esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The protocol selected CHs from all candidate nodes with higher residual energy rates, more neighbors, shorter distances to the BS, and shorter average distances to the member nodes than other candidate nodes and planned the most energy-efficient routing path from the CHs to the BS. Furthermore, the protocol adopts an on-demand re-clustering mechanism instead of clustering periodically or round-by-round like other protocols. The proposed protocol was compared with other latest protocols FRNSEER, FC-RBAT, BOA-ACO, and OAFS-IMFO in four scenarios. The simulation results showed that our protocol is significantly better than other protocols in terms of alive nodes, network lifetime, energy consumption, and throughput. The main contributions of this paper are summarized as follows:  
* We utilized fuzzy logic-based clustering and quantum annealing algorithm-based routing to establish an energy-efficient data transmission mode that can effectively prolong the network lifetime;
* We adopted an energy threshold to filter candidate CHs to save computation time;
* Application-based FIS is used to select appropriate CHs from candidate CHs and considers four parameters as fuzzy input variables: residual energy, the number of neighboring nodes, distance to the BS, node centrality;
* A two-dimensional routing matrix and weight matrix are constructed to obtain the objective function. Moreover, we used the quantum annealing algorithm to evaluate the objective function and then obtain the optimal routing route from CHs to the BS;
* An on-demand re-clustering mechanism is used to maintain clusters locally and globally for computation and message overhead reduction. The remaining parts of the paper are organized as follows: Section 2 reviews the related literature. Section 3 introduces the system model in detail. Section 4 describes the proposed protocol FQA in detail. Section 5 discusses the performance of the proposed methodology with the existing protocols. Finally, Section 6 presents the conclusion.
## 3 Results and Discussion  
### Visual Inspection of the AFSD Parts  
First, experiments were conducted to determine the best AFSD conditions for additively manufacturing DPs that did not have defects like broken deposition layers or excessive flash. The high-strength alloys need special treatment in building continuous multilayers in the solid state, which can be achieved by controlling the factors affecting the deposition process. In previous works [10; 13; 24; 29; 37], it was observed that the rotation speed and deposition feed rate of the consumable rod are critical process parameters for AFSD due to their effects on the generated heat input. Wide ranges of rotation speeds and deposition rates were considered in the present work to investigate the applicability of deposition of AA/7075-T6 on the AA2024 substrate plate. Figure 3 depicts the DPs produced at rotation speeds ranging from 1200 to 200 rpm using different deposition feed rates ranging from 1 to 5 mm/min. It was observed that the DPs processed at a deposition rate of 1 mm/min and at 1200, 1000, and 800 rpm formed discontinuous layers with an excessive flash, as shown in Figure 3a-c, respectively. In addition, at a 1 mm/min feed rate and 600 rpm rotation speed, the AFSD part suffered from tearing (Figure 3d). In contrast, the DPs processed at different deposition feed rates of 1, 2, 3, and 4 mm/min and a constant rod rotation of 400 rpm showed built-up continuously multilayered parts of AA/7075-T6, as shown in Figure 3e-h, respectively. These AFSD parts were fully investigated. On the other hand, the DP processed at 400 rpm and 5 mm/min generated only a deposited layer of AA/7075-T6 on the AA2024 substrate plate (Figure 3i). Finally, the last experiment, involving the lowest rotation speed of 200 rpm and the lowest deposition rate of 1 mm/min, showed sticking between the AA/7075-T6 consumable rod and the AA2024 substrate at the beginning of the AFSD process, as seen in Figure 3j. Based on a visual inspection of all the additively manufacturing deposited materials, it can be noted that the deposition rates of 1, 2, 3 and 4 mm/min at a constant CRRS of 400 rpm succeeded in building continuously multilayered parts.  
Figure 2: Sketch showing indenter locations for hardness measurements of the AA/7075 DP.  
