esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Templates Based Robust Tracking for Robot Person-Following Tasks
## 4 Evaluation
### UGV Dataset  
The UGV dataset is a self-constructed dataset that contains images from a small unmanned ground vehicle that performed person-following tasks in outdoor environments. The robot was following a single-person target in a campus environment under varying road conditions (e.g., brick roads, cement roads, snowy roads, and grasslands) and illumination conditions (backlight, shadow, dawn, and night). The vehicle performed servo moving in accordance with the relative position of the tracked target. We set the target person being followed to pose different challenging situations such as teams wearing similar clothes, partial and full occlusion, etc. The images were collected by an Intel Realsense D435i camera that was rigidly attached to the robot. The camera collected images at 30 fps in the following tasks. We downsampled the frame rate to 10 fps in our dataset. The image resolution is \(640\times 480\) pixels. The robot was following the target person up to speeds of 2 m/s.  
The dataset contains 17 image sequences that vary in the appearance of the tracking targets, the appearance and number of distractors, road conditions, weather, and experiment duration. The detailed information of each subset is provided in Table 1. The distractor information states the attributes of different subsets, including campus environments with pedestrians (PED), the number of pedestrians with different-colored clothes actively interfering (#DAI), number of pedestrians with similar-colored clothes actively interfering (#SAI), illumination variation (IV), and low illumination (LI).
# Microplastics' Detection in Honey: Development of Protocols in a Simulation
## 3 Results and Discussion
### Method 2: PET MPs Detection in Honey through Optical Observation of Fluorescence  
#### 3.2.1 Optical Microscopy  
For the determination of PET MPs' dimensions, the whole filter area was scanned for about 5 h in each filter. All filters from each experiment, with and without pentane as a density separator, were visually studied using the confocal optical microscope. As it was observed from the pentane experiments, the solid honey particles were evenly distributed throughout the sample; however, larger honey particles had moved to the lower extraction phases along with the largest MPs. Thus, MPs with smaller dimensions were observed in the upper extraction phases. Figure 4 depicts optical microscopy pictures of PET MPs from each different extraction phase. The MPs' identification was carried out by Raman spectroscopy. It is worth noticing that sediments of colloid phases were placed on metallic microscopic slides for MP observation, but honey solid particles could not be identified due to fluorescence (either by the 532 nm or 785 nm excitation source). Honey solid particles such as pollen grains, wax, and propolis with PET MPs were detected in the sediments of the remaining samples at the bottom of the Eppendorf tube, as shown in Figure 5a. MPs could not be distinguished from honey waxes. As one can see from Figure 5b, pollen grains were the only particles discriminated, while MPs could not be distinguished from honey solids all over the filter, which is in contrast to the DANS dye approach (see next Section 3.2.2).
## 5 Experiment Simulation
### Performance on the Tree Topology
#### 5.2.4 Comparison in YouTube Traffic Workload  
In YouTube video traffic workload, we set the cache size ratio to (0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0) and represent the simulation results with the log of the x-coordinate. As shown in Figure 9, PB-NCC still keeps its advantage over different evaluation indicators. LCE has a slight advantage in latency when the cache size ratio is small. Results show that PB-NCC works well under a real-world traffic workload. Content popularity in YouTube traffic has a time locality, which means that content popularity changes over time. PROB performs poorly as it cannot always guarantee to keep popular content. LCD and CL4M are also hard to cache contents effectively in time-varying traffic due to their slow diffusion rate of contents.
# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 4 Model Application and Results
### Comparison of Different Models Performance
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site** & \multicolumn{4}{c}{**Pima Gauge Station**} & \multicolumn{4}{c}{**Magdeburg-Strombrücke Gauge Station**} \\ \hline
**Model** & **R** & **R\({}^{2}\)** & **RMSE (t/day)** & **PBIAS (\%)** & **R** & **R\({}^{2}\)** & **RMSE (t/day)** & **PBIAS (\%)** \\ \hline SRC & 0.77 & 0.59 & 464.27 & 15.96 & 0.82 & 0.67 & 475.85 & 17.30 \\ \hline FCF & 0.77 & 0.59 & 468.92 & 44.41 & 0.82 & 0.67 & 541.72 & 46.44 \\ \hline SCF & 0.77 & 0.59 & 461.25 & 25.11 & 0.82 & 0.67 & 533.67 & 44.26 \\ \hline LLR & 0.94 & 0.89 & 235.60 & 8.78 & 0.90 & 0.82 & 339.23 & 3.17 \\ \hline ANN & 0.95 & 0.90 & 233.48 & 3.43 & 0.91 & 0.82 & 335.71 & 2.53 \\ \hline WANN & 0.97 & 0.95 & 160.26 & \(-\)0.73 & 0.95 & 0.90 & 248.26 & \(-\)0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of best models for the prediction of daily SSL. the decomposed time series data are fed using the wavelet transform method. It goes a step further than the simple ANN, as the WT method is used to break down the input time series into component sub-series, which better explain the information contained in short-term, transient events such as SSL peaks, which appear suddenly. Thus, the dynamic features of the studied phenomenon are better translated to the ANN model, which, in turn, enhances and amplifies its predictive capacity.
