esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 1 Introduction
[18] compared different models predicting the shield performance one second in the future and found that LSTM had the best prediction. [PERSON] et al. [19] achieved a better prediction of the average speed in the next 1 min through a hybrid model based on GRUs. However, most of these studies focus on short-term prediction, which has difficulty reflecting the long-term trend of tunnel construction and cannot improve the strong theoretical support for construction decision-making. Several scientists have recently begun looking at predictions over extended periods and distances. [PERSON] et al. [20] used LSTM to predict the penetration of the next ring and the next fifth ring, but the prediction effect was not good, with a coefficient of determination (R\({}^{2}\)) of 0.65, which is much lower than the prediction of the next ring in a short period of time, with a value of R\({}^{2}\) of 0.97. [PERSON] et al. introduced the attention mechanism to build an Attention-ResNet-LSTM hybrid model to predict performance after 20 min and 60 min, but the root mean square errors (RMSEs) were 0.523 and 1.948, meaning the prediction accuracy decreases dramatically with increasing time [21]. It can be seen that there is still a challenge in predicting the performance of tunnelling over long distances or long periods of time [22]. Both LSTM and GRUs are based on RNNs, whose structure restricts them from performing long-distance prediction, which needs to be realized by multi-step recursion. This inevitably produces error accumulation, leading to a significant degradation of the prediction ability, and, therefore, new network architectures need to be sought to solve this problem. The Informer algorithm is a deep learning model designed for long time series prediction. It was presented at the AAAI conference in 2021, where it was recognized as the best paper of the year, primarily for addressing the problem of accuracy degradation in time series prediction [23]. Based on the actual construction decision-making needs [24], this paper defines the research objective as predicting future performance beyond the fifth ring and refers to it as the long-distance prediction. This paper utilizes the advantages of the Informer algorithm to design a shield long-distance tunnelling performance prediction model (LSPP) for shield tunnelling performance after five rings, which reflects the spatio-temporal characteristics of shield tunnelling with the structure of the Informer model and verifies the effect of the new model on the prediction of performance through experiments.
# Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
###### Abstract  
With deep learning approaches, the fundamental assumption of data availability can be severely compromised when a model trained on a source domain is transposed to a target application domain where data are unlabeled, making supervised fine-tuning mostly impossible. To overcome this limitation, the present work introduces an unsupervised temporal-domain adaptation framework for human action recognition from skeleton-based data that combines Contrastive Prototype Learning (CPL) and Temporal Adaptation Modeling (TAM), with the aim of transferring the knowledge learned from a source domain to an unlabeled target domain. The CPL strategy, inspired by recent success in contrastive learning applied to skeleton data, learns a compact temporal representation from the source domain, from which the TAM strategy leverages the capacity for self-training to adapt the representation to a target application domain using pseudo-labels. The research demonstrates that simultaneously solving CPL and TAM effectively enables the training of a generalizable human action recognition model that is adaptive to both domains and overcomes the requirement of a large volume of labeled skeleton data in the target domain. Experiments are conducted on multiple large-scale human action recognition datasets such as NTU RGB+D, PKU MMD, and Northwestern-UCLA to comprehensively evaluate the effectiveness of the proposed method.  
Keywords:unsupervised domain adaptation; human action recognition; contrastive learning; human skeleton data +
Footnote †: journal: _Algorithms_
## 5 Conclusions and Future Work  
To promote the application of spaceborne AIS data in maritime surveillance, this paper proposed a ship classification and anomaly detection method considering ship behavior characteristics. The proposed method makes full use of the advantages of the wide coverage of spaceborne AIS data to extract distinctive behavior characteristics and combined machine learning algorithms to achieve high-performance classification of the five types of ships (i.e., cargo ships, tanker ships, fishing ships, passenger ships, and tug ships). This paper first analyzed and extracted the geometric features and behavior characteristics of ships, and then used the SVM and random forest algorithms to conduct classification and anomaly detection research. Experiments were performed with different classification models and feature combinations. Through a summary of the experimental results, it was found that the classification accuracy of the five types of ships could reach 92.70% with the addition of ship behavior characteristics, and the method achieved better performance in the _Precision_, _Recall_, and _\(F_{1}\)-score_ metrics. In addition, this paper also presented case studies of ship anomaly detection, and the experimental results showed that the proposed method can effectively detect anomalous ships that may forge and transmit inappropriate AIS messages in order to evade monitoring. This is of great significance to ensure maritime economic security and environmental stability, and also proves the effectiveness of the method proposed in this paper.  
