esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This project presents a procedure that applies the Groebner Basis theory to synthesize the IKM of non-redundant open-chain robotic systems. The IKM calculations are performed in a systematic way that do not require any special knowledge of the robot's mechanical structure, besides its Denavit-Hartenberg parameters and the movement range of its actuators. The procedure's output is the IKM, both in C++ and as a MATLAB(r) script, which can be used directly in the robot's control system or to simulate its behavior.  
The performance analysis presented in Section 4 shows that the synthesized IKMs are totally comparable, both in precision and computation time, with the kinematic models calculated by traditional methods. This implies that the developed procedure represents a systematic solution to the Kinematic Problem of non-redundant open-chain robotic systems that is independent of the robot's mechanical structure or its geometry.  
The IKMs synthesized in this work showed how the developed procedure can be applied to non-redundant manipulators that satisfy the in-line wrist condition, such as the PUMA 560, as well as multi-legged walking robots, like the Lynxmotion's BH3-R hexapod. However, it can also be applied to all Cartesian robotic systems and SCARA robots, covering a large range of open-chain robotic systems.  
Conceptualization, J.G.-G., A.V.F., V.M.A., and M.A.D.-R.; Methodology, J.G.-G., A.V.F., and M.A.D.-R.; Software, J.G.-G. and M.A.D.-R.; Validation, J.G.-G., A.V.F., V.M.A., and M.A.D.-R.; Formal analysis, [PERSON]-G., A.V.F., V.M.A., and M.A.D.-R.; Investigation, [PERSON]-G., A.V.F., V.M.A., and M.A.D.-R.; Resources, [PERSON]-G., A.V.F., and V.M.A.; Data curation, J.G.-G. and M.A.D.-R.; Writing--original draft preparation, [PERSON]-G., A.V.F., V.M.A., and M.A.D.-R.; Visualization, J.G. and A.V.F.; Supervision, [PERSON]-G., A.V.F., and V.M.A.; Project administration, A.V.F. and V.M.A.; Funding acquisition, A.V.F. and V.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Plan Nacional de l+D+i, Agencia Estatal de Investigacion del Ministerio de Economia, Industria y Competitividad del Gobierno de Espana, in the project FEDER-CICYT DPI2017-84201-R.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
IKMInverse Kinematic Model  
IKPInverse Kinematic Problem  
D-HDenavit-Hartenberg  
DoFDegrees of Freedom  
RMSRoot Mean Square (Error)
## 2 Materials and Methods
### Mood-Based Playlists  
We aim to develop a user-friendly application that generates mood-based Spotify playlists. Using a random forest model, the application analyzes a user's current emotional state, as determined from their entered text. Subsequently, the Spotify API creates a playlist aligned with the identified mood. This application was built using various development tools, including Visual Studio Code 1.97, Google Cloud 3.1.0, Figma 124.4.7, Python 3.13.0, the Spotify Dashboard APK v9.0.12.567, and Android Studio 2024.3.1 on a Microsoft Windows 11 operating system. The app was developed using the Flutter framework to create a mobile application that interacts with a Flask API for emotion analysis and Spotify API for playlist generation. The app analyzes emotions by sending text input to the Flask API, which returns predicted emotions with percentages. The app retrieves songs from a local CSV file and generates a Spotify playlist based on these emotions. The app also handles Spotify authentication, allowing users to connect their accounts and authorize  
Figure 2: The process of building and deploying a random forest model for classifying the mood of songs using the Spotify API and Google Cloud Platform.  
playlist creation. The UI provides text input, emotion selection, playlist creation, and song shuffling. Loading screens are displayed during API calls, and error messages are shown for issues with the backend or Spotify. This demonstrates a comprehensive approach to building a mood-based music discovery application within a Flutter-based mobile interface.  
In brief, this initial setup of our Flutter application, including key library imports, covers data conversion, UI development, platform interaction, HTTP requests, CSV handling, URL launching, deep linking, asynchronous operations, process running, and loading animations. The main() function and runApp() call initiate the application. The complete implementation includes additional random forest model integration code, Spotify API interaction (authentication, track searching, and playlist creation), and UI design for user input and playlist display. The system block diagram illustrating the user view is shown in Figure 3.  
