esa-sceva/satcom-chunk-collection huggingface dataset:
# Inverse Solution Error Analysis and Correction of Beam Steering System Based on Risley Prisms
## 5 Conclusions  
In this paper, the inverse solution errors of Risley prisms induced by incident light tilt and prism rotation axis tilt are investigated. A correction algorithm based on pointing-field transformation is proposed. Based on a more accurate error model of Risley prisms, the systemic errors are obtained by parameter identification, thereby obtaining an accurate forward solution. Since the actual beam trajectory and pointing field are deformed and deviated under the impact of incident light tilt and prism rotation axis tilt, pointing errors will be generated when using the two-step method for the inverse solution. We simulate the variation of the inverse solution error over the entire pointing field. A correction method based on pointing-field transformation is proposed to correct the inverse solution error. The simulation results show that the maximum value and RMS value of inverse solution error decrease by 80.9% and 87.16%, respectively. Since the experiment is limited to a small deflection angle range, the error descent rate of the experimental results is better than the simulation results. The maximum error and the RMS value of error in the experiment are reduced by 94.08% and 95.18%, respectively. The correction method provides valuable guidance for improving the accuracy of Risley prisms.  
Conceptualization, methodology, and theory, Y.S.; simulations, Y.S.; modelling and data analysis, Y.S.; experiments, Y.S.; writing--original draft, Y.S.; writing--review and editing, J.W., L.H. and S.W.; funding, supervision, and project administration, S.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data supporting reported results can be obtained from the corresponding author. The authors declare no conflict of interest.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 3 Public-Key Cryptography with an Authentication Table Key Management Scheme (PCAT)
### Node Adding  
In the proposed scheme, the nodes store the information required to identify other nodes for all the time in which they are active. Therefore, PCAT is compliant with mobile networks and it is able to manage the insertion of new nodes into the network after the first deployment. However, for each new node added to the network after the initial deployment, a specific row in the authentication table is required, so the size of the table imposes an upper bound to the total quantity of nodes.  
Before the deployment, the administrator of the network generates the data for the maximum number of nodes compliant with the selected dimension of the authentication tables, even if the quantity of initial nodes is lower. All the tables also include the rows not matched to the initial nodes. A node added to the network after the initial deployment is set up with a packet of still available data, i.e., ID, keys, bit mask and authentication table. The new node executes the normal handshake routine.  
A node that was removed from the network because of a failure or low battery is reintroduced in the network without changing its memory. Thus, it is still able to continue its previous tasks. However, if the node must be changed, a new node with the same initial security information can be deployed. The new node will execute the routine described in Figure 3. The nodes that already share a pairwise key with a reintroduced node do not answer its Hello messages. However, the new node does not share a pairwise key with them, so it answers with an Ack1 message to their Hello messages. When a node receives an Ack1 message from a node already matched to a pairwise key, it checks the authenticity of the credentials, and then sends back a reintroduced second acknowledge message (RAck2), in order to check if the node is authentic and to establish a new key. In contrast to Ack2 messages, an RAck2 also contains a random challenge (Chall) encrypted with the public key of the new node. The new node answers with a reintroduced third acknowledge message (RAck3), which includes the same data of an Ack3 message and the decrypted random challenge (ChallDEC). The verification of the challenge demonstrates that the new node is authentic and that it needs a new pairwise key.
# Biologically-Inspired Learning and Adaptation of Self-Evolving Control for Networked Mobile Robots
## 2 Biologically-Inspired Kalman Filter Based RBFNN Control
### Evolutionary KF-RBFNN Control  
#### 2.4.1 Modified GA with Levy Flight  
The GA developed by [PERSON] is a search algorithm that is inspired by [PERSON]'s theory of natural evolution. This evolutionary algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction, thereby producing offspring of the next generation. This stochastic optimization technique starts with a set of solutions (chromosomes), called a population. This paradigm employs probabilistic rules to evolve a population from one generation to the next via the genetic operators: reproduction, crossover, and mutation. This paradigm is widely used to solve multidimensional optimization problems. When applying a GA to deal with optimization problems, an initial population of feasible solutions is generated. Each feasible solution is encoded as a chromosome string. These chromosomes are evaluated using a predefined fitness function or objective function based on the optimization problems. Figure 5 presents the flowchart of a GA. The initial population is randomly generated and the fitness function is defined before the GA evolutionary process begins. This study employs tournament selection, single-point crossover and single-point mutation strategies to develop a modified GA paradigm.  
Figure 4: Block diagram of the KF-RBFNN control scheme.  
Figure 5: Flowchart of evolutionary GA.  
