esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 5 Discussion
### Accuracy
In addition, mapping surface water extent in urban areas is very complex from the perspective of both optical and SAR data. For optical images the main challenge is building shadows, whereas SAR data is confounded by multiple other factors including layover effects from tall buildings and corner reflections resulting in double/triple bounce effects [88]. Our algorithm is designed with large-scale operational monitoring of surface water in mind and whilst rates of urbanization in China have quadrupled over the past four decades [72], urban areas still represent only a fraction of the landscape. Therefore, our approach handles the urban complexity with a relatively simple masking approach that allows urban waters to be detected within a specific seasonal window when the sun is high in the sky and building shadows at a minimum. More studies dedicated to mapping and monitoring surface water in urban areas are needed to further resolve this specific challenge (see e.g., Reference [89]).
## 2 Materials and Methods
### Elastic Constants for Parametric Study
#### 2.2.2 Poisson's Ratios and Ratio \(\
u/\
u^{\prime}\)  
When considering a broad spectrum of rock types, typical values for Poisson's ratios range from numbers as low as 0.05 up to 0.4 [33]. However, certain quartz-rich sandstones and siltstones with open microcracks and -pores may even display auxetic material behaviors with negative values for the Poisson's ratios [34]. Values higher than 0.5 are also documented in the literature for anisotropic rocks, especially for the transverse Poisson's ratios \(\
u^{\prime}\) in highly anisotropic rocks [27]. [PERSON] et al. [5] reported the majority of values for \(\
u_{12}(\widehat{=}\
u)\) of anisotropic rocks to fall in between 0.1 and 0.35, while \(\
u_{13}(\widehat{=}\
u^{*})\) varies to a larger extent from 0.1 to 0.7. The parameter \(\
u^{*}\), as used by [PERSON] et al. [5], is a different representation of the transverse [PERSON]'s ratio \(\
u^{\prime}\), both of which are related via \(\
u^{\prime}/E^{\prime}=\
u^{*}/E\). As stated by [PERSON] [32] for shales, the [PERSON]'s ratio \(\
u^{\prime}\) can be smaller, larger or equal to the value of \(\
u\) depending on existing inclusions of kerogen, microcracks or pores with a low aspect ratio and parallel orientation to the bedding planes.  
Despite the wide ranges of possible values for the anisotropy ratio \(\
u/\
u^{\prime}\), sometimes simplified assumptions are made in rock mechanics, such as \(\
u/\
u^{\prime}=E/E^{\prime}\)[25]. A slightly different relationship is provided by the three-parameter formulation of the transversely isotropic stiffness matrix from [PERSON] and [PERSON] [35], requiring only three instead of five parameters to be calibrated. In this case, the relationship between the single parameter ratios is given by \(\alpha=(E/E^{\prime})^{1/2}=\
u/\
u^{\prime}=G/G^{\prime}\).  
For the herein conducted parameter study, the transverse Poisson's ratio \(\
u^{\prime}\) was assumed to be constant with a value of 0.15. This was carried out to prevent excessively low values for \(\
u\) when made dependent on \(\
u^{\prime}\) based on selected ratios \(\
u/\
u^{\prime}\). Considering chosen ratios for \(E/E^{\prime}\) of 1.0, 1.5 and 2.0 and applying the above mentioned relationships,  
Figure 3: Frequency distribution of anisotropy ratios \(E/E^{\prime}\) for various rock types as (**a**) studied by [PERSON] [28], including more than 200 samples in his study from 4 different lithological classes of anisotropic rocks and (**b**) as listed by [PERSON] and [PERSON] [27]. In both datasets, rocks displaying exceptional values for anisotropy ratios greater than 3.5 were present, but were not considered in this figure due to the study’s focus on weakly to moderately anisotropic rocks. The limit ratio sometimes defined in literature to distinguish weakly to moderately and highly anisotropic rocks is 2.0 [16].  
let the authors come up with chosen values for \(\
u/\
u^{\prime}\) of 0.5, 0.667, 1.0, 1.5 and 2.0. This corresponds to assumed values for \(\
u\) from 0.075 to 0.3.