Figure 4 displays the impact of applying a constant 400 rpm rotation speed, and 1, 2, 3, and 4 mm/min deposition feed rates on the diameter and height of the DPs. It can be observed that the height of the AA7075-T6 DPs decreased with increasing deposition feed rate, while the diameter of the DPs increased. The plasticity of AA7075-T6 through the AFSD is governed by the developed heat generation due to stirring action throughout the process. This heat input is directly related to the consumable rod's deposition rate and rotation speed, as well as adjusting the machine's axial downward force.  
Figure 3: Shows photographs of the deposited materials formed by the AFSD technique processed at different rotation speeds (1200–200 rpm) and various feed rates (1–5 mm/min) and the visual inspection remarks.
## 3 Results
### Response Latencies  
Response latencies could only be estimated for trials that elicited a clear response (see Methods). Thus, not all trials yielded a value and therefore the average value from a participant for each stimulus type was used in the analysis. The subsequent group averaged response onset latencies for each stimulus type are presented in Figure 9, for TA, AD, SCM and OO. Responses latencies were generally longest in TA and shortest in OO, reflecting the conduction distances from the stimulus source to the target muscle. The latencies in TA were consistent across all three stimulus conditions with times of 127.5 \(\pm\) 14.6 ms, 125.4 \(\pm\) 5.8 ms, and 126.3 \(\pm\) 12.7 ms for DISPLACEMENT, STARTLE, and COMBINED, respectively (\(F_{2,15}\) = 0.05, \(p\) = 0.96). Responses in AD were also consistent across stimulus conditions with times of 104.9 \(\pm\) 12.7 ms, 104.0 \(\pm\) 5.3 ms, 99.1 \(\pm\) 9.0 ms for DISPLACEMENT, STARTLE, and COMBINED, respectively (\(F_{2,15}\) = 0.30, \(p\) = 0.74). In contrast, response latencies in SCM and OO were significantly slower with DISPLACEMENT alone, than with STARTLE or COMBINED stimuli, whereas the response times in STARTLE and COMBINED were not different. For SCM, the response latencies were 107.6 \(\pm\) 14.3 ms, 82.8 \(\pm\) 4.9 ms, and 74.1 \(\pm\) 8.5 ms for DISPLACEMENT, STARTLE, and COMBINED, respectively (\(F_{2,15}\) = 6.15, \(p\) = 0.01). For OO, the response latencies were 77.4 \(\pm\) 17.1 ms, 47.5 \(\pm\) 2.0 ms, and 48.4 \(\pm\) 9.3 ms for DISPLACEMENT, STARTLE, and COMBINED, respectively (\(F_{2,15}\) = 24.72, \(p\) < 0.001).  
Figure 8: Trial-by-trial mean (SE) mechanical and muscle response amplitudes for participants who habituated to the STARTLE stimuli initially. (**A**) Participants subsequently received the DISPLACEMENT, followed by the COMBINED stimuli. (**B**) Participants subsequently received the COMBINED stimuli, followed by the DISPLACEMENT. Asterisks indicate differences identified by [PERSON]’s HSD comparisons (\(p<0.05\)). Blue squares, DISPLACEMENT; Red circles, COMBINED; Green triangles, STARTLE. Closed symbols indicate data points used in the statistical analysis. TA, tibialis anterior; AD, anterior deltoid; SCM, sternocleidomastoid; OO, orbicularis oculi; COPAP, center of pressure anterior–posterior; MVC, maximum voluntary contraction.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Crop-Wise Analysis
#### 4.5.4 Lettuce Romaine Crops  
The first crop used for the comparison is \"Lettuce-romaine-4 wk\", class number 11 from the Salinas data. For this crop, the proposed method outperformed the state-of-the-art methods 3 DGSVM and SS3 FC with higher accuracy of 96.52%. Methods CNN-MFL and WEDCT-MI were slightly more accurate than the proposed method. The second crop used for the comparison is \"Lettuce-romaine-5 wk\", class number 12 from the Salinas data. For this crop, the proposed method outperformed the state-of-the-art methods  
Figure 10: Comparison of crop classification accuracy Indian Pines: (**a**) Corn (**b**) Soya Salinas (**c**) Fallow (**d**) Lettuce.  
