esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Yield Optimization
### Failure Allowance of 1%  
The nominal design points for when 1% failure probability was allowed under tail and kurtotic distributions are illustrated in Figure 10. Monte Carlo simulation points are included to depict the distributions, and these were determined by applying the inverse Kumaraswamy CDF transformation with relevant shape parameter values to uniformly distributed random numbers generated by the MATLAB rand() function. As is shown, depending on the distributions assigned to the random variables, the optimal design point for the DGR changes. If the distributions of the UFC and room spacings are such that smaller values are more likely to be realized, then larger nominal values need to be  
Figure 9: Joint Kumaraswamy PDFs (denoted by Pr) for UFC and room spacings at a nominal point of \((x_{1},x_{2})=(1.5,20)\) and tolerances of \(x_{1}\pm 0.1\) and \(x_{2}\pm 1.5\), assuming independence. (**a**) Parameters \(\hat{a}=[1,1]\) and \(\hat{b}=[5,5]\), (**b**) Parameters \(\hat{a}=[8,8]\) and \(\hat{b}=[8,8]\).  
specified for the DGR design to ensure the maximum temperature does not exceed 100 \({}^{\circ}\)C, e.g., see Figure 10a. If the opposite is true, then smaller nominal values that are closer to the boundary of the feasible region can be chosen without an unfavorable increase in system failure, e.g., see Figure 10b. The tail and kurtotic distributions are quite extreme cases, and they were considered to demonstrate the need for either larger or smaller nominal UFC and room spacings within the DGR. Regardless of what the actual distributions for UFC and room spacings may be, yield optimization can provide insight for specifying an appropriate DGR design--with accuracy dependent on available information in Figure 9.
## 4 Discussion
First, the same nanofilled composite material was used for both the aged and repair layers, which may have favored inter-material compatibility and potentially exaggerated the bond strength results. While this choice minimized compositional variability, it limits generalizability to clinical cases where materials often differ. Second, the in vitro nature of the study does not replicate complex oral conditions such as saliva, occlusal forces, or long-term degradation, which may affect adhesive performance. Third, the finite element analysis did not include mesh convergence testing or sensitivity analysis, and while boundary conditions were standardized, further refinement is required to enhance clinical relevance. Finally, displacement values observed in FEA were interpreted within the constraints of simplified loading protocols; future research should integrate cyclic fatigue testing and multi-material interfaces to better reflect functional longevity in clinical scenarios. Based on the results of this study, both silane-containing universal adhesives and the combination of separate silane pretreatment with a conventional adhesive significantly improved the repair bond strength. However, given the comparable performance of these two approaches and the simplified application protocol, universal adhesives with integrated silane may be preferred in clinical practice. They reduce the number of steps and the potential for operator error, offering a more efficient and predictable option for composite repair without compromising bond effectiveness.
## 3 Materials and Methods
### Analysis of Heavy Metals in Salmon Side Stream Materials  
The presence of As, Hg, Cd, and Pb in side stream materials of farmed salmon was studied. Muscle, heads, viscera, skin, and tailfins were mineralized in a microwave oven (MARS, CEM, Vertex, Spain). Approximately 0.30 g of sample was placed in a Teflon reactor vessel. Next, 1 mL of H\({}_{2}\)O\({}_{2}\) (30% \(v/v\)) and 4 mL of HNO\({}_{3}\) (14M) were added and the digestion was conducted under a microwave irradiation power of 800 W at 180 \({}^{\circ}\)C for 15 min. The digested samples were left to cool at room temperature. After eliminating the nitrogenous vapor, they were filtered and brought up to volume with distilled water.  
The identification and quantification of toxic metals was carried out using an inductively coupled plasma spectrometer mass detector (ICP-MS, Agilent model 7900). The analytical conditions were as follows: carrier gas (1.07 L/min), Ar gas flow (15.0 L/min), reaction gas (He), RF power (1550 W), nebulizer pump speed (0.10 rps), and RF matching(1.80 V). To correct matrix-induced signal fluctuations and instrumental drift, internal standard solutions of \({}^{72}\)Ge, \({}^{103}\)Rh, and \({}^{193}\)Ir (ISC Science) at 20 \(\upmu\)g/g were used. For the quantification of As, Cd, and Pb, standard calibration curves from 0 to 1000 \(\upmu\)g/L were used. As for the quantification of Hg, a standard calibration curve from 0 to 100 \(\upmu\)g/L was utilized. Limits of detection (LODs) were calculated according to the following equation: LOD = 3 sB/a where \"3 sB\" is 3 times the standard deviation at zero concentration and \"a\" is the slope of the calibration curve. LOD values obtained for As, Hg, Cd, and Pb were 0.012, 0.0015, 0.004, and 0.0015 \(\upmu\)g/L, respectively. The concentrations of heavy metals in the digested blank (distilled water) were subtracted from the values of samples. The results were expressed as \(\upmu\)g of element/g of side stream material in wet weight. To confirm the accuracy of the method, the fish protein powder DORM-3 was used as the Certified Reference Material for Trace Metals. It was prepared and analyzed simultaneously to the salmon samples. The recovery percentages were 98%, 86%, 76%, and 77% for As, Hg, Cd, and Pb, respectively.
