esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study
### Theoretical Insight into d-Regular Bipartite Graphs  
This subsection is concerned with _d_-regular bipartite graphs, one of this paper's primary points of interest. First, let us focus on their definition provided in Definition 1 and their sample topology shown in Figure 5.  
**Definition 1**.: _A graph \(G\) is said to be d-regular bipartite if [37; 38]:_  
**Definition of d-regular graphs: the degree of each vertex from **V** is the same and equal to d;  
**Definition of bipartite graphs: its vertex set **V** is splittable into two disjoint subsets such that two vertices from the same disjoint subset are not linked to one another.  
Thus, _d_-regular bipartite graphs are a graph type that is regular and bipartite, as seen above. In other words, regular graphs can be understood as graphs with pre-defined degree sequences and gain significant attention in terms of both the practical and theoretical views [39]. Bipartite graphs are graphs where each edge belongs to an odd number of bonds and are also often points of interest. As one can see in the literature [39; 40; 41], regular bipartite graphs have found applications in various areas and contexts (e.g., finding the perfect matching, quantum machine learning, Poincareand log-Sobolev inequalities, etc.) over the past decades. As further shown in [42; 43; 44; 45], bipartite graphs are often used in social networks to describe scenarios where a mutual connection exists only between two distinct sets (e.g., supporting learning, majority opinion diffusion, the core-periphery problem, joining a collaboration network, joining a bibliographic network, etc.). As further seen from the papers, regular graphs are also frequently applied in social networks. As shown in [46], regular and irregular bipartite graphs also have applications in biology. The authors of that paper present Nash equilibria for games that are played on these topologies. Moreover, it is discussed in the paper that bipartite graphs can be applied to many interactions, such as  
Figure 4: Network with three broken conditions required to be met by consensus algorithms and error-free execution—min-consensus is applied.  
Figure 5: Example of 3-regular bipartite graphs formed by fourteen vertices.  
plant-animal mutualisms, pollinator webs, seed dispersal, etc. As further seen from [47], regular bipartite graphs also have applications in networks. The authors of that paper apply the Cartesian and tensor product approaches as a tool to decompose bipartite circulant graphs into numerous graph categories. In addition, the application of the mentioned graph types is also seen in other areas such as medicine, linguistics, physics, etc. [48; 49; 50].
## 1 Introduction
When the surrounding environment is more complex, the traced rays will frequently be reflected, diffracted, or scattered in RT, which results in considerable time being consumed in evaluating their traces and calculating the electric field. As a result, the calculation efficiency is very low. The single deterministic model is inadequate in modeling radio wave propagation in the millimeter wave. Therefore, it is necessary to study the scattering effect and the fusion of RT and the PE. A method is proposed in this study--namely, the proposed 3 DFPHPCM--based on three-dimensional RT and a three-dimensional PE. The method takes the scattering effect into account and introduces the end field of the three-dimensional vector PE to the initial field of three-dimensional RT that can reduce the number of transmitting rays and improve the accuracy of the single deterministic model. The structure of the remainder of this paper is as follows. In Section 2, the relevant propagation modeling theory is introduced. In Section 3, a hybrid propagation channel model based on three-dimensional RT and the three-dimensional vector PE is presented. In Section 4, the millimeter-wave channel measurement at 39 GHz in urban microcellular environments is described. In Section 5, the hybrid propagation channel model is analyzed. Finally, the conclusions are presented in Section 6.
# Detection of Weak Radar Return Signals Based on Pseudo-Time Domain Algorithm
###### Abstract  
When the signal-to-noise ratio (SNR) falls below \(-10\) dB, signals are considered weak. Radar systems, particularly those on small platforms like drones and satellites, often face this challenge due to limited power, resulting in low transmit power and weak signals. Additionally, ambient noises in the detection environment can further obscure the echo signal, leading to low SNR. Satellite-based radar systems are further constrained by processing capabilities and limitations in data transmission bandwidth and communication time windows. As a result, received radar signals are often transformed into a frequency spectrum and relayed to ground stations for offline processing. To address these challenges, this study proposes a novel method that treats the frequency spectrum of the echo signal as an impulse signal and interprets the frequency domain as a \"pseudo-time domain\". By applying conventional time-domain waveform detection methods, such as wavelet analysis, in this \"pseudo-time domain\", we can significantly enhance the success rate and accuracy of offline detection for weak echo signals. Numerical simulations show that this approach can handle SNR as low as \(-34\) dB or even lower, which achieves notable denoising effects and improves signal quality. In cases where the SNR reaches \(-40\) dB, the detection success rate exceeds 95% in most instances. Outdoor experiments also maintain a 90% accuracy rate even at \(-34\) dB SNR.  
