esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks
## 1 Introduction  
Signal processing and analysis is important for exploring complex systems, especially when the systems are treated as black boxes. Many mathematical methods can be employed to make signal analyses. A signal is a carrier of information, which can be described by Shannon entropy [1; 2]. Entropy can be treated as one of the complexity measures [3; 4]. The Hausdorff dimension proved to be equivalent to information entropy [5]. A number of mathematical and empirical relationships have been found between entropy and fractal dimension [6; 7; 8; 9]. Based on box-counting method, normalized fractal dimension was proved to equal normalized information entropy [10]. Thus, fractal dimension can be employed to reveal hidden information in spatial signals. Fractal theory has been applied to signal processing related to urban traffic and transportation [11]. Among various fractal-based signal processing and analyses, the most significant ones include reconstructing phase space, rescaled range analysis (R/S analysis), wavelet analysis, and spectral analysis. Spectral analytical processes include power spectral analysis for time series and wave spectral analysis for spatial series. Spectral exponents has been demonstrated to connect fractal dimension, and are associated with the Hurst exponent in R/S analysis [12; 13; 14].  
Generally speaking, signals are represented by time series coming from physical systems or social systems. In fact, signals fall into three categories; the first is temporal signals, which can be reflected by time series data; the second is spatial signals, which can be reflected by spatial data; the third is hierarchical signals, which can be reflected by cross-sectional data indicative of rank-size distribution. Different signals representdifferent aspects of natural and social systems, and can be characterized by different parameters. The hierarchical signals can be regarded as generalized spatial signals, since cross-sectional data are based on spatial random sampling. Many mathematical methods can be employed to analyze signals based on time series, including moving average (MA) analysis, auto-regression (AR) analysis, auto-regressive moving average (ARMA) analysis, power spectrum analysis, and R/S analysis. Under certain conditions, the methods for time series can be properly improved and applied to spatial signal analysis [15]. For example, MA and AR can be replaced by spatial autocorrelation analysis and spatial auto-regression analysis, power spectrum analysis can be replaced by wave-spectrum analysis. This paper is devoted to modeling and analyzing spatial signals for geographical systems such as cities and traffic networks. The spatial analysis can be combined with time series analysis. The sections are organized as follows: firstly, the mathematical models for spectral scaling relations are presented; secondly, case studies are made by taking Chinese cities as examples; thirdly, related questions are discussed; and, finally, the main points are summarized to conclude this work.
# Inversion and Analysis of the Initial Ground Stress Field of the Deep-Buried Tunnel Area
## 2 Multiple Linear Regression Inversion Principle of Initial Ground Stress Field
### Establishment of the Three-Dimensional Geological Model  
#### 2.1.1 Determination of the Model Range  
In the process of initial ground stress regression inversion, two principles [35; 36] are generally followed to determine the calculation range: (1) the model calculation range includes all the main areas of the tunnel area, and the main geological structures existing in the area, etc.; (2) the boundary of the model is clear and transparent, and it is best to choose in the ridge or river valley. The former is to make the model contain as much data as possible. The more abundant the measured ground stress data, the more accurate and reliable the curve fitting effect will be. The ground stress field inverted by the model is more consistent with the ground stress field in the actual stratum. The latter is for better positioning of the model, and it is easier to impose a single control condition in the process of numerical simulation.