## 3 Results
### Determination of Surfactant Properties  
The surface tension and interfacial tension against n-heptane were analyzed at ambient conditions for the aqueous solution of crude MEL. The present MEL reduced the surface tension of water from 72 mN m\({}^{-1}\) to 32.75 mN m\({}^{-1}\) at a 0.1% (_w/v_) concentration. The interfacial tension at the same concentration decreased from 32.46 mN m\({}^{-1}\) (water/n-heptane) to 4.45 mN m\({}^{-1}\) (MEL solution/n-heptane), which was sufficiently low to realize the emulsifying power of hydrocarbon droplets by MEL. The GC chromatogram of MEL showed assorted peaks at 7.73, 8.14, and 8.91 min, corresponding to MEL-A, MEL-B, and MEL-C, respectively.  
Figure 6 depicts the surface tension reduction profile with an increase in the molar concentration of MEL. The MEL produced from acid oil was shown to have a critical micelle concentration (CMC) of 2.8 \(\times\) 10\({}^{-6}\) M with 29.3 mN m\({}^{-1}\) surface tension at the CMC (\(\gamma\)CMC). In the literature, the CMC of the MEL produced from _Pseudozyme antarctica_ with soybean oil as substrate had been estimated to be 2.7 \(\times\) 10\({}^{-6}\) M [11], which was comparable to the obtained results. Similarly, [PERSON] et al. had reported a high value of 6.0 \(\times\)10\({}^{-6}\) M for the CMC and 25.1 mN m\({}^{-1}\) for the minimum surface tension for MEL-C produced from _P. hubeinsis_ (KM-59) in a fed-batch culture grown on soybean oil as the carbon source [17].  
The surface excess (\(\Gamma\)) for the present molecule, calculated by [PERSON]'s adsorption isotherm, was 0.807 molm\({}^{-2}\). From the surface excess, the area occupied per molecule at the interface was also calculated as 20.5 A. As expected, all these properties of MEL showed ample hydrophilicity, retaining an excellent surface-tension-lowering activity.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acid Oil (g L\({}^{-1}\))** & **MEL (g L\({}^{-1}\))** \\ \hline
80 & 1.74 \(\pm\) 0.033 \\
120 & 2.84 \(\pm\) 0.05 \\
160 & 3.32 \(\pm\) 0.1 \\
200 & 3.82 \(\pm\) 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extraction of MEL by heat treatment.  
Figure 6: Calculation of the CMC value from the surface tension vs. concentration plot.  
Indeed, as previously reported, the MEL produced by _P. aeruginosa_ grown on soybean oil and sucrose showed CMC values as 1.02 \(\times\) 10\({}^{-4}\) M and \(\gamma\)CMC 29.85 mN m\({}^{-1}\), which were higher than those shown by the present MEL synthesized by using acid oil [31]. The significant difference in the previously mentioned surface excess (1.29 mol.m\({}^{-2}\)) and area per molecule (129 A) for the same molecule may be attributed to the difference in the chain length of the hydrophobic group at the C-2\({}^{\prime}\) position on the mannose molecule. However, the observed area per molecule of MEL (60 A) at the interface was substantially different from that of MEL (129 A) reported by [31]. Figure 7 shows the biosynthetic pathway of the _Pseudozyma_ spp.
# Adaptive Model-Free Coupling Controller Design for Multi-Axis Motion Systems
## 2 Multi-Axis Systems and Recurrent Fuzzy Neural Networks
### System Description
In this study, we utilize the RFNN to developa model-free adaptive coupling controller to result in high precision and synchronization control of machine tools. In the following, a brief introduction of RFNN is given.
