esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Trajectory Initialization Based on MDF-Clustering  
In complex environments, traditional trajectory initialization algorithms are often hindered by issues such as noise interference, lack of universality, and limited robustness, especially when dealing with high-level clutter. These limitations are particularly evident in scenarios with high clutter and maneuvering targets. To overcome these challenges, this paper proposes a novel trajectory initialization algorithm based on multidimensional fuzzy clustering (MDF-clustering). By incorporating multidimensional feature information such as speed and irradiance, the algorithm dynamically assigns feature weights to accurately determine trajectory affiliation. This approach not only enhances the accuracy and robustness of trajectory initialization but also adapts effectively to rapidly changing and complex environments.
## 2 Design of the Lunar Exploration Hexapod Robot
### Structural Design of the Legs of the Lunar Exploration Hexapod Robot  
The mechanical leg is an important part of the hexapod robot, and its leg structure needs to be able to ensure that the robot can walk flexibly on the Moon while having a good load-bearing capacity. In this paper, the robot legs are set up as hip, knee and ankle joints according to the leg structure of a hexapod insect; the hip joint provides the power of the robot to move forward, and the knee and ankle joints provide the power of the robot to move up and down. At the same time, the motors at the joints are connected in series  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specification** \\ \hline Operating voltage & 5-7.2 V \\ Operating speed (at no load) & 0.13 s/60\({}^{\circ}\) \\ Running current (at no load) & 100 mA \\ Stall torque (at locked) & 15 kg cm \\ Stall current (at locked) & 1.5 A \\ Idle current & 5 mA \\ Operating frequency & 50–330 Hz \\ Pulse-width range & 500–2500 us \\ \hline \hline \end{tabular}
\end{table}
Table 2: Servo’s detailed parameters.  
Figure 4: RDS3115 servo.  
Figure 3: Leg structure of hexapod insects.  
with the robot's leg linkage to ensure that the leg structure of the robot is compact and the rotation angle is larger in the three joints so that the leg can have a larger movement. In view of the strong adsorption of lunar soil, the mechanical leg of the lunar exploration hexapod robot was designed: There are holes on both sides of the mechanical leg fixed to the motors, which are rotated by the motors to drive the legs to move; the legs are fixed between the ankle joints and the foot ends by increasing the connecting rods. At the same time, the connecting rods adopt a skeletonized design so that the robot will shake off the sticky adhesion with its own vibration when it is walking. For the extreme, unstructured terrain of the lunar surface, the design of the robot's foot end is also essential. [PERSON] team has conducted a variety of comparative analyses of robotic feet suitable for extreme lunar terrain, including planar, spherical and planar-spherical hybrid designs. Under a 14 kg load, it is found that the planar foot ends will slip due to insufficient contact when in contact with rocks, lunar craters and other environments, while the spherical design has a better contact area and higher stability [24]. Therefore, in this paper, the foot end of the robot is designed as a spherical foot, which can effectively reduce the impact of the complex lunar environment on the robot's walking. The specific mechanical leg structure is shown in Figure 5 (dimensions of 68 mm, 63 mm and 148 mm).
# Resonant Fatigue Tests on Drill Pipe Connections with Different Geometries and Sizes
###### Abstract  
In this study, a resonant bending fatigue test rig, designed and implemented by the University of Pisa, is presented, providing a detailed description of the set-up of the machine, the strain gauges calibrations, and the control system used with the main electronic devices. Several geometries of drill pipe connections and pipe samples made of different materials were tested, and all the obtained experimental fatigue results are presented in the paper and compared to previous experimental data. Fractographic images are provided to clearly show that, in two kinds of drill pipe connections, the crack initiation was found at the connection zone, whereas for another connection geometry, it was found at the pipe body. In order to interpret these latter results, a discussion about the section modulus of bending of the various sections of the drill pipes was provided, along with an FE model of a specific zone of one of these connections.  
drill pipe connection; resonant bending fatigue test; strain gauge; upset region; pipe body
# Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model
## 4 Conclusions  
In this work, the application of the ANFIS model was investigated to predict the arsenate removal efficiency from aqueous solutions using different adsorbents at different conditions. Based on the promising results of this study, the following conclusions can be drawn:  
* An efficient ANFIS model was successfully developed to predict the adsorption removal of arsenate from water. High values of R% and R\({}^{2}\) with low values of MSE/RMSE were reported for both training and testing phases.
