esa-sceva/satcom-chunk-collection huggingface dataset:
# On End-to-End Intelligent Automation of 6G Networks
## 4 6G End-to-End Network Automation Challenges
### Heterogeneous Service Orchestration  
The second challenge facing 6G network automation and orchestration is the heterogeneity of service definitions. This stems from the notion of the convergence of the physical, human, and digital worlds that 6G aims to achieve. As a result, new and heterogeneous services with varying performance targets emerge [92; 93]. One aspect of these services is the multitude of underlying access technologies that will support them due to the multi-connectivity requirement in 6G networks. For example, 6G networks allow the combination and integration of fixed access with wireless access technologies. Therefore, 6G orchestration frameworks need to be able to optimally select the suitable interface in the integrated access network in a dynamic manner while accounting for each technology's infrastructure availability, capability, and topology. This is particularly important given the fact that current interface selection frameworks (through network slice templates and service descriptors) are static and infrastructure-agnostic. Therefore, it is desired that the instantiation of these new services is customizable by allowing the automatic adaptation to the network capabilities, resource availability, and the adopted technologies at the corresponding nodes. This is critical to ensure that the service deployment process is optimized and enhances the user's Quality of Experience (QoE) across multiple and possibly changing network infrastructures.  
In addition to the above, 6G orchestration frameworks should also be able to support a richer and more versatile set of software-based components. This means that such frameworks need to be able to handle combinations of service definitions that use varying software modules, including virtual machines, containers, microservices, and serverless functions, simultaneously. Moreover, the orchestration frameworks need to handle different service decomposition patterns and functional splits. This is important in order to be able to define and offer the different required fine-grained services within the network continuum. Furthermore, the extreme-edge devices need to be considered as part of the 6G network slicing mechanisms to ensure that E2E orchestration is offered. This means thatthe 6G orchestration frameworks should be able to aggregate the necessary resources (APIs, component definition primitives, service definition primitives, etc.) from the different stakeholders while still maintaining proper isolation between them.
# Development of a Metabolic Syndrome Prediction Model Using HOMA-IR and Multivariate Factors
## 2 Materials and Methods
### Variables
#### 2.2.3 Building a Predictive Model Using a Logistic Regression Analysis  
Multiple logistic regression analysis was used to predict the probability of MetS occurrence. HOMA-IR levels, gender, age, and DM status were used as independent variables. The regression coefficient of each variable was estimated using a logistic regression model, defined by the following logit function:  
\[\text{logit(p)}=\beta_{0}+\beta_{1}\times\text{HOMA\_IR}+\beta_{2}\times\text {Sex}+\beta_{3}\times\text{Age}+\beta_{4}\times\text{Diabetes Mellitus}\]  
The coefficients were as follows:  
\(\beta_{0}=-4.067\)(intercept), \(\beta_{1}=0.448\)(HOMA-IR, \(\beta_{2}=0.391\)(sex: men = 1, women = 0), \(\beta_{3}=0.033\)(age),  
\(\beta_{4}=0.649\)(Diabetes Mellitus = 1, non-Diabetes Mellitus = 0)  
The predicted probability p was calculated using the logistic function in the following form.  
