esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Analysis and Parametric-Sensitivity Analysis
### Parametric-Sensitivity Analyses
Figure 7A shows that when \(0\leq t<13\) d, SC < 0 and when \(t>13\) d, SC > 0, indicating that the settlements are negatively correlated with \(\alpha\) before 13 d, and positively correlated with \(\alpha\) after 13 d, which also proves the law obtained from the previous analysis of the effect of differential order \(\alpha\) on settlement: there is a point on the creep curve that divides it into two stages, and the law of increase and decrease in settlements in the two stages is opposite to the increase in \(\alpha\). Moreover, \(SC_{\alpha}=0\) when \(t=0\) and \(t\rightarrow+\infty\), which also proved the law that the change in \(\alpha\) has no effect on the instantaneous and final settlements. Figure 7B,C show that \(SC_{G_{1}}<0\) and \(SC_{G_{2}}\leq 0\), indicating that the settlements are negatively correlated with \(G_{1}\) and \(G_{2}\). Moreover, \(\big{|}SC_{G_{1}}\big{|}\) decreases gradually with time, t, and tends to be stable, whereas \(\big{|}SC_{G_{2}}\big{|}\) increases gradually with time, t, and tends to be stable, and when \(t\rightarrow+\infty\), \(\big{|}SC_{G_{1}}\big{|}=\big{|}SC_{G_{2}}\big{|}=1.995\times 10^{-4}\), thus the changes in \(G_{1}\) and \(G_{2}\) affect the final settlement. \(SC_{\eta}=0\) when \(t=0\) and \(t\rightarrow+\infty\), thus the change in \(\eta\) does not affect the instantaneous and final settlement. Figure 7D shows that \(SC_{\eta}<0\), indicating that the settlements are negatively correlated with \(\eta\). In addition, \(\big{|}SC_{\eta}\big{|}\) increases first and then decreases with time \(t\), implying that the influence of \(\eta\) on settlements first increases and then decreases. By calculation, \(-6.2\times 10^{-3}\leq SC_{\alpha}\leq 6.4\times 10^{-3}\), \(-2.1\times 10^{-4}\leq SC_{G_{1}}\leq-1.995\times 10^{-4}\), \(-1.995\times 10^{-4}\leq SC_{G_{2}}\leq 0\) and \(1.7\times 10^{-6}\leq SC_{\eta}\leq 0\). Thus, \(\alpha\) is the most sensitive to the settlement effects, followed by \(G_{1}\) and \(G_{2}\), while \(\eta\) is the least sensitive. Therefore, significant attention should be given to \(\alpha\), \(G_{1}\) and \(G_{2}\) when the displacement inverse analysis method is used to invert the geotechnical parameters.
## 1 Introduction
are fixed, so that it is feasible to use them to determine and describe the chart. In summary, the chart can be seen as a special item, and the purpose of this paper is to recommend these special items to users. The method based on GCN can predict the relationships between different items, and the inductive method and mini-batch meet requirements for running on computers with lower computing power. Therefore, this paper uses them as the basis for the model. It should be noted that the research in this paper focuses on the chart recommendation problem in the data visualization scenario of BI, as well as the problem of recommending high-scoring items for users in other similar situations. Therefore, further exploration is needed for specific integration and application methods. The PreferSAGE section of the model in this paper refers to PinSAGE [9] and makes improvements to it. Firstly, the sampling method of PinSAGE was extended, expanding its importance sampling based on node visits to consider both spatial and logical importance simultaneously. Secondly, in order to alleviate the problem of information loss caused by multiple convolution operations in the information aggregation process, skip connections [10; 11] directly connecting to the outermost node features and corresponding pooling layers were added to the innermost aggregation process. The Slow-acting Collaborative Filtering (SCF) part of this model is an integrated method for the recommendation results of conventional collaborative filtering algorithms, which has a slow effect. The experimental results demonstrate that the PreferSAGE-SCF model proposed in this paper can achieve better performance compared to baselines in solving the tasks of analyzing charts' recommendations for data visualization and recommending high-scoring items for users in similar situations. The contributions are as follows:  
1. An analysis was conducted on the requirements for recommendation models that can be applied to the data visualization of BI. 2. We proposed an inductive heterogeneous graph recommendation model for high-scoring items, PreferSAGE. 3. A collaborative filtering algorithm integration method, SCF, was designed with limited influence. 4. Experiments were conducted on a collected dataset and a public dataset, and the results demonstrated the effectiveness of the model in performing high-preference item recommendation tasks. The remainder of the paper is organized as follows. In Section 2, related works are introduced. In Section 3, the characteristics and requirements of data visualization are explained, and the PreferSAGE-SCF model is constructed. In Section 4, experiments and evaluations are conducted. Finally, the conclusion, current relevant issues, and future research directions are presented in Section 5.
