esa-sceva/satcom-chunk-collection huggingface dataset:
# Remotely Controlled Robot Swarms: A Structural Analysis and Model for Structural Optimization
## 2 Materials and Methods
### Simulation Program  
The investigation was performed through computer simulations, which were partly validated by experiments. The simulation program was prepared using Delphi object software (see Appendix A). Each robot was modelled as an object. The structure of the object depended on the swarm structure and on the role of the robot in the swarm. The field in which the robots moved was modelled as a 2D array of records: the movement resistance value, determined by the array coordinates on hexagons on the ground; and the state of occupancy of the hexagon by a robot or field.  
The field of simulated action was divided into hexagons in order to speed up the simulation process, because the shape of a piece of field was similar to a circuit and made it easier to detect the distance between the robots.  
The values could be constant or change over time.  
The results of the simulations were displayed as a sequence of robot positions on the field. The robots were traced using a path, which was compared with their assumed path. The robots were represented by circles of different colours, marking their roles in the swarm.  
The robot's position was calculated as the value of the integrator versus the time of its speed vector components.  
Generally, it is assumed that robots in a swarm keep their distance from one another within a defined range. When robots move, their speed is decreased when their distance to another robot in the movement direction is too small, or increased when the distance grows.  
The simulation was based on a few scenarios that involved running through the test field on a determined path. The operator controlled the swarm's movement with keyboard arrows, as in a video game. One of the study problems was developing a swarm that was tolerant of failures, e.g., a loss of radio connectivity, a breakdown, or the loss of a few members.
# Adaptive Modulation Scheme for Soft-Switching Hybrid FSO/RF Links Based on Machine Learning
## 3 Threshold Estimation and Data Set Generation by Machine Learning
### Basic Parameters  
#### 3.2.1 Link Budget  
LB refers to the remaining signal power or bandwidth in a communication link. Suppose \(LB_{FSO}\) denotes the LB of the FSO link, and it can be expressed as [14]  
\[LB_{FSO}=P_{1}+|S_{r}|-\alpha_{FSO,atmo}-\alpha_{geo}-\alpha_{sys}, \tag{11}\]  
where \(P_{1}\) is the transmitter power, \(S_{r}\) is the receiver sensitivity, \(\alpha_{geo}\) is geometrical attenuation, \(\alpha_{sys}\) is system losses, and the atmospheric attenuation in the FSO link is denoted as \(\alpha_{FSO,atmo}\), which is a collective representation of \(\alpha_{1,fog}\) and \(\alpha_{1,rain}\). Suppose \(LB_{RF}\) denotes the LB of the RF link, and it can be expressed as [11]  
\[LB_{RF}=EIPR+G_{r}-\left(\frac{E_{b}}{N_{0}}\right)_{reg}-R-kT-L_{s}-\alpha_{ RF,atmo}, \tag{12}\]  
where \(EIPR\) is the effective isotropic radiated power, \(G_{r}\) indicates the receiver antenna gain, \((\frac{E_{b}}{N_{0}})_{reg}\) represents the lowest normalized SNR, \(R\) indicates the bit rate of the system, and \(k\) is the Boltzmann constant. The receiver noise temperature is presented as \(T\), \(L_{s}\) represents path loss, and \(\alpha_{RF,atmo}\) is the atmospheric attenuation under the RF link, given by \(\alpha_{2,rain}\) and \(\alpha_{2,fog}\).
## 3 Connectivity-Based Multipolling MAC Protocol
For efficient and reliable uplink data transfer by the connectivity-based multipolling MAC protocol, the bitmap technique can assign a bit to each recipient MAC address in the multipolling frames. The APs can indicate whether the previous uplink data transfer from the node of the corresponding MAC address was successful by setting the bit assigned to a recipient MAC address to zero or one. The connectivity-based multipolling process consists of two phases: the initialization phase and the multipolling phase. During the initialization phase using the PCF, APs poll each sensor in a round-robin manner, and each sensor responds with the data or null frame on which the connectivity information is piggybacked. Using the connectivity information collected from sensors, the APs initiate the multipolling phase by transmitting the multipolling frames, and the sensors polled by the multipolling frames respond with their data or null frames, on which the change in the connectivity information is piggybacked, in a serially connected manner. This process is depicted in Figure 1. In Figure 1b, node 3 hears the transmission signals from node 2, while node 1 hears the transmission signals from node 3, and so on.
