esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Progress and Hydrodynamic Design of Integrated Motor Pump-Jet Propulsion
## 3 IMP Bearing and Hydrodynamic Design
### Hydrodynamic Design
#### 3.2.2. Cavitation  
Cavitation is a very important hydrodynamic phenomenon that occurs in IMPs and causes severe vibration, noise, material damage, and other problems. Consequently, cavitation in IMPs has piqued our interest throughout the years. Few scholars have summarized the related papers to boost research on cavitation in IMPs. Turbulence is involved in the majority of cavitation occurrences. Both the cavitation model and the turbulence-modeling approach influence the numerical accuracy of cavitating turbulent flow simulations. The latest advances in the study of cavitation in IMPs are reviewed in this section to provide a clear picture and detailed explanation of the methods for cavitation modeling as well as the accomplishments based on IMP thruster numerical simulations (see Table 4).  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Year** & **Reference** & **Geometry of the Model** & **Type of Cavitation/Vortex** & **Cavitation Model** & **Findings** \\ \hline \hline
**2009** & [66] & & & 1. & A vortex lattice approach (MPUF-3A) and a boundary (PROPCAV) & 1. & A vortex lattice approach (MPUF-3A) and (PROPCAV) \\  & & & & & (MPUF-3A) and boundary element (PROPCAV) \\  & & & & & (MPUF-3A) appeared to fairly cavitation and thrust \\  & &
# Mining Pool Selection under Block WithHolding Attack +
## 5 Numerical Results
### Results and Discussions
#### 5.2.1 No-Attack Case  
First, we consider the case without BWH attack (i.e., \(a_{12}=0,a_{21}=0\)). In this case, we set the minimum required hash powers of the two pools as \(\omega_{1}=30\) and \(\omega_{2}=20\) and the block sizes of both pools as \(s_{1}=s_{2}=100\). Under this parameter setting, we obtain an NE (\(0.4,0.6\)) by calculating (27) with \(a_{12}=0\). Figure 2(a) shows the corresponding phase portraits. The figure shows that all paths converge to the point \((0.4,0.6)\), indicating the stability of the NE obtained from theoretical analysis. This result is consistent with the simulation results in Figure 2(b) and the results in [24], indicating the correctness of the theoretical analysis. In addition, the ratio of the population of pool 1 to that of pool 2 in the stable population state is the inverse ratio of the hash powers of pool 1 to that of pool 2. This means that the revenue of a miner depends only on the required hash power of the pool to which he or she belongs in this case.  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(N\) & Number of miners & 5000 \\ \(M\) & Number of mining pools & 2 \\ \(C\) & Fixed revenue from the coinbase & 1000 \\ \(\rho\) & Transaction fee per unit block & 2 \\ \(T\) & Average block generation interval & 600 \\ \(p\) & Power charge required for unit hash power & 0.001 \\ \(\frac{1}{Tc}+b\) & Propagation delay parameter & 0.005 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings.
## 4 Characteristics and Models of Dynamic Parameters of GRS
### Dynamic Parameter Models of GRS  
Some scholars have proposed formulas for fitting the dynamic elastic modulus \(E_{d}\) and the number of loading cycles \(N\)[33; 44]. The form of the proposed formulas is the same (see Equation (8)). [PERSON] et al. [44] established the relationship between the dynamic elastic modulus \(E_{d}\) and the confining pressure \(\sigma_{3}\). [PERSON] et al. [33] explored the effects of frequency \(f\), dynamic load amplitude \(\sigma_{d}\), initial partial stress \(\sigma_{s}\), and confining pressure \(\sigma_{3}\) on \(E_{d}\).  
\[E=A\ln N+B \tag{8}\]  
where \(A\) and \(B\) are fitting parameters, and \(N\) is the number of load cycles.  
The model fitting parameters proposed above are related to the load and not to the soil properties. Therefore, the two models are presented to predict the dynamic shear modulus and damping ratio considering soil properties (D-W cycles and fine particle content).  
