esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Tunnel-Boring Machine (TBM)  
An Earth Pressure Balance Shield (EPB) TBM was selected for use in cohesive soils with high clay, mud, or silt contents and low water permeability. The TBM's cutting wheel excavates the soil while also serving as support for the tunnel face to prevent settlement. Additives such as foam are used to condition the excavated material for conveyance [20,21]. Ten gantries are installed behind the shield, housing mechanical devices for transporting segments, handling grout, and supplying hydraulic and electric energy. The gantries also contain primary cooling circuits and the primary air ventilation system. The TBM had a total length of 110 m and required a crew of 20 workers, including various roles such as the pit boss, lead miner, TBM driver, erector driver, ring builders, segment feeder and belt extension operators, TBM engineer, fitters, electricians, surveyor, cleaner, and assistants.  
The TBM cycle is divided into the following parts:  
1. Advance Preparation: The logistic process is adjusted to ensure that the grout and segment trains arrive just as the ring build is completed. Grout is pumped from the grout car into the permanently installed grout tank on the gantry, and segments are loaded into the segment feeder's unloading area.
2. Advance Mode: Once the ring build is finished, the TBM driver activates the advance mode. The driver controls various parameters, including the EPB pressure in each sensor, the advance rate, tail skin articulation, tail seal compound, shield articulation, foam injection, screw conveyor, belt scales, and cutting wheel. The duration of this phase generally ranges from 15 to 40 min.
3. Grouting: While the TBM is in advance mode, the grout man injects grout into the last constructed ring to prevent settlements. Grout is injected via four independent grout pumps, each serving one grout line in the tail skin.
4. Ring Build: After the advance is completed and the tail skin clearance is measured, the lead miner, erector driver, and two miners start constructing the ring, following a predefined sequence generated via an informatics program. The ring build usually takes between 10 and 20 min.
5. Ancillary Operations: Other essential operations for TBM progress include belt extension, track extension, pipes extensions, and other necessary services.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 4 Algorithms for Multiple GWs Placement
### Algorithm of PGL
1:Compute capture circle and the ICD circle decided by each ordered pair of SNs \((s_{1},s_{2}),(s_{1},s_{3}),\cdots,(s_{n},s_{n-2}),(s_{n},s_{n-1})\);
2:Build the set of pixels, \(P=\{p_{(i,j)}\ |\ 0\leq i\leq R,0\leq j\leq C,(i,j\in Z)\}\) according to the number of pixels in row and column, and initiate all pixel gray levels to zero;
3:Build the set of ordered pair of SNs, \(V=\{(s_{1},s_{2}),(s_{1},s_{3}),\cdots,(s_{n},s_{n-2}),(s_{n},s_{n-1})\}\) according to the number of the SNs;
4:while\(K\leq M\)do
5:for each pixel \(p_{(i,j)}\in P\)do
6:for each capture circle and ICD circle do
7: If the pixel \(p_{(i,j)}\) locates in the ICD circle, that is, outside of the decoding crescent but inside of the capture circle, add \(\alpha\) to the gray level of this pixel and record the related pair of SNs \((s_{i},s_{j})\) which decides the capture circle;
8: If the pixel \(p_{(i,j)}\) locates in the decoding crescent, add \(\beta\) to the gray level of this pixel, and record the related pair of SNs \((s_{i},s_{j})\) which decides the ICD circle;
9:endfor
10:change to next pixel \(\{p_{(p,q)}\in P\}\);
11:endfor
12: Sort all of the pixels \(\{p_{(i,j)}\in P\}\) from the maximum gray levels to minimum;
13: Set the location of \(K^{th}\) GW to be the location of the pixel \(p_{(x,y)}\in P\) with the maximum gray levels;
14: Remove the pixel \(p_{(x,y)}\) out of the set \(P\) and get the new set of the pixels \(\{p_{(a,b)}\in P:p_{(a,b)}\
eq p_{(x,y)}\}\);
15: Remove all of the recorded pairs of SNs \(\{(s_{i},s_{j})\in V\}\) connected to the pixel \(p_{(x,y)}\) with gray level added by \(\alpha\) in Step 7 out of the set, then renew the set \(V\);
16: Remove all of the recorded pairs of SNs \((s_{i},s_{j})\) and \((s_{j},s_{i})\) in set \(V\), connected to the pixel \(p_{(x,y)}\) with either gray level added by \(\beta\) in step 8 out of the set, then renew the set \(V\) again;
17:\(K=K+1\);
18:endwhile
```  
**Algorithm 2** Algorithm of PGL  
Figure 9: PGL Algorithm with 3 SN and 2 GW. The algorithm of WBG is expected to have better performance than PGL because of the precision of the OP locations, but at a cost of higher computational complexity. The PGL algorithm can significantly reduce the number of computations performed, although estimating the number of pixel points necessary for effective GW placement is not straightforward. However, the accuracy of GW locations will connect to the pixel density of the deployment area, as we show in Section 5.3.
