esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Allocation on Blockchain Enabled Mobile Edge Computing System
## 6 Conclusions  
In this article, we thoroughly analyzed the resource allocation policy in blockchain-based MEC system. A new consensus mechanism: PoL, is applied in the system to maintain the stability of blockchain and improve the efficiency of edge computing servers. The optimization target of resource allocation consists of four parts, that is the process delay and cost of servers, which are common in traditional MEC models, and the mining delay and mining reward based on aforementioned PoL consensus. Different weight coefficients are set for these indicators in the reward function to explore what the impact of each indicator on the policy. Furthermore, to better capture the long-time trend of incoming tasks, TCN enabled A3C algorithm is used to solve the resource allocation policy, which can make the policy convergence faster and more stable. However, our work also has some deficiencies. The policy can not adjust according to the arrival rate of tasks. Furthermore, due to the limitation of computing resources, the simulation environment is quite different from real-world environment, which contains more servers and clients. Furthermore, it is only a theoretical research, but the actual software system will be more complicated. In the future, we first hope to improve the algorithm so that it can adjust the allocation policy more flexibly. Secondly, we hope to extend the algorithm in large-scale application scenarios to verify its generality. Furthermore, finally, we hope to complete the implementation of blockchain-based mobile edge computing system in the laboratory and continuously optimize and adjust it.  
**Author Contributions:** Conceptualization, X.Z.; Formal analysis, X.Z., F.Y. and Y.Z.; Investigation, X.Z., F.Y. and Y.Z.; Methodology, X.Z.; Supervision, F.X.; Validation, X.Z. and Y.Z.; Visualization, X.Z.; Writing--original draft, X.Z. and Y.Z.; Writing--review and editing, X.Z., Y.Z. and F.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Research Innovation Fund for College Students of Beijing University of Posts and Telecommunications and Beijing Natural Science Foundation-Haidian Frontier Project \"Research on Key Technologies of wireless edge intelligent collaboration for industrial internet scenarios (L202017)\".  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Fracture and Deflection of Orthodontic Miniscrews--A Systematic Review
## 4 Discussion
The mechanical inferiority of a tapered screw design could derive from the fact that the weakest part of an MS is the tip of the screw. The area of the tip presented in studies had significantly lower fracture torque values when compared to the MS neck [15]. Moreover, a deformation of the tip was the most commonly (84.5%) noted shape distortion in MSs retrieved from patients after clinical use [8]. The blunting of the tip results in its decreased cutting ability, which can strongly hinder the self-drilling efficacy of the screw. An MS design that allows adequate self-drilling is clinically a highly desired feature as it simplifies the insertion procedure, removing the need for pre-drilling. However, in light of this review's findings, the safe placement of MSs without pre-drilling can only be attained at 1 mm or 2 mm thickness of the cortical bone. In the case of particularly dense bone or a cortical layer exceeding 3 mm, the increase in MS fracture risk leads to the recommendation of pre-drilling [17]. As in every surgical procedure for implant placement, the insertion of MSs must be preceded by microbiological control through proper sterilization processes. Despite the commercial availability of most MSs in single sterile packages, some require sterilization in the office. Steam sterilization proved to have no detrimental influence on insertion torque values, whereas dry heat sterilization affected mechanical resistance by lowering the fracture torque value [14]. Finally, amidst the articles included in this review, quite noteworthy are the publications exploring the possibility and limitations of reusing MSs. The aforementioned study by [PERSON] et al. compared unused and retrieved MSs [8]. Besides tip deformation, surface composition analysis of used MSs found debris of carbon, calcium, and phosphorus. Thus, retrieved MSs exhibited decreased cutting ability due to deformation of the tip structure and surface contamination. MS reusability may also cause concern when tooth root contact occurs during the MS insertion and clinicians are advised to remove the screw and reinsert it in a better location. However, the analysis of retrieved MSs by scanning electron microscopy exhibited varying amounts of deformation and blunting at the MS tip and its threads on root contact [18]. Considering those results, the reusability of self-drilling MSs should be regarded with caution. The biomechanical risk associated with the increased insertion torque (MS fracture) and the biological consequences associated with the higher insertion torque (excessive insertion force may hinder soft tissue healing and may induce microcracks and bone damage) strongly advocates for the avoidance of the clinical use of retrieved mini-implants.