weak signal; SNR; discrete wavelet transform Article
## 2 System Model
### Channel Model  
Considering the widely dispersed DAS transmitters, we assume a composite fading channel with both small-scale and large-scale fading, which correspond to Rayleigh fading and log-normal shadowing, respectively. The received signal at the receiver is \(2\times 1\) vector and noted as  
\[\mathbf{r}(t)=\sqrt{P_{t}}\,\mathbf{h}(\mathbf{d})s(t)+\mathbf{z}(t), \tag{1}\]  
where \(\mathbf{r}(t)=[r_{1}(t),r_{2}(t)]^{T}\), and \(P_{t}\) is the transmit power at each transmitter. Also, \(s(t)\) is the desired signal transmitted to the receiver, while \(\mathbf{z}(t)=[z_{1}(t),z_{2}(t)]^{T}\) is additive white Gaussian noise (AWGN) vector. We assume unit noise power for \(z_{i}(t)\) for simplicity. In addition, the channel vector is expressed as  
\[\mathbf{h}(\mathbf{d})=[h_{1}(d_{1}),h_{2}(d_{2})]^{T}, \tag{2}\]  
where \(h_{i}(d_{i})\) is the channel gain from Tx\(i\) to Rx. As in [3], the composite fading channel can be modelled such that its power \(\gamma_{i}=|h_{i}(d_{i})|^{2}\) is decomposed into  
\[\gamma_{i}(d_{i})=\alpha_{i}\Omega_{i}, \tag{3}\]  
where \(\alpha_{i}\) and \(\Omega_{i}\) represent the squared envelopes determined by small-scale and large-scale fading, respectively. We assume that small scaling fading follows Rayleigh fading, thereby the conditional probability density function of \(\alpha_{i}\) for given shadowing realization \(\Omega_{i}\) follows the exponential distribution ([3], Equation (2.7)), which is given by  
\[f_{\alpha_{i}}(x|\Omega_{i})=\frac{1}{\Omega_{i}}\exp\left(-\frac{x}{\Omega_{ i}}\right), \tag{4}\]  
where \(x\geq 0\). On the other hand, we assume that shadowing \(\Omega_{i}\) follows log-normal distribution ([3], Equation (2.53)):  
Figure 2: Network topology model.  
\[f_{\Omega_{i}}(x)=\frac{\xi}{\sqrt{2\pi}\sigma x}\exp\left[-\frac{(10\log_{10}x- \mu_{i})^{2}}{2\sigma^{2}}\right], \tag{5}\]  
where \(x>0\) and \(\xi=\frac{10}{\ln(10)}\approx 4.3429\). Also, \(\sigma\) is the standard deviation of the log-normal shadowing in dB scale. In addition, \(\mu\) is the mean of the log-normal shadowing in decibel (dB), which is determined by the path loss as  
\[\mu_{i}=10\log_{10}\left(\frac{1}{d_{i}^{n}}\right)=-\frac{n}{2}10\log_{10} \left(l^{2}+\frac{\Delta_{I}^{2}}{4}\right) \tag{6}\]  
where \(n\) is the path-loss exponent.  
Therefore, as in [7; 25], the probability density function of \(\gamma_{i}\) is expressed as  
\[f_{\gamma_{i}}(x)=\int_{0}^{\infty}\frac{1}{\Omega_{i}}\exp\left(-\frac{x}{ \Omega_{i}}\right)\frac{\xi}{\sqrt{2\pi}\sigma\Omega_{i}}\exp\left[-\frac{(10 \log_{10}\Omega_{i}-\mu_{i})^{2}}{2\sigma^{2}}\right]d\Omega_{i}, \tag{7}\]  
where \(x>0\).
