esa-sceva/satcom-chunk-collection huggingface dataset:
# Electronically Controllable Phase Shifter with Progressive Impedance Transformation at K Band
## 2 Materials and Methods: Phase Shifter Working Principle and Design
### Complete Phase Shifter Design and Performance  
The phase shift provided by a loaded coupler is not enough to guarantee a phase variation higher than 360\({}^{\circ}\). Therefore, in the complete device, two cascaded sets are connected. Figure 5 provides the complete phase shifter model, together with its performance results. The desired band in the design is at least of 1.5 GHz, centered at 18 GHz.  
Figure 4: Design results of the reflective LC circuit with progressive impedance transformation: (**a**) simulation design; (**b**) phase shift produced.  
Figure 5: _Cont._  
Figure 5: Simulation results of the 18 GHz phase shifter, (**a**) design; (**b**) arg(S\({}_{21}\)); (**c**) Aarg(S\({}_{21}\)) (related to min arg(S\({}_{21}\)) value, 2.2 pF); (**d**) \(|\)S\({}_{21}\); (**e**) \(|\)S\({}_{11}\).  
The shifter design presents proper matching in the desired band and a loss level of around 6 dB in the worst case. Considering that the working band is centered at 18 GHz, this loss level can be considered an appropriate value, if compared to commercially available devices. The phase shifting capability of the shifter is around 600\({}^{\circ}\), almost twice the phase variation obtained for the reflective circuit, as expected.  
The high operating frequency band (central frequency 18 GHz) is highly demanding and limiting in terms of design and manufacturing. In our work, best efforts are concentrated in having an easy-manufacturing device with reasonable performance results. Thus, the device is based on planar microstrip technology, which is almost in its frequency limit of operation. All the manufacturing and mounting has been carried out in our laboratory. The photolithography manufacturing process introduces a manufacturing size uncertainty of around 100 microns. The substrate is a Neltec N9217 (0.508 mm thickness, relative permittivity of 2.17, and \(<\)0.0008 loss tangent). The lumped elements (the MA46585 varactors) are welded by means of conductive epoxy. The device circuit design dimensions are provided in Table 1.
## 1 Introduction
Moreover, the use of many flow entries by the SFC protection mechanism results in increased control messages between the SDN controller and switches and flow table overflows in software-defined switches. Hence, it must use the minimum number of flow entries for rerouting the traffic to and from the backup SF to reduce the flow table occupancy problem. Our previous study used source routing to route traffic through SFC and presented failure recovery as a use-case to show its effectiveness, but it exhibited some limitations in terms of flow table design [11]. To address the aforementioned SFC protection requirements and limitations of [11], this paper extends our previous work to a hybrid SFC protection mechanism that provides more robust traffic steering through a refined flow entry update mechanism. The proposed hybrid SFC protection mechanism (HP-SFC) segregates SFC into segments, where a segment is defined as a path from one primary SF to the next \((S\!F_{i}\!,\!S\!F_{(i+1)})\). Each segment is covered by a single backup SF, and the failure of the primary SF or any network element in the path to it is taken as the failure of the whole segment. HP-SFC reroutes the traffic from the starting point of the failed segment to the backup SF, and from there, it uses the new segment to route traffic to the next primary SF in SFC. The proposed approach resembles global protection from the segment perspective, and from the SFC point of view, it is similar to local protection; hence, it is termed the hybrid protection mechanism. Moreover, flow entry updates required by HP-SFC to reroute the traffic from failed SF to backup SF are reduced through a novel per-SF labeling technique. The contributions of this paper can be summarized as follows:  
* A novel SF labeling technique for traffic steering and rerouting in SFC that reduces the flow table occupancy in the software switches and Service Function Forwarders (SFFs) and improves network capacity;A new and simplified flow entries' update process for traffic re-routing, which parallelizes the sending of update messages and requires fewer flow entry updates, consequently reducing the recovery delay and control overhead;
* A hybrid protection approach that combines the merits of local and global protection to balance the tradeoff between end-to-end transmission delay and the cost of a protection mechanism in terms of additional resources in network entities;
* A comprehensive evaluation and analysis of HP-SFC in Mininet with two distinct topologies representing a data center and enterprise networks. The remainder of paper is as follows. Section 2 defines SFC creation, operation, and routing techniques and discusses current protraction approaches in the literature for SDNs and SFC. Section 3 presents the HP-SFC architecture and describes the hybrid protection mechanism through the proposed per-SF labeling technique. A proof-of-concept emulation environment is discussed in Section 4 along with the detailed evaluation results based on different topologies. The concluding discussion on the merits and limitations of HP-SFC is presented in Section 5 along with future directions for improvement.
