esa-sceva/satcom-chunk-collection huggingface dataset:
# LAMDA Controller Applied to the Trajectory Tracking of an Aerial Manipulator
## 3 Proposed Controllers  
The problem of motion control of the aerial manipulator robot is solved with the control scheme proposed in Figure 2, which solves the problem of trajectory tracking control in an autonomous way.  
Two cascaded subsystems are considered for the controller design:  
Kinematic control: A minimum-norm control with saturation at maneuverability velocities is considered, for which \(\tilde{\xi}_{d}(t)\) and \(\tilde{\xi}_{d}(t)\) describe the desired position and velocity, respectively, of the operating end of the aerial manipulator robot. The control error is defined by \(\tilde{\xi}(t)=\tilde{\xi}_{d}(t)-\tilde{\xi}(t)\). Therefore, the control target is defined as  
\[\lim_{t\to\infty}\tilde{\xi}(t)=0\in R^{m} \tag{9}\]  
Dynamic Compensation: The main objective is to compensate the dynamics of the aerial manipulator robot, thus reducing the velocity tracking error. For this purpose, this work considers the implementation of four control techniques: (i) Kinematic, (ii) Inverse Dynamics; (iii) SMC Sliding Mode; and (iv) LAMDA, in order to evaluate the behavior of the controllers. The implemented controllers receive the desired maneuverability velocities \(\mu_{c}(t)\) as inputs calculated by the kinematic controller and generates velocity references \(\mu_{ref}(t)\) or the motion control of the aerial manipulator robot. The velocity control error is defined as \(\widetilde{\mu}(t)=\mu_{c}(t)-\mu(t)\). Therefore, the control objective is to ensure that,  
\[\lim_{t\to\infty}\widetilde{\mu}(t)=0\in R^{n} \tag{10}\]
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model  
IDEASSat was a 3U CubeSat whose mission objective was to observe plasma irregularities in the ionosphere. The elements of a space mission include launch vehicles, payloads, satellites, mission operations (MOS), and satellite tracking telemetry and command (TT&C) [9]. This section will introduce the various elements of the IDEASSat mission, including satellite sub-systems, payload, satellite flight software, and ground communication station and related environmental verifications.
# Performance and Parameter Sensitivity Analysis of the PEMFC Flow Channel with Porous Baffles
## 5 Conclusions  
In this paper, a porous baffle channel model of PEMFC is proposed, and a 3D simulation verifies the performance of channel PEMFC with the porous baffles. The influence of the straight channel structure parameters of the porous baffles on the performance of PEMFC was investigated. The results of the simulation and discussion are as follows:  
1. Compared with traditional single-channel PEMFCs, the PEMFC with four porous baffles was found to be better at high current density. At 0.4 V, the average current density of the PEMFC increased by 12.8% and the maximum power density increased by 12.7%.  
Figure 10: Effect of the number of baffles on the performance of PEMFC when the porosity is 0.3, the baffle thickness is 0.8 mm, and the voltage is 0.4 V.  
Figure 9: Variation of average current density with the thickness and baffle porosity of the PEMFC with four baffles at 0.4 V.  
2. Porous materials were used as baffles to improve the uniformity of the local distribution of oxygen and the concentration under the ribs where the baffles were located, thus improving the performance of the PEMFC.
3. The influence of the structure parameters of the porous baffles on the performance of the PEMFC can be described as follows: as the porosity of the baffle and the number of baffles increase, the performance of the PEMFC gradually increases. With the increase in the thickness of the baffle, the performance of the PEMFC shows a trend of increasing and then decreasing, resulting in two peaks.  
Conceptualization, Y.S.; writing--review and editing, C.C.; visualization, M.W.; supervision, S.L.; project administration, D.X.; funding acquisition, D.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Zhejiang Province LQ20E060012.  
This study did not involve humans or animals.  
This study did not involve humans.  
This study did not report any data.  
The authors declare no conflict of interest.  
The porosity  
The density (Kg/m3)  
The pressure (Pa)  
The mass fraction of substance  
Two-phase conversion coefficient (1/s)  
The geometrical area (m2)  
Relative humidity  
Velocity (m/s)  
The source  
The potential  
The mole fraction of substance  
Viscosity (Pa/s)  
The flux (mol/(m2 s))  
The substance type, H2, O2, N2 or H2O  
The computational domain, GDL or CL  
Two-phase water, liquid or gas.  