## 4 Computational and Mathematical Aspects
### Constitutive Laws of Masonry Materials
\begin{table}
\begin{tabular}{c c c} \hline
**Sl. No.** & **Reference** & **Formula** \\ \hline
1 & [PERSON] (1907) [76] & \(f_{av}=\frac{1}{3}f_{bc}+\frac{2}{3}f_{mc}\) \\
2 & [PERSON] (1963) [77] & \(f_{av}=0.68f_{bc}{}^{1/2}f_{mc}{}^{1/3}\) \\
3 & [PERSON](1982) [78] & \(f_{av}=0.83f_{bc}{}^{0.66}f_{mc}{}^{0.18}\) \\
4 & [PERSON] and [PERSON] (1986) [79] & \(f_{av}=0.317f_{bc}{}^{0.531}f_{mc}{}^{0.208}\) \\
5 & [PERSON] (1987) [80] & \(f_{av}=0.275f_{bc}{}^{0.5}\) \\
6 & [PERSON] (1995) [81] & \(f_{av}=(v_{fu}f_{bc}+0.8v_{fu}f_{mc})/10\) \\
7 & [PERSON], Boyd and Flanagan (1997) [82] & \(f_{av}=0.3f_{bc}\) \\
8 & AS 3700 (2001) [83] & \(f_{av}=K_{u}K_{u}f_{bc}{}^{0.5}\) \\
9 & [PERSON] and [PERSON] (2002) [84] & \(f_{av}=0.3266f_{bc}\times(1-0.0027f_{bc}+0.0147f_{mc})\) \\
10 & Eurocode 6 (2005) [85] & \(f_{av}=Kf_{bv}{}^{0.7}f_{mc}{}^{0.3}\) \\
11 & [PERSON], [PERSON] and [PERSON] (2007) [86] & \(f_{av}=0.317f_{bc}{}^{0.86}f_{mc}{}^{0.134}\) \\
12 & [PERSON], [PERSON], [PERSON] and [PERSON] (2007) [87] & \(f_{av}=0.63f_{bc}{}^{0.68}f_{mc}{}^{0.32}\) \\
13 & [PERSON], [PERSON] (2013) [88] & \(f_{av}=0.35f_{bc}{}^{0.68}f_{mc}{}^{0.25}\) \\
14 & [PERSON], [PERSON] (2013) [89] & \(f_{av}=0.53f_{bc}+0.93f_{mc}-10.32\) \\
15 & [PERSON] and [PERSON] (2014) [72] & \(f_{av}=0.88f_{bc}{}^{0.75}f_{mc}{}^{0.18}\) \\
16 & [PERSON], [PERSON] and [PERSON] (2014) [90] & \(f_{av}=0.75f_{bc}{}^{0.75}f_{mc}{}^{0.31}\) \\
17 & [PERSON] (2016) [91] & \(f_{av}=0.69f_{bc}{}^{0.6}f_{mc}{}^{0.35}\) \\ \hline \end{tabular} \(f_{av}\), masonry compressive strength; \(f_{lev}\), brick compressive strength; \(f_{mc}\), mortar compressive strength; \(v_{u}\), relative volume of unit \(v_{m}\), relative volume of mortar; K is a constant in Eurocode 6 formula, modified according to the National Annex for different countries. The value of this constants in the UK is 0.52 [92] while in Greece is 0.20-1.00 depending on brick/block unit properties and their arrangement; \(K_{u}\) is a factor in Australian AS 3700 code [83] that accounts for the ratio of unit height to mortar joint thickness (1.3 for blocks of 190 mm high blocks and mortar joints with 10 mm thickness); \(K_{m}\) is also a factor in Australian AS 3700 [83] code that accounts for bedding type (1.4 for full bedding and 1.6 for face-shell bedding).  
\end{table} 
Table 1: Formulae for the estimation of masonry compressive strength. to be the most reliable for the determination of masonry compressive strength [93] among a plethora of proposed equations available in the literature [76; 77; 78; 79; 80; 81].
# Chord Recognition Based on Temporal Correlation Support Vector Machine
## 5 Experimental Results and Analysis
### Experimental Results Compared with State-of-the-Art Methods  
We compare our method with the following methods from MIREX 2013 and MIREX 2014.  
* CB4 and CB3: [PERSON] and [PERSON] [35]
* KO1 and KO2: [PERSON] and [PERSON] [36]
* NMSD1 and NMSD2: [PERSON], [PERSON], [PERSON] [37]* CF2 : [PERSON], [PERSON], [PERSON] [38]
* NG1 and NG2: [PERSON] [42]
* PP3 and PP4: [PERSON] and [PERSON] [39]
* SB8: [PERSON] and [PERSON] [40]
* MIREX 2014:
* KO1: [PERSON] and [PERSON] [51]
* CM3: [PERSON], [PERSON] [41]
* JR2: [PERSON] [52]  
More details about these methods can be found from the corresponding MIREX websites [53]. The results of this comparison are presented in Figure 9.  
Figure 8: Recognition rates with different features.  
Figure 7: Comparison of ground truth and estimated chord with PCP (**top**) and EPCP (**bottom**).  
The recognition rate of our TCSVM method with ELPCP is 82.98%. The recognition rate of our TCSVM (ELPCP) approach is similar to the best-scoring method (KO1) in MIREX 2014.  
The results of the 2015 edition of MIREX automatic chord estimation tasks can be found on the corresponding websites [54]. Table 1 shows the comparison of the recognition rates of the 2015 edition on the Isophonics 2009 datasets. The chord classes are as follows: Major and minor(MajMin); Seventh chords(Sevenths); Major and minor with inversions(MajMinInv); Seventh chords with inversions(SeventhsInv). The recognition rate of our TCSVM method is higher than other methods except the rates of the MajMinInv chords.  
