esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 5 Discussion
### Discussion of Method Effectiveness  
The ground-based multi-algorithm fusion path planning technique driven by multi-source data proposed in this study effectively makes up for the lack of real-time adaptability and the global optimization capability of traditional path planning methods in complex dynamic environments. Previous studies have mostly focused on a single algorithm (e.g., A*, Dijkstra, or reinforcement learning models) or relied on limited data sources (e.g., static maps or GPS trajectories), which show limitations when dealing with scenarios such as unstructured road conditions, variable weather, and traffic bottlenecks.  
The contributions of this study are reflected in the following aspects:  
1. **Multi-source heterogeneous data fusion mechanism:** Unlike traditional path planning that relies only on GPS or static map information, this study organically integrates satellite navigation, ground sensing (e.g., cameras, inductive coils), and real-time traffic flow data, which provides a richer and more dynamic environment sensing support for path decision making.
2. **Innovative multi-algorithm fusion architecture:** By combining the heuristic global search capability of the A* algorithm and the policy self-learning advantage of DQN in complex local scenes, better path planning performance is realized. Compared with the existing literature that applies A* and deep reinforcement learning separately, this study demonstrates the performance improvement after their fusion: in the simulation test of an urban road network, the average time consumed for planning paths is reduced by 61.90% compared with DQN; the risk factor is reduced by 55.56% compared with A*; and the success rate of dynamic obstacle avoidance is improved by 8.5 times.
3. **Improvement of system robustness and adaptability:** The method can be dynamically adapted to heterogeneous terrains, such as dense urban road sections, rugged  
Figure 6: Comparison of path length, planning time, risk coefficient, and obstacle avoidance rate for A*, DQN, and A*-DQN.  
roads in mountainous areas, and transportation hubs, which significantly improves the environmental adaptability of path planning. Compared with the DQN-only approach, the fusion strategy shows higher stability and response efficiency in dynamic obstacle prediction and unexpected event response.
4. **Complementary to existing research:** Most of the current literature focuses on the improvement of the algorithm performance itself, and less on the algorithm synergy mechanism supported by multi-source data. This study fills the gap and systematically proposes a complete path optimization framework from data fusion, algorithm design to system integration, which has strong engineering promotion potential and academic innovation value.
## 4 Conclusions  
In this paper we propose a hybrid parallel network for AMC problem. Our hybrid parallel network extracts features through a parallel structure. This parallel structure extracts spatial features and temporal features simultaneously, and concatenates them with attention mechanism. Three attention mechanisms are applied in the proposed method for reweighting the output to highlight the more discriminate parts in the features. After the last layer, instead of the conventional softmax function, we apply a cos-similarity metric method named AM-softmax to help us compress with-in class cosine angular distance. We first explore the influence factors of our proposed method including the number of hybrid parallel modules and the hyper parameters of AM-softmax function. Then we analyze the accuracy of each modulation category in our experiments and find that most of modulation categories can be discriminated well for an SNR of 0 dB. We also compare the result of AM-softmax function with softmax function while our model is fixed. The average accuracy of AM-softmax and softmax are 87.33% and 86.36%, respectively, which confirms that AM-softmax can achieve similar performance to softmax within the margin of error. Finally, we compare our proposed method with other existing methods. The experiments prove the effectiveness of our proposed method. The worst accuracy is 75% at \(-\)4 dB while the best accuracy of the baseline model just reaches 74%. The average accuracy of our method is at least 5%, which is better than the comparison methods. As a result, our proposed method achieved competitive performance. Although our hybrid parallel structure can achieve a competitive performance, the computational complexity is not a concern. However, computational complexity plays an important role in the practicability of AMC methods. Our future work is to maintain the classification accuracy while reducing the computational complexity at the same time. Although AM-softmax has achieved good results on the AMC problem, it has not shown its superiority to softmax. We will continue to work on cosine-similarity distance to explore more possibilities. Another future work is to apply our hybrid parallel network to a more complicated and larger dataset. If our work can handle more complicated environment, this will prove the superiority of our method.  