# 3D Shape Generation via Variational Autoencoder with
## 3 Proposed 3D-VAE-SDFRaGAN
### 3D-VAE-SDFRaGAN Framework  
A novel approach called 3D-VAE-SDFRaGAN is proposed to learn the latent representation of a 2D image and generate 3D mesh-based shapes that are similar to the 2D image. The use of the learned latent space of a 2D image from the 2D-VAE network as input to the SDF-generator network is the key innovation of this approach. A relativistic average GAN loss function is used to train the SDF-generator network, allowing it to generate high-quality 3D shapes from both the 2D image and SDF. The architecture of the 3D-VAE-SDFRaGAN consists of three main components: a 2D-image encoder network \((E)\), an SDF-generator network \((G_{sdf})\), and an SDF-discriminator network \((D_{sdf})\). The 2D-image encoder network compresses the 2D image into a latent representation that can be used as input to the SDF-generator network. 3D mesh-based shapes are generated by the SDF-generator network using this latent representation. The generated shapes are evaluated by the SDF-discriminator network, providing feedback to the SDF-generator network to continually improve its ability to generate high-quality 3D shapes. Figure 2 depicts the overall architecture of the 3D-VAE-SDFRaGAN, which demonstrates the three key components and their interaction. The overall architecture of the proposed 3D-VAE-SDFRaGAN is comprised of three components: (a) the encoder network, which converts a 2D image to latent spaces; (b) the generator network, which produces an SDF from a 2D image latent vector; and (c) the relativistic discriminator network, which evaluates the likelihood of an actual SDF being more authentic than a randomly selected generated SDF from the generator. The 2D image is encoded through a 2D-image encoder network in Figure 2a to obtain its latent vector \((z_{img})\). The latent vector \((z_{img})\) is concatenated with random noise \((z)\) before sending to \((G_{sdf})\) network as shown in Figure 2b to generate a fake SDFs \((sdf_{gen},sdf_{z})\). Next, the \((sdf_{gen},sdf_{z})\) is concatenated with \((sdf_{real})\) as input for \((D_{sdf})\), as shown in Figure 2c, to return the prediction score.
## 4 Results
### Decay Rate  
The DR controls the effective radiating length of the rail. The simulation and the measurement of the DRs are shown in Figure 12, which is presented in the form of one-third octave spectra.  
Figure 12 shows that the DRs of two waves are quite different. The first wave (marked by the circles) holds a high DR at all frequencies, which is called the near-field wave. The second wave (marked by the triangles) only has a high DR in the low-frequency range, which is called the propagating wave. As the near-field wave is attenuated significantly, the transmission characteristics of the rail vibrations in the longitudinal direction is mainly determined by the propagating wave. Thus, this paper primarily investigates the DR of the propagating wave.  
According to the simulation result, the vertical rail resonance does not occur below \(f_{\mathrm{v}}\). Consequently, the propagating wave  
range are mainly transmitted in the downward direction while those at high frequencies are primarily transmitted along the rail.  
The measurement and the simulation of the DR of the propagating wave match well. Below the center frequency of 160 Hz, they coincide with each other. In the high-frequency range, the measured DR keeps the low values as the simulated DR does. Therefore, the periodic track model and the STMM are adequate for studying the attenuation characteristics of rail vibrations in the longwise direction.
## 4 Simulation Setup and Experimental Results
### Discussion
Herein lies one of the strengths of our protocol: on the one hand, it prevents packet injection and is able to make the CHs identify quickly which packets not to process (affecting the savings in energy consumption); on the other hand, it prevents an attacker from rescuing useful information from a transmission; and finally, before the imminent fall of a node, another one is immediately selected. In the extension of the proposal, where DLT is used, the fall of a node or its compromise can alert (since smart contracts can identify the pattern of an attack) and disconnect all nodes until the source of the attack is solved or identified.
