esa-sceva/satcom-chunk-collection huggingface dataset:
# Pointing Accuracy of an Operational Polarimetric Weather Radar
###### Abstract
We then show and discuss the variation of pointing accuracy as a function of solar azimuth and elevation for two specific days based on dedicated measurements from the Hohenpeissenberg research radar and contrast those results to the bias estimates of the whole data set of more than 1000 solar boxscans. The beamsquint based on those data are computed to assess the characteristics of the dualpol antenna assembly. Bias estimates based on solar boxscans, which are considered as the reference, are then compared to the bias estimates from operational monitoring. A section is then dedicated to the characteristics of the two built-in angle encoders which can be separately used to tag the pulses with angle data. The relative difference of the computed pointing bias based on data that are tagged by the respective encoder provide insight on the to be expected pointing accuracy depending on the source of the angle data. Furthermore, a boxscan continuously consists of antenna movement in clockwise (cw) and counter-clockwise (ccw) direction. By analyzing only data with cw or ccw movement, we quantify the backlash due to the encoder and the gear-drive assembly. The methodology of using dedicated solar boxscans is then applied to four operational radar sites of the DWD weather radar network, and the resulting pointing bias estimates are discussed. We then summarize the main findings of this work and provide recommendations in the conclusions.
###### Abstract  
The commercially available nanoscale zerovalent zinc (nZVZ) was used as an adsorbent for the removal of malachite green (MG) from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration) and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO) and artificial neural network coupled with genetic algorithm (ANN-GA) were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12%) was compatible with the experimental value (90.72%). Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (\(\Delta G^{0}\), \(\Delta H^{0}\) and \(\Delta S^{0}\)) were calculated from the Van't Hoff plot of InKc vs. 1/T in order to discuss the removal mechanism of MG.  
malachite green; nanoscale zerovalent zinc; artificial intelligence; artificial neural network-genetic algorithm; artificial neural network-particle swarm optimization 2018 7 3 309/app8010003
# Improving End-To-End Latency Fairness Using a Reinforcement-Learning-Based Network Scheduler
## 4 Conclusions
NAT-HA showed an average latency fairness index that was 4% lower than the RL-based scheduler in one topology and similar in the other topologies. MED-HA had the same average latency fairness index as the RL-based scheduler in all topologies. In addition, the RL-based scheduler and MED-HA showed a higher average maximum latency fairness index than other schedulers in all topologies. The proposed RL-based scheduler is implemented using a value-based RL algorithm. However, since the value-based RL algorithm cannot be applied in an environment where the action space size is infinite, it is difficult to apply the proposed method as the topology grows. Therefore, we will propose an RL-based scheduler using policy-based RL, such as the proximal policy optimization algorithm, and investigate the performance of the RL scheduler in a large-scale network. Moreover, to account for a more realistic environment, we propose a RL model that enhances QoS fairness among high-priority traffic by either integrating it with an existing priority scheduling algorithm or extending the state to include more network parameters. In addition, we will propose an RL-based scheduler that can learn and operate in real time without operating in unit time by making various traffic characteristics such as packet length, burst size, and input rate, which can perform asynchronous learning. To this end, we propose an RL model that optimizes processing delay using computational complexity as an evaluation index, as well as a method for calculating estimated delay time considering processing delay. Conceptualization, J.K. and J.J.; methodology, J.K. and J.R.; software, J.K. and J.R.; validation, J.K., J.J. and J.H.L. ; formal analysis, J.K.; investigation, J.K.; resources, J.K.; data curation, J.K.; writing--original draft preparation, J.K.; writing--review and editing, J.J. and J.H.L. ; visualization, J.K.; supervision, J.J.; project administration, J.J. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT, Ministry of Science and Information and Communication Technology) (No. 2020R1F1A1058591). Not applicable.