Users input their mood by selecting a pre-defined emoticon shortcut or typing a text description. If text is entered, a sentiment analysis model, hosted on the cloud, processes the text to determine the emotional tone. This emotional data and the direct input from mood shortcuts are then fed into the random forest model. The model uses this mood information, likely referencing a database of mood-to-music mappings (represented by the CSV block), to create a playlist. Finally, the system leverages the Spotify API to create the generated playlist within the user's Spotify account and make it ready for immediate playback. A mobile interface allows users to interact with the system conveniently.
# Reinforcement Learning-Based Routing Protocols in Flying Ad Hoc Networks (FANET): A Review
## 4 Flying Ad Hoc Networks
### Unmanned Aerial Vehicles (UAVs)
For example, internet broadcasting, remote sensing, and navigation [46]. Additionally, drones can be divided into two groups based on size: small and large. * **Large UAVs:** These drones are commonly used in single-UAV systems to carry out some specific missions. They are expensive. Furthermore, their maintenance and repair costs are very high because their structure is complex. Figure 4: Types of UAVs (**a**) rotary-wing UAV (**b**) fixed-wing UAV. * **Small UAVs:** These drones can be used in multi-UAV systems and swarms, and are very useful for civilian applications because they have many advantages, including suitable price and less maintenance and repair costs compared to large UAVs. Furthermore, they have simpler structures and include lightweight equipment, such as a cheap body, small batteries, lightweight radio module, and microprocessor. However, their ability is less than larger UAVs. These drones have a short flight time and low coverage range because they are limited in terms of weight and payload. These restrictions can be a serious issue in important missions, for example, search and rescue scenarios.
# Estimating Interference with a Two-Dimensional Viterbi Algorithm for Bit-Patterned Media Recording
## 3 Proposed Model
### BPMR Channel  
The readback signal from the BPMR channel suffers from 2D interference and Gaussian noise and can be written as:  
\[y[j,k]=a[j,k]*h[j,k]+w[j,k], \tag{32}\]  
where * is the convolution operation; \(j\) and \(k\) are the discrete indices along the down- and cross-track directions, respectively; \(y[j,k]\) is the readback signal; \(w[j,k]\) is additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma^{2}\); and \(h[j,k]\) is the BPMR channel pulse response, as follows:  
\[h[j,k]=P\Big{(}jT_{x},kT_{q}-\Delta_{off}\Big{)}, \tag{33}\]  
where \(P(x,\,q)\) is a 2D Gaussian function used to represent the 2D island response of the BPMR channel, as in [29].  
\[P(x,q)=A\exp\left(-\frac{1}{2c^{2}}\Bigg{[}\bigg{(}\frac{x+\Delta_{x}}{PW_{x} }\bigg{)}^{2}+\bigg{(}\frac{q+\Delta_{q}}{PW_{q}}\bigg{)}^{2}\Bigg{]}\right), \tag{34}\]  
where \(A\) is the peak amplitude (in this study, \(A=1\)); \(x\) and \(q\) are the down- and cross-track directions, respectively; \(\Delta_{x}\) and \(\Delta_{q}\) are the down- and cross-track bit-location fluctuations, respectively; \(c\) is the constant, which represents the relationship between the standard deviation of the Gaussian function and \(PW_{50}\) (\(c=1/2.3548\)) [23]; and \(PW_{x}\) and \(PW_{q}\) are the \(PW_{50}\) components of the down- and cross-track pulses, respectively. Finally, we defined TMR for the BPMR system as  
\[TMR(\%)=\frac{\Delta_{off}}{T_{q}}. \tag{35}\]  
Figure 2: Proposed detection model.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 5 Conclusions
Fourth, adapting the principles of _XSQ-Learning_ to function approximation architectures, particularly in deep Reinforcement Learning contexts (e.g., DQN or DDPG), would enable _XSQ-Learning_ to tackle real-world applications with high-dimensional continuous state spaces, such as autonomous robotics and dynamic path planning in uncertain environments  
Additionally, relaxing the similarity threshold using linguistic quantifiers--such as \"almost all\" or \"many\" objects of the universe--represents a promising avenue to further enhance the flexibility and interpretability of the update mechanism. Formalizing these ideas with intermediate quantifiers, as explored in the literature on fuzzy logic, could provide richer similarity criteria that better handle uncertainty and domain-specific nuances. The success of _XSQ-Learning_ also raises intriguing theoretical questions about the relationship between state space geometry and learning efficiency. Our results suggest that the algorithm's performance advantage correlates with the \"similarity structure\" of the environment--the degree