esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Stability  
The results of the stability experiments are presented in Table 4. The accuracy of enavogliflozin ranged from 85.56% to 103.6%, and the precision ranged from 1.911% to 5.846% under the three different stability test conditions. The acceptance criterion for all stability studies was an accuracy of 85-115% relative to the nominal concentration (100% by definition). As there were no significant differences observed between each measured concentration and the nominal concentration under various stability treatment conditions, these results provided evidence that enavogliflozin in mouse plasma was stable up to 6 h at 25 \({}^{\circ}\)C for bench-top stability, stable for 24 h in the autosampler after sample treatment, and remained stable over three freeze-thaw cycles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Nominal Concentration (ng/mL)** & **Measured Concentration (ng/mL)** & **Precision (\%)** & **Accuracy (\%)** \\ \hline Autosampler stability (10 \({}^{\circ}\)C, 24 h) & & & \\
15 & 15.52 \(\pm\) 0.907 & 5.846 & 103.5 \\
250 & 255.8 \(\pm\) 13.38 & 5.232 & 102.3 \\
2000 & 1853 \(\pm\) 54.45 & 2.939 & 92.63 \\ Freeze–thaw stability (3 cycles) & & & \\
15 & 15.55 \(\pm\) 0.382 & 2.454 & 103.6 \\
250 & 255.2 \(\pm\) 10.07 & 3.945 & 102.1 \\
2000 & 1716 \(\pm\) 38.61 & 2.250 & 85.81 \\ Bench-top stability (25 \({}^{\circ}\)C, 6 h) & & & \\
15 & 15.03 \(\pm\) 0.437 & 2.905 & 100.2 \\
250 & 227.7 \(\pm\) 11.56 & 5.074 & 91.09 \\
2000 & 1711 \(\pm\) 32.69 & 1.911 & 85.56 \\ \hline \hline \end{tabular} Data represented as mean \(\pm\) SD (\(n\) = 3).  
\end{table}
Table 4: Stability of enavogliflozin.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analyte** & \begin{tabular}{c} **Nominal** \\ **Concentration** \\ **(ng/mL)** \\ \end{tabular} & \begin{tabular}{c} **Extraction** \\ **Recovery (\%)** \\ **(\(n\) = 3)** \\ \end{tabular} & \
\begin{tabular}{c} **CIV (\%)** \\ **(\(n\) = 6)** \\ \end{tabular} &
\begin{tabular}{c} **Matrix** \\ **Effects (\%)** \\ **(\(n\) = 6)** \\ \end{tabular} & **CV (\%)** \\ \hline Enavogliflozin & 15 & 88.79 \(\pm\) 2.654 & 2.989 & 99.09 \(\pm\) 5.104 & 5.151 \\
250 & 81.82 \(\pm\) 2.861 & 3.497 & 103.6 \(\pm\) 3.449 & 3.329 \\  & 2000 & 79.75 \(\pm\) 3.190 & 4.000 & 103.2 \(\pm\) 1.126 & 1.091 \\ IS & 200 & 85.76 \(\pm\) 5.879 & 6.856 & 96.93 \(\pm\) 6.236 & 6.433 \\ \hline \hline \end{tabular} Data represented as mean \(\pm\) SD (\(n\) = 3 or 6).  
\end{table}
Table 3: Extraction recoveries and matrix effects for enavogliflozin and IS.
