esa-sceva/satcom-chunk-collection huggingface dataset:
# Rapid Estimation of TVWS: A Probabilistic Approach Based on Sensed Signal Parameters
## 1 Introduction
Figure 1: Block diagram of the proposed rapid estimation method: the first block collects information on \(W(p)\), \(Xo(p)\), geolocation, and time stamp. The second block detects Primary Users (PU) and unused channels, stochastic and mathematical information, and Receiver Operation Characteristics (ROC). The third block applies the rapid method to estimate Television White Spaces (TVWS). The presented paper is derived from the research detailed in [6]. Other interesting applications envisaged are the usage of TVWS in smart grid communications, as is explained in [10], or in Cognitive Vehicular Networks (CVN), as proposed in [11], or the indoor access to TVWS, also known as TV Gray Spaces (TVGS), detailed in [12, 13]. Also, its application in rural communications networks with Wireless Regional Area Network (WRAN) has been mentioned in [14, 15]. To materialize the abovementioned solutions, a REM complemented with a novel low-cost sensing technique would allow for identification of frequencies that may be used for DSA. The motivation for this research is to take advantage of the unused radio spectrum to provide smart access to this much-needed resource. With this in mind, we proposed a method that estimates the presence of TVWS in a rapid and trustworthy manner, so that SUs can also be provided access to the spectrum demanding wireless services and applications. In the last years, the world has experienced impressive growth in wireless communication technologies and the appearance of new devices and services at an unprecedented pace. This fast change has created a never-seen-before demand for the electromagnetic spectrum in data transfer. The actual spectrum allocation shows a curious characteristic: we found parts in which the spectrum is crowded and some other parts in which it is underutilized [8]. Therefore, we can affirm that there is a duality of scarcity and wastage of this resource [2, 3]. The rest of the manuscript is organized as follows. Section 2 provides a spectrum sensing overview for this research. Section 3 describes the data collection, analysis procedure, and signal representations. The rapid estimation method is detailed in Section 4, and the corresponding experiments and results are shown and analyzed in Section 5. Finally, Section 6 presents the conclusions and future work.
## 1 Introduction
These attempts are categorized into three classes of integral approaches, the time-domain polynomial fitting to remove the trend error [14], the band-pass filtering method [15], and FFT implementation with low-frequency cutoff and attenuation attempts [16; 17; 18], have demonstrated their good applicability in engineering problems. Nevertheless, these approaches still have flaws with respect to error control and fidelity. Recent years have also seen the application of Hilbert-Huang transform and wavelet transform [19; 20; 21; 22], such as the least-squares wavelet and cross-wavelet analyses [23]. However, these transforms have not yet been well established for signal conversion. This paper proposes a Walsh Transform and Empirical Model Decomposition (EMD)-based Integral algorithm (WATEBI) to obtain the desired velocity and displacement from acceleration. To demonstrate the level of robustness and adaptiveness of this algorithm, three case studies are presented. The first study is a numerically simulated vibration of a single-degree-of-freedom (SDOF) system driven by white noise. The other two cases are seismic recordings including ground acceleration, velocity and displacement, respectively, measured in two typical earthquakes.
## 3 System Framework Overview
### Application Layer
#### 3.3.1 Services Cloud  
This cloud component is employed for providing various types of services. According to the proposed model, agricultural stakeholders (farmers, agronomist engineers and agricultural products merchants) can subscribe to the mobile cloud structure and by using the context which is deployed in it, they are able to receive request context-aware services concerning the observed conditions of the cultivations as well as to provide rules for control actions by using simple query mechanisms. Sometypical examples of such services include automated, symptom detection, emergency, consultancy, and software service. Their rules as well as the corresponding control actions are presented in Table 3. Since a service rule is retrieved it can be easily described with control actions based on the ontology abstraction of the proposed entity-based context model. All rules are stored inside a context repository of the context-aware middleware cloud and can be retrieved by employing a suitable service provisioning technique [110].  
Given that the implemented services are considerably large in number, as they may be implemented by several diverse stakeholders and differentiate among various plants or cultivations, it is almost impossible to be deployed and installed in a single machine. By integrating all services into the cloud, the issue of resources can be resolved and by describing them with a unique model the service-mapping task can be simplified. Several platforms providing cloud services for PA are available as for example The Open Ag Toolkit (OpenATK) which incorporates internet-based cloud services in widely available, low-cost mobile computing technologies [111].