Figure 10 shows the confusion between the chords using SVM and Figure 11 shows the confusion between the chords using TCSVM. The \(x\)-axis is the ground truth chord and the \(y\)-axis is the estimated chord. Comparing the two images suggests that TCSVM reduces the rate of erroneous identifications for a more reliable result.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **MajMin** & **MajMinInv** & **Sevenths** & **SeventhsInv** \\ \hline CM3 & 54.65 & 47.73 & 19.29 & 16.17 \\ DK4 & 67.66 & 64.61 & 59.56 & 56.92 \\ DK5 & 73.51 & 68.87 & 63.74 & 59.72 \\ DK6 & 75.53 & 63.56 & 64.70 & 54.01 \\ DK7 & 75.89 & 70.38 & 58.37 & 53.53 \\ DK8 & 75.89 & 64.77 & 66.89 & 56.94 \\ DK9 & 76.85 & 74.47 & 68.11 & 66.08 \\ KO1 & 82.19 & 79.61 & 76.04 & 73.43 \\ TCSVM & **82.98** & 79.22 & **77.03** & **76.52** \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of recognition rates of the 2015 edition on Isophonics 2009 datasets.  
Figure 9: Comparison of recognition rates with state-of-the-art methods on Mirex’09 dataset.
# Process-Oriented Tolerance and Variation Management: Review and Classification
###### Abstract
Surrogate models, vector models, and skin-model shapes are used, among other things, for this purpose [10]. In contrast, the approach of [PERSON] et al. is more reactive and focuses on the use of user-oriented methods such as the key characteristics flowdown and tolerance chains. This is intended to ensure the practical application of the approaches in the planning and optimization of assembly systems [11; 13]. In addition to these approaches, which describe themselves directly as process-oriented, there are other methods that can also be used for process-oriented tolerance management. For instance, the variation analysis and the modeling of variation propagation are crucial components of tolerancing, especially in a process-oriented approach [14; 15; 16]. Different types of modeling exist for this purpose. For example, the SoV approach with the state space model is widely used, as demonstrated in the aforementioned publications of [PERSON] et al. [2] and [PERSON] et al. [9]. Another option is to model variation propagation using dual quaternions, as shown by [PERSON] et al. [4; 17; 18; 19]. Figure 1: Process-oriented tolerancing based on [1; 3]. These examples demonstrate the variety of terms in this context, indicating a lack of overview and structure in the extended definition of process-oriented tolerancing. Therefore, the authors define \"process-oriented tolerance and variation management\" (PTVM) to outline this subject. Additionally, this paper proposes a classification for existing methods and approaches in the context of PTVM. This paper is subdivided as follows: After the introduction in Section 1, the method of the literature research follows, and the classification is explained in Section 2. Section 3 provides the classification results and the main approaches. Based on that, a discussion of the classification follows in Section 4. The paper concludes with a summary and an outlook in Section 5.
## 2 High-Precision Confidence Interval Weight Assignment Method for Acoustic Ranging  
### Acoustic Localization Error Factors  
Acoustic localization techniques based on the time-of-flight (TOF) [5; 6; 7] method primarily suffer from errors in two aspects of the ranging process, namely the measurement of transmission time and the calibration of the speed of sound. During the time difference measurement, the transmission time from the acoustic signal emitter to the receiver is composed of the propagation time of the acoustic signal through the air and the demodulation processing time after the signal is received. Due to potential obstructions or the inherent multipath effect along the shortest transmission path, the propagation time in the air may not be the minimum, leading to an overestimation of the measured distance.  
Additionally, as the amplitude of the acoustic wave decreases with increasing propagation distance, signal loss can occur at too great a distance, limiting the range of acoustic ranging. Conversely, when the distance between base station nodes is too short, the propagation distance of the acoustic wave is too brief, leading to signal loss at the receiving end; thus, acoustic ranging cannot be too close either. In summary, acoustic ranging errors increase rapidly when the distance is either too close or too far; only within an appropriate ranging confidence interval can the precision of acoustic ranging be ensured.  
For the pre-estimation calculation method of coordinate positioning, three considerations are necessary. First, the distances measured must be at the same moment; second, the pre-estimation calculation method must be fast enough to ensure the timeliness of the coordinates; and third, the obtained distances must be of high precision and reliable. This paper discusses a scenario based on low-speed mobile terminals, which can be approximated as static coordinate estimation. Therefore, the primary focus of this paper is on the high-precision confidence interval of acoustic ranging and how the positioning algorithm ensures that the obtained coordinates are of high precision and reliably accurate.