# Dual-Window Superpixel Data Augmentation for Hyperspectral Image Classification
## 5 Conclusions  
In this work, the DWS data augmentation framework for superpixel-based DL classification of large hyperspectral scenes is presented. DWS relies on patch extraction using a superpixel segmentation obtained by the application of the SLIC algorithm in order to reduce the complexity of the classification process. The extracted patches undergo patch subdivision, creating two regions over which transformations are independently applied. Four data augmentation techniques based on the DWS framework using rotate and flip transformations are proposed: inner-rotate, dual-rotate, inner-flip and dual-flip. These techniques can also be used for pixel-based classification with minimal changes.  
A comprehensive comparison of the proposals to other data augmentation techniques from the literature was carried out for both superpixel and pixel-based classification scenarios in terms of classification accuracy and execution times. The results obtained show that the proposed DWS approach successfully manages to reduce overfitting and increase the generalization capabilities of the resulting models. Execution times are also reduced when compared to traditional pixel-based classification schemes. Based on the results obtained, the DWS-based dual-rotate 16\(\times\) is the preferred augmentation technique when processing small, low-resolution scenes, and dual-flip 16\(\times\) is the best method for large, high-resolution scenes.  
Conceptualization, F.A.; data curation, A.A.; formal analysis, F.A. and D.B.H.; investigation, A.A.; methodology, F.A. and D.B.H.; resources, A.A., F.A. and D.B.H.; software, A.A.; supervision, F.A. and D.B.H.; validation, A.A., F.A. and D.B.H.; visualization, A.A.; writing--original draft, A.A.; writing--review and editing, A.A., F.A. and D.B.H. All authors have read and agreed to the published version of the manuscript.  
The images of the Galicia dataset were obtained in partnership with the Babcock company, supported in part by the Civil Program UAVs Initiative, promoted by the Xunta de Galicia. This work was supported in part by Ministerio de Ciencia e Innovacion, Government of Spain (grant numbers PID2019-104834 GB-100 and BES-2017-080920), and Conselleria de Educacion, Universidade e Formacion Profesional (grant number ED431C 2018/19, and accreditation 2019-2022 ED431G-2019/04). All are co-funded by the European Regional Development Fund (ERDF).  
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.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AA & average accuracy \\ CNN & convolutional neural networks \\ DL & deep learning \\ DWS & dual-window superpixel \\ FE & feature extraction \\ HSI & hyperspectral image \\ OA & overall accuracy \\ RO & random occlusion \\ Spp. & superpixels \\ UAV & unmanned aerial vehicle \\ \end{tabular}
# Weibo Text Sentiment Analysis Based on BERT and Deep Learning
## 5 Experiment and Analysis
### Experimental Results
BERT dynamically converts words into word vectors, and then converts the word vectors It is sent to the BiLSTM network to extract context information, and then different word vectors are given different degrees of attention through the attention mechanism, and are finally sent to the output layer for classification. \(-\)CNN + Lexion: We remove the convolutional and pooling operations. The pooling operation here refers to the parallelization of max pooling and average pooling, and then connect the pooled results (the same below), and retain external sentiment dictionary. Through this experiment, the influence of sentiment dictionary on the model can be explored. \(+\)CNN \(-\) Lexion: We retain the convolutional and pooling operations, but do not introduce the external sentiment dictionary, through this experiment to learn about the effect of the CNN on the model. \(+\)CNN + Lexion: All components of the model are retained. The experimental results are shown in Table 7 and Figure 8. Compared with removing the CNN and the sentiment dictionary in the meantime, the accuracy of the experiment is increased by 2.7% when all components are retained. After adding the CNN, the accuracy is improved by 1.3%. This is because the CNN can extract local features and reduce the dimension of the feature vector. In addition, the average pooling result is added to the max pooling result to retain important features as much as possible and increase its robustness. It can also observe that the introduction of a sentiment dictionary can strengthen the performance of the model, and the accuracy of the experiment is increased by 1.6%. This is because some emotional words with strong emotional color play a significant role in judging the sentiment tendency of the entire text; we weight the word vectors through the introduced sentiment dictionary to improve the sentiment strength of the word vector, thereby improving the accuracy of classification. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \(-\)CNN \(-\) Lexion & 89.7\% & 90.9\% & 87.8\% & 89.3\% \\ \hline \(-\)CNN + Lexion & 91.0\% & 91.9\% & 89.8\% & 90.8\% \\ \hline \(+\)CNN \(-\) Lexion & 91.3\% & 93.3\% & 88.8\% & 91.1\% \\ \hline \(+\)CNN + Lexion & 92.4\% & 92.5\% & 92.4\% & 92.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison model experiment results.