Conceptualization, R.Z.; methodology, R.Z. and Z.Y.; software, Z.Y.; formal analysis, R.Z., Z.Y., Z.W. and S.Z.; investigation, R.Z.; writing--original draft preparation, R.Z.; writing--review and editing, R.Z., Z.Y., Z.W. and S.Z.; visualization, R.Z.; supervision, Z.Y.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"the National Natural Science Foundation of China\" (Grant No. 61871157 and 62071143).  
Not applicable.  
Figure 11: The comparison between different AMC methods.
# A Velocity-Aware Handover Trigger in Two-Tier Heterogeneous Networks
## Overall Handover Procedures
### Handover Process Description  
The primary concept of the recommended scheme is to foresee the handover choice, taking into account the velocity of the UE. Since the MIH can gather the RSS got from the serving AP, we utilize the MIHF functionality to obtain an N sample of RSS for \(\Delta_{t}=\tau\).  
In view of Equation (3), we calculate the variance \(\sigma^{2}(\tau)\) continuously in order to track and check if \(\Delta\Theta\geq 1\%\). Once this condition is fulfilled, the UE will use the global positioning system (GPS) to get the velocity value. At the point when the velocity is accessible, the lifetime (\((\Delta t)_{lifetime}\)) for the UE in the coverage area of the serving AP is recalculated utilizing the present velocity. Then, we confirm if (\((\Delta t)_{lifetime}\)) is greater than the time required to play out a handover (\((\Delta t)_{HO}\)), else we should begin the handover procedure to maintain a strategic distance from the HOF. Figure 4 resumes the process. To limit the PP impact, we ought to get the (\((\Delta t)_{lifetime}\)) before becoming associated with the new AP.  
In the wake of checking the condition (\((\Delta t)_{lifetime}<(\Delta t)_{HO}\)), the UE sends the \"link going down\" primitive to trigger the handover procedure as demonstrated in Figure 5. The serving AP will begin gathering data from Media-Independent Information Services (MIISs) for seamless handover by sending \"MIH get information\" primitive. Once the data on neighboring APs is gathered, a serving AP starts to rank them in accordance with user preferences (in our situation the rating criterion reflect the value of the RSS). The consecutive stage will be the confirmation of resource availability in the candidate APs; this progression will be coordinated by the serving AP utilizing the \"MIH N2N HO candidate query\" primitive of the MIH system to limit the utilization of energy by the UE. At that point, the asset arrangement is overseen by the \"MIH N2N HO commit\" primitive. Now, we can perform PMIPv6 handover. Once the \"link up\" primitive is activated, at the new AP the PMIPv6 begins taking care of new node by sending a Proxy Binding Update (PBU) to the LMA. After the gathering of the PBU, the LMA refreshes the lifetime of the MN's entrance in the binding cache table and answers with a Proxy Binding Acknowledgment (PBA) message. At that point, begins transmission of the buffered data through the tunnel between the LMA and the new MAG. For resource discharge, the \"MIH N2N HO complete\" primitive is used.  
Figure 4: Flow chart for handover decision-based velocity.
## 3 Results
### Flow over Periodic Triangular Hills  
In the last verification case, we applied the hybrid and reference methods to simulate a well-defined flow passing over a series of triangular hills, which repeat along a channel in a periodic fashion. Figure 16 shows a schematic of the flow domain.  
The flow was driven by a body force \(F\) along the horizontal direction. Periodic boundary conditions were applied in the horizontal direction. No-slip boundary conditions were imposed at the top and bottom boundaries and the surface of the hill. The velocity field was initialised with zero values everywhere. The reference pressure was set to zero at the left-bottom corner. For the geometric parameters, the height of the channel was \(L=0.1\) m and the height of the hill was \(h=0.02\) m. The kinematic viscosity was set to \(\
u=10^{-6}\) m\({}^{2}\) s\({}^{-1}\), and the body force was set to \(F=5\times 10^{-5}\) m s\({}^{-2}\). With this setting, the flow would eventually develop to a state comprising a time-periodic motion. The maximum horizontal velocity \(U_{max}\) was about 0.04 m s\({}^{-1}\). Based on \(U_{max}\), \(h\), and \(\
u\), the Reynolds number of the flow simulated here was about 800.  