* The parametric investigation of the current study can be used to optimize the parameters and, hence, increase the removal efficiency. The relative ranking of the most dominating parameters, in the modeling of the arsenate adsorption process, were as follows: pH, arsenic initial concentration, contact time, adsorbent mass, inoculum size, and then temperature.
* Prediction of the removal of single or multi-component heavy metal/s can be investigated using an appropriate ANFIS model.
* Deep learning can solve complex problems that need to find hidden patterns from the available input data. Therefore, advanced artificial intelligence models such as the Long Short-Term Memory (LSTM) model are highly recommended to be used in the future.
* Developing a smart system as an alternative tool to predict the arsenic (or other heavy metal/s) removal from contaminated drinking/ground/irrigation/wastewater is very important to save cost and time. Further investigation in this regard is currently under consideration.  
All authors contributed significantly to the completion of this article, but they had different roles in all aspects. Conceptualization, M.A.-Y.; methodology, M.A.-Y. and T.H.H.A.; software, T.H.H.A.; formal analysis, M.A.-Y. and T.H.H.A.; investigation, M.A.-Y. and T.H.H.A.; data curation, M.A.-Y. and T.H.H.A.; visualization, M.A.-Y. and T.H.H.A.; validation, M.A.-Y. and S.R.; project administration, M.A.-Y.; funding acquisition, M.A.-Y.; writing--original draft, M.A.-Y., T.H.H.A. and S.R.; writing--review and editing, M.A.-Y., T.H.H.A. and S.R. All authors have read and agreed to the published version of the manuscript.  
This research and the APC were funded by the Deanship of Scientific Research at King Faisal University (Saudi Arabia), Annual Research Program (Grant No. 160152).  
Not applicable.  
The authors gratefully thank the Deanship of Scientific Research at King Faisal University (Saudi Arabia) for the financial support under the Annual Research Program (Grant No. 160152).  
The authors declare no conflict of interest.
# Analyzing Six Indices for Online Short-Term Voltage Stability Monitoring in Power Systems
## 3 Simulation Scenarios
### SVS Indices Implementation
#### 3.2.1 Le  
For use of \(LE\) for SVS assessment, we need to take \(N\) initial samples. By taking \(N\) as equal to 30, the system LE evolutions for defined scenarios have been computed until time 2 s and shown in Figure 3.  
We expect LEs to have a negative amount for stable states and positive in unstable cases. As shown in Figure 3, for unstable states (d, e, f), LEs are positive. However, in stable cases (a, b, c), LEs evolution oscillates between negative and positive amounts. This problem can be solved with choosing different parameters like increasing time interval or taking more initial samples. With applying  
Figure 3: System LE evolution for six defined scenarios: (**a**) Case a, (**b**) Case b, (**c**) Case c, (**d**) Case d, (**e**) Case e, (**f**) Case f.  
phase rectification (PR), which is proposed in [13], LE accuracy dependence on parameter setting will disappear. However, in this paper, we focus on SVS monitoring with simple computing indices and in time less than 2 s. Then, without using PR, we try to propose an instruction which can separate stable and unstable cases by \(LE\).  
As expected, at unstable cases, LEs are positive in most of the considered time intervals. In the case (c), which implies a critical stable case with noticeable voltage sag, LEs take positive and negative amounts frequently. Case (b) shows stable conditions without significant voltage sag. LEs take positive amounts only in some time instances, while they have a negative amount at most of the considered time. Hence, it can be concluded that LE evolution for stable/unstable cases are mostly negative/positive in time intervals less than 2 s. Case (a), which is related to stable condition and voltage magnitude after disturbance, has insensible fluctuation, and LE evolution isn't mostly negative or positive. This problem can be fixed with taking a bigger amount for \(N\). Figure 4 depicts LEs evolution in case (a) for \(N=60\). As shown in this figure, LEs are negative in most of time interval.  