# Characterization of Deburring by Abrasive Flow Machining for AL6061
## 3 Preliminary Testing  
### Deburring System  
Figure 1 is a schematic diagram of the system used in this study for the deburring experiment. Two concentric hydraulic cylinders and pistons were coupled to a drive point connected to a linear actuator for reciprocating motion. The cylinders were connected to the specimen by hoses to accommodate the abrasive flow medium. A disposable protrusion with the burr edge prepared by milling was assembled in the middle of the specimen.  
The specimen was made of AL6061, with dimensions of 120 mm \(\times\) 40 mm \(\times\) 35 mm, as shown in Figure 2. The side inlets were machined by a 12 mm drill for hose fittings. A fluid channel with 1.5 mm width and 3 mm depth was machined on the top face. In the middle of the channel, a disposable protrusion with 4.25 mm length was assembled into a recess, leaving a 1 mm local gap, as shown in Figure 3.  
The definition of flow length \(L\) is given by  
\[L=VF/A \tag{2}\]  
where \(VF\) is the total flow volume of the abrasive medium, and \(A\) is the local cross-sectional area of the flow path above the protrusion with 1 mm width and 3 mm depth.
# Numerical Simulation Study on Welding Process of Upper Frame of Hydropower Unit
## 3 Simulation Analysis of T-Joint Welding Based on TEPM
### Finite Element Modeling
The front and back ellipsoids are characterized by the following mathematical formulas:  
\[Q_{f}(x,y,z)=\frac{6\sqrt{3}Qf_{f}}{\pi a_{f}bc\sqrt{\pi}}\exp\left\{-3\left( \frac{x^{2}}{a_{f}^{2}}+\frac{y^{2}}{b^{2}}+\frac{z^{2}}{c^{2}}\right)\right\},x>0 \tag{1}\]  
\[Q_{r}(x,y,z)=\frac{6\sqrt{3}Qf_{r}}{\pi a_{r}bc\sqrt{\pi}}\exp\left\{-3\left( \frac{x^{2}}{a_{r}^{2}}+\frac{y^{2}}{b^{2}}+\frac{z^{2}}{c^{2}}\right)\right\},x\leq 0 \tag{2}\]  
where \(Q\) is the power of the heat source, \(Q=\eta UI\), \(U\) is the arc voltage, \(I\) is the welding current, and \(\eta\) is the heat source efficiency, taken as 0.8; \(f_{f}\) and \(f_{r}\) are the heat distribution functions of two different half ellipsoids at the front and rear, and \(f_{f}+f_{r}=2\); \(a_{f}\) and \(a_{r}\) are the lengths of the front and rear half ellipsoids, respectively; \(b\) is half of the width of the ellipsoid; \(c\) is the depth of the ellipsoid. After the heat source model was determined, its shape parameters were verified and adjusted to ensure that the simulated molten pool's morphology aligned with the actual one. In this paper, the finalized shape parameters for the double ellipsoidal heat source model are as follows: 4.833 for parameter \(a_{f}\), 9.667 for parameter \(a_{r}\), 5.5 for parameter b, and 6 for parameter c. Figure 8 shows a comparison between the simulated and actual molten pool morphologies. The similarity in shape and size between the two molten pools demonstrates the model's accuracy.
# Integrated Earthquake Catalog III: Gakkel Ridge, Knipovich Ridge, and Svalbard Archipelago
## 3 Results
### Magnitude Unification in the Integrated Earthquake Catalog  
During the second stage of creating the integrated earthquake catalog, the magnitude unification of events was carried out. The integrated catalog contains multiple definitions of the magnitude of different types from various agencies. Different magnitudes are presented in different periods and different parts of the considered region. A preliminary study showed that some magnitude estimates significantly changed over time: \(ML^{\rm NAO}\) magnitude changed in 2009, \(MD^{\rm BER}\) magnitude changed in 2010, and \(ML^{\rm DNK}\) magnitude changed in 2015 (Figure 10).  
