esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Inspired by CHADC [16] and the triple-step control method [30], this study establishes a composite controller comprising three parts: a modified feedback controller, a compound disturbance compensation controller, and a feedforward controller. The modified feedback controller is designed to suppress the vibration analyzed in the dynamics model. The compound disturbance compensation controller is designed by the friction feedforward method and the residual disturbance observation. On the basis of suppressing the vibration and the composite anti-disturbance control, this study emphasizes the structural advantages of SISP, and further enhances the dynamic performance via the feedforward controller. In summary, compared with the current state-of-the-art research, the major contributions of this study are as follows. 1. To analyze the vibration, the platform base and the motor rotor are treated as an interaction subsystem and the motor rotor and the load are treated as a two-mass-like subsystem. Consequently, this study proposes a multi-body dynamics model including the controller for SISP. 2. To suppress the vibration, compensate the multi-source disturbances, and to achieve high performance, the composite controller is designed by three components based on the idea of triple-step control. The proposed method sets the basis for the engineering design of SISPs with a light base support. The remaining sections of this paper are organized as follows. In Section 2, the dynamics model for SISP is established. In Section 3, the SISP model considering light base support characteristics is analyzed to evaluate the vibration. Section 4 proposes an improved control method that comprises the modified PI feedback controller, the compound disturbance compensation controller, and the feedforward controller. The experimental setup and results are described in Section 5. Finally, Section 6 presents the conclusions of this study.
###### Abstract  
This paper addresses the critical issue of unwanted interference in airborne GNSS receivers, crucial for navigational safety. Previous studies often simplified the problem, but this work offers a comprehensive approach, considering factors like Earth's reflective properties, 3D calculations, and distinct radiation patterns. It introduces Spatial Interference Distribution Expression Heat-map and Operation Efficacy Plot graphs to visualize interference distribution along flight paths. The results highlight the significance of physical configuration and distance from interference sources on receiver performance. The algorithm developed can assess interference effects on GNSS receivers and aid in selecting optimal flight paths for minimal interference. This research enhances understanding and management of unintentional interference in airborne navigation systems.  
GNSS; navigation; interference; spoofing; aircraft; antenna; radiation pattern; mathematical modeling; simulation; receiver +
Footnote †: Eöole de Technologie Supérieure, Université du Quebec, Montreal, QC H3C 1K3, Canada; [EMAIL_ADDRESS]  
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# Comparison of Manual and Automated Capillary Morphometry Measurements in Oral Mucosa: A Pilot Study
## 3 Results  
### Caliber  
The comparison between the automated and manual methods for measuring capillary caliber revealed significant differences across all examined sites.  
* In the right buccal mucosa, the automated method yielded a mean value of \(19.996\pm 1.30\)\(\upmu\)m, compared to 17.645 \(\pm\) 3.05 \(\upmu\)m for the manual method, with a mean difference of 2.352 \(\upmu\)m (\(p\) = 0.038).
* In the left buccal mucosa, the automated method recorded a mean of 19.534 \(\pm\) 1.08 \(\upmu\)m, while the manual method reported 16.682 \(\pm\) 2.09 \(\upmu\)m, with a mean difference of 2.852 \(\upmu\)m (\(p\) = 0.001).
* In the lower labial mucosa, the automated method showed a mean of 18.533 \(\pm\) 1.81 \(\upmu\)m, compared to 16.169 \(\pm\) 2.61 \(\upmu\)m for the manual method, with a mean difference of 2.364 \(\upmu\)m (\(p\) = 0.031).
* In the upper labial mucosa, the automated method displayed a mean of \(18.647\pm 0.900\)\(\upmu\)m, versus 16.082 \(\pm\) 2.59 \(\upmu\)m for the manual method, with a mean difference of 2.565 \(\upmu\)m (\(p\) = 0.008).  
Across all sites, the automated method tended to overestimate capillary caliber compared to the manual method, with mean differences ranging between 2.352 \(\upmu\)m and 2.852 \(\upmu\)m. This discrepancy may be attributed to the software's greater sensitivity in detecting capillary structures or to a potential systematic overestimation. Despite these differences, both methods demonstrated a normal distribution of the data, as confirmed by the Shapiro-Wilk test (\(p\) > 0.05). The results suggest that, although the automated method is faster and potentially less prone to human error, it may require further calibration to enhance measurement reliability, particularly in clinical contexts where precision is critical.  