For the reference method, the computational domain was discretised with 153,200 velocity points in the flow region and 800 boundary points. The number of pressure points generated in a staggered fashion was 153,681. For the hybrid method, the velocity and boundary points were the same as those for the reference method. In addition, 7200 virtual points beneath the bottom boundary and the hill surface were generated, and a regular grid of \(400\times 402\) points was used for solving the pressure Poisson equation. Here, a small portion of the regular grid was below the bottom boundary. For both methods, the grid size was about \(L/4  
For a simulation of 10,000 time steps, the total wall clock time of the hybrid solver was about 840 s, and the total wall clock time of the reference solver was about 2075 s. The hybrid solver was 147% faster than the reference solver. In other words, the speedup of the hybrid solver was 2.47 times.  
Figure 17: Comparison of numerical solutions obtained by the reference method (**left**) and the hybrid method (**right**) for the fourth test case at the time when the horizontal velocity at Monitoring Point 1 reaches the median in the accelerating phase. Shown from top to down are the contours of the horizontal velocity component \(U\), the vertical velocity component \(V\), and the pressure \(P\), respectively.
# Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading
## 3 Load Analysis
### Variable Loads
#### 3.2.3 Ice Load  
Ice and snow attached to the structural surface can significantly increase the variable loads in flexible and light structures. In particular, the radio mast is very sensitive to changes in the wind-exposed surface. In addition, the ice covering can increase the volume and the surface of the structural elements more than twice due to the low thickness of the central pole. The recommendations in [43] provide several scenarios for ice coverings. In the absence of more detailed evaluations, it is customary to consider an ice sleeve formation that is 12.5 mm thick. After the estimate of the wind loads, the influence of the ice sleeve formation on the structure is considered by assuming an additional exposed surface equal to 15% of the original one.
## 4 Discussion  
### Theoretical Contributions  
Cognitive enhancement: The reduction in simulation error rates (42% \(\rightarrow\) 5%) supports the technological medium embodiment hypothesis, demonstrating how virtual-physical interactions can enhance spatial cognition. This phenomenon has been verified in multiple studies. For example, one study explored the application of virtual reality (VR) in skill training and found that high-immersion VR simulations may assist in improving skill learning [16]. Another study indicated that motion cues in virtual environments could improve users' spatial perception by adjusting monocular depth cues, thereby reducing interception errors [17]. In large-scale virtual city exploration, users' familiarity with interactive maps increased task accuracy [18].  
Ethical paradoxes: System dependency challenges the \"technology enhances autonomy\" hypothesis, prompting a re-evaluation of the boundaries of human-AI collaboration. The intervention of AI may delay or complicate decision-making processes, leading to confusion about roles and responsibilities [19]. AI automation may also cause skill degradation and a reduction in task diversity, thereby affecting employees' intrinsic motivation and engagement [20]. The application of AI in political decision making may challenge traditional democratic participation models. While AI excels in data analysis and strategic reasoning, it may also lead to a lack of transparency and accountability [21]. In summary, system dependency not only challenges the \"technology enhances autonomy\" hypothesis but also forces us to redefine the boundaries of human-AI collaboration. When designing and applying AI systems, it is essential to strike a balance between technological advantages and human autonomy for more effective collaboration [22]. Therefore, the non-linear relationship between privacy and performance (\(\epsilon\) = 0.3 threshold) contradicts the unified federated learning strategy and advocates for tiered data classification standards.
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 3 Topic Overview
### Centralized vs. Edge Approach for Jamming Detection  
When presenting a ML-based approach in an IoT setting, the question of where the AI model should be placed often arises. By their nature, IoT devices, and in turn drones, are usually resource-constrained, both in terms of computational power and also in terms of internal storage and battery capacity [24]. This means that complex AI models and algorithms are usually not feasible to be run on the device itself.  
