esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Modeling Agents
### Micro-Grid Agent  
This agent must transmit information about energy costs to field layer agents; it is also responsible for monitoring the optimal performance of field layer agents while observing the constraints imposed by the upstream network and reducing the operating costs of the micro-grid.  
The electrical equilibrium equation is defined as follows:  
\[P_{T}(t)+P_{WPP}(t)+P_{WT}(t)+P_{INV}(t)-P_{REC}(t)\pm P_{NET}(t)=P_{ED}(t) \tag{3}\]  
The \(AC\) power in the inverter is calculated using Equation (4):  
\[P_{INV,\;AC}(t)=P_{Inv,\;DC}(t)\alpha_{Inv} \tag{4}\]  
Additionally, the \(AC\) power in the rectifier is obtained from Equation (5).  
\[P_{Rec,\;AC}(t)=\frac{P_{Rec,\;DC}(t)}{\alpha_{REC}} \tag{5}\]  
The thermal equilibrium equation is also defined as follows:  
\[P_{TT}(t)+P_{TFC}(t)+P_{B}(t)+P_{TS}(t)=P_{TD}(t) \tag{6}\]  
The micro-grid agent checks the above equilibrium equations, and the system must operate in such a way that the above conditions are met.  
The amounts of air pollutants emitted from the operation of micro-turbines, fuel cells, rubbish burning units, and the boiler in the micro-grid, in kg/kWh, are obtained from Equation (7):  
\[Emission=\sum\limits_{t=1}^{24}\{E_{T}(t)+E_{FC}(t)+E_{WPP}(t)+E_{B}(t)\} \tag{7}\]  
Micro-grid performance must be optimized with the following constraints:  
\[\frac{Emission}{\sum\limits_{t=1}^{24}P_{ED}(t)}\leq Emission_{max} \tag{8}\]  
where \(Emission_{max}\) is the maximum value of the pollutants, and is equal to 0.66 kg/kWh.  
The objective function of total costs of the micro-grid is defined by the following equation:  
\[\begin{array}{ll}\mathit{Obj.\;Function}=&\sum\limits_{t=1}^{24}\left\{ \mathit{C}_{f,T}(t)+\mathit{C}_{OM,T}(t)+\mathit{C}_{S,T}(t)+\mathit{C}_{f. \;FC}+\mathit{C}_{OM,FC}(t)+\mathit{C}_{S,FC}(t)\right.\\ &+\mathit{C}_{f,WPP}(t)+\mathit{C}_{OM,WPP}(t)+\mathit{C}_{S,WPP}(t)+\mathit{C }_{OM,WT}(t)+\mathit{C}_{OM,TS}(t)\\ &+\mathit{C}_{OM,HT}(t)+\mathit{C}_{OM,ES}(t)\}\end{array} \tag{9}\]  
It should be noted that the electrical power of micro-turbine agents, fuel cells, rubbish burning units, electrical storage agents, and \(P_{Net}\) are considered to be decision variables.
# Towards a Collection of Security and Privacy Patterns
## 3 Security Patterns
### Authorization
#### 3.5.2 Authorization Enforcer  
The Authorization Enforcer [14] describes who is authorized to access specific resources in a system, in an environment in which we have resources whose access needs to be controlled. It indicates, for each active entity that can access resources, which resources it can access and how it can access them.  
As depicted in Figure 13, the Subject class describes an active entity that attempts to access a resource in some way. The Protected Object class represents the resource to be protected. The association between the subject and the object defines an authorization, from which the pattern gets its name. The association class Right describes the access type (for example, read and write) the subject is allowed to perform on the corresponding object. Through this class, one can check the rights that a subject has on some object, or who is allowed to access a given object.  
The two most common implementations of this pattern are Access Control Lists and Capabilities. Access Control Lists (ACLs) are kept with the objects to indicate who is authorized to access them, while Capabilities are assigned to processes to define their execution rights. Access types should be application-oriented.  
Figure 13: Essential components of Authorization Enforcer pattern—class diagram [14].