(1) Dynamic shear modulus model  
From Figure 16, it can be observed that the relationship between the dynamic shear modulus and the number of load cycles (\(N>20\)) can be approximated by a straight line through the transformation of \(G=\frac{G_{d}-G_{d10}}{G_{d1000}-G_{d10}}\). Therefore, all the data in Figure 16 are fitted according to the following formula:  
\[\frac{G_{d}-G_{d20}}{G_{d1000}-G_{d20}}=a_{G}\log(N)+b_{G},\;20\leq N\leq 1000 \tag{9}\]  
where \(G_{d20}\) and \(G_{d1000}\) are the dynamic shear modulus after 1 and 1000 loading cycles under C0 and T0. \({}^{\prime}a_{G}\)' and \({}^{\prime}b_{G}\)' are the fitting parameters and \(N\) is number of load cycles. The fitting results are shown in Figure 16 and Table 9.
# Analysis of Hydrological Characteristics of Blue Nile Basin, Nishe Watershed
## 3 Results and Discussion
### Model Calibration  
It is required to determine the calibration after determining the sensitive parameters on streamflow simulation at the outlet of the watershed. In the Nashe watershed, the model was calibrated using the sensitive parameters in the selected upstream, middle, and downstream sub-basins utilizing SUFI-2, GLUE, Parasol and PSO algorithms. The observed and simulated streamflow were compared using flow data of 1987-1999 and the results showed a good agreement. The objective functions show that the result is in an acceptable range based on the monthly flows at selected stations (Table 4). In the first two years (1985-1986) flow data were initially reserved as the start-up period to mitigate unknown initial conditions. A considerable number of studies have shown that the data series selected for model calibration and validation should be representative of the various phenomena experienced by the catchment [43,44].
# Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network
## 2 Problem Formulation  
Assume that a communication-limited network of \(N\) radars tracks a common target. The radar network is under malicious FDI attacks, which deteriorate the target estimation by injecting unknown and time-varying attack variables into the radar's measurements. In this paper, we propose a novel DSE method that can combat FDI attacks for radar networks and provide high accuracy target tracking results. In this section, we introduce the topology structure, target dynamics and radar measurement model.
## Materials and Methods
### Selection of Environmental Covariates
#### 2.3.4 Organism Covariates  
The normalized difference vegetation index (NDVI) data came from the National Ecological Data Center [[https://www.nesdc.org.cn/](https://www.nesdc.org.cn/)]([https://www.nesdc.org.cn/](https://www.nesdc.org.cn/)) (accessed on 19 July 2023), and the dataset was obtained by processing Landsat 5/7/8 remote sensing data based on the Google Earth Engine cloud computing platform. The vegetation net primary productivity (NPP), net ecosystem productivity (NEP), and gross primary productivity (GPP) data were collected from the National Earth System Science Data Center [[https://www.geodata.cn/](https://www.geodata.cn/)]([https://www.geodata.cn/](https://www.geodata.cn/)) (accessed on 1 November 2023). The climate production potential (CPP) was calculated using the Tharthwaite Memorial model in OpenGMS [[https://geomodeling.njnu.edu.cn/](https://geomodeling.njnu.edu.cn/)]([https://geomodeling.njnu.edu.cn/](https://geomodeling.njnu.edu.cn/))  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Type** & **Covariate** & **Abbreviation** & **Scale** & **Source** \\ \hline \multirow{8}{*}{Human activity (6)} & Topographic ruggedness & \multirow{2}{*}{TRI} & \multirow{2}{*}{30 m} & \multirow{2}{*}{Extracted from DEM data} \\  & index & & & \\  & Multiresolution index of & & & \\  & ridge top flatness & & & \\  & Multiresolution index of & & & \\  & valley bottom flatness & & & \\  & Land use & TDLY & 30 m & Third National Land Survey \\  & Nighttime light & NL & 500 m & [[https://www.geodata.cn/](https://www.geodata.cn/)]([https://www.geodata.cn/](https://www.geodata.cn/)) (accessed on 11 January 2024) \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table}
Table 1: _Cont._(accessed on 28 January 2024). The potential evapotranspiration (PET) data were obtained from a previous publication [32].
