esa-sceva/satcom-chunk-collection huggingface dataset:
# On Sharing Spatial Data with Uncertainty Integration Amongst Multiple Robots Having Different Maps
## 1 Introduction  
Mobile robots are increasingly being used to automate many tasks; tasks which are mostly dull, dangerous, or demanding are a good fit for autonomous robots. The industrial sector has already benefited a lot from 'factory robots'. Recently, a new class of robots called'service robots' have been increasing. These robots are used to provide several common services like cleaning and delivering, dispatching and moving items. These service robots are also used for specific tasks like patrolling and escorting people. Generally, multiple robots are used for such tasks in large service areas as there are several advantages. One of the major advantages of using multiple robots is wide area coverage.  
Multiple robots can cover a large area and perform several tasks simultaneously. Task parallelism is possible as different robots can perform different tasks at the same time. Some robots may be cleaning, some patrolling, while others may be delivering items to specific locations. Fault tolerance is another advantage of multi-robot systems. Even if one of the robots goes out of service the entire service does not stop as other robots can finish the task. Moreover, with task coordination, multiple robots can perform the task efficiently and quickly.  
However, with the introduction of multiple robots in a system, there are several challenges which need to be addressed. Among these problems, effective communication between the multiple robots is a major challenge. Communication forms the basis of other major modules like task coordination, task distribution and collective execution. Accurate and content rich information is important for the successful execution of many tasks.  
Although there are many benefits of sharing spatial information in a multi-robot system, in this paper, we consider the case of sharing obstacle information in a multi-robot system. The environments at many service places, like hospitals and warehouses, are very dynamic with moving entities and new obstacles. To navigate autonomously in such environments, robots need a map of the environment and need to localize themselves within it. This is generally achieved through a SLAM (Simultaneous Localization and Mapping) [1] module. Generally, if one robot finds a new obstacle in the environment, it only updates its own map. The other robots do not benefit from this knowledge. However, if the robot shares this knowledge with other robots along with updating its map, other robots can update their maps and plan better paths with the real-time information. This is shown in Figure 1, in which, Robot R1 finds a new obstacle and blocked path at the center passage of the service area and shares the spatial coordinates of the obstacle with other robots R2, \(\cdots\), R5 which can use this information in generating optimal trajectories. The extension of use-case scenarios other than obstacle information sharing is straightforward.
###### Abstract  
Wind characteristics (e.g., mean wind speed, gust factor, turbulence intensity and integral scale, etc.) are quite scattered in different measurement conditions, especially during typhoon and/or hurricane processes, which results in the structural engineer ambiguously determining the wind parameters in wind-resistant design of buildings and structures in cyclone-prone regions. In tropical cyclones (including typhoons and hurricanes), the inconsistent wind characteristics may be in part ascribed to the complex flow structure with the coexistence of both mechanical and convective turbulence in the boundary layer of tropical cyclones. Another significant contribution to the scattered wind characteristics is due to various measurement conditions (e.g., terrain exposure and height) and data processing schemes (e.g., averaging time). The removal of the inconsistency in the field-measurement system may offer a more rational comparison of measured wind data from various observation platforms, and hence facilitates a better identification scheme of the wind characteristics to guide the urban planning design and wind-resistant design of buildings and structures. In this study, an analytical framework was firstly proposed to eliminate the potential observation-related effects in wind characteristics and then the wind characteristics of seven field measured tropical cyclones (four typhoons and three hurricanes) were comparatively investigated. Specifically, field measurements of wind characteristics were converted to a standard reference station with a roughness length of 0.03 m, observation duration of 10 min for mean wind and averaging time of 3 s for gusty wind at a 10 m height. The differences of the measured wind characteristics between the typhoons and hurricanes were highlighted. The standardized turbulent wind characteristics under the analytical framework for typhoons and hurricanes were compared with the corresponding recommendations in standard of American Society of Civil Engineers (ASCE 7-10) and Architectural Institute of Japan Recommendations for Loads on Buildings (AIJ-RLB-2004).  
