esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Needs and Trends in Wind Farm Design and Optimization
### Commercial Software
The optimization procedure aims to define the best location of a predefined number of WTs while accounting for multiple restrictions such as the minimum distance between WTs, terrain inclination, flow inclination, effective turbulence intensity (which is estimated from both the measured turbulence intensity at the wind farm site and the simulated turbulent kinetic energy), wind shear, and extreme winds (which are estimated by the method of Independent Storms) [362]. The optimization process is repeated while varying the number of WTs (\(N\)) in the range \(1\leq N\leq N_{max}\), where \(N_{\max}\) is a predefined maximum number of WTs. The output of the overall optimization process is a concave curve describing the NPV variation of the project as a function of the number of installed WTs, which in turn define the best number of WTs and the best layout design. Two different algorithms may be selected to perform the optimization task; the local search Simulated Annealing metaheuristic algorithm, which may or may not take into account wake effects, modeled by the Jensen-Katic model, during the optimization process; or a local search calculus-based algorithm that optimizes the wind farm layout while considering a relaxed mixed integer nonlinear model. The optimization process ignores the civil engineering and electrical layout design and any other environmental impacts during the optimization process. AWS Truepower OpenWind(r) Enterprise [46,363] software is a general GIS tool for wind resource assessment (based on a mass-conservation wind flow model) and wind farm micro-siting optimization (which include both wake and turbulence effects in energy conversion, which are modeled using different engineering wake and turbulence models). OpenWind(r) implements either the AEP\({}_{\textrm{Farm}}\) or the CoE as objective performance metric during the wind farm micro-siting optimization, in which the decision variables are the number and position of WTs and the civil engineering (auxiliary roads) and the electrical infrastructure layout design, while considering existing infrastructure and multiple physical constraints. The software includes the possibility of performing shadow flicker, visual impact, noise impact (based on the ISO 9613 [315]), uncertainty and control (e.g., WT scheduling) assessments. Such assessments, however, are not considered during the optimization process. OpenWind's solution procedure is based on an iterative non-deterministic greedy heuristic algorithm, in which the WT coordinates are modified by random Gaussian-distributed perturbations. The number of WTs is updated iteratively depending on predefined WT capacity factor thresholds. A set of different heuristic rules are adopted to obtain feasible solutions during the optimization process (_i.e._, a set of restrictions are given to the program, which in turn define feasible zones in which WTs can be located).
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
###### Abstract  
UAV technology is advancing rapidly, and variable-sweep wing UAVs are increasingly valuable because they can adapt to different flight conditions. However, conventional control methods often struggle with managing continuous action spaces and responding to dynamic environments, making them inadequate for complex multi-UAV cooperative formation control tasks. To address these challenges, this study presents an innovative framework that integrates dynamic modeling with morphing control, optimized by the multi-agent deep deterministic policy gradient for two-sweep control (MADDPG-VSC) algorithm. This approach enables real-time sweep angle adjustments based on current flight states, significantly enhancing aerodynamic efficiency and overall UAV performance. The precise motion state model for wing morphing developed in this study underpins the MADDPG-VSC algorithm's implementation. The algorithm not only optimizes multi-UAV formation control efficiency but also improves obstacle avoidance, attitude stability, and decision-making speed. Extensive simulations and real-world experiments consistently demonstrate that the proposed algorithm outperforms contemporary methods in multiple aspects, underscoring its practical applicability in complex aerial systems. This study advances control technologies for morphing-wing UAV formation and offers new insights into multi-agent cooperative control, with substantial potential for real-world applications.  