From these results, instructions for SVS monitoring by Lyapunov exponents are proposed as follows:  
1. The number of initial samples should be appropriate, for example taken equal to 60.
2. When LE's evolution are mostly negative in certain intervals, it implies a stable state.
3. If LE's evolution in a considered interval is mostly positive, it indicates short-term voltage instability.
4. If LE's evolution isn't mostly negative or positive, it represents a critical status with significant voltage variation.  
However, it is noted that the method based on LE should be enhanced with more modifications to guarantee correct work for all cases.
## 6 Conclusions  
This study introduced and evaluated a novel cone-top pile foundation system that combines the load-spreading efficiency of a conical base with the deep anchorage of a central pile, connected via a rolling joint to enhance flexibility and structural performance.  
1. The proposed design demonstrated superior performance compared to shallow and classical conical foundations. Laboratory and field tests showed reduced settlement and higher load-bearing capacity, particularly under vertical loading and horizontal tensile soil deformation.
2. Numerical simulations using PLAXIS 2D closely replicated experimental results, validating the axisymmetric modeling approach for vertically loaded conical systems and highlighting stress concentration behavior at the cone-pile-soil interface.
3. Horizontal tensile strains in the soil increased foundation settlements by up to 19% in field tests. However, the deeper embedment and geometry of the cone-top pile foundation helped mitigate these effects, making it more resilient to deformation.
4. The rolling joint configuration between the conical base and the superstructure reduced stress transmission and accommodated ground movement, which is especially valuable for seismic zones and underground mining areas or layered soils.
5. Practical benefits of the cone-top pile foundation include the following: - Increased resistance to both vertical and lateral loads, due to deeper embedment; - Reduced settlement and improved stress distribution, from optimized geometry; - Enhanced shear and displacement resistance, making it suitable for challenging ground conditions and critical infrastructure.  
These findings support the cone-top pile foundation as a robust and adaptable solution for use in soft, anisotropic, or strain-prone soils, where conventional shallow or rigid foundations may be inadequate.  
Conceptualization, A.Z. and A.S.; methodology, B.B.; software, A.O.; validation, B.B., A.Z. and A.S.; formal analysis, A.S.; investigation, A.S.; resources, A.Z.; data curation, A.S. and B.B.; writing--original draft preparation, A.S. and A.Z.; writing--review and editing, A.S.; visualization, B.B. and A.O.; supervision, A.Z.; project administration, A.Z.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan, grant number AP13268861 +Development of structures of smart foundations in undermined and seismically hazardous territories*.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors are grateful to the colleagues of Geotechnical Institute of the L.N. Gumilyov Eurasian National University and \"KGS-Astana\" for the provided administrative and technical support.  
The authors declare no conflict of interest.
## 1 Introduction
To deal with these impairments in practical implementation, the principles of conventional FFE/DFE and improved algorithms based on FFE/DFE are first illustrated in Section 4. Moreover, VNLE and equalization based on machine learning are also discussed here. Furthermore, a summary of various equalization technologies is given in Section 4. Finally, conclusions are drawn in Section 5.
## 2 Constructing the Probability Model of Typical Faults
 \tag{7}\]  
Considering short-circuit faults between each network as independent events, the probability of m-order short-circuits faults (\(P_{m}\)) is as follows. \[P_{m}=1-\prod_{i=1}^{K_{t_{0}}}\left(1-\prod_{j=1}^{m-1}q_{ij}\right) \tag{8}\]  
Setting \(P_{max}\) to the maximum of short-circuit faults probability \(q_{ij}\), we obtain the following relationship:  
\[P_{m}\leq 1-\left(1-P_{max}{}^{m-1}\right)^{K_{t_{0}}} \tag{9}\]  
Setting \(P_{Mm}\) to the upper bound of m-order short-circuit faults probability \(P_{m}\), its value is as follows. \[P_{Mm}=1-\left(1-P_{max}{}^{m-1}\right)^{K_{t_{0}}} \tag{10}\]  
\(P_{max}\) is a value far less than 1. With 0.001 as the step size of \(P_{max}\) change, \(P_{Mm}\) varies with m, as shown in Figure 1.  