# An Efficient Blockchain Transaction Retrieval System
## 2 Literature Review
### I2B+ Tree  
I2B+ tree is an improved IB+ tree proposed by [PERSON] in 2020 [40]. It is an efficient index structure for time retrieval. It combines B+ tree and interval tree, which can effectively manage time interval information and retrieve time characteristics. It has good results and supports range retrieval. Specifically, the I2B+ tree is formed by the expansion of the B+ tree (n-ary tree). The I2B+ tree includes two types of nodes: internal nodes (its child nodes are leaf nodes) and leaf nodes (its father nodes are interval nodes). The internal node stores three tables: one table stores the addresses of its child nodes, the second stores the ordered node keys, and the last table stores the maximum value. Leaf nodes store ordered node key pairs and corresponding values. Similar to B+ tree, front and back pointers are set between leaf nodes and middle layer nodes to support range retrieval see Figure 2.  
Figure 1: The Bloom filter.  
At the same time, to enhance the management of time, the I2B+ tree has two unique parameters: _order_ and _alpha_. The _order_ defines the child nodes maximum number that a node can have. The _alpha_ represents an empirical factor. It affects selecting the separation point of the leaf node's child nodes. This parameter adjusts the balance of space and query time. The bigger _alpha_ value results in a higher split point value. Therefore, fewer time splits occur, resulting in less storage and lower query efficiency. Compared with IB+, the biggest difference of the I2B+ tree is that the I2B+ tree increases the front and back pointers between the non-leaf nodes, which can reduce the spatial fragmentation rate during tree splitting and enhance the retrieval ability.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 3 Research and Applications of RO to TC Forecast  
This section details how the RO measurement benefits TC forecast. Section 3.1 states the current status of TC forecast. Section 3.2 presents case studies that demonstrate how the RO measurement impacts TC forecast. In Section 3.3, we discuss how the assimilation operator, mainly the non-local excess phase operator, optimizes the TC forecast.
# The Effect of Apple Juice Concentration on Cider Fermentation and Properties of the Final Product
## Materials and Methods  
### Apple Juice Clarification and Concentration  
This study used apple juice (Brix 9.80%; pH 3.38; titratable acidity 3.93 g L\({}^{-1}\) in malic acid equivalent) industrially pressed from ripe Antei apples (150 L). Half of the juice (75 L) was clarified and concentrated. Clarification was based on the method described by [PERSON] et al. (1978) [20]. The juice was warmed to 40 \({}^{\circ}\)C from the storage temperature; 5 g hL\({}^{-1}\) of commercial pectinase (Rapidase; Oenobrands SAS, Monferrier-sur-Lez, France) and 5 g hL\({}^{-1}\) of glucoamylase (AMG 300L; Novozymes, Copenhagen, Denmark) were added. After 30 min at 40 \({}^{\circ}\)C, 100 mL hL\({}^{-1}\) of silica sol (Baykisol\({}^{\circledR}\) 30; E. Begerow GmbH & Co., Langenlonsheim, Germany) and 10 g hL\({}^{-1}\) of dissolved gelatin (SIHA Clarifying Gelatine; E. Begerow GmbH & Co., Langenlonsheim, Germany) were added as flocculants. Flocculation occurred within 1.5 h at 40 \({}^{\circ}\)C. Clarified juice was filtered and concentrated using a vacuum concentration system (Didacta Italy; Fruit juice and syrup line, code 640,022). Due to volume restrains, the process was performed in three batches of 25 L. Each batch was concentrated separately from 9.8% to 30% Brix. The batches were then pooled and concentrated to the final Brix value of 67.8%. For fermentation, the concentrate was diluted with distilled water to 10% Brix (pH 3.45; titratable acidity 3.97 g L\({}^{-1}\) in malic acid equivalents).