# New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs
## 4 Discussion
The AI model may have difficulty evaluating the margins accurately in these cases, marking bulging or concave borders at times. Our study also showed that the CTR and TCD measurements made based on the automatic segmentation were better discriminators of cardiomegaly when cardiac silhouette enlargement resulted from elevated LVEDV when compared to cardiomegaly due to LVM enlargement. Therefore, when using CXRs with an obscured heart border or with the suspicion of LVH, supervision by a radiologist is recommended. Several studies have evaluated the segmentation of the chest anatomy to estimate the CTR for cardiomegaly [15; 34; 36; 37; 38]; however, there is a lack of studies investigating the TCD. Importantly, we observed that the values of sensitivities were greater for the direct manual TCD measurements when compared to the CTR measurements while maintaining satisfactory specificity. Moreover, the McNemar test revealed that more diagnoses of cardiomegaly established based on the TCD measurements, rather than CTR, were in agreement with the cardiomegaly diagnosis established using CMR. This may result from the fact that TCD appears to be influenced by fewer factors, e.g., chest deformities affecting the transverse chest diameter rather than CTR. Some studies suggest that the CTR is not a sufficient indicator of cardiomegaly, and therefore, consider the introduction of another cut-off point for CTR or suggest TCD assessment as a better method [5; 31; 39]. Hence, future studies should be conducted to investigate the use of AI algorithms, not only in CTR measurements, but also in TCD calculations. There are some limitations in our dataset and methods which should be acknowledged. The chest radiographs and CMR studies were not always performed on the same day. Furthermore, our study was retrospective in nature, the tested population was quite small, and we included data only from a single center. Future studies incorporating data from multiple institutions will be beneficial for increasing the generalizability and robustness of the model. The majority of the patients in our study had previously been diagnosed with cardiovascular diseases, which could affect proper segmentation due to the presence of various conditions obscuring the lung and heart borders. Therefore, our results may not be fully applicable to a general population without comorbidities.
# Incorporating Clinical Imaging into the Delivery of Microbeam Radiation Therapy
## 2 Materials and Methods
### Planning CT  
The imaging protocol, as proposed in this work utilises the adjoining Monash Biomedical Imaging (MBI) facilities. MBI is a designated medical imaging facility housing many clinical and research scanners with a focus on human and pre-clinical research projects. A direct passageway connects MBI and IMBL for the convenience of transporting patients between imaging and treating rooms without the need for auxiliary patient transport services. MBI offers two CT scanners suitable for pre-clinical image-guided radiotherapy purposes; An Inveon Multimodality System small animal scanner and a Siemens SOMATOM go.Up clinical CT scanner. Scans were performed and optimised for small animal imaging using each of these CT scanners and assessed for suitability in image-guided MRT with a particular interest in image quality, tumour contrast, patient dose, patient throughput and ability to facilitate future work involving larger animals or humans.  
The Inveon Multimodality System integrates many imaging modalities onto a single versatile platform. For this study, the scanner was operated in CT only configuration. In this mode, the CT system has an adjustable field of view from \(4.4\,\mathrm{cm}\times 4.4\,\mathrm{cm}\) to \(10\,\mathrm{cm}\times 10\,\mathrm{cm}\) and a maximum voxel resolution of \(20\,\mathrm{\SIUnitSymbolMicro m}\times 20\,\mathrm{\SIUnitSymbolMicro m}\times 20\, \mathrm{\SIUnitSymbolMicro m}\). This scanner is considered for use in image-guided MRT largely for the ability to resolve tumour locations and fine anatomical details within small animals.  
As the Somatom go.Up is a clinical CT scanner indented for use with human sized targets, the choice of reconstruction kernel is not intuitively clear when scanning small animals. A rat sized phantom was constructed to determine the best scan parameters including X-ray kVp energy and reconstruction kernel. The phantom consisted of a \(25\,\mathrm{mm}\times 25\,\mathrm{mm}\times 55\,\mathrm{mm}\) RMI 457 Solid Water(r) cuboid with 1 mm of bone equivalent material on two side faces of the Solid Water(r). An 8 mm diameter hole was drilled to 30 mm depth into the phantom and partially filled with 2 mL of a 2 mg/mL iodine in water solution. The iodine concentration chosen represents what is expected to accumulate in the tumour of a rat injected with iodine contrast. Scans were performed at 80 and 100 kVp as they produced maximum intensity close to the K-edge of iodine (33 keV). Scans were reconstructed with all available reconstruction kernels.
