esa-sceva/satcom-chunk-collection huggingface dataset:
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## Abbreviations  
DDS Drug delivery system  
SBA Santa Barbara amorphous  
MCM Mobile crystalline matter  
FSM Folding shield mechanism  
TUD Delft University of Technology  
HMS Hollow mesoporous silica  
LCT Liquid crystal templating  
CTAB Cetyl trimethylammonium bromide  
HDTMA Hexadecyltrimethylammonium  
PS-b-PAA Polystyrene-b-poly (acrylic acid)  
TEOS Tetreathyl _ortho_-silicate  
GSs Gemini surfactants  
NTA \(N\)- tributylamine acetate  
SDS Sodium decylsulphate  
TMOS Tetramethyl _ortho_-silicate  
TPOS Tetrapy _ortho_-silicate  
TMS & Trimethoxysilane TBOS & Tetrabutoxysilane CMT & Critical micelle of the temperature CP & Cloud point EO & Ethoxy CPP & Critical packing parameter EG & Ethyleneglycol RS & Sudan red MB & Methylen blue MPS & 3-Mercaptopropyl trimethoxysilane S-M & Suzuki-Miyaura TDDCs & Targeted drug delivery carriers ATP & Adenosine triphosphate BSA & Bovine serum albumin PAA & Polyacrylic acid RAFT & Reversible addition-fragmentation chain transfer HDMD & hexadecylmethylenediamine PI & Propidinium iodide PDDA & Poly (dimethylethylldi-allylammonium chloride) MMPs & Matrix metalloproteinase  
DOX & Doxorubicin & Methyl orange MR & Methyl red MG & Malachite green EB & Everazol blue & 4-NP & 4-nitropenol RhB & Rhodamine B PDA & Polydopamine DBT & Dibenzothiophene DMDBT & Dimethyldibenzothiophene BT & Benzothiophene DT & Dithiophene THPC & Tetrakis (hydroxymethyl) phosphonium chloride MNPs & Magnetic nanoparticles MMNP & Magnetic mesoporous nanoparticle & \(\varepsilon\)-calc & \(\varepsilon\)-caprolactone DMC & Dimethylcarbonate HMDS & Hexamethyldisilazane MSM & Mesoporous silica microspheres LA & Lewis acid XRD & X-ray diffraction cAMP & Adenosine monophosphate G-Ins & Gluconic acid-modified insulin PNIPAM & Poly(N-isopropylacrylamide) LCST & Lower critical solution temperature [BsAlm][OTf]/SCF & [1-allyl-3-(butyl-4-sulphonyl) imidazolium] [trifluoromethane-sulphonate]/silica cellular foam SC & Silica cellular & \(\beta\)-CD & \(\beta\)-cyclodextrintrin MPS & 3-(trimetoxyl) propylmethacrylate AB & Azobenzene PI & Polyimide
## 3 Methodology
### Savitzky-Golay Filter (S-G Filter)  
The Savitzky-Golay filter (S-G filter) is a technique used for data smoothing, which smooths the data by fitting a polynomial to the original signal. Compared to other smoothing methods, the S-G filter better preserves the characteristics of the data, such as peaks and widths. It is widely used in fields such as chemistry, spectroscopy, and signal processing, and is particularly effective for smoothing noisy data. The S-G filter can effectively remove random noise while retaining the important characteristics of the signal.  
The essence of the S-G filter is to fit a subset of data points to a low-order polynomial and then use the values of this polynomial to replace the original data points. First, the size of the sliding window (usually an odd number to ensure a central point) and the degree of the polynomial are determined. The window size dictates the number of data points used for each fitting, while the polynomial degree determines the complexity of the fitting.  
\[P(x)=a_{0}+a_{1}x+a_{2}x^{2}+\ldots+a_{n}x^{n} \tag{5}\]  
Figure 4: The map of PMW-LST after Singular Spectrum Analysis reconstruction on (**a**) 1 January 2018, (**b**) 2 January 2018, (**c**) 3 January 2018, and (**d**) 4 January 2018.  