## 2 Materials and Methods
### Protein Corona Sample Preparation  
In this study, three different nanoparticle coatings were subjected to two different serum types and incubated for 24 h. Gold nanoparticles of 30 nm were purchased from NanoHybrids (Austin, TX, USA). Particles were placed in aqueous dispersions with one of the following coatings: polyethylene glycol (PEG), carboxylic acid (COOH), or amine (NH\({}_{3}\)). The primary particle size of each nanoparticle was measured as 30 \(\pm\) 2 nm using transmission electron microscopy (TEM). The different coatings provided a variety of charges among the coatings, with the PEG producing a neutrally charged surface, COOH producing a negatively charged surface, and NH\({}_{3}\) producing a positively charged surface. This variance assisted the determination of the effects of surface charge on the formation of the protein corona. The serum types used included fetal bovine, termed serum-type 1 (ST1), and equine, termed serum-type 2 (ST2; Equitech-Bio Inc., Kerrville, TX, USA). Particles were incubated with serum at a 1:1 ratio at 37 \({}^{\circ}\)C for 24 h. Coronas were separated from particles in two steps. First, the particle-corona suspension was centrifuged to remove excess proteins not adhered to particle surfaces. Second, the proteins were removed from the particle surface using a lysis buffer of 8 M urea at 95 \({}^{\circ}\)C for 5 min and then centrifuged to separate the proteins from the nanoparticles.
## 5 Results
### Comparison with Standard Priority Rules  
For evaluation of the presented algorithm, we compare the performance of the \(PR=GAWS\) with the \(PR=SPR\) on 25 models \(M\) from the MPSPLIB, which essentially differ from the previously considered models by the instances. The experiments vary with respect to the coefficient of variance \(cv_{lognorm}\) and the objective function \(f\). The parameter initial copy rate \(ICR\) is still considered \(ICR=0.6\). The parameters of the GA are the same as in Section 5.4. Common SPRs serve as comparison criteria [49; 50].  
The presented algorithm achieves under stochastic influences without Pareto criterion in 50% better results than the considered SPR (Figure 11). The second best SPR MWRK (Most Work Remaining) achieves better results in 12% of the experiments, the third best SPR EDD (Earliest Due Date) achieves better results in 8% of the experiments, and the fourth best SPR FIFO achieves better results in 6% of the experiments. The remaining SPRs achieve the best results in 1% on average.  
Looking at the three best PRs (GAWS, MWRK, and EDD) in Figure 12, it can be seen that all three PRs are represented on all ranks, with rank = 1 being the best PR and rank = 20 being the worst PR. GAWS is 95% among the 10 best PRs (sum of the relative frequency of GAWS from \(rank=1\) to \(rank=10\)). SPR EDD is among the ten best SPRs in 70% of the experiments (sum of the relative frequency of EDD from \(rank=1\) to \(rank=5\)). It is remarkable that the MWRK comes last \((rank=20)\) in almost 10% of the experiments.  
In the following, the saving potential is quantified if the CPR GAWS ranks first (Table 3). On average, there is a saving of 3.3% compared to \(rank=2\) regarding the objective value. Compared to the average performance, there is a saving of 10.8%, and compared to the worst SPR, there is a saving of about 25%.  
If CPR GAWS does not occupy rank = 1, there is an average loss compared to \(rank=1\) of \(-10.6\%\) regarding the objective value (Table 4). Compared to the medium performance of the other SPRs, an average saving of 3.63% is still achieved. Comparing CPR GAWS to the worst SPR, an average saving of 18.9% is achieved. In summary, the presented CPR can achieve an average improvement over a randomly selected SPR.  
