esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Long-Term Visual Object Tracking via Low-Rank Sparse Learning for Re-Detection
## 4 Experiment
### Results and Analysis
#### 4.2.1 OTB-2013 Benchmark
Figure 6: Precision plots (**left**) and Success plots (**right**) of OPE on OTB-2013 dataset. The values in the legend indicate the quantitative comparisons of distance precision at the threshold of 20 pixels and overlap success rate at the conventional thresholds of 0.5 (\(IOU>0.5\)). Figure 7: Precision plots (**left**) and Success plots (**right**) of OPE on OTB-2013 dataset. The values in the legend indicate the quantitative comparisons of distance precision at the threshold of 20 pixels and overlap success rate at the conventional thresholds of 0.5 (\(IOU>0.5\)). 1. Out of view Figure 9a visualizes the tracking results on the box video sequence. We can observe that most trackers fail after the box in the video sequence moves out of view. Nevertheless, in Frame #495, our proposed tracker can re-detect the target accurately. 2. Occlusion Figure 9b shows that the freeman video sequence encounters occlusion over Frames #40 to #270. It is evident that occlusion may cause drift of the appearance model and lead to failure eventually. Among these trackers, our proposed tracker can achieve accurate tracking and outperforms the baseline (Staple) and most trackers. 3. Scale variation In practical applications, the relative movement between target and camera causes the change of the target scale. Figure 9c shows that KCF, SAMF, SAMF_CA, LCT and TLD cannot adapt to the change of the target scale well. However, our proposed approach can track the singer precisely. 4. Out-of-plane rotation The out-of-plane rotation may cause the appearance of the target to change greatly. In Figure 9d, only the tracker proposed by us can successfully track the ironman. 5. Low resolution \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **BC** & **MB** & **DEF** & **IV** & **IPR** & **LR** & **OCC** & **OPR** & **OV** & **SV** & **FM** \\ \hline Ours & 0.796 & 0.732 & 0.871 & 0.766 & 0.761 & 0.628 & 0.848 & 0.815 & 0.865 & 0.775 & 0.723 \\ Baseline (Staple) & 0.739 & 0.512 & 0.693 & 0.643 & 0.667 & 0.508 & 0.701 & 0.685 & 0.659 & 0.658 & 0.576 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The success rate (at the conventional thresholds of 0.5) of 11 attributes occurred on the OTB-2013 dataset. Figure 8: Success plots over 11 challenging attributes on OTB-2013 dataset. The values in the legend indicate the overlap success rate at the conventional thresholds of 0.5 (\(IOU>0.5\)). Low resolution may make it impossible to obtain detailed information about the target, which is a challenge for accurate tracking. In Figure 9e, our tracker runs in low resolution. Our proposed tracker, TLD and ECOhc can track the panda precisely.
## 2 Materials and Methods
### Data and Pre-Process
#### 2.1.2 Remotely Sensed Data  
NDTI from Landsat 5/7/8  
In this study, NDVI (normalized difference vegetation index) was used to determine the vegetated and non-vegetated surface, and the Landsat data ([[http://glovis.usgs.gov/](http://glovis.usgs.gov/)]([http://glovis.usgs.gov/](http://glovis.usgs.gov/))) including Landsat5 Thematic Mapper (TM), Landsat7 Enhanced TM (ETM+), and Landsat8 Operational Land Imager (OLI) were applied for NDVI calculation according to Equation (3). If NDVI \(\leq\) 0.1, then the surface was defined as non-vegetated, and vice versa.  
\[NDVI=\frac{B_{IR}-B_{R}}{B_{IR}+B_{R}} \tag{3}\]  
where \(B_{IR}\) is the reflectance of the near-infrared band (band4 of TM/ETM+ and band5 of OLI), and \(B_{R}\) is the reflectance of the red band (band3 of TM/ETM+ and band4 of OLI). In order to ensure the consistency between the NDVI calculated from the different Landsat sensors, the NDVI calculated from TM/ETM+ (namely NDVI\({}_{\text{TM}}\)/NDVI\({}_{\text{ETM+}}\)) was transferred to match the one calculated from OLI (namely \(NDVI_{\text{OLI}}\)) according to [25]:  
\[NDVI_{OLI}=1.0778 NDVI_{TM}-0.0848 \tag{4a}\] \[NDVI_{OLI}=1.0747 NDVI_{ETM+}-0.0839 \tag{4b}\]  
In this study, only the clear-sky Landsat bands, whose qc value from their cloud mask provided by the Landsat Ecosystem Disturbance-Adaptive Processing System (LEDAPS) atmosphere correction tool (version 3.3) [26] was 0/3/4, were used to compute NDVI. If the NDVI was unavailable for some day, then the nearest NDVI that was within an 8-day period (4 days before and after) was replaced. By referring to previous studies, the surface was classified as vegetated if NDVI > 0.1, and vice versa.  
