esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Residual Stress in Surface Layer on Plastic Yield Inception
## 2 Methods
The simulation is performed in two steps. The first step is to introduce residual stress in the surface layer by setting a thermal mismatch. Thermal stress is a key source of residual stress in the surface layer. Thermal stress in the surface layer can be calculated using the following expression [6]:  
\[\sigma=\frac{E_{l}}{1-\theta_{l}}(\alpha_{s}-\alpha_{l})\Delta T \tag{1}\]  
where \(E_{l}\) and \(\theta_{l}\) are the Young's modulus and Poisson's ratio of the surface layer, respectively, and \(\alpha_{l}\) and \(\alpha_{s}\) are the coefficients of thermal expansion (CTE) of the surface layer and the substrate, respectively. In the simulations, \(\alpha_{s}\) was set to be zero, and \(\alpha_{l}\) was set to a nonzero value. By changing the temperature difference \(\Delta T\), the magnitude and state of the residual stress (tensile stress or compressive stress) in the surface layer can be controlled. Thus, equi-biaxial residual stress is induced in the surface layer, which is a typical residual stress state for films/coatings [24]. It should be clarified that the settings of \(\alpha_{l}\), \(\alpha_{s}\), and the temperature change are only used to induce residual stress in the surface layer and do not change the material properties or influence subsequent contact calculation. In the second step, a uniformly distributed load was applied, and the contact was simulated. The material parameters used in the finite element simulation are listed in Table 2. The critical load and locations for the yield inception with different residual stress levels, surface layer thicknesses, and material parameters were calculated. To facilitate the analysis of the results, the parameters and calculation results were nondimensionalized. The thickness \(t\) of the surface layer was nondimensionalized using the radius \(R\) of the hemisphere to obtain the dimensionless surface layer thickness \(t/R\). The residual stress \(\sigma_{r}\) was nondimensionalized using the yield stresses of the surface layer \(Y_{l}\) to obtain the dimensionless residual stress \(\sigma_{r}/Y_{l}\). The critical yield load of the contact systems with the surface layer was nondimensionalized, using the calculated results without the surface layer (pure substrate material) as the base value, to obtain the dimensionless critical load \(P_{c}/P_{c0}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Elastic Modulus (GPa)** & **Yield Strength (MPa)** & **Poisson’s Ratio** \\ \hline Surface layer & 200, 300, 400 & 600, 952, 1200 & 0.3 \\ Substrate & 100, 200 & 200, 600 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Material parameters used in the finite element simulation. Figure 3: Mesh of the computational domain.
## 1 Introduction
Due to the surface roughness and end hook, the milling steel fiber has a similar bond strength to crimped steel fiber, but it is markedly higher than hooked-end steel fiber. The indentation steel fiber has good mechanical anchorage which could transfer tensile stress to the matrix during the pull-out process, and in turn the matrix splitting may be caused [30]. The helix steel fiber with a flattened end would pull out in normal strength concrete, of 42-54 MPa; however, it would rupture in super-high-strength concrete than 134 MPa [28]. The twin-twisted steel fiber has a higher peak and residual pull-out load compared to the hooked-end steel fiber, and it can significantly improve the toughness and cracking resistance of concrete [31]. However, even though some comparative studies focused on several types of steel fiber, the bond properties were always evaluated separately and without a suitable method [28; 30], except for a study in which the pull-out energy per unit volume of steel fiber was used to compare the pull-out energies of steel fibers with different embedded volumes [30]. This leads to the fact that the manufacture of steel fiber is only based on a basic cognition of SFRC, and no in-depth studies have been done on the mechanism of synergistic working of steel fibers with concrete matrix. Therefore, SFRC can be produced with many kinds of steel fibers. This causes confusion on how to select a suitable steel fiber to reach the expected properties of SFRC. Inherently, a reliable bond of steel fiber in concrete matrix is the foundation of steel fiber reinforcing concrete. The evaluation of bond properties is the focus of this study, and so is optimizing the morphology and geometry of steel fiber and the economic use of steel fiber. Based on the preliminary study of a multi-index synergistic revaluation of bond behavior of single hooked-end steel fiber [32], an experimental study was carried out on the bond properties of different deformed steel fibers in this paper. Meanwhile, to reflect the reasonable composition, workability, and hardened performance of the mortar in concrete, and to simulate the bond condition of steel fibers in SFRC, the mortar used in this study was wet sieved from fresh self-compacting SFRC. Fourteen trials of pull-out specimens were prepared with different types of deformed steel fibers, including hooked-end, crimped, indentation, milling, and large-end. After that, the synergistic bond properties of per unit weight steel fibers that corresponded to the loading conditions of cracking resistance, normal serviceability, and ultimate bearing capacity of concrete were evaluated. Finally, the adaptability of different deformed steel fibers was proposed with the expectation of reinforcing the strength and toughness of concrete.