The Bland-Altman plot (Figure 4) reveals that the mean difference between the two methods is 2.53 \(\upmu\)m, confirming the tendency of the automated approach to overestimate capillary caliber compared to the manual measurement. Furthermore, the limits of agreement (\(\pm\)1.96 SD) range from \(-\)2.51 \(\upmu\)m to 7.58 \(\upmu\)m, indicating variability between the two approaches. Although most data points fall within these limits, some measurements exhibit larger discrepancies, suggesting that the automated method may have a systematic bias or greater sensitivity in detecting capillary structures.
# Compact Fuzzy Systems Based on Boolean Relations
## 5 Application Example of Architecture I: Nonlinear Plant Identification
### Training Algorithm  
For the system training, it is employed the Back Propagation (BP) algorithm that uses the gradients calculated in relation to the parameters [51]. This approach aims at minimizing the \(J_{e}\) error defined as:  
\[J_{e}=\frac{1}{2}[y_{r}(p)-f(x(p))]^{2} \tag{55}\]  
where \(y_{r}\) is the real data, \(f\) the data from the fuzzy system, and \(p\) the variable associated with each training data. The equations used in the training process are:  
\[v_{c}(q+1)=v_{c}(q)-\alpha\frac{\partial J_{e}}{\partial v_{c}}\bigg{|}_{p} \tag{56}\]  
\[v_{l}(q+1)=v_{l}(q)-\alpha\frac{\partial J_{e}}{\partial v_{l}}\bigg{|}_{p} \tag{57}\]  
\[\delta_{i,l}(q+1)=\delta_{i,l}(q)-\alpha\frac{\partial J_{e}}{\partial x_{i,l }}\bigg{|}_{p} \tag{58}\]  
Figure 17: Representation of the neuro-fuzzy system employed.  
\[\sigma_{i,l}(q+1)=\sigma_{i,l}(q)-\alpha\frac{\partial l_{e}}{\partial\sigma_{i,l}} \bigg{|}_{p} \tag{59}\]  
Thus, the parameters are upgraded with:  
\[v_{c}(q+1)=v_{c}(q)-\alpha(f-y_{r}) \tag{60}\]  
\[v_{l}(q+1)=v_{l}(q)-\alpha(f-y_{r})Y_{l} \tag{61}\]  
\[\delta_{i,l}(q+1)=\delta_{i,l}(q)-\alpha(f-y_{r})v_{l}(q)Y_{l}\frac{2(x_{i}[p] -\delta_{i,l}(q))}{\sigma_{i,l}(q)^{2}} \tag{62}\]  
\[\sigma_{i,l}(q+1)=\sigma_{i,l}(q)-\alpha(f-y_{r})v_{l}(q)Y_{l}\frac{2(x_{i}[p] -\delta_{i,l}(q))^{2}}{\sigma_{i,l}(q)^{3}} \tag{63}\]  
According to [51; 63], parameters adaptation of the neuro-fuzzy system involves the following steps:  
1. The neuro-fuzzy system is chosen considering \(M\) and \(N\). According to [63], \(M\) is increased when having more parameters and, consequently, more calculations and higher accuracy.
2. Output calculations for an input-output \((x[p],y_{r}[p])\) in the \(q\)-th training stage.
3. Parameter upgrade \(v_{l}(q+1),\delta_{i,l}(q+1)\) and \(\sigma_{i,l}(q+1)\). Where \(\alpha\) is the learning rate.
4. Return to step 2 taking \(q=q+1\), until the error \(e=f-y_{r}[p]\) is smaller than a value \(\varepsilon\), or until a maximum value of \(q\).
5. Return to step 2 taking \(p=p+1\), taking the next input-output \((x[p+1],y_{r}[p+1])\).
## 3 Experiments and Analysis
### Comparison of Fusion Criteria  
NSCT transform is adopted to decompose the images into low and high frequency sub-band images, and the information extraction influence of different fusion criteria is analyzed. Among them, the shared parameters of the PCNN or IDPCNN model are set as follows: max iteration \(Kn\) is 200, link input attenuation constant \(\alpha_{L}=\)1, link input amplification factor \(V_{L}=\) 1, threshold attenuation constant \(\alpha_{\theta}=\) 0.2, threshold amplification factor \(V_{\theta}=\) 20, and non-linear combination coefficient ratio between link input and external excitation \(CR=\) 1. The specific design schemes are as follows.
