esa-sceva/satcom-chunk-collection huggingface dataset:
# An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
## 4 Results and Discussion
### Recognize Single Vibration Frequency  
The first proposed system scheme was tested by recognizing specific image-based vibration signals with a single frequency. This recognition process is DNN model testing using simulation data. The frequency-recognized data are the first 11 th frequency in the ranges of 50.00-50.50 Hz with a delta of 0.01, as shown in Figure 6. Thus, the frequency-recognized data range is from 50.00 Hz to 50.10 Hz.  
The recognition of single vibration frequency is the basic process of multiple or superposition frequency recognition that holds significant importance as it forms the foundational process for identifying multiple or superposition frequencies, especially in motor vibration analysis. Accurately identifying individual vibration frequencies lays the groundwork for understanding more complex vibration patterns and motor behavior.  
Figure 5: The result of frequency detection loss and accuracy in various ranges. (**a**) Frequency 50 Hz, (**b**) Frequency 100 Hz, (**c**) Frequency 500 Hz.  
It is essential to analyze the resulting probability of each single vibration frequency and then compare it with the superposition frequency to know the performance of the proposed recognition system. The probability value of vibration signal recognition illustrates the frequency recognition accuracy based on the DNN model. The highest accuracy represents the frequency value of the 2D image that can be recognized.  
In Figure 6, the \(x\)-axis represents frequency, and the \(y\)-axis represents the probability value. As shown in the figure, the proposed system correctly recognizes a single vibration frequency in every frequency between 50.00 Hz and 50.10 Hz. The first red line represents frequency 50.00 Hz, with the recognition probability 0.9474 or 94.74%. The following lines are blue, green, and orange, representing frequencies 50.01 Hz, 50.02 Hz, and 50.03 Hz, with the recognition probability of 89.11%, 88.94%, and 95.36%, respectively. The following line is purple, yellow, and dark grey, representing frequencies 50.04 Hz, 50.05 Hz, and 50.06 Hz, with the recognition probability of 94.81%, 82.54%, and 90.91%, respectively. The last four lines, magenta, brown, sky blue, and light green, represented frequencies 50.07 Hz, 50.08 Hz, 50.09 Hz, and 50.10 Hz, with the recognition probability of 80.37%, 91.29%, 78.54%, and 94.15%.
# Utilizing Multilevel Modeling to Measure Neighborhood Dynamics and Their Impact on House Prices
## 2 Background  
### Study Area--Lucas County, OH  
Lucas County is located in the U.S. state of Ohio and is bordered on the east by Lake Erie and on the southeast by the Maumee River, which runs to the lake. Its county seat is Toledo, located at the mouth of the Maumee River on the lake. As shown in Figure 1, the county consists of 11 school districts. As of the 2010 census, there were 441,815 people, 180,267 households, and 111,016 families residing in the county. The population density was 1296.2 inhabitants per square mile (500.5/km\({}^{2}\)). There were 202,630 housing units at an average density of 594.5 per square mile (229.5/km\({}^{2}\)). The county's racial makeup was 74.0% white, 19.0% black or African American, 1.5% Asian, 0.3% American Indian, 2.0% from other races, and 3.1% from two or more races. Those of Hispanic or Latino origin made up 6.1% of the population. In terms of ancestry, 29.8% were German, 13.2% were Irish, 9.7% were Polish, 8.0% were English, and 3.8% were American. Of the 180,267 households, 31.1% had children under the age of 18 living with them, 40.0% were married couples living together, 16.5% had a female householder with no husband present, 38.4% were non-families, and 31.4% were made up of individuals. The average household size was 2.39 and the average family size was 3.01. The median age was 37.0 years. The median income for a household in the county was $42,072, and the median income for a family was $54,855. Males had a median income of $46,806 versus $33,394 for females. The per capita income for the county was $23,981. About 14.0% of families and 18.0% of the population were below the poverty line, including 25.4% of those under 18 and 8.7% of those over 65.  
