esa-sceva/satcom-chunk-collection huggingface dataset:
# _Sci_
## 4 Discussion
### Disadvantages--Acquisition, Tracking, and Pointing; The Atmosphere  
If not appreciated from above, acquisition, tracking, and pointing between a fixed ground station and a \"slow moving\" geosynchronous orbiting satellite is difficult on the best of days. The challenge of introducing two moving objects (e.g., transmitting from an unmanned aerial vehicle in flight to a geosynchronous orbiting satellite) is akin to \"... trying to hit a bullet with a smaller bullet whilst wearing a blindfold, riding a horse\" [98].  
Acquisition, tracking, and pointing requirements expand to include the need for highly accurate ephemeris data, the point-ahead angle, pointing within an error budget, and choosing to not employ a guidance beacon (pointing aid) to maintain low probabilities of detection. While the above requirement for an acquisition, tracking, and pointing accuracy of one microradian was done as both sample mathematical calculation and as hyperbole, potential beam widths of twenty microradians may require pointing accuracies of ten microradian or less [7; 34; 37].  
There is one reprive to acquisition, tracking, and pointing attributed to laser crosslinks using optical links. As most satellites employ solar panels to generate electrical power, those same panels (the photovoltaic panel or other photo-detector components) can serve as a target for an uplink signal. The large surface area panels greatly simplify the requisite precision needed for uplink acquisition, tracking, and pointing [99].  
If the difficulties in mastering the acquisition, tracking, and pointing of two moving targets fails to warrant pause, then the effects of transmitting optical links through the atmosphere must. Near and in-band visible light waves are notoriously fickle for having energy absorbed by the atmosphere. Additionally, atmospheric non-uniformity leads to beam bending and scattering; if strong enough, beam break-up due to loss of coherence.  
However, as evidenced by very recent progress [100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131], determined engineers, partnered with a tenacious defense funding programmer, will overcome the disadvantages to reap the advantages and make U.S. Department of Defense laser crosslinks a reality.
# Frequency-Bounded Matching Strategy for Wideband LNA Design Utilising a Relaxed SSNM Approach
## 3 IM-OM Circles and Frequency-Bounded Mismatch Envelope
### Frequency-Bounded Envelope of Various Mismatch Regions
It is important to note that the specific shape and distribution of these regions depend on the transistor model, bias conditions, and technology. Additional mismatch zones may emerge for other configurations. For example, moderate mismatch regions with both IM and OM exceeding 0.25 may form separate sub-regions. However, for the device studied in this work, such regions overlap significantly with A2 and B2 due to the inherent convergence of IM-OM boundaries. This analysis confirms that the frequency-bounded mismatch map is flexible, scalable, and adaptable to both wideband and narrowband amplifier designs. The categorised zones serve as a visual aid for selecting suitable \(\boldsymbol{\Gamma}_{\text{L}}\) values that meet relaxed SSNM constraints across multiple stages, thereby streamlining the design process and minimising reliance on iterative optimisation. Furthermore, this method extends naturally to much wider bandwidths. The construction procedure remains unchanged: the mismatch envelope is primarily defined by the tangency loci at the start and end of the band. Additional intermediate frequencies may be used to refine the internal structure of the envelope and improve the resolution between regions, especially in ultra-wideband designs. This generalisation makes the approach suitable for a broad range of RF and mm-wave applications with stringent bandwidth requirements.
## 1 Introduction  
Surface plasmons (SPs) are waves that result from the collective oscillations of electrons excited by electromagnetic waves at a metal/dielectric interface, propagating along the metal surface and evanescently confined in the perpendicular direction [1; 2; 3; 4; 5; 6]. SP waves have local confinement characteristics, and their distribution depths are smaller than the wavelength order, i.e., beyond the diffraction limit [2; 7], making SPs a great candidate for use in the production of sub-wavelength optoelectronic devices [8; 9; 10; 11; 12]. By controlling the propagation of SPs at the nanoscale, functions such as focusing and trapping nanoparticles at a much lower intensity level can be achieved [13; 14]. In conventional optics, however, this is not possible, because of the limitations of the diffraction limit, and the trapping force drops quickly as the radius of the particle decreases due to reduced gradient force [15], so the excellent performance of this beyond-the-diffraction-limit technology is used fully to design SP lenses [16; 17; 18] and plasmonic trapping devices [19]. The Archimedes spiral lens gives incident light an additional geometric phase for different handedness [20; 21; 22; 23], and the light field will thus exhibit different functions for particle trapping or particle rotation [14; 24; 25]. In contrast to the traditional 2D plasmonic Archimedes spiral lens (PASL), the Archimedes spirals were engraved on a planar gold film, and a focusing spot or plasmonic vortex can only be formed by constructive interference on the gold film surface when the distance between the two spirals is equal to the surface plasmon polariton (SPP) wavelength. This greatly reduces flexibility for practical applications of these PASLs, such as manipulation of biological cells [26] and stretching of DNA [27; 28].  