multi-UAV; variable-sweep wing; multi-agent deep reinforcement learning; formation control; MADDPG-VSC  
11 September 2024  
Revised: 26 September 2024  
Accepted: 2 October 2024  
Published: 7 October 2024
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### Educational Buildings
#### Exposure in Public and Private Transportation
The output radiation power of the mobile phone was set to 2W for modeling the worst-case scenario in mobile communications. The whole body SAR values in mobile phone users in free space were compared to those inside a vehicle; results illustrated that the maximum SAR induced for mobile phone users in a vehicle is 5% higher than those in free space, with no significant difference for the handedness. The findings demonstrated that the change of passenger count inside the vehicle can alter the maximum SAR value induced in the bodies of passengers. In addition, when a passenger sits next to a mobile phone user, the maximum SAR value of that passenger can be up to about 5% of that of the mobile phone user, with maximum observed value (not averaged) equal to 0.233 W/kg. Two recent studies based on 3D ray-launching method, estimated of electric field amplitude in a car [69] and in a commercial passenger airplane [39]. As to the car, the exposure to ZigBee, GSM and UMTS sources has been estimated both by simulations and by measurements. Higher electric field levels have been observed using ZigBee compared with UMTS or GSM, due to the fact that ZigBee emitting power configuration has been set to 63 mW value, whereas mobile terminals due to power control mechanisms emit at minimum power levels. In all cases, the systems comply with reference levels found in the recommendations proposed by ICNIRP [43]. The obtained values were strongly influenced by topology, morphology and materials of the confined vehicular scenario, which can give rise to high signal levels and hence, specific regions or hot spots to monitor. As to the exposure in airplane [39], the authors used the 3D ray-launching method to assess the exposure to a radio frequency source operating at 900 MHz, 2.4 GHz and 5 GHz in the main cabin. The topology as well as the morphology of the main cabin of the airplane were known to play a key role in the obtained estimation values, since multipath components are the dominant contribution to the overall mechanism of radio propagation losses within the scenario. Moreover, the strong metallic content within the main cabin emphasized the multipath components, leading to strong signal variation, especially in the vicinity of the radiation sources. Estimations of the exposure in different exposure scenarios (with passenger seats and without passenger seats), showed that there were some regions of the cabin in which compliance with ICNIRP [43] as well as IEEE C95.1 standards [48] was not fulfilled, as values higher than 80 V/m were observed for 5 GHz and higher than 160 V/m for 2.4 GHz, for height from the ground equal to 1.7 m.
# Characterization of Deburring by Abrasive Flow Machining for AL6061
## 4 New Objective Function  
### Need for a New Objective Function  
An ideal objective function represents the actual deburring performance. Table 6 outlines the values of shear stress, local curvature, and [PERSON]'s objective function [18], at each flow speed along with the deburrted area for the flow length of 200 km.  
Figure 14: ANSYS Fluent CFD model for preliminary testing: (**a**) tetrahedral mesh for the specimen; (**b**) physical specimen.  
Figure 15: Observation points for streamline geometry and shear stress in the CFD model: (**a**) streamline; (**b**) shear stress.  
When the values of objective function suggested by [PERSON] [18] were compared, they varied from 10.7 N/m to 23.1 N/m when the flow speed increased from 3.3 m/s to 7.8 m/s, respectively. On the other hand, the deburred area for the flow length of 200 km increased from 0.046 mm\({}^{2}\) to 0.763 mm\({}^{2}\). When the value of the objective function increased 2.2 times, the deburred area increased 16.6 times.