###### Abstract  
This paper investigates the potential utilization of the Tunnel Boring Machine (TBM) muck generated from Doha's Metro Gold Line in different construction applications. The properties of the raw TBM muck were studied, and the results were compared to the specifications of Qatari Construction Standards (QCS 2014) of concrete aggregates, fill material under buildings and road subgrades. Compared to the requirements of concrete aggregates, the results indicated that the gradation of the raw TBM muck does not comply with the QCS 2014 requirements, and hence, sieving and screening may be essential. Moreover, the tests' results showed that the properties of the muck meet the requirements of the concrete coarse aggregates, except for the water absorption, loss by magnesium sulphate soundness, loss by Los Angeles abrasion and the acid-soluble sulphate. As fill material under buildings or road subgrades, the gradation of the TBM muck complies with the QCS 2014 requirements, while the liquid limit and plasticity index are higher than the QCS 2014 permissible limits. Additionally, the morphological structure and the elemental composition of the raw TBM muck were determined by employing Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray analysis (EDX), respectively. Digital images were also taken at larger scale to draw a full picture of the TBM muck morphology. A mixture of rough-rounded to angular-elongated shaped particles with relatively large voids could be observed. The EDX analysis demonstrated the presence of silicon (Si) as the predominant component of the muck, which may alter the Coefficient of Thermal Expansion (CTE) values for mixtures prepared using TBM muck. Hence, further investigations should be performed on the mechanical and thermal properties of mixtures containing TBM muck as aggregates' replacement, and further work should be directed toward this end.  
TBM muck; physical; chemical and mechanical tests; EDX; recycling; construction; sustainability; aggregates +
Footnote †: journal: Applied Sci. 2021, 11, 8623. [[https://doi.org/10.3390/app11188623](https://doi.org/10.3390/app11188623)]([https://doi.org/10.3390/app11188623](https://doi.org/10.3390/app11188623))
## 4 Discussion
While these findings indicate the potential benefits of aromatherapy, it is important to note that the current scoping review on the combination of VR and aromatherapy addressed similar outcomes. However, it only covered a small portion of the benefits associated with essential oils. This limitation highlights the need for further research to more thoroughly explore the therapeutic potential of combining VR and aromatherapy for older adults, pointing to valuable directions for future studies. Among the outcomes explored in the literature, memory warrants specific attention. It is believed that the close connection between the olfactory system and brain regions involved in emotion and memory--such as the amygdala and hippocampus--plays a significant role in the powerful memories often triggered by smells or tastes. This phenomenon is known as the \"Proustian effect\" [44], which aligns with the psychological concept of episodic memory, where specific sensory cues evoke vivid, autobiographical memories. Studies have shown that olfactory and auditory cues in virtual environments enhance users' sense of presence and memory of the environment, an effect that visual stimuli alone cannot achieve [45]. Additionally, research indicates that the introduction of smells can help focus users' gaze on specific areas related to the scent, making it easier to remember those areas compared to scentless environments [46]. While haptic feedback has been incorporated into multisensory VR systems, the integration of smell and taste remains underexplored, especially in traditional VR setups that primarily rely on audiovisual stimuli. A systematic review of 105 studies noted that olfactory displays are not as readily or affordably available as visual and auditory systems in immersive media experiences [47]. The primary challenge to the inclusion of olfactory stimuli in these environments is the lack of accessible and cost-effective olfactory displays [48]. Given that memory and cognitive training have shown moderate to large beneficial effects in conditions like mild cognitive impairment and dementia [49], the combination of VR and aromotherapy offers a promising avenue for further exploration and potential therapeutic applications in these areas. Additionally, recent research on VR has been shown to improve the quality of life in community-dwelling older adults, further supporting its potential to enhance overall well-being [50]. In exploring the future potential of the combination of VR and aromatherapy interventions, recent studies on the metaverse have shed light on the significance of user perceptions, usability, and social influences, all of which can contribute to enhancing immersive therapeutic experiences [51].