In this work, a single three-dimensional (3D) PASL has been investigated by finite element method (FEM). By controlling the angular momentum of the plasmonic near-field directly, a focusingspot or a plasmonic vortex field can be formed in the central hollow of the 3D PASL. We study the effects of several parameters, such as the initial spiral radius \(r_{0}\), the horizontal distance between successive turns of the spiral _gap_ and the height of the structure \(h_{0}\), on the focusing spot/plasmonic vortex position and the focusing intensity in detail. We find that the focusing spot/plasmonic vortex position can only be adjusted by varying the height of the structure. It should be noted that our 3D PASL is free from the rigid structural constraint of the adjacent helical spiral pitch that is included in the 2D PASL for the sake of constructive interference. This makes our designed 3D PASL structure much more flexible, and it offers greater potential than the 2D PASL for many practical applications, including micromechanical systems, optofluidic devices, microbiology and biomedical photonics, particularly in the analysis of conformational changes in DNA or proteins by providing controllable trapping or a controllable local vortex turbulence [29].
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 2 Background on SRv6 Network Programming
### SRv6 Network Programming
#### 2.2.2 H.Insert  
This behavior is applied by the source node in the case of the inline SRH version. When the source node receives a packet whose destination address matches with a locally instantiated SID associated with the H.Insert behavior, it does what follows. Firstly, it embeds the SRH into the IPv6 header. Then, it sets the last segment of the list as the packet destination address. Finally, the SRH and IPv6 header fields are properly set, and the packet is forwarded.  
Differently from the H.Encaps.Red, where the final destination (e.g., the service address) is in the inner IPv6 header, in the H.Insert case, that address must be included in the segment list. The latter requires removing the SRH at the penultimate segment to make the procedure transparent or if the service is SRv6 unaware. The H.Insert behavior is less scalable compared to the H.Encaps.Red because the same SRv6 policy (i.e., list of segments) cannot be applied to multiple services. The reason is that in the case of H.Insert, the service address must be included in the SRH segment list. In the case of H.Encaps.Red instead, using an outer IPv6 header, the service address can be left in the destination address field of the inner IPv6 header. H.Encaps.Red allows the exploitation of the same SRv6 policy for all the packets that require the same list of segments.
## 3 Materials and Methods
### Instrumentation and Acquisition System
#### 3.2.5 Transimpedance Amplifier (TIA) Module  
The transimpedance amplifier (TIA) module converts the photons detected by the photodetectors into mixed voltage signals from multiple wavelengths. The module hosts one female DB15 connector for interfacing with the TBI probe. For the ZenTBI, three identical differential TIA circuits were implemented, one per photodiode. In this particular TIA topology, the photodiode is connected between the negative inputs of two operational amplifiers, each with a gain resistor in a negative feedback configuration. Their outputs are driven into a differential amplifier, allowing the common-mode rejection of coupled noise. For PPG applications, it is important to use op-amps with large bandwidth and open loop gain, as well as include a phase compensation capacitor to provide stability to the circuit [18]. Accordingly, the TIA circuits use an OPA2380 (bandwidth = 90 MHz and open loop gain = 130 dB), that includes a carefully calculated compensation capacitor (2.9 pF).  
Moreover, the signal amplification is manually adjusted by two external resistors that are connected to the differential amplifier INA331. The gain of this specific op-amp is described in its datasheet as G = 5 + 5 \(\cdot\) (R2/R1), and since the common noise is cancelled before signal amplification, the detection of small amplitude PPG becomes easily possible. The equal gain of the distal photodiodes transimpedance circuits is key to ensure \(\frac{\partial A}{\partial\rho}\) linearity; therefore, R1 and R2 are trimmers that allow a precise gain configuration. The  
Figure 3: Current source schematic design.  