## 4 Platform Design
### Gateway Node Design  
The gateway prototype shown in Figure 4 is an evolution of an earlier prototype using a small ATmega324P microcontroller (32 KiB FLASH, 2 KiB SRAM, 1 KiB EEPROM) that was continuously relaying the data from the sensor nodes to the application server using the on-board general packet radio service (GPRS) modem, with almost no power management. It helped with testing the sensor node and server prototypes and with developing the modem driver and the recovery mechanisms from various GPRS connection errors.  
The evolution shown in Figure 4 uses an ATmega644P microcontroller (64 KiB FLASH, 4 KiB SRAM, 2 KiB EEPROM), which has more SRAM and allows more extensive sensor node message aggregation. It uses a TI CC1101 transceiver for in-field communications, compatible with the sensor  
Figure 4: Top view (**a**) and bottom-edge view (**b**) of the gateway node prototype (50 \(\times\) 50 mm). The layout of the balun (a) limits the RF performance and the battery charger of the modem is not efficient for energy harvesting.  
node transmitter. The GPRS modem (on the back side of the board; see Figure 4b) is a Siemens TC65i with an embedded TCP/IP stack and a charge regulator for lithium-ion batteries, which can be used for energy harvesting.  
It is worth noting that the gateway node prototype is cost- and energy-effective, since it uses hardware resources that are typical for a low-end WSN sensor node (except for the GPRS modem).
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
### Key Terms Explanation
#### 1.1.3 Elliptic Curve Cryptography (ECC)  
The algebraic behavior of elliptic curves over finite fields provides the basis for the public-key encryption known as elliptic curve cryptography (ECC). ECC is a relatively new and potent cryptographic approach, with various benefits compared to more established public-key encryption technologies such as RSA and Diffie-Hellman.
## 3 Lattice Construction Algorithm
### Special-Case Workarounds and Continuity Fallback Mechanism
#### 3.4.3 Geometric Continuity Fallback for Sharp Angles and Intricate Input  
In the general node construction step, the length of struts converging to the same node is adjusted such that they are not intersecting each other. If two edges enclose a sharp angle, the corresponding struts have to be cut far from the respective node to avoid intersections. But these struts still need to be connected smoothly to other struts. As a result, the constructed smooth transitions with ruled surfaces may intersect each other. To overcome this error, the cross-boundary tangent condition in the definition of strut transitions is dropped. Ruled surfaces are used instead of \(G^{1}\)-continuous ruled surfaces (Figure 8(b)). Consequently, the resulting node is partly \(G^{0}\)-continuous instead of \(G^{1}\)-continuous.  
Even tough the discussed special case workarounds and fallback mechanism of the general node construction step are able to handle various node topologies, the construction of single nodes may still fail or may even be rendered impossible due to intricate input, e.g., regarding the relation of strut radii and length and resulting self-intersections. In order to not interrupt the construction algorithm in this case, a complete fallback mechanism to \(G^{0}\)-continuity is integrated (Figure 8(c)). A ruled surface is constructed for each edge converging to the node and they are joined by Boolean operations. Due to the parallelised approach, the construction of the respective node can be repeated independently of the other nodes in the lattice frame in a post-processing step if \(G^{1}\)-continuity is required in each node. The repetition can be conducted, for example, with more sensitive special-case recognition parameters or adjusted radii inputs.