## 4 Stability and Altitude Tracking Performance Analysis  
In this section, the stability of the closed-loop system and the tracking performance will be discussed. The main result is given by the following theorem. **Theorem 1:**_Consider the saucer-shaped UAV altitude tracking closed-loop system formed of nonlinear model (1); the ESOs (15) and (27); the second-order differentiators of virtual control signal \(\mu_{r}\), \(\theta_{r}\) and \(q_{r}\); and the control law (29) with (9), (16) and (23). Assume that Assumption 1 is satisfied; then the proposed control scheme can guarantee the following: (1) All the signals of the resulting closed-loop system remain bounded. (2) The system output tracking error converges to a sufficiently small neighbourhood of zero by selecting appropriate design parameters._  
**Proof:** In the first half of the proving process, we mainly focus on the convergences of ESOs. Redefine the scaled ESO estimation error vectors for ESOs (15) and (27) as \(\xi_{x}=\left[\xi_{1x},\xi_{dx}\right]^{T}\), in which \(\xi_{1x}=\left(x-\frac{\kappa}{x}\right)/\zeta_{1x}\), \(\xi_{dx}=d_{x}-\overset{\wedge}{d}_{x}\) and \(x=\mu,q\). The time derivative of \(\xi_{x}\) can be computed as  
\[\left\{ \begin{array}{l}\dot{\xi}_{1x}=\frac{\xi_{dx}}{\zeta_{1x}}-\frac{w_{1 x}\xi_{1x}}{\zeta_{1x}}-w_{1x}w_{2x}\Big{(}\frac{1}{\zeta_{2x}}-\frac{1}{ \zeta_{1x}}\Big{)}\text{sat}\Big{(}\frac{\xi_{1x}}{w_{2x}}\Big{)},\\ \dot{\xi}_{dx}=-\frac{w_{2x}}{\zeta_{1x}}\xi_{1x}-\frac{w_{2x}w_{2x}}{\zeta_{1 x}}\Big{(}\frac{\zeta_{1x}}{\zeta_{2x}^{2}}-1\Big{)}\text{sat}\Big{(}\frac{ \xi_{1x}}{w_{2x}}\Big{)}+\dot{d}_{x}\end{array} \right.\quad x=\mu,q.
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 2 Summary of the BDS B1 Signal  
As mentioned above, two civilian signals, B1I and BIC, will be broadcast simultaneously in the B1 frequency band in the future BeiDou global system [14]. From the viewpoint of the designer of the BeiDou system, the B1I and BIC signals have been designed independently and are expected to be processed in receivers as two separate signals. In this section, the characteristics of the B1I and BIC signals will be introduced respectively, and then the characteristics of the virtual broadband signal B1-ADS composed of B1I and B1C will be analyzed and its excellent potential for ranging performance will be discussed.
# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
## 2 Material and Methods
### Image Preprocessing  
In this study, we employed Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the image quality of mammograms within the CBIS-DDSM and INbreast datasets. The optimization of CLAHE parameters was crucial to achieving a balance between enhancing the contrast of mammographic images and preventing the introduction of noise that could potentially obscure critical diagnostic details. After an extensive empirical evaluation aimed at maximizing the efficacy of lesion detection, we determined the optimal settings for CLAHE to be a clip limit of 2.0 and a tile grid size of 8 \(\times\) 8. These parameters were chosen to ensure localized contrast enhancement that is essential for highlighting the subtle features of lesions, while the clip limit was set to mitigate the risk of over-enhancing the image, which could lead to noise amplification. This careful parameterization allows for a significant improvement in the visibility of pertinent features in mammograms, thereby facilitating the more accurate detection and analysis of breast cancer indicators. Following this enhancement, each image was resized to a uniform dimension of 416 \(\times\) 416 pixels, as shown in Figure 1.  
