esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Definition and Properties of Distributed Order Calculus
### Establishment of the Distributed Order Rheological Model and Operator Analysis
\begin{array}{l}f(t)=L^{-1}\bigg{[}\frac{1}{\
u+\int_{a}^{b}\omega(\gamma)s ^{\gamma}d\gamma}\bigg{]}=\\ \frac{1}{2\pi i}\int_{\delta-i\infty}^{\delta+i\infty}\frac{e^{\delta t}}{ \
u+\int_{a}^{b}\omega(\gamma)s^{\gamma}d\gamma}ds\quad(\delta>0)\end{array}  \tag{29}\]  
where the critical path of Equation (29) is shown in Figure 3.  
Figure 3: Contour integral of the inverse Laplace transform. The premise for the calculation of Equation (29) was to judge whether the operator \(\widetilde{f}(s)\) had poles, that is, points whose denominators were zero. Suppose that there were poles in the integral domain of \(\Omega=\{s\in c:0<r<|s|<R,|Arg(s)|<\pi\}\), Equation (30) could be calculated. \[s=xe^{i\theta},\ \ \theta\in(-\pi,\pi) \tag{30}\]  
where \(x\) and \(\theta\) are variables. By substituting Equation (30) into the denominator in Equation (28), Equation (31) could be obtained. \[ \begin{array}{l}\
u+\int_{a}^{b}\omega(\gamma)s^{\gamma}d\gamma=\
u+\int_{a }^{b}\omega(\gamma)x^{\gamma}e^{i\theta\gamma}d\gamma\\ =\
u+\int_{a}^{b}\omega(\gamma)x^{\gamma}\cos(\theta\gamma)d\gamma+i\int_{a}^ {b}\omega(\gamma)x^{\gamma}\sin(\theta\gamma)d\gamma=0\end{array}  \tag{31}\]  
According to the assumption, Equation (32) could be obtained. \[\left\{ \begin{array}{l}\
u+\int_{a}^{b}\omega(\gamma)x^{\gamma}\cos(\theta \gamma)d\gamma=0\\ \int_{a}^{b}\omega(\gamma)x^{\gamma}\sin(\theta\gamma)d\gamma=0\end{array} \right. \tag{32}\]  
For Equation (32), when \(0\leq\gamma\leq 1\), \(0\leq\theta\gamma\leq\pi\) and \(0\leq\sin\,(\theta\gamma)\leq 1\). Only when \(\theta\gamma=k\pi\) and \(k\) was an integer, sin (\(\theta\gamma\)) = 0. However, \(\theta\in(-\pi\),\(\pi\)), so k could not be an integer. On the other hand, \(\
u\) > 0, Equation (29) was not valid. Therefore, the operator \(\widetilde{f}(s)\) had no poles in the area of \(\Omega=\{s\in c:0<r<|s|<R,|Arg(s)|<\pi\}\). Next, the complex plane was cut. Equation (29) could be calculated by converting the contour integral into Hankel's integrals. According to the definition of a fulcrum, the operator \(\widetilde{f}(s)\) had two fulcrums, that is, \(s\) = 0 and \(s\) = \(\infty\). The complex plane was cut along the negative real axis to obtain the single-valued branch. The contour integral (Figure 3) was converted into the Hankel integral path (Figure 4). Let \(s=xe^{-i\pi},x\in(0,\infty)\) in the secant of a lower branch, Equation (33) could be obtained along the path of the Hankel integral. \[f_{1}(t)=\frac{1}{2\pi i}\int_{0}^{\infty}\frac{e^{-xt}}{\
u+\int_{a}^{b} \omega(\gamma)x^{\gamma}e^{-i\pi\gamma}d\gamma}dx \tag{33}\]  
Let \(s=xe^{i\pi},x\in(0,\infty)\) in the secant of an upper branch, Equation (34) could be obtained. \[f_{2}(t)=\frac{1}{2\pi i}\int_{0}^{\infty}\frac{e^{-xt}}{\
u+\int_{a}^{b} \omega(\gamma)x^{\gamma}e^{i\pi\gamma}d\gamma}dx \tag{34}\]  
Figure 4: Path of Hankel integral. For Equations (33) and (34), the path integral of Hankel was 0 along a small circular path with a radius r of 0. Equation (35) could be obtained. \[
# Human Pose Estimation Based on Lightweight Multi-Scale Coordinate Attention
###### Abstract  
Heatmap-based traditional approaches for estimating human pose usually suffer from drawbacks such as high network complexity or suboptimal accuracy. Focusing on the issue of multi-person pose estimation without heatmaps, this paper proposes an end-to-end, lightweight human pose estimation network using a multi-scale coordinate attention mechanism based on the Yolo-Pose network to improve the overall network performance while ensuring the network is lightweight. Specifically, the lightweight network GhostNet was first integrated into the backbone to alleviate the problem of model redundancy and produce a significant number of effective feature maps. Then, by combining the coordinate attention mechanism, the sensitivity of our proposed network to direction and location perception was enhanced. Finally, the BiFPN module was fused to balance the feature information of different scales and further improve the expression ability of convolutional features. Experiments on the COCO 2017 dataset showed that, compared with the baseline method YOLO-Pose, the average accuracy of the proposed network on the COCO 2017 validation dataset was improved by 4.8% while minimizing the amount of network parameters and calculations. The experimental results demonstrated that our proposed method can improve the detection accuracy of human pose estimation while ensuring that the model is lightweight.  
