esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 1 Introduction
### Pedestal Looseness Failure  
Pedestal looseness failure occurs when the pre-tensioning force on pedestal bolts is lost, resulting in the relative displacement of the connecting parts and leading to failure [17].  
In the 1990s, [PERSON] [18] and [PERSON] [19] introduced a conventional model for failure in rotor bearing pedestal foundations. This model utilizes segmental linear stiffness and damping to explain the abrupt alteration in support stiffness and damping when failure occurs. Furthermore, [PERSON] [18] devised pertinent validation tests for this model, and the numerical and experimental findings exhibit strong concurrence. As a result, the majority of previous investigations on failures caused by the loosening of the rotor system foundation have utilized this model. [PERSON] [20] developed a model to study the loosening of a bearing assembly. The model is based on the traditional foundation loosening model and analyzes the time-frequency domain characteristics of the system's steady-state response. [PERSON] [21] utilizes the traditional loosening model to examine a twin-spool rotor system and investigates the impact of rotational speed on the dynamic behavior of the rotor system with the loosening bolt.  
Currently, the classical pedestal looseness failure model effectively simulates the actual bolt loosening in bearing seats. Comprehensive investigations of the twin-spool rotor-bearing system with looseness failure should be the primary focus of future research. Analyzing failure reaction patterns in various geometries, materials, and operating settings is part of this process. Conducting thorough experimental study and designing safe and feasible experiments for loosening failure are also crucial. In this article, we focus on comprehensively analyzing the transmission of vibration and the dynamic behavior variation of each component with different looseness parameters for a twin-spool rotor-bearing system.
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 2 Materials and Methods
### In-SAR Techniques  
In this study, the 20 Sentinel-1A SLC images were processed through the SARscape 5.2 module based on the ENVI platform. Before MT-InSAR processing, the whole Sentinel-1A images were cut to match the study area. Figure 2 is a flow chart showing the methodologies of the PS and SBAS techniques.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Acquisition Date** & **Perpendicular Baseline (m)** & **Temporal Baseline (Days)** \\ \hline
1 & 16 March 2017 & \(-\)43.33 & 624 \\
2 & 9 April 2017 & \(-\)62.63 & 600 \\
3 & 8 June 2017 & \(-\)26.47 & 540 \\
4 & 26 July 2017 & \(-\)19.04 & 492 \\
5 & 31 August 2017 & \(-\)57.05 & 456 \\
6 & 30 October 2017 & \(-\)66.49 & 396 \\
7 & 23 November 2017 & \(-\)49.25 & 372 \\
8 & 2 December 2017 & 98.54 & 336 \\
9 & 27 February 2018 & 14.07 & 276 \\
10 & 23 March 2018 & 13.47 & 252 \\
11 & 28 April 2018 & 39.54 & 216 \\
12 & 22 May 2018 & \(-\)58.30 & 192 \\
13 & 27 June 2018 & 45.53 & 156 \\
14 & 21 July 2018 & \(-\)73.93 & 132 \\
15 & 19 September 2018 & \(-\)65.98 & 72 \\
16 & 25 October 2018 & 82.42 & 36 \\
17 & 30 November 2018 & 0.00 & 0 \\
18 & 17 January 2019 & 8.88 & \(-\)48 \\
19 & 22 February 2019 & \(-\)9.50 & \(-\)84 \\
20 & 18 March 2019 & 38.60 & \(-\)108 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sentinel-1A single look complex (SLC) dataset for Tianjin study area (the perpendicular and temporal baseline is relative to the image received on 30 November 2018).  
Figure 2: Logical workflow for the persistent scatterer (PS) and small baseline subset (SBAS) methods. GCP stands for ground control point.
## 2 Materials and Methods
### Multi-Scale Feature Extraction Module  
ASPP (Atrous Spatial Pyramid Pooling) is a multi-scale feature extraction module. Aiming at the problem of resolution loss due to the change of image size in the traditional pooling and upsampling process, ASPP adopts parallel dilated convolution with different dilated rates to obtain receptive fields at different scales, which, combined with the global information extracted from the global pooling layer, realizes the multi-scale feature extraction under the condition of preserving the resolution. Its structure is shown in Figure 4. This module includes four parallel convolution steps and one global pooling step. In order to avoid extreme situations where the filter degrades and cannot capture contextual information when the dilation rate is close to the size of the feature map, a convolution layer with a kernel of \(1\times 1\) is set in parallel convolution operations, which can be regarded as a dilated convolution with a high dilation rate; and from this, features can be extracted.  
