esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Species-Specific Control of the HrpG/HrpX Regulon by Diffusible Signal Factor  
Diffusible signal factor (DSF) is a _cis_-unsaturated fatty acid that serves as a cell to cell signal molecule that is considered as the main quorum-sensing signal in _Xanthomonas_[95]. DSF was reported to play a role in the virulence of many _Xanthomonas_ bacteria by controlling multiple traits, including biosynthesis and dispersion of exopolysaccharides (EPS), motility, siderophore production, biofilm formation, and surface attachment [96]. DSF is sensed in recipient cells by the RpfC sensor that transmits the signal to the HD-GYP response regulator RpfG to degrade the internal signal molecule cyclic di-GMP [97]. While cell-to-cell signaling was reported to play a significant role in coordinating the expression of virulence genes in multiple pathogenic bacteria, the effect of DSF on the HrpG/HrpX regulon appears to be strain specific. Transcriptome analyses of DSF biosynthesis and signaling mutants in _Xoo_ and _Xcci_ did not identify the significant regulatory effect on _ltrpG_, _ltrpX_, or T3 SS genes in artificial media [98; 99], and _in planta_ in the case of _Xcci_[100]. On the other hand, DSF was reported to play a role in the regulation of the _ltrp_ regulon in _Xcc_. Analysis of _Xcc rpfC_ mutant identified a significant reduction in the expression of _ltrpG_, _ltrpX_, and their downstream targets [101]. This reduction was also accompanied by reduced HR response in resistant plants and almost complete abolishment of virulence in Chinese radish [101].
# Name-Based Security for Information-Centric Networking Architectures
## 2 Background and Related Work
### Decentralized Identifiers  
Decentralized Identifiers (DIDs) are a new type of identifier that is globally unique, resolvable with high availability, and cryptographically verifiable [5]. From a high level perspective, a DID system can be viewed as a _registry_ where key-value pairs are stored. The key part of this pair is the  
Figure 1: Identity-Based Proxy Re-Encryption (IB-PRE) example. MSK = Master Secret Key; SK = Secret Key; RKGen = Re-Encrypt Key Generation.  
DID, which can be any arbitrary string. The value is a _DID document_, i.e., a JSON-LD [6] encoded document that contains (among other information) public keys that can be used for linking a DID to that document, service endpoints related to that DID, as well as auxiliary information that can be used for verifying the integrity of the document (e.g., a digital signature) [2]. A registry can be implemented using blockchains, distributed ledgers, (decentralized) P2P networks, or other systems with similar capabilities. It should be noted that DID specifications do not dictate how a registry is implemented. Instead, they define the methods that it should implement.  
Suppose that an entity (the prover) wants to prove to another entity (the verifier) that he is the owner of a DID. The verifier retrieves the corresponding DID document form the registry and extracts the public key associated with that DID. Then, using an \"authentication\" method that depends on the type of the public key, which is also defined in the DID document, authenticates the prover (e.g., the verifier generates a challenge, and the prover must provide a digital signature for that challenge).  
Compared to traditional security certificates, DIDs (and DID documents) have some significant advantages. Firstly, a DID acts as an indirection point towards some public information (i.e., the DID document, which may include a certificate), and any modification to this public information does not affect the DID. Secondly, DID documents are stored in a registry (maintained by a 3\({}^{rd}\)party), and before any other operation, a verifying entity may lookup this registry and obtain the latest version of the DID document. This property is of importance when it comes to revocation. Thirdly, DIDs offer \"self-sovereignty,\" i.e., DID owners (and only them) can freely manage their DIDs and the corresponding DID documents.
Sensors_**2013**, _13_, 6882-6899; doi:10.3390/s130606882  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Advanced Respiratory Motion Compensation for Coronary MR Angiography**  
**[PERSON] 1,** and [PERSON] 1,2,3,4**  
* Division of Imaging Sciences & Biomedical Engineering, the Rayne Institute, King's College London, 4 th Floor, Lambeth Wing, St Thomas' Hospital, London, SE1 7 EH, UK; E-Mail: [EMAIL_ADDRESS]
* Wellcome Trust and ESPRC Medical Engineering Center, King's College London, London, SE1 7 EH, UK
* BHF Centre of Excellence, King's College London, London, SE1 7 EH, UK
* NIHR Biomedical Research Centre, King's College London, London, SE1 7 EH, UK
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +44-20-718-85441; Fax: +44-20-718-85442.  
