esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Materials  
CBN (\(>\)98%) and MG-132 were purchased from ChemFaces Biochem. (Wuhan, Hubei, China) and Cayman Chem. (Ann Arbor, MI, USA), respectively. Dimethyl sulfoxide (DMSO), collagen (type I), luciferin/luciferase, PD98059, SB203580, SP600125, bovine serum albumin (BSA), prostaglandin E\({}_{1}\) (PGE\({}_{1}\)), (E)-3-(4-methylphenylsulfonyl)-2-propenenitrile (BAY11-7082), heparin, aspirin and 5,5-dimethyl-1 pyrroline N-oxide (DMPO) were purchased from Sigma (St. Louis, MO, USA). The anti-phospho-p38 MAPK Ser182 polyclonal antibody (pAb), anti-I\(\kappa\)B\(\alpha\) (44D4), anti-phospho-I\(\kappa\)B\(\alpha\)(Ser32/66) (SA5), anti-phospho-c-JNK (Thr183/Tyr185), anti-p38 MAPK and anti-NF-\(\kappa\)B p65 monoclonal antibody (mAb), as well as anti-phospho-p44/p42 ERK (Thr202/Tyr204) and anti-phospho-NF-\(\kappa\)B p65 (Ser36) pAb, were purchased from Cell Signaling (Beverly, MA, USA). The anti-\(\alpha\)-tubulin mAb was purchased from NeoMarkers (Fremont, CA, USA). CBN was dissolved in 0.1% DMSO.
# Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
## 3 Data and Results
### Benchmark Functions  
Figure 3 shows the average convergence graph for the 30 runs for Rosenbrock, Pinter, G02, and Pathological functions of PSPSO compared with other PSO variants. In almost all functions, the PSPSO was able to find the solution before 25% of the total iterations. Of all the benchmark functions only in (f12) Pathological and (f19) Styblinski- Tang was the PSPSO not able to converge in less than 25% of the total iterations. However, for the Pathological function, the PSPSO was still able to outperform the others in finding a better solution in the end. Figure 2: Sample constant values of a single particle during the first 200 iterations of f20 Trigonometric 2. (**a**) **x** shows the function value of a selected random particle with the other particles (in red), the blue line indicates the best value. The values of this particle’s \(\omega\), \(\gamma\), and \(\lambda\) are shown in (**b–d**), respectively. The mean cost, standard deviation (std) of the costs, and average computational time in seconds of the 30 runs are recoded in Tables 8 and 9 for each PSO variant for every benchmark function. The results of Tables 8 and 9 are summarized in Table 10 by ranking the performance of each algorithm. It can be seen that in almost all functions that the PSPSO was ranked 1 except for separable, non-scalable, and multimodal functions where it ranked second. The PSOCO ranked second on almost all functions, performing better in discontinuous, separable, non-scalable, and multimodal functions but performed poorly in constrained function. Overall, the PSPSO ranked first, followed by the PSOCO, CPSO, and APSO tied on third, ScPSO and PSO on fifth, and lastly WICPSO. Figure 3: Average convergence of partitioned step particle swarm optimization (PSPSO) compared with different variants of particle swarm optimization (PSO) for (**a**) Rosenbrock, (**b**) Pinter, (**c**) G02, and (**d**) Pathological.
and operation. Consequently, both the service stratum and the transport stratum of an IMS/NGN network have to be properly designed so that strictly determined values of CPP parameters are not exceeded, which would result in dissatisfaction of users with the provided services.  
To achieve this aim, it is necessary to determine how network and traffic source parameters affect guaranteed CPP parameters, which is the goal of this paper. We use our analytical traffic model of a multidomain IMS/NGN network with an MPLS-based transport stratum, proposed in [11], to thoroughly examine the impact of all input parameters on mean Call Set-up Delay (\(E(CSD)\)) and mean Call Disengagement Delay (\(E(CDD)\)) calculated for various types of successful call scenarios. The performed research and discussion enable the indication of parameters that are very important, less important and irrelevant from the point of view of the network operator and designer.  