3 DGSVM, CNN-MFL, and WEDCT-MI with higher accuracy of 97.51%. Method SS3 FC was slightly more accurate than the proposed method. The third crop used for the comparison is \"Lettuce-romaine-6 wk\", class number 13 from the Salinas data. For this crop, the proposed method and CNN-MFL had 100% accuracy and outperformed the remaining state-of-the-art methods.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 5 Experiments and Results
### Experimental Results and Performance Evaluation
#### 5.2.2 The SAR Imaging Processing System with Heterogeneous \"CPU + FPGA\" Architecture
The waveforms of the SAR imaging results from the \"CPU + FPGA\" imaging system and MATLAB in the range and azimuth directions are shown in Figure 21. The quality evaluation results of SAR imaging for point target are shown in Table 11.  
From the above imaging results, the whole SAR imaging system is shown to have a good performance when dealing with area targets and point targets after the superscalar pipeline storage exchange engine is embed. For point target imaging, our \"CPU + FPGA\" heterogeneous SAR system imaging results are not very different from MATLAB in terms of peak sidelobe ratio and integral sidelobe ratio. The processing performance of the entire SAR imaging system is analyzed below. Imaging processing time is the most significant performance of spaceborne SAR real-time imaging performance. Therefore, it is one of the essential key factors to measure an SAR imaging processing system. Since many operations, such as data storage and exchange and data calculation and processing, are required during the imaging process, these operation times have a crucial impact on the total SAR imaging processing time. Therefore, it is of great significance to improve the performance of each part of the engine in the system. In this paper, we compare the imaging processing times of several recent SAR imaging processing systems. Table 12 shows the results of the comparison between the imaging processing time of the \"CPU + FPGA\" heterogeneous imaging processing platform embedded with the superscalar pipeline storage exchange engine and several recent SAR imaging systems. Figure 22 shows that the SAR imaging processing system using the superscalar pipeline storage and exchange engine proposed in this paper leads to a large improvement in processing time and storage access bandwidth compared with other processing systems developed in recent years. The system architecture is used for \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Result** & **Range** & **Range** & **Azimuth** & **Azimuth** \\
**Source** & **PSLR/dB** & **ISLR/dB** & **PSLR/dB** & **ISLR/dB** & **ISLR/dB** \\ \hline MATLAB Imaging & \(-13.34\) & \(-10.25\) & \(-13.02\) & \(-10.17\) \\ “CPU + FPGA” Imaging & \(-13.31\) & \(-10.23\) & \(-13.01\) & \(-10.08\) \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The quality evaluation results of SAR imaging for point target. Figure 21: The waveforms of the SAR imaging results from the “CPU + FPGA” imaging system and MATLAB in the range and azimuth directions. (**a**) Range; (**b**) Azimuth. dation on the ground platform, but is also meaningful for the on-orbit real-time processing of spaceborne SAR.
# Bounce: A High Performance Satellite-Based Blockchain System
## 2 Related Work
### Blockchain Protocol Landscape
#### 2.1.1 Permissionless Protocols  
Among the many permissionless approaches, proof of work and proof of stake are the most popular. Proof of work requires the would-be appenders to the blockchain to solve a computationally difficult problem. The first agent that does so has the right to append a block on the blockchain. The original Bitcoin protocol [1] introduced this core idea and many successors follow it. This approach can, unfortunately, fail whenever malicious principals control a majority of the computing power [1; 2]. Proof of work also incurs high energy costs with its associated environmental burden. According to one estimate [4], Bitcoin's annual electricity is about the level of entire nations, e.g., Pakistan or Ukraine. Observers [5; 6] have noted the curious fact that the energy consumed by Bitcoin has historically been roughly proportional to the value stored among its clients.  