to which nearby states share optimal policies. Formalizing this relationship could lead to new methods for environment characterization and algorithm selection. In conclusion, _XSQ-Learning_ represents an advance in similarity-based RL, offering practitioners a practical tool for accelerating policy convergence without sacrificing stability. Its empirical performance and theoretical properties suggest potential utility in applications requiring efficient policy convergence. Conceptualization, A.Y.R.G. and R.E.L.D. ; methodology, A.Y.R.G. ; software, R.E.L.D. ; validation, R.E.L.D. and S.M.A.S. ; formal analysis, A.Y.R.G. ; investigation, R.E.L.D. and S.M.A.S. ; writing--original draft preparation, R.E.L.D., A.Y.R.G. and M.G.S.C. ; writing--review and editing, M.G.S.C. ; supervision, A.Y.R.G. and M.G.S.C. ; project administration, A.Y.R.G. ; funding acquisition, A.Y.R.G. All authors have read and agreed to the published version of the manuscript. This research was funded by National Council of Humanities, Sciences and Technologies (CONAHCYT) of Mexico grant number PCC 320301. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:
## 3 Results and Discussion
### Analysis of Deviation in the Inverter Based Power System  
As the inverter is controlled using an 8-bit converter, the accuracy of the electric motor speed control will depend on the output voltage. The minimal value of a step is 19.608 mV for each bit, as presented in Table 1. The graph presented in Figure 15 is based on the relation between the deviation of the electric motor speed controlled using the AO converter and its deviation is dependent on the value of the controlled voltage. It was shown (Figure 15) that due to the use of digital inverter control, the error depends on the output voltage for lower voltage values and the deviation increases up to 8%. However, for speeds above 600 rpm, the error value will drop below 4% and obtain a value below 2% for a nominal speed of 1500 rpm and for speeds of 2400 rpm 1%.  
Figure 14: Effect of workpiece diameter on the deviation in cutting speed for the conventional power system of a TSB 16 lathe at \(v_{c}=150\) m/min.  
Figure 13: Influence of workpiece diameter on the real value of the cutting speed for the conventional power system of a TSB 16 lathe for a target cutting speed of \(v_{c}=150\) m/min.  
A further example of using the inverter system for electric motor speed control is presented in Figure 16. Machining within the full range of lathe speeds, presented as the diameters of the lathe workpiece, was done with only two years (G1-D3 and G1-D2). The thicker the diameter, the lower the electric motor-speed values, hence, the deviation increased with the diameter as shown in the graph presented in Figure 17.  
Figure 16: Relation between workpiece diameter and electric motor speed (for \(v_{c}\) = 150 m/min) using gears G1-D3 and G1-D2 (power system with inverter).  
Figure 17: Effect of workpiece diameter on the deviation in cutting speed (for \(v_{c}\) = 150 m/min) with G1-D3 and G1-D2 gears (power system with inverter).  
Figure 15: Relation between minimum and maximum electric motor speeds and their deviation for a drive system regulated by an inverter.
## 4 Experimental Analysis
### Results and Discussion
#### 4.4.2 Qualitative Results  
**RQ2:**: _How does the proposed framework, WGA-SWIN, achieve accurate and visually detailed 3D reconstructions compared to state-of-the-art methods on ShapeNet with different input view counts?_  
In this evaluation, we compare our method, both WGA-SWIN and WGA-SWIN+, following SoTA works from [10; 11; 13] using different numbers of view inputs from the ShapeNet dataset. The reconstruction results visualized in Figure 4 highlight the effectiveness of the proposed WGA-SWIN framework in handling the 3D reconstruction of multi-view tasks with various quantities of input (5, 10, 15, and 20) views on various ShapeNet objects. However, WGA-SWIN and WGA-SWIN+ frequently outperform state-of-the-art methods such as LRGT, UMIFormer, and GARNet by capturing both global and local fine-grained object details. Even with only five input views, WGA-SWIN demonstrates the ability to reconstruct the overall shape and basic features of an object more efficiently than competing methods. In addition, by increasing the number of views to 10, 15, and 20, the framework shows significant improvements in retaining complex details that are often overlooked by other methods such as sharp edges, smooth surfaces, and fine structures. This advantage is due to the Window Grouping Attention mechanism, which efficiently captures inter-view relationships, and the progressive hierarchical decoder, which refines voxel grids while preserving structural integrity.  