Figure 2: The number of training and validation samples at each half-hour during 9:30-14:30 hours.
## 2 Problem Description, the Proposed Approach, and Aircraft Dynamics
### Aircraft Dynamics  
The longitudinal state-space dynamics of the Boeing 747-100/200 model obtained through Jacobian linearization at the trim point as reported in [53] can be expressed in state-space form as,  
\[\dot{x}(t)=Ax(t)+Bu(t),\ y(t)=Cx(t)+Du(t) \tag{1}\]  
Here, \(A\in R^{n\times n}\), \(B\in R^{n\times m}\), \(C\in R^{p\times n}\) are the system, input, and output matrices, respectively, \(x(t)\in R^{n\times 1}\) is the state vector, \(u(t)\in R^{~{}m\times 1}\) is the input or control vector, and \(y(t)\in R^{p\times 1}\) is the output vector. The longitudinal state vector, control vector, and output vector can be defined as \(x=\left[\alpha\ q\ V_{TAS}\ \theta\ h_{e}\right]^{T}\), \(u=\left[\delta a\ \delta e\right]^{T}\), and \(y=\left[\gamma\ a\ \theta\ q\ V\ h_{e}\right]^{T}\) where the five states are the angle of attack \(\alpha\) (rad), pitch rate \(q\) (rad/s), the true velocity \(V_{TAS}\) (m/s), pitch angle \(\theta\) (rad), and altitude \(h_{e}\) (m). There are two control inputs, the stabilizer deflection \(\delta a\) (rad) and elevator deflection \(\delta e\) (rad). The measurements available are flight path angle \(\gamma\) (rad), aircraft acceleration \(\dot{V}\) (m/s\({}^{2}\)), pitch angle \(\theta\) (rad), pitch rate \(q\) (rad/s), velocity \(V\) (m/s), and altitude \(h_{e}\) (m).
## 4 Materials and Methods
### Angiogenic and Osteogenic mRNA Expressions of DFO-Loaded PLGA-PEG-PLGA NPs-Treated hBMSCs  
The qRT-PCR was performed to evaluate the effects of the released DFO on angiogenic and osteogenic differentiation gene expressions in hBMSCs. Before examining angiogenic-related gene expressions, hBMSCs were cultured for 12 or 48 h in control medium (\(\alpha\)-MEM supplemented with 10% FBS) containing blank or DFO-loaded NPs. Meanwhile, to monitor the expression alterations of osteogenic differentiation genes, hBMSCs were cultured in osteogenic differentiation medium mentioned above with NPs added. After incubating for 7 days, 1 mL of Trizol (Invitrogen, Carlsbad, CA, USA) was added, incubated overnight at 4 \({}^{\circ}\)C to extract total cytoplasmic RNA. Then, cDNA was synthesized using 1 \(\upmu\)g of RNA and PrimeScript First Strand cDNA Synthesis Kit (Takara, Japan). Finally, gene expression was evaluated by qRT-PCR using a SYBR Green qPCR kit (Takara) in combination with ABI 7500 Real-Time PCR System (Applied Biosystems, Waltham, MA, USA). The relative levels of the mRNA was normalized to that of endogenous reference gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The primers were synthesized as follows: VEGF: forward, 5\({}^{\prime}\)-GCACCCATGGCACAGAAGGAGG-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-CCTTGGTGAGGTTTGATCCGACATA-3\({}^{\prime}\); bFGF: forward, 5\({}^{\prime}\)-GGAGAAGGGCACCCTCACATCAAG-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-CCAGTTCGTTTCAGTGCACATACCAA-3\({}^{\prime}\); TGF-\(\upbeta\)1: forward, 5\({}^{\prime}\)-GGTCACCCGTGCATATG-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-GTGAGCTGAAAGCAAATGTTG-3\({}^{\prime}\); Osteocalcin: forward, 5\({}^{\prime}\)-GGCGCTACCTGATCAATG-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-TCACCCAACTCGTCACAGC-3\({}^{\prime}\); Runx-2: forward, 5\({}^{\prime}\)-CAAAGTGGCAGCCTTTGGTC-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-TCACAGCTCGGATTGAGCCTCA-3\({}^{\prime}\); GAPDH: forward, 5\({}^{\prime}\)-CGGATTTGGTCGTTATGGC-3\({}^{\prime}\), and reverse, 5\({}^{\prime}\)-GTCATACCAGGAATGGTTG-3\({}^{\prime}\).
