esa-sceva/satcom-chunk-collection huggingface dataset:
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 2 Related Works
Geo-cast-based [29] routing is proposed, which takes into account 3D movement and forwards data toward the destination location. In [30], authors proposed an ant-colony-based location optimization problem scheme that considers the location of each UAV and assigns scores according to locations. A Geographic-Spray and wait routing scheme is introduced in [31] for rescue scenarios, where UAVs move along predefined locations; therefore, it is easy to predict the path. To enhance the efficiency of position-based routing protocols, clustering techniques have been adopted by [32; 33; 34], where UAVs transfer data through cluster heads. GEO-D [35] is a class of position-based routing algorithms in which the source node forwards the message to its neighboring node within the communication range. During the high-speed movement of UAVs, the selected node might not be within the communication range or can have some signal issues; therefore, the message can experience a high delay or be dropped. Similarly, another position-based routing protocol known as the GEO Most Forward (GEOMF) routing algorithm [36] is similar to GEO greedy, but uses the projection technique on the 3D surface to choose the shortest path from source to destination. MF may fail when the neighboring nodes are not on the projection plane. Another scheme is GEO Compass [37], which forwards the messages in a particular direction or angle towards the nearest node. The compass scheme also suffers when there is no neighboring node in a specific direction. In addition, the calculated direction can be a long path to the destination place. In other words, the compass routing scheme may suffer from the never-ending loop; therefore, the network can suffer from high delays and a lower packet delivery ratio (PDR). The above literature review justifies that the different routing schemes and their combination provide a better solution for an infrastructure-less wireless multi-hop network, especially in terms of link stability during communication. The topology of the infrastructure-less network changes dynamically due to the high speed of nodes. It is challenging to have a stable routing protocol to meet the requirement of the high mobility speed of UAVs in a 3D infrastructure-less network. Furthermore, position-based routing protocols have two drawbacks: (1) higher delay because there is no predefined path from source to destination and (2) communication range, i.e., if the source UAV node does not have any neighbor in its communication range, data cannot be forwarded. At the same time, topology-based routing schemes suffer due to the high mobility of UAVs and have extra overhead. Therefore, to overcome these issues, a novel hybrid routing is proposed in this paper. In the below Table 2, the comparative literature of previous routing protocols is highlighted.
## 4 Results
### Multi-Dimensional Algorithm Performance Validation  
The normalized confusion matrix (Figure 4a) demonstrates 98.4% diagonal accuracy (\(\kappa=0.972\)) across four gesture classes (n = 12,000 trials), with Class 3 emergency commands showing 99.1% true-positive rate under ISO 13264 testing protocols. Off-diagonal blue entries (0.9% mean misclassification) primarily occur between adjacent kinematic categories (Classes 1-2), attributable to similar joint angular velocities (\(\Delta<5^{\circ}/\mathrm{s}\)). The 0.08% false-positive rate for incidental non-command motions (Class 0) meets SIL-2 safety thresholds, validated through 32-subject anthropometric trials with Bonferroni-corrected significance (\(p<0.001\), [PERSON]'s d = 1.73).  
Pareto frontier analysis reveals optimal parameterization (\(\lambda=0.5\)), balancing 97.3% recognition accuracy with 48.2 ms inference latency (\(\sigma=3.1\) ms), achieving 22% speed improvement over baseline (\(\lambda=1.25\)) without statistical accuracy loss ([PERSON]'s \(t\)-test, \(p=0.12\)). The nonlinear trend (R\({}^{2}=0.89\)) confirms parallel attention architectures reduce latency variance by 63% in high-velocity scenarios (80 km/h vibration tests), critical for sub-50 ms emergency response requirements.  