# Pharmacokinetics of Marine-Derived Drugs
## 3 Pharmacokinetics Studies in Animals
### Absorption
#### 3.1.1 Algal-Derived Compounds
The predicted in vivo blood-brain barrier penetration indicated moderate absorption by the central nervous system [77]. Fucoxanthin (**5**) is an abundant carotenoid present in algae and diatoms. It shows multifunctional biological activity and has been used as a valuable nutrient [78; 79]. The pharmacokinetics of fucoxanthin isolated from _L. japonica_ was studied in mice after peroral administration (160 nmol per mice). At one hour after administration, metabolites fucoxanthinol and amarouciaxanthin A were detected in the plasma of mice. Although the T\({}_{\text{max}}\) for both metabolites was 4 h, the C\({}_{\text{max}}\) for fucoxanthinol was about twice as high compared with amarouciaxanthin A. The difference in the AUC between these two metabolites was approximately 1.4-fold [80]. Fucoxanthin (2 mg/kg) was quickly metabolized to fucoxanthinol, which was detected 5 min post-injection in rats. The AUC for fucoxanthin was about 2.5-fold higher when compared with fucoxanthinol. After peroral administration (65 mg/kg), the absorption rate was much slower. The same quick conversion of fucoxanthin to fucoxanthinol was noted. Both compounds were detected in plasma 0.5 h post-administration. The T\({}_{\text{max}}\) for fucoxanthin was 7.7 h vs. 11 h for fucoxanthinol (Table 2) [42]. Astaxanthin (**6**) (3,3'-dihydroxy-\(\beta,\beta\)-carotene4,4'-dione) is a common pigment existing in some bacteria, crustaceans (shrimps, lobsters), and fish (salmon), yet microalgae and phytoplankton are the most abundant sources of astaxanthin [81; 82]. It shows multiple pharmacological effects including anti-cancer [83], anti-diabetic [21], geoprotective [84], and neuroprotective [85] effects, and shows use for the treatment of ocular diseases [86]. Commercially available astaxanthin was i/v and perorally administered to rats. Differences were observed in astaxanthin pharmacokinetics after i/v injection (5-20 mg/kg) and peroral administration (100 and 200 mg/kg). The AUC after i/v injection at 20 mg/kg was y 55% greater than at 5 mg/kg. The C\({}_{\text{max}}\) after peroral administration at 200 mg/kg was higher than that at 100 mg/kg, but the difference, as well as the difference in T\({}_{\text{max}}\), were statistically not significant. The AUC after peroral administration at 200 mg/kg was about twice that at 100 mg/kg. The AUC of astaxanthin in plasma after peroral administration was 90.1% lower than after i/v injection to the portal vein of rats (Table 2) [52].
# VR Sickness Evaluation Method Using Recurrence Period Density Entropy
## 4 Discussion
To validate the significance of RPDE change values between the two sessions with distinct VR sickness, statistical tests were conducted, confirming the significance of RPDE changes between the two sessions. Therefore, although the RPDE values themselves may not serve as direct indicators of VR sickness, changes in individual RPDE values can be considered as indicators of VR sickness. In this study, the utility of RPDE was demonstrated by analyzing the cycle of heartbeats observed in ECG data. This study identified a relation between the changes in RPDE values derived from cardiac cycle data and VR sickness. However, this study has several limitations that need to be acknowledged. First, no direct correlation was observed between the RPDE values and SSQ scores, raising doubts about the reliability of RPDE as a dependable indicator. Second, the experimental data were limited. While the sample size was increased through bootstrapping, the scarcity of original experimental data samples may have led to estimated parameter distributions that did not accurately reflect the distribution of the actual population. Third, considering the potential influence of other unidentified factors on VR sickness, a cautious approach is required when using RPDE values alone to measure the sickness. Finally, the data used in this experiment included comparisons between groups with significant differences in VR sickness symptoms. However, typically, such comparisons rely on contrasting groups that have watched the content with those that have not. Therefore, additional validation is required to consider RPDE values as conventional indicators of VR sickness. Because of these limitations, ensuring confidence in the RPDE values derived from cardiac cycle data used in this study was challenging. Therefore, conducting expanded research using periodic physiological data, including ECG data, is crucial to verify the potential of RPDE as a novel and common metric. In conclusion, this study utilized ECG data, the most representative periodic biological data, to derive RPDE values and confirmed the relation between changes in RPDE values and VR sickness. These findings suggest the potential utility of RPDE values as a new metric for measuring VR sickness.