Moreover, we assumed that the magnitude ratio may vary in different seismically active zones. We identified three sub-regions: Svalbard, Knipovich Ridge, and Gakkel Ridge (Figure 2). Magnitude unification was independently performed in these three sub-regions.  
As the reference scale, we chose the magnitude based on the seismic moment determined by GCMT. We analyzed correlation ratios between various magnitudes and proposed conversion formulas for obtaining \"proxy-\(MW\)\" estimates. We used a simple shift-type ratio \(M=m+a\) for almost all magnitudes, except for surface wave magnitudes \(MS^{\rm ISC}\), \(MS^{\rm MOS}\), and \(MLH^{\rm GSR}\), for which linear ratios were used.
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.9 Configuration
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Group** & **Sub-Group** & **Name** & **Purpose** & **Controller** & **Performance** \\ \hline \multirow{4}{*}{Policies} & Roadmap [439] & Reconfiguring policies for traffic patterns & SOX, Maestro traffic engineering & Discuss performance of traffic engineering \\ \cline{2-5}  & \multirow{2}{*}{Sincon [440]} & \multirow{2}{*}{Traffic shaping} & \multirow{2}{*}{ONOS, POX} & CC throughput, forwarding times increased by 3.8, 2.86 \\ \cline{3-5}  & & & & \\ \cline{3-5}  & & & & \\ \hline \multirow{4}{*}{Routing} & \multirow{2}{*}{AMPS [442]} & Application specific multipath flow routing & \multirow{2}{*}{AMPS} & High availability of high priority flow unloaded path \\ \cline{3-5}  & & & & \\ \cline{3-5}  & \multirow{2}{*}{DRL-QoS [443]} & Application QoS aware routing & — & Improved routing performance in complex networks \\ \cline{3-5}  & & & & \\ \cline{3-5}  & \multirow{2}{*}{App-RS [444]} & Application based routing parameters & Floodlight & Better performance for all application classes than CORouting \\ \cline{2-5}  & & & & \\ \cline{2-5}  & \multirow{2}{*}{DLB [446]} & Dynamic load-balancing considering congestion & Floodlight & High throughput, low delay, packet loss in fat-tree DCN \\ \cline{2-5}  & & & & \\ \cline{2-5}  & \multirow{2}{*}{ELB-MC [447]} & Controller load status, weight based balancing & Floodlight & Low communication overhead in control plane \\ \cline{2-5}  & & & & \\ \cline{2-5}  & \multirow{2}{*}{ALBLP [448]} & Use link state prediction to compute paths & Ryu & 23.7\%, 11.7\% better than OSPF, Q-learning \\ \cline{2-5}  & \multirow{2}{*}{Aloba [449]} & Use resource consumption metrics & Floodlight & Low overhead while reaching network requirements \\ \cline{2-5}  & & & & \\ \cline{2-5}  & \multirow{2}{*}{S-ICM [450]} & Uses measured network delay of packets & Onix & Better at avoiding system saturation than HFA, RR \\ \cline{2-5}  & & & & \\ \cline{2-5}  & \multirow{2}{*}{SBLB [451]} & Maximize resource utilization, minimize user response time & Floodlight & Significant reduction in average response, reply time \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Network application scenarios.
# Rapid Planning of an Assembly Path by Reusing the Prior Path
## 3 The Path Planning Algorithm Based on PPR  
### Prior Path Reuse in the PPR Algorithm  
The PPR algorithm proposed in this paper improves the RRT * algorithm by developing some prior path reuse strategies to rapidly generate an asymptotically optimal assembly path by reusing the prior paths after the path exploring tree is expanded to the prior path space. The main steps of assembly path reuse in the PPR algorithm are as follows:  
Step 1. A new node \(x_{new}\) is generated with a given step \(S\) on the line connecting the random node \(x_{rand}\) and the nearest node \(x_{narr}\), as shown in Figure 4.  
Figure 4: Generation of \(x_{new}\). Figure 3: Prior path space. Step 2. The position of \(x_{new}\) in the configuration space is detected to determine the next step: (1) in the obstacle space \(C_{obs}\), return to Step 1; (2) in the free space \(C_{free}\), optimize the exploring tree with the RRT * algorithm; and (3) in the prior space \(C_{prior}\), proceed to Step 3 for path reuse planning. Step 3. Two connection nodes \(p_{connect}\) and \(x_{connect}\) are identified for path reuse by using a dual-tree fusion strategy on the prior path tree \(T_{prior}\) and the exploring tree \(T_{exploring}\), respectively, as shown in Figure 5.  
Step 4. The new branch connecting \(x_{connect}\) and \(p_{connect}\) is added to \(T_{exploring}\), and a path \(\Gamma_{i}\) obtained by backtracking is added to the prior path set \(\psi\), as shown in Figure 6.  
