esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
One major reason to use additive technologies for space applications is the mass reduction, since lowering between 40% and 90% of the component mass is possible by means of additive manufacturing [9; 10; 11]. The reduction in mass is directly related to the reduction in the overall costs associated with the launch of a spacecraft. Another benefit that impacts the efficiency of the manufacturing process is the capability to manufacture complex parts faster by means of 3D-printing technologies than by traditional methods, while also lowering the costs (up to 70%) [12]. It is to be noted that some geometries cannot be manufactured by classical machining due to their shapes and dimensions. However, the use of AM technologies for space applications has yet to be widely used due to their main challenges: modeling the manufacturing process and predicting the final properties of the printed part [9]. During recent years, however, the latter was improved, and the issue of surface properties (AM surfaces usually have a poor roughness and show breaking lines [13]) can be managed by a multitude of post-processing techniques. The actual space ecosystem aims to create new high-performance space products by actively reducing the limitations imposed by the traditional manufacturing processes/concepts. Thus, this paper falls within the ESA's vision of implementing new manufacturing technologies for space applications, enabling design freedom, performance improvement, and cost and time reductions, from concept to manufacturing. The targeted system that will benefit from using additive manufacturing (AM) processes is the spacecraft thermal control system; more precisely, the mechanical pump fluid loop active system. The component under study is the centrifugal closed impeller, the main component that influences the pump performances. Due to the low volume and complex design that formative or subtractive methods are unable to produce, the additive manufacturing technology seems to be best suited for the closed impeller. At this point, there are only a few centrifugal impellers made by AM, with small dimensions, tested and evaluated accordingly in [14; 15; 16; 17]; however, a dedicated process with iterative steps and a focus on identification of printing parameters' influence over the hydraulic performances of the impellers was not identified in the literature. Considering the above, the main focus of this paper is on the design, manufacture, and test activities of AM closed impellers made from different metallic materials (aluminum, titanium, and Inconel alloys) and an evaluation of their hydraulic performances as a function of both the interior roughness and material.
## 3 Results and Discussion
### Characterization of the Rods and Rod with ARP
#### 3.3.4 Terpolymer-ARP Interaction Study  
The analysis of the FTIR spectra of all the blank rods revealed the following signals: (i) at 3015 cm\({}^{-1}\) (stretching of CH), (ii) at 1757 cm\({}^{-1}\) (stretching of C=O), (iii) in the range of 1500-1350 cm\({}^{-1}\) (deformations of CH\({}_{3}\)), (iv) in the range of 1300-1000 cm\({}^{-1}\) (stretching of C-O), (v) at 933 cm\({}^{-1}\) (deformations of CH), and (vi) at 878 cm\({}^{-1}\) (deformations of CH). The spectra for the rods with ARP (TD,L 21 rod-ARP, Tl. 59 rod-ARP, and Tl. 77 rod-ARP) did not show significant differences in the signals revealed (Figure 8), which suggests the lack of interactions between the terpolymers and ARP.  
These findings reflect the DSC study. The first and second heating runs of the blank rods and the rods with ARP (Figure 6 and Table 3) did not reveal additional endotherms and exotherms except those identified as crystalline ARP (Figure 2), which also indicates the lack of interaction between ARP and the terpolymers. In another study of the physical mixture of ARP and d,l-PLGA, the DSC curves also did not reveal additional thermal events [41]. According to the literature, the absence of additional endothermic or exothermic peaks is interpreted as indicating a lack of drug-polymer interactions [42; 43].
