esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Antioxidant Capacity: DPPH, ABTS and FRAP Results  
The antioxidant capacity of the samples was studied using the FRAP, ABTS and DPPH assays (Figure 2). The DPPH assay showed antioxidant capacities in the range of 507-850 \(\mu\)mol Trolox g\({}^{-1}\) FW, lower than the values obtained for FRAP (743-1083 \(\mu\)mol Trolox g\({}^{-1}\) FW) and ABTS (679-1003 \(\mu\)mol Trolox g\({}^{-1}\) FW) analysis, as has been reported previously[30]. The DPPH and ABTS assays showed different antioxidant activities. The raspberry extracts had higher ABTS values relative to the DPPH scavenging activity, meaning there may be compounds in the samples that were responsible for the difference. Similar results were obtained by using these three methods in four tropical leafy vegetables[31]. In this sense, the ABTS assay measures the direct free radical inhibition by all the antioxidants in the raspberry extract (hydrophilic and lipophilic), whereas the DPPH assay is only applicable to hydrophobic systems[32; 33].  
The results of this work revealed a considerable variation in the antioxidant activity of the different raspberry cultivars. In the FRAP and DPPH assays, a similar trend was observed for all the samples,  
Figure 2: Total polyphenol content (TPC) (mg gallic acid equivalents (GAE) 100 g\({}^{-1}\) FW), 2,2-azinobis (3-ethylbenzothazoline-6-sulfonic acid) diammonium salt (ABTS), ferric-reducing antioxidant power (FRAP) and 2,2-diphenyl-l-picrylhydrazyl (DPPH) (\(\mu\)mol Trolox 100 g\({}^{-1}\) FW) measurements for different raspberry cultivars from different origins; FW: Fresh weight. Results are expressed as mean \(\pm\) standard deviation of three replicates for each sample (\(n=3\)). Different letters (a,b,c,d,e) represent statistically significant differences (\(p<0.05\)).  
the antioxidant capacities being maximal in the \"Adelita\" samples (S1 and S9), which could be due to their higher TPC values as previously described. The main phenolic compounds of raspberries may block free radicals and prevent the reactions caused by a single active oxygen atom [14]. On the other hand, \"Lupita\", \"Enrosadira\" and \"P04\" from Spain were always the cultivars with the lowest antioxidant capacity, their results being in line with the low values of TPC obtained for these samples previously reported in this work. Considering the TPC and antioxidant capacity values of the samples measured by FRAP, ABTS and DPPH methods, no clear differences were observed between the raspberries corresponding to the \"Adelita\" cultivar and the samples belonging to the other evaluated cultivars. Thus, a multidisciplinary statistical approach is proposed in this study.
# Lightweight Design of Multi-Objective Topology for a Large-Aperture Space Mirror
## 3 Application Examples of the Topology Optimization Method
### Topology Optimization Results  
The topology optimization results tend to converge through 53 iterations, as shown in Figure 3. Figure 4 shows the optimal material distribution of the primary mirror. The material in the red area makes a great contribution to structural stiffness and needs to be retained or enhanced, and the material in the blue area has little effect on structural stiffness and should be removed. From the topology optimization results, we can infer that the elements on the lines between the three fixed surfaces should be preserved, and the inner ring and outer ring should be connected with the elements between the three fixed surfaces. The elements with high relative density form a triangular net configuration, which is generally considered to possess a high stiffness.  
Figure 1: Primary mirror mounted on bipod flexures.  
Figure 2: The 2/3 FEM model of the primary mirror.  
According to the relative densities, the elements retained in the topology optimization result are divided into three types of ribs, as shown in Figure 5. The initial design based on topology optimization is shown in Figure 6a. Although topology optimization has led us to obtain a conceptual optimal design, it could not be implemented in engineering directly because of the unequal quilting effects caused by the unevenly distributed ribs during mirror fabrication [13].  
In order to reduce the local deformation, we add auxiliary ribs into the topology optimization. And we make some lightening holes in the material concentration area for further weight loss. The specific design criteria can refer the following:  
1. The additional ribs pass through the center of the areas without material filling to reduce the local deformation;  
Figure 4: Optimal material distribution of the primary mirror.  
Figure 5: Material distribution under different relative densities.  
Figure 3: Iteration history of topology optimization.  