The rest of the paper is organized as follows. Section 2 contains a review of the related work. Section 3 describes the structure and operation of our analytical traffic model of a multidomain IMS/NGN network with an MPLS-based transport stratum in terms of the performed investigations (presented in Section 4). Section 5 summarizes the paper.
## 2 Antenna Design
### Antenna Parametric Study  
#### 2.2.1 Parametric Study on Loaded Lumped Components  
In this subsection, two parametric studies are presented: on the value of the loaded inductor L3 and the two parameters of the equivalent model of the PIN diode Rp and Cp The simulation results are obtained by performing an EM-circuit co-simulation using HFSS, Figure 3.  
The proposed design procedure does not need an accurate measurement of the material properties. Using the results of the parametric studies, we will be able to compensate the shift in frequency between the simulation and the measurement results, and obtain the target resonant frequencies.  
The second parameter that controls the lower and the higher resonant frequencies of the antenna in the OFF state is the value of the inductor L3. The resonant frequency at 401.5 MHz can be achieved by using an inductor of 17 nH in the place of L3, as shown in Figure 4a. We see that the higher resonant frequency shifts to lower frequencies when the inductance L4 increases, Figure 4b. This frequency shift can be compensated by adjusting the length l\({}_{2}\) of the stub-like. With l\({}_{2}\) = 23 mm, we obtain a resonant frequency at 866 MHz.  
As above-mentioned, l\({}_{1}\) is set to 14.3 mm to obtain a resonant frequency a little bit higher than 466 MHz in the ON state of the PIN diode. This makes tuning the resonant  
Figure 4: Simulated input reflection coefficient of the antenna for varied parameter L3 when PIN diode is OFF (Cp = 0 fF, Rp = 15 K\(\Omega\) and Ls = 0.6 nH): (**a**) at lower frequency; (**b**) at the higher frequency.  
Figure 3: Schematic of the EM circuit co-simulation (HFSS) of the modeled antenna within enclosure, the PIN diode and the loaded lumped components: (**a**) PIN diode equivalent in ON state; (**b**) PIN diode equivalent model in OFF state.  
frequency much easier by increasing the inductance L4, than by tuning the length l\({}_{1}\). In Figure 4a, we see that when the diode is OFF and L3 = 0 nH, the antenna resonates at 484 MHz. However, when the diode is ON and L4 = 0 nH, L3 = 17 nH, the resonant frequency is 476 MHz, as shown in Figure 5. This means that the resonant frequency in the ON state depends also on the inductance L4, which is used for tuning the resonant frequency in the OFF state.
## 2 Materials and Methods  
### Microalga Initial Stock Cultivation Conditions  
The microalgae studied was procured from HEALTHALGAE Sweden AB (Uppsala, Sweden). Its cultivation was performed using Spirulina Medium, as described in [47]. It is composed of four (4) stock solutions (Table 1) and the ingredients used were procured by Sigma-Aldrich (St. Louis, MO, USA), Carl Roth (Karlsruhe, Germany), Chem-Lab NV (Zedelgem, Belgium), and ACS Chemicals (Ahmedabad, India). An initial stock of _A. platensis_ culture was axenically cultivated using the produced Spirulina Medium, with an initial pH of 9.4, constant aeration of \(\sim\)1 L/min, and illumination of \(\sim\)160 \(\mu\)mol photons m\({}^{-2}\)s\({}^{-1}\) of photosynthetically active radiation (PAR) in light/dark cycles of 12 h:12 h, using a full spectrum LED array (LUMATEK, ATTIS 200 W). Temperature was kept constant at 27 \(\pm\) 0.5 \({}^{\circ}\)C using a water bath.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Stock Solution I** & \multicolumn{2}{l}{**Stock Solution II**} & \multicolumn{2}{l}{**Stock Solution III**} & \multicolumn{2}{l}{**Stock Solution IV**} \\