Figure 1: Census tracts and school districts in Lucas County, Ohio.
## 2 Materials and Methods
### The Functions Required by the Visually Impaired People
#### 2.2.1 Recognition of Outdoor Environment and the Training of FAST-SCNN  
To lead the visually impaired user to walk safely on the sidewalk and crosswalk, we designed the assistive system to identify the sidewalk or crosswalk in front of the visually impaired user. Fast Segmentation Convolutional Neural Network (Fast-SCNN) [23] is a real-time semantic segmentation model on high-resolution image data and offers an efficient computation on embedded devices with limited memory. In addition, Fast-SCNN has fewer parameters than those in other well-known segmentation networks [24]. Therefore, we employed the Fast-SCNN as the segmentation model to recognize the outdoor environment, including sidewalks, crosswalks, stairs, and asphalt roads. Here we suppose that only sidewalks and crosswalks are walkable for visually impaired people.  
To train the Fast-SCNN, we collected significant photos captured from the perspective of pedestrians on the street around the test field to form the training dataset. The most common outdoor environments for pedestrians, including sidewalks, crosswalks, stairs, and roads, were involved in our dataset. To be precise, we collected the training data (available via the link [[https://github.com/kev72806/TW-NCU-ICIP-Lab-dataset](https://github.com/kev72806/TW-NCU-ICIP-Lab-dataset)]([https://github.com/kev72806/TW-NCU-ICIP-Lab-dataset](https://github.com/kev72806/TW-NCU-ICIP-Lab-dataset)) (accessed on 24 October 2021)) on sunny days during daylight hours and in four different seasons. The training data set includes 22,798 photos, in which there are 3673 original captures and other augmented data. Since the images were collected by ZED 2 video continuously, we randomly adopted one-tenth of the total images as the validation set and screened out some similar images in the set. Furthermore, we used MATLAB Image Labeler to label our data at the pixel level. The total data distribution is shown in Table 1. Use the following methods to do the data augmentation: 1. Randomly rotate the image anti-clockwise or clockwise within 5 degrees and crop 80% of the original size from the center, then enlarge it back to its original size; 2. Change the image's brightness by an arbitrarily finite degree; 3. Flip the image horizontally. Then let the Fast-SCNN network be trained using those training data. Table 2 shows the data or parameters used in the network training.  
Figure 2: The software structure of the assistive system.  
After training, the Fast-SCNN outputs a segmentation result for the image in front of the user to identify sidewalks, crosswalks, stairs, and asphalt roads in their walking path. Figure 3 shows two examples of semantic segmentation results, where the left and right parts are the original capture and its segmentation result, respectively. The green, blue, grey, and red colors represent crosswalks, sidewalks, asphalt roads, and stairs, respectively. Table 3 lists the performance of Fast-SCNN with pixel accuracy and Intersection over Union (IoU).
# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 2 Related Work
The mechanism proposed in [29] also minimizes ON-OFF traffic by selecting video rates higher than the observed throughput to allow the clients to download segments continuously. However, this approach work would lead to poor performance in a cellular network. The reason is that unlike the wired network, the throughput in a cellular network depends on multiple factors, including propagation distance, fading, interference, and user mobility. The authors in [30] use a fuzzy-based adaptation mechanism that uses the moving average of the observed throughput and playback buffer level variations to minimize the video rate switches. Then, the fuzzy logic system selects the video rate based on the difference between the moving average and real values. [PERSON] et al. [31] proposed a three-input fuzzy controller including throughput, buffer level, and buffer level variations to enhance the user experience. The existing algorithms employ fixed control rules to pick the video rate based on the observed throughput and playback buffer level. Furthermore, the algorithms ignore important video client and content characteristics including segment duration and buffer size. This leads to the inconsistent performance of the algorithms under different video content and client settings. Different from existing works, the proposed fuzzy-based algorithm adapts video rates by exploiting not only estimated throughput and playback buffer level but also video player settings and video content characteristics. The objective of the proposed approach is to achieve optimal performance across a variety of video client settings and QoE objectives.