_Received: 15 April 2013; in revised form: 15 May 2013 / Accepted: 21 May 2013 / Published: 24 May 2013_  
**Abstract:** Despite technical advances, respiratory motion remains a major impediment in a substantial amount of patients undergoing coronary magnetic resonance angiography (CMRA). Traditionally, respiratory motion compensation has been performed with a one-dimensional respiratory navigator positioned on the right hemi-diaphragm, using a motion model to estimate and correct for the bulk respiratory motion of the heart. Recent technical advancements has allowed for direct respiratory motion estimation of the heart, with improved motion compensation performance. Some of these new methods, particularly using image-based navigators or respiratory binning, allow for more advanced motion correction which enables CMRA data acquisition throughout most or all of the respiratory cycle, thereby significantly reducing scan time. This review describes the three components typically involved in most motion compensation strategies for CMRA, including respiratory motion estimation, gating and correction, and how these processes can be utilized to perform advanced respiratory motion compensation.  
**Keywords:** magnetic resonance imaging; coronary magnetic resonance angiography; respiratory motion correction; respiratory navigation
# Estimating Carrot Gross Primary Production Using UAV-Based Multispectral Imagery
## 1 Introduction
Other, less commonly used VIs correlating with GPP have been also proposed, including Clgreen [17, 18], Credredge [20], red-edge NDVI (RENDVI) [20], the modified chlorophyll absorption ratio index (MCARI) [20], MERIS terrestrial coefficient index (MTCI) [20], soil adjusted vegetation index (SAVI) [1], water difference vegetation index (WDVI) [1], wide dynamic ratio vegetation index (WDRVI) [17], WDRVI green (WDRVIgreen) [18], land surface water index (LSWI) [10, 22], and green-red vegetation index (GRVI) [29]. The spectral data used in the computation of VIs can be the result of different types of data acquisition platforms, such as spaceborne (satellite sensors), manned airborne vehicles, or unmanned aerial vehicles (UAVs) [26]. The resolution of satellites is relatively low, and the possibility of cloud cover limits the retrieval of primary productivity data at the field scale [30]. The case of manned airborne-based RS technology is limited by its temporal and spatial resolution and high cost [31]. In recent years, there have been important developments in UAV-based RS technology (or drones) in terms of cost, weight reduction, and operability (e.g., pre-programmed flights) [26, 32, 33]. As such, UAV-based RS can increase the scale and the resolution of primary productivity observations, overcoming the shortcomings of satellite and manned airborne platforms [30]. It is also important to have ground truth data, since different vegetation species will have different contributions to carbon fluxes, depending on the time of the year [9]. While the ability to use Vis, such as NDVI, EVI, and NIRv to track GPP using satellite-based platforms has been well established [28], the operational capability to accurately estimate GPP at the farm scale using UAV platforms has not been widely analyzed [30]. In particular, GPP estimates from remote sensing platforms and VIs have been conducted for maize [1, 10, 15, 18], paddy rice [16], soybean [15, 18], wheat [20], evergreen and deciduous forests [14, 21], woody savanna [21], broad-leaved pine mixed forest, temperate steppe, alpine shrubland, alpine marsh, and alpine meadow steppe [22]. They found linear relationships between the daily GPP and VIs over different study areas. Less attention has been paid to deriving GPP from spectral data for vegetable crops using UAVs. The carrot crop (_Daucus carota_ L.) plays a very important role in food security in Colombia and is one of the traditional consumption crops, with around 11,000 hectares planted in 2017, mainly by small producers, with an average yield equal to 30 t ha\({}^{-1}\)[34]. The main producing regions are Cundinamarca, Boyaca, Antioquia, and Narino. In this context, the present study aims to estimate the GPP of carrot crops from UAV-based multispectral VIs. The performance of this relationship has been evaluated based on field data collected during one growing season (2021) through the EC system measurements.
## 3 Using LTE/5G-based Public Mobile Land Networks for Autarkic Microgrid Blackout Recovery
### Access Barring Mechanisms  
In 3 GPP mobile communications systems up to and including LTE, MTC and H2H communication compete for the same radio resources in case spectrum is shared. UEs used by normal subscribers have a randomly allocated access class (AC) value between 0 and 9 [14], which is stored in the USIM card. Specific high priority UEs may also be allocated AC 11 to 15, e.g., for emergency services.  