Figure 3: The map of original FY3C PMW-LST on (**a**) 1 January 2018, (**b**) 2 January 2018, (**c**) 3 January 2018, and (**d**) 4 January 2018.  
where \(n\) is the order of the polynomial, and \(a_{0}\), \(a_{1}\), \(a_{2}\)\(\ldots\)\(a_{n}\) are the coefficients of the polynomial. These coefficients are calculated by minimizing the sum of squared errors given by the following formula:  
\[E=\sum_{i=-m}^{m}[y_{i}-P(x_{i})]^{2} \tag{6}\]  
where \(m\) is the window radius ((window size \(-\) 1)/2), \(y_{i}\) is the value of the data point within the window, and \(x_{i}\) is the corresponding independent variable value.  
By solving this least squares problem, we can obtain the polynomial coefficients and then use this polynomial to calculate the new data point value at the center of the window. This process is repeated for all data points, ultimately resulting in a smoothed data sequence.  
Applying the Savitzky-Golay filter, the decadal NDVI images are converted into continuous, seamless, and smooth image sequences through local polynomial fitting. In this process, the window size is set to 11 and the polynomial order to 2, to minimize the sum of squared errors at each time point and generate new smooth NDVI values. By selecting an appropriate window size and polynomial order, this method effectively smooths out random fluctuations in the original data while preserving key trends in ecological and environmental changes. This technique not only improves the visual quality of the images but also provides a finer and more continuous dynamic of vegetation cover in the temporal dimension, enhancing the capability for monitoring and analyzing vegetation changes.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
###### Abstract  
High Frequency (HF) communications through ionospheric reflection is a widely used technique specifically for maritime, aeronautical, and emergency services communication with remote areas due to economic and management reasons, and also as backup system. Although long distance radio links can be established beyond line-of-sight, the availability, the usable frequencies and the capacity of the channel depends on the state of the ionosphere. The main factors that affect the ionosphere are day-night, season, sunspot number, polar aurora and earth magnetic field. These effects impair the transmitted wave, which suffers attenuation, time and frequency dispersion. In order to increase the knowledge of this channel, the ionosphere has been sounded by means of narrowband and wideband waveforms by the research community all over the world in several research initiatives. This work intends to be a review of remarkable projects for vertical sounding with a world wide network and for oblique sounding for high latitude, mid latitude, and trans-equatorial latitude.  
HF; vertical sounding; oblique sounding; skywave; ionosphere; communication; Doppler spread; delay spread; SNR +
Footnote †: journal: Sensors
# An Optimized Vector Tracking Architecture for Pseudo-Random Pulsing CDMA Signals
## 4 The Proposed VTL Architecture
### Architecture of the IUPPM-VTL  
Figure 5 shows the block diagram of the IUPPM-VTL. The pulse control module generates the pulse signal \(H^{i}(t)\) and calculates intervals \(T_{k-1}\) between two adjacent active timeslots according to the predetermined pseudo-random permutation. The Integrate and Dump (I&D) module only integrates during the active timeslots, avoiding interference of other PRPS. The discriminator outputs the carrier phase error \(\overline{\Delta\phi_{k}}\) and the code phase error \(\overline{\Delta\tau_{k}}\). Then, the IUP pre-filter based KF filters \(\overline{\Delta\phi_{k}}\) and \(\overline{\Delta\tau_{k}}\) in irregular periods according to the intervals \(T_{k-1}\). Meanwhile, the estimated errors of the pre-filter are the inputs of the navigation filter. The input measurements are firstly predicted to ensure the same sampling time. Thus, it will not lead to performance degradation due to inconsistent input sampling times. Finally, the corrections estimated by EKF are used to adjust the channel NCO through the line of sight (LOS) projection.  
Figure 4: Schematic diagram of input sampling for navigation filter.  
Figure 5: Block diagram of the IUPPM-VTL.
## 5 Conclusions  
In this study, using different characterization techniques, two commercially available polyureas were compared in terms of their physical, chemical, thermal, and mechanical properties, to evaluate their capacity to be used as used as protective coatings in buildings against blasts.  
The characterization performed on the two polyureas showed that both materials presented comparable thermal properties, with similar degradation stages and temperatures. In the physical characterization of the polyureas, no relevant differences were realized between the two materials tested in terms of density, water absorption, and dimensional stability. Observations of SEM tests displayed that the surface of polyurea A is heterogenous while the other polyurea's surface is homogeneous. The chemical composition of both polyureas, ascertained using the EDX technique, is similar, with only the presence of titanium on polyurea B being the most relevant difference.  
