esa-sceva/satcom-chunk-collection huggingface dataset:
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 6 Results
### Delivery Probability  
In this subsection, two different metrics are presented. On the one hand, QPDP takes into account only those messages that were accepted in each node's buffer. On the other hand, EPDP also considers the discarded messages due to traffic overflow. As commented, EPDP must be strictly lower than QPDP. The relation between both metrics provides an estimation of the amount of lost traffic because of excessive buffering time. Figure 12 depicts QPDP, whilst Figure 13 illustrates EPDP results.  
It can be observed that once a message is queued, the probability of correctly delivering it is above 82% in all the scenarios. In the case of unsaturated traffic, the impact of distance on this metric is quite reduced, due to the number retransmissions. However, as commented below, both delivery time and EPDP increased dramatically with distance. An increment on the delivery time would imply more channel resources per message, which may tend to a buffer overflow. It is also observed in Figure 12 that under saturated traffic conditions the QPDP is significantly higher for Scenario 1 than for the MAC-only situation. This exotic situation is derived from the increased disconnection probability at those distances. During some periods, due to the disconnection, the remaining nodes have more relaxed CAP restrictions.  
Figure 11: Obtained average throughput versus distance. (**a**) Depicts unsaturated traffic whilst (**b**) corresponds to saturated traffic.  
As commented above, EPDP is smaller than QPDP. For unsaturated traffic, the trend is similar for all the three scenarios, presenting an EPDP around 0.9 at 3 m. The number of required retransmissions starts to be significant at this distance and some traffic is lost at each node due to overflow. On the other hand, traffic-saturated scenarios presented EPDP values below the MAC-only reference simulation in all the cases. As commented during the throughput subsection, there are distances at which collisions may occur with a higher probability at each scenario. Moreover, the shape of throughput and EPDP curves are closely related as expected.
# ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles
###### Abstract  
The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific andtechnological approach for prolonged underwater autonomous operations of seabed survey activities, either in the deep ocean or in coastal areas. The proposed approach combines a hybrid Autonomous Underwater Vehicle capable of moving using either thrusters or as a sea glider, combined with an Unmanned Surface Vehicle equipped with satellite communication facilities for interaction with a land station. Both vehicles are equipped with energy packs that combine hydrogen fuel cells and Li-ion batteries to provide extended duration of the survey operations. The Unmanned Surface Vehicle employs photovoltaic panels to increase the autonomy of the vehicle. Since these missions generate a large amount of data, both vehicles are equipped with onboard Central Processing units capable of executing data analysis and compression algorithms for the semantic classification and transmission of the acquired data.  
autonomous underwater vehicles; unmanned surface vehicles; seabed survey; habitat mapping; autonomous ocean monitoring
## 3 Results and Discussion  
### Physicochemical Analysis  
The physicochemical properties and particle size parameters of volume mean diameter [D(4.3)] and surface area mean diameter [D(3.2)] of wheat flour and hemp protein are presented in Table 1. As shown in Table 1, significant differences were recorded in all examined parameters. The protein content of the hemp protein was approximately five times higher compared to that of the wheat flour, indicating that its addition could potentially increase the protein content of the fortified bread. The mean particle size [D(4.3)] values of hemp protein were significantly higher than those of wheat flour. The water holding capacity of the flour and hemp protein was 0.80 g/g and 1.52 g/g, respectively. Similar results for hemp protein concentrates were also reported by other researchers [42]. The WHC of wheat flour is related to the damaged starch content, i.e., damaged starch absorbs more water [43] while the WHC of protein is related to the amino acid profile, surface hydrophobicity, conformation, pH [44], and the purification methods [45]. [PERSON] et al. [42] reported that the WHC of hemp protein concentrates ranges from 1.3 g/g to 1.8 g/g. In addition, hemp protein exhibited significantly higher swelling values than wheat flour. This is probably related to the larger particle size of the hemp protein, resulting in increased porosity [46]. The swelling capacity is affected by the amylose and amylopectin ratio [47]. Both samples exhibited unimodal particle size distribution. However, the wheat flour exhibited a more uniform distribution, with a higher concentration of particles within the 1 to 300 particle size range, whereas hemp protein showed a broader distribution within the 1 to 500 particle size range (Figure S1). This might be related to the chemical composition of the hemp protein and the processing method used in its production.
