esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## 2 Materials and Methods  
Figure 1 shows the machine learning-based framework for creep rupture life prediction. This study aims at establishing a high-accuracy machine learning-assisted creep life prediction model by screening critical features from a set of a large number of variables. The machine learning procedure that is applied in the current research contains five primary steps, namely, dataset construction, data preprocessing, feature screening, model training and evaluation, and final prediction. In the beginning, creep data for modified 9 Cr-1 Mo steel are collected from a variety of sources, and the input features associated with the target variable, namely creep rupture life, are determined. Specifically, the determination of input features should be based on both physical mechanisms and experimental observation. The constructed dataset is then preprocessed by normalization and logarithmic transformations, which helps to increase modeling accuracy. Before training the machine learning model, feature screening is performed with the primary goal of identifying the critical features that influence the model prediction via correlation filtering and sequential feature selection methods. After determining a critical number of important features, several machine learning algorithms are employed for model training with k-fold cross-validation. The most appropriate model is determined by evaluating some error indicators of various models, including the coefficient of determination (\(R^{2}\)) and root mean squared error (RMSE). Finally, the developed model is applied for the prediction of creep rupture life using new data, and the predicted results are compared with the actual values.  
Figure 1: Machine learning-based framework for creep rupture life prediction.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 2 Proposed Approach
### Low-Power Hardware Development
#### 2.1.2 Interfaces and Data Transmission  
The RPI Zero 2W is equipped with various interfaces, including I2C, SPI, and USB, offering flexibility in connecting peripheral devices. Since the MCU natively supports only one USB connection, a USB 2.0 high-speed four-port hub controller (FE1.1S, Terminus) was integrated to expand this capability, allowing up to four USB devices to be connected simultaneously [32]. Among these connected devices were a USB webcam and an LTE CAT M1 modem. The selected webcam, a Logitech C920 Pro, provides high-quality 1080p (Full HD) image capturing with features such as auto focusing [33]. The LTE CAT M1 modem (WD-N522S, Woornient) facilitates real-time data transmission by connecting the system to a cloud server [34]. The LTE modem is particularly suited for IoT applications due to its relatively low power consumption of 150 mA and broad network coverage, making it ideal for remote monitoring. The data are transmitted from the system to the cloud server using the Remote Network Driver Interface Specification (RNDIS) protocol of the LTE modem. The data size is approximately 2 KB, requiring minimal bandwidth for transmission. The printed circuit board (PCB) layout of the proposed system integrates various components into a compact design optimized for efficient operation. As shown in Figure 3, the PCB  
Figure 2: Schematic diagram of designed EDS.  
includes key sections such as the RPI Zero W2, the EDS circuit, the LTE modem connector, and the charging circuit.
## 4 Optimization of LT-MZI Structure
This observation underscores the pivotal role that waveguide dimensions, which in turn contribute to the total path length of the device, play in shaping its spectral characteristics. In the design of an LT-MZI, the gap between the two waveguides of a 3 dB directional coupler significantly impacts the power division across the wavelength spectrum. The spacing between the waveguides determines the coupling strength between them, which in turn affects the transfer of power from one waveguide to the other. In the context of a wavelength spectrum, variations in g can lead to changes in the interference pattern observed within the coupler. When g is small, i.e., 100 nm, the FSR of the LT-MZI is 57 nm, which increases beyond 80 nm as g approaches 175 nm (see Figure 4d-g). Therefore, precise control over the waveguide separation is crucial for optimizing the power division and maintaining spectral integrity in the LT-MZI structure.