DC outputs of both photodiodes were compared using a homogeneous phantom to set equal gains.
# Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors
As the moving direction is relative stable in each segment, we can apply and update a KF for the segment to correct the headings. Finally, we devise another KF to derive locations in each segmented path by fusing the BLE-based positioning and the PDR. As a result, the BLE-based method lays the foundation for the fusion workflow and overcomes the drifting of the PDR. We implemented this approach in an Android smartphone, and conducted real-time tests on a floor with an area of 44 m \(\times\) 17 m. As we attach importance to turns, different routes with sharp turns (180\({}^{\circ}\) and 90\({}^{\circ}\)) were selected. The experiments regrading our fusion approach were conducted with starting location errors and heading orientation noises. The results demonstrate the proposed BLE-based method indeed improves the positioning accuracy of iBeacon. Moreover, the whole fusion approach can effectively alleviate the positioning error caused by inaccurate initial positions and orientation noises. The remainder of this paper is organized as follows. Section 2 briefly introduces the related work on iBeacon/BLE techniques and PDR. Section 3 elaborates on our research method including BLE-based method, PDR, and the fusion workflow for indoor positioning. Section 4 presents our experimental results and Section 5 discusses them with positioning accuracy. Finally, Section 6 concludes this paper with some future work.
# High-Efficiency Adsorption of SARS-CoV-2 Spike 1 Protein by Plasma-Modified Porous Polymers
## 3 Results and Discussion
### The Adsorption Efficiency of S1 Protein was Quantified by ELISA  
ELISA was used to detect the concentration of residual S1 protein in the solution [39; 40]. As shown in Figure 4a,b, the adsorption process of S1 protein in solution by MPP was rapid. When soaked in 0.5 \(\upmu\)g/mL protein solution for 30 s and 1 min, the adsorption efficiency of MPP can reach 98.15% and 99.99%, respectively. When soaked in 0.1 \(\upmu\)g/mL protein solution for 30 s and 1 min, the adsorption efficiency is 97.72% and 99.95%, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline
**Chemical Contents** & **Binding Energy (eV)** & **MPP (\%)** \\ \hline C (at. \%) & 284.6 & 83.97\% \\ O (at. \%) & 532.64 & 12.81\% \\ N (at. \%) & 400.19 & 3.22\% \\ O/C (\%) & \(-\) & 3.98 \\ C\(-\)C & 284.6 & 85.18\% \\ C\(-\)O & 285.7 & 10.25\% \\ O\(-\)C=O & 289.8 & 3.20\% \\ C=O & 287.8 & 1.37\% \\ \hline \end{tabular}
\end{table}
Table 1: Chemical contents and functional groups of C1s on MPP.  
Figure 3: The expression of remaining SARS-CoV-2 S1 protein and the adsorption stability of MPP was analyzed by western blot. (**a**) Expression of SARS-CoV-2 S1 protein in solution after adsorption; (**b**) Gray value analysis of SARS-CoV-2 S1 protein band; (**c**) Expression of SARS-CoV-2 S1 protein in solution after MMP adsorption for 10 min; (**d**) Expression of SARS-CoV-2 S1 protein in solution after MMP adsorption for 4 h; (**e**–**h**) Gray value analysis of SARS-CoV-2 S1 protein band.  
Further, to explore the reusability of the modified adsorbent, we used 0.1 g raw MaPR and 0.1 g MPP ten consecutive times (1 min each time). The results indicated that the adsorption efficiency of the raw resin and the modified resin decreased by different degrees during the repeated adsorption process. The adsorption efficiency of the raw MaPR decreased from 66.99% to 30.21%, while the adsorption efficiency of the MPP decreased from 95.95% to 63.37% (Figure 4c). In short, the MPP can be reused at least 10 times, and the adsorption efficiency can still remain above 60%.  
The enhancement of the MPP adsorption efficiency on SARS-CoV-2 S1 protein may involve two key factors: the pore structure and chemical functional groups. During plasma discharge, electrons and charged particles accelerate in the electric field and bombard the material surface. As a result, many micropores and mesopores are created. On the other hand, depending on the ionization and dissociation process of plasma, many active species are generated, such as O, OH, O\({}_{3}\)[41], which can destroy the original chemical bonds of resin and embed new oxygen-containing functional groups and nitrogen-containing functional groups (C\(-\)O, O\(-\)C\(=\)O, C\(=\)O, \(-\)NH).