wind characteristics; boundary layer; typhoon; hurricane; field measurement +
Footnote †: journal: Applied Sciences
## 4 Results
### SWAT Model Calibration and Validation  
Table 2 shows that the most sensitive parameter of the SWAT daily streamflow calibration in the KRB was manning's value for the main channel (CH_N2), followed by the baseflow alpha factor (ALPHA_BF), groundwater \"revap\" coefficient (GW_REVAP), channel effective hydraulic conductivity (CH_K2), initial SCS CN II value (CN2), threshold water depth in the shallow aquifer for flow (GWQWN), groundwater delay (GW_DELAY), available water capacity (SOL_AWC), threshold depth of water in the shallow aquifer for \"revap\" to occur (REVAPMN), deep aquifer percolation factor (RCHRG_DP), surface runoff lag time (SURLAG), and soil evaporation compensation factor (ESCO). The result is in agreement with a SWAT monthly streamflow sensitivity analysis of the KRB conducted by [PERSON] et al. (2018), where the GW_REVAP, ALPHA_BF, CN2, and CH_K2 are among the sensitive parameters in the basin. In KRB, groundwater related parameters are sensitive because groundwater is being used as one of the main freshwater sources for domestic, agricultural, and industrial uses. However, SWAT is unable to simulate groundwater flow accurately due to its simplified groundwater concept ([PERSON] et al., 2016). Hence, continuous improvement of the SWAT groundwater module and amore groundwater parameters for calibration are required to improve SWAT modelling in high groundwater withdrawal regions.  
The original and final iterations of the SWAT calibration parameter ranges are listed in Table 2. All the original parameter ranges follow the recommended ranges available in the SWAT-CUP tool,  
Figure 3: Box plots of (**a**) Correlation Coefficient (CC); (**b**) Root Mean Square Error (RMSE) (**c**) Relative Bias (RB) and (**d**) Mean Absolute Difference (MAD) of the GPM IMERG products against gauge for daily precipitation assessment from 12 March 2014 to 31 December 2016.  
except the GW_REVAP parameter. We slightly increased the GW_REVAP original range from 0.02-0.2 to 0.02-0.4. This is because the SWAT model tends to underestimate the evapotranspiration processes in tropical regions [36], so the range of the GW_REVAP parameter was modified to allow more evapotranspiration process in the KRB. The calibration and validation of the SWAT daily streamflow simulation are illustrated in Figure 4. Based on the NSE and R\({}^{2}\) performance rating recommended by [PERSON] et al. [34], the SWAT model showed very good and good performances for the calibration (NSE = 0.84 and R\({}^{2}\) = 0.85) and validation (NSE = 0.74 and R\({}^{2}\) = 0.79) periods, respectively. On top of that, the SWAT model underestimated the daily streamflow of calibration and validation periods by 12.8% and 16.4%, respectively. Figure 4 presents an underestimation of the peak flow during the big yellow flood event in the end of December 2014. These findings further support the results reported in the Johor River Basin [23] and KRB [28] where the SWAT model was unable to match the peak flows.
# ZnO/Ag Nanocomposites with Enhanced Antimicrobial Activity
## 2 Materials and Methods
### Characterization  
The crystal structures of the different ZnO/Ag nanocomposite samples were studied using X-ray diffraction (XRD) measurements. To do so, an X-ray diffractometer (D4 Endeavor, Burker-ASX) equipped with a Cu K\(\alpha\) radiation source was used. Data was collected by step-scanning from 10\({}^{\circ}\) to 80\({}^{\circ}\) with a step size of 0.05\({}^{\circ}\)/26 and 5 s counting time per step. The morphology and the size of the ZnO/Ag nanocomposites were observed using a JEOL JEM-1010 200 kV Field Emission Transmission Electron Microscope (TEM 200) equipped with a JEOL EM-24830 FLASH digital camera with a CMOS sensor, offering a resolution of 2 k \(\times\) 2 k. The maximum resolution achieved was 0.23 nm. The microscope was also equipped with a STEM DF/BF image acquisition system with a resolution of 1 nm and an Aztec TEM Ultim Max microanalysis system from Oxford with a drift silicon sensor without a window of 80 mm\({}^{2}\) and a resolution of 127 eV for the Mn K\(\alpha\) line.  
