esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Traffic-Volume-Monitoring System
### Mains-Free-Power Supply Measurement Points  
One of the problems to be solved during the measurements was the power supply to the measurement points (the video cameras, wireless network, LTE system network, and camera servo systems). In the previous solution, we used one power source system based on one solar panel, one battery, and an external power mains for one measurement system for the whole car park at a rest area (Figure 3). However, in the new solution for the new measurement plan, we wanted to keep our measurement points highly mobile. In addition, the cost of connecting power lines played a significant role in selecting power sources. So we used measurement points with an independent power supply. We used two measurement points for each rest area measurement system. Power was based on a photovoltaic panel and a battery (Figure 5).  
Figure 5: Actual photo of one of the installed measurement points with a mains-free supply system.  
A solar panel with a maximum power of 60 Watts and a battery with a capacity of 40 Ah enabled the cameras to work continuously, even if only one of every three days was sunny. Despite this, we tried to take measurements in the spring-summer-autumn time from equinox to equinox. This selected period of the year for measurements guarantees continuous measurement at night. Although in the target systems, we will additionally use a permanent power source from the power grid for a reliable power supply.
## 5 Modification of Active AP Configuration Algorithm
### Extension of the Algorithm's Procedure  
_Host association improvement_ in the first phase of the algorithm procedure is modified:  
1. Change the association of each host to the interface of AP which provides the highest throughput using Equation (4) within the associable AP interfaces. At this stage, compute the cost function \(E_{2}\) and keep it as the best found cost function, \(E_{2}^{best}\).
2. Find the interface of AP that provides the lowest host throughput using Equation (7). Then, make the _modifiable hosts_ list that are associated with the AP interface, which can be connected with another AP interfaces.
3. Choose one host at random from the list of _modifiable hosts_. Then, this host is associated at random with other associable active AP interface. Compute the new cost function \(E_{2}^{new}\).
4. Replace \(E_{2}^{best}\) with the newly found \(E_{2}^{new}\), if \(E_{2}^{new}>E_{2}^{best}\), and retain the new AP-host association. If not, go back to the previous association and select the best cost function \(E_{2}^{best}\).
## 2 Governing Equations
### Incompressible Fluid Solution  
For comparison, it is necessary to consider the case of vibration in the incompressible fluid situation. The control equation can be listed as follows:  
\[\left\{\begin{array}{l}m\frac{\partial^{2}U}{\partial t}+k\cdot U+\Delta p =0\\ \frac{\partial u}{\partial t}+\frac{u}{r}=0\\ \frac{\partial u}{\partial t}+u\frac{\partial u}{\partial t}+\frac{1}{\rho_{o }}\frac{\partial p}{\partial r}=0\end{array}\right.. \tag{26}\]  
where the symbols are the same as in Section 2.1. The boundary condition in the structural equation in this case is the same as Section 2.1, while the boundary condition in the fluid aspect is:  
\[\left\{\begin{array}{l}u|_{r=r_{s}}=u_{s}(t)\\ p|_{r=R}=p_{0}\end{array}\right. \tag{27}\]The following solution can be concluded according to the continued equation of Equation (26) combined with the boundary condition of \(\left.u\right|_{r=r_{s}}=u_{s}(t)\):  
\[u(r,t)=\frac{r_{s}}{r}u_{s}(t) \tag{28}\]  
Also, the general solution of pressure can be obtained below from the momentous equation of Equation (26) by substituting Equation (28) into it:  
\[p(r)=-\rho_{0}\frac{\partial u_{s}}{\partial t}\ln(R/r)-\frac{1}{2}\frac{u_{s }^{2}}{r^{2}}+C \tag{29}\]  
At last, the specific solution of this case may be deduced as follows combined with the boundary condition of \(\left.p\right|_{r=R}=p_{0}\):  
\[\Delta p=p-p_{0}=\rho_{0}r_{s}\frac{\partial u_{s}}{\partial t}\cdot\ln(\frac {R}{r})+\frac{1}{2}\rho_{0}u_{s}^{2}\frac{r_{s}^{2}}{r^{2}} \tag{30}\]  