Hilbert-Huang transform visualization identifies three dominant intrinsic mode functions (IMF1-IMF3) governing gesture dynamics, with convex regions (z \(>0.5\)) showing 92.7% correlation ([PERSON]'s \(\rho=0.83\)) to radar micro-Doppler signatures. Critical braking patterns occupy the high-curvature zone (x\(\leq\)[\(-0.5\), 0.5], y\(\leq\)[\(-1.0\), \(-0.8\)]), exhibiting 3.1\(\times\) higher Mahalanobis distance (\(p<0.01\)) from non-emergency clusters, confirming effective spatiotemporal separation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Proposed Method** & **HMM Baseline** & **CNN Baseline** & **LSTM** \\ \hline Accuracy (\%) & 99.1 & 87.2 & 93.5 & 95.3 \\ Latency (ms) & 48.3 & 120.5 & 82.7 & 72.1 \\ False Positive Rate (\%) & 0.08 & 1.25 & 0.45 & 0.34 \\ Power Consumption (W) & 5.2 & 8.7 & 7.1 & 6.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Multi-modal system performance comparison.  
Risk Priority Numbers (RPN) distribution reveals 78% of errors (Severity \(\geq\) 15) originate from infrared multipath interference (Occurrence = 6.8/h), mitigated through adaptive thresholding to 2.1/hour post-optimization (Cohen's d = 1.73). The accuracy-severity inverse correlation (r = \(-\)0.79, \(p\) = 0.008) validates redundant sensor fusion efficacy, with dual-modality validation reducing high-severity (RPN > 15) failures by 93% in edge-case mobility scenarios (\(\Delta\)v > 2.3 m/s, \(\alpha\) < 0.05).  
This multi-faceted validation framework provides system-level evidence (\(\alpha\) = 0.01 confidence across all metrics) for deploying the architecture in ASIL-D environments, achieving V-model development rigor through traceable mapping between design specifications and experimental outcomes.
###### Abstract  
Vehicle-to-grid (V2G) systems play a key role in the integration of electric vehicles (EVs) into smart grids by enabling bidirectional energy flows between EVs and the grid. Optimizing V2G operations poses significant challenges due to the dynamic nature of energy demand, grid constraints, and user preferences. This paper addresses the optimization challenges in V2G systems and explores the use of artificial intelligence (AI) methods to tackle these challenges. The paper provides a comprehensive analysis of existing work on optimization in V2G systems and identifies gaps where AI-driven algorithms, machine learning, metaheuristic extensions, and agile optimization concepts can be applied. Case studies and examples demonstrate the efficacy of AI-driven algorithms in optimizing V2G operations, leading to improved grid stability, cost optimization, and user satisfaction. Furthermore, agile optimization concepts are introduced to enhance flexibility and responsiveness in V2G optimization. The paper concludes with a discussion on the challenges and future directions for integrating AI-driven methods into V2G systems, highlighting the potential for these intelligent algorithms and methods.  
vehicle-to-grid systems; optimization algorithms; artificial intelligence; energy Article Optimization Challenges in Vehicle-to-Grid (V2G) Systems and Artificial Intelligence Solving Methods
## 6 Results and Discussion
### Localization Error Analysis  
We present localization error results based on a real-time positioning system developed for the study and an analysis of the confusion matrices.  
Our real-time system correctly recognized the room in which the human subject was holding the phone 29 out of 35 times. The specific reference points were correctly recognized 41 out of 50 times. Since it is not very representative to use location distance error for room identification, we have put effort into using location error for evaluating the specific reference point estimation.  
In [PERSON] et al., a localization error mathematical model was formulated based on prediction error. In this study, we formulated a Cartesian coordinate system shown in Figure 11 for the floor plan in Figure 5 in order to compute the localization error. Basically, we computed the Euclidean distance between two reference points. Once the predicted reference point is the same as the actual reference point, the localization error is zero; if not, then the localization error is the Euclidean distance between the actual and the predicted reference point. The cumulative distribution functions (CDF) of the localization error exhibited for both the Morse and Morlet-based feature sets' errors are presented in Figure 12a,b. The CDF results begin showing convergence at about 3 m, indicating a general localization accuracy realized by the CWT features. However, faster convergence was exhibited by the augmented feature sets, and this occurred close to 2 m. Conclusively, our proposed approach provides improved location accuracy compared to a sizable number of existing indoor positioning solutions, some of which were used for validating this study.  
Figure 11: A location error determination system using a Cartesian coordinate graph.