## 3 Results and Discussion
### Effect of Steel Casing on Axial Force  
Pile shaft strain data can be obtained by computer. Axial force of the pile shaft can be obtained by Equation (4).  
\[P=E\varepsilon A \tag{4}\]  
where P is the axial force of the pile; E is Young's modulus of the pile; \(\varepsilon\) is strain of the pile; and A is the cross-sectional area of the pile.  
Figure 11: Lifting value of ultimate bearing capacity.  
The effect of steel casing on the axial force of steel tube-reinforced concrete piles is shown in Figure 12.  
As shown in Figure 12, whether there is steel casing or no steel casing, at the same depth, when the pile top load gradually increases, the axial force of the pile shaft shows a gradual increasing trend. Under the same level load, when there is no steel casing, the axial force of the pile shaft changes slowly along the direction of pile, and the axial force curve of the pile shaft is relatively flat; when there is steel casing, the axial force of the pile shaft decreases rapidly along the direction of the pile, and the curve becomes steeper. For example, when the load is 1800 N, the depth is from 0-8 cm, and when the steel casing length is 0 cm, 8 cm, 12 cm, 16 cm, and 20 cm, the slope is approximately 55, 139, 142, 144, and 148, respectively, see Equation (5). The main reason is that as the length of the steel casing increases, more axial forces are transmitted to the soil through the form of shaft resistance.  
\[\text{S}=\frac{\text{P}_{\text{i}}-\text{P}_{\text{i}+1}}{\Delta\text{Z}} \tag{5}\]  
where S is the slope of two axial forces; P\({}_{\text{i}}\) is the axial force of the pile, i is the number of strain gauges, 1-6 from top to bottom; and \(\Delta\text{Z}\) is depth difference of two strain gauges.
## 4 Simulation Results
### Validation of the Multi-Linear Model  
In this section, the effectiveness of the proposed multi-linear model is tested via two simulation cases: response of step signal and response of random signal. In both cases, the flue gas mass flow rate is fixed at 0.13 kg/s. In the first case, the mass flow of lean solvent and extracted steam steps are applied at 15,000 s and 50,000 s, respectively. Since the process operates around 80% CO\({}_{2}\) capture rate, we chose the local linear model of 80% CO\({}_{2}\) capture rate for comparison. In the second case, we make the system operating in a wide operating range by imposing random input signals that fluctuate around the steady-state input of 70% CO\({}_{2}\) capture rate with a large amplitude. The local linear model with the 70% capture rate was selected for comparison. The input signals for Case I and Case II are shown in Figures 11 and 12, respectively, and the corresponding validation outputs are presented in Figures 13 and 14, respectively.  
Figure 11: Input signals for Case I.  
Figure 10: Fitness value of the local models (blue lines: fitness of CO\({}_{2}\) capture rate; red lines: fitness of reboiler temperature).  
Figure 14: Model validation results for Case II (solid line in red: the multi-linear model; dotted line in blue: the local linear model; dashed line in black: process measurement.).  
Figure 12: Input signals for Case II.  
Figure 13: Model validation results for Case I (solid line in red: the multi-linear model; dotted line in blue: the local linear model; dashed line in black: process measurement).  
It can be observed in Figures 13 and 14 that the proposed multi-linear has a very high prediction accuracy of the MEA-based post-combustion CO\({}_{2}\) capture process. Both the dynamic and steady-state behaviors of the process are precisely predicted with minor error. Therefore, the multi-linear model can achieve improved control performance when applied to model-based advanced control system design. Although the local linear model correctly captures the trends and time constant of the step response, it has large error in predicting the dynamics with random input signals, which will lead to the degradation of the control performance when the CO\({}_{2}\) capture plant operates flexibly under a wide operating range.