**(500 mL)** & \multicolumn{2}{l}{**(500 mL)**} & \multicolumn{2}{l}{**(900 mL)**} & \multicolumn{2}{l}{**(100 mL)**} \\ \hline
**Component** & **Amount** & **Component** & **Amount** & **Component** & **Amount** & **Component** & **Amount** \\ \hline NaHCO\({}_{3}\) & 13.61 g & NaNO\({}_{3}\) & 2.50 g & ZnSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O & 1 mL of 1 g/L & FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O & 0.70 g \\ Na\({}_{2}\)CO\({}_{3}\) & 4.03 g & K\({}_{2}\)SO\({}_{4}\) & 1.00 g & MnSO\({}_{4}\)\(\cdot\)4H\({}_{2}\)O & 2 mL of 1 g/L & Na\({}_{2}\)-EDTA (Titriplex III) & 0.40 g \\ K\({}_{2}\)HPO\({}_{4}\) & 0.50 g & NaCl & 1.00 g & H\({}_{3}\)BO\({}_{3}\) & 5 mL of 2 g/L & Distilled water & \\  & & & & & 2 g/L & \\ Distilled water & & MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O & 0.20 g & Co(NO\({}_{3}\))\({}_{2}\)\(\cdot\)6H\({}_{2}\)O & 5 mL of 0.2 g/L & \\  & & CaCl\({}_{2}\)\(\cdot\)2H\({}_{2}\)O & 0.04 g & Na\({}_{2}\)MoO\({}_{4}\)\(\cdot\)2H\({}_{2}\)O & 5 mL of 0.2 g/L & \\  & & FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O & 0.01 g & CuSO\({}_{4}\)\(\cdot\)5H\({}_{2}\)O & 0.005 g/L & \\  & & Na\({}_{2}\)-EDTA & Na\({}_{2}\)-EDTA & Na\({}_{2}\)-EDTA & Na\({}_{2}\)-EDTA & \\  & & (Titriplex III) & 0.08 g & (Titriplex III) & 0.40 g & \\  & & SL III and SL IV & 5 mL & Distilled water & \\  & & Distilled water & & & & \\ \end{tabular}
\end{table}
Table 1: The composition of the stock solutions used for the preparation of Spirulina Medium [47].
## 5 Simulation Validation and Discussion  
In this section, the cooperative use of IMD and GPT is to be evaluated using PSCAD/EMTDC. Problem case scenarios representing drawbacks of connecting existing IMD and GPT are simulated, and these results are compared with simulations of implementation of the proposed cooperative operation method. Results demonstrate the feasibility of the cooperative use of IMD and GPT, and the operating characteristics of IMD and GPT according to the capacities of the inductor and capacitor will be discussed in further sections.
## 4 Modified Functional Observer with Unknown Input
### State-Space Structure of the Observer and the EID Estimator  
To cancel the negative effects of the unknown inputs, the estimated EID \(\hat{d}_{e}(t)\) can be newly constructed as  
\[\hat{d}_{e}(t)=\hat{u}_{f}(t)-u_{c}(t), \tag{36}\]  
where \(\hat{u}_{f}(t)\) is the estimate of the system input \(u_{f}(t)=u_{c}(t)+d_{e}(t)\). To achieve this goal, \(\hat{u}_{f}(t)\) and \(\hat{d}_{e}(t)\) are first estimated using the aforementioned extended method. Then, the desired control input \(u_{c}(t)\) is obtained by filtering the estimated EID through a well-designed low-pass filter.  
The accessorial variable \(\vec{d}_{e}(t)\) is updated in real-time by filtering \(\hat{d}_{e}(t)\) with the low-pass filter of the order \(n_{f}(\geq m)\), described by  
\[\dot{x}_{f}(t)=A_{f}x_{f}(t)+B_{f}\hat{d}_{e}(t), \tag{37a}\] \[\vec{d}_{e}(t)=C_{f}x_{f}(t), \tag{37b}\]  
where \(A_{f}\in\Re^{n_{f}\times n_{f}}\), \(B_{f}\in\Re^{n_{f}\times m}\), and \(C_{f}\in\Re^{m\times m_{f}}\) are the system, input, and output matrices, respectively.  