Step 5. The minimum cost path \(\Gamma_{min}\) is selected as the final path from \(\psi\) of all prior paths obtained after sampling \(N\) times. The cost of a path \(\Gamma\) is defined by the path length cost as \(Cost(\Gamma)=\sum_{i=1}^{n-1}dis(q_{i},q_{i+1})\), where \(dis(q_{i},q_{i+1})\) denotes the Euclidean distance. The above-mentioned steps illustrate the prior path reuse method in path planning from the start node \(x_{start}\) to the end node \(x_{end}\). After the exploring tree is expanded to the prior space, the node pair (\(x_{connect}\), \(p_{connect}\)) connecting the exploring tree and the prior path tree is determined by the dual-tree fusion strategy, and then a path expressed as \(x_{start}\)-\(x_{connect}\)-\(p_{connect}\)-\(x_{end}\) is obtained by backtracking from the end node \(x_{end}\) to the start point \(x_{start}\). The pseudo-code of the PPR algorithm (Algorithm 1) is as follows:  
Figure 5: Connection nodes. Figure 6: Path backtracking.
# Switched Observer Design for a Class of Non-Linear Systems
## 3 Switched Observer Design
### Limited Observer Gain Norm  
In actual applications, it is frequently undesirable for the LMI-based observer design to yield high observer gains. Since \(L_{\sigma}\) is not a decision variable, we are unable to directly restrict it; instead, we must limit its norm. Specifically, let \(\chi\) be the highest permitted norm for the observer gain \(L_{\sigma}\). Thus, the following inequality holds  
\[L_{i}^{T}L_{i}=M_{i}^{T}P_{i}^{-2}M_{i}\leq\chi I\]  
From the Schur complement, we obtain  
\[\begin{bmatrix}\chi I&M_{i}^{T}\\ M_{i}&P_{i}^{2}\end{bmatrix}\geq 0. \tag{50}\]  
As the matrix inequality in (50) quadratically depends on the decision variable \(P\), we propose the following relaxation  
\[P_{i}-\epsilon I\geq 0\qquad\begin{bmatrix}\chi I&M_{i}^{T}\\ M_{i}&\epsilon^{2}I\end{bmatrix}\geq 0 \tag{51}\]  
that has to be jointly solved along with the above LMIs in order to confine the norm of \(L_{i}\), with \(\epsilon>0\) denoting a given small positive scalar that the designer will assign.  
On the other hand, an optimization problem may be used to find a solution to the stabilization problem that maximizes the estimation \(\hat{\mathcal{X}}\) of the stability region. Since \(\hat{\mathcal{X}}_{j}\) is an ellipse, we can maximize the volume of \(\hat{\mathcal{X}}\) by formulating an convex optimization problem. An algorithm for the switched observer design with an estimate stability region is provided below, based on the preceding developments.  
**Remark 2**: _Notice that Algorithm 1 sets noteworthy improved features on a flexible design framework in terms of LMIs by ensuring not only exponential stability and guaranteed \(H_{\infty}\) cost for the estimation error dynamics, but also optimum guaranteed stability regions of convergence._
## 1 Introduction
Although some prior work on the colorimetric sensing of edible oil VOCs has been performed, earlier research mainly focused on VOCs released by degraded oil using a sensor made with a single colorimetric indicator. [PERSON] et al. fabricated a Schiff's reagent-loaded poly(vinyl alcohol) nanofiber mat to detect aldehyde VOCs released during oil oxidation [28]. Alternatively, [PERSON] et al. reported a hydroxylamine sulfate pH indicator-impregnated chitin film for the same purpose [29]. Recently, colorimetric VOC sensor arrays have been introduced to detect adulteration in edible oil. [PERSON] et al. used a nine-unit colorimetric VOC sensor comprising pH indicators and metalloporphyrins to detect soybean oil and sesame oil adulteration in extra virgin olive oil [30]. [PERSON] et al. reported a six-unit pH indicator-based VOC sensor to detect sunflower oil and sesame oil adulteration in quince oil [31]. Both studies reported achieving satisfying results. However, little is known about the applicability of colorimetric sensors in analyzing the processing-originated differences in the aroma of fragrant sesame oil or the detection of refined oil in fragrant sesame oil. Here, we present a 14-unit paper-based colorimetric sensor for sesame oil VOC analysis, in which each sensing unit could act as a colorimetric \"receptor\" (Figure 1a). We report the first sensor multiplexing study that combines three groups of colorimetric indicators into one sensor array and targets key VOCs in sesame oil. pH indicators, carbonyl-sensitive indicators, and solvatochromic dyes were used to build this sensor array. The sensor was validated with both chemical standards and 55 commercial sesame oil samples. The results generated by the sensor were compared to gas chromatography-mass spectrometry (GC-MS) data and analyzed by both cluster and regression analyses. Experimentally, the sensor demonstrated the potential to be a simple and rapid tool for the visualization of the VOC profile of fragrant sesame oil. On one hand, it could reveal the difference in aroma caused by the processing technique; on the other hand, it could also detect the change in the VOC profile due to the presence of refined oil in fragrant sesame oil (Figure 1b).