The feature maps input into the ASPP module are simultaneously processed into parallel convolutional and pooling layers for feature extraction. The first convolutional layer uses a \(1\times 1\) convolution kernel to perform convolution operations on the input feature maps, and the results are output after batch normalization. The feature maps are input into the second to fourth convolutional layers, which are convolved with the dilated convolutional layers with a convolution kernel of \(3\times 3\), and given dilated rates of 6, 12, and 18, respectively, to obtain the multi-scale features under different sizes of the receptive fields; the results are also normalized in batches to output a feature map. The pooling layer firstly uses adaptive mean pooling to compress the feature maps of each channel to \(1\times 1\), respectively, to extract the features of each channel, and then obtains the global features. Then it uses a \(1\times 1\) convolution kernel to further extract the features and reduce the dimension of the feature maps. Finally, the upsampling is carried out based on bilinear interpolation to restore the size of the feature maps. The extracted features are concatenated to obtain a feature map that is five times the size of the original feature map. A \(1\times 1\) convolution is used to reduce the number of feature map channels to the expected value and perform batch normalization after dimensionality reduction. Finally, the feature maps output by ASPP module is obtained.  
Figure 4: ASPP Multi-scale Feature Extraction Module.
# Focused Ultrasound in the Rehabilitation Setting:
## 4 Discussion and Future Directions  
This review addressed both lower-intensity and higher-intensity FUS applications. Higher-intensity FUS (HIFU) has been used primarily for thermoablation and mechanical disruption, such as in movement disorders (e.g., ET and PD), musculoskeletal (MSK) conditions, and neuropathic pain management (e.g., peripheral nerve reactivation in amputees, refractory pain in knee osteoarthritis through thermal denaturation of nociceptors). On the other hand, LIFUS has been employed for neuromodulation, including cognitive and motor rehabilitation, as well as psychiatric and neurological conditions. Examples include transcranial FUS for modulating cortical circuits to improve reaction times or reduce errors in cognitive tasks. LIFUS has also been explored for cancer-related neuropathic pain management with minimal side effects.  
Many studies about FUS reported preliminary results, emphasizing the need for further research to comprehensively analyze the potential of focused ultrasound (FUS). Notably, the absence of direct implications for rehabilitation in the reviewed literature should encourage researchers to explore this emerging field, particularly its applications for patients with various disabilities. Given the broad range of FUS applications, including chronic pain management, neuromodulation, and tumor ablation, numerous conditions could potentially benefit from this technology. However, expanding the body of literature is essential to better understand its efficacy, safety, and clinical utility across diverse patient populations.
## 3 Rule-Based Classification
### Definitions of Data Anomaly Classes
#### 3.1.3 Outlier  
The identification of the _Outlier_ class involves data points that deviate significantly from the rest of the measured data. Examples of the _Outlier_ class are shown in Figure 5. A statistical approach can be used to determine the _Outlier_ class. The initial step was to detrend the data to eliminate any underlying trends (e.g., constant, linear, or higher-order). Subsequently, all the peaks in the measured data were obtained. Three conditions were considered for classifying the time-history data to be classified as _Outlier_:  
* The magnitude of the maximum peak (\(\Downarrow_{p}\)) divided by the standard deviation of the time history (\(\sigma_{t}\)) is greater than a pre-defined threshold; \[r_{outlier,1}=\oint_{p}/\sigma_{t}\] (1)
* The magnitude of the maximum peak divided by the mean of the time history (\(\mu_{t}\)) is greater than a pre-defined threshold; \[r_{outlier,2}=\oint_{p}/\mu_{t}\] (2)
* A data segment was selected right after the maximum peak. The ratio of standard deviations of the data segment (\(\sigma_{d}\)) and standard deviation of the raw time history (\(\sigma_{t}\)) is less than a pre-defined threshold. \[r_{outlier,3}=\sigma_{d}/\sigma_{t}\] (3)  
Figure 4: Examples of the _Minor_ class.  
