esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 4 Implementation and Validation
### Performance Evaluation  
To gain a better understanding of the performance aspects of our solution, we have conducted several practical experiments. In particular, we deployed a network service consisting of two communication endpoints (see Figure 4). Each of them was connected to an instance of an L25 VNF, which was in turn attached to an edge router. Both edge routers were provided with a back-to-back connection. The communication endpoints and the edge routers were prototyped as Linux virtual machines (Ubuntu 16.04.5 LTS). Regarding the L25 VNFs, we used the implementation described in Section 4.1. The network service has been deployed as a set of interconnected virtual machines on a single server computer with sufficient resources, to preserve the results of our performance evaluation from external interference. After the deployment, the L25 VNFs were configured to connect both communication endpoints to the same VLAN. This way, both endpoints were interconnected at layer-2 through the L25 platform, being their data traffic transmitted within an IPsec-protected VXLAN tunnel between the L25 VNFs.  
Figure 3: Implementation of the L25 VNF.
## 2 Materials and Methods
### Hydration and Thermodynamic Considerations
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Mix No.**} & **Active/Inert Glass Phase** \\ \hline \multirow{3}{*}{2 (50\% slag)} & (SiO2)\({}_{6.0653}\)(Al2O3)\({}_{0.9651}\)(Fe2O3)\({}_{0.0494}\)(CaO)\({}_{7.9955}\)(MgO)\({}_{1.9639}\)(SO3)\({}_{0.0102}\)(K2O)\({}_{0.0478}\)(Na2O)\({}_{0.033}\) = + 6.0653 SiO2 + \\  & 1.9302 AlO\({}_{2}\)\({}^{-}\) + 0.0988 Fe0\({}_{2}\)\({}^{-}\) + 7.8955 Ca\({}^{+2}\) + 1.9639 Mg\({}^{+2}\) + 0.0102 SO4\({}^{-2}\) + 0.0956 K\({}^{+}\) + 0.066 Na\({}^{+}\) - 17.831 H\({}^{+}\) \\  & + 8.9155 H2O; log\(\_\)k = \(\pm\)999, Vm = 401.6064 g/cm\({}^{3}\) \\ \hline \multirow{3}{*}{3 (34\% slag)} & (SiO2)\({}_{6.0702}\)(Al2O3)\({}_{0.9659}\)(Fe2O3)\({}_{0.0494}\)(CaO)\({}_{7.992}\)(MgO)\({}_{1.9655}\)(K2O)\({}_{0.0479}\)(Na2O)\({}_{0.0331}\) = + 6.0702 SiO2 + 1.9318 \\  & AlO\({}_{2}\)\({}^{-}\) + 0.0988 FeO2\({}^{-}\) + 7.902 Ca\({}^{+2}\) + 1.9655 Mg\({}^{+2}\) + 0.0958 K\({}^{+}\) + 0.0662 Na\({}^{+}\) - 17.8664 H\({}^{+}\) + 8.9332 H\({}_{2}\)O; log\(\_\)k \\  & = \(\pm\)999, Vm = 401.6064 g/cm\({}^{3}\) \\ \hline \multirow{3}{*}{3 (6\% RCGP)} & (SiO2)\({}_{12.2273}\)(Al2O3)\({}_{0.1307}\)(Fe2O3)\({}_{0.0227}\)(CaO)\({}_{2.8507}\)(MgO)\({}_{0.4286}\)(K2O)\({}_{0.0566}\)(Na2O)\({}_{1.0634}\) = + 12.2273 SiO2 + 0.2614 \\  & AlO\({}_{2}\)\({}^{-}\) + 0.0454 FeO2\({}^{-}\) + 2.8507 Ca\({}^{+2}\) + 0.4286 Mg\({}^{+2}\) + 0.1132 K\({}^{+}\) + 2.1268 Na\({}^{+}\) - 8.4918 H\({}^{+}\) + 4.2459 H\({}_{2}\)O; log\(\_\)k \\  & = \(\pm\)999, Vm = 401.6064 g/cm\({}^{3}\) \\ \hline \multirow{3}{*}{3 (51\% slag)} & (SiO2)\({}_{6.0702}\)(Al2O3)\({}_{0.9659}\)(Fe2O3)\({}_{0.0494}\)(CaO)\({}_{7.992}\)(MgO)\({}_{1.9655}\)(K2O)\({}_{0.0479}\)(Na2O)\({}_{0.0331}\) = + 6.0702 SiO2 + 1.9318 \\  & AlO\({}_{2}\)\({}^{-}\) + 0.0988 FeO2\({}^{-}\) + 7.902 Ca\({}^{+2}\) + 1.9655 Mg\({}^{+2}\) + 0.0958 K\({}^{+}\) + 0.0662 Na\({}^{+}\) - 17.8664 H\({}^{+}\) + 8.9332 H\({}_{2}\)O; log\(\_\)k \\ \cline{1-1}  & \(\pm\)999, Vm = 401.6064 