## 4 Design and Implementation
### High-Level Design
```
1:def ESH_search(key):
2:#get the target_segment and target_bucket
3:hashval=hash(key);
4:try:
5:target_segment = get_segment(hashval)
6:#check current state
7:target_bucket = get_bucket(hashval)
8:if get_bucket(hashval)!= target_bucket then
9: goto retry:
10:# activate the bucket lock
11:endif
12:lock = target_bucket.state
13:if lock not set then
14: goto retry:
15:search_result = target_bucket.search(key)
16:if search_result is not in target_bucket.search(key) then
17:# in case overflow was there, search in the segment. 18:search_result = target_bucket \(\leftarrow\) next
19:target_segment.target_bucket \(\leftarrow\) next
20:endif
21:search_result = target_bucket.search(key)
22:endif
23:if search_result is not NULL then
24:return search_result;
25:else
26: search_result = NULL
27:endif
28:return search_result
```  
**Algorithm 2** ESH key-value search algorithm
## 2 Modal Analysis and Natural Frequencies
### Definition  
Modal analysis is a common technique for characterizing the dynamic behavior of a structure through its modal parameters, including natural frequency, damping factor, modal mass, and mode shape. This analysis can be conducted either experimentally or mathematically [10]. In mathematical modal analysis, the goal is to decouple the structural equations of motion, allowing each equation to be solved independently. When exact solutions are not feasible, numerical methods like finite-element or boundary-element approaches are employed to approximate the results. In experimental modal testing, the structure is excited by applying a measured force at one or more points, and the resulting responses are recorded at specific locations. These measurements are used to develop frequency response functions.  
Natural frequencies and mode shapes are two key aspects of modal analysis. Natural frequencies are the precise frequencies at which an item vibrates spontaneously when it is disturbed. For example, when a guitar string is plucked, the sound is caused by the natural frequency at which the string vibrates. Mode shapes depict how an item moves or changes shape while vibrating at a natural frequency. For example, consider a drumhead. It swings up and down in a rhythmic way and vibrates when contacted. The drumhead's mode shape at a specific natural frequency is represented by this motion.
###### Abstract  
As industrial systems grow larger and more interconnected, timely fault detection is essential to minimize downtime, enhance reliability, and reduce costs. However, conventional methods focus on reactive maintenance, limiting their ability to detect faults before escalation. Additionally, fault propagation in large-scale systems can degrade detection performance. To address these challenges, we propose an auto-associative shared nearest neighbor kernel regression method for fault detection in complex industrial processes. Inspired by attention mechanisms, the proposed approach assigns higher weights to relevant training data. Shared nearest neighbor is used to assess similarity between faults and training data, rescaling distances accordingly. These adjusted distances are then utilized in auto-associative kernel regression for fault detection. The performance of the proposed method is evaluated by applying it to benchmark data from the Tennessee Eastman Process and a real-world, unplanned shutdown case concerning a circulating fluidized bed boiler. The experimental results show that the proposed method can detect anomalies up to 2 h earlier than conventional fault detection methods.  
Keywords:fault detection; shared neighbor; auto-associative kernel regression; fluidized bed boiler +
Footnote †: journal: applied sciences  
Article
# An Improved Hilbert-Huang Transform for Vibration-Based Damage Detection of Utility Timber Poles
###### Abstract  
In this study, vibration based non-destructive testing (NDT) technique is adopted for assessing the condition of in-service timber pole. Timber is a natural material, and hence the captured broadband signal (induced from impact using modal hammer) is greatly affected by the uncertainty on wood properties, structure, and environment. Therefore, advanced signal processing technique is essential in order to extract features associated with the health condition of timber poles. In this study, Hilbert-Huang Transform (HHT) and Wavelet Packet Transform (WPT) are implemented to conduct time-frequency analysis on the acquired signal related to three in-service poles and three unserviceable poles. Firstly, mother wavelet is selected for WPT using maximum energy to Shannon entropy ratio. Then, the raw signal is divided into different frequency bands using WPT, followed by reconstructing the signal using wavelet coefficients in the dominant frequency bands. The reconstructed signal is then further decomposed into mono-component signals by Empirical Mode Decomposition (EMD), known as Intrinsic Mode Function (IMF). Dominant IMFs are selected using correlation coefficient method and instantaneous frequencies of those dominant IMFs are generated using HHT. Finally, the anomalies in the instantaneous frequency plots are efficiently utilised to determine vital features related to pole condition. The results of the study showed that HHT with WPT as pre-processor has a great potential for the condition assessment of utility timber poles.  