Notice that Equations (37a) and (37b) gives a general input-output form, in which \(x_{f}(t)\) does not have any direct physical implication and \(n_{f}\) depends on the particular structure of this subsystem, which depends on the application. Furthermore, it is necessary to take \(d_{e}(t)\) into account to estimate the system state \(\hat{x}_{e}(t)\), and the state observer can be constructed as  
\[\dot{\hat{x}}_{e}(t)=A\hat{x}_{e}(t)+Bu_{f}(t)+L_{c}[y_{c}(t)-\hat{y}_{c}(t)], \tag{38a}\] \[\hat{y}_{c}(t)=C\hat{x}_{e}(t)+Du_{f}(t), \tag{38b}\]  
where \(\hat{x}_{e}(t)\in\Re^{n}\) and \(\hat{y}_{c}(t)\in\Re^{p}\) are the estimated state and output vectors, respectively, and \(L_{c}\in\Re^{n_{f}\times p}\) is the estimation gain. With the estimated state \(\hat{x}_{e}(t)\), the control input in Equations (7a) and (7b) is derived as  
\[u_{c}(t)=u_{f}(t)-\vec{d}_{e}(t), \tag{39}\]  
with  
\[u_{f}(t)=-K_{c}\hat{x}_{e}(t)+E_{c}r(t), \tag{40}\]  
where \(K_{c}\) and \(E_{c}\) are determined using Equation (5) and by considering the system to be disturbance-free, since the EID \(d_{e}(t)\) of the unknown input and output disturbances as well as tracking errors have theoretically been merged to the control input terminal.  
**Lemma 1** ([22; 23]).: _Let \((A,B)\) be the pair for the given open-loop system, and let \(h\geq 0\) represent a prescribed degree of relative stability. Then, the eigenvalues of the closed-loop system \(A-B\overline{R}_{o}^{-1}\left(BP+N_{o}^{T}\right)\) will lie to the left of the vertical line \(-h\), with the matrix \(P>0\) being the solution of the Riccati equation_  
\[(A+hl_{n})^{T}P+P(A+hl_{n})-(PB+N_{o})\overline{R}_{o}^{-1}\left(B^{T}P+N_{o} ^{T}\right)+Q_{o}=0, \tag{41}\]_where the matrix \(I_{n}\) is the identity matrix, while \(N_{o}=C^{T}Q_{o}D\), \(\overline{R}_{o}=R_{o}+D^{T}Q_{o}D\), \(R_{o}>0\), and \(Q_{o}\geq 0\) are weighting matrices with appropriate dimensions for the corresponding optimal regulator design._
## 4 Multicast Routing Approaches in DTN
### Energy-Aware Multicast Routing in DTN
Node C will be selected as relay first because it has the highest residual energy, followed by node A with the second highest residual energy. EASDM selects a subset of nodes as forwarding nodes from the high-centrality node set and reserves the remaining nodes for future use. Node E is further chosen as relay due to the highest residual energy in the low-centrality set. It will select relays from the low-centrality node set if the constraint is not satisfied. This scheme supports multiple multicast sessions while maintaining the expected delivery rate. The author claimed that EASDM can sustain the network for a longer time by relieving some of the high centrality nodes. However, this scheme does not integrate awareness of the relay interest in the message. Energy-Aware Multiple-Data Multicast: [PERSON] et al. proposed the Energy-Aware Multiple-Data Multicast (EAMDM) along with EASDM in Reference [60]. The EAMDM is based on the MDM [33] that select relays based on social community characteristics. The differences between EASDM and EAMDM are that the latter includes the available buffer sizes of the potential relays and a priority metric based on the delivery time constraints of data items to make forwarding decisions. In a set of data items with the same delivery ratio, each data item has different size and delivery time constraint. A data item with a shorter delivery time constraint will have a higher priority to be relayed first. Potential relays will be selected from the encountered neighbors with enough buffer to accommodate this data item and high delivery probability to the destinations. A node with the highest residual  
Figure 11: Relay selection in the EASDM scheme [59]. energy will be selected as the relay. This process continues until all the performance constraint is satisfied before proceeding to the next data item with lower priority. Group Encounter Based PRoPHET: The authors proposed the Decentralized Post-Disaster Resource Management [61] (DPDRM) scheme to manage and exchange information on resources, such as medicines and food of each camp site. DPDRM utilizes smart phones carried by volunteers to provide inventory sharing and resource discovery functionalities. The inventory sharing module shares the resource inventories periodically to a specific group of nodes and the latter enables a query on a specific resource to be disseminated to get a quick response from any node holding resource inventories. Both modules employed Group Encounter Based PRoPHET (Ge-PRoPHET) routing protocol to facilitate sharing of resource inventories and query dissemination. Ge-PRoPHET is adapted from PRoPHET to provision group-based multicasting (groupcasting) for post-disaster communication based on group delivery predictability (GDP), and replicates messages only if the encountered nodes have a better chance of meeting any member node of a destination group. This scheme achieves a reduction in average energy consumption due to transmission by minimizing unintended replication of resource inventories and queries.