## 2 Related Work  
### Sparse Attention  
The sparse attention mechanism addresses the issue of excessive computational complexity inherent in self-attention models like the Transformer. This mechanism has gained widespread use in natural language processing (NLP) and has demonstrated notable efficiency improvements in tasks involving high-dimensional data, such as computer vision. [PERSON] et al. [21] proposed a sparse attention approach that encourages the model to focus on a smaller subset of the input speech. Their structured sparse attention technique forced the model to focus on contiguous speech blocks, avoiding the need for attention over numerous individual sound primitives. This method improves efficiency in processing speech data. In the realm of speech emotion recognition, [PERSON] et al. [22] combined sparse autoencoders with attention mechanisms to prioritize speech frames containing strong emotional cues. This reduced the data required for effective labeling, contributing to more-efficient emotion state prediction. Their approach was compared with existing cross-language speech emotion recognition algorithms, showcasing its potential in multilingual contexts. [PERSON] et al. [23] integrated both an adaptive sparse attention mechanism and an adaptive monotone attention mechanism into a Transformer-based end-to-end automatic speech recognition (ASR) model. By replacing softmax functions with A-entmax functions, their model effectively highlighted crucial information in input speech, improving ASR performance. [PERSON] et al. [24] proposed a speech separation algorithm utilizing sparse attention to isolate and generate clean speech from mixed audio sources, each containing multiple speakers. They incorporated a sparse attention structure into a Transformer-based speech separation model, significantly enhancing the model's ability to separate overlapping speech signals. [PERSON] et al. [25] introduced an attention-enhancing coded speaker embedding technique to simulate speaker evaluation. In their model, X-vector embeddings extracted from spectral features were passed through a one-dimensional convolutional neural network (CNN), augmented with an attention module. The processed output was then fed into a sparse autoencoder, with the encoded vectors used in a scoring mechanism based on long short-term memory (LSTM) networks. [PERSON] et al. [26] introduced a probe-sparse self-attention mechanism in Conformer to optimize the computation process of self-attention. Their method sparsely processes the attention function, speeding up inference and reducing memory consumption. A sparsity measure based on Kullback-Leibler divergence was used for each query to determine whether the attention function should be computed, offering further computational efficiency.
# New RED-Type TCP-AQM Algorithms Based on Beta Distribution Drop Functions
###### Abstract  
In recent years, Active Queue Management (AQM) mechanisms to improve the performance of TCP/IP networks have acquired a relevant role. In this paper, we present a simple and robust RED-type algorithm together with a couple of dynamical variants with the ability to adapt to the specific characteristics of different network environments, as well as to the user's needs. We first present a basic version called Beta RED (BetaRED), where the parameters can be tuned according to the specific network conditions. The aim is to introduce control parameters that are easy to interpret and provide a good performance over a wide range of values. Secondly, BetaRED is used as a framework to design two dynamic algorithms, which we will call Adaptive Beta RED (ABetaRED) and Dynamic Beta RED (DBetaRED). In those new algorithms, certain parameters are dynamically adjusted so that the queue length remains stable around a predetermined reference value and according to changing network traffic conditions. Finally, we present a battery of simulations using the Network Simulator 3 (ns-3) software with a two-fold objective: to guide the user on how to adjust the parameters of the BetaRED mechanism, and to show a performance comparison of ABetaRED and DBetaRED with other representative algorithms that pursue a similar objective.  
congestion control; active queue management; random early detection; beta distribution; stability
## 2 Related Works
### Water Quality Monitoring Solutions  
Data collection can be considered as one of the main tasks of an IoT network. However, for large-scale WSN deployment, the task of data collection can be challenging and depends on the complexity of the geographical environment. Generally, the task of data collection can be divided into two types: static data collection and dynamic data collection [33]. In the static method, nodes of a WSN convey their collected data through a multi-hop network, while in the dynamic method, a movable data collector, like a drone, collects data of distributed nodes. Compared with the static methods, dynamic data collection can offer some benefits such as reducing the energy consumption of nodes for data transmission, enhancing network coverage, and extending the capability and flexibility of WSNs to operate in a different type of environment.  
Mainly, there are two approaches for water quality monitoring in the farming industry, either via in situ measurement or by aerial imaging from the drone and, in rare cases, there is a combination of both. However, from the prior search, there is no reliable solution yet related to water quality monitoring, which relies on drone-based wireless communications.  