## 4 BILSTM-Transformer Based Bems Model
### Offline Training Based on the BiLSTM-Transformer System Architecture
#### 4.3.2 Training Dataset  
The training dataset consists of the input and output historical data of the system, aiming to provide high-quality training samples and reference data for the BiLSTM-Transformer network. The dataset is generated by the above-mentioned Building Energy Management System (BEMS) model based on simulated annealing algorithm (SA), which reasonably schedules the charging and discharging behavior of electric vehicles, optimizes the power exchange and power cost of the power grid, and simulates the attack and data loss scenarios in the power grid. Each time step in this optimization process provides rich historical data for subsequent machine learning model training.  
Specifically, the training dataset contains the sum of the load \(P_{\text{grid}}(t)\) of the shopping mall, the \(P_{\text{demand}}(t)\), and the charging and discharging power of the electric vehicle \(P_{\text{EVi}}(t)\), and the current electricity price \(C(t)\). These input features are generated by the BEMS system in different time periods, corresponding to the Pgrid changes in the shopping mall at each time step. Through this process, the training dataset can also reflect the charging and discharging behavior of electric vehicle groups under different electricity prices and power demands.  
Considering the large differences in the numerical range of these input features, this study adopts a min-max normalization method to scale all data to the interval [0,1] [0, 1] [0,1]. Since BiLSTM-Transformer is sensitive to the scale of the data, this normalization process helps to improve the training stability and convergence speed of the model.  
In addition, the construction of the dataset also takes into account the data loss in the attack simulation, so as to ensure that the system can still make reasonable scheduling decisions in the face of possible data incompleteness in practical applications, and the corresponding network structure (see Section 4.5). By processing these incomplete data, the dataset can provide diverse scene inputs for the BiLSTM-Transformer network, and enhance the generalization ability and robustness of the model.  
Since the BiLSTM-Transformer-based EV-dispatching management system is a data-driven technology, in order to ensure the effectiveness of the model in large-scale EV-charging and -discharging scheduling, the training dataset must cover a variety of different dispatching scenarios and power demand situations. In this way, the network can be provided with sufficient samples to have good generalization capabilities to cope with the diverse scheduling requirements in the actual environment.
# Enhancing Saga Pattern for Distributed Transactions within a Microservices Architecture
## 2 Literature Review
### Distributed Transactions in Microservices
#### 2.1.1 Two Phases Commit Protocol  
One of the most popular patterns to implement distributed transactions in a microservice architecture is the two-phase commit protocol (2 PC) [7]. In this protocol (see Figure 1), a coordinator is the component that controls transactions and contains the logic for managing them, while the microservices (participating nodes) execute their local transactions. With the 2 PC protocol, a distributed transaction is executed in two phases. In the first phase or what is known as the prepare phase, the coordinator asks the participating nodes to commit the transaction. Thus, a yes or no response will be returned. In the second phase (i.e., the commit phase), when a yes response is received by the coordinator from all participating nodes, the coordinator, in turn, asks all nodes to commit. Note that the coordinator asks all participants to rollback their local transactions upon receiving at least one negative response.  
Although the 2 PC is viewed as a useful way to implement distributed transactions [8], the coordinator can become a single point of failure. Moreover, the overall performance of the transactions depends on the slowest service as all other services need to wait until the slowest service finishes its confirmation. Thus, it does not perform very well in large-scale and highly loaded systems [9].
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 3 Dangampo Site: Equipment Testing
### Experimental Setup
#### 3.1.1 Side Scan Sonar  
The SiSS is a survey system most widely used in the field of marine research. A towfish, which scans the seafloor, is submerged and towed behind a boat or ship. The transducers installed on both sides of the towfish emit sound waves and receive the acoustic return from the seafloor. The recording device connected to the towfish measures the sound pressure of the returning sound waves and expresses the track of the towfish as a single line. The transducers continuously emit sound waves while being towed over the seafloor, resulting in continuous lines that connect to form a plane image.  
An SiSS survey was conducted at the Dangampo site before the sediment removal survey on the morning of October 26 using a DSME Utech S-150 SiSS, with a frequency range of 400-1250 kHz. Owing to its small footprint and simple structure, the equipment could be taken aboard a rubber boat to sweep the site (500 m (latitudinal) \(\times\) 200 m (longitudinal)), and a frequency of 400 kHz was used. Using a 105 m swath, i.e., a strip of seafloor covered by one sweep, it was possible to attain more than 100% overlap between adjacent swaths by conducting three round trips along the latitudinal axis. Additionally, it was possible to identify the SRA at the site. To obtain high-quality results, we repeated the survey several times using the following two combinations of frequency and swath: 400 kHz and 105 m, and 1250 kHz and 75 m.