## 4 Discussion
### Impact of Different Tillage Practices on Soil Physicochemical Properties  
The highest organic matter content was observed under conventional CK, followed by SS, with the lowest levels under ST. This indicates that without straw incorporation and through the use of no-till practices, the soil may retain more organic matter due to less disturbance and slower decomposition rates compared to straw incorporation scenarios [50]. This may be due to reduced soil disturbance in no-till practices, which helps retain more organic material on the soil surface or in the shallow layer, thus enhancing the organic matter content [51; 52].  
Although the ST treatment showed the lowest organic matter content, it had relatively high available nitrogen, slightly lower than that under CK. This suggests that while straw incorporation may contribute to nutrient input, the tillage method plays a key role in determining nutrient availability. Ridge tillage may promote the transformation of nitrogen into forms that are not immediately accessible to plants [53; 54].  
Available phosphorus levels varied to some extent across treatments. The differences may reflect variations in phosphorus mobilization mechanisms influenced by tillage practices. Strip tillage may promote microbial activity, which in turn enhances the solubilization of available phosphorus [55; 56].  
Available potassium levels were higher under CK compared to the other treatments. This may be related to the reduced soil disturbance in no-till systems, which helps maintain soil structure and minimize nutrient loss through leaching [57; 58; 59].  
Differences in soil pH across treatments also suggest that tillage practices influence soil chemical properties. These changes in acidity can affect nutrient availability and overall crop growth conditions [60; 61; 62].  
Different tillage practices affect soil physical and chemical properties to varying degrees. Under CK, higher levels of organic matter are maintained, along with more stable nutrient conditions. These factors contribute to improved soil structure stability and the preservation of long-term fertility. In contrast, strip tillage and rotary tillage with straw incorporation may cause short-term fluctuations in certain nutrient contents. However, these practices enhance overall soil health by promoting microbial activity and nutrient cycling. Understanding how such soil properties interact with pesticide behavior--including adsorption, mobility, and degradation--is essential for assessing both environmental impacts and food safety.
# Convolutional Neural Network with an Elastic Matching Mechanism for Time Series Classification
## 1 Introduction  
Time series classification (TSC) is an important research topic in data mining communities [1]. It has a wide range of applications in human activity recognition [2], speech analysis [3], electrocardiogram (ECG) monitoring [4], and biological research [5].  
Deep learning is a subfield of machine learning concerned with deep structures with adjustable parameters. Many deep learning architectures exist for TSC. Compared with other classical architectures such as the multilayer perceptron and recurrent neural network (RNN), the convolutional neural network (CNN) has become one of the most prevalent architectures for TSC in recent years [6]. However, the CNN architecture is sensitive to temporal distortion [7], such as differences in rates and local translation within a pattern [8].  
Many studies have been conducted on temporal distortion for TSC. One of the most representative studies is on dynamic time warping (DTW). In conjunction with a one-nearest-neighbor (1 NN) classifier, DTW achieves great success in TSC. Compared with the lock-step matching in Euclidean distance (ED) [9], elastic matching is exploited in DTW to achieve invariance in temporal distortion. However, DTW is a global distance measure that discards the matching information [8]. In addition, DTW could match two series that have dissimilar local structures [10].  
Inspired by the elastic matching in DTW, an elastic matching mechanism combined with CNN called Elastic Matching CNN (EM-CNN) is proposed in this paper. Instead of lock-step alignments between the time series and convolutional kernels as CNN, a matching matrix is used to adaptively learn the alignments between these in the EM-CNN. The EMCNN is an architecture that learns the matching relationship and convolutional kernel simultaneously. The primary contributions of this paper are concluded as follows:  
* An elastic matching mechanism is proposed to measure the similarity between the time series and convolutional kernels. This mechanism can be extended to different architectures based on the CNN.
* The experiments performed on 85 University of California, Riverside (UCR) time series datasets [11] demonstrate that the proposed mechanism improves the performance of CNN on classification tasks.  
The remainder of this paper is organized as follows. This paper briefly reviews the related work in Section 2. In Section 3, an elastic matching mechanism is proposed to learn the matching relationship between the time series and convolutional kernels. Next, the experiments are performed on 85 UCR datasets, and the results are analyzed in Section 4. Additional discussion is presented in Section 5. Finally, a conclusion is provided in Section 6.