## 2 LPD Signal Design and Detection Method
### Complexity Analysis of Receiver  
In this section, the complexity of the proposed receiver is investigated. For a spectrum sparse signal with bandwidth \(NDf_{\text{B}}\), the wideband bandpass filter is used for signal extraction. Different from the traditional receiver, the multiphase clock should be employed to obtain \(L\)-sampled copies. The sampled signal, which has the highest power, is chosen for processing in the following steps. Therefore, a total of \(M\) analog to digital converters(ADCs) is needed. Assuming the power of the sampled signal is calculated over \(Q\) samples, the selection combining step consumes \(QM\) times multiplier, \((Q-1)M\) times add operation, and \(log_{2}(L)\) times comparison operation.  
There are also two other possible architectures of receivers for the designed LPD signal. The first receiver architecture uses parallel demodulators for each subcarrier and post-detection combining to recover the signal \(s(t)\). Each demodulator needs a narrow band filter and ADC. The complexity and power consumption of the receiver will grow in direct proportion to the subcarrier number \(N\). The second receiver architecture uses direct base-band sampling or radio frequency bandpass sampling method. The sampling rate should be at least twice the bandwidth of the LPD signal as \(2 NDf_{\text{B}}\) that performance requirements for ADCs will be ultra high. As mentioned above, the proposed under-sampling detection method has lower implementation complexity and hardware requirements.
###### Abstract  
Nano-reinforced composites are widely studied by the scientific community. The main factors affecting the final nanocomposite performance are the filler type and content, as well as the duration of the dispersion. In this work, we report the effects of Multi-Walled Carbon Nano Tubes (MWCNTs) and milled Carbon Black (CB) dispersion in epoxy resin on the electrical and mechanical properties of the resulting composites. Impedance Spectroscopy (IS) was utilized to assess the dielectric properties of the specimens. The mechanical properties were evaluated by fracture toughness tests, while Scanning Electron Microscopy (SEM) was performed to study the influence of the reinforcement on the failure mechanisms acting on the fracture surfaces of the specimens. IS results for epoxy/CNT systems revealed the creation of a 3D conductive network for concentrations above 0.3 wt. %, while CB did not result in the formation of such a network for filler contents up to 2 wt. %. However, the synergistic effect of CNTs/CB was successfully manifested by both the optimal electrical properties and the 81% enhanced fracture toughness in comparison to the neat resin. Fractography confirmed the aforementioned results and revealed the fracture mechanisms of all systems, such as crack pinning and deflection, and particle pull-out phenomena.  
nanocomposites; hybrid composites; impedance spectroscopy; fracture toughness; fractography Article
# Ontology-Based Spatial Data Quality Assessment Framework
## 3 Discussion
### Case Study
#### 3.1.2 Results of the Data Quality Assessment  
After creating SfO and converting the data into RDF, SDQO ontology is imported by SfO. Figure 14 shows a road feature (Yol8644) with its property values. This feature crosses two buildings, which is forbidden. Therefore, an error with error code 1 (DQR_SWRLCrosses) is associated.  
Figure 13: Data conversion.  
The results of the quality assessment are shown on the sample parts from tested data for visual comprehensibility. Erroneous features are represented in yellow in Figures 15-18.  
Out of 4395 roads, 70 (1.6%) cross over 100 of the 20,596 buildings, which represents 2.1%, and is shown in Figure 15. Additionally, 587 parcels from a total of 33,641 (1.7%) overlap with other parcels, as illustrated in Figure 16. Furthermore, 1030 buildings out of 20,596 (5%) overlap with other buildings, as shown in Figure 17. Finally, 212 buildings (1% of 20,596) are not entirely within a cadastral parcel is presented in Figure 18. These are top-semantic consistency errors. The framework allows inspection of other ISO19157 quality elements like completeness and attribute consistency.  
The assessment results classified after inference in the resulting ontology are displayed in Figures 19 and 20.  
Figure 14: A sample road feature (Yol8644).  
Figure 15: Roads that cross buildings (erroneous roads and buildings are shown in yellow).  
Figure 16: Overlapping cadastral parcels (light yellow or red).  
Figure 17: Overlapping buildings (light yellow or red).  
Figure 18: Buildings that are not within cadastral parcels (painted in red).  