## 3 Transmitter Hardware Impairments or 'RF Features' Overview
### PN Models  
Typically, the phase noises are random noises, modelled via random time waveforms \(\phi(t)\) and characterized by their power spectral density (PSD), denoted here via \(S_{\phi}(f)\). A non-ideal local oscillator generating a waveform of amplitude \(A(t)\) at the oscillator frequency \(f_{o}\) outputs a signal \(x(t)\) of the form [35]:  
\[x(t,f_{o})=A(t)cos\big{(}2\pi f_{o}t+\phi(t)\big{)} \tag{1}\]  
The PSD of the PN is typically modelled via a power law noise [44; 45]:  
\[S_{\phi}(f)=\sum_{n_{\phi}=0}^{4}\frac{k_{n_{\phi}}}{4\pi^{2}f^{n_{\phi}}} \tag{2}\]  
where \(f\) is the frequency, \(k_{\phi}\) is a constant parameter of the model and \(n_{\phi}=0,\ldots,4\) are the summation parameters, defining the PN type, e.g., \(n_{\phi}\in\{0,2\}\) corresponds to a white-noise model (with 0 for additive white noise sources external to the oscillator and 2 for additive white noise sources internal to the oscillator), \(n_{\phi}=\in\{1,3\}\) corresponds to a flicker PN (i.e., 1 for flicker phase noise and 3 for flicker frequency noise), and \(n_{\phi}=4\) corresponds to a random-walk PN.  
The usually adopted model for GNSS signals is to ignore everything except the white-noise PN model at \(n_{\phi}=2\) in eq. (2). In this case, the PN PSD is simplified to \(S_{\phi}(f)=\frac{\sigma_{\phi}^{2}}{4\pi^{2}f^{2}}\) with \(\sigma_{\phi}^{2}\) being the variance of the white noise [35]. Without a loss of generality, this white-noise PN is also the model adopted in what follows. Nevertheless, extensions to other PN PSDs are straightforward and can be easily incorporated in our model. An example of another PN PSD model can be found for example in [46] where a combination of terms at \(n_{\phi}=0\) and \(n_{\phi}=2\) was considered.  
The on-board GNSS local oscillators are atomic clocks based on rubidium/cesium clocks [37]. Typical spoofer local oscillators have lower stability than classical atomic clocks and they rely on technologies such as oven-controlled crystal oscillator (OCXO) or temperature-controlled crystal oscillator (TCXO). This can be modelled with a lower PN variance \(\sigma_{\phi}^{2}\) for genuine GNSS transmitters than for spoofers.  
A typical measure of the PN PSD is through the so-called Allan variance \(\sigma_{A}^{2}(\tau)\) given by [47]  
\[\sigma_{A}^{2}(\tau)=\frac{8}{(2\pi f_{o}\tau)^{2}}\int_{0}^{\infty}S_{\phi}( f)sin^{2}\big{(}\pi f\tau\big{)}df \tag{3}\]  
Usually, it is very difficult to extract \(S_{\phi}(f)\) from Equation (3), and as discussed for example in [47], there might be several \(S_{\phi}(f)\) functions matching the measured \(sigma_{A}^{2}(\tau)\)Nevertheless, for the purpose of RFF, we are not interested in measuring the exact \(S_{\phi}(f)\), but we only consider it as one of the HW features at the transmitter, with the assumption that the spoofer and the genuine GNSS transmitters have different PSDs \(S_{\phi}(f)\).
