\)) is deleted until the \((t+|V^{t}|+H-1)\)-th step if \(v_{i}\) is the right (resp., left) extremity of \(V^{t}\). Without loss of generality, we assume that \(v_{i}\) is the right extremity of \(V^{t}\) in the following. Figure 5 depicts the situation.  
Assume for contradiction that \(A\) explores the \(H\) nodes within \(|V^{t}|+H-1\) steps under the scheduling. Since \(e_{i+H-1}\) is missing until the \((t+|V^{t}|+H-1)\)-th step, \(A\) never reaches \(v_{i+H}\). Therefore, \(A\) must explore at least one node on the left side of \(V^{t}\). This and exploring \(H\) nodes take at least \(|V^{t}|+H-1\) steps; a contradiction.  
Now, apply the above claim from the first step repeatedly. When applying the claim for the \(p\)-th time, \(|V^{t}|=(p-1)H+1\) and then it takes \(|V^{t}|+H-1=pH\) steps. Note that we can apply the claim while \((p-1)H+1\leq n-2H+1\), i.e., \(p\leq\lfloor(n-H)/H\rfloor\). We then derive the lower bound of the exploration time, \(\sum_{p=1}^{\lfloor(n-H)/H\rfloor}pH=\Omega(n^{2}/H)\).
# Topology and Parametric Optimization-Based Design Processes for Lightweight Structures
## 4 Results
### Hollow Plate
#### 4.1.1 Topology Optimization of a Hollow Plate  
This is the most common design workflow. The general idea is to identify an optimized layout through TO and then use it as a design base for further optimization with a size optimization. Firstly, the initial design of the Hollow Plate was topologically optimized using the LSM. The objective function of the optimization was the compliance of the structure, and the response constraint the minimization of its mass in a percentage. The area where the boundary conditions were applied, as well as the plate's hole, were excluded from the optimization region (frozen area). The maximum identified mass reduction of the Hollow Plate, for a factor of safety (FOS) equal with two, was 59.82%. The optimized design was either validated as it is, redesigned and validated, or further optimized with a PO. The conducted design processes here are the (1) topology optimization with no redesign (TO_NR), (2) topology optimization with redesign (TO_R), and (3) topology optimization with redesign and parametric shape optimization (TO_R_PO). The authors used the following rule concerning the maximum of the von Mises stress, \(\sigma_{\max}\leq 125\) MPa, which corresponds to a FOS \(\geq 2\) (Von Mises factor of safety). Many iterations were applied to processes (1) and (2), from the TO to the validation study, etc., in order to stick to this stress rule. Concerning the size optimization in process (3), the LHS was applied as the DOE method with 50 samples. The Kriging method and the MOGA were used for the creation of the response surface and the Pareto fronts, respectively. The final designs of the aforementioned processes are depicted in Figure 9.  
Figure 8: (**a**) The original design of the Hollow Plate, as well as the finite element model, (**b**) the Finite Element Analysis (FEA) of the Hollow Plate (\(\sigma_{\max}\) = 33.2 MPa), and (**c**) the design space for the Topology Optimization (TO).  
Figure 9: The three design processes in the TO workflow: (1) Topology optimization with no redesign (TO_NR), (2) topology optimization with redesign (TO_R), and (3) topology optimization with redesign and parametric shape optimization (TO_R_PO).  
Concerning process (3), the design taken from process (2) was parametrized. At this level, a parametric shape optimization was conducted using small changes in the chosen factors (thicknesses and radiuses) with the procedure described in Section 4.1.2.
## 3 Exfoliative Ichthyosis  
The discovery of cystatin A (_CSTA_) gene mutations in association with exfoliative ichthyosis [36] is an example of the successful implementation of combining SNP microarray analysis with targeted NGS to determine the genetic cause of disease.  
Autosomal recessive exfoliative ichthyosis (OMIM #607936) is characterised by palmoplantar skin peeling and dry scalp skin, with trauma and moisture aggravating the condition [36]. Microsatellite linkage analysis of two related Bedouin families initially suggested linkage of the disease to chromosome 12q13, which contains the type II keratin cluster [37].  
[PERSON] _et al._ (2011) [36] revisited this family and applied whole genome homozygosity mapping which revealed a common block of homozygosity between affected Bedouin patients on chromosome 3q21 as the likely disease gene location. Sequence capture and NGS of this region was then performed and revealed a splice site mutation in _CSTA_, which was found to segregate with exfoliative ichthyosis in the Bedouin family. This locus was missed in the microsatellite genome scans performed by [PERSON] _et al._ (2003) [37] due to markers for this region being uninformative. Sanger sequencing of _CSTA_ in a different family with exfoliative ichthyosis revealed a homozygous nonsense mutation which also segregated with disease [36]. In a subsequent study, WES revealed a novel homozygous nonsense mutation in _CSTA_ in a large family with acral peeling skin syndrome [38] with similar clinical features to the patients reported in [PERSON] _et al._ (2011) [36].  
Cystatins are cysteine protease inhibitors which are thought to have a protective function against endogenous and external proteases, and to potentially modulate the degradation of intra- and extracellular proteins (reviewed in [39]). CSTA has been identified as a constituent of the cornified envelope [40] and is expressed in the suprabasal layers of the epidermis, the highest expression of which is in the granular layer [36,41]. CSTA is secreted by keratinocytes _in vitro_ and has also been found in sweat, and is believed to have a protective role by inhibiting the proteolytic activity of dust mite allergens Der p 1 and Der f 1 [42]. CSTA levels have also been implicated as prognostic markers in different cancers [43, 44, 45].  
Characterisation of skin from exfoliative ichthyosis patients with _CSTA_ mutations revealed widened intercellular gaps in the lower epidermis, whereas the upper epidermal layers appeared normal with no evident barrier defect [36]. Experiments using an _in vitro_ keratinocyte cell knockdown model showed an adhesion defect in response to mechanical stress, and an organotypic _CSTA_ knockdown model showed similar abnormalities to the patient skin [36]. This finding is indicative of CSTA having a key role in keratinocyte adhesion in the basal epidermal layers and that loss of CSTA causes a predisposition to epidermal splitting. There were no obvious abnormalities in a murine model with a chromosomal deletion, which included the _Csta_ gene [46], although investigation of a skin phenotype was not described.
# The Customized Automatic Processing Framework for HY-2A Satellite Marine Advanced Products
## 3 The Components of HY-2A Capf
### The Integrated Algorithmic Environment  
The integrated algorithmic environment possesses a set of predefined algorithms, and offers a place to store users' customized algorithms. The predefined algorithms are developed in Python and can be used to generate typical ocean dynamic environment products, which will be discussed in the next section. On the other hand, in order to run algorithmic executables and scripts uploaded from users, the integrated algorithmic environment should provide different runtime environments  
Figure 4: The GUI of the product order module for a customized wind field processing workflow.  
to execute algorithms developed in different languages. The current version supports three runtime environments: Python, IDL and Windows Shell.