Raman scattering measurements were performed using a Hobiba Jobin-Yvon FHR-640 monochromator coupled with a CCD detector in backscattering configuration through a specific probe designed at the Catalonia Institute for Energy Research (IREC). The spectra were excited by gas (325 nm) with a density power of \(\sim\)10 W/cm\({}^{2}\). Calibration of the spectral position was performed by imposing the main peak of single-crystalline Si to 520 cm\({}^{-1}\). For each sample, the measurements were carried out at 9 points with a laser spot diameter of \(\sim\)70 \(\upmu\)m, which made it possible to exclude the local variations due to different crystalline orientations of the grains and possible inhomogeneities between specific grains. UV-visible (UV-Vis) diffuse reflectance spectroscopy of the samples was performed to study the optical properties of the ZnO/Ag nanocomposites using a CARY 500 SCAN VARIAN spectrophotometer in the 220-600 nm range. BaSO4 was used as a reference.
# Application of Surge Arrester in Limiting Voltage Stress at Direct Current Breaker
## 3 Calculation of the Current Zero-Crossing Time
### Stage 2 (\(t_{1}\) through \(t_{2}\))  
An RLC circuit is formed, comprising line inductance and resistance, C2, and the voltage source, as  
\[\frac{d^{2}I_{d2}}{dt^{2}}+\frac{R}{L}\frac{dI_{d2}}{dt}+\frac{I_{d2}}{LC_{2} }=0;\;I_{d2}=I_{u2} \tag{1}\]  
where \(I_{u2}\) and \(I_{d2}\) are the upstream and downstream currents at this stage, respectively, and \(L\) and \(R\) are the total line inductance and resistance. In case of an underdamped response, \(\frac{R}{2L}\leq\sqrt{\frac{1}{LC_{2}}}\); i.e., \(C_{2}\leq\frac{4L}{R^{2}}\). Equation (1) yields \(I_{2}(t_{21})=k_{1}e^{-\alpha I_{21}}\text{cos}(\omega_{d}t_{21}+\varphi)\), where \(\alpha=\frac{R}{2L}\), \(\omega_{d}=\sqrt{\omega_{0}^{2}-\alpha^{2}}\), and \(\omega_{0}=\sqrt{\frac{1}{LC_{2}}}\). By using the initial conditions \(I_{2}(0)=I_{fault}\) and \(V_{C2}(0)=0\), it is possible to calculate \(\varphi\) and \(k_{1}\) as \(\varphi=-tan^{-1}\left(\frac{V_{s}-RI_{fault}}{\Gamma\omega_{d}I_{fault}}+ \frac{\alpha}{\omega_{d}}\right)\) and \(k_{1}=\frac{I_{fault}}{\text{cos}(\varphi)}\). \(t_{21}\) is the time when \(V_{C2}=V_{SAB}\), where \(V_{SAB}\) is the surge-arrester blocking voltage.