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 5 Pointing Correction Verification
### Radar QPE and Verification with External Data  
Assessing the impact of tilt and its correction on the accuracy of QPEs allows external verification of the tilt correction using ground data while also being indicative of the benefit of the correction for future end users. The assessment conducted here compares radar QPEs to precipitation surfaces produced by the interpolation of raingauge networks and assesses performance by comparing areal averages calculated over hydrological catchments. This approach was used in preference to a direct assessment against raingauge measurements at point locations because catchment averages are more indicative of whether improvements due to the tilt correction will be carried forward to a final use such as hydrological modelling, and they are also less sensitive to any potential localised radar artefacts. The verification was undertaken considering the project as a whole but also for each individual time period. This allows each individual correction to be assessed independently while also considering whether the overall performance of the data improves for the project.  
The verification is not a perfect assessment of the tilt correction given the underlying complexities of radar QPE [6; 27], but here we demonstrate the value of this approach when dealing with a polarimetric processing chain, which is described in the following section. This verification is also only possible due to the orographic environment the radar was deployed in, where partial beam blockage (PBB) from high topography at multiple ranges from the radar has a significant impact on radar visibility and power returns. We demonstrate this in Section 5.2.3, where the widespread availability of rainfall observations from regions of varying blockage severity is illustrated, followed by a discussion of the results of the comparison and their value for assessing the tilt correction.  
Figure 5: Solar disk plots of observed solar interference before (**left**) and after (**right**) correction for tilt and pointing offset. Elevation offset is shown on the \(y\)-axis and azimuth offset on the \(x\)-axis. Data for the entire field campaign (November 2018 to December 2020) are shown, with the colour scale indicating the difference between the observed solar power flux and the closest-time DRAO observations scaled to X-band.
## 2 Frequency Characteristic by Exchange of Frequency Containment Reserves Control
### Load-Frequency Control  
From the perspective of power system operation [16], The LFC is considered a closed-loop system that adjusts generator setpoints for active power output based on wide-area transmission data to maintain grid frequency and scheduled tie-line power between asynchronous zones. Using an HVDC connection, LFC's primary goal is to ensure zero steady-state errors in a cross-border interconnected power system. The shifting from LFC blocks is necessary for HVDC operation, some of which increment or decrement power from the system. A speed droop depending on reserves of power and time iteration is enforced with primary control. Additionally, during the arresting and recovery phase, the main control will turn on the frequency containment reserve (FCR). Furthermore, the LFC model within MIDC systems often includes high-level control applications. As mentioned in Section 1, the reserve operation balancing arrangement that will be applied in this research is the E-FCR control. Moreover, the vulnerability of balancing configuration within two different size regions is explained in [17], which includes several potential cyber attack operations in the balancing arrangement system. In addition, Figure 2 depicts the specifics of the cyber attack that address the MIDC system's several weak points particularly in red cross sign symbol.  
Figure 2: Several vulnerable point attack in MIDC system.
## 2 System Modeling
### DC Motor Model  
A classical brushless direct current motor was used for propulsion on each wheel of the AGV, and the motor model equations were obtained by using the free body diagram of this DC motor. In Figure 3, the schematic representation of the classical DC motor is given. In this figure, \(\rm V_{motor}\), \(\rm I_{motor}\), \(\rm R_{i}\), \(\rm L_{i}\), \(\rm E_{i}\) and \(\rm\tau_{i}\) denote voltage of the ith motor, current of the ith motor, electrical resistance and inductance of the motor, electromotor voltage and torque of the ith motor, respectively.  
According to this diagram, the transfer function that gives the connection between the motor supply voltage and the motor rotational speed is given below [45].  
\[\frac{\rm\dot{\theta_{i}(s)}}{\rm V_{motor}(s)}=\frac{K_{i}}{\rm(Js+b)(L_{i}s+ R_{i})+K_{i}^{2}}\left[\frac{rad/\sec}{V}\right] \tag{13}\]  
where \(\rm J\), \(\rm K_{i}\), \(\rm(K_{i}=K_{e}=K_{t})\) and \(\rm b\) denote the moment of inertia of the rotor, the motor constant and motor' viscous friction constant. The used values of each DC motor's parameters are given in Table 2. Using Equation (13), the voltage from DC/DC converters was converted to wheel angular velocities for use in dynamic and inverse kinematic models of AGV.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value (Unit)** & **Parameter** & **Value (Unit)** \\ \hline Mass of load on AGV (\(\rm M_{t}\)) & 45 (kg) & Radius of wheel (R) & 0.055 (m) \\ Mass of AGV (\(\rm M_{p}\)) & 10 (kg) & Radius of cylinder (r) & 0.015 (m) \\ Mass of wheel (\(\rm M_{k}\)) & 1.5 (kg) & Distance in y axis (l) & 0.49 (m) \\ Total mass (\(\rm M_{pc}\)) & \(\rm M_{p}+4M_{k}+M_{t}\) (kg) & Distance in x axis (h) & 0.46 (m) \\ Moment of inertia of AGV (\(\rm I_{p_{rp}}\)) & 0.0178 (kg\(\cdot\)m\({}^{2}\)) & Coefficient of friction (f) & 0.002 (kg\(\cdot\)m\(/\)s) \\ Moment of inertia of wheel (\(\rm I_{k_{rp}}\)) & 0.0139 (kg\(\cdot\)m\({}^{2}\)) & Total AGV weight (\(\rm G_{r}\)) & 9.81\(\rm M_{pc}\) (N) \\ Total moment of inertia (\(\rm I_{pc}\)) & \(\rm I_{p_{rp}}+4I_{k_{rp}}\) (kg\(\cdot\)m\({}^{2}\)) & Reaction forces (\(\rm N_{i}\)) & \(\rm G_{r}/4\) (N) \\ Axial inertia of each wheel (\(\rm I_{k}\)) & 0.0005 (kg\(\cdot\)m\({}^{2}\)) & Radius of cylinder (r) & 0.015 (m) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The values of the AGV’s parameters.  