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
###### Abstract  
This paper presents a robust framework for monocular camera pose estimation by leveraging high-fidelity, pre-built 3D LiDAR maps. The core of our approach is a render-and-match pipeline that synthesizes photorealistic views from a dense LiDAR point cloud. By detecting and matching keypoints between these synthetic images and the live camera feed, we establish reliable 3D-2D correspondences for accurate pose estimation. We evaluate two distinct strategies: an Online Rendering and Tracking method that renders views on the fly, and an Offline Keypoint-Map Tracking method that precomputes a keypoint map for known trajectories, optimizing for computational efficiency. Comprehensive experiments demonstrate that our framework significantly outperforms several state-of-the-art visual SLAM systems in both accuracy and tracking consistency. By anchoring localization to the stable geometric information from the LiDAR map, our method overcomes the reliance on photometric consistency that often causes failures in purely image-based systems, proving particularly effective in challenging real-world environments.  
camera pose estimation; LiDAR mapping; 3D reconstruction; visual SLAM 2025  
6
# Late-Stage Optimization of Modern ILP Processor Cores via FPGA Simulation
## 2 Conventional Processor Design Flow  
Figure 1 illustrates conventional processor design flow for architecture optimization based on simulation. It can be conceptually identified as following four stages: (1) Given the target applications or representative benchmarks, a high-level simulator is used to explore the architectural design space. It determines the potential designs space by using rough estimates of performance, power, chip area and pin count, etc. (2) Architectural simulations which define the microarchitecture or the internal organization of the processor, for example, the number of arithmetic-logical units (ALUs), the size of the caches, the number of processor pipelining stages, the structure of branch predictor, etc. (3) Register transfer level (RTL) simulations which model the processor core at a lower abstraction level. It incorporates full function as well as bit-accurate timing. For simulation acceleration, the commercial emulator and FPGA prototyping are often used. (4) If test chips are available, then chip benchmarking can be done with performance monitor unit (PMU). This evaluation results can be used to direct the next round processor design.  
These four main steps require different benchmarking simulations. The key optimization steps for processor core design are Stage 2 (early-stage) and Stage 3 (late-stage). Though the results at Stage 4 using chip benchmarking in the form of test chip or previous generation chip would be adequately fast and accurate, this method is design non-reconfigurable and cost unbearable. Furthermore, it cannot obtain all the required information for analysis. Most simulation-based architectural optimizations focus at Stage 2 using softwarebased simulators. This is because they are easily to implement and usually flexible through parameterizations, allowing a large variety of experiments to be performed. There are also some FPGA simulators to do early-stage optimization with simplified or abstract models, for example, FAME simulators at level 001 to 111 [17], Fabscalar [18]. For stage 3, the initial RTL implementation can be optimized through RTL simulation. The software-based simulation can also be used for such purpose. These simulation methods are relatively slow. Though one can use the truncated applications, sampled benchmarks or reduced input data set to reduce the simulation time, these methods would also possibly lose simulation accuracy and leave the performance results in doubt. This leads to a fact that only after tapeout of the processor design, we can get its accurate performance results.
## 3 Analysis and Clustering Methodology  
This section describes the methodology followed to analyse and group RTN data collected simultaneously from \(N^{\prime}\) different measurement locations across a given area. The approach focused on investigating how many different acoustic environments, in terms of RTN, could be distinguished in the area of interest, e.g., a city district or a neighbourhood, based on the spectro-temporal analysis of real acoustic data gathered in that area. In order to focus on RTN, other noise sources, such as trains, airplanes, horns, sirens, birdsongs, dogs barking, works, people, rain, etc., should be removed beforehand to avoid any potential bias due to their occasional presence in the clustering and analysis of RTN distributions.  