## 4 Advanced Sensors  
Impedimetric sensors are manifold and can be developed for manifold applications. In [6] [PERSON] et al. realized an immune sensor for label-free and rapid detection of antigens with a low detection limit and a broad linear detection range of 0.01-10 ng/mL. The experimental results show that the realized impedimetric sensor based on PEN flexible materials is suitable for immunoassay experiments.  
For the detection of anal sphincter injuries occurring during natural deliveries, a rectal probe called the ONIRY Probe has been designed. In [7], [PERSON] et al. carried out clinical studies on twenty women after natural delivery who were enrolled in the study and divided into two groups referring to the stage of a perineal tear. Several diagnostic tests have been carried out in addition to impedance spectroscopy including three-dimensional endo-anal ultrasound, anorectal manometry, and physical examination as a reference. The results show that impedance spectroscopy is a promising diagnostic technique for anal sphincter injury detection.  
The measurement of soil parameters can provide important information about the stability of earthwork structures. In [8], [PERSON] et al. propose the use of impedance spectroscopy to measure changes in density and volume caused by contact erosion. Experimental investigations show a correlation between the volume change of the soils and impedance values. Several investigations have proven that material transport can be detected by impedance spectroscopy.
## 2 Methods
### Profile Retrieval  
In this section, we describe the methodologies developed and used to retrieve the molecular profile expectation of the signal, the determination of the end-of-ground-layer height, and the cloud search algorithm. We present the inversion methods used to determine the extinction coefficients of the ground layer and clouds.  
The _Layer Calculations_ LPP software module described here is specific to the elastic channels at 355 nm and 532 nm. These calculations are used to identify and characterize atmospheric layers, such as the ground layer, cloud layers, and possibly the stratospheric Junge layer. Raman channels are then analyzed taking into account the height ranges of these layers. The analysis is based on the algorithms presented in Fruck et al. [9], with several modifications explained below, mainly to speed up the computation.  
The procedure starts with preparatory steps that include the retrieval of molecular profiles, the computation of the system constant, and the identification of free troposphere ranges.  
**System constant.** We precalculate a system constant \(C_{0}\), which can be obtained either analytically, if the system is sufficiently well characterized, or through an absolute LIDAR calibration [9]:  
\[C_{0}(\lambda)=\ln\left(\frac{E(\lambda)\lambda A}{\hbar f_{s}}\right)+\ln( \left(\beta(h_{\text{LIDAR}}))\right)+\ln(\xi(\lambda)) \tag{41}\]  
where \(\beta(h_{\text{LIDAR}})\) is the average molecular backscatter coefficient at the altitude of the site, and the other parameters have been introduced in Equations (1) and (4).
## 2 Materials and Methods  
### High-Frequency Radar  
The HF radars used in this study were OSMAR-S100 compact high-frequency surface wave radars [34] developed at Wuhan University. Compared to large-scale array-type ground wave radar, compact HF radar has advantages such as miniaturization of equipment, small-occupied area (antenna aperture less than hundreds of meters), low power consumption, and convenient antenna equipment installation and maintenance. OSMAR-S100 HF radars, as networked radars, operate at a frequency between 13 and 16 MHz, with a synthesized detection area (113\({}^{\circ}\) E-114.4\({}^{\circ}\) E, 21.4\({}^{\circ}\) N-22.6\({}^{\circ}\) N) not less than 10,000 km\({}^{2}\). A total of six radar systems were installed at Wushunde (in Zhuhai), Hengqin, Hengshan, Guishan, Miaowan, and Dangan Islands. During the study period between 17 July 2022 and 13 August 2022, radar data were collected at a spatial resolution of 2 km and a time interval of 20 min. Points with a data acquisition rate of more than 80% were selected to ensure reliability of the data (Figure 1). In machine learning model training and prediction, radar data were interpolated into hourly data to align with other time series.  
The ERA5 dataset, a reanalysis of global climate data from the European Centre for Medium-Range Weather Forecasts (ECMWF), was utilized to derive standardized winds at a height of 10 m. The spatial resolution of the wind data was 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\). To obtain tidal information, the TPXO 8.0 global tidal model created by [PERSON] and [PERSON] [35] from Oregon State University was utilized. In the radar observation area, a regional PRE model with a minimum grid size of 100 m was utilized to simulate model data, which were compared to radar and ADCP measurements in our prior research [36]. This study utilized wind, tide, and model data from 17 July 2022 to 13 August 2022, which were averaged to hourly data and smoothed using a 4-h running mean to remove high-frequency signals and align with the radar data.