human pose estimation; attention mechanism; multi-scale feature extraction Article
# Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
## 2 Materials and Methods
### The Vector TopK Matching Technique  
In previous studies on few-shot counting tasks, the task is often defined as a key-value problem. Specifically, given a key representing an instance to be queried in an image, the task is to find the value, which corresponds to the number of instances to be counted in the image. Researchers commonly use the pest instances in the test set as keys to query the test set images for matching regions. However, due to the limited number of samples in the dataset and test set, this approach often leads to long-tail effects and poor domain adaptation caused by environmental differences. As shown in Figure 5, when using the same image of a woodlouse, satisfactory results can be achieved. However, when using woodlice from other scenes as counting instances, the accuracy significantly decreases. This is because changes in the environment, such as variations in lighting angles, blur the features of the queried instances.  
Addressing this issue, this study attempts to transform the problem of querying the value in the test set images using instances outside the test set as keys. It proposes a method that utilizes instances outside the test set as keys to match the key vectors in the test set images, and then uses the test set key vectors to query the values in the test set. Therefore,  
Figure 5: Choose samples beyond the frame of the image as counting samples.  
this study chooses a simple approach that uses the minimum cosine distance between vectors to compensate for the data differences between the visual segmentation model and the counting model. The vector similarity matching aims to match the known pest feature vectors to the most similar K feature vectors in the segmented and processed block image vectors using the SAM. Specifically, it calculates the distances between all vectors after SAM segmentation and a small number of samples in the vector database, and then takes the average of all distances to determine the final similarity. The final results of the network are shown in Figure 6.  
Figure 6: Identifying the most similar sample through matching.  
The hyperparameter K in the TopK tag matching process was set to a fixed value of 10 after conducting preliminary experiments. This value was chosen as it offered an optimal trade-off between the diversity of the tag matches and the computational performance of our model. Further exploration of the impact of varying K on the model's performance is a potential area for future research.  
Additionally, this study explores another approach. It calculates the similarity between all feature vectors after segmentation and the top K of the most similar vectors from a small number of samples in the vector database. As long as the average similarity reaches a certain threshold, it is considered as a selection criterion.
# A Bilevel Optimization Approach for Tuning a Neuro-Fuzzy Controller
## 4 Bilevel Optimization for Tuning a NFC  
In this work, the parameter tuning of an NFC is approached as a bilevel optimization problem to minimize an error function. In order to evaluate this proposal, it is compared with the method described in [13] using the same error function. This function is presented in (23), where \(e\) is calculated as the difference between a signal control and the behavior result of applying the NFC to a system.  
\[f(\vec{x})=\sum_{i=1}^{k}(|e_{i}(\vec{x})|+L|e_{i}(\vec{x})|) \tag{23}\]  
In (23), the decision variables in \(\vec{x}\) include the number of membership functions (\(m\)), which must be an integer; the continuous variables are the weights of the neural network \([w_{1},w_{2},...,w_{n}]\), where \(n=m^{2}\); the parameters of the membership functions (\(\psi_{j,k}\)) and (\(\sigma_{j,k}\)); and the learning rate constants \(\eta_{\phi},\eta_{e}\) and \(\eta_{r}\). \(L\) is a scalar used to manipulate the change over time that the control applies to the system.  
One of the main contributions of this paper is the formulation of an optimization problem to apply a metaheuristic algorithm for solving the complete tuning of the NFC. The differential evolution (DE) algorithm was selected as the base metaheuristic for this proposal because it has been successfully applied in diverse optimization scenarios and has demonstrated competitive performance [28; 29; 30]. However, a significant challenge arises in this specific optimization problem due to the variability in the size of the weight vector \([w_{1},w_{2},...,w_{n}]\) since \(n\) changes according to the number of membership functions. This variability conflicts with the fixed-size solution vectors required by DE. To overcome this challenge, a bilevel optimization approach [31] is proposed, where the objective of the first or lower level is to determine the number of membership functions and perform a preliminary tuning of the NFC. The second (upper) level improves the tuning and finds the weights set randomly in the lower level (noise).