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
## 4 Query Processing
### Time Complexity for Query Processing  
Most of the previous queries are processed in two steps. First, the driver node traverses the GlobalRMOI to find the ReqPids, if needed. The average case for the first step is \(O(|\text{ReqPids}|\log x)\) and \(O(|\text{ReqPids}|k\log x)\) for multistage queries. Lookup query needs \(\lceil\textit{tpn}/\beta\rceil\) steps to find the ReqPids. Second, the worker nodes process the selected RDD partitions and conduct a global reduction for the multistage queries. For simplicity, we will show the complexity of the average cases for the sequential processing of the second step. Then, we will analyze the parallelism factor. The complexity of the range query is \(O(|\text{ReqPids}|R\log y)\), where \(R\) is the number of results. The parallelism depends on the distributions of the ReqPids, where the worst case is to have the maximum ReqPids on one worker node. The interval query has similar complexity except that it traverses the interval B-tree. The complexity of processing \(k\)-\(CRQ\) is \(O(|\text{ReqPids}|kR\log y+|\text{ReqPids}|k^{2}R)\). The first term is the complexity of the local reduction. During the local reduction, the data are being prepared for the global reduction by using a hash set on \(Tid\), which offers, in the average case, constant time for the add and contain operations. The second term is the complexity of the global reduction which is dominated by the Filter transformation. Spark leverages a hash map technique to keep the GroupBy complexity linear in the average case. The parallelism factor depends on the distribution of ReqPids among the cluster, especially on the local reduction. When \(LP=1\), there is no need for a global reduction, which reduces the complexity into \(O(|\text{ReqPids}|kR\log y)\). The other continuous queries have similar complexity, and the difference is in the LocalRMOI traversing. For the lookup query, the complexity of the average case is \(O((\textit{tpn}/\beta)p)\), and the parallelism factor depends on the number and the distribution of the required partitions to the number of the processors. The complexity of the average case of \(LTQ\) is \(O(s+m)\), where the worker nodes need to scan \(T\) and aggregate segments on \(Tids\) locally by using a hash map to keep the complexity linear. After that, they conduct a global aggregation on \(Tids\) and return the max length.
# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
## 3 Proposed Deep Learning-Based MIMO Receiver
### Complexity Analysis  
Let \(N_{ZF}\) be the number of times the ZF receiver is used out of \(N\) channel realizations during the inference phase, and \(N_{ML}=N-N_{ZF}\) be the number of times the ML receiver is adopted. Then, the average number of candidate searches in the demodulation process that determines the computational complexity of the proposed receiver is given by  
\[C_{\text{proposed}}=\frac{N_{ZF}}{N}C_{ZF}+\frac{N_{ML}}{N}C_{ML}. \tag{24}\]  
Figure 2: The block diagram of the proposed receiver.  
We use the number of real multiplications required for symbol replica generation and the calculation of squared Euclidean distance with the \(M\) modulation order as the measure of the computational complexity of both receivers, \(C_{ML}\) and \(C_{ZF}\). According to [27; 28], the ML receiver requires \(4N_{T}N_{R}M\) and \(2N_{R}M^{N_{T}}\) real multiplications for symbol replica generation and the calculation of squared Euclidean distance, respectively. Therefore, we use \(C_{ML}\) in (24) by \(4N_{T}N_{R}M+2N_{R}M^{N_{T}}\). On the other hand, for the ZF case, \(M\) and \(2M\) real multiplications are required for symbol replica generation and the calculation of squared Euclidean distance, respectively. Since those real multiplications are necessary for each data stream, we use \(C_{ZF}\) in (24) by \(3N_{T}M\). Consequently, the computational complexity of the proposed receiver can be expressed by  
\[C_{\text{proposed}}=\frac{3N_{ZF}}{N}N_{T}M+\frac{N_{ML}}{N}\Big{(}4N_{T}N_{R}M+2 N_{R}M^{N_{T}}\Big{)}. \tag{25}\]  
Considering that the ML receiver is adopted if the ZF receiver cannot guarantee a certain level of SER performance when the MIMO channel is given, increasing the threshold \(\eta\) leads to higher \(N_{ZF}\). Namely, using a value of \(\eta\) that is too large or too small would cause the proposed receiver to operate as the ZF receiver or the ML receiver, respectively. Therefore, it is important to use the appropriate threshold to compromise between error performance and computational complexity.