\[\text{p}=\frac{1}{1+\text{e}^{-\log\text{it(p)}}}\]
# RCLED Optimization and Nonlinearity Compensation in a Polymer Optical Fiber DMT System
## 4 Nonlinearity Mitigation
### Nonlinear Compensation
Divide \(\mathbf{X}_{i}\) into equally \(N_{x}\) slots, the \(i^{\text{th}}\) sub-portion pilot can be expressed as \(\mathbf{X}_{i}=[x_{i,1},x_{i,2},\cdots,x_{i,N_{x}}]^{T}(i=1,2,\cdots,M_{L})\). Step2.for\(j=1\) to \(M_{L}\)do \(\hat{w}=\eta_{j}\); for\(i=1\) to \(N_{x}\)do \(\hat{x}_{new}=\hat{w}\cdot x_{j,i}\); \(\hat{P}_{\text{new}}=g\left(\hat{x}_{\text{new}}\right)\); \(P_{\text{linear}}=L\left(x_{j,i}\right)\); \(\hat{\varepsilon}=P_{\text{linear}}-\hat{P}_{\text{new}}\); \(\hat{w}=\hat{w}+\mu_{L}\cdot x_{j,i}\cdot\hat{\varepsilon}\); endfor \(\varepsilon\left(j\right)=\hat{\varepsilon}\); \(w_{j}=\hat{w}\); endfor Step3.\(\eta=\mathbf{W}\). Step4.if\(\sqrt{\frac{\Sigma\left|\varepsilon\right|^{2}}{M_{L}}}>\Psi\),then \(P_{\text{Cnt}}=P_{\text{Cnt}}+1\); if\(P_{\text{Cnt}}>P_{\text{Num}}\),then \(N_{x}=\kappa\cdot N_{x}\); \(\mu_{L}=\lambda\cdot\mu_{L}\);  go to Step1. endif  go to Step2. endif Step5.if\(P_{\text{Cnt}}<P_{\text{Num}}\) and \(\sqrt{\frac{\Sigma\left|\varepsilon\right|^{2}}{M_{L}}}<\Psi\)then  break. else\(P_{\text{Cnt}}=P_{\text{Cnt}}+1\), go to Step2. endif Output: The gain value vector \(\eta\), the overheads of pilot signal \(P_{\text{Cnt}}\), the RMSE \(\sqrt{\frac{\Sigma\left|\varepsilon\right|^{2}}{M_{L}}}\). ```  
**Algorithm 1**An adaptive iterative algorithm to obtain the gain values \(\eta\). In essence, Steps 1 to 5 iteratively find the gain vector \(\eta\). Additionally, in Step 4, the \(N_{x}\) and \(\mu_{L}\) are also adaptively adjusted by \(\kappa\) and \(\lambda\) to fulfill the minimum symbol overheads requirements. According to the accuracy demands of LUT prediction and the training expenses, the favorable \(\Psi\) and \(P_{\text{Num}}\) are empirically selected to guarantee algorithm convergence. Moreover, the system computational complexity \(\mathcal{O}\left(N_{x}\cdot M_{L}\right)\) are directly related to the slot number \(N_{x}\) and the LUT size \(M_{L}\). Therefore, the accuracy of LUT pre-distorter must be balanced with the computational complexity. Different from the conventional methods, where the gain value is updated only once in each iteration, the proposed algorithm takes at least \(N_{x}\) times updates in one iterative cycle and fewer pilot signals are consumed. Additionally, both the bit redundant and overheads are reduced. Moreover, the \(N_{x}\) and \(\mu_{L}\) can be adaptively adjusted in the training procedure by the factor \(\kappa\) and \(\lambda\), respectively.
# Fire Resistance of Ultra-High-Strength Steel Columns Using Different Heating Rates
## 5 Comparison and Discussion of the Results  
In this section, a comparison and discussion about the results of the simulations of CHS and hybrid (HB) columns are presented. It was observed that, for both cases, load level was the parameter that showed the most influence on the critical temperature. In the model, without considering the effect of creep, the heating rate exhibited a significant influence on the critical time but a weaker, yet still important, influence on the critical temperature. This was particularly notable for long columns in both cases, as seen in Figures 13 and 18.  
For both cases, long UHSS columns also exhibited a greater reduction in fire resistance for higher load levels (greater than 60%). The effect of thickness was almost negligible, especially for the CHS columns.  
Figure 21: The effect of the hybrid column length.  
Figure 20: Hybrid columns at ambient temperature with (**a**) length of 3 m (**b**) length of 6 m.  
Since both column elements use the same material for the tubular element, one can directly compare their thermal and mechanical resistance. This can be observed in Figure 22, which illustrates a comparison between the critical temperature equations, where Figure 22a-c refers to columns with length of 0.78 m, 3 m, and 6 m, respectively.  