g/cm\({}^{3}\) \\ \hline \multirow{3}{*}{3 (9\% RCGP)} & (SiO2)\({}_{12.2273}\)(Al2O3)\({}_{0.1307}\)(Fe2O3)\({}_{0.0227}\)(CaO)\({}_{2.8507}\)(MgO)\({}_{0.4286}\)(K2O)\({}_{0.0566}\)(Na2O)\({}_{1.0634}\) = + 12.2273 SiO2 + 0.2614 \\ \cline{1-1}  & AlO\({}_{2}\)\({}^{-}\) + 0.0454 FeO2\({}^{-}\) + 2.8507 Ca\({}^{+2}\) + 0.4286 Mg\({}^{+2}\) + 0.1132 K\({}^{+}\) + 2.1268 Na\({}^{+}\) - 8.4918 H\({}^{+}\) + 4.2459 H\({}_{2}\)O; log\(\_\)k \\ \cline{1-1}  & = \(\pm\)999, Vm = 401.6064 g/cm\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Slag and RCGP glass phases equations for the reactive and unreactive phase. Figure 3: Alkali distribution coefficients, Kd, used.
## 3 Distributed Target Tracking Systems
### Phase III: Target Positioning
#### 3.3.1 Geometrical Positioning with Particle Filtering  
The Bayesian particle filter [2] method may be preferred for object positioning because it is robust to noisy measurements, it allows for flexible information transmission, and it can be robust to lost or lossy data. Particle filter is an algorithm of estimation used to estimate the unknown target position from state equations. The objective is to find feasible position to make the error of state vector \(x\) minimum. The state vector is represented as a set of random samples updated and propagated with the algorithm. One of the main advantages of this approach is that the tasking sensor carries along a complete distribution of estimates of the target position. Therefore, the distribution is inherently a measure of the accuracy of the positioning system. If a given task requires certain accuracy, it is possible to determine if that level of accuracy is currently available. Therefore, our approaches may be computationally affordable by sensor nodes.  
The idea in [24], using known sensor positions and the bounding-box algorithm to extrapolate the unknown target position, inspires us to choose a proper prior density for generating initial samples. Figure 3 shows an example how the measurement information (e.g., distance information) can be used to obtain the \(x\) and \(y\) coordinate bounds of the unknown target. Therefore, the unknown target combines its bounds on the coordinates to form a bounding box, which provides a good set of initial samples for the particle filtering. In this work, each sub-cluster member uses Bayesian particle filter to estimate the unknown target position and performs target positioning with angle-of-arrival (AOA) information or hybrid time-of-arrival/angle-of-arrival (TOA/AOA) information. The particle filter method is shown in Table 1.  
\begin{table}
\begin{tabular}{l} \hline
1. _Initialization_: Generate a set of random samples \(x_{k}(i)\), \\ \(i=1,2,\ldots,N_{PF}\) from the prior density at time \(k=0\). \\  Each sample of the state vector is a “particle”. \\ \(2.\)_Prediction_: Each random sample is passed through the state \\  equation to obtain samples from the prior density at time \\ \(k+1\). Thus \\ \(\hat{x}_{k+1}(i)=\Phi x_{k}(i)+\Gamma\lambda_{k}(i)\) \\ where \(\lambda_{k}(i)\) is a sample drawn from the probability density \\  function of the system noise, \(\Phi\) is related to the mobility model, \\  and \(\Gamma\) is an identity matrix. \\ \hline \end{tabular}
\end{table}
Table 1: The Particle Filtering Methodology.  