Generally speaking, \(R\) is a large value and the vibration acceleration, \(\partial u_{s}/\partial t\), may be of the same order with a magnitude of \(u_{s}\). Therefore, the second term in the specific solution of Equation (30) can be neglected and the approximate result can be derived as the following statement:  
\[\Delta p=\rho_{0}r_{s}\frac{\partial u_{s}}{\partial t}\cdot\ln(\frac{R}{r}) \tag{31}\]
# An Effective Adaptive Combination Strategy for Distributed Learning Network
## 2 The ATC Diffusion LMS Algorithm
### ATC Algorithm  
The main target of distributed estimation algorithm is to generate an estimate \(\mathbf{w}_{k}(t)\) of \(\mathbf{w}_{o}\) at each node \(k\) and time \(t\) in a distributed manner. For the diffusion strategy, generally, each node \(k\) executes a local adaptation step to obtain an intermediate estimate \(\mathbf{\psi}_{k}(t)\), then all the nodes share their intermediate estimates to their neighbors, and finally, each node linearly combines all the intermediate estimates received from its neighbors under some combination weights. The detailed steps of ATC diffusion LMS algorithm are  
\[\mathbf{\psi}_{k}(t) =\mathbf{w}_{k}(t-1)+\mu_{k}\mathbf{n}_{k}(t)\mathbf{e}_{k}^{*}(t), \tag{2}\] \[\mathbf{w}_{k}(t) =\sum_{l\in\mathcal{N}_{k}^{\prime}}a_{l,k}\mathbf{\psi}_{l}(t), \tag{3}\]  
where \(\mathbf{e}_{k}(t)\triangleq d_{k}(t)-\mathbf{w}_{k}^{\mathrm{H}}(t-1)\mathbf{u}_{k}(t)\) is the prior estimate error and \(\mu_{k}>0\) is the step size for \(k\in\{1,2,\ldots,N\}\). The combiner \(a_{l,k}\) is the weight of intermediate estimation from node \(l\) during the combination step of node \(k\). Moreover, the non-negative combination matrix \(\mathbf{A}=[a_{l,k}]\) satisfies [1,12]  
\[a_{l,k}\geq 0\text{ if }l\in\mathcal{N}_{k},\;a_{l,k}=0\text{ if }l\
otin\mathcal{N}_{k},\;\mathbf{a}_{k}^{\mathrm{T}}\mathbf{1}=1, \tag{4}\]  
with \(\mathbf{a}_{k}\) denoting the \(k\)th column of \(\mathbf{A}\). Notice that \(\mathbf{A}\) is left-stochastic since the entries of each column are non-negative and sum to 1 [19,20].
# Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
## 2 Materials and Methods
### Parameters Test  
Few studies have provided detailed information about the influence of network structure and parameter tuning on network performance. These issues, however, are crucial to the accuracy of dispersion curve inverting. So, we calculated the relative errors of the number of training samples, learning rates, and different network layers to discover their relationships.
# Development of a Multiplex PCR Assay to Monitor Living Modified Cottons in South Korea
## 3 Results and Discussion
### Application of the Developed Multiplex PCR Method  
A novel multiplex PCR method for 8 LM codons was developed and applied to nine samples from LMO monitoring in 2017. As a result, areas a, b, and c were identified as non-LM cotton. Areas d, e, f, g, h, and i represented LM cotton. The sample from area d was identified as LLCOTTON25; area e was identified as MON531; area f was identified as 281-3006 x MON88913; area g identified as MON531 x MON145; area h was identified as MON88913 x MON15985 x MON531; and area i was identified as MON15985 x MON531 x LLCOTTON25 x GHB614 (Figure 5A). Nucleotide sequencing analysis of PCR products was conducted, and all results confirmed that the target sites were amplified correctly (Figure 5B). If MON15985 and MON531 were detected at the same time, they were considered to be MON15985, because MON15985 was produced by introducing the new cassette into the MON531 [(20)]. The areas where LM codons were located will be monitored further in the following years to record the rediscovery of LM cotton. Currently, there is comprehensive monitoring of LMO in South Korea, and multiplex PCR could be used for efficient utilization of the limited budget and workforce.  
Figure 3: Gel loading analysis of PCR products of eight LM events. PCR products of Non-LM, GHB119, MON88913m MON15985, LLCOTTON25, MON1445, GHB119, 281-3006, and MON531 were electrophoresed on a 2.5% agarose gel (Lane M: 100 bp DNA Ladder; Lane 1: Non-LM; Lane 2–9: New primers).  
Figure 4: Multiplex PCR experiments were performed under various conditions. (**A**) Electrophoresis gel image using multiplex eight primer PCR method on LM cotton with varying conditions, including concentration of genomic DNA and eight primer sets. (**B**) Schematic diagram of the optimal multiplex PCR conditions.  