## 3 Nanocellulose
### Cellulose Nanofibryl (CNF)  
CNFs contain both a crystalline region and an amorphous region of cellulose in an alternate fashion, and thus represent 100% cellulose. CNFs are also named as cellulose nanofibers, nanofibrillated cellulose (NFC) and microfibrillated cellulose (MFC). These are made up of entangled bundles of flexible, long nanofibrils that can be extracted from cellulose through mechanical disruption (homogenization, microfluidization, grinding, cryo-crushing, high-intensity ultrasonication), during which the cellulose fibrils are cleaved within the longitudinal axis (Figure 2). The long thread-like fibrils present in CNFs are 1-100 nm in diameter and 500-2000 nm in length. It is expected that CNFs have axial tensile strengths and Young's modulus of approximately 3 GPa and 136 GPa, respectively. Furthermore, CNFs exhibit thixotropic and shear-thinning behavior when they form gels in water [19; 41; 42]. Comparing CNFs with CNFs, the former has a longer length, a higher aspect ratio (length-to-diameter), a larger surface area and a higher concentration of hydroxyl groups, which makes it more easily accessible for surface modifications [43]. Due to their extremely high aspect ratios and fibrillar shape, CNFs in particular exhibit exceptional potential in a variety of applications, such as flexible electronics, high performance polymer composites, separation media and adhesive binders [44]. While mechanical disintegration is usually applied to release the nanofibers, the procedure, being very energy-consuming, is believed to be a significant hurdle to the effective production of CNFs. Nevertheless, introduction of various pretreatment techniques added before the mechanical processes, as one of the pre-requisites with the goal of lowering energy consumption and making the surface hydrophobic, may aid in a successful commercial production of CNF [19; 41; 44].
## Materials and Methods
### Analysis Method of Pyr in Cucumber  
A modified QuEChERS method was used to extract Pyr from the cucumber samples. The specific operation was as follows--1.0 g of cucumber leaves or roots sample were weighed into a 50 mL polyethylene plastic tube, then 1 mL deionized water and 5 mL acetonitrile were added to extract the Pyr, by oscillating on a vortex oscillator (VX-III, Beijing Tajin Technology Co.,Ltd., Beijing, China) for 10 min. Then, 3.0 g of NaCl was added into the mixture. After high-speed centrifugation, 1 mL of the acetonitrile supernatant was taken out for purification. For the leaf samples, 10 mg of MWCNTs and 60 mg of MgSO\({}_{4}\) was used as the loose sorbents. For the root samples, 60 mg of MgSO\({}_{4}\) was added into the acetonitrile supernatant.  
High performance liquid chromatography-tandem mass spectrometry (HPLC/MS/MS, Thermo TSQ Quantum Ultra, Seymour Fisher Technology Co., LTD, Shanghai, China) was used to determine the concentrations of Pyr. Separation was achieved on a reversed-phase column (Hypersil GOLD-C18, 1.9 \(\upmu\)m, 2.1 mm \(\times\) 100 mm, Agilent, Santa Clara, CA, USA) at 30 \({}^{\circ}\)C. The mobile phase consisted of acetonitrile and 0.1% formic acid (70/30, \(v/v\)). The injection volume was 5 \(\upmu\)L and the flow rate was kept constant at 0.3 mL/min. The positive ionization mode was chosen for Pyr. Product ion mass spectra were obtained in electrospray ionization using collision-induced dissociation and the collision energy was optimized for two selective ion transitions. Two multi-response monitoring (MRM) ion transitions (m/z 413.19\(\rightarrow\)145.28, m/z 413.19\(\rightarrow\)205.31) were chosen for confirmation, and the more sensitive ion transitions (m/z 413.19\(\rightarrow\)145.28) was used for quantification. The capillary voltage was 3500 V. The carrier gas was nitrogen with gas flow of 8 L min\({}^{-1}\) and the nebulizer gas kept at 35 psi. The retention time of Pyr was 2.84 min.  
The concentration of Pyr in fresh weight was measured using the above method.
## 4 Improved Tight Combination Model
### Design of Extended Kalman Filter
#### 4.4.2 Measurement Equation  
The measurement equation of the tight combination based on the improved UWB/IMU is shown in Equations (25) and (26).  
\[Y(k)=HX(k)+Dv(k) \tag{25}\] \[Y(k)=\begin{bmatrix}\triangle d_{1,k}\\ \triangle d_{2,k}\\ \vdots\\ \triangle d_{n,k}\end{bmatrix} \tag{26}\]  
The corresponding measurement information is the ranging difference between IMU and UWB.  
