esa-sceva/satcom-chunk-collection huggingface dataset:
# Supply-Demand Dynamic Matching in Cloud Manufacturing Based on Hypernetwork Model
## 3 Modeling of the SDM Problem Based on the Hypernetwork Model
### Hypernetwork Model
#### 3.2.1 Manufacturing Service Network Model (_S_Net_)  
The manufacturing service network, denoted as _S_Net_, comprises numerous service nodes interconnected by edges. The specific representation of the manufacturing service network model (_S_Net_) is defined by Equations (1)-(5).  
\[S\_Net=<S,E^{S},W^{S}>, \tag{1}\] \[S=\{s_{i}|i=1,2,...,NoS\},\] (2) \[s_{i}=<Q^{i}_{S},St^{i}_{S},Et^{i}_{S},C^{i}_{S},B^{i}_{S},\eta^ {i}_{S}>,\] (3) \[E^{S}=\{e^{S}_{ii^{\prime}}|e^{S}_{ii^{\prime}}=0,1;i,i^{\prime} =1,2,...,NoS\},\] (4) \[W^{S}=\{w^{S}_{ii^{\prime}}\in(0,1]|e^{S}_{ii^{\prime}}=1;i,i^{ \prime}=1,2,...,NoS\}. \tag{5}\]  
In this context, \(S\) denotes the set of service nodes, where \(NoS\) represents the total number of service nodes. The set \(E^{S}\) corresponds to the edges that indicate the relationships between these services. The parameters \(Q^{i}_{S}\), \(St^{i}_{S}\), \(Et^{i}_{S}\), \(C^{i}_{S}\), \(B^{i}_{S}\), and \(\eta^{i}_{S}\) denote the service quality, the earliest available time, the latest available time, the unit cost required for manufacturing, the maximum manufacturing capacity, and the credibility of service providers, respectively.  
When \(e^{S}_{ii^{\prime}}=1\), an edge exists between the two nodes, indicating that the services provided by these two nodes are similar and can execute similar manufacturing tasks. In dynamic environments, if one service fails, a similar service can act as an alternative provider to complete the original task. The variable \(w^{S}_{ii^{\prime}}\) represents the functional similarity between pairs of similar services, reflecting their similarity in attributes such as cost, time, and quality. Hence, \(w^{S}_{ii^{\prime}}\) is only defined when \(e^{S}_{ii^{\prime}}=1\).
## 2 Intent-Estimation- and Motion-Model-Based Collision Avoidance Method
### Intent Estimation
For each state vector \(M\), the posterior probability with each component \(\lambda_{k}\) of the GMM can be obtained, and the best matched component satisfies the following equation:  
\[i=\text{argmax}[P(M|\lambda_{k})]\quad k=1,\cdots,K \tag{12}\]  
where \(i\) is the subscript of the component. To obtain an intent predictor, trajectories of 200 drivers were collected, including trajectories on straight roads and winding roads, and used in the training process. The data used are shown in Table 2.  
To obtain ideal results, the length of \(M\), i.e., the value of \(n\), should be chosen carefully. The performance is improved with increased \(n\), although the real-time ability of the system decreases, as shown in Figure 5.  
The growth rate of the accuracy decreases, and it is limited when n is larger than five. The accuracy reaches 93.5% when n is five. In this paper, the system runs at 25 Hz, and n is set as five. It means that the intent-estimation process needs approximately 200 ms, which is acceptable.
## 1 Introduction  
Research efforts in the field of flexible GHz nanoelectronics have been gaining significant momentum recently. Research and commercial institutions are investing substantial resources in the design of flexible systems and sensors [1; 2; 3; 4; 5; 6; 7], including the development of smart conformal devices and tags, wearable electronics, internet of things (IoTS) and other electronic gadgets that are low cost, scalable to large areas, and mechanically robust, which is beyond the capability of conventional bulk semiconductor technologies. Moreover continuous advancements in the material synthesis of intrinsically flexible nanomaterials such as graphene, h-BN, carbon nano-tubes (CNTs) and transition metal dichalcogenides (TMDs, e.g., molybdenum disulfide--MoS\({}_{2}\)) [8,9] have encouraged their prospects for practical electronic application. These materials are naturally more suitable for integration with flexible, soft or glass substrates owing to their two dimensional nature and can potentially offer the electronic performance needed for low-power GHz systems.  