## 1 Introduction
The AMF exhibits robust behavior with respect to steering vector mismatches and more computational efficiency than many other detectors as the generalized likelihood ratio test (GLRT) [21; 22; 23; 15]. The ABORT can reject interference signals orthogonal to the nominal steering direction, which modifies the null hypothesis to the unwanted signal assumed to be orthogonal to the nominal steering vector. In this paper, a priori knowledge-based adaptive clutter cancellation and moving target detection techniques for SAR-GMTI are applied to the distributed spaceborne SAR systems. The technique is applied to the formation flying that the distributed satellites exhibit as they fly along the same orbit arc as seen from the ground. The phase centers of these satellites are aligned to form a sparse linear array [1]. A priori knowledge, such as the roads information predicted from the road network database, is integrated into the adaptive processor to reduce moving target steering vector mismatch. Two traditional adaptive detection techniques, which include the adaptive matched filter (AMF) and the adaptive beamformer orthogonal rejection test (ABORT), are exploited to detect moving targets in clutter. If the road network is so dense that the azimuth displacement and the moving target steering vector estimation may be ambiguous for different positions and orientations of the roads [11; 12; 13; 14], the multiple hypothesis testing technique (MHT) is proposed to detect the targets and resolve the potential ambiguities. The detection, classification and relocation scheme are exploited to detect the presence of a target, correctly classify, and relocate it to the corresponding roads. Performance is investigated by means of Monte Carlo simulations in terms of probability of false alarm, detection, and classification. To verify the distributed spaceborne SAR processing scheme and simplify the instrument hardware design, our research group designed the commercial simulation software named SpaceBorne Radar Advanced Simulator (SBRAS) [24; 25]. Simulation results developed on SBRAS and performance analysis demonstrate the effectiveness and robustness of the proposed method. The paper is organized as follows: Section 2 reviews the signal model under Gaussian assumption of clutter and states the adaptive clutter cancellation technique. Section 3 analyzes the moving target steering vector mismatch and integrates a priori knowledge to the adaptive clutter cancellation processor. Section 4 analyzes two traditional adaptive detection techniques and proposes the MHT technique. Performance has been investigated either analytically or by means of Monte Carlo simulation in terms of probabilities of false alarm, detection, and correct classification. Section 5 gives the simulation experimental results and analysis based on SBRAS. Section 6 gives the conclusions. This paper is an extension of our conference papers presented at the IET International Radar Conference in 2009 [15] and at the International Conference on Computer Application and System Modeling in 2010 [22]. This paper is an extension of the first author's PhD thesis [23].
## 3 Results and Discussion  
### UAV-MS and DOP Creation  
With the MS sensor, a total of 4840 images were taken. The images were collected from 484 locations, considering that the MicaSense RedEdge-MX Dual Camera sensor from one location records the area simultaneously with ten different spectral bands. The MS model with a spatial resolution of 4.14 cm consists of ten separate spectral bands, the order of which can be independently changed and combined to find the best spectral bands layout for the observed objects. DOP was also generated in Agisoft Metashape 1.5.1. using 927 images, yielding a spatial resolution of 1.88 cm.  
Figure 8: Distribution of RT and FT.  
Figure 9: Thousand randomly distributed accuracy assessment points.
# Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference
## 2 Controller Design
### Lateral Control
#### 2.2.2 Pure Pursuit Controller with a Front Axle Reference  
Since the kinematic model ignores the lateral dynamic characteristics of the driverless bus [15], PPC-FAR achieves poor tracking performance in the driverless bus. Ignorance results in the oscillation of the driverless bus. Only adjusting the look-ahead distance is not efficient enough. In that case, the PP is modified by moving the reference to the front axle to relieve those influences.  
Figure 5: The structure of the lateral controller.  
Figure 6: Pure pursuit geometry.  
As shown in Figure 7, PPC-RAR calculates the desired steering angle by \((x_{rr},y_{rr})\). However, PPC-FAR uses \((x_{d},y_{d})\) to obtain the radius \(R^{\prime}\) and then calculates the steering angle. It is evident that the radius of PPC-RAR is larger than PPC-FAR. As the assumption in Section 2, the larger radius results in a smaller gap between the kinematic model and the real bus. In that case the instability caused by the kinematic model is released in PPC-FAR. The benefits of the front axle reference are verified in Section 3.  
The PPC-FAR is illustrated in Figure 8. \(O\) is the circle center and \(R^{\prime}\) is the radius of the arc. \(l^{\prime}{}_{d}\) is the look-ahead distance. A is the center of the front axle. \(B\) is the preview point. \(C\) is the middle point of \(A\) and \(B\). \(D\) is the center of the rear axle. \(O\) is the intersection point of line \(OC\) and line \(OD\).  
According to the extended kinematic model, the steering angle of PPC-FAR is calculated as:  
\[\delta^{\prime}=\tan^{-1}\!\left(\frac{L}{\sqrt{R^{\prime}{}^{2}+L^{2}}}\right) \tag{7}\]  
where \(\delta^{\prime}\) is the steering angle in PPC-FAR.  
[PERSON] verifies the relationship between the tracking accuracy and stability in [16]. Similarly, to the traditional PP algorithm, tracking accuracy is scarified to improve the stability of path tracking. The look-ahead distance is calculated as \(k\cdot v_{t}\) (\(k\) is coefficient and \(v_{t}\) is current velocity). However, if the velocity is close to zero, the look-ahead distance becomes zero, too. That causes an unstable control system. To solve this problem, Equation (8) is applied to calculate look-ahead distance \(l^{\prime}{}_{d}\):  
\[l^{\prime}{}_{d}=k_{l}\cdot v\ +\ L_{offset} \tag{8}\]  
where \(k_{l}\) is the proportional coefficient, and \(L_{offset}\) represents the offset of the look-ahead distance.  