To mitigate the overfitting issue, we expanded the INbreast benchmark's dataset using various geometric transformation-based augmentation methods. Flipping was employed, involving both horizontal and vertical axis transformations. We also applied image rotation at angles of 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\) to the original training samples. Lastly, we introduced noise injection, utilizing a matrix with a random Gaussian distribution. These methods effectively diversified the dataset, creating varied orientations of the same image and making it more robust in recognizing and classifying images with varying levels of noise and distortion. These augmentation methods collectively contribute to a more comprehensive training regime, reducing the risk of overfitting while improving the model's performance.  
Figure 1: Dataset sample images: (**a**) the original image, (**b**) the enhanced image, and (**c**) the enhanced and resized image.
## 2 Theoretical Basis  
### Graph Convolutional Network  
The graph convolutional operation is specialized for graph structure data, which can describe the inter-relationships between nodes with the representation of edges. Unlike traditional CNNs, which operate on grid-like structured data, graph convolution can effectively model complex, non-Euclidean structures such as sensor networks, social networks,and molecular graphs. An undirected graph can be expressed as \(G=(V,E,A)\), where \(V\) is the node set with \(N\) nodes, \(E\) is the edge set that describes the connectivity between nodes, and \(A\in\mathbb{R}^{N\times N}\) is the weighted adjacent matrix.  
The spectral-based GCN denotes convolution operation by using graph Fourier transform, which depends on the eigenvalue decomposition of the normalized Laplacian matrix \(L\) of the graph [40]. The normalized Laplacian matrix \(L\) can be expressed as:  
\[L=I_{N}-D^{-1/2}AD^{-1/2}=U\Lambda U^{\mathrm{T}} \tag{1}\]  
where \(I_{N}\) is the identity matrix; \(D=diag\Big{(}\sum_{j}A_{ij}\Big{)}\) is the degree matrix; \(U\) is eigenvectors of the normalized Laplacian matrix \(L\); T represents the transpose operator; and \(\Lambda\) is the diagonal matrix of eigenvalues.  
Then, the spectral graph convolution can be defined as:  
\[Z=g_{\theta}\cdot x=Ug_{\theta}(\Lambda)U^{\mathrm{T}}x \tag{2}\]  
where \(g_{\theta}\) is a filter in the Fourier domain; \(\theta\in\mathbb{R}^{N}\) is a learnable parameter; \(x\in\mathbb{R}^{N}\) is the input signal; and \(\cdot\) is the graph convolution operator.  
This approach allows the convolution operation to capture the global graph structure. However, directly computing \(U\) via eigenvalue decomposition is computationally expensive, which suffers from \(O(N^{3})\) complexity. To reduce the computational complexity, the \(K\)-order truncated Chebyshev polynomial is used to approximate the filter \(g_{\theta}\)[41]. The approximation of graph convolution can be formulated as:  
\[Z=g_{\theta}\cdot x\approx\sum_{k=0}^{K}\theta_{k}T_{k}\Big{(}\widetilde{L} \Big{)}x \tag{3}\]  
where \(\widetilde{L}=(2/\lambda_{\max})L-I_{N}\), in which \(\lambda_{\max}\) is the largest eigenvalue of \(L\); \(T_{k}(x)=2 XT_{k-1}(x)-T_{k-2}(x)\), with \(T_{0}(x)=1\) and \(T_{1}(x)=x\).  
Then, the first-order approximation of Chebyshev polynomials is used to derive the graph convolution operation [42]. The graph convolution can be formulated as:  
\[Z=\Big{(}I_{N}+D^{-1/2}AD^{-1/2}\Big{)}X\Theta \tag{4}\]  
where \(X\in\mathbb{R}^{N\times C}\) is the input signal with \(C\) input channels; \(\Theta\in\mathbb{R}^{C\times F}\) is a learnable parameter matrix; and \(F\) is the number of output channels.