It is notable that, in most cases, hybrid columns exhibit critical temperature values below those of the CHS columns. However, it is crucial to emphasise that the hybrid columns can endure a significantly higher applied load and experience less resistance loss with increasing length. Additionally, when analysing the results from Figures 16 and 20, it can be observed that hybrid columns (HB) showed less loss of load-bearing capacity in ambient temperature with increasing length compared to CHS columns. While for 3 m and 6 m lengths, CHS columns exhibited 37% and 11% reduction in their capacity obtained at 0.78 m length, respectively, hybrid columns showed 98% reduction at 3 m and 58% at 6 m.  
Therefore, while hybrid columns generally display a slightly lower critical temperature than CHS columns, they can withstand a much higher load for the same load level. This difference underscores the robustness and potential advantages of using hybrid columns in structural applications where both thermal and mechanical performance are critical.  
Finally, to list the design equations for CHS and hybrid columns, one can refer to Table 17 for CHS columns and Table 18 for hybrid columns.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Length [m]** & **Thickness [mm]** & **Design Equation** \\ \hline
0.78 & 3.1 & \(-0.0024U^{3}+0.3547U^{2}-20.072U+915.51\) \\
0.78 & 6 & \(-0.00247U^{3}+0.404U^{2}-22.332U+944.7\) \\ \hline \hline \end{tabular}
\end{table}
Table 17: Design equation for critical temperature for CHS columns.  
Figure 22: Comparison of equations for critical temperature.
## 2 Materials and Methods  
### Materials  
Keltan 6160D (structure depicted in Figure 1a) was used as the EPDM matrix and was received from ARLANXEO (The Hague, The Netherlands). The rubber was peroxide cross-linked with di(_tert_-butylperoxyisopropyl) benzene (Perkadox 14-40 B-PD; PO) from Nouryon (Amsterdam, The Netherlands). Corax N660 from Orion Engineered Carbons (Senningerberg, Luxembourg) was applied as reinforcing carbon black filler. In addition, the tall-oil modified phenolic novolac resin SP-6701 (PF) was purchased from SI Group (Schenedady, NY, USA), and the corresponding hardener hexamethylenetramine (Rhenogran Hexa-80; HMT) was obtained from RheinChemie Additives (Cologne, Germany). The chemical structure of the PF resin and hardener are shown in Figure 1b,c. The studied zinc-based coagents Sartomer SR 633 (ZDA) (Figure 1f) and Dymalink 634 (ZDMA) (Figure 1e) were purchased from Cray Valley (Exton, PA, USA), whereas Rheonfit TRIM (Figure 1d) was supplied by RheinChemie (Cologne, Germany). Other additives included the process oil PLI P 460 from Petronas (Kuala Lumpur, Malaysia) and the antioxidants Vulkanox HS and Vulkanox ZMB2/C5, both from Lanzess (Cologne, Germany). The toluene for equilibrium swelling and gel content determination was purchased from Sigma Aldrich (St. Louis, MO, USA). All chemicals have been used without further modification.