## 2 Materials and Methods
### Animal Experiment
#### 2.2.3 Preparations of Experimental Diet and CBZ Solution  
Commercially supplied total-pathogen-free pellet feeds (for breeding mice and rats) from Velaz s.r.o, Prague, Czech Republic, were used to feed the untreated experimental animals. The main nutritional values of the commercially supplied pellet feed, as declared by the manufacturer, are moisture = 11.1%, crude ash = 6.1%, crude fibre = 4.5%, crude protein = 22.5%, Ca = 7.06 g/kg, K = 10.14 g/kg, Mg = 2.06 g/kg, Fe = 191 mg/kg, Se = 0.26 mg/kg, Zn = 85.0 mg /kg, vitamin A = 26,250 IU/kg, and vitamin E = 133 mg/ kg. For the preparation of the diet to feed the treatment animals, defined portions of individual soils were mixed with the commercially supplied pellet feeds to obtain a final percentage of 10% soil (\(w/w\)) of the final weight of the diet. The commercially supplied pellet feeds were milled to a fine powder and thoroughly mixed with the experimental soils. Then, the feed-soil homogeneous mixture was re-pelleted to serve as an experimental diet for the investigated animals. The 10% soil rate was chosen according to previous experiments [52]. The final content of As in the feeding mixtures was 1.25 mg/kg for the low As diet and 166 mg/kg for the high As diet.  
Regarding the preparation of CBZ (>99% purity, purchased from Sigma-Aldrich, St. Louis, MO, USA), 10 ng of CBZ per litre of tap water was prepared freshly every week and divided among individual 500 mL water bottles to serve as the drinking water for the CBZ and As + CBZ groups. The choice of 10 ng/L CBZ was based on its realistic concentration in freshwater bodies [4].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Elements**} & **Citro Soil** & **Mokrsko Soil** & **Preventive Values * \\  & **mg/kg** & **mg/kg** & **mg/kg** \\ \hline As & 12 \(\pm\) 1.1 & 1304 \(\pm\) 131 & 20.0 \\ Be & 0.778 \(\pm\) 0.014 & 0.545 \(\pm\) 0.018 & 2.0 \\ Cd & 0.165 \(\pm\) 0.046 & 0.265 \(\pm\) 0.005 & 0.5 \\ Co & 6.08 \(\pm\) 0.36 & 11.7 \(\pm\) 0.0 & 30.0 \\ Cr & 25.3 \(\pm\) 0.4 & 16.0 \(\pm\) 0.1 & 90.0 \\ Cu & 10.7 \(\pm\) 0.1 & 30.8 \(\pm\) 6.4 & 60.0 \\ Ni & 14.0 \(\pm\) 0.5 & 8.23 \(\pm\) 0.46 & 50.0 \\ Pb & 12.4 \(\pm\) 0.6 & 10.2 \(\pm\) 0.8 & 60.0 \\ V & 34.7 \(\pm\) 0.0 & 91.3 \(\pm\) 1.3 & 130.0 \\ Zn & 39.4 \(\pm\) 0.7 & 76.6 \(\pm\) 0.2 & 120.0 \\ \hline \hline \end{tabular}
* = based on Public Notice No. 153/2016 for monitoring and protecting agricultural soil quality in the Czech Republic.  
\end{table}
Table 2: Pseudo total contents of As and other selected risk elements (mean \(\pm\) standard deviation in mg/kg) in Citro and Morrsko soils and their respective preventive values.
## 4 Results
### Comparison with Other GPP Products at Eddy Covariance Flux Sites  
Furthermore, we estimated global GPP at daily and 1 km resolution using the EF-LUE model developed in this study by taking the global gridded data listed in Table 3 as forcing, and compared with other four exiting GPP products (listed in Table 4) for the period of 2002-2018. We first compared the estimated GPP by the EF-LUE model with other  
Figure 5: Comparisons of 8-day mean GPP between the EF-LUE model and the model without EF against the ground measurements at four cropland flux tower sites. Black solid dots indicate the GPP measurements by eddy covariance system. The blue line and red line represent the GPP estimated by the EF-LUE model and the model without EF, respectively.  