Figure 5: Application of the multiplex PCR method for the collected, candidate LM cotton samples by LMO monitoring. (**A**) Analysis of multiplex PCR. (**B**) Determination of events confirmed by nucleotide sequencing analysis. The circles indicate detected events (area a: Non-LMO; area b: Non-LMO; area c: Non-LMO; area d: LLCOTTON25; area e: MON531; area f: 281-3006 \(\times\) MON88913; area g: MON531 \(\times\) MON1445; area h: MON88913 \(\times\) MON15985; area i: MON15985 \(\times\) MON531 \(\times\) LLCOTTON25 \(\times\) GHB614; line p: Eight cotton events). If MON15985 and MON531 were detected at the same time, they were considered MON15985.
# Digital 3D Technologies for Humanities Research and Education: An Overview
## 4 Epistemics and Methodology
### Epistemic Challenges of 3D Modelling in Humanities  
What epistemic challenges are related to 3D modelling? The analogue research process in history studies traditionally comprises the investigation of a historical object either directly or via historical sources, which researchers inspect visually to answer a specific research question (cf. Figure 5). The virtual 3D techniques (Figure 6) add the transformation layers of digital modelling and computer graphical visualization--potentially losing, altering, or adding information, e.g., by interpretation or selection. This increases the complexity of the research process and potentially causes additional bias in history research due to non-transparency and fuzziness. The 3D reconstruction of no longer extant objects adds another main issue. Digital 3D modelling approaches like CAD strive to show a holistic building and \"make it hard to be vague\" ([301], cited according to [139,302], also [139,302]) by requiring exact measurements and 3D shape information of all architectural parts. Consequently, 3D reconstructions force their creators to complete missing information that available sources--potentially biased, incomplete, and low-quality--cannot provide, e.g., about parts of buildings not shown in images or impossible to read. This approach contrasts with the _problem-centric approach_ in history studies, the leading paradigm for over 50 years, where the attempt to \"show how it actually was\" [303] has usually given way to consequently basing research on available historical sources (e.g., [304,305]). A consequent issue is how to overcome this clash between sparse, questionable, partial, or missing historical information and the demands for holistic information to achieve digital 3D reconstruction of past architecture.  
Figure 5: The analogue research process: A historical column (1) is either directly receivable or documented with sources (2), which is perceived by the researcher (3) to investigate a specific research question (4).
# A Comprehensive Review of the Application Characteristics of Biodiesel Blends in Diesel Engines
## 7 Compatibility  
### Lubricity  
Lubricity is the ability of a fluid to reduce friction and adhesion between two contact surfaces. Fuel lubricity is of great significance to the life of the engine, especially for components such as fuel pumps and fuel injectors that require fuel self-lubrication [18]. Most studies show that, compared with diesel, biodiesel often has better lubricity under the same conditions. [PERSON] et al. tested the lubrication of palm biodiesel-diesel blends with different concentrations through the four-ball wear test, and found that the diameter of the steel ball wear marks decreased with an increase in the concentration of biodiesel [17]. High quality jatropha biodiesel was tested by [PERSON] et al., who found that the blend with 15 vol% biodiesel has the best lubricity. However, the lubrication effect cannot continue to improve due to the increase in biodiesel concentration [140]. Figure 18 shows the effect of biodiesel on fuel lubricity and viscosity. In some studies, biodiesel has also been used as an additive to improve lubricity [19].  
Figure 16: (**a**) Combustion noise and (**b**) engine external noise of different fuels [132].  
Figure 17: atotal (total vibration acceleration) of diesel, biodiesel and their hydrogen enriched blends [136].  
Better lubricity depends on the main components of biodiesel such as fatty acid methyl ester, and a few other ingredients such as glycerides, antioxidants, and sterols, which can effectively improve the lubricity of the fuels [20; 141]. The ester molecules in biodiesel form a relatively stable protective film on the metal surface, which helps to prevent the sliding surface from contacting [17]. In addition, the residual polar compounds such as free fatty acids and monoacylglycerol in the production process of biodiesel can also enhance the lubricating effect, and the O atoms in the fatty compounds have a strong polarizing effect [141]. Some scholars found that the oxidation process of biodiesel affects the lubrication. During this process, the ester is oxidized to various fatty acids, which will cause fuel degradation and corrosion of the materials, thereby deteriorating the lubrication effect [17].
