esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Thermal Expansion Machine Frame Designs Using Lattice Structures
## 3 Results  
### Low Planar CTE Lattice Results  
The concept design presented in the previous section has several design parameters that may be varied to control the resulting structure's CTE. These include the original length of the lattice \(L\), the original diameter of the cylindrical inner part \(D\), and the ratio between the CTEs of the cylindrical inner part material and the lattice outer part material \(\alpha_{2}/\alpha_{1}\). The ratios \(D/L\) and \(\alpha_{2}/\alpha_{1}\) were varied, respectively, from 0.30 to 0.73 (the maximum ratio that the lattice was designed) and from 1.10 to 2.20. Examples of the resulting lattice cells are shown in Figure 8.  
Figure 9 shows that the concept lattice unit cell provides in-plane CTEs (\(\alpha_{X,Z}\)) from approximately \(-62\times 10^{-6}\) K\({}^{-1}\) to 96 \(\times\) 10\({}^{-6}\) K\({}^{-1}\) by varying the design parameters. \(\alpha_{X,Z}\) data were fitted with a first order polynomial surface function of the form  
\[\alpha_{X,Z}=a+b\left(\frac{\alpha_{2}}{\alpha_{1}}\right)+c\left(\frac{D}{L}\right) \tag{1}\]  
Figure 8: Illustration of the top view and the side view of (**a**) the \((D/L)=0.30\) model; (**b**) the \((D/L)=0.50\) model; and (**c**) the \((D/L)=0.73\) model.  
Figure 7: Finite element mesh density convergence for the low-CTE lattice cell.  
which provides a guide allowing designers to customise the CTE of the proposed lattice structure. The fit provides an accurate description of the data (with \(R^{2}\) = 0.994) over the examined range of \(D/L\) and \(\alpha_{2}/\alpha_{1}\).  
The resulting fit parameters, with 95% confidence bounds, were found to be a = (253 \(\pm\) 10) \(\times\) 10\({}^{-6}\) K\({}^{-1}\), b = (\(-\)132 \(\pm\) 5) \(\times\) 10\({}^{-6}\) K\({}^{-1}\), and c = (\(-\)20 \(\pm\) 10) \(\times\) 10\({}^{-6}\) K\({}^{-1}\), which can henceforth be used to determine the \(D/L\) and \(\alpha_{2}/\alpha_{1}\) parameters to achieve a pre-defined CTE.  
The CTEs of Nylon 12 and UHMWPE are 109 \(\times\) 10\({}^{-6}\) K\({}^{-1}\) and 200 \(\times\) 10\({}^{-6}\) K\({}^{-1}\)[36], respectively. The ratio of \(\alpha_{2}/\alpha_{1}\) for our chosen materials was, therefore, approximately 1.83, meaning that a unit cell made of such materials can provide CTEs between 7.8 \(\times\) 10\({}^{-6}\) K\({}^{-1}\) and 1 \(\times\) 10\({}^{-9}\) K\({}^{-1}\).
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Physical Strategies
#### 5.2.3 Photodynamic Therapy (PDT)  
Photodynamic therapy (PDT) (Figure 3c) is a physical strategy that uses reactive oxygen species (ROS) generated by photosensizers to induce tumor cell death. [PERSON] et al. developed a nanoparticle system designed for PDT that responds to ROS. These NPs encapsulate both doxorubicin and a photosensitizer, accumulating in tumors due to the EPR effect. During PDT, ROS trigger the release of doxorubicin and the photosensitizer by degrading the linker between the polyethylene glycol (PEG) coating and doxorubicin. This release mechanism enhances ROS production within the tumor, improving the therapeutic effectiveness of the treatment [185].  
However, the effectiveness of PDT is hindered by the hypoxic conditions commonly found within tumors, which limit the therapy's reliance on the consumption of singlet oxygen. Overcoming this limitation remains a key area of research for improving PDT-based cancer treatments [186].