# The Evolution and Development Trends of LNG Loading and Unloading Arms
## 4 Challenges and Opportunities
### Opportunities
Innovations in High-Precision Positioning and Docking Technology  
Marine loading arms achieve precise positioning and rapid docking through technologies such as vision recognition, laser guidance, 5G transmission, and BeiDou positioning. Equipped with intelligent docking systems, they can automatically adjust posture to avoid leakage caused by docking deviations. In land-based LNG loading and unloading operations, high-precision positioning and fast, accurate docking between loading arms and tankers are critical to improving operational efficiency and safety. In recent years, advanced positioning technologies like BeiDou satellite positioning, laser positioning, and vision recognition have made significant advancements and are increasingly applied to land-based LNG loading arms. Through the synergistic application of these technologies, loading arms can quickly and accurately identify the interface positions of ships or tankers, automatically adjust posture, and achieve precise docking, effectively preventing safety incidents such as leakage due to alignment errors. Simultaneously, the development of advanced docking mechanisms and sealing technologies ensures tight and reliable connections during docking, further enhancing operational safety and stability. For example, intelligent flexible docking devices can compensate for displacement caused by imprecise berthing, uneven ground, or swaying, ensuring sealing tightness and stability. 4. Integration with Emerging Energy Technologies  
As the hydrogen energy industry grows, future marine LNG loading arms may need to expand functionality to accommodate hydrogen-powered transport ships. Through technological development, loading arms can adapt to hydrogen's unique properties (e.g., ultra-low boiling point, high diffusivity) to enable compatibility or transition between LNG and hydrogen handling equipment, opening up new business opportunities. Both marine and land-based loading arms are actively integrating with the Internet of Things (IoT) and big data. Marine systems connect to IoT networks for device intercommunication, using big data analytics to uncover operational patterns and fault predictions, optimizing workflows. Land-based LNG loading arms leverage the IoT to upload real-time operational data to management platforms, enabling predictive maintenance via data analysis to ensure continuous operations and enhance overall efficiency.
## 4 Experimental Results
* mmWave and Terahertz Wireless Communications
* In millimeter-wave (mmWave) and Terahertz (THz) systems, signal waveforms often suffer from path loss and hardware nonlinearities. Applying masked autoencoder-based compression and recovery could help enhance robustness and reduce the overhead associated with channel estimation or signal regeneration. * Underwater Optical Communications
* The underwater environment exhibits strong multipath fading and scattering. The MAE-based technique can be adapted to reconstruct distorted waveforms received after transmission through turbid water, thereby improving effective throughput and error resilience. * Biomedical Time-Series Signal Compression
* Electrocardiogram (ECG) and Electroencephalogram (EEG) signals are also examples of multivariate time-series data. Our method can be used for efficient compression and reconstruction of these signals in wearable devices with limited transmission bandwidth, while preserving signal integrity for downstream diagnostic use. * Structural Health Monitoring (SHM)
* Sensor networks deployed for SHM generate large volumes of time-series vibration or strain data. By applying sparse coded masking, data can be compressed at the edge before transmission to a central server, reducing bandwidth consumption while retaining reconstruction fidelity for anomaly detection.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 1 Introduction
### Background and Related Works
In [27], the reliability of the short-packet-based UAV relay communication system was taken as the optimization objective, and from the paper, it could be seen that the optimal transmit power and UAV locations could be obtained. In [28], a two-way communication model for UAVs was proposed. In the forward link, short packets are transmitted with control information and the backward link transmits the information collected through the long packets. Until now, there have been relatively few studies combining CR and NOMA techniques for UAV communications. In [29], the authors considered maximizing the total rate of secondary users in a cognitive radio network by optimizing the relevant parameters. In addition, [30] investigated the problem of optimizing the safe transmission rate for UAVs. However, the effect of the decoding error probability on UAV communication has not been researched.