Figure 3: An example of obtaining the \(x\) and \(y\) coordinate bounds of the unknown sensor D by the distance and position information of the known sensors A, B, and C.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 5 Overview of WUR Architectures
### Conventional Heterodyne OFDM Receiver  
Figure 12 shows the architecture of a typical modern heterodyne receiver for cellular communication [46]. Due to the coherent nature of the NB-IoT WUS, it can only be demodulated via a coherent quadrature receiver similar to this. A matching network and a band-select bandpass filter (usually off-chip) provide impedance matching and reject out-of-band blockers.  
Figure 11: Block diagram for baseband processing of LP-SS signal.  
Figure 12: Architecture of a conventional heterodyne receiver.  
We identify several power-hungry components as design challenges, which are typically omitted in the ULP receivers in Section 5.2:  
1. Frequency Synthesis: To demodulate coherent signals such as QAM and QPSK, accurate phase information is needed, necessitating quadrature local oscillator (LO) generation for down conversion and a high-performance phase-locked loop (PLL) to minimize the center frequency offset (CFO) and sampling frequency offset (SFO). These blocks often account for half or more of the total power consumption in many receivers.
2. Analog-to-Digital Converter (ADC): Higher-order modulations, such as 64-QAM, require a high-resolution ADC, which is high-power, to digitize the baseband analog signal with sufficient resolution for digital signal processing and demodulation. In addition, according to the Nyquist sampling theorem, a signal with bandwidth B requires a minimum sampling frequency of 2B. For high-data-rate signals with a large bandwidth, a high clock frequency is required for the ADC, increasing the power overhead. It should be noted that for NWUS, which only occupies a 180 kHz bandwidth, the sampling frequency is significantly relaxed.
3. Low-Noise Amplifier (LNA): The LNA is the first stage of the receiver and therefore dominates the noise performance. It must have a good NF to meet stringent sensitivity/coverage requirements. This translates to a high power consumption due to the fundamental power-noise tradeoff. Circuit design techniques can be applied to reduce power consumption, as discussed in Section 5.2.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Bilinear Interpolation  
Bilinear interpolation can produce a smoother interpolation than that achieved using the nearest neighbor method ([PERSON] et al., 2016). Thus, it was applied to transform fields from various grids of the ESMs into the formal 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) grid. In this approach, g (\(n_{1}\), \(n_{2}\)) is defined as a linear combination of the values of its four nearest neighbors. Given the four neighbors with coordinates f (\(n_{10}\), \(n_{20}\)), f (\(n_{11}\), \(n_{21}\)), f (\(n_{12}\), \(n_{22}\)), and f (\(n_{13}\), \(n_{23}\)) (i.e., the four nearest neighbors of f (\(n_{1}\), \(n_{2}\))), the geometrically transformed field g (\(n_{1}\), \(n_{2}\)) is computed as:  
\[g(n_{1},n_{2})=A_{0}+A_{1}n_{1}+A_{2}n_{2}+A_{3}n_{1}n_{2}. \tag{1}\]  
The bilinear weights \(A_{0}\), \(A_{1}\), \(A_{2}\), and \(A_{3}\) are found by solving:  
\[\left[\begin{array}{c}A_{0}\\ A_{1}\\ A_{2}\\ A_{3}\end{array}\right]=\left[\begin{array}{c}1\;n_{10}\;n_{20}\;n_{10}n_{20} \\ 1\;n_{11}\;n_{21}\;n_{11}n_{21}\\ 1\;n_{12}\;n_{22}\;n_{12}n_{22}\\ 1\;n_{13}\;n_{23}\;n_{13}n_{23}\end{array}\right]^{-1}\left[\begin{array}{c }f(n_{10},n_{20})\\ f(n_{11},n_{21})\\ f(n_{12},n_{22})\\ f(n_{13},n_{23})\end{array}\right]. \tag{2}\]