## 3 Extraction of Bistatic RCS from Passive Radar Measurements  
### Range-Doppler Map Generation  
A preliminary preprocessing stage synchronized the received data stream to the start of the DVB-T symbol. This also included a potential frequency offset correction due to a non-GPS locked oscillator at the transmitter and/or at the receiver side [26]. The direct signal was then retrieved by demodulating and remodulating the direct signal component from the received signal and by using the result as a reference [27]. The range compression was achieved by resorting to a reciprocal filter (RpF) [21] between the remodulated reference signal and the surveillance signal. The RpF inherently performs the range compression in a batch-wise process for each DVB-T symbol. The RpF removes any unwanted underlying information from the transmitted DVB-T signal and hence reduces the side lobes in both range and in Doppler that could potentially contaminate the target's range-Doppler cell [28]. The range-Doppler map (RDM) was created right after the RpF, by simple Doppler processing of \(M_{s}\) consecutive range compressed DVB-T symbols. The relevant signal processing parameters are listed in Table 2. The multiple available parallel receiving channels of the LORA-11 system were exploited to retrieve multiple estimates of the target RCS, which were then averaged.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Description** & **Value** \\ \hline \multicolumn{3}{c}{**DVB-T signals parameters**} \\ \(f_{c}\) & DVB-T signal carrier frequency & 482 MHz \\ \(T_{s}\) & OFDM symbol duration & 1120 μs \\ \hline \multicolumn{3}{c}{**Processing parameters**} \\ \(B\) & Signal bandwidth & 9.142 MHz \\ \(M_{s}\) & Number of OFDM sysymbols per CPI & 512 \\ \(CPI\) & Coherent Processing Intervall & 573 ms \\ \(\Delta r_{B}\) & Bistatic range resolution (\(c/B\)) & 32.79 m \\ \(\Delta f_{D}\) & Bistatic Doppler resolution (\(1/T_{s}\)) & 1.74 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of parameters of the analyzed DVB-T signal, as well as the parameters of the measurement campaign and signal processing.  
Figure 3: LORA-11 deployed at Dübendorf airfield during the trials.  
An exemplary cumulative RDM with highlighted target track of the PC-12 is shown in Figure 4 for a single receiving channel.
# Blind Image Deblurring Based on Local Edges Selection
## 2 Method  
### Local Edges Selection Method  
Traditional methods obtain image edges by global filtering. Gradient filters consider two or three pixels in the neighborhood, which tends to ignore longer range dependencies [4]. In contrast, image patches can model more complex image structures in larger neighborhoods [27]. So recently some patch-based image deblurring methods are proposed, such as image deblurring based on patch priors [4; 28] and internal patch recurrence [27], etc. The dark channel [29] and bright channel [30] are useful matrices based on local information of image, some previous methods use them in haze removal [29] and image restoration [30; 31]. Different from all these methods, in this paper, we innovatively select image edges by using dark channel and bright channel, and build the deblurring model.  
Dark channel [29] is obtained by finding the minimum value in an image patch, which is defined as follows:  
\[\Phi_{1}(x)(w)=\min_{w\in N(w)}\biggl{(}\min_{c\in[R,G,B]}x^{c}(u)\biggr{)} \tag{6}\]  
where \(\Phi_{1}(x)\) is the dark channel of \(x\), \(N(w)\) is an image patch centered at w, and \(x^{c}\) is the \(c^{th}\) channel of the RGB color image. Similarly, the bright channel [31] represents the maximum value in an image patch, which is defined as follows:  
\[\Phi_{2}(x)(w)=\max_{w\in N(w)}\biggl{(}\max_{c\in[R,G,B]}x^{c}(u)\biggr{)} \tag{7}\]  
Based on the bright channel and dark channel, we propose a new image edges selection method. The edge of an image is composed of points whose brightness changes obviously, so the edge of an image can also be regarded as the junction of the \"bright\" area and the \"dark\" area. By subtracting the bright channel image from the dark channel image, the complete edges of an image can be obtained. So in the proposed method, the edges of image \(x\) can be obtained as follows:  
\[\hat{x}=\Phi_{2}(x)-\Phi_{1}(x) \tag{8}\]  
The proposed method can select the edges of color images as well as gray value images. In Equations (6) and (7), when the input image is a gray-value image, the dark channel and bright channel can be obtained by Equations (9) and (10):  
\[\Phi_{1}(x)(w)=\min_{u\in N(w)}(x^{c}(u)) \tag{9}\]  
\[\Phi_{2}(x)(w)=\max_{u\in N(w)}(x^{c}(u)) \tag{10}\]