non-destructive testing; timber utility poles; vibration based damage detection; wavelet packet transform; empirical mode decomposition; Hilbert-Huang transform +
Footnote †: journal: Applied Sciences
## 2 Methods and Data
### Datasets  
In this study, we prepared two datasets for the performance comparison experiments (Table 1). The first dataset (hereafter referred to as the Landsat dataset) included simulated Landsat images. The subarea over Cheongyang and Gongju in South Korea, with a spatial extent of 9 km by 9 km, was extracted from a Landsat-5 image on 30 April 2013 (Figure 2a). This subarea was selected because there are various land-cover types, including a river, mountains, croplands, and built-up. The shortwave infrared (SWIR) band was experimentally selected as a target band, and the red and near infrared (NIR) bands at a 30 m resolution were used as fine resolution auxiliary variables by considering their reasonably good correlations to the SWIR band. The original 30 m SWIR band was aggregated to a 150 m resolution, and the aggregated SWIR band at a 150 m resolution was then regarded as a coarse resolution image. The final goal for the experiment using this dataset was to predict the 30 m SWIR band from the 150 m one using two 30 m bands.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Variable** & **Spatial Resolution** & **Remark** \\ \hline \multirow{4}{*}{Landsat} & SWIR band & 150 m & Target variable \\ \cline{2-4}  & Red band & 30 m & Auxiliary variables \\ \cline{2-4}  & NIR band & 30 m & \\ \hline \multirow{6}{*}{SM} & AMSR-2 soil moisture & 10 km & Target variable \\ \cline{2-4}  & NDVI & & \\ \cline{1-1} \cline{2-4}  & LST & & \\ \cline{1-1} \cline{2-4}  & Elevation & & \\ \cline{1-1} \cline{2-4}  & Cropland fraction & & \\ \cline{1-1} \cline{2-4}  & Forest fraction & & \\ \cline{1-1} \cline{2-4}  & Water fraction & & \\ \cline{1-1} \cline{2-4}  & Barren fraction & & \\ \cline{1-1} \cline{2-4}  & Built-up fraction & & \\ \cline{1-1} \cline{2-4}  & Latitude & & \\ \cline{1-1} \cline{2-4}  & Longitude & & \\ \hline \end{tabular}
\end{table}
Table 1: List of datasets used for spatial downscaling experiments (NIR: near infrared; SWIR: shortwave infrared; NDVI: normalized difference vegetation index; LST: land surface temperature).  
Real satellite-based SM products (hereafter referred to as the SM dataset) were used as the second dataset. Monthly AMSR-2 soil moisture products at a 10 km resolution between May and October from 2015 to 2017 over South Korea were used as the input coarse resolution images (Figure 2b). Ten variables were prepared to be used as fine resolution auxiliary variables. The two dynamic variables, which temporally varied, were the MODIS monthly normalized difference vegetation index (NDVI) and monthly LST products. The eight static variables, which remained unchanged over time, were elevation, the fractions of five land-cover types (cropland, forest, water, barren, and built-up), latitude, and longitude. The spatial resolution of all auxiliary variables with different spatial resolutions was set to 1 km. Thus, the output of the experiment using this dataset was the soil moisture value at 1 km.