# Kernel Learning by Spectral Representation and Gaussian Mixtures
## 2 The Concept of Kernels  
The main idea of using kernel methods is to obtain the distance between samples on higher dimensional. Thus, avoid mapping the samples to higher dimensional spaces and using the inner product for such process. In other words, let \(\mathcal{X}\subseteq\mathbb{R}^{D}\) be the input set, where \(D\in\mathbb{N}\), let \(\mathcal{K}\) be a feature space, and suppose the feature mapping function is defined as \(\mathbf{\varphi}:\mathcal{X}\rightarrow\mathcal{K}\). Hence, the kernel function, \(\kappa:\mathcal{X}\times\mathcal{X}\rightarrow\mathbb{R}\), has the following property:  
\[\kappa(\mathbf{x},\mathbf{x}^{\prime})\equiv\langle\mathbf{\varphi}(\mathbf{x}),\mathbf{\varphi}( \mathbf{x}^{\prime})\rangle,\]  
where \(\mathbf{x},\mathbf{x}^{\prime}\in\mathcal{X}\). Thus, \(\mathbf{\varphi}\) and the feature space can be implicitly defined. Now, let \(\left\{\mathbf{x}_{i}\right\}_{i=1}^{N}\) be the set of samples, \(\kappa:\mathcal{X}\times\mathcal{X}\rightarrow\mathbb{R}\) be a valid kernel, and \(\left\langle\cdot,\cdot\right\rangle\) be a well defined inner product. Then, the elements at the Gram Matrix, \(K\in\mathbb{R}^{N\times N}\), are computed using the \(\kappa\) mapping, \(K_{ij}:=\kappa(\mathbf{x}_{i},\mathbf{x}_{j})\). Given this definition, [PERSON] [20] makes an in-depth study of the class of kernel from a statistics perspective, i.e., the kernel functions as a co-variance function. He pointed out that kernels have a spectral representation which can be used to represent their Gram matrix. Based on this representation, the proposed algorithm learns the Gram matrix by using a Gibbs sampler to obtain the structure of such matrix.
## 2 Sampled Apodized Grating Waveguide Structure and Design
### Optical Modes Analysis
\right] \tag{8}\] \[y_{S} = \frac{4(n_{P}^{2}+n_{S}^{2}n_{P}^{2})}{n_{P}^{2}+2n_{W}^{2}n_{P}^{ 2}+2n_{W}^{2}n_{W}^{2}+3n_{W}^{2}n_{S}^{2}}\] \[y_{P} = \frac{2(2n_{P}^{2}+n_{W}^{2}+n_{P}^{2}n_{P}^{2})}{n_{P}^{2}+2n_{ W}^{2}n_{P}^{2}+2n_{W}^{2}n_{S}^{2}}\] \[y_{N} = \frac{4(n_{P}^{2}+n_{S}^{2}n_{P}^{2})}{n_{P}^{2}+2n_{N}^{2}n_{P} ^{2}+2n_{S}^{2}n_{S}^{2}+3n_{N}^{2}n_{S}^{2}}\]  
Finally, this Eigen Equation can be solved by Matlab Software. In the calculation, only the maximum eigenvalues are considered. They and their eigenvectors correspond to the modes in the waveguide. The matrix eigenvalue is the square of the mode propagation constant, and the corresponding eigenvectors correspond to the intensity distribution of the mode field. Figure 3a,b shows the relationships between \(N_{\textit{clad}}\) and \(b\) as well as \(N_{\textit{conv}}\) and \(b\) for TE and TM modes at 1550 nm wavelength, respectively. \(N_{\textit{clad}}\) and \(N_{\textit{core}}\) are defined as the effective refractive indices of the region with and without Al strips, respectively. It can be found that when \(b\) is chosen at 2.5 \(\upmu\)m, there are only TE\({}_{0}\) and TM\({}_{0}\) modes in the core waveguide along \(Y\) axis. It can also be found the value of \(N_{\textit{core}}\) for TM\({}_{0}\) mode in the core and \(N_{\textit{clad}}\) for TM\({}_{1}\) mode in the cladding are 1.480 and 1.457, respectively. The value of \(N_{\textit{core}}\) is larger than that of \(N_{\textit{clad}}\). Further, there is TM\({}_{1}\) mode in the cladding belongs to the surface plasmon polariton (SPP) mode in the Al cladding structure. The effective refractive index  
of TM\({}_{1}\) mode in the cladding is larger than that of TM\({}_{0}\) mode in the core waveguide, so there is no mode coupling between them. The definition of \(W\) is located in the high refractive index region of the grating, which is consistent with the general definition of grating. Figure 3c shows the effective refractive index (\(n_{\mathit{eff}}\)) of the waveguide related to \(c\) when \(b\) is 2.5 \(\upmu\)m. The value of \(c\) is designed as 5 \(\upmu\)m to enable TM\({}_{0}\) and TM\({}_{1}\) modes transmitting in the region between metal strips. The effective indices of TM\({}_{0}\) and TM\({}_{1}\) optical modes are 1.47243 and 1.46371. The value difference between TM\({}_{0}\) and TE\({}_{0}\), TM\({}_{1}\) and TE\({}_{1}\) are smaller than \(1.5\times 10^{-3}\), respectively. The value of \(c\) and \(b\) are designed as 5 \(\upmu\)m and 2.5 \(\upmu\)m, respectively. Figure 3d illustrates the cross-section optical field of this structure using the COMSOL software. The optical power in the form of TM\({}_{1}\) mode can transport in the waveguide.