Figure 3: Schematic diagram of ith DC motor.
# Power Control Techniques for Interference Management-
## 3 Analysis and Findings  
This section presents the literature search's findings on power control techniques based on the results obtained in Section 2. A bibliometric analysis of the literature obtained from the databases was conducted using the R package Bibliometrix (version R-4.4.2) [8] software. In this study, we considered four types of analyses, as discussed in Section 2.
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### Training and Validation  
The SM measurement data are provided daily; however, the sub-daily availability of the CYGNSS observations for each ISMN site is not reduced to daily basis in this study. That is to say, for a particular day-of-year around one of the ISMN stations, if there are multiple CYGNSS SPs that fall into the 9-km grid that centers the site coordinates, all of them are included in the analysis. In such a case, a constant SM value is assumed for all of those multiple CYGNSS observations due to being in an ISMN proximity on the same day. This is considered feasible because the geophysical parameters (such as NDVI, VWC, elevation, slope, and h-value) corresponding to each CYGNSS observation would differ from each other due to the spatial variation, which in turn could explain variations in the CYGNSS observations despite uniform SM values.  
After the quality control flags are applied, there are a total of 4808 reference samples (distinct feature vectors) from 18 ISMN stations, spanning from the 77 th day-of-year of 2017 (starting date of the publicly available CYGNSS science mission data) to the end of 2018. The training and validation sets are organized with the help of a 10-fold cross validation fashion (N-fold in general [65]) as follows: training and validation are performed together in a total of 10 iterations. In each iteration, (i) 10% of the total data samples are randomly selected and excluded from the training data; (ii) the ANN model is trained by using the data samples corresponding to the remaining 90% of the data set; (iii) validation of the trained model is performed on the excluded data samples. By this method, the trained model is tested by using \"unseen\" data in every iteration. (iv) The predicted SM values are stored in the prediction pool with their corresponding reference SM values. (v) 10% of the data excluded are turned back into the training data set. The next iteration is performed similarly to the following exception: The random selection of new 10% of the data to be excluded for validation purposes is handled in a way such that samples in this validation set were never chosen into the validation set of any previous iteration. This regulation ensures that the training/validation split method validates the ANN model over the entire data set after 10 iterations are finished. (vi) When all 10 iterations are run, the prediction results for the entire data set with corresponding reference SM values are stored. (vii) Performance assessment of the entire data set is performed in order to obtain overall and per-ISMN-site statistical performance metrics such as RMSE, unbiased RMSE (ubRMSE), bias, and Pearson's R (correlation coefficient) [66]. The overall training and validation approach is illustrated in Figure 7.
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
###### Abstract  
The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, a genetic algorithm, which consists of a boundary search heuristic (BSH), a linear program, and a dual simplex method, is developed for an UA-FLP. To test the performance of the proposed technique, several test problems taken from the literature are used in the analysis. The results show that the proposed heuristic performs well with respect to solution quality and computational time.  