Graphene is a single sheet of carbon with very high room temperature mobility (\(>\)10,000 cm\({}^{2}\)/V\(\cdot\)s), high cutoff frequency (\(>\)300 GHz), and high electrical and thermal conductivity [2,3,10]. These properties of graphene make it a suitable candidate for the design of high frequency analog communication circuits, such as amplifiers, mixers, demodulators, and speakers [11]. Recent work on graphene circuits for RF applications includes: demonstration of graphene three stage integrated amplifier on Si wafer [12] and demonstration of flexible graphene circuit for a variety of digital modulation schemes [13].  
In this paper we present the state of the art flexible graphene field effect transistors (GFET) with high intrinsic fr of 18 GHz that has recently been improved to 100 GHz [14]. We also present the design and realization of the first all graphene based flexible radio frequency system. The following section discusses the design and fabrication of GFETs. Results and discussion section discuss in more detail the small signal modeling, electrical measurements (DC and RF), and mechanical flexibility and reliability evaluation of GFETs. We also discuss the design of individual graphene based radio frequency system blocks--flexible graphite patch antenna, graphene based frequency translation block (GFET doubler and demodulator), graphene speaker, and the demonstration of the first graphene based AM radio receiver.
## 2 Methods
### Spectral Clustering  
SC works by considering the dataset as an undirected graph \(G=\{V,E\}\), where the vertices \(V\) represent the data points (the patients), and the edges \(E\) are weighted according to similarity between vertices (i.e., the edge connecting two very similar patients \(a\) and \(b\) has a high weight \(w_{a,b}\)). In this context, the problem of clustering can be defined as finding \(k\) partitions (or cuts) \(A_{1},A_{2},\ \dots,\ A_{k}\) in the graph such that (1) the edges among partitions have small weights (patients in different clusters are dissimilar), (2) the edges within a partition have high weights (patients within a cluster are similar), and (3) the partitions are of similar sizes (there are no partitions with very few patients, which in general would not be very useful).  
This, however, has been proven to be an NP-complete problem, for which SC has been proposed as an approximation obtained by relaxation of some of its constraints [34]. Even if no theoretical guarantees exist regarding the quality of the solution to the SC problem compared to the original NCut, it is still widely used due to the fact that it results in a standard linear algebra problem that can be efficiently solved. As a very short summary, SC works by (a) building a similarity matrix \(W=\left(w_{i,j}\right)\) (we employed the pairwise Gaussian similarity between points with \(\sigma=0.1\), which is a typical choice for data in \(\mathbb{R}^{n}\)), (b) constructing a normalized graph Laplacian matrix \(L_{rw}=I-D^{-1}W\), where \(I\) is the identity matrix, and \(D\) is a diagonal matrix with values \(d_{i,i}=\sum\limits_{j\in V}w_{i,j}\), (c) getting the eigenvectors associated with the \(k\) smallest eigenvalues of \(L_{rw}\) (hence the name: Spectral Clustering), and (d) applying k-means clustering over these, and transferring the labels over to the original data points. Although the concept of SC is general and thus any clustering algorithm could be applied, in this work we make use of the most usual choice, which is k-means.  
Figure 1: Comparison of three clustering algorithms on six different two-dimensional datasets. Points of the same color have been assigned to the same cluster by the algorithm (modified from [33]).  
The optimal number of clusters was chosen with the eigengap heuristic, which recommends using the number of clusters for which the eigengap (or gap between consecutive eigenvalues) is maximized [34]. This heuristic is based on the theoretical result that, for a graph with \(k\) disconnected clusters, the first \(k\) eigenvalues have a value of zero, while the rest have much higher values. Hence, understanding the real clustering problem as a perturbation of some underlying ideal clustering with \(n\) clusters, the first \(n\) eigenvalues should have very similarly low values, and the rest should be higher. Figure 2 shows the application of this heuristic to our problem, where the first largest eigengap would suggest using a single cluster (which would be of no use), and the second largest eigengap suggests using three (which is the number of clusters that was eventually employed).