## 3 Traffic Data Sensing and Gathering
### Machine-to-Machine Communications
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **SIGFOX** & **LoRa** & **Clean IoT** & \begin{tabular}{c} **NB LTE-M** \\ **Release 13** \\ \end{tabular} & \begin{tabular}{c} **LTE-M** \\ **Release 12/13** \\ \end{tabular} & \begin{tabular}{c} **EC-GSM** \\ **Release 13** \\ \end{tabular} & \begin{tabular}{c} **5G** \\ **(Targets)** \\ \end{tabular} \\ \hline \begin{tabular}{l} **Range/MCL** \\ **(Maximum Coupling Loss)** \\ \end{tabular} & \(<\)13 km & \(<\)11 km & \(<\)15 km & \(<\)15 km & \(<\)11 km & \(<\)15 km & \(<\)15 km \\  & 160 dB & 157 dB & 164 dB & 164 dB & 156 dB & 164 dB & 164 dB \\ \hline \multirow{3}{*}{**Bandwidth**} & Unlicensed & Unlicensed & \begin{tabular}{c} Licensed \\ 7–900 MHz \\ 200 kHz \\ \end{tabular} & \begin{tabular}{c} Licensed \\ 7–900 MHz \\ \(<\)900 Hz \\ \end{tabular} & \begin{tabular}{c} Licensed \\ 7–900 MHz \\ 200 kHz or \\ dedicated \\ \end{tabular} & \begin{tabular}{c} Licensed \\ 7–900 MHz \\ 200 kHz or \\ shared \\ \end{tabular} & \begin{tabular}{c} Licensed \\ 7–900 MHz \\ shared \\ \end{tabular} \\ \hline \begin{tabular}{l} **Data Rate** \\ \end{tabular} & \(<\)100 bps & \(<\)10 kbps & \(<\)50 kbps & \(<\)15 Mbps & \(<\)1 Mbps & \(<\)10 kbps & \(<\)1 Mbps \\ \hline \
\begin{tabular}{l} **Battery Life** \\ \end{tabular} & \(>\)10 years & \(>\)10 years & \(>\)10 years & \(>\)10 years & \(>\)10 years & \(>\)10 years \\ \hline
\begin{tabular}{l} **Availability** \\ \end{tabular} & Today & Today & 2016/2017 & 2016/2017 & 2016/2017 & 2016/2017 & Beyond 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Connectivity overview for M2M communications/M2M devices [44].
## 5 Active Coating and its Control Algorithm
### The Scattering Suppression System and Algorithm
#### 6.2.8. Calibration  
To ensure the accuracy of our measurement of acoustical pressure \(p(\mathbf{r},t\ )\), we need to have identical calibration within each bearing group. We assume that the output of each microphone depends linearly on acoustical pressure: \(P_{m}(t)=a_{m}\,p_{m}(t)+b_{m}\) ( \(m=1,2,...,N_{P}\)). Coefficients \(a_{m}\), \(b_{m}\) have slow temporal drift and need fast identification of them. The relation (like (18), (21), (29)) between time delay and media particle displacement is constant along the propagation of front plane border of wave. These relations give us parameters \(a_{m}\), \(b\)m,\(\ldots\), if the coordinates of microphones are known exactly and natural acoustical noise is very weak in comparison with the incident waves.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Data Imputation  
Once the dataset was collected, the next step was to analyze the missing samples and then extrapolate the missing samples using the data imputation technique. Figure 2 shows the flowchart for extrapolating the missing samples. It can the seen that the data imputation was performed in four major steps, which include missing samples identification, NULL values insertion, data segmentation, and data imputation.
## 3 Implementation
### Access Layer
#### 3.2.1 Web Console  
The most convenient way to access F3 BFS is by using the web console, which is typically hosted in a web browser. Through the web console, users can manage stored data, upload new datasets, manage the F3 BFS cluster, and perform routine cluster maintenance tasks.  
There are, however, some limitations of the data accessing feature in the web console, restricted by the capabilities of the web browser. For example, users cannot upload a dataset larger than 500 MB or a download dataset larger than 5 GB to a cluster in a web console, and the IO speed is also limited to 1 MB per second. Therefore, we only recommend that users perform lightweight tasks via the web console.