# Enhance Text-to-Text Transfer Transformer with Generated Questions for Thai Question Answering
## 2 Literature Review
### Recent Research in Question Answering  
Most recent research works on NLP focus on developing language models to use with many tasks including the QA task. Most language models use the Transformer model as a part of their processing because the Transformer model has proved that it can reach higher performance than older-style NLP models, such as BiDAF [6], that use Long Short-Term Memory (LSTM) [12]. BERT [13] is the first Transformer based language model that uses only the encoder part of the Transformer. There are two sizes of BERT models: \(BERT_{Base}\) with 12 layers of Encoder and \(BERT_{Large}\) with 24 layers of Encoder. In the experiment, BERT could achieve state-of-the-art performance in QA tasks. \(BERT_{Base}\) could reach 80.8 and 88.5 EM and F1 scores, respectively, \(BERT_{Large}\) could also reach 84.1 and 90.9 EM and F1 scores, respectively, when tested with SQuAD 1.1, while BiDAF could achieve only 68.0 and 77.3 EM and F1 scores, respectively.  
There were several trials to develop a BERT model to better predict span, which is more appropriate with QA tasks. SpanBERT [14] is one of these. SpanBERT changed some functions of the pretraining process by using span masking instead of token masking, and adding a Span Boundary Objective to train the model to predict the masked span with adjoining words. SpanBERT used \(BERT_{Large}\) architecture and applied a described method. SpanBERT was tested with the SQuAD 1.1 dataset to evaluate its performance, and achieved 88.8 and 94.6 EM and F1 scores, respectively.  
However, neither of those developments was chosen to use in our work because WangchanBERTa was considered a better model than BERT, and SpanBERT must be pre-trained with Thai documents before using, which is not convenient to use.
# Investigating BIM Implementation Barriers and Issues in Pakistan Using ISM Approach
## 1 Introduction
### Building Information Modeling (BIM)  
Autodesk was the first to present 2D computer-aided design in 1982. 2D CAD provides better documentation, precise drafting and saves time. Because of the user-friendly behavior of AutoCAD, the architectural, engineers and construction professionals moved towards AutoCAD technology [5].  
According to [PERSON] [6], [PERSON] and [PERSON] were the first who presented the idea of BIM in 1970. For two decades, this arrangement of demonstrating was utilized under various nomenclature, for example, virtual structure, shrewd article and item model [7]. BIM has an edge over conventional CAD 2D models, such that the BIM model can be viably utilized for planning, designing, construction and operation tasks of the projects [8]. Design, engineering and construction experts are inclined towards BIM for the successful and productive structure and development of the board [9]. Building information modeling is not just a product, but is a total procedure [8; 10]. In 1992, the term BIM was officially utilized, and in the 21 st century, a program called \"Revit Software\" was developed. Later on in 2004, a similar program was offered to Autodesk and they acquired numerous changes in it. Presently, that product is named as \"Autodesk Revit.\" This technology has acquired progressive variations in the building design field [11].  
Most of the issues can easily be solved using BIM. In BIM, the creation of intelligent computer models helps in integrating the planning, designing, execution and operation phases [12]. Autodesk360 cloud, also known as A360, provides a central room in the cloud for project documents and the individuals dealing with a task can easily access the files. Autodesk360 cloud is a community-oriented program through which all the team members communicate, and they upload their files to the A360 drive, as shown in Figure 1. The project team can view and access all the shared information and designs in the central workspace anywhere, anytime.  
Figure 1: A360 Cloud.