In the future, research works will focus on ship feature extraction and classifier design. The performance of AIS data ship classification could also be improved by extracting additional features, such as ship draught, _ROT_, and _COG_ in AIS messages. The fusion of AIS data and remote sensing images for ship classification is also a future research direction, which could further promote the application of AIS data in maritime surveillance.  
Conceptualization, Z.Y.; methodology, Z.Y.; software, Z.Y.; validation, X.S., H.Z. and L.Y.; formal analysis, Z.Y. and X.S.; investigation, Z.Y.; resources, X.S. and L.Y.; data curation, Z.Y.; writing--original draft preparation, Z.Y.; writing--review and editing, Z.Y., X.S., H.Z., L.Y. and Y.W.; visualization, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
My-1C/HY-2B data were obtained from [[https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)]([https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)) (accessed on 5 August 2021). The authors would like to thank NSOAS for providing the data free of charge. The authors would also like to acknowledge the reviewers for evaluating this work.  
The authors declare no conflict of interest.
# Deformation and Stress Analysis of Pile-Supported Immersed Tunnels under Seismic Loads
## 2 Methodology
### Numerical Model
#### 2.2.1 FEM Mesh and Boundary Conditions  
C3D8P elements are considered for piled consolidation drainage, while C3D8R elements with reduced integration and hourglass control were used for tunnels, gravel cushions, and backfill. Mesh sizes are smaller near piled foundations and tunnels, while foundations away from piled foundations and tunnels are gradually increased in size, and the maximum element size is less than 5 m [35]. This is done to ensure computational efficiency and accuracy. The dimensions of the seabed are 112 m in the X-direction, the model width is 3 \(S\) in the Y-direction (\(S\) is the pile spacing), the SCP depth is 15 m in the Z-direction, and the bottom boundary of the model is 3.1 \(D\) from the end of the piles (\(D\) is the diameter of the piles) [32].  
Tip of the pile restraint is considered to play a vital role in dynamic load analysis. [PERSON] et al. [36] stated that the lateral deflection of the pile at the ground surface can be reduced by about 50% due to pile fixed restraint. The fixed tip of the pile restraint also reduces the maximum bending moment of the pile and produces a bending moment at the tip of the pile in the opposite direction to the piled foundation [37; 38]. During the actual seabed improvement technology, the surface of the SCP composite foundation does not slide relative to the gravel cushion and can be considered as a fixed contact in the finite element analysis. The contact surface of the pile soil is face-to-face contact. That is, the tangential direction is frictional contact, the normal direction is hard contact, and the strength discount factor is 0.8. The model is constrained around the normal direction displacement, and the bottom surface is constrained for X, Y, and Z direction displacement.  
Finite element analysis can only simulate finite space, and the seabed is surrounded by half-infinite space bodies Kou and Yang [33]. Seismic waves lack reflection and damping effects at the boundaries, which are inconsistent with the actual wave field in the half-space [39]. Therefore, viscoelastic boundaries are set up in addition to the constrained displacements for realizing the reflection and damping of seismic waves. The restraining effect of neighboring soil materials is simulated. This is described in detail in Section 2.2.3.
## 2 Background and Conventional Approaches Related to RQ1 and RQ2  
This section provides a brief background on the problem setting and explores various conventional methods related to each research question.