A comparison between the results of quality assessment using the proposed method and manual spatial operations in the QGIS software (QGIS 3.28) is handled to validate the effectiveness of the proposed method. To identify inconsistent features using QGIS, a series of spatial queries are designed. For instance, the \"select by location\" tool is used to implement the \"forbidden-type rule\", \"roads must not cross over buildings\" thereby identifying roads that are crossing over buildings as inconsistent data. A sample dataset was created with intentional inconsistencies. The results generated by the proposed method demonstrate full consistency with those obtained through QGIS software.
## 4 Results and Discussion
### Kernel Function  
The defect sensitivity kernel represents the probability of a wave sent from location S to pass at location x and then to arrive at location R after period of time t [11]. Considering the source images due to the four straight boundaries of the plate, the detection diameter \(Z_{detection}\) is 260 mm when \(t=45\) us, as shown in Figure 6 based on Equation (5). The diffuse wave field is mostly sensitive to the defect in this range. In fact, the diffusion model simplifies the energy distribution and neglects the diffuse field-defect interaction after \(t=65\) us, as previously shown in Figure 2. All the pressure amplitudes are equal to zero in that period. The whole space is then discretized into \(300\times 300\) elements for the possible location of the defect, and a further mesh refinement gives no more localization resolution improvement.  
Figure 5: Decorrelation coefficients varied according to time.  
Figure 6: Sensitivity kernels and detection zone.
# Research on Bone Stick Text Recognition Method with Multi-Scale Feature Fusion
## 2 Fundamentals
### Focal Loss Function  
The proportion of each type of text sample in the bone stick text data set is unbalanced. The categories with large samples are close to 300 images, and some samples have only a few images. The uneven distribution of samples will lead to an over-recognition of classes with large samples and an insufficient recognition of classes with small samples. [PERSON] et al. proposed focal loss [20] to solve the class imbalance problem by reducing the internal weight. This function focuses on using data sets with sparse and complex samples for training. Although the number of simple samples is large, it contributes little to the total loss. Focal loss is an improvement of the cross-entropy function. The definition of the original cross-entropy loss function [21] is shown in Equation (3).  
\[L_{CrossEntropy}=-ylogy^{\prime}-(1-y)log(1-y^{\prime}) \tag{3}\]  
The modulation factor is multiplied by the original cross-entropy loss function, which is defined as Equation (4)  
\[L_{FocalLoss}=-\alpha(1-y^{\prime})^{\gamma}logy^{\prime}-(1-\alpha)(1-y)y^{ \prime}log(1-y^{\prime}) \tag{4}\]  
Where \(L_{CrossEntropy}\) represents the loss value generated by the cross-entropy loss function, \(L_{FocalLoss}\) represents the loss value generated by the focal loss function, and \(y\) is the actual category; \(y^{\prime}\) is the predicted value of the category; the initial setting of \(\alpha=NAN\) can be used to balance the uneven number of positive and negative samples themselves, i.e., category imbalance; \(\gamma\) is a positive adjustable parameter, and the setting of \(\gamma~{}=~{}2\) attenuates the error contribution of easy-to-identify samples so that the model can focus more effectively on the hard-to-classify samples during training, and for accurately classified samples \(y^{\prime}\) tends toward 1 and the modulation factor \((1-y^{\prime})^{\gamma}\) tends toward 0, so that the loss value is reduced; and for misclassified samples, \((1-y^{\prime})^{\gamma}\) converges to 1, and the modulation factor has no effect on the loss function. Compared with the cross-entropy loss, the focal loss does not change for misclassified samples and becomes smaller for accurately classified samples. It is equivalent to increasing the weight of the inaccurately classified samples in the loss function. By reducing the weights of simple negative samples, the imbalance between positive and negative and complex and easy samples in the data set can be alleviated. Moreover, the direction of network optimization avoids being affected by a large number of simple negative samples. This solves the problem of sample imbalance and does not need to calculate complex weight mapping, so the model can better capture the signal features.
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 2 Methodology
### Exclusion Criteria  
Exclusion criteria are used to reject sources that do not meet strict conditions in order to narrow the search. We excluded publications that:  
* had limited access to the content, or the publication was unavailable (EC1);
* were related to environmental safety, e.g., warehouse fires (EC2),
* were related to the method of data storage itself (EC3);
* predicted the location of workers in the context of stationary robots (EC4);
* were concerned with trajectory prediction in a context other than the warehouse (EC5);
* used mainly vision modules because of the cost and methods used (EC6).