## 4 Problem Formulation and Methodology
### Proposed Methodology
#### 4.2.1 Data Cleansing and Linguistic Marker Identification  
Next, the data cleaning process was performed on _myPersonality_ and _Sentiment140_ datasets to remove all the irrelevant textual attributes such as Uniform Resource Locators (URLs), punctuations, and symbols as well as non-English strings. Then, spelling correction and lowercase transformation were conducted to reduce the size of the training attributes and enhance the prediction tasks. After pre-processing, lemmatization using the Natural Language Toolkit (NLTK) and the stemming process were performed for each word in _myPersonality_ and _Sentiment140_ instances so that each morphological form was mapped into its root term. In this study, both datasets were stemmed using the Porter Stemmer Algorithm [49] to increase the density of the training data. This stemming process was different from the stemming process performed in the preliminary study, which was carried out to identify the root terms for valence identification. Subsequently, the Part-of-Speech (POS) tagging was conducted to extract the adjectives, nouns, verbs, and adverbs as these grammatical elements often served as hints to indicate sentiments [12]. For instance, the status message \"motherfuck..(ing) norwegian bureaucratic bullshit. yet again\" was tagged as \"motherfuck/VB norwegian/JJ bureaucratic/JJ bullshit/NN./yet/RB again/RB\". The abbreviations VB, JJ, NN, and RB refer to the grammatical attributes of verb, adjective, noun, and adverb, respectively. Afterwards, the relevant sentiment words labeled as JJ, NN, and VB (e.g., motherfuck) were extracted, and training documents were tokenized using regular expression where all the text attributes except word and white space were removed. Then, the word attributes were transformed using the Bag-of-Words (BoW) mechanism, where the probabilities of each distinct word attribute \(a\) represented as \(p(a_{1}a_{2},\ldots,a_{n})\ =\ p(a_{1}),p(a_{2}),\ldots,p(a_{n})\).
## 3 Simulation Results
### Statistical Evalution  
Extensive case studies with respect to various test systems, various load patterns, various load levels, and various \(Q\)-limit violations have been conducted. The proposed approach can accurately and rapidly determine the point of voltage collapse for all tested cases. The statistical results are summarized in the following.  
The average timing results under the selected 500 simulation runs for each test system are listed in Table 5, showing that the average CPU time of the proposed approach is about three to four times faster than that of the the conventional CPFLOW approach. This means that a fast computation of voltage collapse point can be achieved by using the proposed method.
## 6 Conclusions  
In this paper, we study the transmission rate optimization problem for a dual-hop multi-relay OFDM based IoT network, where DF relay supports SWIPT technique. The joint optimization of power and subcarrier allocation aims to maximize the transmission rate. By adopting the time-sharing strategy, we convert the mixed integral program problem with multi-constraint to the convex optimization problem which can be solved by Lagrange dual method. The effects of the source transmit power, the number of relays and the energy conversion efficiency on the performance of the proposed algorithm have been investigated. Simulation results demonstrate that the proposed algorithm has higher transmission rate compared with other benchmark algorithms when ensuring the relay has no additional energy supply. In particular, when \(P_{\mathrm{s}}=0.5\) w, our proposed algorithm achieves the improvement in the transmission rate about 2.8%, 3.4% and 43% over the Algorithms 2-4.  
In the future work, we will consider the relay selection optimization, in which not all of the relays will be allocated subcarriers to forward information. Moreover, We will study the amplify-and-forward relaying protocol in SWIPT enabled multi-relay IoT system by considering the source and destination direct link. Besides, it is also an interesting direction to study a two-way multi-relay SWIPT enabled IoT system which is able to achieve higher spectrum utilization.  
Conceptualization, G.L.; Data curation, B.L. and H.P.; Formal analysis, G.L.; Investigation, P.S. and G.Z.; Writing--original draft, W.L.; Writing--review & editing, W.L.  
This work was supported by the National Natural Science Foundation of China under Grant No. 61871348.  
The authors declare no conflict of interest.