## 2 Analysis of Two-Step Method
### Equivalent ODE  
For an additional analysis of the influence of \(\beta_{2}\) on the convergence of (4), we consider an approach based on the ODE, which is constructed as a continuous analogue of the iterative method. At present, this approach is widely used for the analysis of optimization methods [7; 21; 22; 23; 24; 33; 32].  
Let method (4) for quadratic function \(f(x)=\frac{a}{2}x^{2}\), where \(x\in\mathbb{R}\), \(a>0\), be considered. This function can be treated as a quadratic approximation of the arbitrarily smooth function, which has its minimum zero value in point \(x=0\). Application of (4) leads to the following difference equation:  
\[x^{k+1}=x^{k}-ha(x^{k}+\beta_{2}(x^{k}-x^{k-1}))+\beta_{1}(x^{k}-x^{k-1}). \tag{22}\]  
Figure 2: Plots of the dependence of the optimal convergence rate on logarithm of \(\kappa\).  
Let us introduce function \(x(t)\), where \(t\) is defined as \(t=k\sqrt{h}\), so \(x(t)\approx x^{\frac{t}{\sqrt{h}}}=x^{k}\) and \(x(t+\sqrt{h})\approx x^{k+1}\), \(x(t-\sqrt{h})\approx x^{k-1}\). Equation (22) can be rewritten as  
\[\frac{x^{k+1}-x^{k}}{\sqrt{h}}=-\sqrt{h}ax^{k}+(\beta_{1}-ha\beta_{2})\frac{x^ {k}-x^{k-1}}{\sqrt{h}}. \tag{23}\]  
Let the new parameters \(\gamma_{1}>0\), \(\gamma_{2}\geq 0\) be introduced: \(\beta_{1}=1-\gamma_{1}\sqrt{h}\), \(\gamma_{2}=\sqrt{h}\beta_{2}\) and the following new variable is considered:  
\[m^{k+1}=\frac{x^{k+1}-x^{k}}{\sqrt{h}}.\]  
So, (23) is rewritten as  
\[\frac{m^{k+1}-m^{k}}{\sqrt{h}}=-ax^{k}-(\gamma_{1}+a\gamma_{2})m^{k}. \tag{24}\]  
For \(h\to 0\), we find that (24) is rewritten as \(\dot{m}=-ax-(\gamma_{1}+a\gamma_{2})m\) and with the use of \(m=\dot{x}\), we obtain the following second-order ODE:  
\[\ddot{x}=-ax-(\gamma_{1}+a\gamma_{2})\dot{x}. \tag{25}\]  
The case of HBM corresponds to \(\gamma_{2}=0\)[7] and the ODE describes the dynamics of a material point with unit mass under a force with a potential represented by \(f(x)\) and under a resistive force with coefficient \(\gamma_{1}\). Thus, if \(\gamma_{2}\
eq 0\), we have the following mechanical meaning of \(\beta_{2}\): this presents an additional damping effect on the solution of the ODE (25) and, as a consequence, on the behavior of method (4). With the use of proper values of \(\beta_{2}\), we can realize the damping of oscillations related to the non-monotonic convergence of the method. This is typical for the case of \(\kappa\gg 1\)[13]. In Section 3, this will also be illustrated for the minimization of non-quadratic convex and non-convex functions.
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 5 Experimental Validation
### Power Consumption of the LV-VSC  
Owing to the extreme low dc capacitor voltage, low voltage switching devices are chosen for the LV-VSC, which are MOSFETs FDD86110 (100 V, 50 A) with on-state resistance of 10.2 m\(\Omega\). Based on that, Figure 26 shows comparative results of the calculated and measured power loss (\(P_{d}\)) between the proposed LV-VSC in series with the filter capacitor and the one in series at the inverter output in [34],  
Figure 24: Comparative waveforms with a shunt rectifier load at the PCC (\(L_{g}=0.1\) pu.) (**a**) without the proposed method. (**b**) with the proposed method.  
Figure 25: Transient behavior of the grid-connected inverter when \(L_{g}=0.1\) pu.  
as a function of the inverter output current (\(i_{o}\)), in which the power loss of the LV-VSC is mainly from switching devices and dc capacitors.  
As seen, the power loss of the LV-VSC in [34] increases obviously with the inverter output current because the current through the LV-VSC equals to the inverter output current. However, the power loss of the proposed LV-VSC in series with the filter capacitor is maintained around 1W since the small value of capacitor current through the LV-VSC is independent on the inverter output power. For example, the capacitor current through the proposed LV-VSC is maintained around 0.4 Arms in the case study regardless of the increase of inverter output current. Thus, the efficiency of the entire system is not sacrificed since the VA rating of the proposed impedance compensator is sustained extremely low.
# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 2 Related Works  
### Deep-Learning-Based Pedestrian Detection Methods  
With the success of convolutional neural networks [16; 17; 18; 19] in generic object detection, significant progress has been achieved in the pedestrian detection task. Most existing pedestrian detection methods are proposed using two-stages, based on a region-based  
Figure 2: \(l_{2}\)-norm distances and IoU values of detection box (red) and ground truth box (green). (**a**) As an example, when \(l_{2}\)-norm is 12, the IoU of the two boxes is 0.43. (**b**) With the same \(l_{2}\)-norm, the IoU of the two boxes is 0.69. A higher IoU value indicates better detection localization.  
convolutional neural network (R-CNN) framework [18; 20; 21]. Two-stage pedestrian detectors generate a set of region proposals, and then classify the proposals into the pedestrian or the background classes and regress the coordinates in the second stage. For example, RPN+BF [4] uses the region-proposal network (RPN) to get candidate predictions which are then refined by the boosted forest (BF). For multi-scale pedestrian detection problems, MS-CNN [5] generates proposals by exploiting multi-scale feature maps. SA-Fast RCNN [22] contains two sub-networks for detecting pedestrians with a large- and a small-scale, respectively. DIF-RCNN [3] considers context information by integrating a deconvolution module, and enlarges the receptive field to enhance the detection performance of small-scale instances. One-stage pipelines have also been explored for pedestrian detection. ALF [6] is a lightweight pedestrian detector based on a single shot multi-box detector (SSD) architecture. It introduces an asymptotic localization fitting module that stacks multiple predictors to infer the anchor boxes step-by-step. The recent, state-of-the-art method, CSP [1] uses a single fully convolution network (FCN) with an anchor-free setting. It simplifies pedestrian detection as a center and scale prediction task.
## 3 Results
### Testing Results--Data 2  
Data 2 represent an object that is practically free of flat surfaces. Figure 7 shows the point cloud colored according to the \(T\) values (flat surfaces in red, rugged surfaces in blue). Rather than architectural decorations, the high detail originates from the defects, chinks, and cracks in this object. However, even these are important for an accurate recording of the object. A closer look reveals also ledges and other projections. On a standard laptop, the \(E_{3}\) calculation took less than 1 s, its transformation to \(T\) was practically immediate and the dilution took 0.2 s.  
Figure 8 shows examples of the processed cloud for the walled part of the chapel with a = 0.02 m and different values of b. Table 3 shows the numerical values indicating the reduction in the number of points; using the same example as in Data 1, i.e., a = 0.01 m and b = 0.02 m, 0.03 m or 0.05 m, the number of points in the cloud decreased to 90%, 82% and 70%, respectively.  
Figure 7: Data 2—original point cloud colored according to the \(T\) values; red—flatest parts, blue—most curved/rugged parts.  
The extremely low value of RMSD in Table 3 for a = 0.01 (0.1 mm for uniform dilution) is caused by the fact that in this dilution, practically no points were removed from the origi  
Figure 8: Data 2—examples of dilution: a = 0.02 m in all cases; (a) uniform dilution (i.e., a = 0.02 m); (b) b = 0.04 m; (c) b = 0.06 m; (d) b = 0.10 m.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**a [m]** & **b [m]** & \
\begin{tabular}{c} **Points in** \\ **the Cloud** \\ \end{tabular} & **\% Original 1** & **RMSD [m]** &
\begin{tabular}{c} **RMSD\({}_{\text{e}}\)** \\ **[m]** \\ \end{tabular} & **\% Uniform 2** \\ \hline original & **-** & 407,532 & 100 & **-** & **-** & **-** \\ \hline
0.01 &  
nal cloud (only 0.7% of points were removed) due to the original resolution being approx. 0.01 m. For this reason, evaluation using RMSDE gives a complementary perspective on the success of the dilution.  
Figure 9 then shows a detail of Data 2, where (a) is a detail of the original cloud and (e) of the progressively diluted cloud. Again, we can see a lower density of points in the flat surfaces while density is preserved in the rugged parts such as the visible stones, bricks, or edges.  
Similarly to the details presented for Data 1, we can again see that increasing a leads to a major loss in detail, and although uniform dilution with the same a provides visually slightly better results than progressive dilution with 10 times higher b, it reduces the number of points by about half while preserving sufficient detail even in data of such complexity.