For complete evaluation of the presented CPR GAWS, it is necessary to assess the fulfillment of individual project objectives according to the Pareto criterion (Table 5). For this purpose, the solutions are divided into solution fronts (Section 4.2). CPR GAWS is on average in 90% of the experiments in the first front. There are no significant deviations when considering individual parameters. The only difference shows the parameter instance _iz_. A performance analysis of the other SPRs shows that no  
Figure 10: Performance of the proposed algorithm depending on the single parameters.
## 2 Investigations Focusing on Overall Accumulated-Bed Characteristics
### Investigations with Single-Sized and Single-Signed Solid Particles
#### 2.1.3 Modeling Studies
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{5}{c}{**Modeling for Single-Sized and**} \\ \multicolumn{5}{c}{**Single-Shaped Solid Particle Cases**} \\ \hline
**Empirical Constant Index** & **Value** & **Empirical Constant Index** & **Value** \\ \hline \(a_{1}\) & \(-\)0.425 & \(a_{2}\) & 1.36 \\ \hline \(b_{1}\) & \(-\)0.103 & \(b_{2}\) & 3.76 \\ \hline \(c_{1}\) & \(-\)0.407 & \(c_{2}\) & 0.589 \\ \hline \(d_{1}\) & \(-\)0.161 & \(d_{2}\) & \(-\)0.661 \\ \hline \(e_{1}\) & \(-\)0.585 & \(e_{2}\) & \(-\)1.60 \\ \hline \(f_{1}\) & 0.456 & \(f_{2}\) & \(-\)0.545 \\ \hline \(g_{1}\) & 0.035 & \(g_{2}\) & 0.490 \\ \hline \(h_{1}\) & \(-\)0.137 & \(k_{2}\) & 162,755 \\ \hline \(k_{1}\) & 0.2049 & & \\ \hline \end{tabular}
\end{table} 
Table 2: Empirical constants in the predictive models for the particle bed (or mound) height. Figure 6: Experimental and predicted results on (**a**) bed height and (**b**) repose angle (“Exp.” and “Pred.” mean experimental and predicted results, respectively) [63; 65]. On the other hand, [PERSON] et al. proposed a model to predict the repose angle under different experimental conditions by using [PERSON]'s \(\pi\) theorem:  
\[\theta=26 Re^{-0.38}Fr^{0.15}Ar^{0.20} \tag{3}\]  
Figure 6b shows the experimental and predicted results. However, due to their limited experimental data, the model should be extended further with consideration of particle size, density, liquid properties, and experimental dimensions. In a word, the empirical model proposed for predicting the particle bed heights and repose angles for DBF behavior in cases of single-sized and single-shaped particles can provide relatively reasonable predicted results in the case of spherical particles. However, it should be further improved to better and more rationally estimate the effects of non-spherical particles.
## 3 Target Detection and Tracking Algorithm  
The key approach adopted in the proposed UAV vision-based earthquake-induced structural displacement monitoring framework is to reproject the image points to the 3-D world space based on the world-to-image point correspondence. As such, reliable tracking of certain image feature points is crucial to achieve reasonable accuracy and consistency over all image frames. This section introduces two methods for tracking the movement of the image feature points in the image frame coordinates, namely the feature points tracking-based algorithm for square checkerboard patterns (Section 3.1) and the Hough Transform-based algorithm for concentric circular patterns (Section 3.2).  
For both methods presented in this section, there are three initialization steps before applying the algorithms for target detection and tracking:  
* Lens distortion correction is applied to each video frame to obtain distortion-free image frames (see Section 4.2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference Target** & **Target Pattern Type** &
\begin{tabular}{c} **Target Dimension** \\ **[cm \(\times\) cm]** \\ \end{tabular} & \multicolumn{1}{c}{**Number of Targets**} \\ \hline \multirow{3}{*}{Stationary Targets (_10 targets_)} & Type A 1  & 45.7 \(\times\) 45.7 & 3 \\  & Type B & 45.7 \(\times\) 45.7 & 5 \\  & Type C & 20 \(\times\) 20 & 2 \\ \hline \multirow{6}{*}{Moving Targets (_28 targets_)} & Type 1 & 20 \(\times\) 20 & 4 \\  & Type 2 & 20 \(\times\) 20 & 5 \\ \cline{1-1}  & Type 3 & 20 \(\times\) 20 & 7 \\ \cline{1-1}  & Type 3\({}_{\text{R}}\) & 20 \(\times\) 20 & 4 \\ \cline{1-1}  & Type 3\({}_{\text{M}}\) & 15 \(\times\) 15 & 2 \\ \cline{1-1}  & Type 3\({}_{\text{S}}\) & 10 \(\times\) 10 & 2 \\ \cline{1-1}  & Type 4 & 20 \(\times\) 20 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of reference targets used in this study.  