The incorporation of graphite is an interesting option for a couple of reasons. Graphite is a very cheap, abundant, and non-toxic material. Moreover, it exhibits a stable electrochemical response and low-volume expansion upon lithium insertion, and it has one of the lowest lithiation potentials of 0.05 V [3]. Graphite was already successfully applied in combination with PDCs [35; 37; 38; 39]. It is worth noting that the addition of graphite to the ceramic material may induce different electrochemical properties, depending on the composition of the preceramic polymer and the polymer-to-graphite ratio [35]. A capacity increase, a lower lithiation potential, a higher first-cycle Coulombic efficiency, and a better rate capability were observed for various SiOC/graphite composites, which have been mostly attributed to the improved conductivity, the creation of new lithium storage sites [37; 38], and the appropriate ratio between ordered (graphitic) and disordered carbons [35]. Graphite was also reported to be a good component for Sn-based nanocomposites due to its low volume expansion and good electrical conductivity [5]. Graphite reduces charge transfer resistance and lowers overall polarization [40]. In addition, the graphite/tin composite exhibits a remarkably improved capacity at low temperatures in comparison with pure graphitic anodes [41]. In this work, we combined graphite and SiOC to achieve an exceptional matrix for tin nanoparticles and increase carbon content, which is a crucial component for ensuring good conductivity. The idea behind the addition of graphite was also to increase the tin content in the final material compared with that of the pure carbon-rich ceramic matrix. The ternary silicon oxycarbide/graphite/tin composites were synthesised using a poly(methylhydrosiloxane)-divinylbenzene mixture as a preceramic precursor, tin (II) octaate as a tin source, and graphite flakes. The choice of the preceramic polymer and the tin source was based on the good miscibility of these components as reported by [PERSON] et al. (2013). The main focus of this work was on the influence of graphite with regard to the progress of carbothermal reduction processes as well as the composition, microstructure, and electrochemical behaviour of the final composites.
## 1 Introduction
The horizon length is constantly adjusted such that the absolute residual values of the three sequential estimates with different horizon lengths is minimized and applied for the next three estimates. Because the proposed AHLA method uses results in the estimation process, it can have less computational burden than the previous approach in [34]. Although the estimation performance of the proposed AFMRH filter can be improved by applying the AHLA method, the computational cost of the proposed AFMRH estimators can increase because of a large adjusted horizon length. As the RH filter converges to the Kalman filter as the horizon length increases [13], it is better to choose a Kalman filter that provides the optimal estimate and computational efficiency than an RH filter with large horizon length. Moreover, the adaptive fading factor can also be used to determine whether a temporary model uncertainty or numerical error exists. Thus, a simple but efficient switching AFMRH (SAFMRH) filtering algorithm based on adaptive fading factor and AHLA method is also proposed to improve the computational efficiency. It is expected that the proposed AHLA method and SAFMRH filtering algorithm can improve the estimation performance and computational efficiency for both the nominal and temporarily uncertain systems. The rest of this paper is organized as follows:  
In Section 2, an AFMRH filter is proposed by combining the recursive optimal RH filter with the adaptive fading factor. Then, based on the results of AFMRH filtering, a fixed-lag AFMRH smoothing algorithm is proposed using the Rauch-Tung-Striebel smoothing technique in Section 3. Thereafter, in Section 4, a computationally efficient AHLA method and switching filtering algorithm based on the adaptive fading factor are proposed to improve the estimation accuracy and computational efficiency of the proposed filter and smoother. In Section 5, the performances of the proposed AFMRH estimators are evaluated via numerical simulations. Finally, the conclusions are presented in Section 6.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 5 Conclusions  
This study introduces an enhanced version of the YOLOv11 architecture, built upon the modified YOLOv5 framework, for the effective detection and classification of printed Bangla alphabet characters. The proposed model incorporates a ResNet50 backbone and attention mechanisms to enhance feature representation and detection accuracy. Experimental results demonstrate that our approach significantly outperforms existing models in terms of mean average precision (mAP), precision, recall, and F1 score, based on a custom-labeled Bangla alphabet dataset.  
One of the most challenging aspects of Bangla alphabet recognition is distinguishing visually similar characters and conjunct consonants. Our model exhibits strong resilience in such cases, achieving an accuracy of up to 99.9% under optimal training conditions. We found that training for 150 epochs provided the best balance between accuracy and overfitting, making it the most effective configuration in our experiments.  