Equations (4)-(10) are the probability models of typical faults established in this paper. By this probability model, the upper bounds of the probability of short-circuit faults of each order can be calculated. According to the calculation results, this paper analyzes  
Figure 1: The upper bound of m-order short-circuit fault probability under different \(P_{max}\) conditions. and studies those fault modes with high probability, so as to improve the efficiency of the following algorithms while ensuring the test precision. As shown in Figure 1, the probability of a fault with order greater than 3 approaches 0, so the short-circuit fault model established in this paper only considers short-circuit faults with order no greater than 3.
## 6 Conclusions  
Predicting vehicular flow is one of the central topics in the domain of intelligent mobility. It is a challenging task, influenced by several complex factors, such as spatio-temporal dependencies and external factors. In this study, we have developed a new deep learning architecture, based on convolutions and CMU to forecast the Inflow and Outflow in each region of the smart city. A comprehensive experimental campaign has been conducted on three different real-world case studies. The results showed that consistently outperforms state-of-the-art models in predicting dynamics in all the experiments conducted on the three performance metrics considered. This work also reports and analyzes the results from an ablation study and complexity analysis.  
For future developments, the possible integration of other external factors, such as the territorial characteristics of each geographical area, should be tested. Moreover, it would be appropriate to increase the granularity of the city tessellation, as well as conduct transfer learning experiments to study the applicability of the proposed model to scenarios with a reduced amount of data available.  
**Author Contributions:** Conceptualization, S.F. and M.C.; methodology, S.F. and M.C.; software, S.F.; validation, S.F. and M.C.; formal analysis, S.F. and M.C.; writing--original draft preparation, S.F., M.C. and A.M.; writing--review and editing, S.F. and M.C.; supervision, A.M.; project administration, A.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **BikeNYC** & **TaxiNYC** & **TaxiBJ** \\ \hline ST-ResNet & 230,849,450 & 115,735,786 & 5,459,663,018 \\ MST3D & 33,042,250 & 33,042,570 & 272,483,226 \\
3D-CLoST & 29,613,094 & 9,601,920 & 383,148,804 \\ PredCNN & 1,015,468,288 & 1,015,468,288 & 9,883,813,888 \\ ST-3 DNET & 171,242,496 & 190,130,922 & 1,823,295,898 \\ STAR & 40,449,706 & 78,231,530 & 928,100,922 \\ ACFM & 41,687,924 & 93,643,568 & 621,498,864 \\ \hline STREED-Net & 130,047,738 & 130,047,738 & 1,067,063,882 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational complexity (in number of FLOPs).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **BikeNYC** & **TaxiNYC** & **TaxiBJ** \\ \hline ST-ResNet & 906,272 & 458,304 & 2,696,992 \\ MST3D & 668,218 & 668,378 & 8,674,370 \\
3D-CLoST & 13,099,090 & 19,477,648 & 72,046,714 \\ PredCNN & 3,967,906 & 3,967,906 & 4,827,842 \\ ST-3 DNET & 540,696 & 617,586 & 903,242 \\ STAR & 161,052 & 310,076 & 476,388 \\ ACFM & 182,065 & 270,581 & 969,893 \\ \hline STREED-Net & 582,673 & 582,673 & 765,497 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of trainable parameters.
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
## 2 GNSS Use in Geodesy and Jamming
### Signal-to-Noise-Ratio and Carrier-to-Noise Density Power Ratio  
The signal-to-noise-ratio (SNR) is used to express the signal strength, which implies the signal quality. It can be estimated during the correlation between the received and replica signals and is an expression of the signal strength. SNR is defined as the ratio of the post-correlation signal power \(P_{S}\) to the noise power \(P_{N}\)[36]:  
\[\mathrm{SNR}=10\cdot log\Big{(}\frac{P_{S}}{P_{N}}\Big{)}=10\cdot log\Big{(} \frac{S}{\sigma_{N}}\Big{)}^{2}[dB], \tag{5}\]  
where \(S\) stands for amplitude of the correlation peak and \(\sigma_{N}\) represents the standard deviation of the noise. SNR expresses the amount by which a signal level exceeds its noise in decibels.  