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
## 5 Results
### IR-UWB Transmitter
#### 5.1.2 Link Budget for Uplink  
It is important to analyze how the transmitter would fit in the complete chain, with a transmit/receive antenna pair, communication through 3-7 mm of skin, and an external receiver. For this purpose, we look at the link budget. Our IR-UWB transmitter delivers \(-27\) dBm. The FCC regulations impose approximately a \(-9\) dBm limit for a 2 GHz bandwidth in the UWB 3-10 GHz spectral mask of \(-41.3\) dBm/MHz. For the \(-27\) dBm output, the consumed power is 0.15-0.3 mW on 1.3 V supply. The S21/link loss through 3 mm skin is found to be approximately 20 dB from electromagnetic simulations (the worst-case scenario at 7 mm skin thickness gives an extra 5 dB loss around the target 3-5 GHz band), which means that \(-47\) dBm will reach the LNA of the external receiver or \(-52\) dBm at 7 mm skin thickness. This seems well within reach. A similar antenna skin interface design was shown in [9], which agrees with our simulated loss. The simulation was carried out in SIMULIA Studio Suite with antenna dimensions of 12 mm by 12 mm, thickness of 1 mm and insulator coating of 1 mm. Reported UWB receiver IC sensitivities are about \(-70\) dBm to \(-65\) dBm [23; 24; 39], which implies that we have an excess margin of 18-23 dB for our application at 3-7 mm assuming a \(-70\) dBm sensitivity of the external receiver. BER performance assumes that, at this sensitivity, the \(<\)\(10^{-3}\) target is reached [23; 24], otherwise the sensitivity is lowered, taking into account the 18-23 dB excess margin. Finally, error detection and protection methods such as linear and convolutional codes could further increase the link margin which is already in excess.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 2 Background  
The fundamental issues of laser-generated ultrasonics and attractor-based analyses are briefly reviewed in this section. The framework for damage detection and quantification is provided. Transforming the dynamic properties of a structure into a geometric figure in the state space is shown to be especially useful for damage detection and quantification.