Crossover and mutation are important operations for generating new individuals in the GAs. The performance of a GA is sensitive to the control parameter setting. The probabilities of crossover and mutation are significant parameters that influence the convergence performance of a GA. The use of unsuitable probability for crossover and mutation can result in poor convergence performance. More precisely, choosing suitable parameter values is a problem dependent task and requires previous experiences. Most studies adopt fixed crossover and mutation probabilities; this paper employs the Levy flight which is a specialized random walk to increase the search diversity, expressed by:  
\[Levy(\beta)=\left|\frac{\Gamma(\beta+1)\times\sin\left(\frac{\pi\beta}{2} \right)}{\Gamma\left(\frac{1+\beta}{2}\right)\times\beta\times 2^{(\frac{\beta-1}{2})}} \right|^{\frac{1}{\beta}}, \tag{10}\]  
where \(\Gamma\) denotes the gamma function and \(\beta\) is a constant \((1<\beta\leq 3)\). The proposed modified GA is then applied to optimally set the initial parameters of the KF-RBFNN.
# FM-UWB: Towards a Robust, Low-Power Radio for Body Area Networks
###### Abstract  
The Frequency Modulated Ultra-Wideband (FM-UWB) is known as a low-power, low-complexity modulation scheme targeting low to moderate data rates in applications such as wireless body area networks. In this paper, a thorough review of all FM-UWB receivers and transmitters reported in literature is presented. The emphasis is on trends in power reduction that exhibit an improvement by a factor 20 over the past eight years, showing the high potential of FM-UWB. The main architectural and circuit techniques that have led to this improvement are highlighted. Seldom explored potential of using higher data rates and more complex modulations is demonstrated as a way to increase energy efficiency of FM-UWB. Multi-user communication over a single Radio Frequency (RF) channel is explored in more depth and multi-channel transmission is proposed as an extension of standard FM-UWB. The two techniques provide means of decreasing network latency, improving performance, and allow the FM-UWB to accommodate the increasing number of sensor nodes in the emerging applications such as High-Density Wireless Sensor Networks.  
Keywords:UWB; FM; low-power; Wireless Body Area Networks (WBAN); Frequency Division Multiple Access (FDMA); multi-user; transceiver +
Footnote †: journal: Sensors
## 3 Methodology
### Genetic Algorithm for the Parameter Estimation
Once the new population is obtained, the process is repeated from the evaluation step as long as the stop criterion is not reached. When the stop criterion is reached, the GA delivers the estimated parameterized model for the active power forecasting. In the case of the THD prediction, the aforementioned process is carried out with only two differences. The first is related to the objective function. For the THD model, Equation (6) is used:  
\[ \begin{array}{c}H_{i}=u_{1}x_{1,\;i}+u_{2}x_{2,i}+u_{3}x_{3,i}+u_{4}x_{4,i}+u _{5}x_{5,i}+u_{6}x_{6,i}+u_{7}x_{7,i}+u_{8}x_{8,i}+u_{9}x_{9,i}+\\ u_{10}x_{10,i}+u_{11}x_{11,i}+u_{12}x_{12,i}+u_{13}x_{13,i}+u_{14}x_{14,i},\end{array}  \tag{6}\]  
where \(H_{i}\) is the \(i\)-th value of the estimated THD;  
\(x_{1,i}\) is the \(i\)-th value of the sun irradiance;  
\(x_{2,i}\) is the \(i\)-th value of the cell temperature;  
\(x_{3,i}\) is the \(i\)-th value of the DC voltage signal;  
\(x_{4,i}\) is the \(i\)-th value of the DC current signal;  
\(u_{1}\), \(u_{2}\), \(u_{3}\), \(u_{4}\), \(u_{5}\), \(u_{6}\), \(u_{7}\), \(u_{8}\), \(u_{9}\), \(u_{10}\), \(u_{11}\), \(u_{12}\), \(u_{13}\), and \(u_{14}\) are the constant weights determined by the GA. The objective function for the THD forecasting is selected in this way because Equation (3) is an expression that involves quadratic terms; therefore, these types of terms should be considered in the model for the estimation. Moreover, electric parameters are not conventionally related with the THD. However, in this case, the voltage and currents signals represent an important part of the internal behavior of the PV system and this is why their use in the estimation of THD is being proposed only in the PV generation process. To obtain the THD prediction, the GA is trained using THD testing signals. With these two modifications (objective function and test signal), it is possible to perform the THD prediction. The methodology is performed a total of 24 times, one for each day of the parameterization dataset. The final weights for the two models are the average of these 24 results. Finally, the proposed methodology is used to perform the forecasting of the active power and the THD of eight different days from those used for the estimation of the parameterized model. These days come from four different seasons of the year, and they present different weather conditions from each other. This way, it is possible to evaluate the variability associated with the specific climatic changes of each season, but also the variability that results from the lack of sunlight when there are clouds in the sky.