## 3 Attacks in Blockchain
### Detecting and Mitigating Malicious Blockchain Attacks
#### 3.3.2 Smart Contract Vulnerabilities  
Smart contracts, being self-executing and immutable, are susceptible to vulnerabilities that can lead to significant security breaches. Detection techniques for these vulnerabilities are broadly categorized into static and dynamic analysis methods.  
Detection Techniques Static analysis methods include the following:
* Symbolic execution: Code is analyzed with symbolic values, enabling the generation of algebraic terms and propositional formulas to uncover logical errors [71; 72].
* Control flow graph (CFG) construction: Represents execution flow as a directed graph, aiding understanding of the program's structure [71; 72].
* Pattern recognition and rule-based analysis: Matches code against predefined secure patterns or known vulnerabilities [57; 72].
* Decomposition analysis: Translates lower-level bytecode into higher-level representations for easier parsing and vulnerability identification [71].
* Formal verification: Uses mathematical proofs to ensure the program adheres to its specified properties [73]. Dynamic analysis methods include the following:
* Runtime Execution Trace: Captures the sequence of instructions executed during runtime for anomaly detection [71].
* Fuzz Testing: Provides structured data as inputs to trigger unexpected behavior, such as crashes or abnormal execution paths [72].  
Mitigation Strategies  
To enhance smart contract security, the following measures are crucial:  
* Adopting established design patterns and best practices to minimize coding errors.
* Implementing robust access control mechanisms to prevent unauthorized interactions.
* Conducting thorough security audits and regular code reviews to identify potential vulnerabilities.
* Utilizing comprehensive testing methods, including fuzz testing and formal verification [73].
* Deploying upgradeable contracts to allow future improvements without disrupting the system.
* Monitoring deployed contracts and addressing detected vulnerabilities proactively [71; 72].
## 3 Results and Discussion
### Experimental Data  
The high-speed imaging showed that the internal air-core is fully developed in all tested regimes, cylindrically shaped, and being larger in diameter at the exit orifice which is a well-known phenomenon [6; 28], see Figure 4. With increasing \(Re\), the air-core diameter slightly increases as discussed in [7], for diameter values see Table 2, where both \(d_{acc}\) and \(d_{ac11}\) grow almost linearly with increasing \(Re\). The prominence of the surface waves and distortions decreases with \(Re\) but the distortions of the discharged liquid sheet increase.  
The measured swirl velocity profiles are identical in all axial positions, see Figure 5. This is expected since it is a basic assumption of all inviscid models. The velocity profile represents a Rankine vortex; however, the viscous losses reduce its peak velocity. The measured velocity can be interpolated by a simple equation \(w^{b}=const\), where \(b\) is an empirical constant which is proportional to the viscous losses [29].  
With increasing \(Re\), the relative swirl velocity slightly increases, which suggests smaller viscous losses with higher \(Re\), see increasing values of \(b\) constant from 0.8 to 0.9 for \(Re=1000\) and 4000, respectively. Increasing relative swirl velocity causes an increase in the air-core diameter. This is in alignment with decreasing \(C_{D}\), as shown in Table 2, which also well correlates with the enlarging air-core.  
The axial velocity was measurable only in positions \(d_{ac8}\) and \(d_{ac11}\) due to limited optical access. It has a local maximum near the atomizer wall and a global maximum at the air-core surface (Figure 6). The position and relative value of both maxima remain the same for all measured regimes.  
Figure 4: Typical results obtained from HS imaging. From left: \(Re=1000\), 2000 and 4000.  
The turbulence intensity of the swirl velocity is between 7 and 9% with a slightly decreasing trend with \(Re\), but with much higher values near the air-core and swirl chamber walls where it reaches the values above 30%. It was found independent of the axial position. The small influence of \(Re\) suggests that no transition effect occurs in the studied range of \(Re\) values between 1000 and 4000. However, the values of the turbulence intensity must be taken with respect to the long measuring volume of the backscatter LDA, which may introduce a false indication of turbulence flow character.