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 3 Materials and Methods
### Feature Extraction and Fusion  
RGB and point clouds represent two entirely different data modalities. Inspired by PointMBF [24], our method adopts U-Net-like network architectures for both branches and performs bidirectional feature fusion at multiple scales. One branch takes RGB images as input, while the other processes point cloud features. In the following sections, we will  
Figure 1: The pipeline of our Fusion Feature Matching with Transformer (**FFMT**). It takes a pair of RGB-D images as input and estimates the relative pose between them. The RGB-D images first pass through a feature extraction and fusion module. Both the RGB branch and the point cloud branch adopt U-Net like structures, utilizing CNN and KPConv, respectively, for feature extraction. A multi-scale bidirectional feature fusion mechanism is employed between the two branches. After obtaining the extracted features, the features are flattened into 1D vectors and combined with positional encoding. The augmented features are then processed through a series of alternating self-attention and cross-attention layers. Based on the extracted features, coarse correspondences are determined based on Lowe’s ratio. These correspondences are refined through several RANSAC iterations to obtain precise matches. Finally, the estimated transformation is computed using a least-squares method. The entire model is trained end-to-end with differentiable rendering.  
provide a detailed explanation of our method and elaborate on the differences between our approach and PointMBF [24].
###### Abstract  
To optimize the workflow of civil engineering construction in a harbour, this paper developed a framework of the contaminant leaching assessment carried out on the stabilized/solidified dredged soil material. The specimens included the sampled sediments collected from the in situ fieldwork in Arendal and Kongshavn. The background levels of the concentration of pollutants were evaluated to assess the cumulative surface leaching of substances from samples over two months. The contamination of soil was assessed using a structured workflow scheme on the following toxic substances, heavy metals--As, Pb, Cd, Cr, Hg, Ni, and Zn; organic compounds--PAH-16 and PCB; and organotin compounds--TBT. The numerical computation and data analysis were applied to the results of geochemical testing creating computerised solutions to soil quality evaluation in civil engineering. Data modelling enabled the estimation of leaching of the contaminants in one year. The estimated leaching of As is 0.9153 mg/m\({}^{2}\), for Ni--2.8178 mg/m\({}^{2}\), for total PAH-16 as 0.0507 mg/m\({}^{2}\), and for TBT--0.00061 mg/m\({}^{2}\) per year. The performance of the sediments was examined with regard to permeability through a series of the controlled experiments. The environmental engineering tests were implemented in the Swedish Geotechnical Institute (SGI) in a triplicate mode over 64 days. The results were compared for several sites and showed that the amount of As is slightly higher in Kongshavn than for Arendal, while the content of Cd, Cr, and Ni is lower. For TBT, the levels are significantly lower than for those at Arendal. The algorithm of permeability tests evaluated the safety of foundation soil for construction of embankments and structures. The optimized assessment methods were applied for monitoring coastal areas through the evaluated permeability of soil and estimated leaching rates of heavy metals, PHB, PACs, and TBT in selected test sites in harbours of southern Norway.  
optimization; quality control; material science; chemical contamination; workflow; soil stabilisation; binders; solidification; leaching; decontamination  
**PACS:** 81.40.Cd; 81.40.Ef; 62.20.Qp; 83.50.Xa; 45.70.Mg; 92.40.Lg; 81.40.Lm; 62.20.M-  
**MSC:** 74E20; 74E30; 74F10; 93C57  
**JEL Classification:** Q00; Q01; Q24; Q55; Q56
## 3 Robust Extended Kalman Filter
### MM Estimation Theory  
In regression analysis, using the least squares method would not be enough to handle outliers or extreme observations. We therefore resort to the MM-Estimation which is known as a robust statistics method providing a more efficient analysis of statistical data with high breaking point as demonstrated in [20; 22].  
The linear equation in this paper is written as follows:  
\[y=GX+e \tag{27}\]  
where \(y\) is the new observation vector after combining system and observation data; \(G\) is the combined system and observation matrices corresponding to \(y\); \(X\) is the unknown state vector and \(e\) is the noise vector corresponding to \(y\).  