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 3 Methods
### Data Processing
#### 3.1.2 Ground Data  
Soil samples were collected from the surface at a depth of 0-5 cm. The geolocation for each sampling point was recorded by a GPS device (GPSmap 76, Garmin Company, Olathe, KS, USA) which has an acceptable accuracy (\(\pm\)1.45 m). A total of 133 soil samples were collected from 33 preselected observation sites from May 2018 to October 2019 (Figure 1c). Ten grams of dried and crushed soil sample was weighed and 50 mL of deionized water was added, mixed well, and allowed to stand. After the solution was layered, a conductivity meter (DDSJ-308A, [PERSON], Yidian Co., Ltd., Shanghai, China) was used to measure the EC value of the supernatant. Then, repeated EC measurements were averaged for each observation site. Linking the soil sampling sites and Landsat imagery was inappropriate due to the high spatio-temporal heterogeneity in the sampling area. As a result, the measured EC values at each site were estimated from the corresponding SI in the high spatial resolution Sentinel 2 imagery collected on 22 April 2019, when the area was uncropped and unfrozen. The statistical crop areas of Bayannur city, including the planting area for spring wheat, corn, and sunflower, were available at the Statistics Bureau of Bayannur City ([[http://tij.bynr.gov.cn/](http://tij.bynr.gov.cn/)]([http://tij.bynr.gov.cn/](http://tij.bynr.gov.cn/)), accessed on 31 August 2021). Notably, most cropland in Bayannur City was in the Hetao Irrigation District. Therefore, the statistics of Bayannur city were used to validate the results.
## 5 Conclusions  
This study is novel because it explores the transcriptome of _S. mycoparasitica_ during biotrophic mycoparasitism with mycotoxicigenic _Fusarium graminearum_, with an emphasis on the analysis of the ATP-binding cassette (_ABC_) transporters. The findings, through phylogenetic analyses and specific functional annotations, provide valuable clues to better understand the biotrophic mycoparasitism of _S. mycoparasitica_ interacting with the _Fusar_iium_ host, which implies that the ABC transporters group of key proteins are involved in S. _mycoparsitica_ virulence and multidrug resistance to toxic substances, including cellular detoxification. Further, diversified ABC family transcripts (-B, -C, -F, and -G) might contribute to enhanced _S. mycoparsitica_ adaptability to the hostile _Fusarium_ host environment or stress factors in an active way of a defense system. It appears that genus-specific selection also contributes to the fungal lifestyle and multiple host transitions. The comparison of hypocreaceous _Trichoderma_ and _Clonostachy_ myocarditis being generalists with melanosporaceous _Sphaerodes_ specialists being adapted to control _Fusarium_ pathogens and associated mycotoxins, suggests that fungal adaptation often requires a selected number of core genes to regulate a particular mycoparsitic lifestyle. A comparative analysis of ABC family genes reveals critical differences between various microparasitic lifestyles, while also pinpointing distinctive evolutionary advantages of _S. mycoparsitica_-specific mycoparsitism, thus, opening new directions for future research on biocontrol of mycotoxicigenic Fusarium pathogens.  
[PERSON] and [PERSON] equally contributed to conceptualization, methodology, investigation, data analysis and interpretation, writing--original draft preparation, writing--review and editing: [PERSON] conceived project idea, acquired funding, and supervised. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a National Sciences and Engineering Research Council of Canada (NSERC) Discovery grant, grant number RGPIN-2017-05286 and the Government of Saskatchewan Agriculturale Development Fund (ID 20160226), and the Saskatchewan Wheat Development Commission (171025-65) external to [PERSON]. [PERSON].  
Not applicable.  
The RNA sequence data are available in NCBI with accession PRJNA757970.  
The authors would like to thank anonymous reviewers of this paper and [PERSON] (WCVM) for technical TEM assistance.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 TKIFRPM Approach  
This section provides a detailed discussion of the proposed top-\(K\) identical frequent regular pattern mining (TKIFRPM) approach for producing topmost-\(K\) IFRs. The approach is able to perform mining on online datasets. The overall TKIFRPM approach/methodology and algorithm are shown in Figure 1 and Algorithm 1 respectively. The TKIFRPM algorithm receives online committed transaction \(tr\) in step 1. The committed transaction \(tr\) is copied into transactions buffer \(tb\) in step 2. Step 3 checks whether the transactions buffer \(tb\) is full or not. If the transaction buffer \(tb\) is full, step 4 applies an online synopsis or summary data structure (explained in Section 4.1), and the synopsis data structure ISI-table is updated when the transaction buffer \(tb\) is found to be full. Step 5 receives all distinct support K 1-itemsets from the ISI-table in the candidate list \(C_{l}\). Step 6 calls the top-\(K\) IFRs mining search procedure to return \(K\) topmost distinct support and regular itemsets of a size greater than 1. Step 8 and 9 return the itemsets in the top-\(K\) list for the current buffer \(tb\).  