# A Baseband Wireless Spectrum Hypervisor for Multiplexing Concurrent OFDM Signals
## 3 Multiplexing Concurrent OFDM Signals
\begin{cases}\rho_{k}B_{k}(i),&0\leq i\leq\frac{N_{\text{vPHY}}}{2},\\ \rho_{k}B_{k}(i),&N-\frac{N_{\text{vPHY}}}{2}+1\leq i\leq N-1,\\ 0,&\text{otherwise}.\end{cases}  \tag{8}\]  
Next, after the down-sampling operation the complex periodic signal \(e^{\frac{-2\pi ni}{N}}\) has its periodicity reduced from \(N\) to \(N/M\) samples, \(e^{\frac{-2\pi ni}{N/M}}\), and therefore, we can rewrite (7) as  
\[y_{\text{wBB}}^{\text{\tiny{$\mathcal{I}_{k}$}}}(nM)=\frac{1}{N} \left[\sum_{i=0}^{N_{\text{vPHY}}-1}F(i)e^{\frac{-2\pi ni}{N/M}}\right.\] \[+\sum_{i=N_{\text{vPHY}}}^{2N_{\text{vPHY}}-1}F(i)e^{\frac{-2 \pi ni}{N/M}}+\cdots+\sum_{i=(M-1)N_{\text{vPHY}}}^{MN_{\text{vPHY}}-1}F(i)e^ {\frac{-2\pi ni}{N/M}}\Bigg{]} \tag{9}\] \[=\frac{1}{N}\sum_{i=0}^{N_{\text{vPHY}}-1}\left[\sum_{n=0}^{M-1} F(i+mN_{\text{vPHY}})\right]e^{\frac{-2\pi ni}{N/M}},\] \[-N_{\text{CP}}\leq nM\leq N-1.\]  
By recalling the definition of \(F(i)\) given by (8), we realize that only half (i.e., \(\frac{N_{\text{vPHY}}}{2}\)) of the first and last terms of the summation in (9) are different from 0, and therefore, it can be re-written as  
\[y_{\text{wBB}}^{\text{\tiny{$\mathcal{I}_{k}$}}}(nM)=\frac{1}{N} \sum_{i=0}^{N_{\text{vPHY}}}F(i)e^{\frac{-2\pi ni}{N/M}}\] \[+\frac{1}{N}\sum_{i=\frac{N_{\text{vPHY}}}{2}+1}^{N_{\text{vPHY }}-1}F\left(i+(M-1)N_{\text{vPHY}}\right)e^{\frac{-2\pi ni}{N/M}}, \tag{10}\] \[-N_{\text{CP}}\leq nM\leq N-1.\]  
Finally, by using (8) and remembering the fact that \(N/M=N_{\text{vPHY}}\), we show that the time-domain representation of the \(k\)-th narrow sub-band, \(B_{k}(i)\), is recovered by the \(k\)-th branch of the channelizer (see Figure 5)  
\[y_{\text{nBB}}^{\text{\tiny{$\mathcal{I}_{k}$}}}(n)=\frac{1}{N} \sum_{i=0}^{N_{\text{vPHY}}-1}\rho_{k}B_{k}(i)e^{\frac{-2\pi ni}{N_{\text{vPHY }}}}, \tag{11}\] \[-N_{\text{CP}}^{\text{vPHY}}\leq n\leq N_{\text{vPHY}}-1,\]  
where \(N_{\text{CP}}^{\text{vPHY}}=N_{\text{CP}}/M\) is the cyclic prefix length of the vPHYs. It is important to mention that this analysis is the same for any other narrow sub-band centered at any of the \(N\) subcarriers of the hypervisor's IFFT module. Additionally, it is also important to notice that each one of the narrow sub-bands, which are multiplexed by the hypervisor, can have different widths. The only requirement is that the width of individual sub-bands is a divisible multiple of \(N\)In this case, if a device is receiving more than one of the multiplexed narrow sub-band signals, then, it has to employ a non-uniform channelizer [20], as polyphase filter-bank channelizers only extract equally spaced spectrum chunks.