The work in [34] used a drone-based thermal camera for estimation of water evaporation in a much finer spatial scale for irritation and water resource management. The work in [35] suggested using a multispectral image from the drone and compared the measurement with the in situ measurement by using portable water sensor equipment on site. The authors measured turbidity and chlorophyll a in their study. However, the authors concluded that they still have to rely on the in situ measurement and still need lots of aerial data to achieve reliable water quality measurement from a drone.  
Similar work was carried out in [36], where the authors derived water quality parameters from the chlorophyll a, turbidity, and surface water temperature by using hyperspectral and thermal imaging. The data acquired were then compared against the in situ measurement using the WISP-3 ground sensor (to measure chlorophyll a and turbidity) and a laser thermometer to measure surface water temperature.  
The earlier work to combine in situ and drone measurements can be traced from the project developed by Aerotestra [37]. The prototype works by immersing the water sensor while the drone is temporarily floating on a lake. The sensor measures the temperature reading, and there is a plan to extend the number of sensors, including pH, salinity, and dissolved oxygen.  
Finally, the work in [38] attempted to use several water sensors mounted on the drone to measure the water quality parameters, such as DO, EC, pH, and temperature. This prototype has been shown to make semiautonomous in situ water quality measurements from predetermined waypoints. The results showed small measurement differences (maximum 3.8%) between the prototype and the in situ reading from a commercial probe.
## 4 Results  
### 2D Pore Structure  
#### 4.1.1 SEM Analysis  
Under the scanning electron microscope, the types of pores observed in the SK1-S-2, SK1-S-4, SK1-S-5, SK1-S-6, SK1-S-9, and SK1-S-11 samples were intergranular pores and intragranular pores, but no organic pores were observed. The pores were mainly intergranular pores, including mineral intergranular pores, organic-mineral intergranular pores, and clay mineral intergranular pores. Most of the mineral intergranular pores and organic-mineral intergranular pores were narrow or acicular, and a few were round or sub-circular. The pore diameters observed under the microscope were about 0.1-1 \(\upmu\)m. The intergranular pores of the clay minerals were mostly in the shape of long strips, and the pore size was about 0.1 \(\upmu\)m (Figure 2a).  
Under the scanning electron microscope, the pore types of samples SK1-S-1, SK1-S-8 and SK1-S-12 were observed as mineral intergranular pores, organic-mineral intergranular pores, mineral intragranular pores, and organic pores. Mineral intergranular pores were mainly observed in SK1-S-12 with a few organic pores. The pores of SK1-S-1 and SK1-S-8 were mostly circular or subcircular, and their sizes were between 0.1 and 0.5 \(\upmu\)m (Figure 2b).  
The pore types observed in SK1-S-3, SK1-S-13, and SK1-S-7 were mainly intergranular pores and mineral intragranular pores, but no organic pores were observed. Intergranular pores of pyrites have developed, which were in the form of microspheres with a pore size of less than 0.1 \(\upmu\)m (Figure 2c).  
Intragranular dissolution pores are those formed by dissolution inside of the particles of feldspar, carbonate, and other soluble minerals. They are formed by port-like dissolution of the particles around the pores. Intergranular dissolution pores are larger than intragrain dissolution pores. The intragranular pores observed in the SK1-S-16 sample were developed in the calcite (Figure 2d).  
In summary, the shale of K\({}_{2}\)n\({}^{1+2}\) in SK-1(S) has mainly developed intergranular pores, including mineral intergranular pores, organic-mineral intergranular pores, and clay mineral intergranular pores. A small number of mineral intragranular pores have developed, including calcite intragranular dissolution pores. The distribution of the organic pores was uneven, and the number of organic pores was small, which shows that K\({}_{2}\)n\({}^{1+2}\) in Songliao Basin has oil generation potential.