## Data and Methods
### The SMAP FT Classification Algorithms
#### 2.2.3 False Alarm Mitigation  
False NPR FT retrievals can occur due to a small FT reference difference or frequent diurnal thaw and re-freeze events [7]. Therefore, two additional processing steps are applied to mitigate summer false freeze and winter false thaw retrievals following the pixel-wise NPR FT classification, consistent with previous product versions [7]. First, if either the Tbv or Tbh value is above the FT threshold of pure water (273 K), the pixel is set to thaw regardless of the FT retrieval results. Second, an ancillary daily geospatial record of estimated 'never frozen (NF)' and 'never thawed (NT)' conditions was used with a 31-day moving window of NPR-derived daily values in each pixel to identify and correct suspected erroneous FT values:  
\[\text{ NeverFrozen}(\text{doy})=\sum\
olimits_{i=\textit{doy}-15}^{\textit{doy} +15}\textit{Freeze\_flag}(i),\text{ \ \ }\textit{NF\_mask}=(\textit{NF}==0) \tag{6}\]  
\[\text{ NeverThawed}(\text{doy})=\sum\
olimits_{i=\textit{doy}-15}^{\textit{doy} +15}\textit{Freeze\_flag}(i),\text{ \ \ }\textit{NT\_mask}=(\textit{NT}==0) \tag{7}\]  
where _NF_mask_ and _NT_mask_ denote the respective 'never frozen' and 'never thaw' masks. The ancillary _NF_mask_ and _NT_mask_ time series used for operational adjustment of the SMAP NPR FT record are obtained when consistent freeze or thaw conditions occur within each 31-day period of an annual FT climatology derived from a long-term (2002-2015) global FT daily record using higher frequency (36.5 GHz) Tbv retrievals [11,28]. Grid cell values where the false alarm mitigation is applied are subsequently assigned a retrieval quality flag in the final product (Section 2.5). Approximately 26% of the FT global domain has significant surface water contamination indicated from a static global water mask. 17.4% of the identified water contaminated grid cells show fractional water (fw) cover within a cell of less than 20%; 22% of water contaminated cells show a fw cover of less than 50%, whereas grid cells with identified higher water fractions were excluded from the FT retrievals. The portion of NPR FT corrections for likely NF and NT conditions vary weekly, and represent approximately 0-35% of the _NT_mask_ and 0-65% of the _NF_mask_ over the global domain.
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 7 Results
#### 7.3.5 Cost Efficiency  
Centralized cloud computing can be beneficial to extend for healthcare organizations since it helps to avoid the investments and maintenance in on-premise infrastructure, which results in considerable expenditure [50]. Some healthcare organizations may expand the resources up or down on demand, meaning that when a healthcare organization requires more computing or storage, it can hire for it. This kind of flexibility enables healthcare organizations to only pay for the requirement of the resources needed, hence avoiding the aspect of having to purchase more resources than required, and therefore cutting down the costs [52]. However, cloud computing centralization models entail some form of data download and upload to the cloud infrastructure that may attract charges relating to the amount and type of data and the bandwidth used. Healthcare organizations should, thus, assess the costs involved in data transfer and endeavor to enhance data transmission with their associated costs. Nevertheless, in decentralized cloud computing, healthcare organizations do not have to invest a lot for infrastructure like big data centers or buy and maintain large servers for every healthcare center as the infrastructure is distributed over many nodes or servers, thus having reduced costs compared to traditional systems [66]. Consequently, in decentralized cloud computing, data can also be stored and processed near their usage locales; hence, there is less needed to move data so far. The computer communication bandwidth costs can be lower when data are to be conveyed within a dispersed network, especially when a heavy amount of traffic is required for data processing or interchange. However, decentralized cloud computing is slightly different and more advanced than the centralized cloud models; there is a need for additional technical know-how and infrastructural support, thus involving a higher cost of establishment, maintenance, and the human resources trained specifically for the job [66; 72]. However, another drawback of cloud computing that may become serious when the network is expanding is that it may become rather slow, as all the applications are decentralized. The maintenance of a predictable quality can be technically disruptive, and the facility may need to spend more on the desired quality. In a decentralized environment, the acquiring and maintenance of data consistency and coordination among queryable nodes is not an easy task, and it also requires consensus, which involves additional cost and technical advancements; unused resources also demand additional costs [86]. Table 4 delineates key distinctions between centralized and decentralized cloud infrastructures within the healthcare information exchange paradigm. As shown in Table 5 the primary differences between centralized and decentralized cloud computing is summarized.
