esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Ground Motion  
The seismic input to the auxiliary building structure was developed through a site response analysis to consider the site amplification effects. The median response spectrum shape for the 5% damping ratio defined in NUREG/CR-0098 ([PERSON] 1978) was adopted as the target response spectrum of bedrock motions [33]. The response spectrum was anchored to a maximum ground acceleration of 0.3 g. The site condition was assumed to be rock, defined by V/A = 36 in\(\cdot\)s/g and AD/V\({}^{2}\) = 6.0, where A is the peak ground acceleration, V is the peak ground velocity, and D is the peak ground displacement D. The target response spectrum of the bedrock is plotted in Figure 5.  
Figure 4: Stress subjected to the dead and seismic loads: (**a**) Shear stress of the 4 th Floor slab; (**b**) bending stress of 4 th Floor cracked in flexure; (**c**) Shear stress of an H2 direction wall; (**d**) bending stress of an H2 direction wall.  
Figure 5: Target, mean, and individual response spectra of the matched ground motions: (**a**) H1; (**b**) H2; (**c**) V.  
Spectral matching of the recorded ground motions was performed on the target response spectrum. Seed ground motions were selected from a total of 89 sets available from the list of ground motions that are appropriate for matching a rock site with the characteristics of the central and eastern United States (CEUS) presented in NUREG/CR-6728. The selected ground motion sets were matched to the target spectrum, and the five sets with the least change in the waveform were selected. See [PERSON] et al. (2022) for the detailed selection process [34]. The selected ground motions are listed in Table 3. The mean response spectrum of the five matched ground motions in each direction so that it is between the upper and lower bound defined by 1.3 and 0.9 times the target response spectrum, respectively, as shown in Figure 5[35].  
Site response analysis was performed for the shear wave velocity profile S1, one of several profiles applied to the design of the auxiliary building of the APR 1400 nuclear power plant. The free-field surface ground motions from the site response analysis were used as seismic inputs to the base of the structure to calculate the ISRS at a number of locations. In addition, the average response spectrum at the ground surface, as shown in Figure 6, was applied to the crack evaluation analysis described above.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No** & **Earthquake Name** & **Year** & **Magnitude** & **Rjb [km]** & **Vs30 [m/s]** \\ \hline
1 & Southern Calif & 1952 & 6.00 & 73.35 & 493.5 \\
2 & Victoria Mexico & 1980 & 6.33 & 13.8 & 471.53 \\
3 & Northridge-01 & 1994 & 6.69 & 4.92 & 2016.13 \\
4 & Kocenal Turkey & 1999 & 7.51 & 31.74 & 347.62 \\
5 & Duzee Turkey & 1999 & 7.14 & 12.02 & 293.57 \\ \hline \end{tabular}
\end{table}
Table 3: Ground motion records.  
Figure 6: Mean response spectra of the surface ground motions obtained from site response analysis: (a) H1; (b) H2; (c) V.
# Wind Speed Measurement via Visual Recognition of Wind-Induced Waving Light Stick Target
## 3 Wind Speed Calculation
### Mechanical Formulation  
To explain the observations made during the wind tunnel calibration, mechanical analysis is performed using the Free Body Diagram (FBD) of the light stick under wind load. When the stick is lifted by wind load in yaw angle of \(\alpha\), the resultant force of wind load \(F_{\text{W}}\) and stick gravity force \(G_{\text{S}}\) is strictly along the stick to balance the reaction tension force at the end of stick, as in Figure 13. In the force equilibrium there is no extra shear force that will rotate the stick and change the yaw angle. Thus, the wind load and gravity force yield the geometry relation in Equation (29) below.  
\[F_{\text{W}}=G_{\text{S}}\cdot\text{tan}\alpha \tag{29}\]  
where \(F_{\text{W}}\) is the overall horizontal wind load and \(G_{\text{S}}\) is the total gravity force on the stick.  