## 2 Background and Related Work
### Related Work
In addition, the generated scenario and its suitability for cyber-incident response exercises in the ICS environment are evaluated quantitatively. Here and after, we consider an attack scenario to consist of several attack procedures that should be executed to achieve an attacker's goal. Figure 1 shows an overview of the proposed framework. The proposed framework is divided into a total of five steps, that are performed after the preparation: threat analysis (step 1), attack information identification (step 2), modeling cyberattack scenarios (step 3), quantifying cyberattack scenarios (step 4), and generating cyberattack scenario (step 5). In the preparation step, all assets attackers may be interested in deployed in the ICS. The threat analysis step outlines the cyberattack scenario by defining the attacker's attack goal, the attack target to achieve the goal, and the attack's impact. The attack information identification step configures the attack procedure, and MITRE ATT&CK's tactics are adopted to assign the attack technique to the corresponding attack procedure. In the attack scenario modeling step, the attack tree is constructed with the analyzed information produced in the previous steps; we augment and modify the standard attack tree to present analyzed information appropriate to the ICS environment. In the attack scenario generation step, attack scenarios are derived from the attack tree. In the quantification step, the scenarios generated are evaluated through the CVSS and DREAD techniques. **Preparation**. The preparation step identifies the assets deployed in the ICS. The assets included in the ICS environment are largely categorized into physical assets, network assets, and software assets. The physical assets are responsible for process control or monitoring and may include assets such as PLCs, EWS, human-machine interfaces (HMIs), and field devices. The network assets include devices for segmenting or connecting multiple sub-networks in the industrial networks and protocols for each communication segment. This can include devices such as switches and routers. Software assets are programs that control physical assets or manage the entire process. We should utilize the ICS environment's schematic diagram or network architecture to identify these assets. Unidentified assets will be excluded from threat modeling or scenario generation if this step is not performed accurately. This may result in deriving an incomplete attack scenario.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Global Navigation Satellite System (GNSS)  
The GNSS can greatly facilitate localization outdoors, as long as LoS exists between a few of the satellites and the receiver. The GNSS consists of four positioning systems, the GPS by the USA, GALILEO by the European Union (EU), GLONASS by Russia, and BeiDou by China. In general, the GNSS localization system employs propagation time estimations, followed by the trilateration localization technique. Therefore, LoS with at least three satellites is required, which can be problematic for indoors, dense urban, and underground environments [69]. In particular, the unknown receiver's position is estimated by using the Earth-based coordinate system, which includes the latitude, longitude, and altitude. Each satellite transmits a unique code, and an identical copy is created at the receiver's side. It includes two carrier waves in the L-Band with frequency range from 1530 MHz to 2.7 GHz, together with the satellite's precise orbit details and a very stable time stamp created by an atomic clock, called ephemeris data [69]. The atomic clock is also used for various other navigation and timing services on the global basis. In what follows, complementary GNSS technologies, which aim to address the GNSS limitations, are presented, including indoor localization.
# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 3 Problem Formulation  
### System Model  
The network deployment problem was formulated as an optimization problem to maximize the performance of the network in terms of bandwidth coverage and longevity. Assume that a set of \(N\) identical UAVs \(U=\{u_{1},u_{2},\ldots,u_{n}\}\) need to be deployed in a given AOI of \(2000\times 2000\) m\({}^{2}\) with a command center located in the middle at \((1000,1000)\)[37]. All UAVs are initially located at the center within a \(10\times 10\) m\({}^{2}\) area and fly at a constant altitude of 100 m. Each UAV is equipped with sensors that can detect users on the ground within a ground-sensing range \((U_{gr})\) of 100 m and can communicate with its neighbors within an air-to-air communication range \((U_{ar})\) of 300 m [22]. A total of \(m\) users, each with a position and a bandwidth requirement \((p_{i},b_{i})\), are distributed over the AOI. Therefore, the users can be represented by a list of pairs: \(\{(p_{1},p_{1}),(p_{2},b_{2}),\ldots,(p_{m},b_{m})\}\). These simulation parameters can be easily changed. In the results section, we demonstrate the performance of our proposed algorithm with various AOIs, numbers of UAVs and users, and different user distributions. Additionally, other parameters, such as the range of UAV-UAV communication, range of UAV-user communication, and initial energy, can also be adjusted as needed. This paper treats the network deployment problem as a search problem and thus channel modeling for communication among UAVs and between UAVs and users is a subject for future work.