## 3 Study Area and Satellite Data
### Overview of Sensors and Satellite Data
\begin{table}
\begin{tabular}{c c c} \hline \hline
**36.8108\({}^{\circ}\) N; 115.9313\({}^{\circ}\) W** & **RapidEye (RE4)** & **FASat-C** \\ \hline UTC Time & 19:22 & 18:51 \\ Incidence Angle & 3.760 & 1.237 \\ Sun Azimuth Angle & 158.437 & 138.372 \\ Sun Elevation Angle & 71.912 & 69.070 \\ Satellite Azimuth Angle & 279.570 & 100.084 \\ Satellite View Angle & 3.401 & \(-\)1.127 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Satellite acquisitions over the Frenchman Flat calibration site. Figure 2: FASat-C NAOMI-1 and RapidEye-MSI Spectral Response Functions (SRF). The modeled atmospheric total transmittance, for the mid-latitude summer atmosphere, is based on MODTRAN code. The absorption features of atmospheric constituents are differently sensed, according to the spectral response functions of each instrument. were downloaded from the National Aeronautics Space Agency (NASA) Earth Explorer server [(80)]. The most recent cloud-free acquisitions were selected (Table 3).
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 5 Comprehensive Analysis of Genomic Comparison Tools: Human and Diverse Genomes
### Analysis of Human and Mouse Genomes  
Performing whole-genome alignment between human and mouse genomes offers valuable insights into evolutionary relationships, genetic conservation, and functional similarities between these species. Through comparing their entire genomic sequences, researchers can elucidate conserved regions, identify genetic variations, and unravel the molecular mechanisms underlying biological processes shared across mammalian species.  
* Mummer4: During the execution of the \"nucmer\" command, we encountered an error related to the construction of the suffix tree, indicating that the input sequence length exceeded the maximum allowable limit. As a result, the alignment process failed to proceed, preventing the comparison between the human and mouse genomes using MUMmer;
* SibeliaZ: The analysis indicated that the human and mouse genomes shared a substantial number of junctions, highlighting common genomic features and evolutionary conservation between the two species. The identification of 1,784,620 blocks suggested regions of significant sequence similarity, potentially corresponding to conserved genes, regulatory elements, or functional domains across the genomes. The coverage level of 15% suggested that a considerable portion of the genomes aligned with each other, indicating a substantial level of evolutionary conservation and shared ancestry between humans and mice.
* Progressive Cactus: This method faced obstacles in achieving the expected alignments between entire human and mouse genomes, mirroring its performance limitations  
Figure 12: Comparative genomic visualization of human Chromosomes 1 and 2, using Circos based on Progressive Cactus output.  
observed in human-to-human genome comparisons. This suggests that the tool may be better suited for shorter read comparisons, such as chromosome-level alignments, rather than whole-genome analyses spanning across species.
* Dialign2: When applied to the alignment of human versus mouse genomes, Dialign2 faced significant challenges, attributable in particular to the substantial size of these genomes. In the same way, difficulties were encountered in the human vs. human genome alignment, resulting in a failure to achieve the alignment. This outcome mirrored the difficulties encountered in aligning large genomes and underscored the importance of refining alignment methodologies to effectively handle such complex datasets.
* GraphAligner: the alignment process using GraphAligner failed, resulting in an empty output GAM file;
* Minimap2: Out of the total reads processed (2589), 98.45% reads were successfully mapped. The high mapping rate of 98.45% indicated a close evolutionary relationship between the human and mouse genomes, facilitating robust alignment of sequencing reads between these species.
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 5 Complexity Analysis  
To quantify the performance of the proposed method, the CNN-OTFS model was used for contrasting the experimental results. It is noted that to make a fair comparison, the network structure of this CNN model is equivalent to that of SeCNN-OTFS, which only replaces the separable convolution with traditional convolution.  