## 2 Materials and Methods
### LIS Modelling, Data Processing and Analysis
The attenuation for the given simulation example, compared to a reference sample (here taken as the same material, but with a slightly larger grain size), is computed and shown in Figure 10 for echoes 2, 3 and 4. Both \(\gamma=3\) and \(\gamma=4\) show a reasonable fit in the considered frequency range, although an optimal value for \(\gamma\) might be in between 3 and 4. Note that the frequency range depends on the echo: up to 55 MHz for echo 2, up to 48 MHz for echo 3 and up to 41 MHz for echo 4. For higher order echoes, scattering and interference of mode conversions becomes more apparent in the less smooth curves. Additionally, the low SNR limits the upper boundary of the frequency range which can be used for the fitting. In order to be able to use a large frequency range, the second echo was chosen for further processing. The choice of a fixed exponent for the fits is always an approximation, since the attenuation \(\alpha(d,f)\sim d^{\gamma-1}\,f^{\gamma}\) depends both on the grain size \(d\) (which might vary in between simulations, but is in the order of 5 to 20 \(\upmu\)m) and frequency \(f\) (which covers a range between 5 and 60 MHz in each simulation). The scattering is considered to be in the Rayleigh regime (\(\gamma=4\)) and in the transition regime (\(\gamma=3\)), as can be seen from the area of interest in Figure 4:1 Since several experimental systems use a constant exponent \(\gamma=3\)[5, 20, 21, 54], this same exponent has been chosen here as a compromise. This constant exponent \(\gamma=3\) has been used for the whole range of grain sizes. After obtaining the attenuation \(\alpha(f)\) a function \(\hat{\alpha}(f)=a+bf^{3}\) is fitted, and the parameter \(b\) is determined. A new parameter  
\[b^{\prime}= \begin{cases}\sqrt{|1000\cdot b|}&\text{for }b\geq 0\\ -\sqrt{|1000\cdot b|}&\text{for }b<0\end{cases} . \tag{6}\]  
is defined, based on [20, 21]. Note that the signs of \(b\) and \(b^{\prime}\) are the same, enabling a distinction between a positive and negative \(b\).
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 4 Results and Analysis  
With the gradual increase of the hours of sunshine, temperature and precipitation; the winter wheat enters a stage of rapid growth, especially from March to May, which is a critical period for accumulating dry matter quality. Although this study analyzes the distribution of winter wheat yield over the entire growing season, the parameters fPAR, the light use efficiency, and the NPP from March to May are focused on to parametrize the improved CASA model.
# Mathematical Foundations and Implementation of CONIKS Key Transparency
## 2 Related Work
### Handling Errors and Exceptions  
Error handling in this implementation involves ensuring robustness and security against various failure scenarios. For example, during Merkle tree construction, care must be taken to handle cases where the number of leaves is not a power of two, which may require padding or handling uneven levels in the tree. For digital signature operations, error handling includes managing exceptions during key generation, signing, and verification processes, such as invalid signatures or corrupted data. This can be achieved by using try-except blocks around critical operations to capture and respond to exceptions gracefully, ensuring that the system remains secure and functional even in the presence of errors.
## 2 Methodology
### Feature Extraction
#### 2.2.3 Standard Deviation (STD)  
Standard deviation quantifies the spread of the signal values around the mean and is used to assess signal volatility [17]:  
\[\text{STD}\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}(x_{i}-\overline{x})^{2}} \tag{4}\]  
where \(\overline{x}\) is the mean of the data. A larger standard deviation implies greater signal variation and may indicate abnormalities or irregular vibrations.
## 2 Materials and Methods
### Material Properties  
The implant materials used in this study were bulk titanium alloy (Ti-6 Al-4V) and effective porous titanium properties. The effective material properties of porous titanium were investigated in the previous study [23] with a body-centered cubic (BCC) structure and had a porosity ranging from 18.1 to 77.65%. Five porosities (77.65%, 62.8%, 47.3%, 29.9%, and 18.1%) of BCC cubes were modeled and simulated under compression test. The elastoplastic material properties of bulk titanium alloy (Ti-6 Al-4V) were assigned to porous cubes. The stress-strain curves of these cubes were plotted to obtain effective Young's moduli of all porous cubes which are reported in Table 1. In this study, the effective Young's moduli were utilized to define five porous implants. The labels for porous implants were selected based on their porosity (represented by \"P\") and Young's modulus. For instance, a porous implant with Young's modulus of 76.71 GPa was labeled as P76 as listed in Table 1. Similarly, P53, P31, P18, and P9 correspond to 53.84 GPa, 31.49 GPa, 18.33 GPa, and 9.11 GPa of Young's moduli. Two distinct bone models were investigated, the normal bone model represents healthy bone, while the osteoporotic bone represents weak bone. Both models exhibited different apparent Young's modulus, as shown in Figure 1b [4]. In finite element models, bone is modeled as a solid with effective material properties of normal and weak cancellous bones. All models were assumed to be homogeneous, isotropic, and linearly elastic. The mechanical properties of the porous titanium (Ti) used in this study, Young's modulus, Poisson's ratio, and titanium porosity, have been reported in previous studies [23; 39] and are summarized in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Young’s Modulus** & **Yield Strength** & **Poisson’s Ratio** & **Porosity** & **Elements** \\  & **(E, GPa)** & **(Ys, MPa)** & **(\(v\))** & **(\%)** & **(\%)** & **(C3D10)** \\ \hline Dense Titanium (Ti-6 Al-4V) & 114 & 1023 & 0.3 & 0.0 & \\ \hline Porous Titanium P76 & 76.71 & 760 & 0.3 & 18.10 & \\ \hline Porous Titanium P53 & 53.84 & 528 & 0.3 & 29.90 & \\ \hline Porous Titanium P31 & 31.49 & 305 & 0.3 & 47.30 & \\ \hline Porous Titanium P18 & 18.33 & 176 & 0.3 & 62.80 & \\ \hline Porous Titanium P9 & 9.11 & 88 & 0.3 & 77.65 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties used in finite element analysis [4; 23; 39; 40].  