Figure 4: The results of multi-view 3D reconstruction on the test set of ShapeNet when facing 5, 10, 15, and 20 views as inputs.  
To deal with objects that have complex geometries, such as airplanes and rifles, our proposed method, WGA-SWIN, excels in reconstructing key features such as heads, back-rests, and tail fins with surprising accuracy. As the number of views increases, its ability to effectively integrate information from other views of the structure becomes more apparent, providing consistent structural accuracy and visual fidelity. However, when evaluated with 20 views, WGA-SWIN+ achieves superior reconstructions, which maintain fine-grained details and structural coherence better than any other approach to ensure its robustness and scalability. These results validate the adaptability and effectiveness of the proposed framework, demonstrate its ability to provide high-quality 3D reconstructions over a wide range of input views, and further establish it as a leading method for multi-view reconstruction tasks.
# Risk Assessment of UAV Cyber Range Based on Bayesian-Nash Equilibrium
## 7 Conclusions  
In this paper, the existing common information system risk assessment method based on the game model is transferred to the UAV cyber range, and the incomplete-information model is adopted, which is more in line with the actual application of UAV risk assessment. A static Bayesian attack and defense game model is established, and the game model is used to calculate a mixed strategy Bayesian-Nash equilibrium and predict the attacker behavior. The improvement observed with the fuzzy Analytic Hierarchy Process and the Minimax Theorem is relatively small, 2.67% and 5.38%, respectively. The improvement observed with the proposed method is higher than that with FAHP and the Minimax Theorem. It is 21.43%, and achieved a good, expected effect. The research results show that the application of the static Bayesian offensive and defensive game model on the UAV cyber range can enable the defenders to predict the intention of the attacker and formulate effective defense strategies even though they lack the basic information of the attacker in the initial stage of the offensive and defensive game, and provide help for the UAV cyber range managers to deploy the optimal defense measures. However, this method is only suitable for phased threat assessment, and it is difficult to carry out a dynamic risk assessment of UAV. Therefore, two issues need to be considered in future work combining actual UAV attack and defense requirements. One is to propose an attack effectiveness prediction method based on the analysis of attack stages to describe attack capabilities in multiple dimensions to ensure the quantification of attack strategies and damage; the other is to improve the accuracy of UAV range security risk prediction through feedback iterative optimization.  
Conceptualization, S.M.; methodology, S.M. and Q.P.; validation, S.M. and Q.P.; investigation, S.M. and Q.P.; resources, S.M.; data curation, S.M. and Q.P.; writing--original draft preparation, S.M. and Q.P.; writing--review and editing, S.M. and Q.P.; visualization, S.M.; supervision, S.M.; project administration, S.M.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 7: Attack and defense game experiment.
## 1 Introduction
* By narrowing the spatial attack window to a small distance to the potential victims on the road, the proposed scheme can force the attackers into the detection range of the line-of-sight (LoS) hardware sensors. Thus, the scheme creates an opportunity for the cooperation between the LoS sensors and the communication for the message contents validation. * By applying the proposed scheme to platooning, potentially the most vulnerable application of V2X to the false position attack, we show that it can mitigate not only the collision risk but also the discomfort from the unnecessary responses to the false position attack.
# Testing and Analysis of MOSFET-Based Absorber Integrated Antenna for 5G/WiMAX/WLAN Applications
## 3 Design Analysis of SRD Pulse Generator and Diode-Based Quasi-Circulator
### Transient 3D Electromagnetic Circuit Simulation of SRD Pulse Generator
This configuration isolates port-3 of the circulator. A similar process occurs when the signal enters at ports-2 and -3, and then we have an active circulator, as shown in Figure 9.  