Although some changes are introduced to cancel out the influence caused by the ignorance in the kinematic model, there still exists a gap between the geometric model and the real bus, especially in  
Figure 8: Pure pursuit control with a front axle reference.  
Figure 7: Pure pursuit control with rear and front axle references.  
high-speed and large curvature conditions. The \(k\) in Equation (9) is used to eliminate the error between proposed model with the real bus:  
\[\delta^{\prime}=k\cdot\tan^{-1}\left(\frac{L}{\sqrt{R^{\prime 2}-L^{2}}}\right) \tag{9}\]
## 3 Results  
### ADSCs Viability with Different YB Concentrations  
The cytotoxic effects of different YB concentrations have been investigated (Figure 1). YB has been added, at different dosages, to ADSCs culture medium. Moreover, the same assay has been used to assess if YB can impact the osteogenic commitment of ADSCs.  
The experimental sessions investigated the ADSCs viability under different culturing conditions. ADSCs viability increased over 21 days, when they were cultured both in BM (Figure 2a) and in ODM (Figure 2b), showing a higher rate in ODM.  
Figure 1: Adipose mesenchymal stem cells (ADSCs) viability measured by a methyl thiazolyltetrazolium (MTT) assay. Viability of control ADSCs and ADSCs treated with 50, 100 or 200 \(\mu\)g/mL of Yunnan Baiyao (YB) at 1, 3 and 5 day of culturing in basal medium. *\(p\) < 0.05; **\(p\) < 0.01; ***\(p\) < 0.001 vs. control (CTRL).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Sequences (5”-3”)** & **Amplicon (pb)** \\ \hline ALP & \begin{tabular}{c} GGCTTCTTCTTGTGGTG \\ CAAATGTGAAGACGTGGGAAATGG \\ \end{tabular} & 181 \\ \hline OC & \
\begin{tabular}{c} GCACGAGCTGGAAGAC \\ AGCAGAGGCACACCCA \\ \end{tabular} & 193 \\ \hline TFRC &
\begin{tabular}{c} TGTHCTGCATAGGCTGGA \\ ACACCCAAACCAGAATCTC \\ \end{tabular} & 222 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The addition of different YB aliquots in cell culture medium led to decreasing cell viability. MTT analysis revealed that YB aliquots over 100 \(\upmu\)g/mL severely reduced the overall cell viability (Figure 1). After having reduced the YB aliquot to 50 \(\upmu\)g/mL, the lower dose of YB did not modulate ADSCs viability until day 14 (\(p>0.05\)), while at day 21 the reduction became significant in both culturing conditions (\(p>0.01\)). In culturing condition with 50 \(\upmu\)g/mL of YB, the viability rate was lower than the control at 14 and 21 days (\(p<0.001\)).
## 1 Introduction  
In general, it is hard to find explicit expression for closed-form solutions for stochastic differential equations (SDEs), see [1; 2; 3; 4; 5; 8; 9; 10; 11; 12; 16] to name just a few monographs. In none of the aforementioned monographs one can find a general algorithm to find such solutions. Nowadays, sophisticated computer packages provide powerful tools to seek for solutions, e.g., MAPLE, MATHEMATICA, MATLAB, _etc_. Our aim is to provide a general algorithm to find explicit expression for the strong solution of initial value problems related to systems of finite-dimensional ordinary SDEs  
\[dX(t) = f(t,X(t))dt+\sum_{j=1}^{m}g^{j}(t,X(t))dW_{j}(t), \tag{1}\] \[X(0) = X_{0}\in\mathbb{R}^{d},\quad t\geq 0,\]  
driven by independent Wiener processes \(W_{j}\) on the filtered probability space \((\Omega,\mathcal{F},(\mathcal{F}_{t})_{t\geq 0},\mathbb{P})\) which is completed with respect to all \(\mathbb{P}\)-null sets. The drift \(f:[0,T]\times\mathbb{R}^{d}\rightarrow\mathbb{R}^{d}\) and diffusion coefficients \(g^{j}:[0,T]\times\mathbb{R}^{d}\rightarrow\mathbb{R}^{d}\) are supposed to be Borel-measurable functions such that the strong solution of the IVP (1) exists (a.s.). The initial value \(X_{0}\) in (1) is completely independent of the increments of all Wiener processes \(W_{j}\) for \(t\geq 0\).  