# Boundary-Aware Hashing for Hamming Space Retrieval
## 2 Related Work
### Hamming Space Retrieval  
Hamming space retrieval is an efficient method for fast search of approximate nearest neighbors in Hamming space. Cross-modal Hamming hashing (CMHH) [27] proposed a pairwise focal loss function based on exponential distribution to solve poor cross-modal hashing methods in hash table lookups. DCH [7] proposed a pairwise cross-entropy loss based on the Cauchy distribution and introduced an up-weighting similar training pair. However, it still returns a large number of zero images for longer code lengths. MMHH [9] proposed a loss based on the maximum marginal \(t\)-distribution to explicitly characterize the Hamming ball, making the model focused on data outside the Hamming ball. Nevertheless, it ignores the distribution of similar and dissimilar data inside the Hamming ball. Unlike them, this work focuses on similar/ dissimilar data outside/inside the boundary through boundary-aware penalization.
# Blockchain Technology for Intelligent Environments
## 5 Blockchains for IoT Systems
### Data Delivery and Storage
#### 5.2.2 Flowchain  
_Flowchain_[71] is a proposal for supporting peer-to-peer IoT networks and real-time transactions over a blockchain system. In Flowchain, each node can mine blocks, referred to as _virtual blocks_, in its own branch. Some of these blocks are valid, while others are invalid. One approach is to characterize the most recently used block of a branch as the only valid block of the specific branch. Only valid blocks contain IoT transaction data. Valid blocks can be merged using a branch merge algorithm to form a single blockchain. Such an approach for mining is used to support real-time data transactions, by avoiding the mining delay that exists in PoW blockchain systems, such as Bitcoin. Characterization of valid blocks considers a PoS mechanism. Chunks of IoT data, along with their hashes, are exchanged over the peer-to-peer network and are stored in the distributed data store between IoT devices. The forwarding of data chunks is based on the Chord [72] protocol.  
Flowchain uses W3C's _Web of Things (WoT)_ ontology as a standards-based model to represent a physical device as an application server, which can run on a high-performance device or a microcontroller. Moreover, a JavaScript runtime environment is used for high-performance devices, while a more lightweight environment is used for resource-constrained devices. Finally, Flowchain supports a gateway mechanism for interoperability between different ledgers within the same IoT peer-to-peer network.
## 6 Sustainability and Economic Viability of Waste Cooking Oil Valorization  
The increasing global emphasis on sustainability and environmental responsibility has spurred a growing demand for bio-based alternatives to conventional petroleum-derived products. As a result, the market prices of environmentally friendly materials such as biolubricants, biosurfactants, bioasphalts, and 3D printing resins have risen significantly [178]. A key factor contributing to this price surge is the high cost of feedstocks, which can constitute more than 50% of the total production expenses. These feedstocks are predominantly sourced from virgin plant-based oils, which require resource- and energy-intensive agricultural operations, including land preparation, planting, irrigation, fertilization, harvesting, and oil extraction. While these activities elevate production costs, they also exacerbate environmental degradation through greenhouse gas (GHG) emissions and increased global warming potential (GWP) [9].  
In contrast, waste cooking oil offers a sustainable and cost-effective alternative feedstock. Typically considered a low-value or even waste material, WCO is readily available from residential, commercial, and industrial sources with minimal or no purchase cost. Its valorization into high-value bioproducts, such as insulating liquids for transformers, can significantly reduce dependency on virgin feedstocks, enhance the economic feasibility of bio-based production chains, and align with circular economy principles. By diverting WCO from improper disposal channels (e.g., drainage systems and landfills), this approach also mitigates environmental hazards such as waterway pollution, soil contamination, and sewage blockages.  