Figure 3: Reference target patterns and dimensions. The three patterns in row one were used as stationary targets and the seven patterns in row two were used as moving targets.  
* Coarse bounding boxes representing the initial regions of interest (ROIs) of each target are manually defined in the reference video frame. The reference video frame is the first frame of each test video, which corresponds to a state at 5 to 10 s before the testing (and hence movement of the specimen) initiates. The pixel dimension of the coarse bounding box in the image frame is 51 \(\times\) 51 pixels.
* The colored video frames are converted into grayscale images since black-to-white contrast is easier to identify using a single value threshold compared to a 3-value threshold required with all RGB channels in a colored image. Moreover, grayscale images are of reduced size; thus, the computational cost of the algorithm is reduced.
# Linear Algorithms for Radioelectric Spectrum Forecast
## 2 Theory and Background
### Seasonal Autoregressive Integrated Moving Average Algorithm  
In general, if a time series exhibits potential seasonality indexed by \(s\), then using a multiplied SARIMA(\(p\)_A,\(q\))(\(P\),\(D\),\(Q\))s algorithm is advantageous, where \(d\) is the level of non-seasonal differencing, \(p\) is the autoregressive (AR) non-seasonal order, \(q\) is the moving average (MA) non-seasonal order, \(P\) is the number of seasonal autoregressive terms, \(D\) is the number of seasonal differences, and \(Q\) is the number of seasonal moving average terms. The seasonal autoregressive integrated moving average algorithm of [PERSON] and [PERSON] [23] is given in the Equation (1),  
\[\mathcal{D}_{p}\left(B\right)\Phi_{p}\left(B^{s}\right)\
abla^{d}\
abla_{s}^{ D}x_{t}=\theta_{q}\left(B\right)\Theta_{Q}\left(B^{s}\right)e_{t} \tag{1}\]  
where \(B\) is the backward shift operator, \(x_{t}\) is the observed time series of load at time \(t\), \(e_{t}\) is the independent, identical, normally distributed error (random shock) at period \(t\); \(\
abla_{s}^{D}x_{t}=\left(1-B^{s}\right)^{D}x_{t}\), \(\Phi_{p}(B^{s})\) and \(\Theta_{Q}(B^{s})\) are the seasonal AR(\(p\)) and MA(\(q\)) operators, respectively, which are defined in Equations (2) and (3),  
\[\Phi_{p}\left(B^{s}\right) =1-\Phi_{1}B^{s}-\Phi_{2}B^{2s}-\ldots-\Phi_{p}B^{Ps} \tag{2}\] \[\Theta_{Q}\left(B^{s}\right) =1-\Theta_{1}B^{s}-\Theta_{2}B^{2s}-\ldots-\Theta_{Q}B^{Qs} \tag{3}\]  
where \(\Phi_{1}\), \(\Phi_{2}\), \(\ldots\), \(\Phi_{\text{p}}\) are the parameters of the seasonal AR(p) model, \(\Theta_{1}\), \(\Theta_{2}\), \(\ldots\), \(\Theta_{Q}\) are the parameters of the seasonal MA(q) [24].  
The Box-Jenkins methodology consists of four iterative steps [25]:  
* Step 1: Identification. This step focuses on the selection of \(d\), \(D\), \(p\), \(P\), \(q\) and \(Q\). The number of the order can be identified by observing the sample autocorrelations (ACF) and sample partial autocorrelations (PACF).
* Step 2: Estimation. The historical data is used to estimate the parameters of the tentative model in Step 1.  
Step 3: Diagnostic checking. Diagnostic test is used to check the adequacy of the tentative model.
* Step 4: Forecasting. The final model in Step 3 is used to forecast the values [26].
## 6 Simulation Setup and Results
### Uplink Use Case
#### 6.7.2 Dss1  
DSS1 resolves a good portion of the interference events, especially in the satellite links. All satellite nodes (GSO-SAT 4, NGSO-SAT 5, and NGSO-SAT 6) are now healthy, maintaining SINR values over 9 dB in almost all cases throughout the simulation run (see Figure 11). FWA links continue to experience high interference, however, making it obvious that we were unable to protect them. Since the FWA nodes cannot dynamically select their channel usage, they cannot recover when interfered. The main problem comes from the GSO link that is very stable and does not suffer from any interference, and hence does not take any action for slot reallocation. To remedy this situation, we have envisioned DSS1+, an upgrade on the CP for DSS1 allowing FWA nodes to inform other systems participating in the collaboration about their interference status. With this upgrade, FWA links can alert other DSS capable nodes and trigger slot reallocation.