# Some Dynamic Aspects of a Sextic Galactic Potential in a Rotating Reference Frame
## 4 Stability Analysis
### Application of the Lagrange Theorem
If \(0\leq\Omega<1\), then it is a local maximum and hence unstable. Similarly, the case for \(\mathcal{E}_{3,4}\) with \(a,c\) replacing \(b,d\). For the equilibrium point \(\mathcal{E}_{5,6}\), the Hessian matrix (Equation (20)) is \[Hess_{V}(\mathcal{E}_{5,6})=\frac{4(\Omega^{2}-1)}{a+3c}\bigg{(} \begin{array} []{cc}(a+c)&\pm 2c\\ \pm 2c&(a+c)\end{array} \bigg{)},\] (23) where the off-diagonal entries have the same sign. The determinant of \(Hess_{V}(\mathcal{E}_{5,6})\) is \(4(\Omega^{2}-1)(a-c)\). If \((\Omega^{2}-1)(a-c)<0\), then the equilibrium point \(\mathcal{E}_{5,6}\) is a saddle and hence unstable. If \((\Omega^{2}-1)(a-c)>0\), then we check the sign of the trace which follows the sign of \((a+c)\). If \(a+c>0\), then the point \(\mathcal{E}_{5,6}\) is a local minimum and hence stable. If \(a+c<0\), then the case where \((a-c)\) and \((\Omega^{2}-1)\) are both negative gives \(\mathcal{E}_{5,6}\) as a local maximum and is thus unstable. The case where both \((a-c)\) and \((\Omega^{2}-1)\) are positive and \(a+c<0\) leads to a contradiction, so it never happens. It should be noticed that in all the above cases, the Hessian matrix becomes null when \(\Omega=1\) and no information about the equilibrium positions can be derived. Consequently, we exclude the case where \(\Omega=1\) from further discussion, as it demands a special investigation outside the scope of this work. Notice that the degree of instability for the maximum equilibrium points is two, and according to [PERSON]'s proposition [41], with the addition of gyroscopic forces, these positions remain stable when they are the minimum for the effective potential in Equation (19), while the unstable maximum equilibrium points for the effective potential can be stabilized by the forces resulting from the rotating frame (gyroscopic forces). Consequently, we must apply the linear approximation for the stability of these positions [40]:
# Seismic Risk Assessment of Chania, Greece, Using an Integrated Computational Approach
## 2 Study Area  
### Geology/Tectonics of the Study Area  
The study covered an area of 28 km\({}^{2}\) in the main part of Chania city. The city is in the west of Crete, being the second-largest town on the island, with an increasing population, >100,000, in the whole municipality and a population of approximately 60,000 in the urban center. In addition, during the summer season, the population is more than doubled due to tourism. The city has famous and well-preserved historical sites located around the old port as well as many important neoclassical buildings in the old town. Crete is one of the most important European natural geological museums, since its geology, its terrain, and its morphotenctics reflect the changes and developments in the Greek terrestrial during the Alpine orogenesis [28]. Moreover, the geological structure of Crete is complex as Crete is located at the edge of the subduction zone between the African and Eurasian plates, i.e., at the convergence zone of lithospheric plates, Eastern Mediterranean, and Aegean microplates, with a net convergence rate of about 4 cm/year [29; 30; 31; 32; 33].  
Due to the extensive stress field of the broader area of southern Greece along the subduction zone, the geology of Crete is characterized by intense tectonic actions, where successive tectonic coverages, of different geological origins and evolutions, are located on top of each other, creating thus a complex tectonic structure. The cover tectonic is associated with successive compressive tectonic events, which resulted from successive periods of crustal extension due to the convergence of the lithospheric plates of Africa and Eurasia during continental collisions. Specifically, the geology around Chania city is complex, as shown in Figure 1. The surrounding area of the old city of Chania is partially covered by Quaternary deposits, while further to the south, there are limestones [34; 35]. In addition, most of the eastern and southern parts of modern Chania city are covered by limestones with different geomechanical properties (Trypalion and Marylimestones). The western and central parts of the basin are covered by Quaternary alluvial deposits (Q), while Marls cover the central part of the city. Most of the faults (denoted by red lines in Figure 1) are aligned in the following directions: E-W, N-S, and NW-SE.