# Design and Lateral Stability Analysis of an Attitude Adjustment Tractor for Moving on Side Slopes
## 4 Lateral Stability
### Coordinate Systems  
Here, two coordinate systems, the slope coordinate system and the tractor coordinate system, are defined. Figure 6 illustrates a tractor laterally traversing a slope of angle \(\theta\). As shown in Figure 6, the tractor and its wheels are at a tilted angle \(\psi_{l}\) against the slope (\(\psi_{l}\) positively increases in the uphill direction). In Figure 6, the side slope coordinate system, \(\Sigma_{s}\), is defined as follows: \(x^{(s)}\) denotes the desired traversing direction, \(y^{(s)}\) denotes the uphill direction, and \(z^{(s)}\) denotes the vertically upward direction against the slope surface, as a right-handed system. The tractor coordinate system, \(\Sigma_{r}\), is achieved by a rotation of \(\Sigma_{s}\) about the \(x^{(s)}\) axis with \(\psi_{l}\) in the uphill direction.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 2 Research Status
### Ground-Based GNSS-R Vegetation Remote Sensing Research  
The most representative research on vegetation parameters using a ground-based remote sensing platform is the GPS-SNR (Signal to Noise Ratio) method and IPT technique (Interference Pattern Technique). The former directly uses the multipath information received by the PBO (Plate Boundary Observatory) site to obtain SNR data, and extracts the effective reflectometer height, phase, and amplitude information, and then carries on the relevant vegetation inversion of water content and NMRI (Normalized Microwave Reflection Index) [23; 24; 25]. The latter employs an improved GPS receiver, that is, on the basis of the existing surveying and mapping receiver; however, they focus on improving the antenna polarization, and they have employed vertical polarization antenna to acquire the surface properties [26; 27]. In order to improve the retrieval accuracy, they have also employed the horizontal polarization antenna for an analysis [28]. The receiver's interference waveform is used to obtain vegetation height information by notch position and number information [26; 27; 28]. It should be noted that a GPS receiver intended to be used with the IPT technique obtains the constructive/destructive pattern of the combined reflected and direct signals.
## 1 Introduction
platensis_ biomass is efficient in passive adsorption, while living cultures combine this with active uptake, sequestering metals intracellularly and improving water quality by releasing oxygen during photosynthesis [39]. This dual mechanism makes _A. platensis_ effective for detoxifying industrial wastewater, mining effluents, and polluted water systems. Its eco-friendly nature and potential for biofuel and other valuable byproducts enhance its appeal for sustainable remediation [40]. Extensive studies were conducted regarding the bioremediation of single metal systems (mono-metal-treated, mono-MT), examining the processes, kinetics, and their removal efficiency [5; 12; 14; 15; 16; 41; 42; 43; 44; 45]. These studies have provided valuable insights into the adsorption and biosorption capabilities of numerous microalgae. However, natural ecosystems often contain complex mixtures of metals, and as such, removal systems must be studied in multi-metal (multi-metal-treated, multi-MT) configurations. The research into the effectiveness of _A. platensis_ bioremediation of multi-MT systems remains limited regarding the use of living cultures [36; 46], and of dry _A. platensis_ biomass [39]. Mixed metals often compete for binding sites, complicating the biosorption process. Variationsin metal properties, such as ionic radii and chemical behavior, further challenge multi-metal removal. Addressing these gaps is crucial for scaling algae-based bioremediation to industrial applications capable of handling real-world scenarios involving multiple contaminants [39]. _A. platensis_ is considered as an effective biosorbent because of its large surface, polysaccharide content, and functional groups, allowing good coordination of the metallic cations. However, most research emphasizes single metal sorption performance, leading, subsequently, to the considerable underestimation of the realistic applications concerning multi-metal situations. The scope of this study is to evaluate the ability of _A. platensis_ in heavy metal remediation of multi-metal wastes. For this reason, microalgae's cultures were exposed to mono- and multi-metal solutions of Cu, Cd, Ni, Pb, and Zn, and their metal adsorption ability was comparatively examined in every case. The heavy metal removal efficiency from _A. platensis_ cultures was evaluated using atomic absorption spectroscopy (AAS). Additionally, the biomass, before and after remediation, was qualitatively examined using spectroscopic and microscopic methods to explore its potential use to other applications, while its growth estimation was performed using Near-Infrared Spectroscopy. These measurements were also performed to evaluate the changes induced by heavy metal stress.