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 2 Related Work
Then the publisher adjusts the sending rate in a TCP-like way based on the received ACKs. Moreover, a retransmission-based reliable multicast transport for the publish/subscribe internet (PSI) architecture (RMITPSI) [30] was proposed to ensure the reliability of multicast transmission. However, it did not provide a congestion control method, nor did it give an appropriate multicast sending rate. For MobilityFirst architecture, [31] proposed a congestion control mechanism that adopts traffic source rate control and explicit congestion notification from routers. But, at the scale of large networks, the overhead of router feedback would be very large. In addition, some NDN-based congestion control mechanisms have been proposed. NDN supports native multicast delivery because data are able to flow back to the multi-interface at the same time as long as they have the same content name [32]. To avoid congestion, a link quality-based congestion control (LQCC) scheme [33] selects the optimal and stable path according to the defined dynamic metrics related to NDN communication and other metrics such as link utilization, cache store, and the mobility impact. A multipath forwarding strategy for content-centricnetwork was proposed by [34] for the purpose of fully utilizing available bandwidth. The collaborative multiple metric interface ranking scheme (CM2) [35] combines multiple metrics with different objectives and applies a decision-making function to rank each interface, which can deliver the requested content with less delay and less overhead. In addition, the new architecture of ICN makes it impossible to directly apply the traditional IP multicast congestion control mechanism. The provision of a congestion control mechanism for ICN multicast is therefore an urgent problem.
## 2 Experimental Approach and Optimization
### Chord Methods & Optimization
Figure 3: Sensor configuration mounted on the sensor plate and four included chord methods. If a method selection would be made for this wavelength range, the method 3 AP1 with the smallest deviation from the amplification factor one would be selected. The sensor distances given in Table 1 are the result of a numerical optimization based on the stated quality criterion \(Q_{Band}\). The distances between two adjacent sensors should be between 80 mm and 200 mm, and the length of the total arrangement should not exceed 550 mm. Each possible configuration is evaluated in 0.5 mm step sizes with a quality factor \(Q_{C}\) for each sensor configuration. First, all \(Q_{Band}\) are calculated and summarized in a matrix \(Q\):  
\[Q= \begin{pmatrix}Q_{1,1}&\cdots&Q_{1,n}\\ \vdots&\ddots&\vdots\\ Q_{m,1}&\cdots&Q_{m,n}\end{pmatrix}  \tag{4}\]  
Each row represents the values of \(Q_{Band}\) for each one-third octave band of a chord method (with \(n\) the total number of one-third octave bands). Consequently, \(Q\) contains four rows representing four chord methods (\(m=4\)). To be able to evaluate a sensor configuration, it is assigned a single value \(Q_{C}\). The variable \(i\) indicates the number for different chord methods:  
\[Q_{C}=\sum_{j=1}^{n}\min_{i}\bigl{(}Q_{i,j}\bigr{)} \tag{5}\]  
For each one-third octave band, the minimal \(Q_{Band}\) is included (column-wise in \(Q\)) and added up for all bands. The configuration with the lowest \(Q_{C}\) resulted in the specified sensor distances listed in Table 1. The four chords are used to achieve a subsequent optimization of the measurement result. For each one-third octave band, the measured value of the chord with the lowest \(Q_{Band}\) value is used and included into the spectrum of acoustic roughness. This procedure is subsequently referred to as optimized acoustic roughness.
# Sizing Methodology of a Fast Charger for Public Service Electric Vehicles Based on Supercapacitors
###### Abstract  
In this article, a new methodology for the sizing of a fast-charging station for electric vehicles is presented. The proposed method is applicable to public service vehicles on urban journeys. Its use has been conceived for vehicles equipped with an energy storage system based on supercapacitors (SCs), which are already functional in several countries. The proposed charging station also uses a bank of SCs of variable capacitance. During the study, mathematical expressions will be obtained for the electrical variables involved in vehicle charging: instantaneous current, peak current, charging time, dissipated energy, and the efficiency of energy transference. From these, each of the components of the system will be dimensioned: the capacitance of the charger with its different variable steps, the initial voltage of the charger, and the current smoothing inductor. The proposed charger presents the advantage of allowing energy to be evacuated to the electric vehicle very quickly and with high performance, all without using an external power source or high-power converters. The proposed architecture minimizes the disturbances that, with conventional methods, would appear on the electrical grid, preventing the installation of fast-charging stations at many grid nodes, as is currently the case. Finally, the charger control algorithm is considerably simplified as it only depends on the initial voltage of the vehicle's accumulator.  