2 The additional ribs pass through the intersection of the main ribs to shorten the transmission path of the force;
3 The arrangement of the additional ribs must not affect the cyclic symmetry of the mirror;
4 Increasing lightening holes in the areas where the material is excessively concentrated.  
Then we construct the verification model using computer-aided design (CAD) software, as shown in Figure 6b.
## 4 The Design of the QKD-Secured V2I System
Angoka uses its ZAP protocol to authenticate RSU devices via their unique DAU-supplied identity and symmetrical keys stored in the KMS. This forms a device private network (DPN) for secure communications. 4. Vehicles entering the DPN are similarly authenticated using ZAP and can then communicate to the RSUs. The Angoka gateway is a computer and networking switch running the Angoka software. It is installed alongside the University of Warwick's V2I server; see Figure 6  
To test the encryption, the following procedures were performed:  
1. A route was planned that had the research vehicle move from a campus starting point and travel the campus one-way road system to exit the campus. The use of the testbed's CPM capability provides awareness of hazards and obstructions for the driver. 2. A compromised RSU was used to transmit faked CPM messages to influence the driver's actions. 3. The aforementioned steps were performed with both the Angoka ZAP turned off and turned on. The above-mentioned tests are used to verify  
* The operation of the V2I system, without security, when it is not under attack;
* How the system can be influenced by faked V2I messages;
* That the QKD-derived Angoka ZAP security protocol functions in the V2I system;
* That the QKD secure system prevents the faked V2I messages.
###### Abstract  
Monin-Obukhov similarity theory (MOST) overestimates the mean vertical velocity gradient in some atmospheric stable conditions, i.e., Richardson number \(R_{f}<0.25\). To obtain a given hub-height inflow velocity for a certain roughness length, this overestimated velocity gradient underpredicts the friction wind speed and the turbulence intensity, potentially influencing wake modeling of a wind turbine. This work investigates the side effects of the breakdown of MOST on wake modeling under stable conditions and makes some modifications to the flow similarity functions to eliminate these side effects. Based on a field measurement in a wind farm, we first show that MOST predicts a larger velocity gradient for the atmospheric stability parameter \(\zeta>0.1\) and proposes new flow similarity functions without constraining \(R_{f}\) to limit the overestimated velocity gradient. Next, different turbulence models based on MOST and a modified one based on the new similarity functions are investigated through numerical simulations. These turbulence models are combined with the actuator disk model (AD) and Reynolds-averaged Navier-Stokes equations (RANS) to model wind turbine wakes under stable conditions. As compared to measurements, numerical results show that turbulence models based on MOST result in a larger wake deficit and a slower wake recovery rate with a root-mean-squared error (RSME) of wake deficit in the range of 0.07 to 0.20. This overestimated wake effect is improved by applying the new similarity functions, and the RSME of wake deficit is reduced by 0.05 on average.  
wind turbine; wake; MOST; turbulence models; similarity functions; stable conditions; wind gradient +
Footnote †: journal: Applied Physics Letters
## 3 System Model
### UAV Power Consumption  
The total power consumption during time period \(t\) is given by Equation (5):  
\[P_{total}(t)=\sum_{i=1}^{V}P_{i}(t)+P_{UAV}^{(V)}(t) \tag{5}\]  
where \(P_{UAV}^{(V)}\) is the power required for operating the UAV during time period \(t\), and \(P_{i}(t)\) denotes the power consumption in the \(i\)th time slot when \(V\) vehicles transmit data or local models to the UAV. The variation in UAV power \(P_{i}(t)\) is modeled in Equation (6), which depends on the uplink power, downlink power, processing power, and flying power:  
\[P_{i}(t)=u_{i}(t)\left(\sum_{k=1}^{k}\gamma_{k}\psi_{i,k}(t)+P_{UAV}^{(1)}(t)\right) \tag{6}\]  
where \(u_{i}(t)\) is a binary variable; \(u_{i}(t)=1\) indicates that in the \(i\)th time slot, the UAV is processing the data from a vehicle, and \(u_{i}(t)=0\) indicates that the UAV is idle which leads to the wastage of energy. Moreover, \(\psi_{i,k}(t)\) denotes the size of the \(k\)th type of vehicular data served by the UAV in the \(i\)th time slot. In addition, the coefficient \(\gamma_{k}\psi_{i,k}(t)\) determines the power consumption during the uplink and downlink of the \(k\)th type of vehicular data, which can either be a local model for different sensor data, basic safety message (BSM), or co-operative perception message (CPM). This paper aims to minimize the weighted total energy consumption by jointly optimizing the transmission window (\(L_{w}\)), transmit power (\(P_{i}(t)\)), and UAV trajectory (\(\mathbf{q}_{(x,y)}\)) while considering the Doppler spread. The UAV energy consumption depends on its flying speed, flying duration, flying time, and communication with vehicles [68]. The trajectory coordinates traversed by UAV are denoted by \(\mathbf{q}_{(x,y)}\) with the flying time upper bound by \(\mathcal{T}\). The trajectory coordinates including all flying and turning points are represented as \(\mathbf{q}_{(x,y)}\in\left[\mathbf{q}_{(0,0)},\mathbf{q}_{(0,1)}\ \cdots\ \mathbf{q}_{(1,0)},\mathbf{q}_{(1,1)}\ \cdots\ \mathbf{q}_{(m,m)}\right]\).