## 8 Discussion
Furthermore, it is very clear that there are plenty of unsolved research needs special attention to explore security (recall Section 5) in healthcare application using WMSNs, as follows:  
1. Public key cryptography: Recent studies have revealed that public key operation may be practical in medical sensors. But private key operations are still too expensive in term of time complexity, so the efficiency of private key operations still needs to be explored. Moreover, the authenticity of public key is highly desirable, e.g., suppose a body sensor receives a public key from a server, then there is the need to check the authenticity of the public key (_i.e._, real or fake). In [56,93] the authors did not consider public key authentication. Since healthcare applications deal with sensitive patient data, in future, researchers should be mindful of the authenticity of public keys. 2. Symmetric key cryptography: Although, symmetric key cryptography is easy to implement and superior to asymmetric key cryptography (_i.e._, public and private key) in terms of time complexity and energy cost, the demerit of symmetric key cryptography for medical sensor networks is that, it is not perfect for key distribution. Therefore, efficient and flexible key distribution protocols need to be designed for healthcare application using WMSNs. 3. Secure routing: Generally, most current secure routing protocols are designed for stationary networks, whereas medical sensor networks provides mobility to their patients and demands for mobility-supported multi-hop routing protocols [7,54,56,57]. Recalling the project described in [7], CodeBlue uses TinyADMR routing which is susceptible to routing loop attacks, grey-hole attacks, and Sybil attacks [49], so secure routing needs special attention for future wireless healthcare research. 4. Security and quality-of-service (QoS): Most healthcare studies consider security as an individual topic, whereas QoS [101-104] with security could become a tradeoff for healthcare applications using medical sensor networks, so security and QoS need to be evaluated jointly.
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 7 Conclusions  
In this article, a detailed review of the flexible antennas for the sub-6 GHz 5G band is discussed, highlighting the choice of the conductive as well as the substrate materials needed for the fabrication of flexible antennas. The choice of the conductive and substrate materials for the flexible antenna fabrication has to be carried out based on the intended applications, capability to integrate over any rigid and non-rigid surfaces, low cost, and ease of fabrication process. The parameters needed for the design and analysis of the flexible antennas are discussed with an emphasis on several antenna parameters such as gain, radiation pattern, bandwidth, efficiency, etc. A bending analysis and a SAR analysis of the antenna and its relevance are discussed in detail concerning many single-elements, array-based, as well as MIMO flexible antennas for sub-6 GHz applications. The specific absorption rate has to be within the permissible level when the antenna is utilized for a wireless body area network (WBAN). The design challenges of the flexible antennas and the best possible solutions are also discussed in this article.  
Conceptualization: [PERSON], [PERSON], P.K. and T.A.; methodology: [PERSON], S.V., S.P., P.K. and T.A.; data citation: [PERSON], S.V., P.K. and T.A.; writing--[PERSON] and T.A.; supervision: [PERSON], S.P., P.K. and T.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
## 2 Related Works  
Location information is fundamental to IoT applications such as infrastructure asset management [8] and infrastructure health [9; 10]. LBS has become increasingly important with the development of IoT. In the face of large and variable business requirements, the design of an ecosystem and easy deployment location method for LBS is challenging [11]. Compared with various localization technologies, such as Bluetooth, computer vision and ultrasonic and infrared, the features of the RFID-based method are non-line of sight (NLOS), low-cost and automatic tracking.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 5 V2V Aids toward Sustainable Transportation Systems
### Cloud-Integrated V2V Network Contributions in ITS  
ITSs are the next game changer because V2V cloud computing presents multiple advantages along the way, shifting the ecosystem of vehicle communication and performance [55]. Employing cloud computing, vehicles can transfer significant amounts of computationally intensive data to remote machines; subsequently, they can obtain access to intensified computational resources and execute massive amounts of data. With this ability, V2V systems can be scaled and become more flexible, allowing complex algorithms and applications that are impossible to implement locally on individual vehicles to run [73]. On the other hand, cloud computing makes data sharing between vehicles and their collaboration possible, which in turn makes the description of traffic and occurrences on the road so precise and current. Furthermore, cloud-based V2V frameworks benefit from these technologies, as they can redesign route maps, reduce traffic, and increase overall traffic management efficiency. In addition, cloud computing underlies the centralized monitoring and regulation of V2V networks, which effectively promotes security and allows for rapid response to emerging dangers or mild conditions [74]. Therefore, V2V cloud computing is likely to redesign the way transport functions in smart cities and give vehicles access to powerful computing resources, which in turn aim to improve safety, efficiency, and sustainability in ITSs.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
### Key Terms Explanation
#### 1.1.2 Cryptographic Hash Mechanism  
A mathematical operation called a cryptographic hash function converts arbitrary sized input data into a fixed-size output presented as a hash value or a message digest. The hash value is a singular presentation of the input data; hence, any modification to the input data will result in a non-identical hash value. Cryptographic hash functions are frequently utilized for many tasks in cryptography, such as digital signatures, message authentication codes (MACs), and password storage.