## 1 Introduction
The eBPF is a general execution engine that provides a general ability to execute specific code efficiently and safely based on system or program events, which enables user mode to implement customized functions; thus, it is very suitable for implementing network flow watermarking for high-rate traffic. As shown in Table 1, the following evaluation indicators are selected to compare the existing network congestion detection methods with the network congestion detection based on flow watermarking:  
1. Carrier: Indicating the detection method. 2. Whether to occupy extra bandwidth: Occupying extra bandwidth will cause the 'observer effect'. 3. Whether to modify the packet content: Modifying the packet content will bring security problems and additional switch overhead. 4. Detection speed: Reacting to the efficiency of network congestion detection. Figure 1: An example of the distortion of IBF. In this paper, we propose a fine-grained network congestion detection method-based flow watermarking, which is implemented in the eBPF environment; it improves the accuracy and speed of network congestion detection with low overhead and easy deployment, which provides theoretical and practical support for network management. The main contributions of this paper are as follows:  
* First, we design a network congestion detection model based on flow watermarking and theoretically analyze the network congestion detection method based on flow watermarking. The flow watermarking is applied to network congestion detection for the first time, which doesn't occupy extra bandwidth, change the payload of the packet, and increase the processing burden. * Second, we propose a fine-grained network congestion detection algorithm to solve the problem that the congestion caused by micro-burst traffic has the characteristics of short duration and rapid change, which makes it difficult to detect by conventional congestion detection methods; it can detect network congestion on a small time scale and we can obtain current network status by analyzing the watermark decoding information. * Third, we introduce eBPF into network congestion detection based on flow watermarking to enable it easy to employ. The experimental results show that the flow watermarking is sensitive to the changes in network status. The fine-grained network congestion detection algorithm can more accurately judge network status.
## 2 Materials and Methods
### Determination of Relative Contents of Volatile Organic Compounds (VOCs) in Rosa roxburghii Fruit  
The relative contents of volatile organic compounds (VOCs) in _R. roxburghii_ fruit were measured by the Gas Chromatography-Mass Spectrometry system (7890-5975C, Agilent Corporation, Santa Clara, CA, USA) equipped with a HP-5 MS column (60 m \(\times\) 250 um \(\times\) 0.25 um, Agilent, USA). The details of this method were as follows:  
Sample pretreatment: ground and mixed _R. roxburghii_ fruit (2.0 g) was placed in a 20 mL headspace vial. Boiling distilled water (4.0 mL) and phenethyl acetate internal standard solution (10.0 \(\upmu\)L) were added, followed by sealing the vial with a screw cap equipped with a silicone headspace pad. The vial was then mixed in a vortex apparatus and each sample was measured three times.  
Headspace solid phase microextraction (HS-SPME) conditions: prior to chemisorption, the 50 um DVB/CAR/PDMS fiber (Stabelflex, 24 Ga, Manual Holder, 3 pk, Santa Clara, CA, USA) in the extraction head (HP-5 MS) was subjected to a 30 min pretreatment at 240 \({}^{\circ}\)C to ensure the absence of residual substances. Subsequently, the fiber was exposed to the headspace vial containing the pre-treated sample, and was desorbed and analyzed at 50 \({}^{\circ}\)C in a water bath with stirring speed of 300 rpm for 30 min. Following this, the GC injector was introduced for 3 min for desorption and analysis.  
Gas chromatography (GC) condition: an HP-5 capillary column (60 m \(\times\) 250 um \(\times\) 0.25 um, Agilent, USA) was utilized with an inlet temperature set at 250 \({}^{\circ}\)C. The temperature ramp-up procedure consisted of an initial temperature of 40 \({}^{\circ}\)C for 3 min, followed by a ramp to 80 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, further increasing to 160 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min with a 30 s hold, then progressing at 2 \({}^{\circ}\)C/min to 175 \({}^{\circ}\)C, and finally at 10 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, maintaining for 7 min. The analysis employed a non-shunt injection mode, using high purity helium carrier gas (purity \(\geq\) 99%) with a solvent delay time of 7 min.  
Mass spectrometry (MS) conditions: an electron bombardment (EI) ion source was utilized with an electron energy of 70 eV. The ion source temperature was set at 230 \({}^{\circ}\)C and the quadrupole temperature at 150 \({}^{\circ}\)C. A full scanning mode was chosen with a mass scan range (\(m/z\)) of 45 to 500 amuQualitative and quantitative analysis: the results were initially analyzed using Chemistry Book and NIST (National Institute of Standards and Technology Standard Reference Database) database queries. Simultaneously, the Retention Index (RI) of C\({}_{7}\)\(\sim\)C\({}_{40}\) normal alkanes was calculated through chromatographic scanning to further identify the VOCs in _R. roxburghii_ fruit. The VOCs were quantified using phenethyl acetate as the internal standard, and the relative content of each compound was determined by comparing it with the area of the internal standard.