## 4 Simulation Results and Discussion
### Poor Geometry Test  
Simulated GPS satellites never have severely poor geometry, because simulation allows the satellite configuration to be manipulated as necessary. However, the geometry may become less than ideal if the receiver unexpectedly loses a pseudolite signal corresponding to a GPS satellite. Therefore, the proposed algorithm is tested for cases involving poor GPS and pseudolite constellation geometry. Once again, five GPS satellites using five corresponding pseudolites are simulated. The simulation procedure and settings except the GPS satellites, pseudolite, and user positions are the same as the feasibility test.  
Figure 15 illustrates the results for good and bad GPS satellite geometries. For each case, the DOPs for GPS are 1.7489 and 14.5487, respectively. The pseudolite DOP is calculated to be 2.0468. With higher DOPs, the _a priori_ position gets worse, and the final position error is also affected. However, unlike the biased _a priori_ position, the final estimated position still matches the true position.  
Figure 14: (**a**) Estimation results for user #1 (**b**) Estimation results for user #2.  
To test for even worse cases, poor pseudolite geometries are used in addition to poor GPS geometry. Figure 16 illustrates the results for high pseudolite DOPs. In each case, the DOPs for the pseudolites are 14.1849 and 25.3421, respectively. For the same _a priori_ positions, poor pseudolite geometry makes the final estimation even worse. Again, unlike the biased _a priori_ positions, the final results match the true user positions.  
Figure 16: (**a**) Results for poor DOP of pseudolites (**b**) Results for worse DOP of pseudolites.  
Figure 15: (**a**) Results for good DOP of GPS satellites (**b**) Results for poor DOP of GPS satellites.  
It can be assumed that any time a pseudolite system is developed, the pseudolites will be arranged in an effective configuration. However, if the user's GPS receiver loses some pseudolite signals, the system will be left with poor geometry. Simulation results show that even when the geometry gets worse, the proposed algorithm works with only a slight loss of accuracy.
# Communication Rate Increase in Drill Strings of Oil and Gas Wells Using Multiple Actuators
## 5 Conclusions  
This paper demonstrates that by using two actuators, one can transmit two data sets simultaneously through drill strings and pipes in order to double the transmission rate in such communication media. The experimental bit error probability performance of the proposed two-actuator new system on a testbed was shown to be about the same as a system that used only one actuator, which can therefore offer only half of the data rate of the new system. Upon using more actuators, one can increase the data rate further.  
Although only a two-channel receiver is proposed and used in this paper to separate and demodulate the data of the two actuators, one can use more receiving channels and sensors to improve  
Figure 11: BERs (**top**) and SNRs (**bottom**) at different receiver positions, with two actuators transmitting two data streams simultaneously. The receivers were the \(y\)-channel and \(z\)-channel of a triaxial accelerometer. The piecewise linear graphs represent the average BERs and SNRs for each of the two data streams versus the receiver position.  
Figure 10: BERs (**top**) and SNRs (**bottom**) at different receiver positions, with two actuators transmitting two data streams simultaneously. The receivers were the \(x\)-channel and \(z\)-channel of a triaxial accelerometer. The piecewise linear graphs represent the average BERs and SNRs for each of the two data streams versus the receiver position.  
the system performance. The benefit of having a multi-channel receiver with one actuator is discussed in References [4; 6].  
In this paper, various combinations of strain and acceleration sensors are considered at the receive side. Due to some properties of strain channels (e.g., smaller delay spreads [5] and their less-frequency-selective behavior) presented in this paper, it appears that a strain sensor receiver and an accelerometer receiver together can offer a good performance when separating and demodulating the two data streams transmitted simultaneously by the two actuators.  
While not discussed in the paper, one can use the additional actuators to induce redundancy at the transmit side to reduce the bit error probability, using space-time codes and space-frequency codes.  
Conceptualization, A.A.; methodology, A.A.; experimentation, E.Z.; writing--original draft preparation, A.A.; writing--review and editing, E.Z. and A.A.; supervision, A.A.  
This work is supported in part by the National Science Foundation (NSF), Grant IIP-1340415.  
The authors declare no conflict of interest.