```
\(r\):=Incoming transaction  Input: \(t_{b}\):=Trasaction buffer \(\theta_{r}\):=Regularity threshold \(\theta_{s}\):=Support threshold, initially zero ISI - tables :=Item support index table (0) flag = 0 // for the merge sort to be performed for the first time on ISI-Tables (1) while ((\(t_{r}=read\ (\ ))!=\varnothing\)) \(t_{b}\ \cup\ t_{r}\) (2) if (\(t_{b}\) is \(full\)) (4) update\(\_1 SI\_tables\{ ISI-tables,t_{b},\ flag\)) \(flag=1\ //\) to perform binary search when tb is full (6) \(C_{l}\leftarrow\varnothing\), \(F_{l}\leftarrow\varnothing\), \(F_{l}\leftarrow\varnothing\), \(Select\_K\_item\ (\{ ISI-tables,\ K,\ C_{l}\,\theta_{r}\ )\) (8) Gen\(\_\)Top-\(KIFRs\)(\(F_{l},\ C_{l},\ \theta_{r},\ \theta_{s},\ top-K\ List\)) (9) Output \(top-K\ List\);
```  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**K** & **Itemsets** & **Regularity/Periodicity** & **Support** \\ \hline
1 & d & 3 & 15 \\ \hline
2 & b & 3 & 13 \\ \hline
3 & a & 3 & 12 \\ \hline
4 & g, h & 2 & 7 \\ \hline
5 & bh, ac, gh & 2 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Top-5 \(IFR\) set after mining batch A and B.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**K** & **Itemsets** & **Regularity/Periodicity** & **Support** \\ \hline
1 & b & 3 & 8 \\ \hline
2 & e, g, h & 2 & 7 \\ \hline
3 & d, be, bh & 3 & 6 \\  & gh & 2 & 6 \\ \hline
4 & a, bg, eh, beh, bhg & 3 & 5 \\  & eg & 2 & \\ \hline
5 & ed, beg, egh, begh & 3 & 4 \\  & gd & 2 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Top-5 \(IFR\) set after mining batch A.
# Validation of the Developed Psychoacoustic Model for Sound Quality Valuation of Washing Machines
## 2 Materials and Methods
### Determination of Sound Metrics  
Objective evaluation of sound quality was performed by the determination of psychoacoustic sound metrics. For the validation process, the same sound metric was used as in the process of developing the psychoacoustic model. For the research, five psychoacoustic parameters were selected (Loudness, Sharpness, Roughness, Fluctuation strength, and Tonality), see Table 1. The psychoacoustic parameters were calculated and determined using Artemis Suite 11.0 software from the Head Acoustic.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{28.5 pt} p{28.5 pt}} \hline
**Metrics** & **Units** & **Definition** \\ \hline \multirow{2}{*}{Loudness} & \multirow{2}{*}{Sone} & \multirow{2}{*}{Loudness is a subjective characteristic of a sound (as opposed to the sound pressure level in decibels, which is objective and directly measurable). Its unit is “Sone”, a one some loudness corresponding to a level of 40 dB for a 1 kHz tone.} \\ \cline{3-3}  & & & Sharpness is a measure of the high-frequency content of a sound; the greater the proportion of high frequencies, the “sharper” the sound. Its unit is the “Accum”, one Acum corresponding to the sharpness of a broadband noise centered on 1 kHz, with a width of 1 critical band and a level of 60 dB. \\ \hline \multirow{2}{*}{Roughness} & \multirow{2}{*}{Asper} & \multirow{2}{*}{Roughness has been used to partially quantify sound quality. The unit of measure is the “Asper”. One asper is defined as the roughness produced by a 1000 Hz tone of 60 dB, which is 100\% amplitude modulated at 70 Hz.} \\ \hline \multirow{2}{*}{Fluctuation strength} & \multirow{2}{*}{Vacil} & \multirow{2}{*}{Fluctuation strength is similar to roughness, though at much lower modulation frequencies.} \\  & & & Fluctuation strength is expressed in units of “Vacil”. The reference value for one vacil is a 1 kHz tone, fully modulated at 4 Hz, at a sound pressure level of 60 dB. \\ \hline \multirow{2}{*}{Tonality} & \multirow{2}{*}{t.u.} & \multirow{2}{*}{Tonality is a sound quality metric aimed at identifying and quantifying the strength of tones in a given noise spectrum. Classic duality produces a value in “Tonality units” (t.u.) on a scale from 0 to 1.} \\ \cline{3-3}  & & & \\ \end{tabular}
\end{table}
Table 1: Psychoacoustic metrics used in objective analysis.  