fast-charging; supercapacitors; electrical analysis; electric public transport +
Footnote †: journal: Applied sciences
## Materials and Methods  
### Samples, Internal Standard, SPME  
Seven different red wines were obtained from various wineries in Iowa. Varieties included two Marechal Foch from separate wineries, two Frontenac from separate wineries, a St. Croix, a Vincent, and a Marechal Foch/Frontenac blend. Wines were not stored after initial opening for analysis. 3-nonanone (99%), CAS 925-78-0 (Sigma-Aldrich, St. Louis, MO, USA), was used as an internal standard (IS) for the semi-quantification of aroma compounds. 3-nonanone was chosen because the compound is odor-active and not present in these wine samples. The final concentration of IS in wine (0.206 mg/L) was achieved by adding 10 uL of IS in ethanol (82.5 mg/L) to each 4 mL of wine. Each wine sample bottle was opened immediately before each analysis, and triplicate runs were performed for each experiment (\(n=3\)). SPME fibers with seven different coatings were purchased from Sigma-Aldrich (St. Louis, MO, USA). These coatings included: 50/30 um Divinylbenzene (DVB)/Carboxen (CAR)/Polydimethylsiloxane (PDMS), 100 um PDMS, 7 um PDMS, 85 um Polyacrylate (PA), 65 um PDMS/DVB, 70 um Carbowax (CW)/DVB, and 85 um CAR/PDMS. All SPME fibers were 1 cm in length. Details of SPME fiber cores, coatings, and the internal structure can be found elsewhere [14].  
The optimized method used for the automated analysis of wine aroma used a 1 cm 50/30 um DVB/CAR/PDMS SPME fiber, 10 min extraction time at 50 \({}^{\circ}\)C, and 2 min desorption in the heated GC inlet. A 10 min incubation in the heated agitator was used to equilibrate VOCs in the headspace of 4 mL of the wine sample in a 10 mL vial, facilitated by the addition of 2 g of sodium chloride.
# Detail-Preserving Shape Unfolding
## 4 Implementation Details
### Initial Exploration  
To evaluate the performance of the proposed method for automatic mesh unfolding, we first discuss the effects of parameter \(\beta\). Intuitively, it affects the local rigidity and the quality of unfolding for a given mesh.  
In Figure 2, the Human model (a) is deformed with three different \(\beta\) which are \(5\times 10^{5}\), \(1\times 10^{6}\) and \(2\times 10^{6}\), and the corresponding results are shown in (b), (c) and (d) respectively. Each of them are obtained with 25 iterations. From these results we can observe that small \(\beta\) helps to stretch the mesh, however many details are lost, while large \(\beta\) facilitates the shrinkage of the shape, but it goes against mesh unfolding. The energy curve defined in Equation (3) with \(\beta\) = 1 \(\times\) 10\({}^{6}\) is shown in Figure 2e. We can clearly see that the energy is almost converged after 15 iterations.  
From the above discussion we can conclude that choosing a small \(\beta\) results in mesh overstretching in the early iterations and enlarging \(\beta\) is a trade-off strategy during iterations. A straightforward strategy is to increase \(\beta\) per iteration. But it is difficult to design a universal increasing function for \(\beta\) which is suitable for all meshes. Another strategy is to use cascading algorithm with maximum 15 iterations for each cascade. In each cascade, the value of \(\beta\) remains the same and it is increased in the next cascade. Figure 3 illustrates the deformed results with cascading algorithm for the same model shown in Figure 2. The shape in (a) is deformed through four cascades with initial \(\beta\) = 5 \(\times\) 10\({}^{5}\), which is increased two times in each cascade. The corresponding results are shown in (b)-(e) and energy curve is plotted in (f). Experiments demonstrate that the cascading algorithm can obtain better results compared with the fixed value of \(\beta\). However, cascading algorithm costs more extra running time. Fortunately, the computation efficiency of our model can be improved by parallel computation in each cascade. Figure 4 illustrates the influences of different initial \(\beta\). We can obtain a series of unfolding results through tuning parameter \(\beta\), which can meet the different requirements of users.  
Figure 2: The influences of \(\beta\). (a) is the original mesh. From (b–d) are the mesh unfolding results with \(\beta\) = 5 \(\times\) 10\({}^{5}\), 1 \(\times\) 10\({}^{6}\), 2 \(\times\) 10\({}^{6}\), respectively. (e) shows the convergence curve of the objective function with \(\beta\) = 1 \(\times\) 10\({}^{6}\).