## 4 Results
### Preliminary Tests with the End-Users  
The prototype was tested by 20 students of the University of Pisa with motor skills disabilities, who might be the target end-users of this system. The aim of the investigation was to determine the effectiveness of the HMIS explained in Section 3.5. The tests were done by dividing the students into three main groups. Each group was characterized by different motors skills and, consequently, by a different HMI. The groups were:  
* Touch (High motor-skills) (12 people)
* Double sensors (Medium motor-skills) (6 people)
* Single sensor (Low motor-skills) (2 people)  
Each group had the same task to complete (pressing a predefined elevator button) in the same amount of time. In this way, it was possible to estimate the performance of each HMI with respect to the level of disability. The test was divided into training and validation phases. The first phase was to explain how to control the robotic arm, showing them how the interface works and executing some tests with the users. The second phase was the pressure of the elevator button from a lift panel of nine buttons. This test showed how the users were able to control the entire system in only few minutes, reducing the training phase and their frustration. Furthermore, the time for all the categories was respected even if the disability level and manual skills were different. At the end of the tests, each student completed a survey relative to the interface used. The survey was made by eight questions. For each question, a value from 0 to 10 was given. The questions were on the usability (two questions), prototype reaction/performance (two questions), propensity to use of the system (one question), HMI usability (two questions), and satisfaction with the functions offered (one question).  
The results were optimistic obtaining a mean of 8.13/10. Because of these good performances, there will be the real possibility of embedding the prototype on a power-wheelchair. Furthermore, the users suggested some nice to have functionalities, such as:  
* Grabbing objects, such as bottles or glasses
* Picking up objects fallen on floor
## 2 Materials and Methods
### PGP Features
#### 2.9.3 Ammonia Production  
[PERSON]'s method was used to estimate the ammonia production of bacterial strains. Aliquots (1.0 mL at 0.1 OD\({}_{600}\)) of the suspension of NaCl resistant strains were inoculated in 9.0 mL of peptone water (1.0 g\(\cdot\)L\({}^{-1}\)peptone, 5.0 g\(\cdot\)L\({}^{-1}\) NaCl). The inoculated peptone water solutions were incubated at 27 \({}^{\circ}\)C for 24 h under stirring at 135 RPM. At the end of the incubation time, the inoculated solutions were centrifuged at 5470 RCF for 20 min at room temperature, and 1.0 mL of the supernatant solution was mixed with 1.0 mL of [PERSON]'s reagent (VWR-- Leuven, Belgium) and diluted up to 10.0 mL of ultra-pure distilled water. Finally, the amount of ammonia in the medium was quantified spectrophotometrically at 450 nm (Shimadzu, UV-1800) and comparing the absorbance values of each bacterial strain with an ammonium-sulfate ((NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\)) standard curve. The standard curve was prepared mixing eight different concentrations of ammonium-sulfate in peptone water, ranging between 1 and 200 \(\mu\)g\(\cdot\)mL\({}^{-1}\), following the procedure above (correlation factor R\({}^{2}\) = 0.98). [27].
# Control of DC Motors to Guide Unmanned Underwater Vehicles
## 3 Results
### Trajectory Tracking Performance Comparison  
This section assembles the target tracking performances on a single multi-plot to bestow qualitative indications of relative superiority of approaches in direct comparison. Figure 9--with corresponding figures of merit in Table 2--illustrates that all methods have an initial transient associated with both the initial conditions and the initial discontinuous step to initiate the square wave. Indirect self-tuner without process zero cancellation (minimum phase plant) peaks around 1.5 while deterministic artificial intelligence peaks around 3.0. Meanwhile, both remaining methods begin with much larger transients. At each switch of the square wave, these two same methods have nearly indistinguishable performances, while slightly more overshoot is qualitatively visible for the indirect self-tuner without process zero cancellation (minimum phase plant). It is noteworthy that after the initial startup transient, deterministic artificial intelligence appears to follow the square wave without noticeable overshoot or settling.  