# Secure Monitoring System for IoT Healthcare Data in the Cloud
## 5 Proposed System
### Involved Technologies
#### 5.1.3 Cloud Computing (CC)  
The transmission of on-demand computing resources, such as storage, processing power, and software programs, through the internet is referred to as cloud computing. Cloud computing enables users to access and utilize these resources remotely from any location with an internet connection instead of relying on local servers or personal devices. The adoption of cloud computing in the healthcare industry has improved the effectiveness, usability, and security of healthcare services. Also, in the field of medicine, CC can give medical professionals like doctors and nurses access to patient records anytime and wherever they are [16].  
The healthcare industry now has access to scalable infrastructure, data-sharing capabilities, cutting-edge analytics, and remote healthcare delivery thanks to the integration of cloud computing with other internet technologies [16]. Collaboration between healthcare providers is facilitated, data-driven decision-making is made possible, and ultimately, patient care and outcomes are improved.
## 2 Background  
### Flash Translation Layer and File System  
Different from hard disk drives that allow \"in-place-update\" operation, the physical blocks in NAND flash memory must be erased before they are programmed. Therefore, a software module called the flash translation layer (FTL) is used in NAND flash memory to handle this constraint. FTL maintains the address mapping table [5, 23, 24, 25, 26], conducts garbage collection operations [27], manages the bad blocks [28], and balances the wear leveling [29] of NAND flash memory.  
According to the granularity of address mappings, there are mainly three types of FTL schemes: page-level FTL, block-level FTL, and hybrid-level FTL. Most of the current FTL designs are based on SLC NAND flash memory storage systems. Block-level FTL and some of the hybrid-level FTLs use the block offset to locate the pages within a block, and the pages may be programmed randomly within a block [30], but for MLC NAND flash memory, programming physical pages with the \"out-of-order\" pattern is prohibited [30]. Therefore, some of these FTLs cannot be applied to MLC NAND flash memory storage systems directly. Since our technique adopts the page-level FTL, it can be applied to MLC NAND flash memory to enhance its reliability.  
Several file systems have been specifically designed for NAND flash memory, such as JFFS/JFFS2 [31], and YAFFS/YAFFS2 [32]. Typically, these file systems are dedicated flash file systems and are aimed at embedded applications. They also achieve some functions of FTL (_i.e._, wear leveling and bad block management). For large-scale storage applications, many general-purpose file systems, such as NTFS [33], FAT [34], and XFS [35], are used without considering the constraints of NAND flash memory. When these file systems are used in the NAND flash memory storage system, additional FTL schemes must be implemented to conceal the special characteristics of NAND flash memory.
## 4 Results and Discussion
### Comparative Discussion  
The discussion of FP-SFOA-DQN-FL is depicted in Table 3. It is crystal clear that FP-SFOA-DQN-FL has attained high accuracy of 0.950, low loss function of 0.104, low MSE of 0.122, minimum RMSE of 0.035, minimum FPR of 0.140, and maximum average precision of 0.909 based on dataset-1 at time step = 200 s.
###### Abstract  
To study wind characteristics over mountainous terrain, the Xiangjiang Bridge site was employed in this paper. The improved boundary transition sections (BTS) were adopted to reduce the influence of \"artificial cliffs\" of the terrain model on the wind characteristics at the bridge site over the mountainous terrain. Numerical simulation and experimental investigations on wind characteristics over mountainous terrain with/without BTS were conducted for different cases, respectively. The research results show that the cross-bridge wind speed ratios and wind attack angles at the main deck level vary greatly along the bridge axis, which can be roughly divided into three parts, namely the mountain (I, III) and central canyon areas (II). The cross-bridge wind speed ratios at the main deck level with BTS is generally larger than that without BTS in the central canyon area (II) for most cases, while the opposite trend can be found in wind attack angles. The longitudinal wind speed ratios of the terrain model with BTS at L/4, L/2, and 3L/4 of the bridge length are larger than that of the terrain model without BTS for most cases. In general, the maximum relative error between numerical results and experimental results is about 30% for most cases.  