# Noise Reduction Mechanisms of an Airfoil with Trailing Edge Serrations at Low Mach Number
## 2 Computational Methodology
### LES with One-Equation Subgrid Scale Model  
The spatial filter operation and Favre-averaged filter are represented as \(\bar{(}\bar{)}\) and \(\bar{(}\bar{)}\) and applied to the governing equations. All variables are non-dimensioqnalized by chord length \(C\) and the mainstream velocity \(U_{0}\). A general curvilinear coordinates (\(\xi\), \(\eta\), \(\varsigma\)) are used for all computations due to applying the boundary-fitted-grid. Thus, the governing equations can be written as [39]:  
\[\frac{\partial\bar{\rho}}{\partial t}+\frac{1}{\bar{J}}\frac{\partial}{ \partial\xi^{k}}\left(J\bar{\rho}\bar{U}^{k}\right)=0, \tag{1}\]  
\[\frac{\partial\left(\bar{\rho}\tilde{u}_{i}\right)}{\partial t}+\frac{1}{ \bar{J}}\frac{\partial}{\partial\xi^{k}}\left(J\bar{\rho}\tilde{u}_{i}\bar{U} ^{k}\right)=-\frac{1}{\bar{J}}\frac{\partial}{\partial\xi^{k}}\left[J\frac{ \partial\xi^{k}}{\partial x_{i}}\left(\bar{p}+\frac{2}{3}\bar{\rho}k_{ggs} \right)\right]+\frac{1}{\bar{J}}\frac{\partial}{\partial\xi^{k}}\left(J\frac {\partial\xi^{k}}{\partial x_{i}}\sigma_{ij}\right)\,, \tag{2}\]  
\[\bar{p}=\bar{\rho}RT, \tag{3}\]  
where  
\[\sigma_{ij}=2\left(\frac{1}{Re}+\bar{\rho}\
u_{ggs}\right)\left(\bar{S}_{ij}- \frac{1}{3}\delta_{ij}\bar{S}_{kk}\right)\,, \tag{4}\]  
\(\rho\) represents the density, \(J\) is the Jacobian of the coordinate transformation, \(\bar{U}^{k}\) is the contravariant velocity, \(k_{ggs}\) is the subgrid scale kinetic energy, \(R\) is the ideal gas constant, \(T\) is the absolute temperature, \(\delta_{ij}\) is the Kronecker symbol, \(Re\) is the Reynolds number (as in \(\rho U_{0}C/\
u\), where \(\
u\) is molecular viscosity), \(\bar{S}_{ij}\) is the rate of strain tensor,  
\[\bar{S}_{ij}=\frac{1}{2}\left(\frac{\partial\xi^{k}}{\partial x_{i}}\frac{ \partial\bar{u}_{j}}{\partial\xi^{k}}+\frac{\partial\xi^{k}}{\partial x_{j}} \frac{\partial\bar{u}_{i}}{\partial\xi^{k}}\right)\,, \tag{5}\]and \(v_{ggs}\) denotes the kinetic viscosity due to SGS turbulence that remains to be evaluated by a SGS model. Here, the SGS model is given in the following form:  
\[v_{ggs}=C_{v}\Delta_{v}\sqrt{k_{ggs}}\,, \tag{6}\]  
where \(C_{ggs}\) is the model constant and \(\Delta_{ggs}\) is the characteristic length given by [PERSON] and [PERSON] [PERSON] and [PERSON] (1980) in the following form  
\[\Delta_{v}=\frac{\bar{\Delta}}{1+C_{k}\bar{\Delta}^{2}|\bar{S}|^{2}/k_{ggs}}. \tag{7}\]  
Here, \(|\bar{S}|^{2}=\sqrt{2\bar{S}_{ij}\bar{S}_{ij}}\) denotes the norm of the rate-of-strain tensor, \(C_{k}\) is the model constant and \(\bar{\Delta}\) is the filter [PERSON].
## 3 Results and Discussion
### Rate Constant for Reactions between the DAP and Hydroxyl Radicals  
Benzoic Acid (BA) was used as a model compound to determine the absolute rate constant of the reaction between DAP and HO\({}^{\bullet}\) radicals. An absolute rate constant of BA with HO\({}^{\bullet}\) radicals of 5 10\({}^{9}\) M\({}^{-1}\) s\({}^{-1}\) was reported in literature [32; 33].  