The third condition is necessary for differentiating the impulse response from an outlier in the time-history data.
## 2 Introduction to the Integral Response Deformation Method  
The computational model of the IRDM for underground structures is illustrated in Figure 1a. On the basis of the fundamental theory of this method, the seismic-induced loads acting on underground structures can be decomposed into three constituent components:  
(1) **Load induced by geotechnical foundation deformation \(\mathbf{f_{\text{E}}}\):**  
This component corresponds to the forces required to enforce free-field deformation at all the soil-structure interface nodes in a soil stratum finite element model with the structure removed, as illustrated in Figure 1b.  
(2) **Load induced by free-field stresses fs0:**  
This component originates from the stresses in the soil medium surrounding the structure. Specifically, free-field displacements are applied at the boundaries of the free-field model, whereas free-field inertial forces are imposed internally. The resultant nodal reaction forces at the interface \(\mathsf{S}f_{\text{s}}^{\,0}\) can be obtained through static analysis, as shown in Figure 1c.  
(3) **Structural inertial force:**  
This load represents the product of the structural mass and seismic acceleration (Figure 1d).  
In practical applications of the IRDM for determining the peak seismic responses of underground structures, the first two load components can be combined for calculation. This process is facilitated by the auxiliary free-field model illustrated in Figure 2, where the structural zone is replaced with free-field geological media. The implementation workflow is as follows:  
(1) **Impose the free-field relative displacements at the critical moment on the soil-structure interface of the free-field model;**2. Apply the free-field inertial forces at the critical moment to the geological media within the interface domain; 3. Perform a static analysis to obtain the nodal reaction forces at the soil-structure interface; 4. The interface reaction forces are combined with the structural inertial forces to formulate the equivalent seismic loads, and these loads are applied to the corresponding nodes in the SSI model; 5. Conduct a static analysis on the SSI model to derive peak seismic responses.
# Co-Design of Mechanical and Vibration Properties of a Star Polygon-Coupled Honeycomb Metamaterial
## 4 Comparison and Analysis of the Bandgap Properties
### Effects of the Configuration Parameters
#### 4.3.3 Distribution of the Inserted Pins  
Each unit cell of the SPCHM contains 14 rings, and this section discusses the influence of different numbers and assembly positions of pins on the bandgap distribution. Figure 9 plots the bandgap distributions of the three SPCHMs with different numbers and assembly positions of copper pins; there are two, five, and seven rings without pins installed, respectively. The first bandgaps are marked in red and the rest in orange.  
Compared to Figures 7 and 8, more bandgaps have been generated in Figure 9, especially in Figure 9c, where 11 bandgaps have been generated. This indicates that the number of installed pins has a great impact on opening bandgaps. However, with the increase in bandgaps from Figure 9a to Figure 9c, the bandwidth of the first bandgap becomes narrow and a contradiction exists between the width of the first bandgap and the quantity of bandgaps. In addition, all the dispersion curves are in the low-frequency region below 6000 Hz, making the proposed metamaterial suitable for wide engineering applications.  
This method can generate multiple combinations based on different numbers and installation positions of pins, and more bandgap properties will be discovered, which provides a design idea for forming various bandgaps and offers guidance and support for research of low-frequency wide-bandgap metamaterials. Furthermore, if installing different materials of pins in the chosen rings, a better bandgap characteristic may be found.  
Figure 8: Effect of pin size on bandgap of the SPCHM: (**a**) \(r_{1}=3\) mm; (**b**) \(r_{1}=3.5\) mm; (**c**) \(r_{1}=4\) mm. The first bandgaps are indicated in red and the rest in orange.  
Figure 9: Bandgap properties of the SPCHM with different number and position of pins: (**a**) two rings without pins; (**b**) five rings without pins; (**c**) seven rings without pins. The first bandgaps are marked in red and the rest in orange.