mountains valley; bridge site; boundary transition section (BTS); wind characteristics; numerical simulation; wind tunnel test +
Footnote †: journal: Applied Physics Letters  
1
# Dynamic Stability Finite Difference Time Domain Analysis of Landfill Based on Hypergravity Test
## 2 Dynamic Calculation Method for Landfill Based on FLAC3D  
The computational analysis of this study is conducted using FLAC3D software, and its calculation method is essentially the finite difference method. The use of tetrahedral calculation elements in the computational analysis can effectively avoid the hourglass problem, where small disturbances during numerical calculations lead to infinite deformation of the element under zero energy dissipation. However, this calculation element cannot meet the deformation requirements in plastic structure analysis. The hybrid discrete theory proposed by [PERSON] and [PERSON] [30] satisfies the deformation requirements by reducing the number of plastic flow constraints while avoiding the hourglass problem, thus solving this problem well. The hybrid discrete method they proposed first discretizes the structure using polyhedral elements, then further discretizes the obtained polyhedral elements into tetrahedral elements, and finally carries out scientific calculations based on constant strain assumption for tetrahedral elements. FLAC3D finite difference analysis, based on Lagrangian description and continuum mechanics theory, adopts this discretization method, which can better satisfy the deformation needs of model structures while ensuring a certain accuracy.  
The node balance equation is  
\[F^{<m>}=M^{<m>}\frac{\mathrm{d}\boldsymbol{v}^{<m>}}{\mathrm{d}t}=[[\frac{ \boldsymbol{T}}{3}+\frac{1}{4}\rho V\boldsymbol{b}]]^{<m>}+\boldsymbol{P}^{<m>},m=1,N^{node}. \tag{1}\]  
where \(<m>\) represents the node m associated with the grid cell. \(N^{node}\) is the number of nodes. \([[\frac{\boldsymbol{T}}{3}+\frac{1}{4}\rho V\boldsymbol{b}]]^{<m>}\) represents the sum of all components of the constant strain element at node m in the grid cell. \(\boldsymbol{P}^{<m>}\) represents the component of external force P at node m.  
Correspondingly, \(M^{<m>}\) can be expressed as follows:  
\[M^{<m>}=[[\frac{1}{4}\rho V]]^{<m>},m=1,N^{node}. \tag{2}\]  
When considering the influence of damping, the equation becomes:  
\[\boldsymbol{F}^{<m>}+\boldsymbol{F}_{D}^{<m>}=M^{<m>}\frac{\mathrm{d} \boldsymbol{v}^{<m>}}{\mathrm{d}t},m=1,N. \tag{3}\]  
where \(\boldsymbol{F}_{D}\) represents the damping force.  
The central difference expressions for node velocity, position, and displacement are  
\[\boldsymbol{v}^{<m>}(t+\frac{1}{2}\Delta t)=\boldsymbol{v}^{<m>}(t-\frac{1}{ 2}\Delta t)+\frac{\boldsymbol{F}^{<m>}}{M^{<m>}}\Delta t,m=1,N. \tag{4}\]  
\[\boldsymbol{x}^{<m>}(t+\Delta t)=\boldsymbol{x}^{<m>}(t)+\Delta t\boldsymbol{ v}^{<m>}(t+\frac{1}{2}\Delta t) \tag{5}\]  
\[\boldsymbol{u}^{<m>}(t+\Delta t)=\boldsymbol{u}^{<m>}(t)+\Delta t\boldsymbol{ v}^{<m>}(t+\frac{1}{2}\Delta t) \tag{6}\]  
The calculation method for landfills under different water and seismic levels is shown in Figure 1.
# Leveraging IGOOSE-XGBoost for the Early Detection of Subclinical Mastitis in Dairy Cows
## 2 Relevant Methods and Theories  
### XGBoost Model  
An ensemble machine learning approach based on decision trees, called XGBoost (eXtreme Gradient Boosting), has gained significant recognition for its exceptional performance in both classification and regression tasks [21]. For classification, it effectively handles binary and multi-class problems through optimized tree structures and node splitting strategies [22]. The algorithm's robust performance against noise and missing values, coupled with its high computational efficiency, has led to successful applications across various domains.  
In regression tasks, XGBoost efficiently manages complex relationships in high-dimensional data [23], achieving notable success through its gradient boosting technology. The algorithm's extensive parameter set significantly influences model performance and training efficiency, making intelligent optimization algorithms essential for parameter tuning and feature selection. This optimization approach not only enhances model performance and generalization capabilities, but also improves the interpretability and practical applicability of machine learning models.
