esa-sceva/satcom-chunk-collection huggingface dataset:
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 5 Design and Development of Satellite Ground Stations
### Equipment of Ground Station
#### 5.2.4. SDR Server  
The SDR server functions as a data storage server and connects to remote user commands. It serves as the central hub for key equipment of the satellite ground station. The details of the critical equipment performing various functions are as follows:  
* Software-Defined Radio (SDR) (Figure 6, item 19): According to the specifications in Table 1, the construction of this ground station involves receiving frequencies from 137 to 1700 MHz and demodulating a variety of signals ranging from digital to analog. The only device capable of meeting these requirements is the SDR. Based on the initial requirements, the system needs to receive signals at 2 Msps or higher to cover satellite reception in orbit. Therefore, the RTL-SDR with an R820T tuner from Rafael Microelectronics, Taiwan and an RTL2832U ADC from Realtek Semiconductor Corp., Taiwan, was chosen, as it perfectly meets these minimum requirements and is the most cost-effective SDR for this application, supporting a wide range of uses.
* Rotator Control: This receives azimuth and elevation angle commands from remote users and forwards them to the rotator controller (Figure 6, item 22), a custom-developed device discussed in Section 5.2.3. This function controls and reports the rotator's position back to the user.
- Data Reporting from RTL-SDR: Users can access data on the SDR sever in two different ways: Accessing through the SDR baseband server, users can directly receive data from the SDR and process the data with SDR client software like SAt-Dump version 1.2.1 or SDR++ version 1.0.4, or accessing through the OpenWebRX server, which acts as a web server, allowing users to view SDR data through a web browser.
- Ground Station Monitoring Camera: This can be controlled via multimedia player software version 3.0.19.
## 4 Experiments and Results
### Quantitative Performance Evaluation of the Framework
#### 4.1.2 Congestion Prediction
In a place such as a train station, where there are often services such as direction guides and ticket counters needed, being able to predict crowd flows in advance will give facility operators additional time to plan for the dispatching of additional service support and potentially help reduce future congestion.
# Adaptive, Observer-Based Synchronization of Different Chaotic Systems
## 8 Conclusions  
The problem of the observer-based master-slave synchronization of completely different chaotic systems was solved under a novel set of assumptions. A modification of the K-filter-based observer was proposed, including feedback from the tracking adaptive controller. The master and the slave are connected by a single signal, which is a practical advantage in prospectus secure communication applications.  
The main, new idea of the proposed approach is to enable cooperation between the adaptive observer and the adaptive tracking controller in a unified closed-loop system. Tracking errors fed back from the controller influence the observer (by a tuning function in adaptive law), improving the overall performance. Application of the tuning functions technique allows us to avoid overparameterization of the whole system--the number of adaptive parameters is the same as the number of unknown parameters. As a result  
Figure 10: (**a**) Synchronization error \(x_{2}-w_{2}\); (**b**) synchronization error \(x_{3}-w_{3}\); design parameters: \(K_{1}=K_{2}=K_{3}=10\), solid line; design parameters: \(K_{1}=K_{2}=K_{3}=15\), dashed line.  
of filter application, the \"explosion of complexity\" in the control law is prevented. The applied adaptive laws are robust; therefore, an uncontrolled increase in adaptive parameters is avoided.  
The proposed controller can synchronize different chaotic systems with sufficient accuracy. Tuning of the design parameters is logical and clear. A designer can trade off the synchronization accuracy against the aggressiveness of the control strategy. The proposed solution was compared with adaptive tracking using all master-state-vector variables. The analyzed examples demonstrate that the obtained accuracy of tracking (synchronization) was similar, despite the smaller amount of information transferred from the master. Additionally, settling time, overshoot, and other control quality measures are comparable to those obtained in the case of full availability of the state vector. The same conclusions can be drawn from the comparison with the results presented in [11].  
The selection of a particular type of nonlinear adaptive observer constrains the class of chaotic systems under consideration. In future research, we plan to explore other types of nonlinear adaptive observers in collaboration with a tracking controller. Although this paper concerns particular chaotic systems and the derivation concentrates on third-order systems, the proposed design technique may be easily adapted to different cases of nonlinear systems.  