## 2 System Model
### GNSS Receiver Measurement Model  
The measurement equation for the \(m\)-th satellite is given by  
\[y^{m}_{\text{GNSS}}[t_{k}]=\tau_{\text{GNSS}}[t_{k}]=h^{m}_{\text{GNSS}}(\text{ s}[t_{k}])+\omega^{m}_{\text{GNSS}}[t_{k}] \tag{1}\]  
\[h^{m}_{\text{GNSS}}(\text{ s}[t_{k}])=\frac{d^{m}_{\text{GNSS}}[t_{k}]}{c}+ \delta[t_{k}] \tag{2}\]  
where \(m\) is the satellite number, \(t_{k}\) denotes the measurement time of the receiver, \(\tau_{\text{GNSS}}[t_{k}]\) denotes the time delay measurement result, and \(h^{m}_{\text{GNSS}}(s[t_{k}])\) indicates the measurement function that converts the state quantity into the observation quantity, where \(\omega[t_{k}]\) models the uncertainty of the measurement, which is modeled as a zero-mean Gaussian random vector with a covariance of \(R_{\text{GNSS}}\). \(R_{\text{GNSS}}\) is composed of different error sources, including satellite and receiver clock errors, satellite orbit errors, and errors such as ionospheric and tropospheric delays, where \(c\) represents the speed of light, \(d^{m}_{\text{GNSS}}[t_{k}]\) indicates the real distance between the terminal and the \(m\)-th satellite, and \(\delta[t_{k}]\) denotes the terminal's clock bias in relation to the GNSS satellites. The GNSS measurement results are not available if the GNSS receiver loses its signal under severe multipath effects or when the carrier-to-noise ratio is extremely low. Therefore, in harsh multipath environments such as navigating in a canyon deep within a city, the multipath effect-suppression algorithm must be considered. This article does not discuss the related research on multipath signal modeling or related methods, for which one can refer to [27; 28].
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 4 Security Analysis and Performance Evaluation
### Security Analysis
#### 4.1.3 Tamper-Proofing and Data Unforgeability  
In the vehicle network layer, the malicious vehicle cannot pretend to become a legitimate vehicle to corrupt the data because it cannot forge a digital signature that is encrypted by the vehicle's private key. In the blockchain edge node, the consortium blockchain provides honest RSUs selected to execute the consensus phase. Therefore, the small numbers of compromised RSUs will not affect the result of the consensus mechanism. Moreover, the PBFT consensus mechanism still works if there are 33% of malicious RSUs. Further, a few malicious RSUs cannot tamper with the stored block due to the chain structure that requires rebuilding the whole chain in the blockchain network layer.
# Blockchain Interoperability for Future Telecoms
## 2 Study Background
### Blockchain Interoperability Use Cases in Telecommunications
#### 2.2.1 Identity Management  
Due to stringent regulatory and privacy requirements, telecom operators must ensure user privacy and security. Therefore, telecom companies are actively exploring blockchain-based identity management solutions. These solutions are anticipated to enhance security and privacy while enabling secure interoperability among various telecom service providers. According to [23], cloud-based cognitive networks require more comprehensive identity management when users seek access to multiple operators. Implementing dynamic spectrum allocation in next-generation 5G and 6G networks requires secure identity management to enable seamless switching between different spectra. Using secure, immutable blockchain technologies enables telecom operators to empower users to maintain control over their identity data and streamline authentication processes across various telecom networks. This authentication process requires secure data exchange between operators. Given regulatory restrictions, whether two or more independent telecom operators can join a single blockchain network and share the same infrastructure is a valid point of discussion. Consequently, implementing one identity solution for multiple networks and consolidating operator user bases into a single platform presents significant regulatory challenges. Due to strict regulatory requirements and data privacy rules, such as the General Data Protection Regulation (GDPR) in the European Union, telecom operators are forced to implement isolated blockchain networks. Secure data exchange layers (cross-chain communication) that provide interoperability with other networks are potential candidates to overcome this challenge. Such an architectural approach can be a standard deployment model for telecom use cases. Once such a secure architecture is deployed, decentralized decision-making can be enabled for many telecommunications use cases, satisfying regulatory hurdles.