# Joint Deployment of Sensors and Chargers in Wireless
## 5 Simulation Evaluation
### Solution of the Charger Deployment
#### 5.3.4 Varying the Transmitting Power of Chargers  
In Figure 10, the results show that as \(P_{s}\) increases, the number of deployed chargers decreases. According to Equations (2) and (3), with the increase of \(P_{s}\), \(P_{h}^{\bar{s}j,c_{x}}\) is gradually increased while keeping \(d(s_{j},c_{x})\) and \(P_{h}^{\bar{s}j,C}\) unchanged, the number of deployed chargers decreases. Both the GH and PSO solutions based on the GHDSAE solution can always deploy fewer chargers than that of GH and PSO solutions based on the GHDS solution. In Figure 10a, the GHDSAE-based GH solution saves 16.50-24.93% (with an average of 19.75%) chargers than that of the GHDS-based GH solution. In Figure 10b, the GHDSAE-based GH solution saves \(-\)2.13-13.5% (with an average of 8.70%) more time than the GHDS-based GH solution. In Figure 10c, the GHDSAE-based PSO solution saves 10.33-28.20% (with an average of 14.53%) more chargers than the GHDS-based PSO solution. In Figure 10d, the GHDSAE-based PSO solution saves 13.05-33.68% (with an average of 18.19%) less time than the GHDS-based PSO solution. As a consequence, the GHDSAE-based solution deployment chargers are superior to the GHDS-based solution deployment chargers, at the cost of a slight increase in time, which is still within an acceptable range. Likewise, both the PSO solutions based on the GHDSAE and the GHDS solutions can always deploy fewer chargers than the GH solution based on the GHDSAE and the GHDS solutions. In Figure 10a,c, the GHDS-based PSO solution saves 12.57-22.20% (with an average of  
Figure 9: Deployed chargers with the increase of sensor perception probability. (**a**) Deployed chargers of GH; (**b**) Solving time of GH; (**c**) Deployed chargers of PSO; (**d**) Solving time of PSO.  
16.72%) more chargers than the GHDS-based GH solution. And the GHDSAE-based PSO solution saves 8.07-19.32% (with an average of 11.40%) chargers than the GHDSAE-based GH solution. In Figure 10b,d, the GHDS-based PSO solution consumes 154.95-457.14% (with an average of 395.25%) more solving time than the GHDS-based GH solution. The GHDSAE-based PSO solution consumes 65.56-422.69% (with an average of 352.50%) more solving time than the GHDSAE-based GH solution. In summary, both the GH and PSO solutions are scalable as the transmitting power grows, the PSO solution always outperforms the GH solution by deploying fewer chargers with the cost of acceptable calculation time expenses, and the aggregation effect of sensors can effectively reduce the deployment of chargers at the cost of slightly incremental computing time.
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 2 Materials and Methods
### Methods  
To generate time series high resolution GPP/NPP, a LUE model based on the downscaling method was adopted in this paper, as shown in Figure 3. First, high resolution (30 m) FPAR and LAI were obtained by using the downscaling method, and high resolution (30 m) temperature and water stress were obtained through interpolation. Second, time series high resolution GPP and NPP were estimated from a LUE GPP/NPP model. To assess the performance of the method, the GPP and NPP estimates were validated with the ground-based field data, and were compared with the MODIS and GLASS vegetation productivity products.
# Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater
## 2 Materials and Methods
### Modelling Methodology  
As illustrated in Figure 5, modelling of the effluent COD by the multi-scale temporal convolutional network (MSTCN) can be carried out in the following three steps:  
1. Fourier transform. Decompose the time series into the frequency domain and identify the \(k\) frequency components with the largest amplitudes. Then, reconstruct the original data into two-dimensional series at different time scales according to the top \(k\) periods.
2. Model training. For each time scale, construct an adjacency matrix to represent the correlations between variables and input it along with the time series data into the GCN layer to capture finer-grained relationships of features in the spatial dimension. Similarly, build a corresponding TCN layer to extract broad historical dependences within the sequences. Then, the representations learned at that time scale can be achieved by feature fusion of the results from GCN and TCN layers, and the final output of the hybrid model is aggregated adaptively from \(k\) scales using a fully connected layer.
3. Evaluation. Root mean squared error (\(RMSE\)), mean absolute error (\(MAE\)) [32], and the coefficient of determination (\(R^{2}\)) [33] are chosen as the metrics to evaluate the prediction accuracy and validate the proposed model performance statistically. The mathematical definitions of them are as follows: \[RMSE=\sqrt{\frac{1}{N}\!\sum\limits_{i=1}^{N}\!\left(y_{i}-y_{pred,i}\right)^{ 2}}\] (8) \[MAE=\frac{1}{N}\!\sum\limits_{i=1}^{N}\!\left|y_{pred,i}-y_{i}\right|\] (9) \[R^{2}=1-\frac{\sum\limits_{i=1}^{N}\left(y_{i}-y_{pred,i}\right)^{2}}{\sum \limits_{i=1}^{N}\left(y_{i}-\mu\right)^{2}}\] (10)  
Figure 4: The architecture of TCN combined with GCN.  