# Potential Marker Genes for Predicting Adipogenic Differentiation of Mesenchymal Stromal Cells
## 3 Results  
Correlational Analysis between Gene Expression before Differentiation and GPDH Activity after Adipogenic Differentiation  
The ability of MSCs to differentiate into a distinct type of cells, such as adipocytes, is thought to vary among MSCs isolated from different tissues and/or donors. To confirm this assumption, we measured the activity of GPDH, an adipogenic differentiation marker, in 15 different MSCs (five jaw and 10 ilium MSCs) from 15 donors at 28 days after induction of adipogenesis. As expected, GPDH activities significantly varied across the MSC lines (Figure 1, \(p<0.001\), one-way ANOVA). However, the GPDH activity of ilium MSCs was 18 times higher than that of jaw MSCs (Figure S1a). On the other hand, after osteogenic differentiation, calcium content, an osteogenic differentiation marker, was not significantly different between jaw and ilium MSCs (Figure S1b).  
Figure 1: Adipogenic differentiation levels of bone marrow mesenchymal stromal cells (MSCs) evaluated by glycerol-3-phosphate dehydrogenase (GPDH) activity at 28 days after adipogenic induction. The activity was normalized using genomic DNA content to show means \(\pm\) standard error (\(n=3\)). 1–15, donor ID numbers; 1–5, jaw MSCs; 6–15, ilium MSCs.  
To screen candidate markers for predicting the adipogenic differentiation ability of MSCs before adipogenic induction, we investigated the correlation between GPDH activities described above and mRNA expression of 95 genes in undifferentiated MSCs using the data from low-density arrays of previous studies [7,10] (Table S2). The expression patterns of 22 out of 95 genes in the 15 MSCs before differentiation significantly positively related to the GPDH activities in the same MSC lines after adipogenic differentiation (Table 1 and Table S2).
## 3 Model and Problem
### Inactivated or Activated Antenna Model  
In this work, we aim to turn off a specific number of array elements in CCAAs while achieving some other optimization objective. Thus, we introduce our inactivated or activated antenna model in this section.  
Specifically, we consider the use of a binary vector \(\mathbf{B}=\{b_{1},b_{2},\ldots,b_{N_{T}}\}\) to denote the antenna's inactivated or activated condition. As shown in Figure 2, we use \(0\) to indicate that the antenna is not turned on, and \(1\) to indicate that the antenna is turned on. Mathematically, this model can be expressed as follows.  
\[b_{l}=\left\{\begin{array}{ll}0,&\text{The $l$th antenna is turned off,}\\ 1,&\text{The $l$th antenna is turned on.}\end{array}\right. \tag{3}\]
# Data Augmentation with Generative Methods for Inherited Retinal Diseases: A Systematic Review
## 2 Materials and Methods
### Search Strategy  
The search was conducted on 1 October 2024, which was the cut-off date for the inclusion of studies. No systematic reviews were found that met our objective. The databases included in this study and the reason why they were chosen can be seen in Table 1. These were selected due to the characteristics that each one possesses to guarantee a balance between the themes of this review.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Identifier** & **Inclusion Criteria** \\ \hline PubMed & It is focused on biomedicine and health, which meets the context of inherited retinal diseases. \\ IEEE Xplore & It is a database specializing in technology and engineering, covering the area of artificial intelligence. \\ Web of Science and Scopus & They are databases with a multidisciplinary scope and scientific impact, which can encompass studies covering both areas. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search databases and reason.  
In the searches, controlled terms (Medical Subject Headings--MeSH terms) and uncontrolled terms (free text words) were used and combined with Boolean operators to create a comprehensive and effective search. The MeSH terms used in the search were \"Retinal Diseases\" (MeSH) and \"Retinal Degeneration\" (MeSH). The rest of the terms used were free-text expressions. This search term, used in this study can be found in Appendix B, Table A2, which contains all the steps for conducting the search.  
Initially, the search was limited to IRDs, but as there were no results from this search, we were \"forced\" to broaden the context of the search to include all retinal diseases. In this way, some literature has already emerged for analysis.  
To ensure methodological rigor, we adopted the PICo method for systematic reviews (Population, Phenomena of Interest, Context), proposed by the Joanna Briggs Institute (JBI) manual [25].  
The inclusion and exclusion criteria for this study are detailed in Tables 2 and 3. One of the inclusion criteria is articles published from 2019 onward, as no relevant studies were found before this period.