The membrane  
The channel  
The potential  
The anode  
The ionic water  
The cathode  
The liquid water  
The maximum value  
The condensation coefficient  
The evaporation coefficient  
The saturation value  
The effective value  
A mixture of N substances
## 4 Numerical Study
### Analysis of Vertical Deformation of Subway Tunnel
The maximum vertical deformation of the subway tunnel caused by the construction of the foundation pit section A and the foundation pit section B is recorded in Table 4. The calculation results of finite element software show that in the first four excavations, the vertical deformation of the subway tunnel caused by section B of the foundation pit is greater than that of section A. However, when all the foundation pit is excavated, the vertical deformation of the subway tunnel caused by section B of the foundation pit is smaller than that of section A. When the fourth layer supports are removed, the vertical deformation of the subway tunnel caused by section A is larger than that of section B. However, when all the supports are removed, the vertical deformation of the subway tunnel caused by section A is smaller than that of section B. The data in Table 4 are plotted as a line graph and recorded in Figure 10. It can be seen from Figure 10 that the variation trend of the simulated value of the maximum vertical deformation caused by the construction of section A and section B is basically consistent with the monitored value. The largest relative error in the construction process of the foundation pit in section A appears in the first excavation condition, and its value is 38.5%. The largest relative error during the construction of the foundation pit in section B also appears in the first excavation condition, and its value is 27.2%. The relative error of the numerical simulation is large, which is mainly caused by the small value of the maximum vertical deformation of the subway tunnel itself. According to the code requirements of the area where the project is located, the absolute settlement of subway structural facilities should be less than or equal to 10 mm. The maximum vertical deformation of the actual subway tunnel caused by the construction of the foundation pit in section A and the foundation pit in section B is 2.31 and 2.42 mm, which are far less than the specification limit.
# Experimental and Numerical Evaluation of Toxic Pool Evaporation
## 1 Introduction  
In the context of land use planning, toxic dispersion modelling resulting from pool evaporation determines the safety distance to be in the hundreds of meters to avoid possible negative human consequences. These distances are based on computations of both the source term evaluation and the dispersion modelling hypothesis. For the purpose of this particular study, the source term is of primary importance. The objective of this paper was to evaluate the time evolution of ammonia solution evaporation rate. This product was selected because it is largely used in the industry in various concentrations, typically for water treatment in nuclear power stations. In most existing models, as described in the next section, the evaporation rate is assumed to be constant.
# A Two-Stage Routing Protocol for Partitioned Underwater Wireless Sensor Networks
## 3 Methodology
### Rerouting
Therefore, the early death of a relay node reduces the number of nodes assigned for the underwater sensing mission. The residual energy threshold ensures delay in the early death of sensor nodes. Secondly, our partition handling mechanism ensures that the load is shifted down the network to the less loaded nodes thus ensuring better distribution of energy consumption across the network. This can be explained with the help of the Figure 10:  
In this example, we assume that only node 2 is directly connected to the UW-Sink. Nodes 3 and 4 are within the range of node 2, which enables them to receive beacon from node 2. Whereas, nodes 5, 6 and nodes 7, 8 and 9 fall within the communication range of nodes 3 and 4, respectively. For the sake of simplicity, we assume that none of the above-mentioned nodes fall within the communication range of any other node except for those mentioned above. Figure 10: Rationale for Residual Energy Threshold. Figure 9: Forwarding Loop. As evident from the topology, the traffic load is highest on node 2, followed by nodes 4 and 3, whereas nodes 5 to 9 have the smallest load which is their own data packets. Intuitively, we can tell that node 2 will reach its residual energy threshold before other nodes. If node 2 keeps forwarding data from this point onwards, it will soon run out of energy and will not be able to continue its role a sensor node. However, if node 2 quits forwarding, a connectivity hole will be created. In the absence of other neighboring nodes, the rest of the network will not be able to reach the UW-Sink, thus bringing down the packet delivery ratio to zero. For the ongoing routing cycle, this issue is resolved by our rerouting mechanism which ensures that the load is shifted down the network to the less loaded nodes, i.e., nodes 3 and 4. Moreover, in the following routing cycles, our partition handling routine solves the issue amicably by shifting the load backwards. In the next routing cycle, node 2 will not send beacon thus none of the neighbors will be able to route traffic through node 2. After the threshold time, the remaining nodes will send requests with increased transmission power. We assume that nodes 3 and 4, being nearer to the UW-Sink, receive a beacon from the UW-Sink which they retransmit. The beacons are received by their neighbors, i.e., nodes 5 to 9. Now nodes 3 and 4 will directly transmit data from nodes 5,6,7,8 and 9 to the UW-Sink. The same process will be followed when nodes 3 and 4 reach their residual energy threshold. This technique effectively distributes energy consumption across the network. Even though farther nodes have to use higher transmission power to send their data that results in higher energy consumption, this technique keeps the network connected. The high residual energy in the downstream network nodes may be wasted if the network disconnects due to connectivity holes.
# Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
## 6 Conclusions  
This study proposes an adaptive filter design scheme based on the DSLMS algorithm to improve the performance of weak signal processing in photoelectric detection technology. A new adaptive filtering algorithm with variable step size is proposed based on the minutiae function, combined with the pair elimination system to effectively reduce the noise component in error and improve the adaptive filter's anti-noise interference ability by utilizing the noise signal's low correlation. The adaptive filtering algorithm realizes an improvement in convergence speed and a significant reduction in steady-state error by adjusting the step factor of weight coefficients by segments. The experimental validation with the FPGA and Matlab shows that this scheme obtains better and more optimal results in noise suppression, accelerated algorithmic convergence, and steady-state error reduction compared to the traditional LMS filter, the variable-step-size LMS filter, and the traditional noise canceller. This scheme's effectiveness and application potential in the field of weak signal processing for photoelectric detection are confirmed.  
Conceptualization, Y.W. and M.W.; Methodology, Y.W., M.W. and W.B.; Software, Y.W. and M.W.; Validation, Y.W., M.W. and Z.S.; Formal analysis, Y.W. and W.B.; Investigation, Y.W., Z.S. and W.B.; Resources, Y.W. and W.B.; Data curation, Y.W. and Z.S.; Writing--original draft, Y.W. and W.B.; Writing--review & editing, Y.W., M.W., Z.S. and W.B.; Visualization, Y.W., M.W., Z.S. and W.B.; Supervision, Y.W. and M.W.; Project administration, Y.W.; Funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not available to the public due to a confidentiality agreement with the company.  
The authors declare no conflict of interest.
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 4 Discussion
### Uncertainties of This Study  
Due to the extensive temporal and spatial scale of this study, the utilization of high spatial resolution satellite imagery, while desirable, was constrained by the limitations of revisit frequency, hindering the continuous monitoring of dynamic water body changes. The 30 m spatial resolution employed in this study resulted in the omission of narrow water bodies, such as ponds and ditches. Future research should integrate optical imagery with ground-based observations to enhance the accuracy of water body delineation.  
Given the arid nature of the Kashgar region, characterized by complex oasis and desert landscapes, MODIS-derived evapotranspiration estimates may exhibit inherent biases. The sparse vegetation cover in arid environments complicates the differentiation between soil evaporation and vegetation transpiration. Subsequent studies should leverage unmanned aerial vehicles (UAVs) or Sentinel-2 data for more precise evapotranspiration estimations.  
This study encountered challenges in effectively distinguishing ice and snow from water bodies. Future research should refine model accuracy and comprehensively analyze the influence of ice and snow melt on local water area variations. During the water level data collection process, inconsistencies were identified in the water level records of several reservoirs. Future studies should implement in situ monitoring devices for long-term water level observations.
## 3 Proposed Model and Its Performance Evaluation
\begin{table}
\begin{tabular}{c c c c} \hline
**Weather Condition** & **Month** & **Visibility** & **Attenuation** \\ \hline Fog & December 2022 & 9.88 km & 0.894 dB/km \\ Fog & January 2023 & 13.5 km & 0.655 dB/km \\ Fog & February 2023 & 10.09 km & 0.876 dB/km \\ Haze & December 2022 & 8.4 km & 1.052 dB/km \\ Haze & February 2023 & 3 km & 4.843 dB/km \\ Clouds & December 2022 & 23.29 km & 0.379 dB/km \\ Clouds & January 2023 & 26.45 km & 0.334 dB/km \\ Clouds & February 2023 & 21.96 km & 0.402 dB/km \\ \hline \end{tabular}
\end{table} 
Table 3: Visibility and attenuation: London region.
# A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm
## 4 Experiments and Evaluations
### Parameter Setting  
Before the experiment, some parameters need to be determined, such as the population size, maximum generation number (MaxGen), crossover probability and mutation probability, where the population size and maximum generation number (MaxGen) are related to the number of buildings and conflicts in each block. If this parameter value is set too small, then it will lead to a small number of chromosomes and too few iterations in each population, which will not be sufficiently evolved to find the optimal solution. If this parameter value is set too large, then increased running costs will ensue. Therefore, these two parameters are determined by [PERSON]'s research [17], which uses experimentally determined heuristics, as shown in Equations (9) and (10).  
\[P_{m}=4*C_{m} \tag{9}\]  
\[G_{m}=15*S_{m} \tag{10}\]  
Figure 4: The original experimental data (1:10,000). (**a**) Original data; (**b**) Result of widening the road.  
where parameter \(P_{m}\) is the population size, \(G_{m}\) is the maximum generation number, and \(S_{m}\) and \(C_{m}\) represent the number of buildings and the number of conflicts in each block, respectively.  