u pacs: 14.30.-c, 14.10306 +
Footnote †: journal: Applied Soft Computing  
**Keywords:** unequal area facility layout problem; fixed dimension facilities; genetic algorithm; boundary search
# A Two-Phase Approach for Predicting Highway Passenger Volume
## 7 Conclusions  
This study overcomes the limitations of existing research on predicting highway passenger volume. The main work and results of this study are as follows:  
1. A two-phase approach, in which Phase I extracts the significant impact factors and Phase II develops a deep learning model to achieve the prediction, was proposed to predict the highway passenger volume with the dataset of multiple urban attributes;
2. Phase I extracted a dataset with 30 significant factors reflecting urban economic level, urban population size and structure, per-capita income and consumption, urban industrial structure, and resource and environments with the RF algorithm and proved that they have a significant impact on highway passenger volume. 3. Phase II developed the deep learning method, DFNN, to predict the highway passenger volume with a mean absolute error of 2066.31 persons per day, improving the predicted accuracy by 8.49% compared to the multiple regression and 2.20% compared to the SVM algorithm. This study contributes to proposing a novel approach for predicting highway passenger volume, but limitations still exist and are worth further study. Recently, deep learning algorithms have been proposed and are expected to be utilized for further improving the predicted accuracy of highway passenger volume as well as increasing the interpretability. As the statistical yearbook only publishes the annual statistics, it is difficult to make a detailed analysis of highway passenger volume in quarters or months. Moreover, it is possible to find data mutation caused by the change of statistical caliber in the statistical yearbook, which affects the predicted accuracy. Therefore, other new datasets can be considered to introduce into future research for more accurate analysis. Conceptualization, Y.X. and W.Y. ; methodology, Y.X. and J.C.; software, R.W. and J.C.; data acquisition, W.Y. and B.L. ; data analysis, Y.X. ; writing--original draft preparation, Y.X. and W.Y. ; writing--review and editing, J.C., B.W. and Z.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (No. 71901059), the Natural Science Foundation of Jiangsu Province in China (BK20180402), the General Project of Humanities and Social Sciences Research of the Ministry of Education (19Y)CZH152), and the Fundamental Research Funds for the Central Universities (2242021R10126, 2242021R10068). Not applicable. The data presented in this study are available on request from the first author. The authors would like to thank the students from the school of computer science and engineering of Southeast University for their assistance with the data collection. The authors declare no conflict of interest. The selected impact factors of urban attributes.
# Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
## 3 Results
### Optimized Parameter Choice  
Combining the results of the previous subsections, we present the resulting images for the optimized values. We propose the following settings: \(r=7\), \(s=8\), \(b=20\), and \(p=0.15\).
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### Path Cost Estimation
#### 3.2.2 Radio Energy Model  
The ELR-W protocol uses the basic model for radio energy consumption discussed in [39]. In this model, energy consumption to transmit and receive \(k\) number of bits over distance \(d\) is determined according to the following equations.  
\[E_{Tx}\left(k,d,n\right)=\ E_{Tx-elect}k+E_{amp}(n)kd \tag{5}\]  
\[E_{Rx}(k)=E_{Rx-elect}\ k \tag{6}\]  
where \(E_{Tx}\) is the energy utilization for transmitting and \(E_{Rx}\) is for receiving the data packet. While \(E_{Txelect}\) and \(E_{Rxelect}\) indicate the energy consumption by the radio operations for the purpose of transmission and reception correspondingly. \(E_{amp}\) is the energy utilization for amplification and \(n\) is the coefficient used for path loss. The values of these parameters depend on the hardware transceivers. We consider these parameters for Nordic nRF2401 [39] which is a low power single chip transceiver commonly used for body area networks. The parameter values are presented in Table 1.
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 2 Materials and Methods
### 2D Electrochemical Process  
The two-dimensional (2D) electrochemical process for the removal of CBZ was carried out in a single-compartment electrochemical cell, working in batch-operation mode. An acrylic reactor (2 \(\times\) 15 \(\times\) 8 cm) was built with a rectangular base with a total volume capacity of 240 mL (Cromotema, Vila Nova de Gaia, Portugal), with the possibility of placing the electrodes at two different distances, 3.5 and 7.5 cm. All electrodes used (BDD, MMO, and STS) had a submerged area of 15 cm\({}^{2}\). An aqueous CBZ solution (150 mL), containing NaCl as electrolyte, was transferred to the electrochemical cell while maintaining an airflow of 3000 cm\({}^{3}\)/min during the process (selected after preliminary studies). An ELITE 802 air pump with 2 outputs of 1500 cm\({}^{3}\)/min each (Hagen, Yorkshire, United Kingdom) was used as the air supplier. The current intensity was maintained constant at 0.1, 0.2, and 0.3 A, corresponding to a current density of 6.67, 13.3, and 20 mA/cm\({}^{2}\), respectively. Besides different current densities, the operational conditions were optimized considering the pH, CBZ and NaCl concentrations, reaction time, distance between electrodes, and anode material (BDD and MMO). Aliquots of CBZ solution (1000 \(\upmu\)L each) were taken at regular periods during the experiments, and the evolution of the CBZ removal was assessed by HPLC. All experiments were performed in triplicate.