###### Abstract  
Background: Modern dentistry demands accurate finish line designs for abutments. CAD/CAM systems enable the fabrication of thin prosthetic structures to fulfill this requirement. The aim of this study is to research the mechanical resistance of customized implant abutments with different types of marginal design in laboratory environment. The null hypothesis is there is no difference in fatigue loading and compression strength in custom implant abutments with chamfer or vertical marginal design. Methods: The study model includes 60 specimens of implant suprastructures, organized into four test groups, by the margin design and used material: Group A--suprastructures, made of monolithic zirconia implant crown and titanium custom abutment with vertical marginal design; Group B--suprastructures, monolithic lithium disilicate implant crown and titanium custom abutment with central marginal design; Group C--suprastructures, made of monolithic zirconia implant crown and titanium custom abutment with chamfer marginal design; Group D--suprastructures, made of monolithic zirconia implant crown and titanium custom abutment with chamfer marginal design. All samples were subjected to fatigue loading test in chewing Simulator CS-4 (SD-Mechatronik, Westerham, Germany) for 1250,000 cycles, at a frequency of 2 Hz. The specimens, which survived, was conducted to compressive strength test in universal testing machine Instron M 1185 (Instron, Norwood, MA, USA). Results: The results analysis highlighted Group A as the most resistant to compressive forces (4411 MPa). Group D was with lowest values (1864 MPa)--twice than Group A. Group B (3314 MPa) had lower results than Group A, but higher than Groups C (3130 MPa) and D. Conclusion: Compression strength significantly depends on the choice of marginal design of implant abutments. Vertical margin design has better performance, that chamfer one.  
CAD/CAM; custom abutments; implants; mechanical resistance; vertical preparation
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### Comparison with State-of-the-Art Designs  
Table 5 highlights the performance of the proposed SPDT switch relative to other published designs at 10 GHz. Although some references report narrower bandwidths, the proposed design features substantially higher isolation (\(-70\) dB) while maintaining acceptable return loss. This is advantageous for wideband receiver front-ends where high isolation is crucial for signal integrity.  
As shown in Table 5, the proposed SPDT switch significantly improves isolation relative to references [47; 48; 49; 50], while retaining an acceptable insertion loss. Moreover, many prior works focus on narrower bandwidths, whereas the presented design has a wideband capability, covering from DC up to about 50 GHz.
## 3 Results and Discussions
### Contact Angle of Aluminum Surfaces  
Contact angle analysis is a good indicator to reveal the hydrophobicity of the material surface. It can also be applied to surface adhesion, as the smaller the angle the better its wettability, and its value is strongly dependent on the micro- or nanostructure of the surface of the material [9]. Considering the untreated samples and the treated sample that exhibited the highest resistance value (9.5 MPa), contact angle analysis was conducted (Figure 4). The results indicated an angle of 72.4 \(\pm\) 4.0\({}^{\circ}\) for the untreated sample and 37.0 \(\pm\) 4.3\({}^{\circ}\) for the treated sample, representing a 48.9% reduction. This significant decrease in the contact angle suggests a substantial improvement in the adhesion of the thermoplastic matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Factor** & **DF** & **Sum of Squares** & **F-Value** & \(p\)**-Value** \\ \hline Model & 10 & 10.9766 & 2.03 & 0.200 \\ Time & 1 & 2.0328 & 3.75 & 0.101 \\ Duty Cycle & 1 & 0.7273 & 1.34 & 0.291 \\ Concentration & 1 & 4.1203 & 7.60 & 0.033 * \\ Time\({}^{2}\) & 1 & 1.5398 & 2.84 & 0.143 \\ Duty Cycle\({}^{2}\) & 1 & 0.1024 & 0.19 & 0.679 \\ Concentration\({}^{2}\) & 1 & 0.5192 & 0.96 & 0.365 \\ Time \(\times\) Duty Cycle & 1 & 0.3856 & 0.71 & 0.431 \\ Time \(\times\) Concentration & 1 & 0.0521 & 0.10 & 0.767 \\ Duty Cycle \(\times\) Concentration & 1 & 0.5717 & 1.06 & 0.344 \\ Lack-of-Fit & 4 & 3.0860 & 9.35 & 0.099 \\ Error Pure & 2 & 0.1651 & & \\ R\({}^{2}\) & & 75.15\% & & \\ \hline \end{tabular} \({}^{*}\)Significant factor for the statistical model.  
\end{table}
Table 5: ANOVA with LSS as the dependent variable.