## 4 Results and Discussion
### Synthetic Workflows
In particular, the task \(T_{18}\) had an estimated peak memory occupancy higher than Spark's available unified memory of 5413.8 MB (i.e., corresponding to a heap size of 9.5 GB): this would bring the IIWM scheduling algorithm to allocate the task to a new stage, but memory would be saturated anyway. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Strategy**} & \multirow{2}{*}{**Task-Scheduling Plan**} & **Number** & **Time (min)** & **Peak Disk** & **Writes** & **On-Disk** \\  & & **of Stages** & **Time (min)** & **Usage (MB)** & **Duration (min)** & **Usage (MB)** \\ \hline \multirow{5}{*}{Full-Parallel} & \multicolumn{3}{c}{\((t_{0})\), \((t_{1}\parallel t_{2}\parallel t_{3})\),} & \multirow{5}{*}{10} & \multirow{5}{*}{356,106.60} & \multirow{5}{*}{11.56} & \multirow{5}{*}{126,867.06} \\  & \((t_{4}\parallel t_{5}\parallel t_{6}\parallel t_{7}\parallel t_{8}\parallel t_{9} \parallel t_{10})\),} & & & & \\  & \((t_{11}\parallel t_{21}\parallel t_{13}\parallel t_{14})\), & & & & & \\  & \((t_{15}\parallel t_{16}\parallel t_{17}\parallel t_{18}\parallel t_{19} \parallel t_{20}\parallel t_{21})\), & & & & & \\  & \((t_{22}\parallel t_{23}\parallel t_{24}\parallel t_{25}\parallel t_{26} \parallel t_{27}\parallel t_{28})\), & & & & & \\  & \((t_{23}\parallel t_{30}\parallel t_{31}\parallel t_{32}\parallel t_{33} \parallel t_{34}\parallel t_{41})\), & & & & & \\  & \((t_{35}\parallel t_{16}\parallel t_{27})\), \((t_{38}\parallel t_{39})\), \((t_{40})\) & & & & & \\ \hline \multirow{5}{*}{IIWM} & \multicolumn{3}{c}{\((t_{0})\), \((t_{1}\parallel t_{2})\), \((t_{3}\parallel t_{4}\parallel t_{5})\),} & \multirow{5}{*}{16} & \multirow{5}{*}{21.70} & \multirow{5}{*}{0} & \multirow{5}{*}{0} & \multirow{5}{*}{0} \\  & \((t_{7}\parallel t_{10}\parallel t_{6}\parallel t_{1}\parallel t_{9})\), & & & & & \\ \cline{1-1}  & \((t_{12}\parallel t_{11}\parallel t_{13})\), \((t_{15}\parallel t_{18})\), \((t_{19}\parallel t_{22}\parallel t_{23})\), & & & & \\ \cline{1-1}  & \((t_{24}\parallel t_{16}\parallel t_{17}\parallel t_{29})\), \((t_{25}\parallel t_{24}\parallel t_{30}\parallel t_{20})\), & & & & \\ \cline{1-1}  & \((t_{35}\parallel t_{21}\parallel t_{14})\), \((t_{28}\parallel t_{26}\parallel t_{27})\), & & & & & \\ \cline{1-1}  & \((t_{33}\parallel t_{22}\parallel t_{34}\parallel t_{31})\), \((t_{33}\parallel t_{36})\), & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Scheduling plan and statistics about execution times and disk usage with 14 GB of RAM. Figure 6: Average peak disk usage and execution time, varying the size of available memory. In such a situation, data spilling to disk cannot be avoided, but the IIWM tries to minimize the number of bytes written and the duration of I/O operations. Even in this scenario, the prediction model achieved very accurate results, shown in Table 11, confirming its forecasting abilities.
# A Hybrid Domain Color Image Watermarking Scheme Based on Hyperchaotic Mapping
## 2 Background
### Discrete Wavelet Transform  
Discrete wavelet transform (DWT) is a key technology in the field of signal processing and image processing, which realizes the multi-resolution analysis of signals or images [23]. In image processing, DWT can decompose the image into four sub-bands: LL, LH, HL and HH. The LL sub-band is rich in the main low-frequency information of the image, and the energy is concentrated and stable. Therefore, the LL sub-band is usually rotated during watermark embedding. The LH sub-band combines the characteristics of horizontal low frequency and vertical high frequency, which are suitable for edge detection. The HL sub-band is used for image enhancement because it carries horizontal high-frequency and vertical low-frequency information. HH sub-bands are sensitive to the texture and edge details of the image, but these sub-bands are also relatively vulnerable to attack. In order to further improve the security of watermark embedding, the LL sub-band can be further decomposed by a double DWT. Figure 3 shows an example of image discrete wavelet transform.  
Figure 3: Example of discrete wavelet transform: (**a**) original image; (**b**) first-order wavelet transform; and (**c**) second-order wavelet transform.  
Figure 2: Lorenz chaotic map encryption effect, (**d–f**) respectively (**a–c**), corresponding to the histogram.