Considering second order reaction kinetics for the reactions between BA and DAP with HO\({}^{\bullet}\) radicals, the rate of the reactions can be given by the equations (Equations (6)-(7)):  
\[r_{BA}=-\frac{d[BA]}{dt}=k_{BA}\times[HO^{\bullet}]\times[BA]=k^{\prime}_{BA} \times[BA] \tag{6}\]  
\[r_{DAP}=-\frac{d[DAP]}{dt}=k_{DAP}\times[HO^{\bullet}]\times[DAP]=k^{\prime}_{ DAP}\times[DAP] \tag{7}\]  
Assuming pseudo-first order for BA and DAP (Equations (8) and (9)):  
\[Ln\left\{\frac{[BA]_{0}}{[BA]_{t}}\right\}=k^{\prime}_{BA}\times t \tag{8}\]  
\[Ln\left\{\frac{[DAP]_{0}}{[DAP]_{t}}\right\}=k^{\prime}_{DAP}\times t \tag{9}\]  
\(L\)Solving the equations for \(k_{DAP}\) gives (Equations (10) and (11)):  
\[\frac{Ln\left\{\frac{[DAP]_{0}}{[DAP]_{t}}\right\}}{Ln\left\{\frac{[BA]_{0}} {[BA]_{t}}\right\}}=\frac{k^{\prime}_{DAP}}{k^{\prime}_{BA}}=\frac{k_{DAP}}{k _{BA}}=1.2308 \tag{10}\]  
\[k_{DAP}=1.2308\times k_{BA}=7.26\times 10^{9}M^{-1}s^{-1} \tag{11}\]  
The plot of Ln([DAP]\({}_{0}\)/[DAP]\({}_{t}\)) vs Ln([BA]\({}_{0}\)/[BA]\({}_{t}\)) during Fenton oxidation of 100 mg/L DAP and 100 mg/L BA (using 1000 mg/L H\({}_{2}\)O\({}_{2}\) and 50 mg Fe\({}^{2+}\)/L at pH 3.2) is given in Figure 7. This  
Figure 6: Effect of initial DAP concentration on the DAP degradation kinetics during the Fenton process (H\({}_{2}\)O\({}_{2}\)/Fe\({}^{2+}\)). Experimental conditions: [DAP]\({}_{0}\) = 40–140 mg/L; [H\({}_{2}\)O\({}_{2}\)]/[DAP] = 10; [H\({}_{2}\)O\({}_{2}\)]/[Fe\({}^{2+}\)] = 20 pH = 3.2.  
plot is linear with a correlation coefficient (R2) higher than 0.99. After least-square regression analysis, a slope of 1.2308 was measured. Based on the competitive kinetic model reported in literature by several studies [32; 33], the absolute rate constant of the reaction between DAP and HO\({}^{\bullet}\) hydroxyl radicals was calculated to be 7.26 109 M\({}^{-1}\) s\({}^{-1}\). This value DAP was in the same range as other aromatic compounds and phenols mentioned by [PERSON] et al. [33].
# Artificial Intelligence vs. Human: Decoding Text Authenticity with Transformers
## 1 Introduction  
The proliferation of large language models (LLMs), particularly those developed by OpenAI, has significantly blurred the lines between human and machine-generated content, amplifying concerns about text authenticity [1, 2, 3]. In today's landscape, where misinformation spreads across various domains [4, 5, 6], distinguishing between human-written and machine-generated text is critical to combating the risks associated with deceptive content [7, 8]. While prior research has often focused on detecting text generated by specific LLMs or domain-specific models (e.g., ChatGPT), our study addresses the broader challenge of distinguishing between human and machine-generated text [9].  
Transformer models, such as Bidirectional Encoder Representations from Transformers (BERT) [10], have emerged as powerful tools in Natural Language Processing (NLP) [11, 12], demonstrating impressive capabilities across a variety of tasks, including text generation (TG) [12]. The Generative Pretrained Transformer 3 (GPT-3), in particular, has excelled across numerous NLP tasks due to its self-attention mechanism, which allows it to weigh each word in the context of a sentence differently, capturing intricate relationships between words and their meanings. As transformer models continue to advance, they have becomefoundational for large-scale self-supervised learning systems [13, 14], revolutionizing other AI and machine learning fields, including time series analysis [15].  
However, the increasing fluency of these models raises challenges in effectively discerning between human and machine-generated text [16]. Our research centers on using transformer models combined with Bidirectional Long Short-Term Memory (BiLSTM) to predict the source of text and address the challenge of text authenticity detection. This work not only advances text authenticity detection systems but also highlights the versatility and effectiveness of transformer-based methodologies in NLP applications.  
The main research question of this paper is as follows: How efficient are transformers in building a classifier that can accurately detect human-written text from machine-generated text? Through experimental analysis and methodology evaluation, we aim to provide valuable insights into this question.  
The structure of this paper is as follows: Section 2 positions this study in the context of existing literature. Section 3 outlines the dataset and methodology based on transformers. Section 4 presents the usability and efficiency of the proposed method through a series of tests, followed by concluding remarks in the final section.