## 3 Coordinated Control Scheme for the Energy Router
### Reference Voltage and Current Compensation Strategy for Mode Switching of the Energy Router
#### 3.2.1 Reference Voltage Compensation  
It is assumed that \(u_{d\_i}\) and \(u_{q\_i}\) are the \(d\) axis and \(q\) axis voltage components generated by the PI regulators of the inner current loop before switching from PQ control to V/f control, while \(u_{d\_u}\) and \(u_{q\_u}\) are the \(d\) axis and \(q\) axis voltage components generated by PI regulators after switching. The PI regulator outputs are in steady state before the main converter switches from PQ control to V/f control. After switching, PI regulator will adjust its outputs from zero to new steady state gradually. This process requires a certain amount of time for adjustment. In order to avoid an abrupt change of the PI regulator outputs during the course of mode switching and realize smooth transition of the voltage modulation signal \(u_{d}\) and \(u_{q}\), the compensation items are added into Equation (4) as shown in Equation (6),  
\[\left\{\begin{array}{l}u_{d}=u_{sd}+\omega Li_{q}+u_{d\_i}+u_{d\_u}\\ u_{q}=u_{sq}-\omega Li_{d}+u_{q\_i}+u_{q\_u}\end{array}\right. \tag{6}\]  
It can also be utilized when the main converter switches from V/f control to PQ control. Equation (6) indicates that the PI regulator outputs before switching are added as compensation during the course of switching. When the main converter switches from PQ control to V/f control, \(u_{d\_i}\) and \(u_{q\_i}\) are used as the compensation items \(u_{d\mathrm{com}}\) and \(u_{q\_i}\) as shown in Figure 5. When the main converter switches from V/f control to PQ control, \(u_{d\_u}\) and \(u_{q\_u}\) are used as \(u_{d\mathrm{com}}\) and \(u_{q\_\mathrm{com}}\).  
Figure 4: Control schematic of the DC/AC converter.
## 4 Simulation Results and Analyses
### Simulation Results  
Figure 9 shows the real solar irradiance and PV cell temperature during the day of 28 May 2019 at Compiegne, France.  
The steady DC bus voltage is 400 V and it reaches a steady state at 9:05, as illustrated in Figure 10, which proves that the strategy works well to balance the powers.  
To extend battery life, the storage current should be limited. Figure 11 presents the storage current limitation for a value of 115 A.  
Storage _soc_ and distribution coefficient \(k\) are shown in Figure 12 where \(k=1\) if only the storage is working and \(k=0\) if only the public grid is working. If \(k\) is between 0 and 1, the power is shared simultaneously between the storage and the public grid during the current limitation periods. To reduce storage damage _soc_ upper limit is 80% and _soc_ lower limit is 20%.  
The powers' evolution is recorded, and the powers' flow is shown in Figure 13. PV sources work in two modes: PV-constrained production control and PV-MPPT control. When the storage and the public grid reach their upper limits, the PV production should be constrained [32].  
Figure 10: DC bus voltage evolution.  
Figure 9: Solar irradiance and PV cell temperature.  
Figure 11: Storage current evolution.  
Figure 12: Storage _soc_ and distribution coefficient \(k\).  
Figure 13: The power flow of DC microgrid.  