In conclusion, the mechanical characterization, the most important set of tests, performed on both polyureas determined that polyurea B has significantly better tensile properties-almost 3000% of maximum deformation capacity compared with approximately 115% of polyurea A, than the other polyurea. This exceptional capacity presented by polyurea B to endure high deformations loads makes for a drastic improvement compared with the tensile performance of polyurea A, which influences the choice of protective coating against blasts in the retrofitting of buildings.  
Conceptualization, C.M.; Investigation, F.L.; Project administration, C.M.; Resources, G.G. and J.M.; Supervision, J.B., F.C., R.F., G.G. and J.M.; Writing--original draft, F.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reasons.  
The authors declare no conflict of interest.
# Windowed Joint Detection and Decoding with IR-HARQ for Asynchronous SCMA Systems
## 5 Conclusions  
This paper introduces a new transmission strategy for the SCMA system using an RC-LDPC code and IR-HARQ. Additionally, we propose a windowed joint detection and decoding algorithm. The simulation results demonstrate that this scheme outperforms the original joint detection and decoding algorithm by approximately 0.5 dB and achieves a higher throughput across a wide range of SNRs.  
Conceptualization, M.G. and M.Z.; methodology, M.G. and B.B.; software, M.G. and M.Z.; validation, M.G. and M.Z.; formal analysis, M.G. and M.Z.; investigation, M.G. and M.Z.; resources, M.G.; data curation, M.G. and M.Z.; writing--original draft preparation, M.G.; writing--review and editing, M.Z. and B.B.; visualization, M.Z. and B.B.; supervision, M.Z. and B.B.; project administration, M.G. and M.Z.; funding acquisition, M.Z. and B.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China under Grant 2021 YFB2900500 and in part by the National Natural Science Foundation of China under Grants 62271380 and 62171356.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
RC rate-compatible IR incremental redundancy SCMA sparse code multiple access HARQ hybrid automatic repeat quest AWGN additive white Gaussian noise  
Figure 7: Throughput of the SCMA system with the IR-HARQ scheme using the windowed joint detection and decoding algorithm with \(D_{\text{max}}=4\).
# Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods
## 3 Results
### Comparison with Adam Optimizer  
Adaptive moment estimation (Adam) [7] is a popular efficient first-order optimizer used in DL. Due to the high sensitivity of Adam to the value of its hyperparameters, it is usually used after the determination of near-optimal values through grid searching strategies, which is a very time-consuming task. It is worth noting that sL-QN-TR approaches do not require step-length tuning, and this particular experiment offers a comparison to optimized Adam. To compare sL-BFGS-TR and sL-SR1-TR to Adam, we have performed a grid search on learning rates and batch sizes, to select the best value of Adam's hyperparameters. We have considered learning rates values in \(\{10^{-5},10^{-4},10^{-3},10^{-2},10^{-1},1\}\) and batch size in \(\{100,500,1000,5000\}\), and we have selected the pair of values that allows Adam to achieve the highest testing accuracy. The gradient and squared gradient decay factors are set as \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\), respectively. The small constant for preventing divide-by-zero errors is set to \(10^{-8}\).  
Figure 4 illustrates the results obtained with the two considered sQN algorithms and the tuned Adam. We have analyzed which algorithm achieves the highest training accuracy within, at most, 10 epochs for different batch sizes. In networks using BN layers, all methods achieve comparable training and testing accuracy within 10 epochs with \(bs=1000\). However, this cannot be generally observed when \(bs=100\). The figure  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LeNet-5** & **ResNet-20** & **ResNet-20 (No BN)** & **ConvNet3 FC2** & **ConvNet3 FC2 (No BN)** \\ \hline MNIST & sL-SR1-TR & both & sL-SR1-TR & both & both \\ F.MNIST & sL-SR1-TR & both & sL-SR1-TR & both & sL-SR1-TR \\ CIFAR10 & sL-SR1-TR & sL-BFGS-TR & sL-SR1-TR & sL-BFGS-TR & sL-SR1-TR \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the best sQN approach for each combination problem/network architecture.  
shows that tuned Adam provides higher testing accuracy than sL-SR1-TR. Nevertheless, sL-BFGS-TR is still faster at achieving the highest training accuracy, as we also previously observed. It also provides testing accuracy comparable to tuned Adam. On the other hand, for networks without BN layers, sL-SR1-TR is the clear winner.  