# Internet of Things Platforms for Academic Research and Development: A Critical Review
## 3 Comparison of Platforms for R and D in Academia

Table 2: _Cont._\"high\", including platforms with the richest set of features, \"medium\", including platforms with a good set of features, but not on par with the first group, \"low\", including platforms only providing basic functionalities, and finally \"absent\", including platforms that do not provide any feature relevant to the criterion under consideration, or for which no information is provided. Note that the rankings by criteria were not combined to determine a global ranking. This would require in fact to use a multi-criteria decision making (MCDM) method [53], such as a simple weighted sum of scores or the more sophisticated analytic hierarchy process (AHP) [54]; in either case, this implies the assignment of weights associated to the importance of the different criteria. Establishing the relative importance of the criteria and the corresponding weights is however part of the research problem definition, that is beyond the scope of the present work. The rankings provided in the following can however constitute the input for a MCDM method, jointly with weights defined by the researcher based on the considered research problem. The results of this qualitative comparison are presented in Table 3. Note that for platforms that provide different set of features between their open source, local installation vs. commercial, cloud-based versions, such as ThingsBoard and ThingSpeak, Table 3 refers to the open source installation. Table 3 highlights that, with the exception of the ThingBox, which can be considered to some extent a niche platform, most platforms are characterized by satisfactory features under most of the considered criteria. However, the FIWARE platform stands out for its consistently high level of features for most criteria, and emerges thus as a very good candidate as the IoT reference platform for academic R and D activities. As also suggested in [7], however, the selection of the platform to support an R and D project should also be based on the specific requirements of the project itself. As an example, if support for multiple communication protocols and integration with external services are key aspects in the project, FIWARE could be definitely considered the best candidate platform, while if ease and flexibility in the installation process is deemed the most important aspect, Kaa and ThingsBoard are the best suited platforms.
## 5 Experimental Results and Analysis
### Spatial Correlation Analysis of Traffic Black Spots  
Spatial correlation is the correlation between phenomena or variables in geographic space. It involves the factor of geographic location and examines whether there is a correlation or similarity between neighboring regions in geographic space.  
In previous studies, the spatial distributions of traffic black spots are generally not considered to be spatially independent of each other, and the probability of them being the same traffic black spot is higher when the physical environments are very similar. Therefore, this paper will analyze the spatial correlation of traffic black spots at road intersections in the Kowloon Peninsula region of Hong Kong through the use of spatial analysis methods such as the global Moran's \(I\).  
Spatial correlation analysis of traffic black spots at road intersections in the Kowloon Peninsula region of Hong Kong can reveal their geospatial distribution patterns, clustering, and the degree of association with other factors. This is of great significance for transportation planning, traffic safety, and traffic management, and helps to improve the efficiency and safety of the transportation system.
## 1 Introduction  
Nuclear magnetic resonance (NMR) is an important biochemical technique for the in vitro determination of protein structure and protein-drug interaction studies [1; 2], which is also being widely used in the field of in-cell NMR [3; 4; 5; 6]. Magnetic resonance spectroscopy (MRS), which is based on NRM principles, is widely being used along with magnetic resonance imaging (MRI) to acquire detailed information about the tissue structures for medical diagnosis [7; 8; 9]. MRS non-invasively provides information on the brain's metabolites concentrations, which aids the early detection of neurodegenerative diseases [10; 11; 12] The current developed techniques and software packages for MRS signal processing retain significant variance in the absolute metabolite quantification. Magnetic resonance (MR) spectra analysis methods can be classified as either black box (data-driven) or basis sets methods [8]. Basis set methods incorporate prior knowledge of metabolites chemical structures in both in-vivo NMR (e.g., LC ModelTM [13], TARQUIN [14]), and in-vitro NMR [15], while the black box does not incorporate prior knowledge, and are completely data-driven, such as QUEST [16].  