## 5 Results
### Empirical Correlations  
Using coupled basket-twisted tape inserts with y/w = 6, 3, and 2, empirical correlations were generated for turbulent flow regimes with Re numbers ranging between 6000-19,500. The \(Nu\) and \(f\) of the plain heat exchanger were correlated based on the experimental results and are provided below in Equations (15) and (16), respectively [37; 38].  
\[Nu=0.0158 Re^{0.825}Pr^{0.4} \tag{15}\]  
\[f=0.5229 Re^{-0.298} \tag{16}\]  
Figure 8: Variation of thermal performance factor with Reynolds number for heat exchanger fitted with basket alone and combined basket-twisted tape inserts.  
Figure 9: Percentage of thermal performance factor enhancement.  
Correlations depending on (\(Re\)), (\(Re\)), Prandtl number (\(Pr\)), number of basket inserts (N), and twist ratio (y/w) were suggested for the case of combined basket-twisted tape inserts as follows [37; 38]:  
\[Nu=0.11618 Re^{0.625}Pr^{0.4}N^{0.541}(y/w)^{-0.011} \tag{17}\]  
\[f=0.6284 Re^{-0.285}N^{0.99}(y/w)^{-0.0001} \tag{18}\]  
The predicted values of the \(Nu\) were found to be within 7.2 percent of the experimental data for the average \(Nu\), as shown in Figure 10.
# Study and Investigation on 5G Technology: A Systematic Review
## 3 Preliminary Section  
### Emerging 5G Paradigms and Its Features  
5G provides very high speed, low latency, and highly salable connectivity between multiple devices and IoT worldwide. 5G will provide a very flexible model to develop a modern generation of applications and industry goals [26; 27]. There are many services offered by 5G network architecture are stated below:  
Massive machine to machine communications: 5G offers novel, massive machine-to-machine communications [28], also known as the IoT [29], that provide connectivity between lots of machines without any involvement of humans. This service enhances the applications of 5G and provides connectivity between agriculture, construction, and industries [30].  
Ultra-reliable low latency communications (URLLC): This service offers real-time management of machines, high-speed vehicle-to-vehicle connectivity, industrial connectivity and security principles, and highly secure transport system, and multiple autonomous actions. Low latency communications also clear up a different area where remote medical care, procedures, and operation are all achievable [31].  
Enhanced mobile broadband: Enhance mobile broadband is an important use case of 5G system, which uses massive MIMO antenna, mmWave, beamforming techniques to offer very high-speed connectivity across a wide range of areas [32].  
For communities: 5G provides a very flexible internet connection between lots of machines to make smart homes, smart schools, smart laboratories, safer and smart automobiles, and good health care centers [33].  
For businesses and industry: As 5G works on higher spectrum ranges from 24 to 100 GHz. This higher frequency range provides secure low latency communication and high-speed wireless connectivity between IoT devices and industry 4.0, which opens a market for end-users to enhance their business models [34].  
Figure 1: Systematic layout representation of survey.  
New and Emerging technologies: As 5G came up with many new technologies like beamforming, massive MIMO, mmWave, small cell, NOMA, MEC, and network slicing, it introduced many new features to the market. Like virtual reality (VR), users can experience the physical presence of people who are millions of kilometers away from them. Many new technologies like smart homes, smart workplaces, smart schools, smart sports academy also came into the market with this 5G Mobile network model [35].