two LUE-based GPP products, i.e., MOD17 GPP [20] and the revised EC-LUE GPP [68], the spectral index based GPP (i.e., GOSIF GPP) [10] and the conductance model based GPP (i.e., the PML-V2 GPP) [69] at an 8-day step for each crop site. Figure 6 showed the boxplot to compare the performance of different GPP products. Generally, the EF-LUE GPP showed the highest R\({}^{2}\), KGE, and NSE and the lowest RMSE. These improvements may be attributed to the optimized potential LUE values (i.e., \(\varepsilon_{max}\)) in different climate zones and the integration of water availability constraint factor (i.e., EF) in the EF-LUE model. The MODIS algorithm used the VPD-based slope function as surrogate for soil moisture status, which partly explained that MOD17 GPP had a higher deviation during severe summer drought caused by the low soil water content [73]. VPD is an effective indicator of atmospheric water demand and might not be sufficient to represent the condition of soil water availability and the effect of soil water stress on the vegetation photosynthetic efficiency. The revised EC-LUE model took into account the effects of diffuse radiation and atmospheric carbon dioxide concentration, but the improvement was limited by the exclusion of EF to reflect the impact of soil water stress on cropland GPP estimation in global scale application.  
The performances of GOSIF GPP and PML-V2 GPP were also showed in Figure 6 for a broader comparison, and these two GPP products were claimed to have high accuracy. The EF-LUE GPP showed comparable or better behavior when compared with these two GPP products, e.g., EF-LUE GPP had the overall highest R\({}^{2}\) and KGE and lower RMSE, indicating the good ability of the EF-LUE model in global GPP estimation. These results also suggest that the LUE-based model (especially the EF-LUE model in this study) is still very promising for global cropland GPP estimation with satisfactory accuracy if the model is properly structured and model parameters are carefully calibrated, and it retains the advantage of easy application when comparing with the complex process-based models.
## 3 Method
### State Space  
In a real working environment, the state information can be vast (and even infinite), making it impractical to enumerate all possible states. Therefore, in practical applications, key features are usually extracted for representation, in order to reduce the computational complexity and improve learning efficiency. In DRL, state information is not only important for the agent to perceive the environment, but also directly affects its decision-making process and the evaluation of long-term rewards. As such, reasonable state design can significantly improve the convergence, convergence speed, and final performance of the algorithm.  
This study aims to train an agent to autonomously control the joint motion of a six-axis robot arm, while avoiding collisions and getting as close as possible to a specified target. The state space \(s_{t}\) can be represented by Equation (5):  
\[s_{t}=[q,p_{e},d_{tar},d_{obs},collision] \tag{5}\]  
where \(q\in R^{6}\) denotes the joint angles of the end effector; \(p_{e}\in R^{3}\) represents the Cartesian position of the end effector's reference coordinate system relative to the target coordinate system; the scalar \(d_{tar}\) indicates the Euclidean distance between the end effector and the origin of the target coordinate system; and \(d_{obs}\) is the shortest Euclidean distance between  
Figure 3: Framework for the DRL algorithm to interact with the environment.  
the end effector and the two types of obstacles. The final component--collision, which can be obtained via the CoppeliaSim API function--is a Boolean value that indicates whether the manipulator has collided with any environmental obstacles.
## 4 Performance Evaluation
### Delay  
The delay computation signifies the time difference between the packet dissemination and delivery at the destination, as shown in Equation (7):  
\[Delay=\frac{\sum(All\;Received\;Packets\;-\;All\;Disseminated\;Packets}{\sum Packets\;Delivered} \tag{7}\]  
The delay is calculated by varying the cluster size and the speed of UGVs. The delay is below 4.5 s by increasing the speed to 60 m/s, which signifies the efficiency of LBCHE, CMN, MRT, and stable IVC links, as shown in Figure 7a. Similarly, Figure 7b illustrates that the delay is 4 s at 10 adjacent clusters because of the CMN scheme, which effectively manages the CMs' joining and leaving. On the other side, the delay is observed to be higher upon variation in the speed and cluster size of UGVs.  
Figure 6: (a) UGAVs-MDVR PDR concerning mobility; (b) UGAVs-MDVR PDR for the number of CMs.  