## 6 Discussion
### Rapid Changes in Spectral Features and Missing Temporal Data in Non-Food Crop Growth Stages
For example, in rice-rapeseed rotation systems, the growth cycles of the two crops differ, with rapeseed's flowering and maturation phases typically occurring after rice's growth cycle, leading to significant spectral feature changes in the time-series data [47]. If the model relies on static phenology windows for classification, these variations in growth cycles and phenological stages will lead to misclassification, especially when there is a significant overlap in spectral features between crops [48; 49]. To address these limitations, future research could incorporate adaptive phenological models to overcome the shortcomings of static phenological assumptions. By integrating dynamic information, such as meteorological data, soil moisture, and temperature, the model can adjust the phenology window in real-time, flexibly adapting to the growth patterns of different regions and crops. Through adaptive mechanisms, the model can dynamically adjust phenological parameters based on actual crop growth conditions, improving classification accuracy and adaptability. Model Deployment Cost Challenges and Optimization Paths: Applications of Cross-Modal Learning and Lightweight Strategies  
The practical application of the PVM model faces high deployment costs, which is particularly evident in real-world applications. The current model relies on large-scale labeled data and high-performance computing resources, such as requiring 9990 labeledsamples and powerful computational devices like the NVIDIA A800 GPU for training and optimization. However, this poses significant financial pressure and resource investment for grassroots natural resource departments. Preparing large-scale datasets often requires substantial manual annotation, and purchasing and maintaining high-performance computing resources also requires considerable funding, limiting the application of these models in low-resource areas. In addition to the high costs of hardware and data, differences in crop growth and environmental conditions across regions mean that labeled data from some areas may not be directly applicable to other regions, affecting the model's accuracy and adaptability. Therefore, the quality and regional adaptability of labeled data are also key issues in model deployment. To address this, future research can explore cross-modal contrastive learning or knowledge distillation techniques to deepen semantic alignment between SAR and optical data, reducing fusion errors for fragmented plots [43]. Introducing lightweight Transformer architectures (such as Mobile-VIT) and self-supervised pretraining strategies can compress model parameters by over 30% and reduce annotation costs by utilizing unlabeled time-series data. Additionally, combining probabilistic soft classification maps and uncertainty modeling techniques can help distinguish fuzzy pixels in \"non-food-food\" transition areas, providing tiered management guidelines for cultivated land protection policies [31; 46].
## 2 Pharmacology of Piperine: In Vitro and In Vivo Studies
### Anti-Cancer Effect of Piperine  
In vitro studies on various cancer cells showed that piperine possesses cytotoxic action (selective toward tumor cells) against several types of cancer, including breast, lung, prostate, cervical, and other cancers.
# Modelling and Measuring Trust in Human-Robot Collaboration
## 4 Results
### Universality of Trust Detection Models  
Deciphering trust in the intricate human-robot dynamics requires many variables. The physiological signals captured during the experiment, including 12 detailed signals related to brain activity (EEG), skin conductance response (GSR), respiratory band extension (RSP), and pupil diameter variations (PLP), offer a variety of information to train a suitable model. However, there are some considerations to ponder in order to choose the most suitable approach. We analyzed three distinct yet complementary approaches in creating a trust detection model.  
The General Approach involved constructing an expansive dataset comprising all participants' data and iterations. This method allowed us to leverage a broad dataset, providing a comprehensive overview of general trust trends. However, it introduced the challenge of potential information leaks between the training and test sets, as participant-specific information was included in both sets. Under this approach, a single model covers all the trust detection needed for every participant.  
In the Leave-One-Out Approach, we addressed the risk of information leaks by training algorithms using the complete dataset, excluding participant-specific information. While this approach maintained a substantial amount of data, it sacrificed personalized information crucial for trust detection, leaving out precise data that could contain the most valuable information. This approach created a specific classifier for each left-out participant and the results were later aggregated.  
The Individual Approach focused on the uniqueness of trust reactions. By exclusively utilizing each participant's individual dataset, this method created personalized algorithms for each individual. Despite having the smallest dataset for training, the Individual Approach allowed for the detection of specific trust nuances in each participant. As in the previous case, a specific model was created for every participant.  
In summary, we created over 100 distinct models through an iterative process involving the use of a pipeline grid search to explore various algorithms, including K-Nearest Neighbors, Support Vector Machines, and Random Forests, and fine-tunning the corresponding hyperparameters to optimize the algorithmic result.  