The gravity force \(G_{\text{S}}\) on the stick is constant, since it is related to density, length, and section area of the stick. However, the wind load \(F_{\text{W}}\) transforms with wind pressure  
Figure 12: Scattering diagram of wind speed vs. yaw angle.  
Figure 13: Mechanical diagram of light stick under wind loads. (**a**) FBD diagram of light stick; (**b**) projection area in different attitudes.  
and projected stick area \(A_{\rm S}\) in a perpendicular plane to wind direction. Wind pressure \(P_{\rm W}\) is the quadratic power of wind speed \(v_{\rm w}\). Thus, we have:  
\[F_{\rm W}=P_{\rm W}\cdot A_{\rm S}=(0.5\rho\cdot v_{\rm w}{}^{2})\cdot[C_{\rm SHP }\cdot(L\cdot d)\cdot\cos\alpha] \tag{30}\]  
where \(\rho\) is the air density, \(C_{\rm SHP}\) is the shape coefficient of the cylindrical object, \(L\) and \(d\) are the length and diameter of the stick as in Figure 13b, respectively.  
With Equations (29) and (30), the mechanical formulation between wind speed and yaw angle is given in Equation (31) below.  
\[v_{\rm w}{}^{2}=C_{\rm AZV}\cdot(\tan\alpha/\cos\alpha) \tag{31}\]  
where \(C_{\rm AZV}=G_{\rm S}/(0.5\rho\cdot C_{\rm SHP}\cdot L\cdot d)\), which is related to stick mass, length and diameter, shape coefficient, and air density.  
With the help of the mechanical formulation in Equation (31), a linear relationship is observed by recalculating the quadratic power of wind speed and the compound trigonometric function of yaw angle from experimental data in Table 1, as depicted in Figure 14. Possible sources of error may include the disturbance of the uniform flow around the stick by the cantilevered supporting arm and the circular rod, which are necessary intrusions for the test, and the friction between the target and the circular rod contact area.  
The results from adopting the fitting from the mechanical formulation are encouraging. The wind speed calculation from the stick yaw angle due to horizontal wind loads is hence given as in Equation (32).  
\[v_{\rm w}=\sqrt{C_{\rm AZV}\cdot(\tan\alpha/\cos\alpha)} \tag{32}\]  
where \(C_{\rm AZV}=45.822\)\({\rm m}^{2}/{\rm s}^{2}\) according to experimental calibration fitting.
# Novel Methodology for Condition Monitoring of
###### Abstract  
In gearboxes, the occurrence of unexpected failures such as wear in the gears may occur, causing unwanted downtime with significant financial losses and human efforts. Nowadays, noninvasive sensing represents a suitable tool for carrying out the condition monitoring and fault assessment of industrial equipment in continuous operating conditions. Infrared thermography has the characteristic of being installed outside the machinery or the industrial process under assessment. Also, the amount of information that sensors can provide has become a challenge for data processing. Additionally, with the development of condition monitoring strategies based on supervised learning and artificial intelligence, the processing of signals with significant improvements during the classification of information has been facilitated. Thus, this paper proposes a novel noninvasive methodology for the diagnosis and classification of different levels of uniform wear in gears through thermal analysis with infrared imaging. The novelty of the proposed method includes the calculation of statistical time-domain features from infrared imaging, the consideration of a dimensionality reduction stage by means of Linear Discriminant Analysis, and automatic fault diagnosis performed by an artificial neural network. The proposed method is evaluated under an experimental laboratory data set, which is composed of the following conditions: healthy, and three severity degrees of uniform wear in gears, namely, 25%, 50%, and 75% of uniform wear. Finally, the obtained results are compared with classical condition monitoring approaches based on vibration analysis.  
Keywords:infrared thermography; thermal analysis; supervised learning; gearbox; sensor +
Footnote †: journal: Applied
## 4 Performance Evaluation of Offshore Communications  
In order to provide some effective reference to the construction of offshore wireless communication networks, this paper will also make a performance evaluation of the communication capability based on the measurements mentioned above.