\[H=\begin{bmatrix}\frac{\mathrm{d}_{1,k}^{\ell m}}{\partial p_{x}^{\ell}}& \frac{\partial a_{1,k}^{\ell m}}{\partial p_{x}^{\ell}}&\mathcal{O}_{2}& \mathcal{O}_{2}\\ \frac{\mathrm{d}_{2,k}^{\ell m}}{\partial p_{x}^{\ell}}&\frac{\partial a_{2, k}^{\ell m}}{\partial p_{x}^{\ell}}&\mathcal{O}_{2}&\mathcal{O}_{2}\\ \vdots&\vdots&\vdots&\vdots\\ \frac{\mathrm{d}_{n,k}^{\ell m}}{\partial p_{x}^{\ell}}&\frac{\partial a_{n, k}^{\ell m}}{\partial p_{x}^{\ell}}&\mathcal{O}_{2}&\mathcal{O}_{2}\end{bmatrix} \tag{27}\]  
where \(H\) is measurement matrix; \(n\) is the amount of data measured by IMU after passing the threshold; and \(v(k)\) is the measurement noise, with \(v(k)\sim N(0,R)\).  
Thereby, the extended Kalman gain can be determined based on Equations (10)-(13), including the transition matrix, observation matrix, and optimal state value of the system.
## 1 Introduction
analyzed the effects of infill orientation by printing three open molds with PPS and 50% CF in different orientations (0\({}^{\circ}\)/90\({}^{\circ}\) and 90\({}^{\circ}\)/0\({}^{\circ}\)), revealing that the 0\({}^{\circ}\) orientation, aligned with the beads, yielded the highest thermal conductivity [16]. [PERSON] et al. evaluated the precision and stability of high-temperature polymer parts before and after thermal cycling [17]. Molds were 3D-scanned to determine geometric deviations and subjected to 120 \({}^{\circ}\)C for two hours, followed by cooling to 65 \({}^{\circ}\)C for 30, 60, and 90 cycles at 100 psi. The results indicated deformations below 0.5% after 200 h of exposure. Fiber-reinforced polymers are commonly used in these applications due to their increased stiffness, dimensional stability, and good machinability [18]. [PERSON] et al. have defined the required rheological behavior for the LFAM processing of various materials [19,20]. Fiber reinforcement also enables higher printing temperatures and improved cooling rates [16]. Companies such as Techner (Clinton, TN, USA), Sabic (Riyadh, Saudi Arabia), Airtech (Huntington Beach, CA, USA), and Maresia(tm). (Cadiz, Andalucia, Spain) offer commercial materials tailored for mold production, ranging from engineering polymers (ABS, ASA, PC) to technical polymers (PEI, PESU, PPS), all fiber-reinforced, with the latter being suitable for demanding autoclave processes [21]. Regarding polymer machining, [PERSON] et al. identified environmental variables, cutting parameters, and polymer properties as key factors influencing surface quality [22]. The impact of cutting parameters on roughness and precision in injection-molded parts has been extensively studied [23, 24, 25, 26, 27], highlighting cutting speed, feed rate, and tool rake angle as critical parameters. Statistical analyses by [PERSON] et al. [25] and [PERSON] et al. [26] confirmed that feed rate is the most influential factor, a finding corroborated by [PERSON] et al. using Taguchi methods on various polyamides, including tool material as a variable [27]. [PERSON] et al. also found feed rate to be the primary parameter in PEEK milling [28]. [PERSON] et al. compared ABS and PLA milling, concluding that ABS requires lower cutting speeds than PLA, which exhibited better surface roughness at higher speeds [29]. [PERSON] and [PERSON] emphasized the importance of avoiding viscosity deformation during machining by staying below the glass transition temperature [30]. [PERSON] et al. evaluated the processability of PEEK, PEI, and PMMA at different temperatures, achieving the best surface quality in the fragile regime [31]. This study presents two experiments designed to determine the optimal printing conditions for ASA with 20 wt.% CF parts intended for machining and to identify the optimal process parameters for both additive manufacturing and milling, ultimately optimizing surface quality for mold production.