# Dual-Polarization Observations of Slowly Varying Solar Emissions from a Mobile X-Band Radar
## 3 Deriving Solar Flux Values Using Observations of a Mobile Radar Working at the X-Band
### The Swiss Confederation Dual-Polarization X-Band Weather Radar
#### 3.2.2 Assessing the Unpolarized Solar Power in dBm at the Entrance of the Antenna Feed
The equivalent area of the antenna is estimated from the (horizontal and vertical polarization) gain, \(\rm{G}\), measured at the test range, using the following equation:  
\[A_{eq}=\;G\frac{\lambda^{2}}{4\pi} \tag{5}\]  
The uncertainties related to the assessment of the equivalent Rx bandwidth are (certainly) smaller than those associated with the estimation of the equivalent area: the bandwidth of the matched filter is 3.6 MHz for the typical transmitted pulse of the X-band radar (pulse width \(\rm{\tau}\) = 333 ns). However, recent investigations [24] have shown that the bandwidth should be extended to a value of 3.78 MHz in order to calculate the power of noise signals. This means a value of 65.77 dBHz on a log-transformed scale. In short, the third step necessary to transform the Rx dBadu into dBsfu is:  
\[\rm{I_{3,2}}=P_{ant}+190\;dB-B_{dBHz}+10\;Log\left(\frac{4\pi}{\lambda^{2}} \right)-G_{dB}. \tag{6}\]  
As a practical example, it can be seen, in Figure 1, that \(\rm{S_{rp}}\) (2 nd MAX and 0.05 dB rounding) was \(-101.90\) dBm on both 21 and 22 February 2016 for H polarization. By applying Equations (4) and (6), it is possible to obtain an estimated solar flux (Sun + Noise) at the X-band equal to 26.06 dBsfu, which can be compared with the DRAO reference value obtained for those two days. The next section deals with how a reasonable comparison can be performed. It is worth noting that although the DRAO references are corrected for clear sky tropospheric attenuation, our radar derived estimates are not compensated. The total atmospheric attenuation at the zenith for the X-band and for a radar site altitude of 500 m above sea level (such as in Payerne, Switzerland) is of the order of 0.1 dB (if a one-way attenuation of 0.013 dB/km at the sea level and an equivalent atmospheric height of 8.5 km are assumed). For the practical example shown in Figure 1, this would correspond to approximately 0.554 dB in the morning and afternoon (angle of Elevation of \(\sim\)10\({}^{\circ}\)) and to 0.1992 dB at an angle of elevation of 30\({}^{\circ}\): hence, a decrease in attenuation of \(\sim\)0.35 dB could be expected, while an approximate difference of 0.2 dB can be observed in Figure 1. However, the clear-sky atmospheric attenuation at mid-day, which is smaller than 0.199 dB (angle of Elevation of the 2 nd MAX is always larger/greater than 30\({}^{\circ}\)) is neglected in Sections 4 and 5.
## 2 Construction of GM-BP Combined Prediction Model  
### Fundamentals of GM-BP Combined Prediction Models  
Both the GM(1,1) grey prediction model and the BP neural network prediction model have their own pros and cons. The advantages of the GM(1,1) model are that it requires small samples and less computation, and it has strong applicability; however, it lacks self-organisation and the ability to learn autonomously, and it can only carry out single-parameter predictions, so when there is a large degree of data discretisation or the phenomenon of multi-parameter mutual coupling, its prediction accuracy will be significantly affected. BP neural networks have a strong nonlinear fitting ability and can handle multi-parameter coupling problems, but their modelling requires massive sample data for training to ensure their prediction accuracy. Therefore, it is often difficult to achieve the desired results if only one forecasting method is used. Considering the characteristics of few samples and multi-parameter coupling in the process of controlling the construction line of large continuous girder bridges, the prediction data of the GM(1,1) model are taken as the input of the BP neural network, and the actual pre-camber degree is taken as the output to form a GM-BP combined prediction model, so that the prediction accuracy of the bridge's pre-camber degree can be improved.