Table 5 presents key metrics, including minimum, mean, and maximum F1 scores, obtained with each training model, showcasing the strengths and nuances of each approach.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
###### Abstract  
Underground pipelines are vital means of transporting fluid resources like water, oil and gas. The process of locating buried pipelines of interest is an essential prerequisite for pipeline maintenance and repair. Acoustic pipe localization methods, as effective trenchless detection techniques, have been implemented in locating underground utilities and shown to be very promising in plastic pipeline localization. This paper presents a comprehensive review of current acoustic methods and recent advances in the localization of buried pipelines. Investigations are conducted from multiple perspectives including the wave propagation mechanism in buried pipe systems, the principles behind each method along with advantages and limitations, representative acoustic locators in commercial markets, the condition of buried pipes, as well as selection of preferred methods for locating pipelines based on the applicability of existing localization techniques. In addition, the key features of each method are summarized and suggestions for future work are proposed. Acoustic methods for locating underground pipelines have proven to be useful and effective supplements to existing localization techniques. It has been highlighted that the ability of acoustic methods to locate non-metallic objects should be of particular practical value. While this paper focuses on a specific application associated with pipeline localization, many acoustic methods are feasible across a wide range of underground infrastructures.  
acoustic localization methods; buried pipelines; wave propagation mechanism; vibro-acoustics +
Footnote †: journal: Applied Sciences  
1
## 2 Principles and Methods
### Technical Routes  
In this study, we adopted \"3S\" technology (\"3S\" is the collective name for remote sensing (RS), geography information systems (GIS), and global positioning systems (GPS)), InSAR radar technology, and machine learning to study and design the program with  
Figure 1: An overview of convolutional neural network with Bayesian optimization used in this research.  
the main line of landslide susceptibility evaluation in the study area. Specifically, (1) the data were collected in the study area; (2) landslides were identified in the study area by combining multi-temporal high resolution imagery and InSAR elevation track data; (3) the landslide susceptibility evaluation system was constructed based on the data research and analysis, and the evaluation factors were analyzed and screened by combining the InSAR deformation data, topography and geomorphology, geological formations, hydrological environment, and human engineering activities; (4) the BO-CNN model was constructed, comparison experiments were performed by combining the BO-RF and PSO-SVM models for comparative experiments, we adopted Arcgis software to draw the susceptibility map, the ROC curve AUC value and frequency method were employed to analyze the differences of the models, which can provide theoretical guidance for landslide susceptibility evaluation. The research and technical route of this study are illustrated in Figure 2.  
Figure 2: Research technologies routes and research workflow.
## 4 Conclusions  
The layered perovskite BaLa\({}_{0.9}\)Pr\({}_{0.1}\)InO\({}_{4}\) was obtained and investigated as a protonic conductor for the first time. The possibility of proton transport is shown. It was proved that isovalent doping Pr\({}^{3+}\)\(\rightarrow\) La\({}^{3+}\) leads to an increase in the crystal lattice size, proton concentration and proton mobility. The proton conductivity value for the doped BaLa\({}_{0.9}\)Pr\({}_{0.1}\)InO\({}_{4}\) composition is 5.0 \(\times\)10\({}^{-6}\) S/cm (T = 400 \({}^{\circ}\)C), in comparison with the 2.7\(\times\) 10\({}^{-7}\) S/cm for BaLaInO\({}_{4}\) composition that is 18 times greater. Further research for a dopant capable of a significant change in its electrical conductivity nature is relevant. Layered perovskites based on BaLaInO\({}_{4}\) are promising materials for application in proton conducting  
Conceptualization, I.A. and N.T.; methodology, I.A. and N.T.; investigation, A.B.; data curation, N.T. and A.B.; writing--original draft preparation, N.T.; writing--review and editing, N.T. and I.A. All authors have read and agreed to the published version of the manuscript. This research was supported by the Russian Science Foundation (grant no 22-79-10003). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. Not conflicts of Interest: The authors declare no conflict of interest.  
Figure 4: The dependencies of conductivity (**a**) and mobility (**b**) of protons vs. temperature for the compositions BaLa\({}_{0.9}\)Pr\({}_{0.1}\)InO\({}_{4}\) and BaLaInO\({}_{4}\)[56].
## 4 Proposed Solution  
Based on the review of the existing security enhancements available for MANETs' environment, a multiple path-based solution is proposed (Figure 1). In the proposed solution for the data transmission, multiple loop free and disjoint paths are utilized. Due to the absence of any CA or heavy computations-based processes during the route establishment phase, it becomes important that integrity and confidentiality of the data transmission should remain intact. So, to fulfill this aspect, the use of asymmetric cryptography and trust of paths is based on direct observations of the source node.