# Ad-RuLer: A Novel Rule-Driven Data Synthesis Technique for Imbalanced Classification
## 3 Method
### Ad-RuLer Oversampling Approach
A \(ratio=0.5\) indicates that at least \(50\%\) of the input data instances should be included to accept the candidate rule. Take, for instance, the potential rule \(r_{1,2}=[\{1,3\},\{5,6\},\text{positive}]\). The complete set of data that could be generated by \(r_{1,2}\) encompasses \([\{1\},\{5\},\text{positive}]\), \([\{1\},\{6\},\text{positive}]\), \([\{3\},\{5\},\text{positive}]\), \([\{3\},\{6\},\text{positive}]\). If we set the _ratio_ to \(0.5\), it implies that half of the data must be present in the input data. If this condition is not met, the potential rule is discarded. **Balanced Resampling**: In the RuLer system, the synthesis of all potential instances of the minority class is achieved using extracted patterns. By using the _ratio_ parameter, the volume of the data generated can be adjusted to approximate the desired quantity as  
Figure 1: The flowchart of Ad-RuLer for imbalanced data processing. closely as possible. However, it is important to note that exact control over the volume of produced data is not feasible. To compensate for this variation, Ad-RuLer introduces a resampling phase. The initial step involves the calculation of \(n\), the difference between the number of synthesized samples \(X_{r}\) and the target sample size \(S\). If \(n>0\), RUS is applied. This process randomly removes \(n\) instances from the synthesized data, thereby aligning the volume to the balanced level. Conversely, if \(n<0\), ROS is implemented. This process randomly replicates \(|n|\) instances from the synthesized data, effectively increasing the minority class volume to meet the targeted level. If \(n\) equals zero, it signifies that the synthesized instances are in perfect alignment with the targeted count, implying that the dataset is already balanced and does not require further adjustments  
```
1:\(X\): input data, \(d\in\mathbb{N}\): user-defined dissimilarity threshold, \(ratio\in[0,1]\): ratio for creating new rules, \(S\): required sample size
2:Oversampled data \(X_{r}^{\prime}\)
3:\(rules\leftarrow RulesCreation(X)\)
4:\(newRules\leftarrow[]\)
5:for\(i=0\)to size of rulesdo
6:\(r1\gets rules[i]\)
7:for\(j=i+1\)to size of rulesdo
8:\(r2\gets rules[j]\)
9:\(pattern\leftarrow dissimilarity(r1,r2,d)\)
10:if\(pattern\)then
11:\(rule\gets createRule(r1,r2,ratio,rules)\)
12:if\(rule\)then
13:\(newRules.append(rule)\)
14:endif
15:endif
16:endfor
17:endfor
18:\(rules.append(newRules)\)
19:\(rules\leftarrow deleteRedundant(rules)\)
20:\(X_{r}\gets samplesCreation(rules)\)
21:\(n\gets NumberDifference(X_{r},S)\)
22:if\(n>0\)then
23:\(X_{r}^{\prime}\gets randomUndersampling(X_{r},n)\)
24:elseif\(n<0\)then
25:\(X_{r}^{\prime}\gets randomOversampling(X_{r},abs(n))\)
26:else
27:\(X_{r}^{\prime}=X_{r}\)
28:endif
29:return\(X_{r}^{\prime}\)
```  
**Algorithm 1** Rule-based Data Synthesizer
## 3 Materials and Methods
### Experiments on a Tissue-Mimicking Phantom  
A tissue-mimicking breast phantom (Model BPB170, Blue Phantom, Redmond, WA, USA) was used to provide a simulated mass, as shown in Figure 3. Five independent scans of the simulated mass were performed using the same Terason ultrasound scanner and the transducer. For each scan, the transmitting central frequency was set at approximately 5, 5.5, 6, 7.5 and 8 MHz by using the software development kit to acquire image RF data corresponding to different frequencies. The RF data comprised 128 scan lines of the backscattered echoes, corresponding to a size of 3.5 \(\times\) 3.5 cm (depth \(\times\) width). The same procedures as mentioned in the previous subsection were used to obtain the B-mode and IMF-based images. For each image, a ROI with a size of 5\(\times\)5 mm was located in the mass to calculate the average intensities of the \(C_{1}\) and \(C_{2}\) images for calculating the IER. The image intensities (\(C_{1}\) and \(C_{2}\)) and the IER as functions of frequency were plotted for comparison.  