# Development and Application of Digital Twin-BIM Technology for Bridge Management
## 5 Conclusions  
The results of developing a digital twin of the Comoro bridge based on BIM in this research can be summarized as follows:  
* The development and application of digital twin-BIM technology for bridge management offer significant advantages in the field of infrastructure maintenance and operation. By integrating the capabilities of digital twin technology with building information modeling (BIM), bridge managers can create a comprehensive virtual representation of the bridge, capturing its physical and functional attributes.
* Digital twin-BIM technology enables the landscape visualization of the Comoro Bridge with the surrounding environment, traffic flow simulation at the area of the target bridge, a finite element analysis for bridge structure performance evaluation, and bridge structure maintenance. Therefore, digital twin-BIM technology has the potential and ability to access accurate and up-to-date information about the bridge's condition that enhances asset management practices, optimizes maintenance schedules, and extends the lifespan of the structure.
* Additionally, digital twin-BIM technology promotes an effective collaboration among stakeholders involved in bridge management, allowing seamless data exchange, information sharing, and coordinated decision-making. This enhances communication, reduces errors, and streamlines workflows, leading to improved efficiency and cost-effectiveness in bridge maintenance and operation.
* Furthermore, the utilization of digital twin-BIM technology in bridge management contributes to enhanced safety by enabling the evaluation of the structural integrity, the assessment of risk factors, and the implementation of preventive measures. This proactive approach minimizes the likelihood of bridge failures, ensuring the safety of users and surrounding areas.  
Overall, the combination of digital twin and BIM technologies presents a powerful tool for bridge management, offering enhanced insights, improved decision-making, and optimized asset performance. As technology continues to advance, it holds enormous potential for revolutionizing the way bridges are designed, built, monitored, and maintained, leading to safer and more efficient infrastructure systems.  
Conceptualization, G.W., E.E.T., P.S. and K.A.; methodology, E.E.T. and G.W.; software, E.E.T. and P.S.; validation, G.W. and E.E.T., investigation, E.E.T. and G.W.; resources, G.W. and E.E.T.; data curation, E.E.T. and G.W.; writing--original draft preparation, E.E.T. and G.W.; writing--review and editing, G.W. and E.E.T.; visualization, E.E.T.; supervision, G.W.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Dispatchable Substation for Operation and Control of Renewable Energy Resources
## 2 Requirement for Dispatch-Able Substation
### Mitigate Fluctuations and Voltage Stability  
The interconnection of renewable generation can cause voltage fluctuations due to its variable output. It is the obligation of the renewable generator to maintain the power factor within a stable range, and in some countries, they are required to sustain the voltage level more actively. In the distribution system, a transformer usually controls the overall voltage level; if the fluctuations are very rapid, the distribution network fails to maintain the voltage within the range defined by the grid code. Developing a grid code for limiting the output fluctuations of renewable generation can improve the voltage stability of the network. Combining ESS with RES can efficiently smooth out fluctuations with low energy loss. Additionally, it can operate as a reactive power source for additional grid support. In the proposed DSS system, ESS can function as a reactive source; however, it is recommended to include additional reactive power sources. The frequent use of ESS for reactive support can reduce the overall life cycle of the devices, consequently increasing the operation cost of the entire system. Thus, the voltage support devices must be selected based on a detailed analysis of the neighboring network.
## 2 Materials and Methods
### Environmental Impact Assessment Method  
The Environmental Footprint (EF) method version 3.1 was chosen to model the environmental impacts based on the ISO 14040/14044 Standards [1; 2]. The approach evaluates multiple categories, and covers the entire life cycle of a product, from raw material extraction to end-of-life, ensuring a holistic view of its environmental footprint. This method emphasizes robust data collection, transparency and alignment with EU Regulations, making it a valuable tool for improving sustainability and regulatory compliance.  
The inventory data were categorized into impact categories following the characterization method outlined in \"Updated Characterisation and Normalisation Factors for EF 3.1\" [12]. Characterization links inventory flows to environmental impacts, calculated by multiplying flows by characterization factors. Normalized results express impactsrelative to reference systems, making comparing the significance of different impacts easier. Table 1 presents the normalization factors used in this study [13]. Table 1 shows the weighting factors applied in this study [14]. The final EF results helped identify critical areas for improvement and support eco-design, and they may be used for marketing or compliance purposes.