# BIM-GIS-Based Approach for Quality Management Aligned with ISO 9001
## 5 Results
### Creation of the Inspection Form  
A point layer, designated \"Quality form\", was created in QGIS to structure the data collected during on-site quality inspections. This layer includes six fields, each corresponding to specific information required for traceability and documentation. These are:  
* Quality control supervisor, which records the name of the inspector in charge of each verification;
* Status, which reflects the compliance result of the activity being inspected;
* Responsible, which identifies the person accountable for quality assurance;
* Comment, which allows inspectors to document additional notes or observations;
* Date, which captures the day the inspection was carried out;
* East coordinate and North coordinate, which record the geographic location of each inspection point using the WGS 84 coordinate system.  
These fields were grouped into attributes according to the three predefined control phases. When necessary, attributes were repeated to accommodate multiple checks within a single phase. A separate group was added for the mechanical strength tests, which are performed later in the process. The resulting configuration of fields and attributes is shown in Figure 12.  
The \"Status\" field was configured using the Value Map customization tool, allowing the inspector to select from three predefined outcomes: \"Complies\", \"Does not comply\", and \"Corrected\". These options appear as a dropdown list during form completion, as shown in Figure 13. While QField supports using structured input types such as lists, text boxes, date pickers, and numeric fields, it does not automatically validate the overall coherence or completeness of the entered information. Therefore, it remains the inspector's responsibility to verify that all data are accurate and fully recorded before submitting the form.  
Visibility constraints were applied to enhance usability during fieldwork so that only the attributes relevant to the current phase are displayed. Once the form was configured, the QGIS project was saved locally and synchronized using the QField Sync plugin. Access rights were assigned to the form creator and the field inspector through their QFieldCloud accounts, allowing collaborative editing and synchronization Figure 14.  
The structure of the inspection form incorporated several key indicators derived from ISO 9001:2015. These include the compliance status of each activity (complies, does not comply, corrected), the responsible party's identification, the date of inspection, and the documentation of nonconformities or additional observations. Spatial traceability was ensured through georeferenced coordinates for each inspection point, which were linked to specific construction activities. These indicators support process control, documentation, and traceability requirements defined by the standard and serve as the basis for subsequent monitoring and continuous improvement.
## 4 Centralized Algorithm
### CE-Based Scheduling Algorithm
#### 4.1.1 Initialization  
Let \(\mathbf{X}^{m}=[x_{(1)}^{m},...,x_{(n)}^{m},...,x_{(N_{\text{RB}})}^{m}]\) (\(x_{(n)}^{m}\in[0,\mathcal{U}_{m}]\)) denote a sample generated according to a given probability, which presents a possible scheduling result of TP \(m\). \(x_{(n)}^{m}=0\) means that no transmission is scheduled on RB \(n\) of TP \(m\). Otherwise, \(x_{(n)}^{m}\) is the ID of the scheduled UE. In the CE method, the scheduling problem is regarded as a stochastic procedure. The distribution of \(x_{(n)}^{m}\) is denoted by \(\mathbf{q}^{m,n}=[q_{0}^{m,n},q_{1}^{m,n},...,q_{(n,m)}^{m,n},...,q_{(n,m)}^{m,n}]\), where \(q_{u}^{m,n}=\mathbb{P}\{x_{(n)}^{m}=u\},u\in[0,\mathcal{U}_{m}]\). Obviously, \(q_{u}^{m,n}\) has the attributes of \(0\leq q_{u}^{m,n}\leq 1\) (\(\forall u,\ m,\ n\)) and \(\sum_{u\in[0,\mathcal{U}_{m}]}q_{u}^{m,n}=1\) (\(\forall m,n\)).  