In network congestion and emergency situations, PLMN restricts traffic by using the Access Class Barring (ACB) and/or Extended Access Class Barring (EAB), functions to bar UEs from connecting to the network. AC barring mechanisms take effect during the random access procedure (RAP) [15], which results in the UE being given an initial uplink bandwidth assignment which it uses for the radio resource control (RRC) procedure. In RRC, admission control functionality checks the UEs' QoS parameters such as QCI (QoS class identifier) and allocation and retention priority (ARP). Successful RRC results in allocation of a radio bearer and an evolved packet system (EPS) bearer, a tunnel between UE and packet gateway (PGW). Hence, to actually achieve the QoS to which it is nominally entitled, the UE must first successfully complete the RAP, which is affected by access barring in congestion or emergencies.  
In ACB [14], UEs with normal AC values generate a random number which must be below a level (the Barring Factor) broadcasted by the eNodeBs for the UE to connect to the network, otherwise the UE must wait for a period defined by the eNodeB. In EAB [15], the eNodeB broadcasts an order to UEs without the required class level to stop transmitting for a period. UEs may be configured to override EAB, effectively giving them a priority over other UEs. ACB or EAB restrictions mean that MTC-type devices which are not already connected would not be able to transmit, limiting the usage of MTC to non-critical or always-connected applications.  
The fact that the number of microgrid MAS MTC devices is of the same order as there are houses or people in the area precludes allocating the devices any special priority handling compared to other MTC devices or H2H users, except during the blackout. Also, giving the MAS MTC devices service during blackout should not interfere with the service to users with higher AC values, such as emergency services and so should keep AC values in the range 0 to 9.  
However, during blackouts, microgrid MTC devices should reliably be able to access the network, while other normal human and MTC-type users are excluded. To achieve this, it must be possible, at least during blackouts, to bypass the network access restriction of MAS MTC devices based on their access class.
## 2 Basic Model Assumptions in Conventional Thin Film Optics
### Material Description  
#### 2.2.1 Dispersion Models  
When restricting on electric dipole interaction in optically isotropic materials (i.e., neglecting any magnetic response), the light-matter interaction results in the induction of a macroscopic polarization of the medium according to [10; 12; 13; 14]:  
\[\mathbf{P}(t)=\varepsilon_{0}\int_{0}^{\infty}\kappa(\xi)\mathbf{E}(t-\xi)d\xi \tag{1}\]  
Here, \(\mathbf{P}\) is the polarization of the medium, \(\varepsilon_{0}\) the vacuum permittivity, \(\mathbf{E}\) the electric field in the medium, and \(\kappa\) the real response function of the medium. \(t\) is the time, and the integration variable \(\xi\) stands for the time delay.  
From the response function, the dielectric function \(\varepsilon(\omega)\) (with \(\omega\)--angular frequency of the incident light) of the medium is straightforwardly calculated according to the following:  
\[\varepsilon=1+\int_{0}^{\infty}\kappa(\xi)e^{i\omega\xi}d\xi=1+\int_{0}^{ \infty}\kappa(\xi)(\cos\omega\xi+i\sin\omega\xi)d\xi=\varepsilon(\omega) \tag{2}\]  
Note that according to Equation (2), the dielectric function must depend on the light frequency (so-called dispersion) and is necessarily a complex quantity. For any real response function \(\kappa(\xi)\), from Equation (2) we immediately find the following:  
\[\text{Re}\varepsilon(-\omega)=\text{Re}\varepsilon(\omega);\text{Im} \varepsilon(-\omega)=-\text{Im}\varepsilon(\omega) \tag{3}\]  
Figure 1: Illustration of the “standard model” of thin film optics. \(I\) is the light intensity; subscripts \(E\), \(T\), and \(R\) denote incident, transmitted, and reflected intensities. All other symbols are introduced in the next paragraph.  