The black box methods decompose the free induction decay (FID) signals in the time domain, and then quantify the metabolites by modeling the decomposed FID signal. Black box methods have been shown effective on sparse data, and long echo time (TE) acquisition; however, the main drawback of black box methods is impracticable results for complex data, such as short TE, due to not including prior knowledge [14; 17]. The number of detected peaks in MR spectra increases using short TE scanning, which allows for the precise quantification of metabolites with short T\({}_{2}\) relaxation time [18]; thus, the pattern complexity of some metabolites increases. Therefore, modeling the metabolites basis sets with a series of single peaks is difficult using existing black box methods.  
The basis sets method match basis sets models developed in the laboratory with the MR spectra of the scanned data in the frequency domain. It has been shown that basis sets methods are more operative for complex data when compared with black box methods. However, one of the shortcomings of the basis sets methods is the existence of unknown components in the complex in vivo data, as a consequence of using basis sets that were developed in laboratory environments [8; 19]. Therefore, the possibility of accuracy loss increases.  
In this study we address the abovementioned issues, by the integration of the truncated HSVD and K-means clustering with centroids initialized using prior knowledge of basis sets developed in the laboratory environments. We used a phantom to generate a ground truth data to compare the accuracy of the proposed algorithm and TARQUIN. Two different brain regions of four subjects were scanned at Texas Tech Neuro-imaging Institute (TTNI) in order to provide in vivo data, allowing for a comparison of proposed approach and TARQUIN.
# Robust Quadratic Optimal Control for Discrete-Time Linear Systems with Non-Stochastic Noises
## 1 Introduction
In this paper, we converted the multi-step quadratic global optimal control problem to multiple one-step quadratic local approximate optimal control problems. Then set-membership filtering and robust optimization are adopted to design the state feedback control strategy which ensures the optimality of control performance (the quadratic cost function about the system states and the control law). The remaining part of this paper is organized as follows: In Section 2, the problem formulation is presented. Then, in Section 3, we present the robust control algorithm based on the set-membership filtering and robust optimization. To show the effectiveness of our proposed method, simulation results are presented in Section 4. Finally, conclusions and future research directions are presented in Section 5. **Notations. ** Throughout the paper, \(S\geq 0\) (\(S>0\)) means that the real symmetric matrix \(S\) is non-negative definite (positive definite). \(I\) is the identity matrix with appropriate dimensions. The ellipsoid \(\Omega(s,S)\) is denoted by  
\[\Omega(s,S)\triangleq\{l\in R^{n}:(l-s)^{T}S^{-1}(l-s)\leq 1\},\]  
where the vector \(s\) is the center of the ellipsoid, the matrix \(S(\geq 0)\) characterizes its shape.
## 6 Use Case: Privacy Identity Management Enrollment
### Authentication and p-ABC Management  
The authentication process begins when the user submits their username and password to the vIdP. Once successfully authenticated, a p-ABC is issued to the user by the Verification System. This credential can be securely stored on the user's device and used to access services that enforce specific access policies. The next steps are the following:  
1. User login: When accessing the application for the first time, the user begins the login process through the AURORA MINDS system.  
* If the user has already been registered by the administrator (as described in Steps 1-3), the user is directed to the Verification System page to log in using their assigned username and OTP.
* If the user has not yet been registered, they must initiate the enrollment process through the designated registration portal, where they will provide the necessary information to receive their login credentials. Once registration is completed, they will be able to authenticate and access the application securely.