Furthermore, \(p_{S}<0\) means that the storage charges EVs, \(p_{S}>0\) represents that PV sources inject excess energy into the storage, \(p_{G}<0\) shows that PV sources and the storage cannot provide sufficient energy for EVs so that the public grid needs to inject electricity into EVs, and \(p_{G}>0\) illustrates that the storage has reached its upper limit so that the excess PV energy is sold to the public grid.  
The results show that the power management strategy is well respected.
## 5 Experimental Results and Discussions
### Evaluation Measures
#### 5.2.5 Residual Energy  
After each data transmission and sensing, some amount of energy is lost in the SNs and the remaining energy availed in the SNs is termed as residual energy. The mathematical expression for _Average_\(\lambda_{\varepsilon}\) is given as,  
\[Average\;\lambda_{\varepsilon}=\frac{1}{q}\sum_{z\in q}\lambda_{\varepsilon}^{z} \tag{32}\]  
The term \(\lambda_{\varepsilon}^{z}\) represents the residual energy availed in the SNs and the amount of sensor node is denoted \(q\).
_Article_  
**Comparative Study of Structural Reliability Assessment Methods for Offshore Wind Turbine Jacket Support Structures**  
**[PERSON] 1\({}^{\copyright}\)**, **[PERSON] 1\({}^{\copyright}\)**, **[PERSON] 2, [PERSON] 3\({}^{\copyright}\) and [PERSON] 4\({}^{\copyright,\copyright}\)  
1 Offshore Energy Engineering Center, School of Water Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0 AL, UK; [EMAIL_ADDRESS] (A.A.S.); [EMAIL_ADDRESS] (A.M.)  
2 School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry CV1 5 FB, UK; [EMAIL_ADDRESS]  
3 Sustainable Manufacturing Systems Centre, Cranfield University, Cranfield, Bedfordshire MK43 0 AL, UK; [EMAIL_ADDRESS]  
4 Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G1 1 XQ, UK  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +44-141-574-5346 check for updates  
**Abstract:** Offshore wind turbines (OWTs) are deployed in harsh environments often characterized by highly stochastic loads and resistance properties, thus necessitating the need for structural reliability assessment (SRA) to account for such uncertainties systematically. In this work, the SRA of an OWT jacket-type support structure is conducted, applying two stochastic methods to predict the safety level of the structure considering various design constraints. The first method refers to a commercial finite element analysis (FEA) package (DesignXplorer(c) from ANSYS) which employs direct simulations and the six sigma analysis function applying Latin hypercube sampling (LHS) to predict the probability of failure. The second method develops a non-intrusive formulation which maps the response of the structure through a finite number of simulations to develop a response surface, and then employs first-order reliability methods (FORM) to evaluate the reliability index and, subsequently, the probability of failure. In this analysis, five design constraints were considered: stress, fatigue, deformation, buckling, and vibration. The two methods were applied to a baseline 10-MW OWT jacket-type support structure to identify critical components. The results revealed that, for the inherent stochastic conditions, the structural components can safely withstand such conditions, as the reliability index values were found acceptable when compared with allowable values from design standards. The reliability assessment results revealed that the fatigue performance is the design-driving criterion for structural components of OWT support structures. While there was good agreement in the safety index values predicted by both methods, a limitation of the direct simulation method is in its requirement for a prohibitively large number of simulations to estimate the very low probabilities of failure in the deformation and buckling constraint cases. This limitation can be overcome through the non-intrusive formulation presented in this work.  