Proof of stake is based on a protocol in which principals use a voting mechanism to reach consensus on new blocks. Each principal's votes are proportional to that principal's monetary stake in the system. Ethereum switched to Proof of stake in September 2022, vastly reducing its energy footprint. This approach can fail, however, if the majority of shareholders in the system conspire to act maliciously [7; 8]. Some researchers [9; 10] have pointed out that malicious agents may control less than \(1/3\) of the stake and yet may still be able to induce forks in the blockchain. Here are some of the subtle attacks on proof of stake protocols: (i) nothing-at-stake attacks [11], (ii) retroactive (\"long-range\") stake compromise [12], (iii) stake-bleeding [13], (iv) race-to-the-door takeovers [5], and (v) selfish endorsing [14]. Furthermore, incentives depend intricately on the reward schedule [15], returns to lending [16], and token valuation [17].
# BeiDou Geostationary Satellite Code Bias Modeling Using Fengyun-3C Onboard Measurements
## 3 Results
### Inconsistency of Linear Models  
We estimated the linear model node values using the approach of [PERSON] and [PERSON] [8] for individual BDS satellites in a least-squares sense based on the 26-day onboard data set. The results are shown in Figure 5. Similar to [PERSON] and [PERSON] [8], groups of satellites with different code bias behaviors are shown. For GEO C01 to C04 satellites, there is no substantial elevation dependence in B1 frequency, but a different behavior can be observed for C05 MP1 under higher elevation angles. About this phenomenon, because we do not have any specific details regarding GEO satellite construction, a new group of MP1 for C05 was taken into account in this study. For IGSO and MEO satellites, there was good agreement with [PERSON] and [PERSON]'s model over the high elevation angles for the derived ones, but clear inconsistency can be observed under the lower elevation angles, which were predominantly less than 40\({}^{\circ}\). [PERSON] et al. [5] thought that this is caused by the Fengyun-3C GNOS receiver, which smooths the BDS code observations when the elevation angle is less than 40\({}^{\circ}\).  
However, it needs to be emphasized that because of the loss of the low-elevation observations from the ascending phase of BDS satellites as mentioned above, the inconsistency between the 2 models derives mainly from the descending signals. As seen in Figure 4, the MP values show clear asymmetry with the elevations and the trend of the descending variations is more flat than that of the ascending, which may lead to flat values of the low-elevation nodes shown in Figure 5. A question thus arises as to whether these strange behaviors reflect the actual BDS signal characteristics or a systematic underestimation of the MP variations.  
Figure 4: An example of the B1 and B2 frequency MP time series and elevation-angle variations of complete satellite passes as a function of time for different BDS satellite types.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 6 Impact of the Butterworth Filter on Classification Performance  
To assess the impact of signal preprocessing, we compare the classification results with and without applying a Butterworth filter to the vibration and acceleration data. As shown in Table 1, filtering ** consistently improves the performance of the model in all configurations**.  
Without filtering, the classifier struggled with increased noise in raw sensor data, leading to lower accuracy and AUC scores. For example, in Configuration A, the accuracy drops from **89.6% (filtered) to 85.1% (unfiltered)**, while the AUC decreases from **0.981 to 0.924**. This trend is consistent across Configurations B and C, reinforcing the role of filtering in improving data quality.  
The improvement in recall across all configurations indicates that filtering helps the model **better capture true positive events**, reducing false negatives. This is crucial for real-time applications, where missing critical road events could affect the reliability of the system.  
In contrast, the rule-based approach maintains high precision but lacks adaptability across different datasets. The Random Forest model, especially with filtering, offers a more **robust and generalizable solution**, making it better suited for real-world deployment.