## 5 Numerical Results and Discussion
### Simulation Parameters
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link Length** & **Argo Node Number** & **Ocean Parameters** & **Malaga Model** & **Scintillation Index** \\  & & **(\(\chi_{T}\), \(\eta\), \(\varepsilon\), \(\omega\))** & **Parameters (\(\alpha\), \(\beta\))** & **(\(\sigma_{1}^{2}\))** \\ \hline  & Node 1 & \((1\times 10^{-6}\), \(10^{-3}\), \(5.116\times 10^{-6}\), \(-0.122)\) & (5.648, 4) & 0.553 \\
26 m & Node 2 & \((1\times 10^{-6}\), \(10^{-3}\), \(2.646\times 10^{-6}\), \(-1.572)\) & (4.284, 2) & 1.102 \\  & Node 3 & \((1\times 10^{-6}\), \(10^{-3}\), \(9.173\times 10^{-7}\), \(-5.498)\) & (3.995, 1) & 1.860 \\  & Node 4 & \((1\times 10^{-6}\), \(10^{-3}\), \(1.240\times 10^{-6}\), \(-3.639)\) & (4.014, 1) & 1.670 \\ \hline  & Node 1 & \((1\times 10^{-6}\), \(10^{-3}\), \(5.116\times 10^{-6}\), \(-0.122)\) & (2.816, 3) & 0.770 \\
30 m & Node 2 & \((1\times 10^{-6}\), \(10^{-3}\), \(2.646\times 10^{-6}\), \(-1.572)\) & (4.045, 1) & 1.523 \\  & Node 3 & \((1\times 10^{-6}\), \(10^{-3}\), \(9.173\times 10^{-7}\), \(-5.498)\) & (4.021, 1) & 2.643 \\  & Node 4 & \((1\times 10^{-6}\), \(10^{-3}\), \(1.240\times 10^{-6}\), \(-3.639)\) & (4.056, 1) & 2.388 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Malaga model parameters for different Argo nodes in our simulation. Figure 2: Scintillation index and the effective received spot size for four Argo nodes: (a) \(\chi_{T}=1\times 10^{-6}\), \(\eta=10^{-3}\); (b) \(\eta=10^{-3}\), \(W_{0}=10\) mm, \(z=26\) m.
## 2 Superiorities of Near-Space Radar Platform
### Relative High Sensitivity and Large Footprint  
Near-space platforms are much closer to the targets than their orbital cousins. Distance is critical to receiving low-power signals. From the radar equation [18] we know that the received power attenuates  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline \multirow{3}{*}{Orbital Altitude(km)} & \multicolumn{3}{c|}{Maximum Pass Time (minutes:seconds)} \\ \cline{2-6}  & \multicolumn{3}{c|}{Angle Above Horizon (degrees)} \\ \cline{2-6}  & 0 & 5 & 10 & 30 & 45 \\ \hline
200 & 7:49 & 5:37 & 4:08 & 1:40 & 1:00 \\ \hline
300 & 9:35 & 7:16 & 5:34 & 2:24 & 1:27 \\ \hline
400 & 11:10 & 8:44 & 6:54 & 3:08 & 1:54 \\ \hline \end{tabular}
\end{table}
Table 1: Pass Times of LEO satellites.  
in the square of the distance from the transmitter to the target, while that of an active antenna attenuates in the fourth power of the transmitter distance. Considering a point at nadir, near-space platforms are 10-20 times closer to their targets than a typical 400 km LEO satellite. This distance differential implies that it could detect much weaker signals (10 dB to 13 dB weaker). Moreover, the footprint covered by near-space sensors are also satisfactory. Figure 2 shows the ground coverage area as a function of looking-down angle for different flying altitude.  
Additionally, being lower than satellites also brings about another advantage to near-space platforms: they fly below the ionosphere. Ionospheric scintillation can have significant effects on radio communication and navigation accuracy. Ionospheric scintillation is very difficult to predict, but it can disrupt satellite significantly. Fortunately, ionospheric scintillation has no impact on near-space sensors.