To analyze the complexity of SeCNN-OTFS and CNN-OTFS, both the number of parameters and the computational cost are considered in this paper. The computational complexity of convolutional layer is given by:  
\[O(K\times K\times C_{\text{in}}\times C_{\text{out}}\times H\times W), \tag{15}\]  
where \(K\times K\) is the filter size, \(C_{\text{in}}\) is the number of input channels, \(C_{\text{out}}\) is the number of output channels, \(H\) and \(W\) are the height and width of the feature map. Separable convolution decomposes a convolution into depthwise convolution and pointwise convolution. The corresponding computational complexity is:  
\[O(K\times K\times C_{\text{in}}\times H\times W), \tag{16}\]and  
\[O(C_{\text{in}}\times C_{\text{out}}\times H\times W), \tag{17}\]  
respectively. Thus, the total computational complexity for SeCNN is:  
\[\text{FLOPs}_{\text{SeCNN-OTFS}}\approx\sum_{\text{layer}}((K^{2}+C_{\text{ out}})\times C_{\text{in}}\times H\times W). \tag{18}\]  
The total complexity for CNN is given by:  
\[\text{FLOPs}_{\text{CNN}}\approx\sum_{\text{layer}}(K^{2}\times C_{\text{in} }\times C_{\text{out}}\times H\times W). \tag{19}\]
# New RED-Type TCP-AQM Algorithms Based on Beta Distribution Drop Functions
## 5 Dynamic Algorithms Based on BetaRED
### The Dynamic Beta RED Algorithm (DBetaRED)  
The idea of the DBetaRED algorithm is, once the target queue delay \(T_{\text{target}}\) is fixed and the target queue length is estimated as in ABetaRED by Equation (10), we introduce another dynamic parameter called virtual target queue length \(\tilde{q}_{\text{target}}\) to correct for deviations between the average queue length \(q_{\text{avg}}\) and the actual target queue length \(q_{\text{target}}\). According to the value of \(\tilde{q}_{\text{target}}\), the value of the mean \(\mu\) (and thus the standard deviation \(\sigma=\theta\cdot\sqrt{\mu(1-\mu)}\)) of the drop probability function are updated dynamically for each time interval \(T_{\text{update}}\) previously established. An outline of the pseudo-code of this new algorithm is given in Algorithm 3.  
In this case we make a straightforward selection of the following parameters:  
\[q_{\min}=0,\quad q_{\max}=B,\quad p_{\max}=1.\]  
The choice of the minimum and maximum thresholds is based on the same reason as we have stated for the ABetaRED algorithm. Regarding \(p_{\max}\), it is well known that the choice of \(p_{\max}\) in RED is linked to the traffic load in the network, where the higher the traffic load the higher the value of \(p_{\max}\) should be. One of the advantages of BetaRED over RED is that we can control the increase of the drop probability function around the target queue length (in a smoothly or sharply way, but always continuously) by means of the \(\theta\) parameter. Thus, the choice of \(p_{\max}=1\) is the most reasonable in order to avoid discontinuities in the drop probability function. Moreover, the numerical results of Figure 3 show that a higher value of \(p_{\max}\) implies better performance. The control parameters Alpha and Beta determine the size of the increase and decrease steps of the virtual target queue length, respectively, with the range of admissible values being \(0<\text{Alpha},\text{Beta}\leq 1\). The tunable parameters to be set by the user in this case are the target delay \(T_{\text{target}}\), the scalar factor \(\theta\) and the averaging weight \(w\).
## 5 Conclusions  
This research has successfully designed, prototyped, and validated a novel ground--air amphibious UAV tailored for search-and-rescue operations in complex, cluttered environments. The core innovation lies in the implementation of a separation cage structure, fundamentally overcoming the critical limitation of sensor occlusion inherent in traditional monolithic cage designs. This architecture, inspired by the fullerene molecular geometry (optimized at \(\lambda\) = 0.375), provides robust omnidirectional protection for the UAV and its surroundings during terrestrial locomotion or collision events, while simultaneously ensuring unobstructed sensor fields of view for cameras and LiDAR--a significant advancement in cage-type UAV design.  
Complementing this, the H-shaped quadrotor frame skeleton enhances aerial stability and aerodynamic efficiency, facilitating precise control within confined spaces. The system demonstrates an exceptional operational capability, achieving a 40 min flight endurance under full payload (1 kg) at 10 m/s while performing real-time object detection via the optimized YOLOv5s-Ghost visual recognition system. The integrated fuzzy adaptive PID controller ensures robust multimodal operation--stable aerial flight, terrestrial locomotion over rugged terrain, and controlled transitions--even under adverse conditions.  
The prototype validation confirms that this integrated design effectively addresses the key limitations of prior systems: eliminating sensor blockage in caged UAVs, mitigating terrain instability and component vulnerability in wheeled amphibious designs, and enabling the precise close-range detection absent in systems like SR-RUAV. Furthermore, the protective cage structure demonstrably enhances safety for both personnel and the operational environment.