Furthermore, the conversion of WCO into value-added products reduces the carbon footprint associated with traditional fossil-derived materials. Reusing WCO prevents emissions from both the waste management process and virgin oil production, contributing to lower net GHG emissions. The integration of WCO into industrial applications represents a practical and scalable approach to supporting green chemistry principles, promotingresource efficiency, and advancing the United Nations Sustainable Development Goals (SDGs), particularly those related to responsible consumption, climate action, and clean industry [179].
# Immuno-PET for Glioma Imaging: An Update
## 4 Targets for Immuno-PET
### EGFR  
The overexpression of the EGFR in many malignancies makes it a suitable molecular target for diagnosing and treating different malignancies [36]. Scandium-44/^44 Sc (with a half-life of 3.9 h) is a relatively new radioisotope with potential applications in clinical PET. [PERSON] et al. demonstrated a ground-breaking achievement for in vivo PET imaging [37]: they generated the Fab fragment of Cetuximab, an mAb known for its strong binding to the EGFR, and radiolabeled it with ^44 Sc at room temperature. To achieve this, the Fab fragment was conjugated with N-[(R)-2-amino-3-(para-isothiocyanato-phenyl)propyl]. CHX-A^#-DTPA stands for -trans-(S,S)-cyclohexane-1,2-diamine-N,N,N^#,N^#-pentaacetic acid. SDS-PAGE and mass spectrometry were used to thoroughly confirm the high quality and purity of cetuximab-Fab. After labeling with ^44 Sc, the potential of this bioconjugate for the PET imaging of EGFR expression was assessed in a human glioblastoma (U87 MG) tumor-bearing mice model. The PET imaging results show that [^44 Sc]-CHX-A^#-DTPA-Cetuximab-Fab was rapidly absorbed in the tumor, with a peak uptake of around 12% ID/g observed 4 h after injection. Importantly, this uptake showed an outstanding tumor-to-background ratio, implying the possibility of same-day PET imaging in future clinical applications. In addition, immunofluorescence labeling was used to correlate tracer uptake in tumor and normal tissues with EGFR expression. This successful immuno-PET imaging strategy, utilizing [^44 Sc]-CHX-A^#-DTPA-Cetuximab-Fab, has the potential to advance clinical practices by aiding in patient selection for EGFR-targeted therapy, and by facilitating the monitoring of the effectiveness of anti-EGFR treatments.
## 3 Results and Discussion
### Prediction of HRR
#### 3.3.1 Determination of \(H_{c}\)  
Provided the combustion efficiency is constant during the whole combustion process for a given fuel, the HRR should be linearly related to the MLR [27; 41]. The HRRs versus MLRs for the three NC samples are presented in Figure 8, where their proportional relation can be observed, and the fitted slopes represent the corresponding actual heats of combustion of the three fuels, i.e., 16.57, 15.20, 13.50 kJ/g for NC-I, NC-E and NC-D, respectively. As claimed in [22], for pure NC from cotton linter with nitrogen content of 11.49-12.14%, the heat of combustion falls in between 10.67 kJ/g and 10.27 kJ/g; for pure NC from wood pulp with nitrogen content of 11.74-12.12%, the value is between 10.50-10.27 kJ/g. In any case, the heats of combustion of pure NC with nitrogen content of 11.86-12% in current tests should be lower than the fitted values in Figure 8. One reasonable explanation for this might be the addition of humectant or plasticizer. The heats of combustion for isopropanol, ethanol and DBP are 33.08 kJ/g, 29.68 kJ/g, and 30.99 kJ/g [30], respectively, which will inevitably upgrade the heat of combustion of NC samples.  
Figure 8: Determination of heat of combustion for: (**a**) NC-I; (**b**) NC-E; and (**c**) NC-D.