The carrier-to-noise ratio (C/N\({}_{0}\)) is identified as the carrier power divided by the noise power spectral density per unit bandwidth. It is expressed as follows [39]:  
\[\mathrm{C}/\mathrm{N}_{0}=\mathrm{C}-(\mathrm{N}-\mathrm{BW})=\mathrm{C}- \mathrm{N}_{0}=\mathrm{SNR}-\mathrm{BW}. \tag{6}\]  
where C represents the carrier power in dBm or dBW; N and N\({}_{0}\) represent the noise power and noise power density, respectively; and BW is the bandwidth of the observation. Typical values in an L1 C/A code receiver are: C/N\({}_{0}\approx\) 37-45 dB-Hz.
###### Abstract  
With the increasing demand for accurate multimodal data analysis in complex scenarios, existing models often struggle to effectively capture and fuse information across diverse modalities, especially when data include varying scales and levels of detail. To address these challenges, this study presents an enhanced Swin Transformer V2-based model designed for robust multimodal data processing. The method analyzes urban economic activities and spatial layout using satellite and street view images, with applications in traffic flow and business activity intensity, highlighting its practical significance. The model incorporates a multi-scale feature extraction module into the window attention mechanism, combining local and global window attention with adaptive pooling to achieve comprehensive multi-scale feature fusion and representation. This approach enables the model to effectively capture information at different scales, enhancing its expressiveness in complex scenes. Additionally, a cross-attention-based multimodal feature fusion mechanism integrates spatial structure information from scene graphs with Swin Transformer's image classification outputs. By calculating similarities and correlations between scene graph embeddings and image classifications, this mechanism dynamically adjusts each modality's contribution to the fused representation, leveraging complementary information for a more coherent multimodal understanding. Compared with the baseline method, the proposed bimodal model performs superiorly and the accuracy is improved by 3%, reaching 91.5%, which proves its effectiveness in processing and fusing multimodal information. These results highlight the advantages of combining multi-scale feature extraction and cross-modal alignment to improve performance on complex multimodal tasks.  
Keywords: bimodal analysis; scene graph generation; graph embedding; economic activity prediction; multimodal data fusion; urban economics
## 3 Proposed Routing Algorithm
### Operation of the Radio-Aware Routing Algorithm
#### 3.2.2 The neighbor table  
The radio-aware routing algorithm consists of three parts: gathering the radio information, route selection, and a fast-reroute scheme. In the first step, the radio information for the wireless network was collected and updated through the cross-layer design [27] at the MAC layer. The radio information indicates the link quality and route failure information which can be updated whenever receiving the interest and data packets from neighbors. Each node has a neighbor table to store the values of routing information. The table was designed to share information between MAC and network layers for cross-layer design.  
When a node receives an interest packet from its neighbor, the node puts all the values of the neighbor node into the table, except the position of a sink. In our assumption, all the nodes are aware of their own position and the sink node's position when the nodes are deployed. However, since a node cannot know the neighbors' position, they have to exchange their position data. In order to exchange the position data, we added the data field to the interest packet. When an interest packet was propagated to neighbor nodes, each node put their position data into the interest instead of previous forwarding node's position data. If a node receives the interest from the neighbors, the node updates its neighbor table with the information of previous node.  
The radio information is updated whenever a node receives both interest and data packets from neighbors to indicate the air time cost and a route failure flag. The air time cost can be calculated from Equation (1). The route failure flag is updated from 0 to 1 when a node fails to transmit sensing data to a target node for making a re-route decision.  
The complete neighbor table was composed of a node ID, the position (x, y) of a node, the air time cost, and route failure flag as shown in Table 1. The Table 1 is a snapshot of node 33 at a simulation. In this case, node 23 would be selected as a next hop at the node 33, because node 23 has the smallest air time cost and the closest distance and the route failure flag is inset.
# Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring Bay in Spring 2021
## 3 Methodology  
This study was carried out on the cloud-based GEE platform, using the JavaScript coding language. In the first step of the study, satellite images acquired in May, June and July 2021 were selected and downloaded from the hub considering the cloud ratio. Since both cloud and mucilage may give spectrally similar reflectances, obstructing the separation via spectral indices and classifiers, cloud-free images were firstly downloaded from the database. In addition, an image including clouds was employed to test the performances of the considered water indices. For this purpose, five water-related indices were employed to extract mucilage-covered areas from the Sentinel-2 images.