## 3 The Reference Architecture
### The Shore Control Centre (SCC)
Figure 2 shows some of the applications composing the SCC in Livorno that enable the control and monitoring of the ship while approaching the berth as well as the current weather and marine conditions to detect potential security and safety issues and support the risk assessment based on IMO/EU standards. The applications we are going to present below are only examples of the subset of functionalities that can be provided considering the data at our disposal, without claims of completeness. The Shore Control Centre application (Figure 2a) is a 2D map visualising the vessels within the port area and the ship that is entering the port. The application shows the trajectory and includes a real-time computation of the optimal route that a given ship should follow to reach the assigned berth. The application can also visualise eventual alerts and alarms that may arise during the manoeuvre in case the ship deviates from the optimal route. Figure 2b shows the digital twinor the ship on a 3D map during the approach to the berth together with the information coming from the shipboard in real-time, e.g., the NMEA 0183 [21] sentences shown on the Electronic Chart Display and Information System (ECDIS) on board the ship. The Vessel Companion Chat(Figure 2c) has a counterpart running on a portable device used on board the ship and allows for the real-time exchange of information from the ship to the shore and vice versa, together with the visualisation on the SCC of information coming from the ship side. Finally, Figure 2d shows the SenseNOES application that allows the visualisation of all the sensors installed in the port area for monitoring purposes, including their position and the last reading they produced.
## 2 Regularized MoLE
### Formulation
The algorithmic solution is shown in Algorithm 1. ```
1:procedureMoLE(\(\xi_{Q}\))
2: Initialize \(\mathbf{\Omega}^{0}\), \(t\gets 0\), Done \(\leftarrow\) FALSE
3:while not Done do
4: E-Step: compute responsibilities, using Equation (5). 5:if\(t>0\)then
6:if\(|Q^{t}(\mathbf{\Omega}^{t})-Q^{t-1}(\mathbf{\Omega}^{t-1})|\leq\xi_{Q}\)then\(\triangleright\) Check convergence. 7: Done\(\leftarrow\)TRUE
8:endif
9:endif
10: M-Step: \(\mathbf{\Omega}^{t+1}\leftarrow\operatorname*{arg\,max}_{\mathbf{\Omega}^{*}}Q^{t}( \mathbf{\Omega}^{*})\)\(\triangleright\) Find new parameters. 11:\(t\gets t+1\)
12:endwhile
13:return\(\mathbf{\hat{\Omega}}\leftarrow\mathbf{\Omega}^{t}\)
14:endprocedure
```  
**Algorithm 1** Expectation maximization (EM) for mixture of linear experts (MoLE) learning. As input, the MoLE receives the convergence threshold \(\xi_{Q}\), and outputs the learned parameters \(\mathbf{\hat{\Omega}}\).
## 5 Conclusions  
The geostatistical approach has proved to be a very useful tool for post-earthquake analysis. This methodology allows the evaluation of the damage observed in a city from a multidisciplinary perspective, facilitating the implementation of seismic risk mitigation strategies with a comprehensive perspective. In the case of the Lorca (Spain) earthquake of 2011, a geostatistical analysis of the data obtained after the earthquake has been carried out to numerically and spatially evaluate the degree of correlation between the level of damage produced in the buildings of the city and different phenomena detected by various studies in recent years. The analysis clearly shows the great impact of phenomena, such as the existence of short columns in the structure of buildings or the importance of local amplification capacity of the ground in the distribution of building damage in the city. Other variables such as the inadequate architectural design of the buildings or urban planning of the streets show more heterogeneous results. Finally, the configuration of some seismological parameters, such as the PGA or the distance to the focus, do not show conclusive results, while that of other variables, such as the age of the buildings or the existence of soft stories, are shown to be non-determinant to elaborate a seismic mitigation strategy. The set of results obtained allow one to prioritize the actions that need to be carried out in the city to protect it against a hypothetical future earthquakes.  