## 4 UAV-TPUA: UAV Trajectory Progress Updating Algorithm
### Angle Measurement of URA  
When using URA, array elements are distributed in the \(yz\)-plane in a rectangular lattice while the array bore sight is along the \(x\)-aixs. For a wave propagation in a direction described by azimuth \(\phi\) and elevation \(\theta\), the wave-vector \(\mathbf{k}\) is given by:  
\[\mathbf{k}(\phi,\theta)=(k_{x},k_{y},k_{z})=\frac{2\pi}{\lambda}(\sin\theta \cos\phi,\sin\theta\sin\phi,\cos\theta) \tag{4}\]  
The steering vector represents the set of phase delays for an incoming wave at each sensor element. For a plane wave that is described by a wave vector \(\mathbf{k}\), with \(N\) elements in an antenna array, steering vector \(\mathbf{w}(k)\) is an \(N\times 1\) complex vector representing the relative phases at each antenna and is given by:  
\[\mathbf{w}(\theta,\phi)=[e^{-j\mathbf{k}\cdot\mathbf{r}_{1}},e^{-j\mathbf{k} \cdot\mathbf{r}_{2}},\ldots,e^{-j\mathbf{k}\cdot\mathbf{r}_{M}}]^{T} \tag{5}\]  
where \(\mathbf{r}_{i}=(x_{i},y_{i},z_{i})\) is the location of the \(i\)th antenna element. To estimate the AoD, the base station goes through a beam training process with a code book containing a set of code words \(C^{P}=\{\mathbf{w}^{1},\ldots,\mathbf{w}^{P}\}\). Therefore, a coarse estimation for the AoD of the propagation path is described as:  
\[p^{*}=\arg\max_{p\in\mathcal{P}}A^{p} \tag{6}\]  
where the beam with code word \(\mathbf{w}^{p}(\theta,\phi)\) maximizes the received power \(A^{p}\) on the UE. Without considering the noise, the ideal received signal strength is given by:  
\[A^{p}=\sqrt{(\mathbf{hw}^{p}(\theta,\phi))^{H}(\mathbf{hw}^{p}(\theta,\phi))} \tag{7}\]  
Thus, using the beam index \(p\), the UE can derive the azimuth (\(\theta\)) and elevation (\(\phi\)) from the positioning assistance data sent to the UE.
## 4 Data Management  
### General Aspects  
Novel SHM systems typically require many sensors to record, collect, and process vast amounts of data, opening up the era of big data in the field. Consequently, an efficient SHM system must also cope with these three main challenges: (1) how to transmit and store data, (2) how to process data to extract meaningful information, and (3) how to make processed data easily serviceable by end-users. To this aim, the recent developments in digital technologies, including the Internet of things (IoT), the development and adoption of new algorithms for data processing (e.g., machine learning techniques), and building information modelling (BIM) are facilitating the management of monitoring datasets [177]. All these relatively new advances contribute to condition-based maintenance, identifying in real-time prognosis and diagnosis, avoiding maintenance based on collapse or guaranteeing non-negligible economic benefits.  
The use of all these technologies has undoubtedly benefited from the development of numerous wireless sensor network (WSN) technologies that permit a real-time, pervasive, non-intrusive, low-cost, and highly flexible data collection and analysis [73; 178], particularly suitable for historic structure SHM [179; 180; 177; 181; 62; 182; 183; 184; 185]. Indeed, the artistic/architectonical value of heritage constructions typically requires employing sensors that minimise the use of cables.  
An efficient management system should also be easily accessible by people to support and guarantee society's engagement (not just people working specifically in the sector, i.e., engineers or architects) in the conservation processes. Concerning cultural heritage management, although an increasing number of countries are encouraging open cultural heritage data and promoting its safety and reuse, systematic digital tools for the preventive conservation and management of cultural heritage are still lacking [144]. Within this scope, it is worth mentioning the recently concluded EU project \"HeritageCare--Monitoring and Preventive Conservation of Historic and Cultural Heritage\" [145; 186; 187], which developed a series of standardised protocols, based on the IoT and Web-GIS system, for the diagnosis and management documentation of built cultural heritage. One of these tools, a web platform combining the latest advances in geodatabase models, interoperability protocols and digitization strategies, named PlusCare [186], was exploited to integrate all data acquired during the digitization of two ancient churches [172]. The system creates enriched digital models to provide owners and curators with an intuitive data-driven tool for planning preventive measures and future interventions based on the actual conservation needs of the building, according to best proactive practices.  
The following sections provide a state-of-the-art description of the latest advances in the IoT-SHM systems, novel algorithms for data processing, and BIM in heritage structures.