## 2 Materials and Methods
### Feature Selection  
MRMD3.0 [29], a robust dimensionality reduction tool employing an ensemble link analysis strategy, was used to identify the optimal feature subset. MRMD3.0 integrates multiple feature ranking algorithms, incorporating the PageRank algorithm, a well-established method for ranking nodes in a network based on their importance [30]. In the context of MRMD3.0, the PageRank algorithm is adapted to model features as nodes in a graph. The \"importance\" or \"rank\" of a feature node is then iteratively calculated based on its  
Figure 2: Schematic illustration of feature extraction by aggregating layer-wise embeddings from PLMs (ESM-1b/ESM2/ProtBert) for protein representation.  
relevance to the target variable and its redundancy with other features. Features that are highly relevant to the target and less redundant with other high-ranking features receive higher PageRank scores, thereby contributing to a more effective feature ranking. The feature ranking algorithms included Information Gain, Gain Ratio, Gini Index, Chi-squared, ReliefF, minimum Redundancy Maximum Relevance (mRMR), and Mutual Information. By combining these algorithms, each with distinct strengths, MRMD3.0 generates a robust and comprehensive feature ranking.  
The feature selection process in MRMD3.0 comprises two key steps. Initially, raw hybrid features are ranked based on importance scores calculated by the ensemble of ranking algorithms. Subsequently, a forward feature selection strategy, combined with 10-fold cross-validation, is used to iteratively add features to the model and assess its performance, measured by classification accuracy. The feature subset achieving the highest accuracy is selected as the optimal set [31]. This two-step strategy leverages the strengths of ensemble learning and PageRank, facilitating the identification of a robust and informative feature set for accurate FRP prediction.  
Furthermore, an XGBoost classifier combined with LambdaRank, a ranking algorithm specifically designed for optimizing ranking tasks [32], was incorporated to complement MRMD3.0. While MRMD3.0 provides an initial robust feature subset, LambdaRank, integrated with XGBoost, serves as a secondary refinement step. It optimizes the feature ranking specifically for the classification task by leveraging gradient boosting to assign importance scores that directly improve the classifier's performance. This enhanced feature selection process results in improved prediction accuracy and model robustness. This integrated approach ensures that the selected features are both informative and optimally ranked for the classification task [33].
# Safe and Policy Oriented Secure Android-Based Industrial Embedded Control System
## 3 Policy Oriented Security Scheme
### Adversary Model  
Here, we consider an adversary model where the attacker tries to physically tamper an embedded system connected to an industrial control network in order to bypass the booting process and obtain access to the Android operating system. Once the adversary has access to the operating system, all the connected devices can easily be manipulated, and confidential data such as control algorithms and machine specifications can be modified or downloaded. To achieve this goal, the adversary can either remove the ALPU chip or attach a similar one. In our developed system, we have enforced two bootloader policies, explained in the previous section, in order to prevent the adversary from booting to the Android operating system.  
To restrict the adversary from tampering with any of the bootloader environment variables, we have disabled the command line interface, which results in an infinite system reset procedure. The flow diagram and the adversary model of the booting process policies are shown in Figure 4a. The standard boot procedures are depicted by the white blocks, while the shaded blocks represent the implemented booting process policies using the ALPU.  
In the figure, the adversary can try to bypass the authentication process by removing the ALPU from the embedded system. However, with the first bootloader policy enforced, the entire system will fall into an unlimited reset procedure unless it is powered off. In case the adversary can get hold of a similar ALPU chip and attach it to the embedded system, the second policy ensures that the system is protected by executing the ALPU authentication process. This means that the system will boot only if the encryption key in the ALPU is the same as the one stored inside the embedded system.  
If, by any chance, the adversary is able to bypass the bootloader policies, the system is also enforced with administrative policies in the Android layer. Here, we assume an adversary trying to indirectly access the embedded system by executing unauthorized and malicious applications, as shown in Figure 4b. Through the developed security manager, the Android application is checked to ensure that it was developed with the encryption API in compliance with the third policy. If not, the security manager kills the entire process and prevents the execution of the application. If the adversary develops an application by reverse engineering the encryption API, the entire system will still be secured by the fourth security policy. The malicious application will not be executed as long as the correct encryption key is not compensated.  
Figure 4: Flow diagram and adversary model of the policy oriented security scheme: (**a**) Booting process; (**b**) Android application layer.