Conceptualization, S.G.-A.; methodology, S.G.-A. and A.T.; software, S.G.-A. and J.L.R.; validation, S.G.-A. and A.T.; formal analysis, S.G.-A.; investigation, J.L.R.; resources, J.L.R.; data curation, S.G.-A. and J.L.R.; writing--original draft preparation, S.G.-A.; writing--review and editing, S.G.-A. and A.T.; supervision, S.G.-A.  
This research received no external funding.  
The authors would like to thank the Town Hall of Lorca, the Ministry of Public Works of the Region of Murcia, and the Insurance Compensation Consortium of the Government of Spain for the assignment of different data and reports from the earthquake. The access to all the information was authorized for scientific use and the data was treated anonymously so that the authors only had cadastral codes from the damaged buildings.  
The authors declare no conflict of interest.
## 5 Discussion
We expect that we can further improve the pCR prediction performance by applying semi-supervised training techniques for heterogeneous medical image data [51]. Segmentation variability also affects the pCR prediction performance of the proposed method. As we performed semi-automatic VOI segmentation using 3D Slicer tool that requires manual adjustment, inter- and intra-observer variability still needs to be resolved. The proposed method uses features extracted from segmented VOI of multi-parametric MRI, and thus the pCR prediction performance will gradually drop as segmentation variability increases. While there is a lack of reliable and validated fully automatic VOI segmentation tools for MRI [52], there have been efforts to develop automated VOI segmentation tools based on deep learning [53; 54; 55]. By using automatic VOI segmentation, we can fully automate the proposed method and perform more reliable and consistent pCR prediction. This study has clinical significance in that it increases the applicability of a new treatment method, such as \"wait-and-see\" without surgery [56; 57] by achieving a higher prediction performance of pCR after nCRT based on a large number of patients. For the past decade, the two-step process of performing nCRT followed by surgery has been considered as the standard treatment. pCR is often achieved after nCRT only, although the surgery was unavoidable even in the case of pCR. This is because we can determine the presence or absence of pCR only after performing surgery. As the wait-and-see method is not included in the internationally standardized medical guidelines, it is currently being conducted only as a clinical study by some professors at our hospital and is not a routine process. Therefore, it is not mandatory for radiologists to provide information on the prediction of pCR. More studies on reliable pre-operative pCR prediction are necessary to establish clinical guidelines for pre-operative pCR prediction, and this study will contribute to it. The inability to compare the results of this study with clinical practice is a limitation of this study, and further research is needed.
## 4 Hydration Model for Cement-Quartz Binary Blends
### Evaluating Strength Development Using the Hydration Model  
In cement-quartz blends, quartz is a chemically inert filler. In hydration heat tests, given the limits of testing equipment, only the first 7 days of heat can be measured. However, with the proposed model, we can calculate hydration heat beyond 7 days. The calculated results are shown in Figure 12. Figure 12a shows the heat normalized by the cement  
Figure 11: Hydration heat analysis: (**a**) analysis results for control specimen; (**b**) analysis results for 7.5% quartz specimen; (**c**) analysis results for the 15% quartz specimen.  
mass. As quartz content increases, heat normalized by the cement mass increases because of the nucleation effect. Figure 12b shows the heat normalized by the cement-plus-quartz mass. As quartz content increases, heat normalized by the cement-plus-quartz mass decreases because of the dilution effect. This means that although the nucleation effect can accelerate cement hydration, it cannot compensate for the reduction caused by the dilution effect.  
Figure 12c shows the relationship between strength and the calculated hydration heat. For all three specimens at all test ages (1, 3, 7, and 28 days), strength is generally an exponential function of hydration heat. The coefficient of determination of regression is 0.9163. In summary, our proposed model can overcome the weakness of hydration heat test machines and expand test results for hydration heat over a longer period.