The algorithm is applicable to both Ito- and Stratonovich-interpreted SDEs (1). It is mainly based on the fundamental theorem of stochastic calculus which originates from [PERSON]'s works [6, 7]. The algorithm can be extended to SDEs integrated in the sense of \(\alpha\)-calculus, see [13, 14]. For the readers, the readership is supposed to be familiar with the basic concepts of stochastic calculus and real analysis (e.g., for an excellent survey, see [15]).  
The paper is organized in 3 sections as follows. After this short introduction, Section 2 presents the algorithm. In Section 3 we discuss its application to solve numerous examples where the solution is and is not known from the literature. An appendix states the fundamental theorem of stochastic calculus, also known as Ito formula, and a theorem on the existence of unique strong solutions of linear SDEs.
## 5 Performance Evaluation
### Experiment 2  
In this experiment, the traffic load of flow 1 is twice the load of flow 2. We vary the rate of flow 2 from 200 kbps to 340 kbps with step of 10 kbps to test the jitter performance of flow 1, network throughput, average end-to-end delay and packet loss ratio of the two mechanisms. The results are shown in Figure 8.  
From Figure 8, it is observed that BLJCAR performs better than the ONC policy in jitter, network throughput, average end-to-end delay and packet loss ratio by considering the queue state of coding node in packet coding algorithm and the effective queue management mechanism. The effective queue management mechanism of BLJCAR can result in improved jitter benefits, alleviates congestion, reduces the queuing delay of packets and allows more data transmissions in each time unit.  
Figure 8 shows that BLJCAR is suitable for the multiple rate case.  
Figure 7: **(a)** Average packet jitter _versus_ offered load; **(b)** Network throughput _versus_ offered load; **(c)** Average end-to-end delay _versus_ offered load; **(d)** Packet loss ratio _versus_ offered load.
## 2 Carbon Trading Mechanism  
### Carbon Trading Cost Model  
The purpose of the carbon emission trading (carbon trading) market is to control the total amount of carbon emissions by means of a market regulation mechanism, and its main target is the source of carbon emissions. The specific measure is to allocate a certain carbon emission quota to each carbon emission source. If the carbon emission of the carbon emission source exceeds the quota, the excess quota must be purchased on the market; conversely, if the carbon emission of the carbon emission source is less than the quota, the carbon emission rights can be sold for profit.  
Firstly, the regulatory authorities assign duty-free carbon emission quotas to all carbon sources (thermal power generating sets such as CFPP, GFPPG) in the system. In a dispatch cycle (\(T\)), the free carbon quota (\(Q_{c}^{i}\)) and the actual carbon emissions (\(E^{i}\)) of generator set \(i\) are as follows:  
\[Q_{c}^{i}=\varepsilon\sum_{t=1}^{T}P_{i,t}\;\;;\;1\leq i\leq N_{c}+N_{g} \tag{1}\]  
\[E^{i}=\mu_{i}\sum_{t=1}^{T}P_{i,t}\;\;;\;1\leq i\leq N_{c}+N_{g} \tag{2}\]  
where \(\varepsilon\) is the free carbon emission quota coefficient per unit of electricity allocation, which is obtained by the weighted average of the marginal emission factor of electricity and the marginal emission factor of capacity in the area [26; 30].  
The carbon trading cost \(C(E^{i})\) of generator set \(i\) is as follows:  
\[C(E^{i})=p_{rc}^{i}(E^{i}-Q_{c}^{i})\;\;;\;1\leq i\leq N_{c}+N_{g} \tag{3}\]  
\[p_{rc}^{i}=\left\{\begin{array}{ll}-\,p_{rc}^{0}(1+N\delta),&E^{i}\leq Q_{c} ^{i}-N\eta\\ \ldots&\ldots\\ -\,p_{rc}^{0}\;(1+\delta),&Q_{c}^{i}-2\eta\leq E^{i}\leq Q_{c}^{i}-\eta\\ -\,p_{rc}^{0}\;,&Q_{c}^{i}-\eta\leq E^{i}\leq Q_{c}^{i}\\ p_{rc}^{0}\;,&Q_{c}^{i}\leq E^{i}\leq Q_{c}^{i}+\eta\\ p_{rc}^{0}(1+\sigma),&Q_{c}^{i}+\eta\leq E^{i}\leq Q_{c}^{i}+2\eta\\ \ldots&\ldots\\ p_{rc}^{0}(1+K\sigma),&E^{i}\geq Q_{c}^{i}+K\eta\end{array}\right. \tag{4}\]  
where \(p_{rc}^{0}\) is the benchmark carbon emission right price.  