**Keywords:** stochastic modeling; reliability index; non-intrusive formulations; structural reliability analysis; offshore wind structures
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cementitious** & **Aggregate Types** & **Variables** & **Porosity (\%)** & **Permeability (mm/s)** & **Compressive Strength (MPa)** & **Reference** \\ \hline
43-grade Ordinary & & & & & & \\ Portland & Over burnt brick & aggregate size & 8–31 & 7–19 & 7–30 & [21] a \\
2-4-cement & & Natural & & & & \\ \hline ordinary Portland & \begin{tabular}{c} Asterpages and \\ (OPC) \\ \end{tabular} & \begin{tabular}{c} Natural \\ aggregates and \\ recycled concrete \\ aggregates (RCA) \\ \end{tabular} & RCA replacement & 18–22 & 8–20 & 3–17 & [22] b \\ \hline Ordinary Portland & \begin{tabular}{c} basalt aggregates \\ (Cement \\ \end{tabular} & \begin{tabular}{c}aggregate size \\ \end{tabular} & 20.50–21.16 & – & 19.86–32.00 & [23] \\ \hline alkali activated & & & & & & \\ materials based on & \begin{tabular}{c} natural gravel \\ \end{tabular} & \begin{tabular}{c} aggregate size and \\ binder type \\ \end{tabular} & 17.7–34.5 & 3.2–16.2 & 11.3–36.2 & [24] \\ \hline type I Portland & \begin{tabular}{c} dolomite \\ aggregate and \\ copper slag \\ \end{tabular} & \begin{tabular}{c} Copper slag \\ replacement \\ \end{tabular} & 20.44–22.77 & 3.05–3.53 & 17.93–23.45 & [25] \\ \hline magnesium & \begin{tabular}{c} waste steel slag \\ phosphate cement \\ \end{tabular} & \begin{tabular}{c} aggregate size \\ \end{tabular} & 25–26 & 6.8–7.1 & 34–38 & [26] c \\ \hline type I cement, & \begin{tabular}{c} type II cement, \\ sulphoaluminate \\ cement and calcium \\ \end{tabular} & \
\begin{tabular}{c} electric arc \\ furnace slag \\ \end{tabular} &
\begin{tabular}{c} cement type and \\ curing condition \\ \end{tabular} & 8.45–21.31 & 4.52–7.62 & 10.02–24.72 & [27] d \\ \hline
53-grade Ordinary & \
\begin{tabular}{c} limestone \\ aggregates \\ \end{tabular} &
\begin{tabular}{c} fly ash content \\ \end{tabular} & 18–35 & 6.1–9.8 & 5.0–10.8 & [28] e \\ \hline \hline \end{tabular}  
* The values detected from observations of the figures where w/c = 0.3 and fine aggregate = 10%. b: The values obtained from P20-S0. c: The values detected from observations of the figures for the damping molding. d: The values obtained for Type I cement, Type II cement, and calcium aluminate cement cured in saturated lime water. e: The values detected from observations of the figures.  
\end{table} 
Table 1: Summary of the variables and properties of pervious concrete.
# Optimization of DC Energy Storage in Tokamak Poloidal Coils
## 3 Need for Energy Storage in Tokamak PF Coils  
As shown in Figure 3, in the PF PSs, even though the power peaks can be extremely high, the average power needed is relatively small. Moreover, the plant duty cycle is limited, especially in the present experimental plants (it is about 100 s/3600 s in DTT [24]).  
To meet the demanding requirements of the PF coils, a robust and efficient storage system is essential, also because the PF coils are only one of the electrical loads necessary to operate an experimental tokamak like DTT [31] or a power station producing energy by nuclear fusion [10]. Such a storage system must be capable of storing large amounts of energy and delivering it precisely when needed.  
A standard solution to support the medium/high voltage transmission/distribution lines in past fusion experiments was based on flywheel generators, which are able to provide a high amount of power in short time intervals without affecting the electrical grid  
Figure 2: Currents and voltages of the PF coils in the DTT reference scenario, including transient phenomena and phases such as plasma breakdown, plasma ramp-up, L-H mode transition, and H-L mode transition.  
system's stability. This is less common today, because it is difficult to find such devices on the market [14] (actually, some new installations revamp old flywheel generators [32; 33]). The other reason is that, approaching tokamaks for energy production, operations are becoming longer and longer, so more flexible technologies are required.  
A viable alternative currently under study for fusion reactors is the DC storage system based on banks of SCs or other high-capacity energy storage devices that can rapidly discharge and recharge to match the dynamic demands of the PF coils. In this case, the AC/DC converter stage can be classified as a \"charger\", which can be rated for a power significantly lower than that delivered to the load.  
The design and implementation of the DC storage system involve several key considerations. First, the storage system must have a substantial energy reserve to support the peak current and voltage requirements. This ensures that the system can sustain the necessary power output during the most demanding phases of plasma confinement. Second, the system must be capable of rapid response to changes in demand, allowing for precise control over the magnetic field configurations. This is crucial for maintaining plasma stability and preventing disruptions; additionally, the DC storage system must be designed with high reliability and efficiency in mind. Any failure or inefficiency in the power supply could lead to instability in the plasma, potentially causing disruptions or damage to the tokamak. Therefore, advanced power conversion and energy storage technologies are employed to achieve the required performance levels.