## 3 Results
### Positioning Performance of the Single System with Different Types of Precise Products  
We chose five precise products from different analysis centers to compare the positioning performance of single systems with different types of precise products. GPS and Galileo use precise products from five analysis centers, while BDS-3 uses precise products from four analysis centers. Specific information about the precise products is given in Table 6.  
Figure 12: Distribution of the convergence time and RMS of kinematic PPP solutions for the single systems (G is short for GPS, E is short for Galileo, and C is short for BDS-3) and dual- system combinations (GE is short for GPS combined with Galileo, GC is short for GPS combined with BDS-3, and CE is short for BDS-3 combined with Galileo) in September 2022. (**a**) Horizontal component of convergence time, (**b**) Horizontal component of RMS, (**c**) Vertical component of convergence time, (**d**) Vertical component of RMS.  
In order to better analyze the impact of using different analysis center products on positioning accuracy, the following figures (Figures 13 and 14 for GPS, Figures 15 and 16 for Galileo, and Figures 17 and 18 for BDS-3) show the distribution of convergence times and RMS at 143 tracking stations using different analysis center products for three single-system PPP solutions over a month. For the convergence time of GPS kinematic PPP solutions, the performance in the horizontal components is consistent with RMS. In the vertical direction, the cod, gfz, grg, and iac products have similar convergence time distributions, and the median convergence time is 17.00 min. The convergence time of the whu product is 17.50 min. For RMS, the grg products performed best, with a median RMS of 1.85 and 3.11 cm in both the horizontal and vertical components, respectively. The cod, iac, and whu products are 1.89 and 3.18 cm, 1.93 and 3.19 cm, and 1.94 and 3.21 cm, respectively. The gfz product performed the worst, with RMS values of 2.30 and 3.83 cm, respectively.  
Figure 14: Distribution of the RMS of the GPS kinematic PPP solutions with five types of precise products for the 143 MGEX tracking stations in September 2022. (**a**) Horizontal component, (**b**) Vertical component.  
Figure 15: Distribution of the convergence time of the Galileo kinematic PPP solutions with five types of precise products for the 143 MGEX tracking stations in September 2022. (**a**) Horizontal component, (**b**) Vertical component.  
Figure 16: Distribution of the RMS ((**a**,**b**) for the horizontal and vertical component, respectively) of the Galileo kinematic PPP solutions with five types of precise products for the 143 MGEX tracking stations in September 2022. (**a**) Horizontal component, (**b**) Vertical component.  
Figure 17: Distribution of the convergence time of the BDS-3 kinematic PPP solutions with five types of precise products for the 143 MGEX tracking stations in September 2022. (**a**) Horizontal component, (**b**) Vertical component.
## 4 Application to an Additive Manufacturing Benchmark Test
### Uncertainty Quantification  
For the uncertainty quantification, we used the normal strains in the x, y, and z directions at z = 9.536. The analysis was conducted using Python libraries, which include Numpy, statistics, and other libraries.  
To begin, a set of 6000 input samples was generated using a Sobol sampling technique. Next, the trained DNN model was used to calculate the x-directional strains along the line z = 9.536 for these input variables. The samples were generated under \(\pm\)15% bound on the baseline input values, which are shown in Table 5. As mentioned before, the values of Young's modulus and the Poisson ratio were modified by multiplying them with the factors \(cY\) and \(cv\), respectively. The values of these factors lie between 0.85 and 1.15, representing \(\pm\) 15% variation. Figure 18a illustrates the uncertainties in the process outputs, which shows that the mean of the data was very close to the actual results; the grey area represents the confidence interval of 95%. The confidence interval was equal to the \(\pm\)2x standard deviation. A similar study was performed with y- and z-directional strains at the exact location and their analysis plots are shown in Figure 18b,c, respectively. The L2 error norm between the mean value and the experimental results for Y strains was 0.6662, whereas, for the z strains, the value was 0.8764. The experimental results for Y strains lay in the 95% confidence interval, but for the z strain, they did not. This may be explained by the simplifications and modeling hypotheses used in the numerical software of the model limitations; for example, the model does not consider the anisotropy in the material properties or the laser power.