# From the Martian Surface to Its Low Orbit in a Reusable Single-Stage Vehicle--[PERSON]
## 3 Results
### Vehicle Subsystems
#### 3.2.5 Guidance, Navigation & Control  
A vital part of the system is tracking of the vehicle during flight, enabling navigation of the vehicle and its control. Sensors are used for tracking along with a set of thrusters to provide full control. Throughout the entire flight, inertial sensors are used for positioning. Additionally, a surface based radar system is used during ascent, reentry, and phasing. Furthermore, star tracking cameras are also used during phasing. During docking, relative navigation through cameras is used. They are usable with sufficient accuracy within 2000 m of the node. Finally, for increasing reliability, landing cameras are used during landing. An Extended Kalman Filter has been used to simulate the state determination accuracy for all flight phases.  
For the reaction control system, control over 6 degrees of freedom is required to execute the pitching, rolling, lateral acceleration, and disturbance corrections that are necessary to complete the mission profile. Thus, a full thruster configuration is used with 32 thrusters providing instantaneous and decoupled control and 6 Degrees of Freedom. An extra thruster is used for reentry pitch control. Decoupling adds weight but provides a lower control complexity and more redundancy. After sizing the RCS, the propellant is chosen to be mono-prop hydrogen peroxide. This choice results from a trade-off on performance, throttleability, complexity, and sustainability [27]. A visualisation of a set of thrusters can be seen in Figure 11. Each engine weighs in at 4 kg, with a tank structural  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element [dB]** & **Charon to Ground** & **Charon to Relay** & **Charon to Node** \\ \hline Transmitter power & \(18.5\) & \(18.5\) & \(18.5\) \\ Line loss & \(-0.88\) & \(-0.88\) & \(-0.88\) \\ Transmitter gain & \(0\) & \(0\) & \(0\) \\ Point loss receiver & \(-0.12\) & \(-0.12\) & \(-0.12\) \\ Space loss & \(-165.8\) & \(-178.3\) & \(-171.4\) \\ Path loss & \(-0.45\) & \(-0.45\) & \(-0.45\) \\ Receiver gain & \(40.2\) & \(34.2\) & \(30.7\) \\ Boltzmann & \(228.6\) & \(228.6\) & \(228.6\) \\ System noise temp & \(-26.0\) & \(-12.8\) & \(-12.8\) \\ Data rate & \(-73.0\) & \(-73.0\) & \(-73.0\) \\ Margin & \(-2\) & \(-2\) & \(-2\) \\ Eb/No & \(19.0\) & \(13.6\) & \(17.0\) \\ BER (\(*10^{-7}\)) & \(0.0035\) & \(0.91\) & \(0.027\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculation of \(Eb/No\) ratios and BER.  
mass of 40.2 kg for each of the two propellant tanks. The total propellant is estimated to be 1243 kg for all manoeuvres plus a redundancy.
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 1 Introduction
The work presented in this paper can be extended to hybrid analog/digital equalization scenarios for massive MIMO systems as presented in [19; 20]. In Section 2 the comparison of receivers that can be used in mMimo systems is presented. In Section 3 the proposed system is described and in Section 4 the IB-DFE, MRC and hybrid receivers are presented, followed by Section 5 where the Bit Error Rate (BER) performance and the correlation factors are calculated for the same receivers. In Section 6, the results are presented and discussed. Lastly, in Section 7, the conclusions achieved are drawn. The following denotation is employed in this paper: In general, the lower case letters denote time-domain variables, while upper case letters denote frequency-domain variables. Bold upper letters represent matrices or vector; \(\mathbf{I}_{N}\) is the identity matrix \(N\times N\); the complex conjugate, transpose and Hermitian of \(\mathbf{x}\) is denoted by \(\mathbf{x}^{\star},\mathbf{x}\mathbf{T}\) and \(\mathbf{x}^{\mathbf{H}}\) respectively. The expectation of \(x\) is represented by \(\mathbb{E}[x]\) and \(\tilde{x}\), \(\tilde{x}\), denotes respectively sample, \"hard decision\" and \"soft decision\" estimation of \(x\).