Figure 1: Recording the sound of the washing machines in a semi-anechoic chamber.
# A Planer Moving Microphone Array for Sound Source Localization
###### Abstract  
Sound source localization (SSL) equips service robots with the ability to perceive sound similarly to humans, which is particularly valuable in complex, dark indoor environments where vision-based systems may not work. From a data collection perspective, increasing the number of microphones generally improves SSL performance. However, a large microphone array such as a 16-microphone array configuration may occupy significant space on a robot. To address this, we propose a novel framework that uses a structure of four planar moving microphones to emulate the performance of a 16-microphone array, thereby saving space. Because of its unique design, this structure can dynamically form various spatial patterns, enabling 3D SSL, including estimation of angle, distance, and height. For experimental comparison, we also constructed a circular 6-microphone array and a planar 4 \(\times\) 4 microphone array, both capable of rotation to ensure fairness. Three SSL algorithms were applied across all configurations. Experiments were conducted in a standard classroom environment, and the results show that the proposed framework achieves approximately 80-90% accuracy in angular estimation and around 85% accuracy in distance and height estimation, comparable to the performance of the 4 \(\times\) 4 planar microphone array.  
sound source localization; active microphone array +
Footnote †: journal: Applied Sciences
# Pilot-Assisted OFDM for Underwater Acoustic Communication
###### Abstract  
Multicarrier techniques have made it possible to wirelessly transmit data at higher rates for underwater acoustic (UWA) communication. Several multicarrier techniques have been explored in the past for wireless data transmission. OFDM is known to fight off inter-symbol interference due to the orthogonality of its subcarriers. However, due to time variations, OFDM suffers from intercarrier interference. As the UWA channel is both a time and frequency variant, channel estimation becomes complex. We propose a pilot-based channel estimation technique and explore two equalizers for improving the error performance of an OFDM-based UWA system. Both the equalizers employ pilot subcarriers to estimate the UWA channel. One equalizer is a least squares (LS) equalizer and the other is a zero forcing (ZF) equalizer. Using computer simulations, it is observed that, for an acceptable error performance, the number of pilots should be one-fourth the number of subcarriers. Moreover, if the energy of the pilots is increased without changing the overall symbol energy, the error performance degrades. It is also noted that both the LS and ZF equalizers give an acceptable error performance with the ZF performing marginally better than the LS. Furthermore, the error performance of the proposed system is evaluated as a function of the transmitter-receiver distance and an acceptable error performance is observed even at 1250 m.  
channel estimation; OFDM; underwater acoustic communication; pilots; equalization; zero forcing; least squares estimation +
Footnote †: journal: Marine Science and Engineering  
1
Footnote 1: email: [EMAIL_ADDRESS]
# A Versatile Shared-Aperture Antenna for Vehicle Communications
## 1 Introduction
Furthermore, various types of antennas have been designed for this purpose, including Yagi antennas [25], pattern-reconfigurable antennas [26], high-gain patch antennas [27], and even phased-array antennas [28]. Figure 1: Vehicular communication network diagram. In this paper, a multi-functional shared-aperture vehicular communication antenna is proposed, and for the first time in the literature, it proposes a structure of reconfigurable antenna and traditional antenna with shared aperture, comprising a microstrip antenna with rotating feed for GPS applications, a circularly polarized cross-dipole antenna for S-band satellite communication, and a pattern reconfigurable antenna tailored for V2V applications. It is proposed to design a 2 \(\times\) 2 microstrip antenna array with a half-wavelength array spacing, which is joined by four S-band circularly polarized antennas and a 5.9 GHz pattern reconfigurable antenna, with high isolation between these antennas and little mutual influence. The pattern reconfigurable antenna achieves scanning capabilities around the perimeter with a single antenna, resulting in higher antenna reuse. To further measure the design difficulty of the shared-aperture antenna, we use the antenna port density as a measure, because the higher the density, the greater the difficulty in achieving isolation between shared-aperture antennas. The antenna port density is the number of antenna ports divided by the area of the antenna array calculated at the lowest frequency wavelength. The port density of this design is \(9/\lambda^{2}\), which indicates that the antenna has a super compact structural design and high design difficulty. Following comprehensive antenna testing, it is demonstrated that the performance of this antenna can effectively meet the diverse communication requirements of automobiles.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 4 Secure IoT Communication and Attack Mitigation