In terms of the formulated model assumptions III and VIII, the optical constants \(n\) (the refractive index) and \(K\) (the extinction coefficient) are obtained from the complex dielectric function according to Equation (4):  
\[n(\omega)+iK(\omega)\equiv\sqrt{\varepsilon(\omega)}\Rightarrow\begin{cases}n( \omega)&=Re\sqrt{\varepsilon(\omega)}\\ K(\omega)&=Im\sqrt{\varepsilon(\omega)}\end{cases} \tag{4}\]
## 1 Introduction
Similarly, the asymmetric turbo-coded cooperative vertical bell labs layered space-time (ATCC-VBLAST) scheme is utilized as a suitable benchmark for ATCC-SM under identical conditions. * A joint iterative ATC decoder for two sources is also implemented at the destination node. The rest of this manuscript is organized as follows. Section 2 introduces the fundamental construction of the ATC with the CMI and proposes two non-cooperative schemes, i.e., ATC-SM and ATC-VBLAST. Section 3 discusses the system model for two coded-cooperative schemes, i.e., ATCC-SM and ATCC-VBLAST. Section 4 demonstrates the joint iterative ATC decoder. The results and discussion are presented in Section 5. Finally, Section 6 draws the conclusion of this manuscript.
# Recent Progress Trend on Abrasive Waterjet Cutting of Metallic Materials: A Review
## 3 Abrasive Waterjet Cutting Process Parameters and Influences  
The abrasive waterjet (AWJ) cutting process incorporates several independent process parameters that directly affect the machine's performances. As illustrated in the cause-and-effect diagram of the AWJM process parameters given in Figure 4, the input process parameters are categorized as follows (1) hydraulic, (2) nozzle, (3) material, (4) abrasive, and (5) cutting. The input process parameters primarily affect the machining performance or output parameters of AWJM application. Learning the specific functions of these influential  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year \& Author** & **Metallic Material** & **Thickness** & **Defects** \\ \hline [PERSON] et al. 2018 [77] & Ti6 Al4V & 5 mm & KTA, MRR, Ra \\ [PERSON] et al. 2019 [61] & AA 6061-T6 & 5, 10, 25, 50 mm & KTA \\ [PERSON] et al. 2017 [80] & AISI D2 Steel & 60 mm & Ra \\ [PERSON] et al. 2018 [18] & SS 304 & 20 mm & Ra \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quality defects in AWJ cutting of metallic with different thickness [18; 61; 77; 80].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Defects** & **Material** & **Images** & **Key Findings and References** \\ \hline Cutting residue, & & & [PERSON] et al. [76] have found quality defects such as cutting residue, kerf taper and striation in cutting AISI 304 using abrasive waterjet machine. These defects are caused by the decreasing energy of the jet. \\ \hline Kerr taper angle & AISI 1090 & & [PERSON] et.al. [62] have identified the kerf taper angle as an intrinsic characteristic of AWJ cutting of AISI 1090 mild steel. They determined that variation in the kerf top width wider and kerf bottom geometries resulted in a higher kerf taper angle. \\ \hline Surface Roughness & Ti-6 Al-4V & & [PERSON] et al. [77] applied AWJ cutting of Ti-6 Al-4V and observed roughness and striations marks in cut surfaces. They discovered that the cut surface finish differs depending in the depth from surface entry of the abrasive jet. \\ \hline Material removal rate, Kerf taper angle & Inconel 600 & & [PERSON] et al. [78] have known quality defects, such as kerf geometric inaccuracy and a low material removal rate in cutting super nickel alloy using abrasive waterjet machine. They established a high occurrence of inaccurate kerf geometries using a high level of water pressure and increasing the traverse speed. \\ \hline Depth of Cut & SS304 & & [PERSON] et al. [15] have determined that one of the challenges faced with cutting stainless steel is achieving a high depth of cut due to its low machinability. They concluded that using abrasive waterjet machine with a high level of pressure settings have increased the depth of cut.  
\end{table}
Table 3: AWJ cutting defects amongst various metallic materials [15; 62; 76; 77; 78].  
variables will be fundamental towards development and improvement initiatives of quality and efficiency of the entire cutting process.
## 3 Methodology
### Dimensionality Reduction  
Given the high dimensionality of image data, we also employ dimensionality reduction techniques as an optional step before clustering to speed up the clustering process and potentially improve its effectiveness. In this study, we consider principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) due to their prevalence and performance in reducing the dimensionality of data [19].  