Figure 9: Response comparison with corresponding results in Table 2. The solid (thin) black line is the commanded square wave trajectory. Roughly coincident with this commanded trajectory (after the initial transient) appears the blue-dashed line, representing the response of deterministic artificial intelligence. The red-dotted line represents the indirect self-tuner without process zero cancellation (minimum phase plant). Indirect self-tuner without process zero cancellation (non-minimum phase plant) is represented by the dashed-green line, while the solid (thick)-black line representing direct self-tuner with filtering (all process zeros cancelled).  
Table 2 lists figures of merit for trajectory tracking, corresponding to the results in Figure 9. The minimum mean and standard deviation of tracking errors are highlighted with bold font indicating that deterministic artificial intelligence was superior in both categories, while very similar mean error (alone) was achieved by the indirect self-tuner without process zero cancellation (minimum phase plant).
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Non Orthogonal Multiple Access
#### 3.4.2 Power Allocation Issues
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Author \& Ref. No.** & **Approach** & **Advantages** & **Limitations** \\ \hline [PERSON], [PERSON] et al. [120] & Two user pairing and access algorithms are proposed to enhance the system capacity of a SIC based NOMA system. & The proposed methods enhanced the system throughput, capacity, and spectral efficiency. & At minimum SNR, the average channel capacity is very small for fixed PA algorithms compared to all the other algorithms. \\ \hline [PERSON], [PERSON] et al. [121] & Fixed PA and CR inspired NOMA schemes are proposed to improve the performance of NOMA systems. & Enhanced the outage probability and data rates of the NOMA system with the increased SNR. & If two neighbouring users are paired then it will provide a better data rate compared to the non-neighbouring users. \\ \hline [PERSON], [PERSON] et al. [122] & Multi carrier NOMA with cooperative game and conventional neural network schemes are used to design a user pairing network. & Enhanced the average channel throughput based on the soft and hard channel capacities with SNR and with the number of users. & As the number users increases the performance loss also increases, which degrades the average spectral efficiency. \\ \hline [PERSON] et al. [123] & Interference-aware user grouping scheme is considered in the NOMA system to minimise the energy consumption. Exchange league concept is used to identify the energy consumption in the case of multi user scenario. & Computational complexity reduced with the proposed algorithm. Total transmit power varies with the number of user groups and the number of individual users. Transmit power increases with the user groups. \\ \hline \end{tabular}
\end{table} 
Table 11: Summary of user pairing and resource allocation algorithms in NOMA systems.
# Hazard Assessment for a Glacier Lake Outburst Flood in the Mo Chu River Basin, Bhutan
## 3 Materials and Methods
### Hydrodynamic Modelling Using HEC-RAS 5.0.7
Mean breach width (\(\overline{\rm B}\)) and formation time (tf) were derived by [PERSON]'s empiric equations [29], which are based on data from 74 embankment dam failures:  
\[\overline{\rm B}=0.27\rm k_{0}V_{w}^{0.32}H_{b}^{0.04}, \tag{1}\]  
\[\rm t_{f}=63.2\sqrt{\frac{V_{w}}{gH_{b}^{2}}}, \tag{2}\]  
where \(\rm V_{w}\) is water volume (m\({}^{3}\)), and k_0 is a factor that accounts for the effect of failure mode and is set to \"1\" for modes other than overtopping. The resulting values for the dam breach are shown in Table 1.  