Main idea, J.K. and P.M.; methodology, J.K. and P.M.; simulations, P.M.; validation, J.K. and P.M.; writing--original draft preparation, J.K. and P.M.; writing--review and editing, J.K.; visualization, P.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Politechnika Lodzka (Lodz University of Technology) The APC was funded by Politechnika Lodzka.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Investigation of AE Signals Waveform and Spectrum  
Fracture predictors are more clearly identified based on the AE signal waveform. Figure 10 shows the waveforms and spectra of AE signals observed during the period of acoustic calm, typical for defect-free specimens (Figure 10a,d), for the specimens with diffusion interlayers (Figure 10b,e) and for specimens with lack of penetration (Figure 10c,f). AE signal waveforms were obtained with the clustering algorithm by which AE signal waveforms were grouped in the cluster in accordance with a correlation coefficient; each cluster joins the signals with a correlation coefficient r > 0.7. For each of three data samples corresponding to defect-free specimens, a specimen with diffusion interlayers, and a specimen with a lack of penetration, one representative cluster was formed.  
For defect-free specimens, the size of the cluster formed with the correlated AE signals is in the order of 8-10% of the total signals number. It consists of short-duration signals with a relatively wide spectrum with a several resonances peaks. For samples with a lack of penetration, the proportion of correlated signals is much higher (more than 40%) and the typical signal waveform has the following features: a longer duration compared to the signals shown in Figure 10a, as well as a narrow-band spectrum corresponding to 120-150 kHz (Figure 10f). For specimens with diffusion interlayers, the proportion of correlated signals is more than 60%, a typical waveform has a high repeatability and represents a short impulse with a duration of about 10-20 \(\upmu\)s (Figure 10b) and a wide  
Figure 10: Typical AE signal waveforms and spectrums for defect-free weld joint (**a**,**d**), for weld joint with diffusion interlayers (**b**,**e**) for weld joint with lack of penetration (**c**,**f**).  
spectrum corresponding to the bandwidth of the measuring channel (Figure 10e), since the waveform and spectra of the AE signals are characterized by some variability. For a more complete analysis of the AE data during the period of acoustic calm, the scatter plot between the amplitude of AE signals and the median frequency of the spectrum was considered (Figure 11). The amplitude characterizes the energy of AE source and the median frequency is an informative parameter characterizes the changes in a frequency spectrum of the AE signal.  
The scatterplot corresponding to defect-free specimen is characterized by minimum variance in amplitudes and frequencies (Figure 11a). The presence of diffusion interlayers causes the scatterplot to expend slightly on an amplitude and more significantly on a frequency (Figure 11b). For a specimen with a lack of penetration, the scatterplot had a maximum deviation (Figure 11c), and the ranges of amplitudes increased insignificantly compared to non-defective case. The wide range of median frequency from 70 to 340 kHz indicates the presence of several AE sources corresponding to deformation processes and destruction in the welded joint area.
###### Abstract  
Scientific workflow applications are collections of several structured activities and fine-grained computational tasks. Scientific workflow scheduling in cloud computing is a challenging research topic due to its distinctive features. In cloud environments, it has become critical to perform efficient task scheduling resulting in reduced scheduling overhead, minimized cost and maximized resource utilization while still meeting the user-specified overall deadline. This paper proposes a strategy, Dynamic Scheduling of Bag of Tasks based workflows (DSB), for scheduling scientific workflows with the aim to minimize financial cost of leasing Virtual Machines (VMs) under a user-defined deadline constraint. The proposed model groups the workflow into Bag of Tasks (BoTs) based on data dependency and priority constraints and thereafter optimizes the allocation and scheduling of BoTs on elastic, heterogeneous and dynamically provisioned cloud resources called VMs in order to attain the proposed method's objectives. The proposed approach considers pay-as-you-go Infrastructure as a Service (IaaS) clouds having inherent features such as elasticity, abundance, heterogeneity and VM provisioning delays. A trace-based simulation using benchmark scientific workflows representing real world applications, demonstrates a significant reduction in workflow computation cost while the workflow deadline is met. The results validate that the proposed model produces better success rates to meet deadlines and cost efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.  