## 1 Introduction
Moreover, the strategy is based on the principle of energy attenuation in the form of oscillation of the mass damper and the creation of mass inertial force in the opposite phase of the applied force to the building. Due to the uncertainty in earthquake prediction and the dynamic features of the structure, such as natural frequencies and the attenuation of different vibration modes, it is often recommended to use more mass dampers with close vibrational frequencies in order to cover vast ranges of main frequencies of the structure [1]. In the case of high-rise buildings, the contribution of higher modes is also important; therefore, the damper tuned only to the first natural vibration mode may lose its effectiveness. In addition, when the structure is exposed to severe earthquakes and it enters the non-linear range, its stiffness is changed which can result in the situation where the damper is not tuned and its efficiency is then lost. Moreover, for medium to hard soils, TMDs should be tuned to the natural frequency of the soil-structure system, and not to the structure alone [7; 27]. The research on TMDs has been much advanced. [PERSON] et al. [28] and [PERSON] et al. [29] confirmed the efficiency of the method in suppressing structural translational vibrations. [PERSON] and [PERSON] [30] introduced a new layout for 4 TMDs to control the torsional mode of a building in addition to its translational mode. [PERSON] and [PERSON] [31] investigated the effectiveness of TMDs in controlling the response of a non-linear 25-story structure under the El Centro earthquake. [PERSON] [32] studied the performance of a building equipped with TMDs exposed to the excitation time history of the Mexico City earthquake. The new idea of a hybrid control strategy combining base isolation and non-traditional TMDs (i.e. TMDs with dashpots directly connected to the ground) has recently been proposed and analyzedby [PERSON] and [PERSON] [33]. They applied a relatively simple numerical model by modelling the superstructure as a single degree-of-freedom system and assuming the linear elastic behavior of the base isolation. Although the results obtained from the study are very promising, further analyses using more detailed structural models are needed in order to confirm the effectiveness of the method. Therefore, the aim of the present paper is to investigate the effectiveness of using base isolation and non-traditional TMDs in suppressing structural vibrations of high-rise buildings with a different number of stories under various far-field and near-field earthquake records applying multi degree-of-freedom models of buildings and assuming a non-linear model of the base isolation system.
## 4 SAR Raw Signal Processing
### Omega-k Algorithm for LFM-CW SAR System
#### 4.2.7 Inverse Fast Fourier Transform in Azimuth  
The inverse fast Fourier transform (IFFT) is applied in the azimuth; however, the inverse transform is no longer applied in the range because it is not necessary, and it is enough to apply the Fourier transform in the range for the image quality to be acceptable.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 3 Architectural Challenges  
With the push to increase connectivity in developing and under-developed countries, regions with a dense population such as cities, towns, and suburban areas are increasingly interconnecting. This trend would accelerate soon, especially with the push towards 5G networks. On the other hand, rural areas are shrinking in size, and the average age of the population is increasing [16], thus providing connectivity is not a priority for network providers. Solutions such as mobile and satellite internet, for direct connectivity to the cloud, are prohibitively expensive. Furthermore, by the nature of rural areas and farmlands, the houses are sparsely positioned, cable and Wi-Fi links are technologically not viable due to signal deterioration. Thus, we identify the following constraints that the architecture must meet:  
* Internet connectivity is of low quality, expensive, or nonexistent.
* Houses are mostly spaced out at a distance of at least 100 m.
* Electricity is not reliable, and blackouts are to be expected.
* The care receivers are unable to troubleshoot or repair IoT devices.
* Servicing can be done from once in few months to once in a few years.
* The systems must be affordable, even for a few participants, and scaling up reduces cost.
* When storage is limited, the most valuable data has a higher priority over the most recent data.  
The lack of persistent Internet connectivity implies a self-sustained network that relies on one or few links for connecting to the cloud. The limitation for Internet connectivity requires an architecture that would support local processing as the data cannot be fully or immediately uploaded to the cloud. The sparse distribution of the houses and the need to minimize cost require long-range wireless network links.  
The lack of reliability of electrical power is one factor that would require a battery backup for the devices. However, difficulties and expenses related to the installation of powered devices, especially in older houses that might not have electrical installations in all rooms, increase the preference for battery-powered devices. The battery capacity should be maximized so that the active time of the devices is sufficiently longer than the maintenance window when the operator could service or replace batteries. To extend the lifetime of the battery-powered devices, it is crucial to have low power and sleep states such that the device would minimize the time needed to remain in a higher power consummation state.