### IPv6 Protocol and IoT Communications
#### 4.1.3 IPv6 Communication Infrastructure for IoT  
Deploying IPv6 for the IoT requires upgrades across network infrastructure, including routers, switches, firewalls, network management systems, and the DNS. Dual-stack technology, supporting both IPv4 and IPv6, is commonly used during the transition period.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indicator** & **Projection** \\ \hline
**Cellular IoT connections over IPv6** & 98\% CAGR from 2020 to 2025 \\ \hline
**Global IPv6 IoT devices** & \textgreater{}50\% by 2023 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Growth indicators for IPv6 IoT adoption.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Advantage** & **Description** \\ \hline
**Addresses global IoT device growth** & Supports massive number of IoT devices \\ \hline
**Efficient address auto-configuration** & Using SLAAC \\ \hline
**Hierarchical allocation** & For route aggregation \\ \hline
**Simplified addressing** & No NAT required \\ \hline
**Enhanced mobility** & End-to-end IP connectivity \\ \hline \hline \end{tabular}
\end{table}
Table 7: Advantages of expanded addressing in IPv6.  
Tunneling mechanisms like 6 over4, 6 in4, and 6 RD allow IPv6 packets to run over existing IPv4 networks. Cellular networks have rapidly adopted IPv6 to support the growth of cellular IoT connections, using carrier-grade NAT to handle IPv4 address exhaustion. IoT platforms and services also need to evolve to support IPv6 connectivity with devices. Cloud, analytics, and application platforms enable the leveraging of sensor data. IoT devices like sensors, cameras, appliances, etc., need IPv6-capable network stacks to connect over the IPv6 infrastructure.  
There are some challenges in IPv6 deployment, including knowledge gaps, buggy IPv6 stack implementations, a lack of IPv6-compliant services, and perceived security concerns. Thorough planning, testing, and training are required for smooth IPv6 deployments, although the long-term benefits outweigh the initial hurdles for supporting massive-scale IoT growth.  
The expanded addressing capability and advanced functions of IPv6 deliver critical benefits for connected IoT devices and platforms. The progressive deployment of IPv6 across networks, services, and devices will empower tremendous IoT innovations in the future.
# A Method for Modelling Business-Critical Architecture Decisions in Engineer-to-Order Companies
## 2 Theoretical Background
### Integration Cross-Functionally and Across the Value Chain  
The importance of understanding dependencies across different functional disciplines and across the value chain is highlighted by [PERSON] et al. [13]. They describe in this paper how the poor integration of product variety with a multitude of cross-value-chain and cross-functional activities can have significant impacts on the costs, lead times, or other business-critical parameters:  
1. The poor integration between product variety and downstream product lifecycle phases, such as maintenance activities, can negatively impact product performance (e.g., customers of wind turbine manufacturers are willing to pay a higher one-time manufacturing price if their maintenance costs can be reduced over a 30-year lifespan);
2. Product variety can lead to a poor integration between mechanical engineering and other disciplines (e.g., offshore structures, such as oil rigs, have a significant integration dependency between mechanical and civil engineering);
3. Design variety drives fewer resources in manufacturing in companies with low product volumes, such as ETO companies. Due to these low volumes, they do not see the traditional benefits of economies of scale.  
Better integration for such cross-functional dependencies has been studied in several areas. For instance, the framework proposed by [PERSON] et al. [14] offers a practical approach to structuring an analysis of how product design impacts the commissioning activities of ETO companies. The focus of aligning the product principles with commissioning performance is highlighted in this paper as a useful means for improving the integration between the two different functional areas of product design and commissioning. [PERSON] et al. [15] supported the related findings through a case study on the test and qualification activities in the architectural design of satellite propulsion systems. Additional methods for cross-functional integration are explored in Section 2.3. Nevertheless, the two aforementioned papers did not explicitly explore the potential of prioritising such architecture decisions on commissioning according to business criticality.