When \(N\) is the sample size, \(\mu\) denotes the mean value, and \(y_{i}\) and \(y_{prd,i}\) are the measurements and the estimated values of the model, respectively. The \(R^{2}\) value ranges from 0 to 1, with values closer to 1 indicating a higher proportion of the variance in the target variable that can be explained by the predictive variables, thus signifying a better fit of the model. Conversely, lower values of \(RMSE\) and \(MAE\) reflect a smaller error between the predictions and observations.
## 1 Introduction
By transforming the matching problem into a combined optimization and matching problem, we have initially optimized the positioning results for the WiFi portion. 2. We employed an AO algorithm to enhance the performance and effectiveness of the PF algorithm by addressing its particle impoverishment problem. 3. We designed two indoor positioning experiments, incorporating four distinct user postures including calling, dangling, handheld, and pocketed mode. These experiments aim to evaluate the positioning accuracy and performance of various algorithms under different real-world scenarios. The experiments were conducted in two indoor settings: a laboratory and a corridor within the building, to assess the effectiveness of the proposed localization algorithm. The experimental results show that the WiFi/PDR localization using the AO-PSO-PF algorithm can reduce the average localization error compared to other methods including WiFi, PDR, PF, FPF, and APF. The structure of the remaining work is organized as follows. Section 2 describes the related work. Section 3 presents the system model including a data filter, WiFi fingerprint database, IDW, PSO, PDR, and AO-PF. Section 4 illustrates the experiment and analysis. Section 5 presents the conclusion.
## 2 Background: Existing Space-Based Monitoring Systems
### Commercial Platforms  
In recent years, commercial platforms have emerged, offering free visualization of open-access satellite imagery. Even though these are not specifically dedicated to volcano monitoring applications, they can be useful to volcano observatories which do not have the capability to download and process large volumes of data.  
EO-Browser and Sentinel-hub Playground in particular ([[https://apps.sentinel-hub.com/sentinel-playground/](https://apps.sentinel-hub.com/sentinel-playground/)]([https://apps.sentinel-hub.com/sentinel-playground/](https://apps.sentinel-hub.com/sentinel-playground/)) and [[https://apps.sentinel-hub.com/eo-browser/](https://apps.sentinel-hub.com/eo-browser/)]([https://apps.sentinel-hub.com/eo-browser/](https://apps.sentinel-hub.com/eo-browser/)), respectively), make it possible to browse within a Web-GIS the complete archive of ESA Sentinel missions (Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P), NASA Landsat missions (Landsat-5, 7 and 8), NASA MODIS imagery, and more. It gives the possibility to export image products in various formats, create time-lapse animation clips, and provides simple statistical analysis tools. Additional features are offered in commercial packages.  
Google Earth Engine (GEE) gives access to several catalogs of open-access satellite imagery and geospatial datasets, and provides a platform to develop and run algorithms on Google's cloud infrastructure at no cost. While the GEE Explorer provides a simple web interface to visualize satellite imagery, the GEE Code Editor ([[https://code.earthengine.google.com/](https://code.earthengine.google.com/)]([https://code.earthengine.google.com/](https://code.earthengine.google.com/))) provides a web-based IDE (Integrated Development Environment) for the GEE JavaScript API (Application User Interface). This allows users to develop algorithms and run them on the cloud without having to download the data. Although useful for processing large amounts of data, such service is not at the moment an immediate resource for volcano monitoring.
# Novel ZIF-67@Bentonite (ZIF-67@BNT) Nanocomposite for Aqueous Methyl Orange Removal
## 3 Results and Discussion
### Analysis of Methyl Orange Adsorption Using ZIF-67@BNT
#### 3.3.3 Adsorption Kinetics and Isotherm Analysis  
The pseudo-first order (PFC), pseudo-second order (PSO), and Avrami models were also employed for the kinetic modeling. Out of these three kinetic models, the Avrami model showed the highest R\({}^{2}\) value of 0.9949 (Figure 12 and Table 5). This suggests that MO adsorption by ZIF-67@BNT involves a potential variation in the adsorption mechanism with the Avrami model indicating a non-linear kinetics and a better capturing of the overall MO adsorption trend as a function of time.  