IaaS cloud; scientific workflow; resource provisioning; scheduling; cost minimization; deadline-constrained +
Footnote †: journal: Future Internet
# Nanotechnology, Nanomedicine, and the Kidney
## 3 Renal Clearance of Nanoparticles
### Size  
Size is the most obvious factor to influence NP renal clearance. For globular biomolecules, such as protein, the size-dependent threshold, i.e., the glomerular filtration regulated by the size, is generally accepted as <5 nm. Typically, molecules with a hydrodynamic diameter (HD) < 6 nm are filtrated, while those with an HD > 8 nm are not. Since NPs have properties distinct from proteins, they could exhibit a distinct renal filtration behavior [26]. Polyamidoamine (PAMAM) dendrimer-based NPs (PDBNP) and quantum dots (QD) have been used to evaluate the size-dependent threshold for NPs [27; 28]. Similarly to conventional molecules, renal clearance studies showed that renal excretion was observed for PDBNPs and QD, with HD of \(\sim\)5 nm [18]. As expected, QD with HD > 8 nm did not exhibit renal elimination but instead accumulation in the liver. Similar trends were observed with silica NPs and gold NPs (AuNPs), where a decrease in HD resulted in an increased renal clearance efficiency. Since the size-dependent threshold was observed from engineered NPs with HD ranging from 2 nm to 6 nm, [PERSON] et al. [29] answered a fundamental question: \"Is this size-dependency in the glomerular filtration still valid at an even smaller size scale?\" Interestingly, they demonstrated that, in the sub-nm regime, just a few atom reductions in the gold-cluster (AuGC) size resulted in a four to nine times reduction in renal clearance efficiency because the glycocalyx trapped smaller AuGC more efficiently.
## 3 Test Results and Discussion
### Stress-Strain Curves  
The curves of time and load are achieved based on the tests and the full stress-strain curve is also obtained by the conversion of mechanical Equation (1):  
\[\sigma=\frac{N}{S},\epsilon=\frac{l-l_{1}}{l} \tag{1}\]  
where '\(\sigma\)' and '\(\epsilon\)' are the strain and stress of the concrete, respectively; '\(N\)' and '\(S\)' represent the axial force and the cross-sectional area of the specimen in the test; '\(l\)' and '\(l_{1}\)' are the height of specimens before and after loading in the test.  
As shown in Figure 6, the OC stress-strain curve shows three stages: first dense, then sparse, and then dense, while the RC shows two stages of first sparse and then dense. It has  
Figure 4: The observation of the specimen under cyclic loading. (The square represents the variation of cracks during loading and unloading).  
the following characteristics: (1) As the number of cycles increases, the internal voids of OC specimens are compacted, and almost no dissipation occurs; subsequently, microcracks are generated in the internal pores, and the dissipated energy increases (Hysteresis loop area increases). The cycle continues to increase, the plastic strain reaches the limit, the internal microcracks cease to develop, and the structure achieves equilibrium. On the contrary, after adding rubber particles, the deformation mainly occurs in the first cycle, followed by rapid compaction, and the total strain is small--there are many pores in the RC specimen, and the plastic deformation value increases under pressure. Once cracking occurs, the flexibility of rubber particles plays a role in buffering, preventing, and dissipating energy. (2) With the increase in rubber particle size, the total strain and elastic-plastic deformation increase. This demonstrates that a certain amount of rubber particles can reduce the dissipation energy of the specimen. As the particle size increases, the bonding ability between the surface of the rubber particles and the cementitious material decreases, the strain of the specimen increases, and the dissipation energy increases. (3) According to [PERSON]'s law \(\Delta\sigma=\Delta E\Delta\epsilon\), each \(\Delta\sigma\) is equal, but \(\Delta\varepsilon_{RC-3}>\Delta\varepsilon_{RC-2}>\Delta\varepsilon_{OC}> \Delta\varepsilon_{RC-1}\); therefore, \(\Delta E_{OC}>\Delta E_{RC-3}>\Delta E_{RC-2}>\Delta E_{RC-1}\). This further explains that, when the specimen is subjected to the same degree of load, the rubber particles have better deformability.  