From Equation (3), we can see that the carbon price of carbon trading (\(p_{rc}^{i}\)) directly affects the cost of carbon trading. Carbon price can be divided into traditional unified carbon price mechanism (UCT) and ladder-type carbon price mechanism (LCT). This paper introduces the reward coefficient [24] on the basis of the ladder-type carbon price mechanism, to give certain incentives and subsidies to energy supply companies whose total carbon emissions are lower than the freely allocated carbon emissions quota. This kind of carbon price carbon trading is called reward and punishment ladder-type carbon trading (RPLCT). The carbon trading price of RPLCT is shown in Equation (8). Figure 1 shows the carbon price of UCT, LCT and RPLCT.
## 2 Materials and Methods  
### Glider Measurements  
The mission site, as illustrated in Figure 1, is situated amidst numerous warm (anticyclonic) and cold (cyclonic) eddies and is approximately 1000 km east of the Luzon Strait, which is an energetic source of internal tides and the primary passage between the Western Pacific (WP) and the South China Sea (SCS). In September 2018, a Slocum glider was deployed from the research vessel ISABU at 20.71\({}^{\circ}\) N and 131.11\({}^{\circ}\) E and actively navigated northeastward to a mesoscale warm eddy core at 21.03\({}^{\circ}\) N and 131.25\({}^{\circ}\) E, which was identified using Level 4 SSH satellite data from the Copernicus Marine Environmental Monitoring Service (CMEMS: [[https://marine.copernicus.eu/access-data](https://marine.copernicus.eu/access-data)]([https://marine.copernicus.eu/access-data](https://marine.copernicus.eu/access-data)), accessed on 8 September 2018). The glider was operated in virtual mooring (VM) mode by making a dive-and-climb from the surface to the depth of 800 m every 2.8 h, during which conductivity-temperature-depth (CTD) data were collected at a frequency of 4 Hz, providing a vertical resolution of approximately 0.5 m. Through the course of the glider measurement, the eddy slowly moved northward, allowing the glider to scan the spatial extent of the eddy from its core to periphery, corresponding to a distance of approximately 50 km (Figure 2).  
These in situ CTD measurements were interpolated and gridded to 1 m vertical and 1 h temporal resolution through a simple linear interpolation process. Further, a low-pass filter with a boxcar window of 10 m in the vertical direction and 5 h in the time direction was applied. To examine the variability of the internal wave system within the eddy, temperature anomalies that are closely related to internal wave behaviors were considered in this study. Temperature anomalies, defined as \(\eta(z,t)=\big{[}T(z,t)-T(z,t)\big{]}\), were computed from the glider CTD data. Here, \(T(z,t)\) represents temperature time series,while \(\overline{T}(z,t)\) is the background temperature obtained through averaging \(T(z,t)\) over the entire observation period. A time-depth map of the temperature anomalies with isotherms is displayed in Figure 3. For the following analysis, we reconstructed multivariate time series from the gridded data by selecting 25 depths separated by 30 m from the upper 50 m to the depth of 800 m. The top surface layer (below 50 m) was excluded owing to the uncertainty of glider measurements induced by the strong surface current, and the 30 m interval between successive depths was adopted in order to avoid spurious spatial correlations due to the gridding processing of raw measurement data.
# Apollonian Packing of Circles within Ellipses
## 3 Results and Discussion  
In Figures 6 and 7, the results of the Apollonian packing procedure, implemented in Matlab(r), are shown for the cases of \(a/b=5/4\) (Figure 6a), \(a/b=2\) (Figure 6b), \(a/b=\sqrt{11}\) (Figure 7a) and \(a/b=5\), (Figure 7b). In all the figures, \(r_{min}=a/150\). The results attest to the good accuracy and reliability of the proposed algorithm for a wide range of values of the eccentricity of the elliptic boundary.  
The figures show evidence of the recursive nature of the branching sequence of circle filling in the elliptic domain, resulting in Sierpinski-like fractal morphogenesis [23]. The choice of simple, highly symmetrical scaffolding initial configurations, especially in the case of low-eccentricity ellipses, e.g., the structure shown in Figure 6a, fosters a reduction in the number of packing circles for a predefined filling accuracy; this choice would reasonably minimize computational complexity in simulations of the electromagnetic scattering of waves by elliptic-cross-section cylindrical structures assembled as a juxtaposition of circular cylindrical scatterers. The results might be easily applied to the modeling of physical fractals [24], allowing the circle radius distribution and the relevant fractal dimension to be numerically studied [25]. Similarly, Apollonian circle packings within non-circular boundaries could find interesting applications in space-filling bearings in connection with the study of tectonics and turbulence modeling [26]. A further analogy may be recognized between the presented structures and the covering of hierarchical nanostructures [27; 28], the tiling of tubular nanostructures or other nanostructure modeling [29; 30; 31].