# An Analysis of MPTCP Congestion Control
## 3 Path Selection
### Path Diversity
#### 3.1.1 Head-of-Line Blocking  
When packets are distributed through different paths, different path delay and loss rates might contribute to out-of-sequence delivery at the receiver. For example, consider two paths between nodes where one node has a lower delay than the other. Packets that are transferred through the lower delay path need to wait at the receiver's buffer until the high-delay packets arrive.  
The delay difference and out-of-order packets at the receiver buffer cause a problem called head-of-line blocking. Head-of-line blocking can lead to a network burst which is not desirable for certain types of traffic. For example, streaming applications since they require a large amount of receive buffer for a good user experience [11].
# Analytical Investigation of Viscoelastic Stagnation-Point Flows with Regard to Their Singularity
## 4 Analytical Solutions of a Near-Wall Stagnation-Point Flow
### Analytical Solutions of the Model Equation  
We firstly solve (76) for \(T_{22}\), which is uncoupled from (75) and (77). Then, Equations (75) and (77) can be successively solved. Using the method of characteristic for (76) yields the characteristic equations:  
\[\frac{dx}{Wxy^{\alpha-1}}=\frac{dy}{-Wy^{\alpha}}=\frac{dT_{22}}{-(2 Wxy^{ \alpha-1}+n)T_{22}-2\beta\alpha y^{\alpha-1}}. \tag{79}\]  
Its solution takes the form:  
\[T_{22}=F_{1}(xy^{\alpha})\cdot y^{2\alpha}e^{-\frac{y^{1-\alpha}}{nW(\alpha-1 )}}+\frac{2\beta}{W}xy^{2\alpha}e^{-\frac{y^{1-\alpha}}{nW(\alpha-1)}}\int y ^{-2\alpha-1}e^{\frac{y^{1-\alpha}}{nW(\alpha-1)}}dy, \tag{80}\]  
where \(F_{1}(xy^{\alpha})\) is an arbitrary function of \(xy^{\alpha}\), and \(xy^{\alpha}=const.\) represents the characteristic curves of the solution. Substituting (80) into (77) and solving the resulting PDE with the same method as above yields:  
\[\begin{split} T_{12}=& F_{2}(xy^{\alpha})\cdot e^{- \frac{y^{1-\alpha}}{nW(\alpha-1)}}-F_{1}(xy^{\alpha})\cdot\alpha xy^{2\alpha-1 }e^{-\frac{y^{1-\alpha}}{nW(\alpha-1)}}+\frac{\beta}{2 nW^{2}}xy^{-\alpha}\\ &+\frac{\beta}{nW^{2}}xy^{\alpha}\big{(}\alpha y^{\alpha-1}+ \frac{1}{2 nW}\big{)}e^{-\frac{y^{1-\alpha}}{nW(\alpha-1)}}\int y^{-3\alpha}e^ {\frac{y^{1-\alpha}}{nW(\alpha-1)}}dy,\end{split} \tag{81}\]  
with the arbitrary functions \(F_{1}\), \(F_{2}\) of \(xy^{\alpha}\). Similarly, substituting (81) into (75) and solving the consequent PDE result in the solution for \(T_{11}\):  
\[\begin{split} T_{11}=& F_{3}(xy^{\alpha})\cdot y^{- 2\alpha}e^{-\frac{y^{1-\alpha}}{nW(\alpha-1)}}-2\alpha F_{2}(xy^{\alpha}) \cdot xy^{-1}e^{-\frac{y^{1-\alpha}}{nW(\alpha-1)}}\\ &+\xi^{2}F_{1}(xy^{\alpha})\cdot x^{2}y^{2\alpha-2}e^{-\frac{y^{1 -\alpha}}{nW(\alpha-1)}}-\frac{2\alpha\beta}{W}y^{-2\alpha}e^{-\frac{y^{1- \alpha}}{nW(\alpha-1)}}\int y^{2\alpha-1}e^{\frac{y^{1-\alpha}}{nW(\alpha-1)}} dy\\ &-\frac{\alpha\beta}{W^{2}}x^{2}y^{-\xi-1}-\frac{\alpha\beta}{nW ^{2}}x^{2}y^{\alpha-1}\big{(}\alpha y^{\alpha-1}+\frac{1}{nW}\big{)}e^{-\frac{ y^{1-\alpha}}{nW(\alpha-1)}}\int y^{-3\alpha}e^{\frac{y^{1-\alpha}}{nW(\alpha-1)}}dy\\ &-\frac{\alpha^{2}\beta}{nW^{2}}x^{2}e^{-\frac{y^{1-\alpha}}{nW( \alpha-1)}}\int y^{-\alpha-2}e^{\frac{y^{1-\alpha}}{nW(\alpha-1)}}dy,\end{split} \tag{82}\]  