# Low-Latency Short-Packet Transmission over a Large Spatial Scale
## 4 Asymptotic Performance Analysis for Long-Distance Short-Packet Transmission
### Asymptotic Analysis with a Fixed Number of Relays  
In this part, we assume that the number of relays is fixed as a constant \(L\). When \(\bar{n}\) or \(\bar{\gamma}\) is large, we have \((\ln(1+\bar{\gamma})-\frac{k\ln 2}{\bar{n}})\approx(\ln(1+\bar{\gamma})\) and \(\frac{(1+\bar{\gamma})^{2}}{\bar{\gamma}(\bar{\gamma}+1)}\approx 1\). Thus, we approximate \(\epsilon\) in Equation (43) by applying Theorem 4.  
\[\epsilon\approx\frac{L\exp\left(-\frac{n\ln^{2}(1+\bar{\gamma})}{2}\right)}{ \sqrt{2\pi\bar{n}}\ln(1+\bar{\gamma})}. \tag{43}\]  
For a large \(\bar{n}\), we present \(\epsilon\) as a function of \(\bar{n}\), i.e.,  
\[\epsilon\approx\frac{\mu_{1}\exp\left(-\theta_{1}n\right)}{\sqrt{n}}, \tag{44}\]  
where \(\mu_{1}=\frac{L}{\sqrt{2\pi\ln(1+\bar{\gamma})}}\) and \(\theta_{1}=-\frac{\ln^{2}(1+\bar{\gamma})}{2}\). In this case, as:  
\[\begin{split}\bar{n}&=\frac{N^{th}}{L}\\ &=\frac{T-\frac{D}{\epsilon}}{L\tau}\to\infty,\end{split} \tag{45}\]the whole problem degenerates into an infinite-blocklength problem. The well-known Shannon formula can be applied to assure \(\epsilon\to 0\) with a given data rate. Moreover, from (44), we can see that \(\epsilon\) decreases nearly exponentially with \(n\).  
For a large \(\tilde{\gamma}\), we present \(\epsilon\) as a function of \(\tilde{\gamma}\), i.e.,  
\[\epsilon\approx\frac{\mu_{2}\tilde{\gamma}^{-\theta_{2}\ln(\gamma)}}{\ln( \tilde{\gamma})}, \tag{46}\]  
where \(\mu_{2}=\frac{L}{\sqrt{2\pi n}}\) and \(\theta_{2}=-\frac{\theta}{2}\). From Equation (46), we can see that \(\epsilon\) decreases nearly exponentially with \(\log(\tilde{\gamma})\), which is an infinitesimal of higher order compared to any fractional polynomials, but a low-order infinitesimal compared to \(\exp(-\tilde{\gamma})\).
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 2 Knowledge-Based Approaches
### Categories of Knowledge Representation and Reasoning Approaches  
In general, there are many knowledge representation forms, each with an associated reasoning approach. In this paper, we divide them into three major categories as depicted in Figure 1. The first category corresponds to logic-based and mathematical formalisms that depart from standard logic and mathematics. Due to practical limitations in the applications of logic under uncertainty, these formalisms have been extended to non-standard logic approaches of reasoning to cope with incomplete and uncertain knowledge. To this end, various forms of reasoning have been developed including abductive, inductive, fuzzy, probabilistic, and many others. The second category consists of structural approaches, typically based on graphs. Reasoning in graphs corresponds to traversing through the graph from a root or a set of nodes, that correspond to the premises, to another non-empty set of nodes, that correspond to the conclusions. The third category refers to data-driven formalisms that accommodate reasoning based on data and/or data-derived models as in digital twin, regression, neural network, and generative AI. It has to be noted that the two last categories, in the description that follows, correspond to the connectionist approach, as depicted in Figure 1. However, they will be defined and evaluated in the sequel separately as they can be used for different purposes. Common to all these different categories is the existence of a knowledge base where predicates and facts, graphs, data, and models that characterize and describe the state of the system are stored. In this section, these different representation and reasoning forms will be described and their applicability in telecommunications systems will be discussed.