The robust method is used to bound the influence of outliers on the \(X\) state estimates. The goal of the robust estimator is to seek the best fitting criterion to the real observations in order to reduce the effect of abnormal data. Generally, a robust estimator minimizes the cost function \(J(r)\) given by:  
\[J(r)=\sum_{i=1}^{n}\mu\left(r_{i}\right)=\sum_{i=1}^{n}\mu\left(y_{i}-G_{i}X\right) \tag{28}\]  
where \(y_{i}\) is the \(ith\) observation, \(G_{i}\) is the transformation vector for the \(ith\) observation, and \(y_{i}-G_{i}X\) is the residual error for the \(ith\) observation. \(\mu(r)\) is the objective function that gives the contribution of each residual to the cost function \(J\left(r\right)\).  
The scaled estimation is used to minimize the weights out of the restrained observations dynamically:  
\[\min\sum_{i=1}^{n}\mu\left(\frac{r_{i}}{s}\right)=\sum_{i=1}^{n}\mu\left(\frac {y_{i}-G_{i}X}{s}\right) \tag{29}\]  
where \(s\) is the scale factor.  
Let \(\varphi\) be the derivative of the objective function for the unknown \(X\), \(\varphi=\left.\mu^{\prime}\right|_{X}\). Differentiating the objective function in Equation (29) and setting the derivative to zero:  
\[-\frac{1}{s}\sum_{i=1}^{n}\varphi\left(\frac{y_{i}-G_{i}X}{s}\right)G_{i}=0 \tag{30}\]  
Which is equivalent to:  
\[\sum_{i=1}^{n}\varphi\left(\frac{y_{i}-G_{i}X}{s}\right)G_{i}=0 \tag{31}\]  
By introducing a weight \(w(r_{s,i})\) to consider the effects of each scaled residual \(r_{s,i}=\frac{y_{i}-G_{i}X}{s}\), Equation (31) can be modified as:  
\[\sum_{i=1}^{n}w\left(r_{s,i}\right)\varphi\left(r_{s,i}\right)G_{i}=0 \tag{32}\]  
The solution of Equation (32) is a weighted least squares estimation. Nevertheless, the weights are determined by the scaled residuals, which rely upon the estimated coefficients, which themselves rely upon the weights. For this reason, the Iterative Re-Weighted Least Squares method is used and presented in section \"Iterative Reweighted Least Squares Algorithm\" (Section 3.3) to optimize the solution. The weight function plays an important role and some variants are presented in Table 1.  
In Table 1, \(r_{i}\) is the \(ith\) observation residual. \(\alpha\) is a tuning constant to optimize the estimation with a balance between a high breakdown point and efficiency of the estimator [22].
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
###### Abstract  
Estimates of crop canopy chlorophyll content (CCC) can be used to monitor vegetation productivity, manage crop resources, and control disease and pests. However, making these estimates using conventional ground-based methods is time-consuming and resource-intensive when deployed over large areas. Although vegetation indices (VIs), derived from satellite sensor data, have been used to estimate CCC, they suffer from problems related to spectral saturation, soil background, and canopy structure. A new method was, therefore, proposed for combining the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) and LAI-related vegetation indices (LAI-VIs) to increase the accuracy of CCC estimates for wheat and soybeans. The PROSAIL-D canopy reflectance model was used to simulate canopy spectra that were resampled to match the spectral response functions of the MERIS carried on the ENVISAT satellite. Combinations of the MTCI and LAI-VIs were then used to estimate CCC via univariate linear regression, binary linear regression and random forest regression. The accuracy using the field spectra and MERIS data was determined based on field CCC measurements. All the MTCI and LAI-VI combinations for the selected regression techniques resulted in more accurate estimates of CCC than the use of the MTCI alone (field spectra data for soybeans and wheat. R\({}^{2}\) = 0.62 and RMSE = 77.10 ug cm\({}^{-2}\); MERIS satellite data for soybeans: R\({}^{2}\) = 0.24 and RMSE = 136.54 \(\mu\)g cm\({}^{-2}\)). The random forest regression resulted in better accuracy than the other two linear regression models. The combination resulting in the best accuracy was the MTCI and MTVIZ and random forest regression, with R\({}^{2}\) = 0.65 and RMSE = 37.76 ug cm\({}^{-2}\) (field spectra data) and R\({}^{2}\) = 0.78 and RMSE = 47.96 ug cm\({}^{-2}\) (MERIS satellite data). Combining the MTCI and a LAI-VI represents a further step towards improving the accuracy of estimation CCC based on multispectral satellite sensor data.  
crop chlorophyll; PROSAIL-D; MTCI; LAI-related vegetation indices; random forest regression Published: 29 January 2021