# Scoring of Human Body-Balance Ability on Wobble Board Based on the Geometric Solution
###### Abstract  
Many studies have reported that the human body-balance ability was essential in the early detection and self-management of chronic diseases. However, devices to measure balance, such as motion capture and force plates, are expensive and require a particular space for installation as well as specialized knowledge for analysis. Therefore, this study aimed to propose and verify a new algorithm to score the human body-balance ability on the wobble board (HBBAWB), based on a geometric solution using a cheap and portable device. Although the center of gravity (COG), the projected point of the center of mass (COM) on the fixed ground, has been used as the index for the balance ability, generally, it was not proper to use the COG under the condition of no fixed environment. The reason was that the COG index did not include the information on the slope for the wobble. Thus, this study defined the new index as the perpendicular-projection point (PPP), which was the projected point of the COM on the tilted plane. The proposed geometric solution utilized the relationship among three points, the PPP, the COM, and the middle point between the two feet, via linear regression. The experimental results found that the geometric solution, which utilized the relationship between the three angles of the equivalent model, enabled us to score the HBBAWB.  
center of gravity; center of mass; human balance ability on wobble board; perpendicular-projection point; kinematics; geometric solution +
Footnote †: journal: Applied sciences
# A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
## 2 Materials and Methods  
### Study Area  
This study was conducted in the US Corn Belt, which includes the following 12 Midwestern states: North Dakota (ND), South Dakota (SD), Nebraska (NE), Kansas (KS), Minnesota (MN), Iowa (IA), Missouri (MO), Arkansas (AR), Wisconsin (WI), Illinois (IL), Indiana (IN), and Ohio (OH) [2; 52]. The US Corn Belt accounts for approximately 75% of corn production in the US [53] and 36% of global corn production [52]. The US accounts for approximately 15% of global corn export [1]. The corn acreage in the study area in 2020 is shown in Figure 1.
## 1 Introduction
### Model Approaches
#### 1.4.1 Statistical Approaches  
In statistical approaches, time series analysis techniques, including auto-regression and exponential smoothing, have long been considered as STLF baseline models. This approach is based on well-established methods and has good interpretability. However, selection of appropriate and sufficient lagged inputs requires expertise. This approach may not be able to capture complex patterns present in the data. Simple averaging models, such as ARIMA and triple exponential smoothing, can be effective for long forecasting horizons, as discussed by [PERSON] et al. [11].
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 6 Open Issue and Future Direction
### Udns  
Interference is one of the main challenges in UDNs because of the presence of dominating interference close to the intended receiver. Therefore, IM is one of the significant issues when UDNs are integrated with other B5G technologies. To overcome this issue, cell coordination and distributed control strategies are implemented to minimize total interference. Overhead signaling is required for cell coordination. The coordination of interference is a sophisticated challenge in UDNs because of the varying BS density, which causes dominant interference in some BSs [115]. As a result, joint IM and handover among different networks while taking into consideration SE and EE require more research. moreover, the EE of an M-MIMO network with an FD relay channel should be investigated for the integration of FD, M-MIMO, and UDNs communication to mitigate the influence of interference [178]. Additionally, effective interference avoidance strategies are critical for the successful deployment of UDNs. Furthermore, the generalized coordinated multipoint (GCoMP) strategy is considered a viable solution for tackling the issue of interference, particularly for cell edge users [179].
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 4 Performance Evaluation
### Evaluate System with Network Simulator
#### 4.2.1 Network Simulator Implementation  
For system evaluations, we built the network simulator, and Figure 8 represents the structure of the simulator. The simulator consists of three components, simulation environment generator, UAV simulator, and AAN simulator. The environment generator receives environmental information, including user distribution and traffic model, and generates traffic map. The UAV simulator simulates UAVs' position, mobility, and communication with considering forwarding table and data rate information. The AAN simulator conducts network simulation whose environment is composed of satellites, UAVs, and user nodes. The simulator generates access a network topology using the information about satellites and UAVs, and creates events based on traffic map. The components of AAN are highly mobile and the network topology changes continuously. Thus, during the simulation running, the simulator continuously updates the network topology and connectivity information based on the updated positions of satellites and UAVs.