where \(F_{1}\), \(F_{2}\), and \(F_{3}\), again, are arbitrary functions of \(xy^{\alpha}\).  
The solutions in the case \(\alpha=2\) are consistent with that obtained by [12; 13].
## 4 Results and Analysis
### Characteristics of Sand-Moving Wind
#### 4.2.2 Characteristics of Seasonal Sand-Moving Wind  
Figure 4a shows the seasonal sand-moving wind frequencies of Qiemo and Ruoqiang. Sand-moving wind frequency peaked in the spring, followed in succession by the summer and the autumn, reaching its lowest value in the winter. The average sand-moving wind velocities of Qiemo and Ruoqiang peaked in the spring (6.15 and 6.34 m/s), followed in succession by the summer and the autumn, and reached their lowest values (5.52 and 5.63 m/s) in the winter (Figure 4b). As shown in Figure 5a,b, the prevailing direction of the seasonal sand-moving wind was consistent with that of annual sand-moving wind in both Qiemo (NE) and Ruoqiang (ENE). The sand-moving wind frequencies of the four seasons presented consistent variation trends in 16 directions. The prevailing directions of sand-moving wind were concentrated in NE and ENE, and sand-moving wind frequencies were extremely weak in SE and NW. The prevailing direction of the sand-moving wind in the spring in Qiemo was NE. Sand-moving wind frequency in NE accounted for 61.3% of the sand-moving wind frequency in the spring in Qiemo. The variation trends in various directions in the autumn and the winter were consistent with those in the spring. In the summer, sand-moving wind frequency presented a blunt bimodal pattern, while the prevailing directions of sand-moving wind were NNE and NE, accounting for 23.8 and 35.5% of the sand-moving wind frequency in the summer in Qiemo, respectively. In the contrary directions SW and WSW, sand-moving wind frequency increased abruptly, and sand-moving wind was disorderly in the summer, accompanied by a complex wind regime. In comparison, in Ruoqiang, sand-moving wind in the spring was jointly dominated by NE and ENE, which accounted for 38.7 and 49.5% of the sand-moving wind frequency in the spring in Ruoqiang, respectively. The variation trends in various directions in the autumn and the winter were consistent with those in the spring. In the summer, the prevailing directions of sand-moving wind in Ruoqiang were NNE and SW, which accounted for 33 and 24.7% of the sand-moving wind frequency in the summer in Ruoqiang, respectively. The sand-moving wind frequency in the summer in Ruoqiang presented a more significant blunt bimodal pattern, suggesting that sand-moving wind was more disorderly in the summer in Ruoqiang.  
Figure 4: Seasonal sand-moving wind frequency and velocity: (**a**) sand-moving wind frequency; (**b**) average sand-moving wind velocity.  
Figure 5: Sand-moving wind frequencies in various directions in the four seasons: (**a**) Qiemo; (**b**) Ruoqiang.