To test the functionality of the designed diode-based quasi-circulator in terms of power routing capability, the output power from port-2 of the pulse generator presented in Section 3.1 is connected to port-1 of the designed circulator, and peak current and voltage values at each port of the circulator are shown in Figure 10a,b, respectively. Table 3 presents each port's current, voltage, and power values. It has been observed that more than half of the power incident on port-1 of the circulator is transferred to port-2. Moreover, port-3 is almost fully isolated with an output power of 192 mW, which is minimal compared to the power incident on port-1. Figure 10: Diode-based quasi-circulator port. (**a**) Current and (**b**) voltage. Figure 9: Three-port active diode-based quasi-circulator.
# Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation
## 5 Conclusions  
This study presents a comprehensive investigation into the long-term deformation behavior of concrete gravity dams on soft rock foundations, with a focus on dynamic parameter inversion using an improved particle swarm optimization (PSO) algorithm. The conclusions are as follows:  
(1) The inversion analysis successfully quantified the elastic moduli of the dam body and foundation, while capturing the time-dependent degradation of the foundation's elastic modulus through an exponential decay model. The identified mechanical parameters provide a critical baseline for evaluating the structural performance and long-term safety of the dam.  
(2) The improved PSO algorithm, incorporating adaptive inertia weight, achieves robust global optimization with stable convergence. Integrated with finite element modeling and real-time monitoring data, the inversion framework enables high-precision displacement predictions of the concrete dam.  
(3) The proposed dynamic inversion method has exhibited significant effectiveness in assessing the long-term performance of the concrete gravity dam constructed on a soft rock foundation. To further advance the research, Bayesian updating theory will be introduced to systematically quantify the uncertainties in both monitoring data and numerical models, with the ultimate objective of establishing reliable risk warning indicators.  
Methodology, T.L.; Software, C.L. and T.L.; Validation, T.S. and W.X.; Data curation, G.Y.; Writing--original draft, G.Y. and C.L.; Writing--review and editing, W.X. and S.C.; Project administration, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (No. 2022 YFC3005403), the National Natural Science Foundation of China (Nos. 52309151 and 52309157), the Open Research Fund of Key Laboratory of Reservoir and Dam Safety Ministry of Water Resources (No. YX323007), and the Science and Technology Project of Power Construction Corporation of China (No. DJ-HXGG-2022-02).  
The data presented in this study are available on request from the corresponding author.  
Author [PERSON] was employed by the company NanJing Hohai Nanzi Hydropower Automation Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 5 Nomological Deductive Reasoning (NDR): A Proposed Solution
### NDR Framework Architecture and Operational Integration with Machine Learning
#### 5.2.3 Integration of Machine Learning and Deductive Reasoning  
In the NDR framework, machine learning (ML) and symbolic reasoning interact sequentially and complementarily. The ML model is responsible for feature extraction--identifying which attributes are most predictive for the task at hand. However, the final decision is not directly adopted from the ML model. Instead, the most influential features are passed onto the reasoning layer for formal evaluation. This decoupling ensures that ML contributes empirical insights by identifying relevant features, and the deductive system ensures interpretability, logical consistency, and compliance with domain laws. This integration follows a two-stage architecture:  
1. **Stage 1--ML Model Prediction**: A supervised ML model (e.g., deep neural network, random forest, or any other model) generates a prediction \(\mathbf{h}_{i}\) for input \(\mathbf{d}_{i}\).
2. **Stage 2--Deductive Reasoning Engine**: The reasoning module takes the input instance \(\mathbf{d}_{i}\), the prediction \(\mathbf{h}_{i}\), and the knowledge base \(\mathbf{L}\) and \(\mathbf{C}\) and then verifies whether the prediction can be deduced from the knowledge base, such as by \[(\mathbf{L}\wedge\mathbf{C}(\mathbf{d}_{i}))\vdash h_{i}\] If the deduction is consistent, the prediction is validated, and a logical explanation is generated. If not, the system raises a **trust warning** or requests further input.