## 2 Materials and Methods
### Processing and Analysis of RNA-Seq Data  
Adapter sequences were trimmed with Trimmomatic v0.36 (ILLUMINACLIP:TruSeq3-SE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:30 MINLEN:36). Read mapping and counting was performed with STAR v2.5.3a (default parameters) using Ensembl _S. cerevisiae_ R64-1-90 genome annotation (genome-build-accessing GCA_000146045.2, UCSC sacCer3). For all samples, no less than 88% of the reads were mapped uniquely. The resulting gene lists were filtered to contain only genes reaching at least 2 counts per million in at least four of the eight libraries. Read counts were normalized using the edgeR [43] TMM approach. A principal component analysis of the resultant normalized and log-transformed matrices was done in the R environment. Differential expression was assessed with edgeR using a generalized linear model, accounting for the interaction between the strain (CRN, CRN/PPN1) and growth conditions (control or manganese excess medium). The genes with the FDR-adjusted \(p\)-value \(\leq\) 0.05 and \(\left|\log_{2}\right.\) (Fold Change)\(\mid\)\(\geq\) 1 were considered differentially expressed. The gene ontology (GO) enrichment analysis was performed with the YeastMine software [44]. The read counts and differential gene expression data are presented in Tables S3 and S4.
## 2 System Model
### LEACH Protocol  
The LEACH protocol is adopted by the nodes in which nodes self-organize into clusters as shown in Figure 1. LEACH operates in two phases: the _setup phase_ and the _steady state phase_.
## 1 Introduction
Previous studies presented sandbody classification based on stacking patterns into isolated, superimposed, and incised types [22; 23]. The current understanding of sandbody genetic types is derived from studies on shoreface, shallow marine, and lacustrine facies in both marine and continental sequences [13; 14; 24; 25; 26]. However, studies on lacustrine delta settings, such as the Shunan area, have primarily focused on classifying sandbody types based on sedimentary microfacies. Common sandbody types identified include distriburately channels in the delta plain, subaqueous distriburately channels, mouth bars, and sheet sands on the delta front, as well as shallow lake bars in lacustrine environments [27; 28]. Although both have advanced the classification of sandbodies, a gap still exists in the integratedanalysis of the Shaximiao Formation. Prior studies often focus on either depositional processes or environmental contexts, such as event-driven sedimentation and complex depositional scenarios [29], without fully integrating these aspects to assess their impact on reservoir quality. This research aimed to fill the existing knowledge gap by systematically investigating the genetic types of sandbodies in the Shaximiao Formation, focusing on their lithofacies characteristics, spatial distribution, and implications for reservoir quality. By analyzing the sedimentary controls and depositional patterns influencing sandbody development, this study refined predictive models for tight sandstone reservoirs in transitional environments, ultimately supporting more effective exploration strategies.
###### Abstract  
The structural characteristics of membrane support directly affect the performance of carrier facilitated transport membrane. A highly porous PolyHIPE impregnated with Aliquat 336 is proposed for Cr(VI) separation. PolyHIPE consisting of poly(styrene-co-2-ethylhexyl acrylate) copolymer crosslinked with divinylbenzene has the pore structure characteristic of large pore spaces interconnected with small window throats. The unique pore structure provides the membrane with high flux and stability. The experimental results indicate that the effective diffusion coefficient \(D^{*}\) of Cr(VI) through Aliquat 336/PolyHIPE membrane is as high as 1.75 \(\times\) 10\({}^{-11}\) m\({}^{2}\) s\({}^{-1}\). Transport study shows that the diffusion of Cr(VI) through Aliquat 336/PolyHIPE membrane can be attributed to the jumping transport mechanism. The hydraulic stability experiment shows that the membrane is quite stable, with recovery rates remaining at 95%, even after 10 consecutive cycles of operation. The separation study demonstrates the potential application of this new type of membrane for Cr(VI) recovery.  
PolyHIPE; pore structure; Cr(VI); diffusion coefficient; transport study +
Footnote †: journal: Journal of Chemical Physics
# Rapid-DEM: Rapid Topographic Updates through Satellite Change Detection and UAS Data Fusion
We demonstrated Rapid-DEM with a case study where we evaluated the impact of topographic change captured in the fused DEM on spatial patterns of surface water runoff in the area identified as the highest priority location.