## 2 Related Works
[16] proposed ERNC, a nonuniform clustering protocol that addresses energy imbalance and inefficient transmission through combined coordinate systems, residual energy, and node density metrics. [PERSON] et al. [17] developed RLOR, a reinforcement learning-based opportunistic routing protocol that adapts routing paths dynamically based on node state to improve reliability and reduce delay. However, fixed parameters of the RLOR may reduce its adaptability to dynamic underwater environments. [PERSON] et al. [18] proposed a dual-hop topology-aware routing protocol, which effectively reduces the likelihood of packet voids and enhances both delivery success and energy efficiency. Inspired by this approach, we incorporated similar ideas into our protocol design to alleviate routing voids. In addition to the aforementioned protocols, numerous recent studies have combined clustering architectures, hierarchical routing, opportunistic forwarding, entropy-driven mechanisms, and game-theoretic strategies to improve the energy efficiency, network connectivity, data delivery performance, and environmental adaptability of UASNs. The CAPTAIN protocol [19] employs a hybrid communication model using intra-cluster optical and inter-cluster acoustic links to support data collection but does not account for ocean current dynamics or node mobility. To address energy loss and packet failures caused by environmental drift, [PERSON] et al. [20] proposed an ocean-current-aware adaptive routing strategy that can dynamically adjust routing paths to enhance robustness. From the perspective of opportunistic routing, [PERSON] et al. [21] proposed a distance vector-based opportunistic scheme that enables nodes to maintain multiple candidate forwarders, thereby improving robustness in environments with unstable links. Inspired by this idea, our work maintains a dynamic neighbor queue to preserve the multi-hop forwarding flexibility. The CELR [22] constructs a hierarchical structure based on hop count and makes forwarding decisions by jointly considering the number of hops and residual energy, effectively mitigating routing voids while enhancing both energy efficiency and connectivity. [PERSON] et al. [23] developed a cluster-based depth source selection routing scheme that improves scalability in underwater environments, while [PERSON] et al. [24] introduced an entropy-driven data aggregation strategy that reduces redundant transmissions and enhances efficiency. Additionally, [PERSON] et al. [25] designed a game-theoretic routing protocol that balances energy consumption and reliability in 3D UASNs by modeling node interactions as strategic games. Although these studies make notable contributions, most either focus on localized or single-layer optimizations while overlooking the constraints of limited energy and computational capacity inherent to underwater sensor nodes.
## 3 Results and Discussion
### Comparison of TAG Profiles
Mean values with a different letters within a row are significantly different from each other at \(p<0.05\). [ENDFOOTNOTE] & 49.14 \(\pm\) 0.05 a  \\ POO & 48 & 15.33 \(\pm\) 0.01 b  & 14.71 \(\pm\) 0.10 c [FONOTE:b]Footnote b: L, Ionic; O, oleic; P, palmitic; S, stearic. 2 Equivalent carbon number (ECN) = carbon number – 2 \(\times\) double bonds. Mean values with a different letters within a row are significantly different from each other at \(p<0.05\). [ENDFOOTNOTE] & 18.59 \(\pm\) 0.17 a [FOOTNOTE:b]Footnote b: L, Ionic; O, oleic; P, palmitic; S, stearic. 2 Equivalent carbon number (ECN) = carbon number – 2 \(\times\) double bonds. Mean values with a different letters within a row are significantly different from each other at \(p<0.05 The cluster analysis, which employed dimensionality reduction using the TAG profiles, was undertaken to address the shielding effect resulting from the absolute dominance of major TAGs. The results are illustrated in Figure 2, revealing distinct characteristics for each camella oil variety. C.O was notably enriched in PLL, OLL, and LLL. In contrast, C.G exhibited a preference for PPO, POO, and PPL, suggesting a different fatty acid composition compared to C.O. Furthermore, the major TAG in C-C.O was OOO, while C.S showed higher levels of OOL and SOO/SSL. These findings suggested that the TAG profiles of each camella oil variety can serve as unique fingerprints for differentiating and evaluating the characteristics of different types of camellia oils. Similarly, [PERSON] et al. [17] showed that a distinct pattern of TAG spectra for vegetable oils was obtained, allowing for the classification of 18 edible vegetable oils into five groups based on their TAG profiles.