Figure 4: Comparison of sL-BFGS-TR, sL-SR1-TR (both with \(l=20\)) and tuned Adam (with optimal learning rate \(lr\)) for different batch sizes (\(bs\)). Learning rates equal to \(10^{-4}\) and \(10^{-3}\) are indicated as lr: 1e-3 and lr: 1e-4, respectively.  
A final remark is that Adam's performance seems to be more negatively affected by large minibatch size than QN methods. For this reason, QN methods can increase their advantage over Adam when using large batch sizes to enhance the parallel efficiency of distributed implementations.
## 2 Materials and Methods  
To obtain the required results to evaluate the NN architectures used in the control structure across different configurations, both real 3D-printed (3- and 5-DOF) and simulated robotic arms (3-DOF) were used, as shown in Figure 3.  
Figure 3: Design of the studied robotic arms for (**A**) simulated and real 3-DOF arms and (**B**) a real 5-DOF arm.  
First, the real 3-DOF planar robotic arm consisted of three 3D-printed bodies made from polylactic acid with segment lengths of \(d_{1}=0.2\) m, \(d_{2}=0.2\) m, \(d_{3}=0.1\) m, respectively. Each body was connected by a pivot joint actuated by XM-540 Dynamixel motors.  
Secondly, the real 5-DOF robotic arm consisted of five 3D-printed PLA bodies. Two DOFs were in the base (universal joint), while the remaining three were pivot joints, as shown in Figure 3. All joints were actuated by Dynamixel motors from the XM series. The segment lengths were \(d_{1}=0.3\) m, \(d_{2}=0.3\) m, \(d_{3}=0.1\) m, respectively.  
The experimental results were used to validate and enhance the simulated results, ensuring that the simulations represented reality. Specifically, we adjusted simulation parameters, including PID gains, inertias, and masses, to ensure that the accuracy of the simulated PD controller matched that of the real PD controller.  
We also assumed that the internal dynamics of the robotic arms were deterministic and repeatable.
## 2 Materials and Methods
### Determination of the Volatile Fraction  
The method for determining the volatile profile follows [PERSON] et al. [20] with minor changes. Using the SPME sampling technique, the volatile profile of the virgin oil samples was described. Approximately 3 g of oil was placed in a 20 mL glass vial with a PTFE-coated silicone septum. A Supelco (Bellefonte, PA, USA) SPME instrument with a 1 cm fiber coated with 50/30 \(\upmu\)m DVB/CAR/PDMS (divinylbenzene/carboxylate/polydimethylsiloxane) was selected for extraction. Prior to use, the fiber was conditioned at 270 \({}^{\circ}\)C for 30 min. The equilibration time for all samples was obtained by heating to 40 \({}^{\circ}\)C for 10 min. After this time, the fiber was exposed to the headspace of the samples for 40 min at 40 \({}^{\circ}\)C to capture and concentrate the volatiles. Finally, the SPME fiber was inserted into the GC injector maintained at 250 \({}^{\circ}\)C in splitless mode for desorption of the collected components. Analyses were performed using the same equipment used to determine the fatty acid profile. The capillary column was Restek Stabilwax. To characterize the volatile composition of extra virgin olive oil samples, the oven conditions were set as follows: initiated at 40 \({}^{\circ}\)C and held for 10 min, then increased to 200 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min, then increased to 250 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min and held for 10 min. Helium was used as a carrier gas at a constant rate of 1 mL/min. The identification and quantification of the detected compounds were performed as described in the previous Section 2.3.