Figure 5: SEM image of rubber concrete of each particle size.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 5 Performance Evaluations
### Experimental Environment  
In order to implement a LoRa network, we used off-the-shelf LoRa products [18]: Multitech mDot for end device and Conduit for LoRa gateway. Both end device and gateway are configured with Korean frequency regulation specified in the LoRa specification ver. 1.02, and 1 gateway and 5 end devices were used in our experiments. Since mDot and Conduit contains the LoRa standard stack, LoRa standard performance is tested based on the original systems, and on the top of LoRa standard, CDL is implemented. Our experiments are conducted on 5 end devices but based on the results from the real experiments we evaluated the performance in extensive environments as shown in Table 2.
## 3 Overview and Theoretical Foundations
### Big Spatial Multidimensional Data Analytics
#### 3.2.2 Spatial Joins  
A spatial join is an indispensable operation in geospatial data analytics, which returns a set of pairs resulting from pairing two georeferenced datasets with a spatial predicate applied, e.g., intersection, proximity, inclusion, etc., [38]. The two datasets are multidimensional spatial objects. A spatial join query example will ask to \"retrieve regions to where every vehicle passes through during previous 3 h in NYC\", which necessitates joining spatial points with a table containing the administrative regions of NYC.  
Mathematically speaking, with two datasets S and Q, a spatial join computes a set of pairs (s, q) that satisfies Equation (1)  
\[\text{S}\ \bowtie_{predicate}\text{Q}=[(\text{s},\ \text{q})\ |\ \text{s}\ \in\text{S},\ \text{q}\ \in\text{Q},\ \textit{predicate}\ (\text{s},\ \text{q})\ ==\text{true}]. \tag{1}\]  
where _predicate_ is a geometric predicate (e.g., intersects, overlaps, touches, etc.).  
When spatial data objects are transformed through the internet, they are normally parametrized in the form of pairs represented by longitudes/latitudes and similar coordinate projection systems, hence they lose their original geometric representation. Having said that, relational join methods, such as sort-merge join, or hash join cannot be applied. Spatial join is a kind of theta-join, which is computationally expensive, as it needs to link tables based on a spatial relationship instead of equality between two attributes.  
Costly spatial operations are typically required to verify join predicate and need to be applied to every point in a survey area, which results in a huge computational overhead, and consequently bringing the system to a halt.  
Ray-casting algorithm (a.k.a. Point-In-Polygon, PIP for short) is a typical costly operation in spatial join. It is required specifically for checking whether a point is contained geographically within a polygon or not. Stock versions of spatial join apply PIP to every single point in the dataset. This is typically solved with dimensionality reduction approaches such as z-order curves, projecting spatial objects to single-dimensional space, thereafter, applying cheaper joins such as equijoin. We show an optimization for such a plain method in the next subsection.  
Spatial join is an indispensable primitive in dynamic smart city application scenarios which require integrating heterogeneous georeferenced datasets (e.g., mobility, pollution and meteorological) for insightful analytics.
## 2 Mid-Infrared Optical Parametric Oscillation Laser (OPO)
### KTiOPO\({}_{4}\) and KTiOAs\({}_{0}\)\({}_{4}\) Optical Parametric Oscillator  
KTP crystal and KTA crystal belong to the isologue, the symmetrical structure of the 2 m point group, which has high hardness and excellent optical properties. They are nonlinear optical materials widely used in frequency conversions. The descriptions of the two crystals are shown in Figure 3.  
It can be seen from the Figure 3 that both KTP and KTA have the characteristics of a high damage threshold. However, compared with KTP crystal, the physicochemical property of KTA crystal is more stable and overcomes the absorption band of KTP crystal, which is near 3.4 \(\upmu\)m. Both crystals have made prominent contributions to the high repetition frequency and high-energy mid-infrared output, and the excellent characteristics of KTP and KTA crystals determine the wide range of their applications. The research progress of KTP and KTA crystals in the mid-infrared band is shown in Table 3.  