## 2 Mathematical Formulation
### Operating Costs Minimization (\(\mathit{FO}_{1}\))  
The objective function \(\mathit{FO}_{1}\) seeks to minimize the daily operating costs of the MG. This function incorporates the cost of both energy purchasing and maintenance associated with the WT, as detailed in Equation (1).  
\[\mathit{FO}_{1}=\min\left(\mathcal{C}_{kWh}\sum_{h\in\Omega_{M}}\sum_{i\in \Omega_{N}}P_{i,h}^{s}\Delta h+\mathcal{C}_{OkM}\sum_{h\in\Omega_{M}}\sum_{i\in \Omega_{N}}P_{i,h}^{wt}\Delta h\right) \tag{1}\]  
In this equation, \(\mathcal{C}_{kWh}\) denotes the unit cost of the energy produced by conventional generators, while \(\mathcal{C}_{OkM}\) corresponds to the operation and maintenance cost of the WTs. The variable \(P_{i,h}^{s}\) indicates the active power supplied by conventional generators at node \(i\) and hour \(h\), whereas \(P_{i,h}^{wt}\) represents the active power generated by the WTs at node \(i\) during hour \(h\). The summation is performed for all hours \(h\in\Omega_{M}\) within the 24 h operation horizon and across all nodes \(i\in\Omega_{N}\) within the MG. The parameter \(\Delta h\) represents the duration of each time interval--1 h in this case. In this objective function, the power supplied by conventional generators may correspond to energy purchased from the main  
Figure 1: Architecture of a WT-based MG.  
grid during grid-connected operation or to energy produced by diesel generators within the MG in islanded mode.
# New Compact Wearable Metamaterials Circular Patch Antennas for IoT, Medical and 5G Applications
## 4 Active Transmitting Compact Circular Patch Antennas  
A basic transmitter block diagram with an active antenna is presented in Figure 10. An active circular patch transmitting antenna is presented in Figure 11. The antenna diameter is 40 mm. In Figure 11, the high-power amplifier (HPA) is an integral part of the antenna. The HPA is an MMIC GaAs MESFET, VNA25. The HPA is connected to the transmitting circular patch. The circular patch is matched to the HPA by a matching output network. The HPA input matching network matches the amplifier port to the transmitter.  
The dimensions of the input and output matching network are less than \(22\times 30\) mm. The amplifier specifications are listed in Table 3. In Table 4 the HPA complex S parameters are listed. The active transmitting circular patch S11 parameters is better than 2:1 in the frequency range from 2 to 3 GHz as presented in Figure 12. The antenna matching network, presented in Figure 13a, was optimized to improve the active antenna bandwidth up to 50% for VSWR better than 3:1. A Smith chart diagram of S11 with and without the output matching network is shown in Figure 13b. The optimized active transmitting antenna S21 parameter, gain on human body, is presented in Figure 14. The active circular patch antenna gain, computed and measured, is \(11.0\pm 2\) dB for frequencies ranging from 1.9 to 3.3 GHz. The active transmitting circular patch antenna output power is around 18 dBm.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**F-GHz** & **S11** & **S11\({}^{\circ}\)** & **S21** & **S21\({}^{\circ}\)** & **S12** & **S12\({}^{\circ}\)** & **S22** & **S22\({}^{\circ}\)** \\ \hline
1.04 & 0.74 & \(-126.2\) & 12.74 & 100.13 & 0.05 & 33.69 & 0.29 & \(-94.96\) \\
1.21 & 0.71 & \(-137.6\) & 11.25 & 92.91 & 0.051 & 30.05 & 0.26 & \(-104\) \\
1.53 & 0.687 & \(-154.2\) & 9.30 & 82.06 & 0.055 & 26.08 & 0.22 & \(-119\) \\
1.75 & 0.67 & \(-164.1\) & 8.24 & 75.31 & 0.06 & 23.14 & 0.20 & \(-128.4\) \\
2.02 & 0.67 & \(-174.6\) & 7.30 & 67.82 & 0.06 & 20.88 & 0.18 & \(-138.8\) \\ \hline \hline \multicolumn{8}{c}{**Noise Parameters**} \\ \hline
**F-GHz** & **N. FMIN** & **N11X** & **N11Y** & **m** \\ \hline
1 & 0.16 & 0.3424 & 52.98 & 0.042 \\
1.9 & 0.30 & 0.368 & 100.93 & 0.03 \\
2 & 0.32 & 0.371 & 106.01 & 0.03 \\
2.4 & 0.39 & 0.383 & 125.79 & 0.03 \\
3 & 0.48 & 0.400 & 153.93 & 0.036 \\
3.9 & 0.63 & 0.430 & \(-167.3\) & 0.06 \\
5 & 0.81 & 0.465 & \(-125.53\) & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: LNA S parameters and noise parameters at the S band.  