Figure 1 shows the block diagram of the proposed clustering methodology. First, acoustic data are gathered from \(N^{\prime}\) locations simultaneously (e.g., from a WASN of \(N^{\prime}\) sensors) of a given acoustic environment. The analysis considers a labelled acoustic database as input, which includes both the simultaneous collection of audio recordings for the period of interest (e.g., one day) together with the corresponding RTN labels. Next, the acoustic data are pre-processed before conducting the subsequent spectro-temporal analyses of RTN. To that effect, the audio passages of interest are selected from the sensed periods, considering if they sufficiently representative in terms of the presence of RTN. As a result, some sensed locations can be discarded (e.g., due to particular technical problems), being \(N\leq N^{\prime}\), the final number of locations considered for the subsequent analyses.  
After conducting the spectro-temporal analysis of the acoustic data for each location considered, the obtained spectro-temporal profiles (STPs) are input into the clustering analysis to automatically obtain a set of STP groups based on the computation of a set of cluster validity indices (CVIs) that drive the selection of the optimal number of STP clusters \(Q^{*}\). Finally, the approach ends with an expert-based analysis and representation step to validate the coherence of the obtained solution, obtaining as output a similarity matrix \(\mathbf{S}\) that shows graphically the differences and similarities between the \(N\) acoustic RTN environments according to the \(Q^{*}\) STP clusters, through the expert definition of a mapping function \(I(i)\), for \(i=\{1,\ldots,N\}\).
# Random Vibration Fatigue Analysis Using a Nonlinear Cumulative Damage Model
###### Abstract
To show numerically how the proposed model works, in a practice analysis, the response stress generated from the loading random vibration is obtained by determining a dynamic load factor (\(\sigma_{dynamic}\)) and the corresponding response acceleration (\(A_{res}\)) factor. Then, based on the generated vibration bending stress (\(\sigma_{vb}\)), the total life cycle \((N_{i})\) is determined by using the Basquin's equation. The cycles of random vibration applied \((n_{i})\) are determined by using the rainflow method. Thus, with these three factors, vibration bending stress (\(\sigma_{vib/bend}\)), the total cycle \((N_{i})\) and the cycles of random vibration, applied \((n_{i})\), the modified model is performed. Since the proposed model depends only on the vibration bending stresses and its analysis to determine them, then, in the application, a single mechanical component (support) made of steel AISI 1025 subjected to random vibration (bending cycling) stress was used. The addressed vibration bending stresses were determined once the component was submitted to the random vibration profile per GR-326, then, by applying the proposed model, the fatigue damage until failure (\(D=1.00\)) was determined after 28 loads of the GR-326 vibration profile. The structure of the paper is as follows. Section 2 presents the theoretical background of the random vibration and nonlinear damage accumulation. In Section 3, the proposed nonlinear model is formulated. Section 4 contains the experimental application (validation's procedure) and its result. Finally, the conclusions are provided in Section 5.
## 3 Results
### Acetic Acid Recovery from Fractionated Hydrolysate by Liquid-Liquid Extraction  
Table 3 summarizes the extraction yields of acetic acid from hydrolysate as a function of the H/S ratio; that is, the ratio of fractionated hydrolysate to ethyl acetate. A low AA extraction yield (14.54% at 25 \({}^{\circ}\)C and 21.59% at 50 \({}^{\circ}\)C) was obtained at an H/S ratio of 1:1. At an H/S ratio of 1:2, the extraction yield of acetic acid increased to 55.73% (at 25 \({}^{\circ}\)C) and 58.59% (at 50 \({}^{\circ}\)C). When the H/S ratio was relatively large, the reaction temperature had some effect, but when the H/S ratio was small, the effect of the reaction temperature was not significant. The highest extraction yield of AA was obtained at an H/S ratio of 1:4. As the H/S ratio decreased, the extraction yield increased, but when the H/S ratio was 1:3 or less, there was no significant difference in yield. This finding is consistent with the results of [PERSON] et al. [51], who evaluated the effect of undissociation of acetic acid in the  
Figure 3: Furfural production and xmg consuming in the fractionated hydrolysate with 0.73 M of ZnCl\({}_{2}\), (**a**) with reaction time, (**b**) with reaction temperature.  
region where the H/S ratio decreases. With an initial acetic acid concentration of 4.54 g/L in the hydrolysate, the amount of acetic acid recovered was 66.1% at an H/S ratio of 1:4 at room temperature (25 \({}^{\circ}\)C) (Table 3). Although this result fell short of the results of [PERSON] et al. (2016), who investigated acetic acid recovery using ethyl acetate and obtained a high extraction yield of 93%, we believe the reason is the interfering action of other by-products in the fractionated hydrolysate.  