For the sake of accelerating convergence, we design the initial probability distribution according to estimated data rates. We first defined \(\mathbb{P}\{x_{(n)}^{m}=0\}=\Pr_{0}\) where \(\Pr_{0}\) is a given value. This probability gives an opportunity to TP \(m\) for scheduling no transmission on RB \(n\). For each \(u\in\mathcal{U}_{m}\), the data rate \(R_{u}^{n}\) is estimated based on the fixed JT CoMP transmission, as given by (2), at the initialization stage. As suggested by C4 in (7), the transmission is considered to be unqualified if \(R_{u}^{n}<R_{\text{thres}}\). To save energy, the proposed algorithm sets the corresponding probability to be zero, _i.e._, \(q_{u}^{m,n}=0\). For the rest of the optional UEs, the probability is defined as \(q_{u}^{m,n}=(R_{u}^{n}/R_{\text{tot}}^{m})\times(1-\Pr_{0})\), where \(R_{\text{tot}}\) indicates the sum data rate of all possible qualified transmissions, defined as:  
\[R_{\text{tot}}^{m}=\sum_{\begin{subarray}{c}k\in\mathcal{U}_{m},\\ R_{k}^{n}\geq R_{\text{thres}}\end{subarray}}R_{k}^{n} \tag{9}\]  
Summarily, the initial distribution of \(x_{(n)}^{m}\) is:  
\[q_{u}^{m,n}=\begin{cases}\Pr_{0},&u=0\\ (1-\Pr_{0})\times R_{u}^{n}/\;R_{\text{tot}}^{m}\;,&u\in\mathcal{U}_{m},\text{ and }R_{u}^{n}\geq R_{\text{thres}}\\ 0,&u\in\mathcal{U}_{m},\text{and }R_{u}^{n}<R_{\text{thres}}\end{cases} \tag{10}\]
# A Short Review of Through-Silicon via (TSV) Interconnects: Metrology and Analysis
## 2 TSV Metrology
### Metal Fill
The temperature of the intact TSV starts to gradually increase, and then, in the center of the TSV sample, the temperature gradually stabilizes and gradually decreases. The overall result shows a certain symmetry (Figure 6a). For TSVs with filling deletion, the heat can only be conducted along the inner wall caused by the complete absence of copper, with the lowest temperature in the center where it is missing and the highest temperature around the inner wall (Figure 6b). For TSVs containing gaps, since the TSV copper pillars contain gaps, when heat is conducted to the defect, heat will continue to transfer along the inner wall of the gap, so heat will accumulate in the gap ring and the temperature will increase (Figure 6c). For TSVs with defects in the bottom cavity, the temperature starts to rise at the defects, then tends to be smooth, and finally drops slowly. Since there are holes in the bottom, there are some barriers to heat conduction. Heat is first conducted along the copper column of the TSV without holes (Figure 6d). Therefore, the presence of defects can be clearly identified by comparing the three defects with the complete TSV. In addition to this, [PERSON] and [PERSON] [36] proposed a multi-tone tuning jitter test method. First, the TSV defect equivalent circuit is obtained, and the multi-tone signal with Gaussian white noise is added as the excitation of the test simulation to extract the peak-to-average ratio for fault detection; the accuracy of this method can reach the micrometer level. [PERSON] et al. [37] used [PERSON]'s deep neural network algorithm to detect faulty TSVs in 3D-IC. Compared with the traditional random gradient algorithm, it has fast performance and a better convergence speed. The simulation results are outstanding in terms of area, line length, delay, running time, and temperature. Figure 6: Thermal images and 3D temperature distribution diagram. (**a**) Intact, (**b**) filling deletion, (**c**) containing gap, and (**d**) bottom cavity (adapted from [36]).
## 1 Introduction
The current study proposes an analytical method by which to assess the benefit of ESS implementation for F/R in electricity markets. First, to capture the basic benefit of ESS for F/R, we developed a method of predicting the SMP, which is the weighted mean of the fuel cost of a marginal plant. Second, we proposed a new scenario-based method to forecast utility economic benefits; this method considers both the electricity market structure and power system operations. The case study results show the diverse profile of the economic aspects of ESS implementation; one can readily infer from the results economic insights pertaining to large-scale ESS implementation. This study contributes to the literature on economics analysis and long-term SMP estimation. this study contributes to the literature on two perspectives. First of all, a benefits analysis of ESS for F/R is conducted in accordance with electricity market in South Korea. And the proposed methodology, Long-term SMP estimation doesn't require large time series data, therefore there shouldn't be too much difficulty with respect to data collection. The remainder of this paper is organized as follows. In Section 2, we develop a novel methodology for assessing the economic benefits of ESS implementation in the F/R electricity market, based on South Korea's national plan for long-term electricity supply and demand [24]. Section 3 addresses the simulation results by using the methods proposed in this study, while our conclusions are presented in Section 4.