In the GA, the genetic operations mainly include crossover and mutation. The frequency at which the genetic operations occur is controlled by the crossover and mutation probability, which determine the global search capability and local search capability of the algorithm. Many scholars recommend choosing a larger crossover probability and a smaller mutation probability. However, in practical applications, the specific value of the parameters is generally obtained through experiments. In this study, to compare with other algorithms, the crossover probability and mutation probability in GA and IGA are 0.8 and 0.08, respectively. Since the BDMPGA has multiple populations, the parameter values assigned to each population are different. According to the suggestion of scholars [PERSON] et al. [40], the crossover probability of each population is randomly assigned within the range of [0.7, 0.9], and the mutation probability of each population is randomly assigned within the range of [0.001, 0.05]. In addition, the number of populations and the number of minimum generations of the optimal individual are all set to 10 in the BDMPGA. Because the conflict is mainly caused by the widening of the road, the principle of displacement is to maintain the accuracy of the position of the buildings as much as possible while resolving all possible conflicts. Therefore, the objective function weights are set in principle as follows \(w_{BR\_conf}>w_{BB\_conf}>w_{TotalDist}\). The specific value is the same as [PERSON]'s work [27], that is, the \(w_{BR\_conf}\) is set to 30,000, the \(w_{BB\_conf}\) is set to 25,000 and the \(w_{TotalDist}\) is set to 1. The values of the parameters used in these three methods are shown in Table 1. The symbol - indicates that the corresponding method does not contain that parameter.
## 2 Materials and Methods
### Data Normalization and Feature Selection
#### 2.5.2 Feature Selection  
The feature selection and ranking for the training of an automated decision system is a crucial and challenging process [39; 40]. Feature selection methods are applied to different fields, including the pharmaceutical and oil industry, voice and pattern recognition, biotechnology, etc. [47]. The aim of these methods is to reduce the features that are inserted into the classifier by keeping out unnecessary ones and, by extension, decreasing noise in multidimensional data. A small number of features allows for better visualization and understanding of the data, fewer operations and storage requirements, and more efficient training and testing processes. Overtraining of the classifier can also be avoided while improving its performance, giving faster results and a better understanding of the training process. An exhaustive search was used in this study for feature selection. This method investigates all feature combinations and yields optimum results [48]. The relatively small dataset of our pilot study allowed for the application of the method that is usuallycomputationally intensive and almost impractical to use with a large number of features and large datasets [41; 42].
## 4 LoRaWAN Issues and Recent Solutions
### Network Scalability
The ALOHA-based MAC protocol of LoRaWAN limits its scalability and represents an obstacle for LoRa to lead other LPWAN technologies as the main representative in the wide-scale deployment of IoT applications. In [50], the authors proposed a distributed queueing algorithm as an improvement to the MAC protocol of LoRaWAN. It uses a tree-splitting technique whose performance is independent of how many nodes share a channel. Unlike ALOHA, the distributed queueing method needs active nodes to contend in contention slots before sending data. The distribution of nodes between two logical queues is accomplished by the application of a set of rules. In the case of collision detection, nodes are categorized into groups and the first logical queue (collision resolution queue), and they wait for their turn to be transmitted. If no collisions are detected, the nodes are organized into the data transmission queue. The grouping of the colliding nodes decreases the collision probability in the later access attempts, since the simultaneous trails are minimized. The authors claimed that the obtained results show that the highest throughput of the modified MAC protocol is independent of the network size and shows improved throughput and delay while saving energy compared with the pure ALOHA MAC of Lo-RaWAN. The performance level of the LoRaWAN technology is determined by the maximum number of nodes that can communicate on a LoRa channel, which determines the technology's scalability in large-scale deployment situations. The study in [51] evaluated the level performance of LoRaWAN technology by investigating the number of packet collisions that might occur. The results show a rapid decrease in efficiency when the amount of devices that rely on the mechanic of communication of the ALOHA protocol is significantly increased. An introduction of a communication channel occupancy management mechanism may be possible to reduce the number of collisions before beginning a proposed transmission. An ADR algorithm can be used to minimize collisions and to allow automatic changes in the data rate if a collision is detected. The approach increases the energy consumption of the LoRa node when applying a collision avoidance method. Another suggestion is to eliminate the ACK mechanism, which could decrease the number of collisions [52].