Numerous institutions for KTP and KTA crystals research have been reported. They have a wide variety of pump sources, and the operation modes are various. According to the latest research, they have achieved high-power and high-quality laser output.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Crystal** & **Year** & **Research Establishment** & **Crystal Parameter** & **Pump Source** & **Mid-Infrared Output** & **Reference** \\ \hline \multirow{6}{*}{2021} & \multirow{3}{*}{2021} & Changchun University of Science and Technology & & & Output wavelength 3.8225 \(\upmu\)m & \\  & & Changchun University of Science and Technology & & 1.064 \(\upmu\)m & Repetition rate 1 MHz & \\  & & Changchun University of Science and Technology & & 5\% mol & Yb\({}^{3+}\) fiber & Average power 2.06 W & \\  & & & & & Optical efficiency 11.38\% & \\  & & & & & Output wavelength 3.4 \(\upmu\)m & \\ \multirow{2}{*}{2021} & \multirow{2}{*}{2021} & Shandong University & 10 \(\times\) 1 \(\times\) 50 mm\({}^{3}\) & 1.064 \(\upmu\)m & Repetition rate 5 kHz & \\  & & & 5\% mol & Yb\({}^{3+}\) fiber & Average power 3.68 W & \\ \multicolumn{6}{c}{} & \multicolumn{6}{c}{Optical efficiency 37\%} \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Description diagram of KTP and KTA crystals.
###### Abstract  
The design of sustainable and efficient materials for efficient energy storage and degradation of environmental pollutants (specifically organic dyes) is a matter of major interest these days. For this purpose, cerium- and ytterbium-based GO/g-C3N4/Fe2O3 composites have been synthesized to explore their properties, especially in charge storage devices such as supercapacitors, and also as photocatalysts for the degradation of carcinogenic dyes from the environment. Physicochemical studies have been carried out using XRD, FTIR, SEM, and BET techniques. Electrochemical techniques (cyclic voltammetry, galvanic charge discharge, and electrochemical impedance spectroscopy) have been employed to measure super-capacitance and EDLC properties. Results show that the gravimetric capacitance calculated from GCD results is 219 Fg\({}^{-1}\) for ytterbium- and 169 Fg\({}^{-1}\) for cerium-based nanocomposites at the current density of 1 A/g and scan rate of 2 mV/sec. The specific capacitance calculated for the ytterbium-based nanocomposite is 189 Fg\({}^{-1}\) as compared to 125 Fg\({}^{-1}\) for the cerium-based material. EIS results pointed to an enhanced resistance offered by cerium-based nanocomposites as compared to that of ytterbium, which can be assumed with the difference in particle size, as confirmed from structural studies including XRD. From obtained results, ytterbium oxide-based GO/g-C3N4/Fe2O3 is proven to be a better electro-catalyst as compared to cerium-based nanocomposites. Photocatalytic results are also in agreement with electrochemical results, as the degradation efficiency of ytterbium oxide-based GO/g-C3N4/Fe2O3 (67.11 and 83.50% for rhodamine B and methylene blue dyes) surpasses values observed for cerium-based GO/g-C3N4/Fe2O3 (63.08 and 70.61%).  
super capacitance; cerium oxide; ytterbium oxide; graphitic carbon nitride; photocatalytic degradation
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 5 Discussion
Differentiated results are achieved for the same network model structure, differing only in input features. When augmented multi-dimensional feature information such as position, velocity, and acceleration as inputs, the model achieved better prediction results than when only inputting three-dimensional features (time, longitude, and latitude). Therefore, the richness of the input features helps to improve the predicted accuracy. In general, the CNN model designed in this paper achieves superior prediction performance for sparse trajectory data. However, it also provokes reflection and indicates directions for future research. It is of great significance to extend relevant features as inputs for better prediction, and combining multi-factor features to achieve track prediction for complex behavioral scenarios such as steering is needed for further research.