# Investigation on Residual Stress Loss during Laser
## 3 Simulation Procedure
### Material Definition  
In order to consider the effect of strain rate as well as the work hardening of the target material during LPT, Johnson-Cook model [30] was adopted as the constitutive model:  
\[\sigma_{f}=(A+B\overline{\varepsilon}^{n})\Bigg{[}1+C\ln\Bigg{(}\frac{\dot{ \overline{\varepsilon}}}{\dot{\overline{\varepsilon}}_{0}}\Bigg{)}\Bigg{[}1- \Bigg{(}\frac{T-T_{0}}{T_{m}-T_{0}}\Bigg{)}^{m}\Bigg{]} \tag{1}\]  
where \(\sigma_{f}\) is the Von-Mises stress, \(\overline{\varepsilon}\) is equivalent strain, \(\dot{\overline{\varepsilon}}\) is rate of plastic strain, and \(\dot{\overline{\varepsilon}}_{0}\) is rate of reference plastic strain. The parameters \(T\), \(T_{0}\), and \(T_{m}\) represent temperature, reference temperature, and melting point of the material, respectively. \(A\), \(B\), and \(n\) indicate the effect of work hardening. \(C\) reflects the strain rate effect and \(m\) represents the thermal effect.  
As LPT only utilizes mechanical effect induced by lasers, Johnson-Cook model can be further simplified by omitting the parameter \(m\) which reflects thermal effect:  
\[\sigma_{f}=(A+B\overline{\varepsilon}^{n})\Bigg{[}1+C\ln\Bigg{(}\frac{\dot{ \overline{\varepsilon}}}{\dot{\overline{\varepsilon}}_{0}}\Bigg{)}\Bigg{]} \tag{2}\]  
The properties of 316 L stainless steel used in simulation are listed in Table 2.
## 4 Experimental Results
### Two-Stream UDN
#### 4.2.3 Comparison with Other Shallow Nets  
Table 3 shows comparison of the two-stream UDN and other shallow nets on Caltech and ETH. All the compared networks have two or three layers, and all their input features are hand-crafted features, such as HOG, gradient, or color self-similarity (CSS).  
Comparing with other shallow nets, the improved UDN gets the lowest score on the Caltech and the second lowest score on the ETH. Comparing with the UDN, the improved UDN improves more obviously on the Caltech. A possible reason is that images in ETH and INRIA have higher resolution, so the global network can extract better features on the upper region, so the local network benefits less. The results indicate that the two-stream UDN works better when the captured image has low resolution. Figure 14 shows some samples in which the two-stream UDN outperforms the other models. False positive samples are in the red box, which means that the negative samples are mistakenly predicted as pedestrians. True positive samples are in the green box, which means that pedestrians are correctly predicted. False negative samples are in the purple box, which means these pedestrians are not detected by the methods. Compared with UDN [21], the two-stream UDN obtains fewer red boxes, which benefits from the Local network for eliminating non-pedestrian samples. Compared with SDN (Switchable Deep Network) [22], which has a similar LAMR with the two-stream UDN, the two-stream UDN predicts more green boxes (Figure 14, image 1, 2, 5 and 7). SDN is a network that  
Figure 12: Scores for true positive samples (in green boxes) of the Caltech (**a**) scores by the Global network, (**b**) scores by the two-stream UDN.  
Figure 13: Scores for false positive samples (in red boxes) of the Caltech. (**a**) Scores by the Global network, (**b**) scores by the two-stream UDN.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **DBN-Isol [15]** & **DBN-Mut [20]** & **UDN [21]** & **SDN [22]** & **MultiSDP [42]** & **Two-Stream UDN** \\ \hline Caltech & 53.29\% & 48.22\% & 39.32\% & 37.87\% & 45\% & 37.66\% \\ ETH & 47.01\% & 41.07\% & 45.32\% & 40.63\% & 48\% & 44.58\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: LAMR of two-stream UDN and shallow networks.  
also emphasizes local features, so it receives a similar score with the proposed two-stream UDN. The SDN gets low-level local feature maps directly divided from low-level global feature maps, but the two-stream UDN learns the local feature maps with a separate branch.  
Figure 14: Detection results of models based on shallow networks. False positive samples are in the red box. True positive samples are in the green box. False negative samples are in the purple box. (**a**) Ground truth; (**b**) DBN-IsoI; (**c**) DBN-Mul; (**d**) UDN; (**e**) SDN; (**f**) MultiSDP; (**g**) Two-stream UDN (ours). Best viewed in color.