# A Survey on the Application of WirelessHART for Industrial Process Monitoring and Control
## 4 Challenges and Design Requirements
### Latency  
Closed-loop processes continuously require real-time reliable sensor data to keep the process stable. If the network experiences any delay in data transmission larger than a specified time, the data cannot be used for effective control actions. Therefore, new critical data must be transmitted through the network to a sink instead of retrying all transmissions [37]. Additionally, packet delay and transmission failure result in the performance degradation of the WHNCS [48]. Forward error correcting, multi-path and multi-SPEED routing protocol (MMSPEED), and routing protocol for low-power and lossy networks (RPL), can be used to minimize the number of retransmission attempts to mitigate the network failure delay [39]. Another possible solution is designing a controller with a predictive nature to compensate the network and plant delay, which will minimize the impacts of unavoidable stochastic delay in the control loop [14; 108].
# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 4 The Experiments
### Qualitative Experiments on Public Datasets  
To qualitatively compare the proposed tracker with other trackers, Figure 10 also shows the tracking results of six tracking algorithms, including TransT [9], MDNet [1], DaSiaRPN [52], SiamRPN [44], Staple [35], and Ocean [43], on five challenging sequences. The five sequences, from top to bottom, are Ironman, Shaking, Bird1, Singer2, and Soccer, respectively. These five sequences encompass nearly 11 different tracking challenges, and some conventional trackers show poor performance in these five sequences, making them prone to losing the object and resulting in tracking failures.  
From Figure 10, we can see that, compared to the bounding boxes of other colors, the red bounding boxes and green bounding boxes have a higher degree of overlap. This also indicates that the proposed tracker, compared to other methods, can achieve better tracking results, and the ECA module can better direct the fused features to pay attention to the object's region. Other trackers, such as TransT [9] and Ocean [43] in the last row, have indirectly demonstrated the robustness of our algorithm under different challenges.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **AUC**(\%) & **P**(\%) \\ \hline GFETrack & 67.3 & 87.0 \\ TransT [9] & 68.1 & 87.6 \\ ToMP-101 [49] & 66.9 & - \\ TREG [50] & 66.9 & 88.4 \\ TrDiMP [45] & 67.5 & - \\ SiamAttn [51] & 65.0 & 84.5 \\ SiamCAR [48] & 61.4 & 76.0 \\ DiMP [42] & 65.4 & - \\ ATOM [41] & 64.3 & - \\ SiamFC [6] & 48.5 & 69.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison experiments of different trackers on UAV123 dataset. The top 2 results are marked in red and blue, respectively.  
Figure 10: The qualitative experiments of six tracking algorithms on five challenging sequences. The five sequences, from top to bottom, are Ironman, Shaking, Bird1, Singer2, and Soccer, respectively.  
Although the proposed method could handle most of the tracking challenges, we notice that the ECA modules may face some challenges and lead to a decrease in matching accuracy under some specific conditions, such as complex backgrounds or when the appearance of the object changes fast. As the proposed GFETrack is still in its theoretical stage, it may encounter hurdles in the real world. Thus, research on the better feature fusion and re-detection methods would be the next task in GFETrack.
###### Abstract
The results showed that the three-dimensional characteristics of the flow in spanwise direction became strong especially in the critical regime. In addition, the wake width became narrower and the drag coefficient decreased, which was observed by [PERSON] et al. [26], too. As for two tandem cylinders, [PERSON] [27] measured \(Cd\) and \(St\) by low-speed wind tunnel tests. It was observed that when the Reynolds number came to the super-critical regime, the jump of \(Cd\) disappeared at \(L/D=3.8\) and the effect of \(L/D\) on \(Cd\) and \(St\) became weaker. [PERSON] et al. [23] investigated the tandem cylinders' flow characteristics in the sub-critical and super-critical regimes. When compared to the examples in the sub-critical regime, the vortex shedding frequencies are higher in the super-critical domain. Moreover, the flow separation positions move backward along the cylinder surface, which causes the reattachment position on the back cylinder to shift forward in the super-critical regime. In summary, few systematic study have been conducted on the flow around three cylinders in equilateral-triangular configuration in the super-critical regime. In the present paper, a three-dimensional numerical simulation of flow past three cylinders in equilateral-triangular arrangement in super-critical regimes (\(Re=3\times 10^{6}\)) is presented at a spacing ratio of \(1.1\leq L/D\leq 3.5\), focusing on how fluctuating forces, flow separation, and vortex shedding frequencies vary with \(L/D\). Five flow patterns are identified by spacing ratio and the characteristics of each pattern are summarized.