PCA is a linear method that identifies the directions (principal components) in which the data vary the most and projects the data onto these directions, discarding less informative ones. On the other hand, t-SNE is a nonlinear method that constructs a probability distribution over pairs of high-dimensional objects in such a way that similar objects are assigned a higher probability, and dissimilar ones a lower probability. The same is done for the low-dimensional counterparts. t-SNE then minimizes the divergence between the two distributions, ensuring that similar objects in high-dimensional space remain close in the reduced space.
## 5 Related Works
### Integrated Cryptographic Approaches for Next-Generation Data Security  
There have been studies exploring emerging encryption approaches to enhance privacy and security in image and sensitive data processing. [PERSON] et al. [86] proposed an image-encryption method that combines the Piecewise Linear Chaotic Map (PWLCM) with the Standard Map to exploit both one-dimensional and two-dimensional chaotic behaviors for increased key sensitivity and diffusion. [PERSON] et al. [87] proposed a chaos-DNA hybrid model that utilizes DNA encoding and logic operations to further obscure the relationship between plaintext and ciphertext. [PERSON] et al. [88] introduced a diversified memristive Hopfield Neural Network (HNN) that generates complex multi-butterfly chaotic attractors to secure Internet of Medical Things (IoMT) data. Similarly, [PERSON] et al. [89] proposed a hidden multiwing HNN system that produces high-dimensional chaos suitable for encrypting remote sensing images. These studies exemplify a research trend focused on enhancing security by increasing algorithmic complexity within the classical computing domain.  
Our paper is in line with these studies in the goal of designing advanced encryption schemes to provide robust security for specialized data, such as images and large-scale files. However, our study distinguishes itself by primarily addressing the security threats posed by quantum computing, a challenge not covered by the chaos-based classical approaches of these studies. By integrating Kyber-based Post-Quantum Cryptography (PQC) for key encapsulation and BB84-based Quantum Key Distribution (QKD) for key masking, our proposed scheme constructs a layered quantum-classical cryptographic framework designed to enhance security in distributed cloud systems. Additionally, our proposed scheme is designed for high-performance, scalable cloud systems. It enables secure and efficient large-scale file protection by decoupling key management from data encryption and supporting parallel AES encryption across distributed slave nodes. This is achieved through centralized coordination and multi-layered, quantum-resilient key masking, a systemic approach designed to balance next-generation security with practical throughput, unlike the algorithm-centric focus of the compared existing works.
# Deformation of Air Bubbles Near a Plunging Jet Using a Machine Learning Approach
## 2 Material and Methods
### Evaluation Metrics and Cross-Validation  
Different evaluation metrics have been used to assess the effectiveness of the predicting models: the coefficient of determination R\({}^{2}\), the Mean Absolute Error (MAE), the Root Mean Squared Error (RMSE) and the Relative Absolute Error (RAE). These well-known metrics are defined below:  
\[R^{2}=\left(1-\frac{\sum_{i=1}^{m}(f_{i}-y_{i})^{2}}{\sum_{i=1}^{m}(y_{a}-y_{i} )^{2}}\right) \tag{15}\]  
where \(m\) is the total number of experimental data, \(f_{i}\) is the predicted value for data point \(i\), and \(y_{i}\) is the experimental value for data point \(i\):  
\[MAE=\frac{\sum_{i=1}^{m}\left|f_{i}-y_{i}\right|}{m} \tag{16}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{m}\left(f_{i}-y_{i}\right)^{2}}{m}} \tag{17}\]  
\[RAE=\frac{\sum_{i=1}^{m}\left|f_{i}-y_{i}\right|}{\sum_{i=1}^{m}\left|y_{a}-y_ {i}\right|} \tag{18}\]  
in which \(y_{a}\) is the averaged value of the observed data.  
Each model has been built by a \(k\)-fold cross-validation process, using a set of about 300 vectors. The \(k\)-fold cross-validation involves the random subdivision of the original dataset into \(k\) subsets. Subsequently, \(k-1\) subsets are used to train the model, while the \(k\)-th single subset is reserved for testing and validating the model. The recurrent cross-validation procedure is carried out \(k\) times: each of the \(k\) subsets is used once as the testing dataset. Finally, the \(k\) results are averaged to obtain a single prediction. In this study, \(k=10\) has been assumed.