A few studies have been conducted to recover acetic acid from biomass hydrolysates. [PERSON] et al. (2018) extracted only 33% of AA from barley straw hydrolysates using ethyl acetate. However, [PERSON] et al. (2018) reported acetic acid recovery of 62% and 83% from wood hydrolysates using TOPO and TAPO, respectively. According to [PERSON] and [PERSON] (2018), if the AA concentration in the initial feed is high (more than 10 g/L), EA single solvent is not suitable to obtain an effective extraction yield and, thus, a co-solvent extraction strategy is required.
# Paneth Cells and Lgr5+ Intestinal Stem Cells in Radiation Enteritis
## 2 The Structure and Functions of the Small Intestine
### Goblet Cells  
Goblet cells are the second most populous IECs that are derived from the Lgr5+ ISCs. Majority of goblet cells are found toward the villous surface of the intestinal crypts [15; 40]. The number of goblet cells increase progressively along the small intestine and the largest number per surface area of zones in the GIT are found in the distal parts of the ileum and the colon. The life-span of goblet cells is normally less than 7 days [17]. The main function of goblet cell in the small intestine is to secrete mucus and the constituents of mucus which is secreted by the goblet cells includes subtypes of mucin (MUC) and trefoil factors (TFIs) [40; 68; 69]. The two main components of mucus which are secreted by goblet cells in the small intestine are secretory MUC2 and membrane-bound MUC3 [40; 68; 69]. The MUC2 forms a carpet on the luminal surface of the epithelium which is permeable to nutrients but impermeable to pathogens. The carpet of mucus which is predominately made up of MUC2 helps to maintain a higher concentration of antimicrobial peptides, which have been secreted by, among others, the Paneth cells in the area adjacent to the surface epithelial cells [40; 41]. The goblet cells are also involved in the sampling of nutrients and the microbiota in the lumen of the small intestine. The mucus which is secreted by the goblet also serves as a nutrient for commensal organisms in some of the niches along the GIT.  
High dose of ionizing radiation can reduce the number and function of goblet cells and lead to a reduction in the quality and amount of MUC2, MUC3, and TFIs that is produced [7]. The poor quality of mucus produced is ultimately not able to ensure the microbiological concentration of antimicrobial peptides and protein produced by the Paneth cells on the luminal surface of the epithelial cells. The lowering of antimicrobial activities and poor quality of mucus result in dysbiosis, an increase in gut permeability, translocation of bacteria, gut inflammation, and systemic inflammation [68]. The trefoil factor normally enhances the growth and development of intestinal epithelium and is especially needed during the repair and regeneration of the intestinal epithelium following radiation-induced damage to the epithelium of the small intestine [70; 71; 72; 73]. Furthermore, goblet cells can de-differentiate and assume stemness if both the Lgr5+ ISCs, reserve ISCs and Paneth cells have been damaged by ionizing radiation [8; 22; 58]
## 2 Small-Signal Modeling of the Distributed WEDES System
### Overview of the WEDES System and Controller Operation Principle
\[SOC_{\text{MX-ref}} = \frac{\left(\delta_{\text{MX1}}\times SOC_{\text{MX1}}\right)+ \left(\delta_{\text{MX2}}\times SOC_{\text{MX2}}\right)+\ldots+\ \left(\delta_{\text{MXN}}\times SOC_{\text{MXN}}\right)}{\delta_{\text{MX1}}+ \ \delta_{\text{MX2}}+\ldots+\delta_{\text{MXN}}}, \tag{2}\]  
\[\lambda_{\text{DC}r} = \frac{\delta_{\text{MXr}}\times\ \alpha_{\text{MXr}}}{\left( \delta_{\text{MX1}}\times\ \alpha_{\text{MX1}}\right)+\ \left(\delta_{\text{MX2}}\times\ \alpha_{\text{MX2}}\right)+\ldots+\ \left(\delta_{\text{MXN}}\times\ \alpha_{\text{MXN}}\right)} \tag{3}\]  