Figure 1: Bone-implant models; (**a**) CAD modeling of bone and dental implants, (**b**) the correlation between cancellous bone apparent Young’s modulus and porosity, and (**c**) load, boundary conditions, and mesh.
# A Scalable Deep Network for Graph Clustering
## 3 The Proposed Method
### Dual Self-Supervised Module  
Through the above two modules, we mechanically combine the DNN module and the Quasi-GNN module, they essentially are all used for unsupervised or supervised learning in different scenarios and we cannot apply them to our depth clustering task directly. Therefore, we need to unify the Quasi-GNN module and the DNN module with the same optimization objective. We set the goal of these modules to approximate the target distribution \(\mathbf{P}\), which makes the results tend to be consistent during the training process, and because of the strong connection between the two modules, we call it a dual self-Supervised module. This module does not require the participation of labels during the training process. First, for the DNN module, we utilize Student's t-distribution as the kernel to measure the similarity between the node embeddings \(\mathbf{e}_{i}\) and the cluster center vector \(\mu_{j}\):  
\[q_{ij}=\frac{(1+\|\mathbf{e}_{i}-\mu_{j}\|^{2}/v)^{-\frac{p+1}{2}}}{\sum_{j^{ \prime}}(1+\|\mathbf{e}_{i}-\mu_{j^{\prime}}\|^{2}/v)^{-\frac{p+1}{2}}}, \tag{17}\]  
where \(\mathbf{e}_{i}\) is the \(i\)-th row of the embedding \(\mathbf{E}^{(l)}\), \(\mu_{j}\) is initialized by the K-means learned by the pre-train auto-encoder, \(v\) is the degree of freedom of Student's t-distribution. \(q_{ij}\) can be seen as the probability of assigning sample \(i\) to cluster \(j\). From this, we can obtain the cluster distribution \(\mathbf{Q}\) about the nodes. To enable nodes to be assigned to different clusters with higher confidence, we calculate the target distribution \(\mathbf{P}\). \[p_{ij}=\frac{q_{ij}^{2}/f_{ij}}{\sum_{j^{\prime}}q_{ij}^{2}/f_{ij^{\prime}}}, \tag{18}\]  
where \(f_{ij}=\sum_{i}q_{ij}\) is the soft clustering frequency. The target distribution \(\mathbf{P}\) normalizes the sum of squares of each distribution in the cluster distribution \(\mathbf{Q}\). By using two distributions to constrain different embeddings, the embedding obtained by the DNN module and the Quasi-GNN module can be considered simultaneously to optimize the embedding and clustering quality jointly. On this basis, we can obtain the corresponding objective function  
\[\mathcal{L}_{clu}=KL(P\|Q)=\sum_{i}\sum_{j}p_{ij}log\frac{p_{ij}}{q_{ij}}. \tag{19}\]  
This objective function constrains the DNN module and we can obtain the superior embeddings for clustering by reducing the KL divergence loss of the two distributions \(\mathbf{Q}\) and \(\mathbf{P}\). In addition, we need to utilize the \(\mathbf{P}\) distribution to constrain the Quasi-GNN module  
\[\mathcal{L}_{mlp}=KL(P\|Z)=\sum_{i}\sum_{j}p_{ij}log\frac{p_{ij}}{z_{ij}}. \tag{20}\]  
To sum up, the final loss can be defined as  
\[\mathcal{L}=\mathcal{L}_{res}+\beta\mathcal{L}_{clu}+\gamma\mathcal{L}_{mlp}, \tag{21}\]where \(\beta\) and \(\gamma\) are constraint coefficients, \(\beta\in[0,1]\) and \(\gamma\in[0,1]\). Algorithm 1 shows the training process of our proposed model.