## 3 Surrogate Modeling
### Design of Experiments  
The purpose of conducting the design of experiments is to generate sampling points for the surrogate model. Since we have no prior knowledge about how the objective output functions vary over the input space, the principle of a sampling plan is to fill the design space with sampling points, as uniformly as possible. For this purpose, a space-filling strategy named Latin hypercube sampling (LHS) for uniform random sampling was employed. The LHS can avoid the over-concentration of the sampled points in a small range and make the sample points evenly distributed in the sampling area. For sampling \(n_{s}\) sample points, \(n_{v}\) design variables are evenly divided into \(n_{sl}\) intervals. The total sampling interval is divided into \(\left(n_{v}\right)^{n_{s}}\) subintervals. When each sample point is projected into any dimension, there is only one sample point in each interval.  
In the aerodynamic shape optimization of the arc-plate bluff body, 50 sample points were taken for the control variables \(a_{1}\) and \(a_{2}\) in the range from 0 to 1. As shown in Figure 2(a), 50 samples for the modeling were generated in the two-dimensional input space. The shapes of bluff bodies are shown in Figure 2(b), corresponding to all the sample points. Taking shape 2 as an example, \(a_{1}=0.02\) and \(a_{2}=0.69\), the angle of arc \(\beta\) is \(0.04\pi\), and the plate length \(L_{t}\) is \(0.69D\). The corresponding bluff body section is approximately T-shaped. For shape 47, where \(a_{1}=0.94\) and \(a_{2}=0.10\), arc angle \(\beta\) is \(1.88\pi\) and the plate length \(L_{t}\) is \(0.20D\). The related bluff body section is roughly circular. For shape 49, where \(a_{1}=0.98\) and \(a_{2}=0.82\), arc angle \(\beta\) is \(1.96\pi\) and the plate length \(L_{t}\) is \(1.64D\). The resultant bluff body section follows the aerodynamic shape studied by [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [35]. It can be concluded that the arc-plate bluff body controlled by two variables can describe a variety of aerodynamic shapes using LHS.  
Figure 3: Design of experiments (**a**) samples points and (**b**) shapes of bluff bodies.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 4 Discussion
### Scaling Above- and Below-Canopy Imagery
While there are geometrical considerations that may play a part (e.g., circular plot areas) when comparing hemispherical imagery to UAV imagery--including how buffer size and area are calculated--the agreement we see among sensors in this system is encouraging for linking below-canopy imagery to UAV imagery, potentially even to satellite imagery. This is an important consideration for pairing UAV imagery with plot-based studies. Additionally, aspects of forest structure can be captured with UAV-based structure-from-motion (SFM) methods where overlapping imagery can be processed using photogrammatical algorithms to construct three-dimensional (3D) point clouds of the canopy that approximate point clouds acquired by aerial light detection and ranging (LiDAR) systems [39; 40]. SFM matched with phenological measurements can potentially clarify the role of forest stature in phenology with high resolution and spatial extent. We also only consider RGB UAV-based imagery when there are other multispectral sensors that provide additional spectral bands (e.g., red edge) that could offer utility in scaling plot data to the stand or landscape. The potential for UAVs to scale plot-based research from the micro- to the mesoscale is enormous, and this work adds even more support to a growing, robust body of research that emphasizes these applications [12; 13; 41]. There are often many different methods that can be employed in order to measure a given variable of interest. For example, in forests, leaf area is a structural parameter of biophysical importance that can be measured many different ways including optically (hemispherical camera, surface reflectance, light absorption), from active remote sensing (LiDAR) [42; 43; 44; 45] or empirically [46]. These methodshave variable results even within the same stand or plot [47] that arise due to methodological assumptions-statistical artifacts from saturation when estimated using light absorption methods that result in saturating values at higher ranges [48; 49], or interference from parsing leaf and wood values [50; 51]. Many leaf-are measurement methods are built on estimating gap fraction, but different assumptions within algorithms can change estimates. Depending on sensor type and perspective, methodological assumptions can similarly impact measurements in phenological studies.