If the SOC value of \(r\)th battery module is larger than others, the corresponding multiplier \(\alpha_{\text{MXr}}\) will be set larger, and vice versa. These multipliers \(\alpha_{\text{MX1}}\) through \(\alpha_{\text{MXN}}\) are then multiplied by  
Figure 2: Illustration of the WEDES controller presented in [1]. enabled/disable values \(\delta_{\text{MX1}}\) through \(\delta_{\text{MXN}}\) to further generate the weighting factors \(\lambda_{\text{DC1}}\) through \(\lambda_{\text{DCN}}\), as given by Equation (3). The sum of weighting factors \(\lambda_{\text{DC1}}\) through \(\lambda_{\text{DCN}}\) always equals to one. These weighting factors are then used in the battery module voltage loop to regulate the output voltage of the battery modules \(V_{\text{MX1}}\) through \(V_{\text{MXN}}\) at the primary side (Tx side), and as a result, achieve bus voltage regulation at the second side (Rx/OBU side). It should be emphasized that due to the inevitable power loss during wireless power transfer (i.e., transmission efficiency is less than 100%), the bus voltage control loop is important to adaptively adjust \(V_{\text{MN-total}}\) value to compensate the conversion ratios and losses of multiple power conversion stages (DC-AC-AC-DC) and realize bus voltage regulation. The relationship between different voltages can be calculated as given by Equation (4). \[\left\{ \begin{array}{c}V_{MX-total}=\left(V_{bus-ref}-V_{bus}\right)\times G _{v-bus}\\ V_{MX-total}=~{}V_{MX1}+~{}V_{MX2}+\ldots+V_{MXN}\\ V_{MXr-DC-ref}=V_{MX-total}\times\lambda_{DCr}\\ V_{bus}=~{}V_{o1}+~{}V_{o2}+\ldots+V_{oN}\end{array} \right. \tag{4}\]  
To summarize, the presented WEDES controller can dynamically control SOC multipliers \(\alpha_{\text{MX1}}\) through \(\alpha_{\text{MXN}}\) to adjust the discharging rate for each battery module to achieve SOC balancing, while keeping \(\lambda_{\text{DC1}}+\lambda_{\text{DC2+}}\ldots+\lambda_{\text{DCN}}=1\) such that the bus voltage is always regulated as \(V_{\text{bus-ref}}\).
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Significance of IoT-Based Healthcare-Monitoring Systems  
Researchers and medical experts are paying close attention to the development of monitoring systems for healthcare. Numerous successful research initiatives in this field have been taken, and many more are now underway [55]. The gap between people treated by healthcare professionals and those in need of treatment is constantly increasing, owing to the fast-expanding population of elderly persons and patients with chronic conditions. The main disadvantage is that healthcare is only available in hospitals; as a result, it is unsuitable for the elderly and impaired persons, and it cannot always satisfy their needs [56]. The IoT, through sensor values and telecommunications, provides an effective and practical answer to the issue of real-time monitoring of the health state of the elderly. It has been demonstrated that the IoT, in conjunction with smart technology, may deliver a variety of upgraded and expanded services. Researchers have created a variety of emergency systems employing sensors and technology that enable intelligent and distant wireless communication. These technologies have been employed in a variety of medical applications, most notably in monitoring the health of elderly people. By recording crucial vital signs, data on general health and risky circumstances may be acquired [57].
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 5 Performance and Complexity Analysis  
In this section, we present the functional correctness and complexity analysis of the proposed architecture for different SNR, WL, and PSS down-sampling factors. We specifically focus on the PSS Search, SSS Search, and DMRS Search blocks using the data samples generated by the transmit PHY. We consider the wireless PHY with specifications in Section II.A and Additive White Gaussian Noise (AWGN) channel between BS and UE. For comparison, we use reference 3 GPP 5G CS PHY with \(D_{PSS}=1\) and floating-point WL.