The interactions between the MO adsorbate and ZIF-67@BNT adsorbent were further evaluated using the adsorption isotherm approach. To that end, the respective MO adsorption isotherm results were fitted to the Langmuir isotherm, Freundlich isotherm, and Redlich-Peterson isotherm and the output was analyzed for possible adsorbate-adsorbent interactions as described earlier [67]. The respective experimental and modeling findings as given in Figure 13 and Table 6 indicate that the three-parameter Redlich-Peterson (R-P) isotherm shows a good match to the experimental MO adsorption isotherm results, which is also confirmed by the \"g\" parameter value of more than 1 [68]. This finding indicates that, instead of a plateau, the adsorption capacity would tend to increase with the mass transfer driving force, i.e., the MO aqueous phase concentration, though the change at higher equilibrium MO concentrations is low.  
Figure 12: Adsorption kinetic model for MO adsorption by ZIF-67@BNT nanocomposite.  
Figure 13: Adsorption Isotherm model for MO adsorption by ZIF-67@BNT nanocomposite.  
This also indicates a complex adsorption mechanism with several adsorbent surface parameters (including the type of surface functional group) initiating the MO adsorption. This topic is probed further.
## 3 Results Analysis
### Planning and Design Stage
#### 3.6.2 Summary  
From the research results of many scholars, it is clear that the field has made much progress in the planning and design stages of assembled M&E systems. However, the literature of many research efforts devoted to this assembled MEP system has revealed that several issues and challenges still need to be solved in the planning and design stages of assembled MEP systems. These include inconsistencies in standards and codes, insufficient consideration of environmental sustainability, technology updating and integration issues, cost and budget constraints, and multi-stakeholder synergies. Many scholars have also made many efforts in this direction, but these technologies still need to be mature enough, and further exploration is needed. The key to solving these problems lies in enhancing the collaborative work in the planning and design stages, describing them as a project ecosystem [73], paying attention to the latest technologies, grasping the frontiers, and searching for innovative solutions under budget constraints.  
The planning and design stage of the assembled MEP system ensures its efficiency and feasibility and lays the foundation for the prefabrication and machining stages. With the design drawings and models finalized, we now turn to the prefabrication machining stage of the assembled MEP, where prefabrication machining, carried out in a controlled environment according to stringent quality standards, will ensure that we can efficiently and accurately construct MEP modules that meet the design requirements.
## 4 Discussion
Instead, it can be reasoned by weak partial correlations with VSI height (with simple jump type height as control variable). The authors selected the investigated variables based on their best knowledge and the current state of research. To improve future optimization of models, the authors recommend identifying predictors that correlate stronger with VSI jump height while showing no co-linearity with the height in simple jump types. The presented data suggest that this is more promising than increasing the sample size. The overall best results for each sex were derived by the improved models during SJ in females (\(R^{2}=0.92\)) and CMJA in males (\(R^{2}=0.78\)). Therefore, these are the recommended jump types during performance testing to allow the best predictions of VSI height if applying the improved models. If the application of improved models is not feasible requiring the use of a single-predictor assessment, the current data suggest the use of SJ and CMJ for females (both: \(R^{2}=0.76\)) and CMJ for males (\(R^{2}=0.69\)). VSI did not correlate any better with CMJA than with CMJ, and CMJ was not superior over SJ in both sexes. This may be surprising as it could be expected that jump types that share more similarities would result in greater correlation. SJ; however, achieved better correlation with VSI height than CMJA. An explanation may be that SJ reflected the capability to reduce the degree of muscle slack and enhance the buildup of muscle [23], allowing for faster power generation that is essential in explosive movements like volleyball jumps [8; 24; 25; 26]. CMJ and CMJA can provide information about the qualities of stretch-shortening-cycle during the countermovement [19], which is an important aspect in all volleyball jumps and VSJ performance [27]. Therefore, measuring both jump types (with and without countermovement) during performance testing can provide differential information about two abilities that are both relevant in volleyball jumping.