Despite these promising results, there are a few limitations. The model has been tested exclusively on printed Bangla characters, and its performance on handwritten or cursive text remains unverified. Additionally, the current dataset does not include noisy backgrounds or real-world scenes, which may affect the model's ability to generalize effectively.  
Looking ahead, we aim to extend this framework to handle handwritten Bangla characters and adapt to more complex image conditions, such as varying lighting, occlusion, and background noise. We also plan to explore real-time Bangla script recognition from video streams and mobile-captured images. These future directions will further test the robustness and applicability of our proposed model in practical OCR systems for low-resource languages.  
Conceptualization, M.A. and N.I.; methodology, N.I.; software, N.I.; validation, N.I. and M.A.; formal analysis, N.I.; investigation, N.I.; resources, M.A.; data curation, M.A.; writing\(-\)original draft preparation, M.A.; writing\(-\)review and editing, C.Y.; supervision, C.Y. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China [Grant Number: 52227901].  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 5 Conclusions  
This article considers safe 3D path planning for a fixed-wing autonomous aerial vehicle. Regarding a 3D workspace domain without grid cells, this study addresses a time-efficient 3D path plan algorithm utilizing a simulated vehicle and simulation nodes. Regarding obstacle-rich 3D environments, the proposed plan algorithm generates a smooth 3D path considering the maximum turn rate of a fixed-wing aerial vehicle. This paper verifies that the proposed 3D path plan algorithm generates a short and safe path in a time-efficient manner. It is proven that the planned 3D path avoids collision with obstacles, while satisfying the turn rate limit constraints.  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline \(Algorithm\) & \(simTime\) & \(meanLen\) & \(varLen\) \\ \hline \([Proposed]\) & 164 & 2464 & 58 \\ \([RRT]\) & - & - & - \\ \hline \end{tabular}
\end{table}
Table 2: Comparison analysis (Scenario 2).  
Figure 7: Regarding Scenario 2, this plot shows \(Dist(n_{j})\) with respect to \(j\), where \(j\in\{1,2,3,...,end-2\}\). The safety radius \(r\) is marked with a red dotted line.  
Through computer simulations of two cluttered environments, one proves the outperformance (low computational load, satisfying maneuverability constraints, and short path length) of our 3D path plan algorithm by comparing it with the 3D RRT-star algorithm [21] by considering the turn angle limits. As our future work, we will prove the outperformance of our 3D path plan algorithm utilizing experiments with real fixed-wing autonomous aerial vehicles. Also, in the future, we will extend the proposed 3D path planner, such that a formation of aerial vehicles can reach the destination, while considering the turn angle limit of each vehicle.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (Grant Number: 2022R1A2C1091682).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflicts of interest.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 2 Materials and Methods
### Coverage Analysis  
Signal propagation was modeled by considering the location of each cellular tower as the transmitter site, and that of each track waypoint as the receiver. For all towers, the transmitting frequency was assumed to be 2.5 GHz, with a transmission power of 10 W and an antenna height of 10 m. Similarly, a receiving antenna with a sensitivity of \(-100\) dBm and a height of 1.5 m was assumed to approximate the mobile phones of injured victims.  
The Longley-Rice irregular terrain model was used to estimate the signal pathloss, since it considers the terrain profile between the transmitters and receivers. Specifically, the signal strength is calculated from the lowest pathloss value across all receivers using  
\[P_{rx}=P_{tx}-\text{pathloss}, \tag{1}\]  
where \(P_{rx}\) and \(P_{tx}\) define the signal power of the receiver and transmitter, respectively. The link margin for each receiver is subsequently obtained using  
\[Rx_{\text{margin}}=Rx_{\text{sensitivity}}-P_{rx}. \tag{2}\]  
A receiver with a negative link margin corresponds to a weak signal that cannot be detected. Consequently, all receivers with a negative link margin are deemed to fall outside the coverage area of a cell-tower and define the required points of interest.  
Figure 2: Digital elevation model of Keswick.