The simulation results show that the MDM dissemination of the proposed protocol is efficient by incurring less delay than MAODV and DSR, even by increasing the mobility of the vehicles. Hence, the MDM dissemination will not be affected by the dynamic mobility of vehicles. The MAODV and DSR are facing delays because of their unstable inter-vehicular links upon the dynamic mobility of the vehicles. The comparison results show that the delay increases upon the rise in mobility, indicating that MDM dissemination will be affected. The major reason for the rise in the delay of MAODV and DSR is the time the network takes to organize and update the inter-vehicular links because of the continuous occurrence of changes in the network. The proposed protocol can adapt itself despite the dynamic mobility of the vehicles, resulting in the efficient delivery of MDMs by incurring less delay compared to the MAODV and DSR.  
The delay is also computed with respect to the number of CMs joining within the cluster. The results show that the delay variation is low even by increasing the number of the CMs within clusters because of the strong inter-vehicular communication links.
# Design and Analysis of an O+E-Band Hybrid Optical Amplifier for CWDM Systems
## 3 Proposed Design of Hybrid Optical Amplifier
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Pump wavelength & 1030 nm \\ Pump power & 500 mW \\ PDF length & 15.7 m \\ Pr\({}^{3+}\) concentration & \(50\times 10^{24}\) m\({}^{-3}\) \\ Core radius of PDF & 1.2 \(\upmu\)m \\ Doping radius of PDF & 0.8 \(\upmu\)m \\ Attenuation of SMF & 0.2 dB/km \\ Numerical aperture of PDF & 0.26 \\ Center wavelength of WFBG & 1310 nm \\ Bandwidth of WFBG & 100 nm \\ Reflection of WFGB & 99\% \\ Injection current & 60 mA \\ Length of SOA & \(5\times 10^{-4}\) m \\ Width of SOA & \(3\times 10^{-6}\) m \\ Height of SOA & \(80\times 10^{-9}\) m \\ Optical confinement factor & 0.3 \\ Differential gain of SOA & \(27.8\times 10^{-21}\) m\({}^{2}\) \\ Carrier density at transparency & \(1.4\times 10^{24}\) m\({}^{3}\) \\ Initial carrier density & \(30\times 10^{24}\) m\({}^{-3}\) \\ Signal attenuation & 0.1 dB \\ Pump attenuation & 0.15 dB \\ Temperature & 300 K \\ Responsivity of PIN & 0.9 A/W \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Main simulation parameters and their values.
## 2 Methods
### Performance Simulation Using the Return Power Budget Method  
In order to estimate the BRL capabilities and to check whether its design [16] was able to meet the CTAO requirements, we first carried out a computational analysis to assess the sensitivity and performance capabilities. We developed a power-budget (or link-budget, see, e.g., [30]) simulation program, originally developed in Eizmendi [31], to calculate the expected return power and signal-to-noise ratio (SNR) for single laser shots at each BRL laser wavelength and at various altitudes while considering different atmospheric conditions. We also computed the time required to achieve a given minimum SNR because of the short time allowed to operate the BRL during standard CTAO operations (see Section 1).
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 2 Materials and Methods
### Pilot Country: Bangladesh  
Banglades was selected as the pilot country according to the first priority target from the preceding research. For example, [PERSON] et al. (2012) reported that Bangladesh was the country with the highest future flood risk among 14 Asian developing countries in the case of a 50-year return period flood under climate change scenarios [4; 32]. Also, Bangladesh is one of the most flood-prone countries in the Asian monsoon region and suffers from flood disasters every year due to its geographical location and extreme rainfall; a rainfall event in 2010 recorded the highest annual rainfall of 5690 mm in the northeast of Bangladesh and the highest annual total rainfall of 4939 mm at the Sylhet station. Table 2 shows floods of different return periods with their corresponding averages of the total inundated area from historical records after 1950 in Bangladesh. For example, two-year return period floods have caused, on average, an inundated area of about 20% of the total area of the country. Among the extremely large floods after 1950, the flood in 1998 (return period: 100 years) affected approximately 100,200 km\({}^{2}\), or about 68%, of the total area of the country. Four events were recorded as the most devastating [33; 34]. Figure 2 shows water level gauge stations (points) with the differential river water depth as an overflow vulnerability indicator in the nationwide river network. The differential river water depth (=H\({}_{\rm max}-\) DL, unit: meter) was calculated by the difference between a historical highest water level and danger level [24]. Red and blue circles of water level gauge stations are categorized as the low differential water depth with the high vulnerability and the high differential water depth with the low vulnerability, respectively.  
Figure 2: Pilot country: water level gauge stations (points) in a nationwide river network (blue lines) of Bangladesh and visited district for field verification (dark gray).