A centralized approach offloads the computational burden to a remote central server, that returns the results of the AI model to the device. While this scheme might be favorable in some cases, in a real-time situation, such as a jamming attack, the latency introduced by the communication with the server reduces the time to react to the threat. Also, as jamming attacks degrade or sometimes completely block the communication between the device and the BS, a centralized approach might not be feasible in this case. A collaborative edge approach faces similar issues, as the communication between the devices would likely be disrupted by the attack. Implementing a lightweight on-device edge approach, on the other hand, while not as precise as a centralized approach, would provide real-time results and would be able to operate even when the communication is disrupted, as is the case in a jamming situation.
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 3 Standalone Remote Sensing (RS) Technologies
### GNSS-Based Technologies
#### 3.1.1 GNSS and Differential GNSS Techniques  
The GNSS is part of a satellite application, providing signals from space that transmit positioning and timing data to GNSS receivers installed on the construction equipment. These receivers then use this data to determine the equipment location [5]. A typical 3D GNSS system, employed in a construction equipment consists of a roving receiver and a reference station [23]. The receiver located on the body of a construction vehicle receives signals from GNSS satellites to determine its position and direction. Besides, in a differential GNSS system the use of a reference GNSS station can enhance the location accuracy by sending corrections to the construction vehicle using a radio modem. These corrections can also be sent to a central unit over the internet through a GPRS modem connected to the levelling system. Furthermore, in a 3D GNSS levelling system, some highly resistant sensors are stuck on the body of a vehicle which make it possible to control any specific action demanded from the equipment in real-time. For applications related to the road pavement which need high accuracy in surface leveling, a 3D control unit processes the signals received from GNSS satellites and the reference station compares them with the stored designed values for quality measurement. A reference station is able to control any number of construction vehicles via onsite radio connections. The accuracy of such a system is around 2-3 cm [23].
# An Improved Image Filtering Algorithm for Mixed Noise
## 3 Experiment
### Experimental Results and Analysis  
In order to verify the suppression effect of the algorithm in this paper on mixed noise with salt and pepper + Gaussian noise, \(6\) classic images were selected in the field of image processing as experimental objects to test the denoising effects and record their data. They are lena, boat, barbara, with \(512\times 512\) pixel; and cameraman, hill, peppers, with \(256\times 256\) pixel. These images are shown in Figure 6. At the same time, those algorithms, including KSVD [38], NCSR [39], OCTOBOS [40], TWSC [41], BM3D [42], and WNNM [44], are introduced to compare the denoising effect with the algorithm we proposed. Figure 5: The flow chart of this proposed algorithm. During the experiment, the following salt and pepper noise density/Gaussian noise variance (\(\rho/\sigma\)) were added to the six images used in the test algorithms: 0.03/0.01, 0.06/0.02, 0.09/0.03, 0.12/0.04, 0.15/0.05. At the same time, some specific metrics are used, including PSNR, RMSE, SSIM, and FSIM, to evaluate the denoising effect of each algorithm. Among these metrics, the larger the PSNR, the SSIM, and the FSIM, the better the denoising effect, while the smaller the RMSE, the better the denoising effect. The values of SSIM, FSIM, and RMSE range from 0 to 1. Tables 1-4 show PSNRs, RMSEs, SSIMs and FSIMs of the 6 images corresponding to different algorithms and different salt and pepper noise density/Gaussian noise variance (\(\rho/\sigma\)). The setting of parameters in each algorithm is specified by the author in the original article. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**PSNR** & \(\rho/\sigma\) & **KSVD** & **NCSR** & **OCTOBOS** & **TWSC** & **BM3D** & **WNNM** & **Ours** \\ \hline \multirow{5}{*}{lena} & 0.03/0.01 & 23.94 & 24.06 & 20.60 & 22.47 & 25.69 & 21.98 & **29.74** \\  & 0.06/0.02 & 21.57 & 21.96 & 18.61 & 20.99 & 24.76 & 20.24 & **28.61** \\  & 0.09/0.03 & 20.13 & 20.63 & 17.55 & 19.93 & 23.64 & 19.38 & **27.40** \\  & 0.12/0.04 & 19.07 & 19.66 & 16.79 & 19.03 & 22.52 & 18.81 & **25.99** \\  & 0.15/0.05 & 18.21 & 18.77 & 16.09 & 18.34 & 21.69 & 18.12 & **24.84** \\ \hline \multirow{5}{*}{boat} & 0.03/0.01 & 23.48 & 23.93 & 20.71 & 22.43 & 25.07 & 21.87 & **26.69** \\  & 0.06/0.02 & 21.16 & 21.52 & 18.71 & 20.71 & 24.02 & 20.15 & **25.92** \\ \cline{1-1}  & 0.09/0.03 & 19.87 & 20.17 & 17.54 & 19.66 & 22.56 & 19.34 & **25.04** \\ \cline{1-1}  & 0.12/0.04 & 18.73 & 19.30 & 16.75 & 18.88 & 21.84 & 18.68 & **24.23** \\ \cline{1-1}  & 0.15/0.05 & 17.94 & 18.59 & 16.19 & 18.15 & _20.84_ & 18.11 & **23.26** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Denoising data of mixed noise by different algorithms on test images (PSNR). Figure 6: The test images.
## 2 Materials and Methods
### Aroma Volatile Analysis by HS-SPME-GC-MS  
Volatile compounds were analyzed according to the method of [PERSON] et al. [24], with minor modifications.  
Solid-phase microextraction (SPME): An aliquot of wine (5 mL) was added to a 20 mL glass headspace vial containing NaCl (1.5 g, Guangnuo Chemical Technology, Shanghai, China); then, internal standard (4-methyl-2-pentanol, 10 \(\upmu\)L, 1008.3 mg/L in ethanol) was added to a final concentration of 2.01 mg/L. The mixture was agitated on an orbital shaker at 250 r/min and 40 \({}^{\circ}\)C for 5 min. The SPME fiber (50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS), length 1 cm Supelco, Bellefonte, PA, USA) was aged at 250 \({}^{\circ}\)C for 10 min, cooled, and then added to the sample vial and agitated at 40 \({}^{\circ}\)C for an additional 30 min  
GC-MS analysis: The GC-MS analysis was performed on an Agilent 7890B gas chromatograph (Agilent, Santa Clara, CA, USA), fitted with a Gerstel PAL RSI 85 autosampler (CTC Analytics AG, Zwingen, Switzerland) and an Agilent 7000D mass-selective detector. Thermal desorption of analytes from the SPME fiber was achieved by splitless injection (straight glass liner, 0.75 mm i.d.) at 240 \({}^{\circ}\)C for 10 min. The column oven temperature program was as follows: initial temperature 40 \({}^{\circ}\)C, held for 5.0 min, increased to 97 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, then to 120 \({}^{\circ}\)C at 2 \({}^{\circ}\)C/min, then to 150 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, then to 220 \({}^{\circ}\)C at 8 \({}^{\circ}\)C/min, then held for 10 min. The MS detector was operated in scan mode (mass range 40\(-\)300 \(m/z\)), the transfer line to the MS system was maintained at 230 \({}^{\circ}\)C, the interface between the GC and the MS was held at 240 \({}^{\circ}\)C, and the electron impact source operated at 70 eV.  
Identification and quantification of volatile compounds: A total of 47 volatile compounds were identified in the wines (Table S2 and Figure S1) using experimentally obtained Kovats retention indices (RI), with the combination of C8-C40 alkane standards and mass spectra in the NIST MS library. Aroma volatile standard mixtures were prepared in a red wine sample. Standard concentrations were selected to bracket the concentrations of each individual compound as closely as possible. All standards were analyzed in duplicate. Any compound for which the reference standard was not available was quantitated based on the standard with the closest structure available. In total, 19 volatile compounds were quantified by standard concentrations and were used for data analysis.