With this information, HEC-RAS generates breach hydrographs. A 90% outburst causes a flood peak of 5700 m\({}^{3}\)/s already 8 min after dam failure begins. For the 70% and 40% outbursts, the corresponding values are 3250 m\({}^{3}\)/s after 12 min and 816 m\({}^{3}\)/s after 26 min. These breach hydrographs serve as input for the two-dimensional hydrodynamic routing. In this step, the channel roughness is the most important parameter, because it controls flow velocity [23]. According to the HEC-RAS manual [27], \(n\)-values between 0.025 and 0.075 are typical. In high mountain environments, channel roughness is high and the values for GLOF modelling found in the literature typically range from 0.03 to 0.15. Supported by empirical data [30] and by field observations on a rafting tour in 2018, we decided to test the sensitivity of this parameter by using three different values: 0.04 (smooth), 0.06 (medium) and 0.08 (rough). When no past GLOF events can be used for calibration, the selection of different n-values within a reasonable range is a practical solution, which was also chosen by most other authors [22; 23; 25; 26]. Channel roughness might change along the course of the river and flood plains or forested areas might have n-values different from those of the river bed. However, we decided not to make a differentiation here, but to use mean \(n\)-values. In our opinion, defining different areas would pretend a level of detail which is not justified by any data or observation. In combination with the three possible outburst volumes, we create an ensemble of 9 model runs or scenarios. From here on, they will be denominated in the following style: \"outburst volume (%)_Manning value\". As an example, 70_0.06 represents an outburst volume of 70% and a Manning value of 0.06. Within this matrix, an average and two extreme scenarios can be defined: a worst case (90_0.04), a best case (40_0.08), and an average GLOF (70_0.06). Figure 3: Trapezoidal dam breach model including height \(\rm H_{b}\), average width \(\overline{\rm B}\), and side-slope ratio z (horizontal to vertical) after Frohlich [29].
## 1 Introduction
The international altimetry community expects continuity and upscaling of Cal/Val services to maintain measurement conformity and error reporting, and also to support the right decisions concerning Earth observation. Such an FRM Cal/Val site will constitute the fundamental mainstay for building up capacity for monitoring climate change in an objective and unequivocal manner with altimetry. It will be capable of assessing any altimeter measurements to absolute reference signals traceable to SI-standards (Systeme International, such as speed of light, absolute time reference, etc.) with different techniques, various processes, and diverse instrumentation and settings. The Permanent Facility for Altimeter Calibration (PFAC) in western Crete, Greece, is such a Cal/Val infrastructure where calibration of satellite altimeters takes place making use of several of the calibration techniques (i.e., absolute indirect, relative direct Cal/Val), both at sea as well as on land using a prototype microwave transponder (Figure 1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Altimetric** & **Specifications for** & & **Specifications for** & **Actual Altimeter** \\
**Mission** & **Sea-Surface Height** & & **Altimeter** & **Noise** \\ \hline Jason-2 and Jason-3 & \(\pm\)3.4 cm & \(\pm\)2.5 cm & \(\pm\)1.7 cm & \(\pm\)1.5 cm \\ SARAL/AltiKa & \(\pm\)3.2 cm & \(\pm\)3.2 cm & \(\pm\)1.5 cm & \(\pm\)1.0 cm \\ HY-2A & \(\pm\)10 cm & \(\pm\)5.0 cm & \(\pm\)4 cm & \(\pm\)2 cm \\ Sentinel3A & \(\pm\)294 cm & \(\pm\)3.0–5.0 cm & \(\pm\)1.3 cm & \(\pm\)3.0 cm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The expected root mean square (RMS) of sea surface height (SSH) and altimeter noise measurements obtained by satellite altimetry. The present paper presents the evolution of the Permanent Facility for Altimetry Calibration in Gavdos/Crete, Greece, along with its infrastructure and instrumentation as of 2001. The latest results are presented for the calibration of past (Jason-1, Jason-2, and SARAL/AltiKa) and current (Jason-3 and Sentinel-3A) satellite altimetry missions using the latest GDR products and employing sea-surface calibration around Gavdos and western Crete. First calibration results of Jason-2 and Jason-3 altimeters are given using the microwave transponder at the CDN1 Cal/Val site on the mountains of Crete and compared against sea-surface calibration. Finally, concise procedures are given for upgrading an existing permanent altimeter calibration facility to meet the fiducial reference measurement status and the uncertainty budget estimation for final results.