Figure 10: Transmitter block diagram with an integral wearable active transmitting antenna.  
Figure 11: Active Circular Transmitting Patch antenna for Internet of Things (IoT) and 5G Applications.  
Figure 12: S11 of Transmitting Circular Patch antenna.  
Figure 13: Transmitting Circular Patch antenna: (**a**) Matching network; (**b**) S11 of the active antenna.
## 3 Research Model and Hypotheses
### Modified TAM  
The TAM posits that an individual's belief, specifically their perceived usefulness and perceived ease of use concerning a certain technology, affects their attitude, usage intention, and acceptance of the technology. The two cognitive beliefs presented in TAM are perceived usefulness and perceived ease of use, which are each defined, respectively, as \"the degree to which a user believes that the use of a certain system would enhance their performance\" and \"the degree to which a user believes that using a certain system would be free of effort [16]\".  
Prior studies have confirmed the effectiveness of TAM when explaining and predicting acceptance of a newly emerging technology [32; 33]. In a training or education context, TAM has been utilized to study an individual's intention to use e-learning or other newly introduced training tools. For example, [PERSON] et al. [34] adopted the TAM to study the factors affecting students' intention to use e-learning. They found that although perceived ease of use had no significant effect on perceived usefulness, both perceived usefulness and perceived ease of use positively affected students' intention to use e-learning. Additionally, [PERSON] and [PERSON] [35] confirmed the positive effect of perceived usefulness and perceived ease of use on an aviation student's intention to use an augmented reality maintenance training system.  
Additionally, in cases where technology adoption is mandatory, satisfaction was found to be an effective indicator of user acceptance. For example, to study the acceptance of web-based training among construction professionals, [PERSON] et al. [24] utilized user satisfaction as a variable replacing usage intention in the TAM model. Similarly, [PERSON] et al. [36] applied 'symbolic adoption' as an alternative to usage intention regarding the acceptance of an enterprise system, and measured the construct through items such as 'I am excited about using the SAP system in my workplace'.  
Therefore, based on prior studies, we defined perceived usefulness as the degree to which a trainee believed that the VR safety training would enhance their performance, and perceived ease of use as the degree to which a trainee believed that the VR safety training would be free of effort. Furthermore, based on studies employing the TAM as a theoretical basis for studying the acceptance of technologies in a mandatory setting, we present the following hypotheses:  
**H1.**: _Perceived usefulness has a positive effect on training satisfaction._
**H2.**: _Perceived ease of use has a positive effect on training satisfaction._
**H3.**: _Perceived ease of use has a positive effect on perceived usefulness._
## 6 Key Issues and Challenges
### Signal Interference  
Interference issues can cause severe problems in the data monitoring of a solar PV system. The network may be affected by interference from other modes of communication. This results in the poor functioning of the modules, a slow rate of data transfer, poor signal strength and discontinuous connections. Signal interference in GSM reduces the quality of the service thereby increasing the loss of revenue [137]. Furthermore, interference in Raspberry Pi occurs by radio frequency from High-Definition Multimedia Interface (HDMI) cable due to the dropping off of Wi-Fi signals.
# Lacase in Biorefinery of Lignocellulosic Biomass
## 3 Significance of Lacase in Biorefinery
### Lacase Sources
#### 3.1.1 Bacterial Lacase Production  
Bacteria are among the earliest reported sources of lacase, with several species from different taxonomic groups known to produce lacase. Bacterial lacase production is influenced by several factors, including the type of bacterium, culture conditions, and substrate availability. Some of the commonly reported bacterial sources of lacase include _Bacillus, Pseudomonas_, and _Streptomyces_ species [37]. Other than bacteria and fungi, few yeast species, including _Candida, Rhodotorula_, and _Saccharomyces_ species [38], and actinomycetes, such as _Streptomyces_, _Nocardiopsis_, and _Micromonospora_ species [39], have been reported.