Figure 3: The experimental setup for imaging the tissue-mimicking breast phantom using the commercial ultrasound scanner.  
Figure 2: Illustration to explain the intrinsic mode functions (IMF) based echogenicity ratio (IER) calculation. The raw RF image data were decomposed into the IMFs) by using the 2D empirical mode decomposition (EMD). Higher-frequency IMFs exhibit larger signal amplitudes and relevant physical meanings; thus, IER was defined using the first (\(C_{1}\)) and second (\(C_{2}\)) IMFs for signal normalization.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### Cyber Security of IECPS
#### 5.4.2 Communication Authentication and Protection  
In IECPS, unified communication between different energy subsystems is becoming increasingly necessary, but it also brings many security issues, which can be categorized into signaling security, streaming media transmission security, and IP backbone network security [212]. For signaling security, the HTTP digest verification mechanism is used to ensure authentication security, while the transport layer security protection mechanism is employed to ensure the encryption of signaling. Streaming media transmission security can be achieved by using secure real-time transport protocol for encryption, ensuring the confidentiality and functionality of streaming media during transmission. As for the security of IP backbone networks, it is typically ensured using firewall technologies, demilitarized zones, and virtual network technologies. The demilitarized zone and virtual network are used to address the issue where users accessing external networks after firewall installation cannot reach internal network servers, and these methods are widely applied by grid operators. With the advancement of communication and authentication protection technologies, more effective communication protection methods, such as quantum communication technology and software-defined networking, are gradually being applied.
## 2 Data and Methods
### Coherence Analysis  
The coherence \(\gamma_{ij}\) between two signals \(i\) and \(j\) provides a measure of the similarity of their respective spectra in a specific frequency band, Equation (4):  
\[\gamma_{ij}(\omega)=\frac{lF_{i}^{*}(\omega)\;F_{j}(\omega)l}{\sqrt{lF_{i}( \omega)l^{2}\;lF_{j}(\omega)l^{2}}} \tag{4}\]  
where \(F_{i}(\omega)\) and \(F_{j}(\omega)\) are the spectra of the signals i and j, respectively, and F\({}_{i}^{*}(\omega)\) is the conjugate of F\({}_{i}(\omega)\). A smoothing on a chosen bandwidth is necessary to obtain values between 0 and 1, where higher values correspond to signals with very similar spectral characteristics [22]. The coherence defined in Equation (4) can be extended to more than two signals through the computation of the cross-spectral matrix, and thus, it is a multi-station analysis method. In our work, we used the signals recorded by the three stations BKSG, BKWG, and SVAG, which were utilized for the computation of spectral moments. As described above, we adopted adjacent 30 s signal windows to apply the coherence analysis to continuous data. The calculation was performed with a smoothing parameter of 20 and focused on a frequency of 4 Hz, in agreement with the spectral features of low-frequency events observed at Vesuvius by [PERSON] and [PERSON] [12]. The result of coherence analysis provides one value that is the coherence of the seismic signals among the three sites. Since low-amplitude LF events are almost always characterized by emergent onset and unclear waveforms in the time domain, the coherence provides more robust information than other mathematical tools in the time domain (such as cross-correlation) because it is based on the spectral characteristics of the analyzed signals. The coherence was computed on the vertical component of ground motion, and more than 28 months of continuous data have been analyzed. The results show that most of signals, at the selected frequency, have a value of coherence less than 0.4. Moreover, we have observed that the signals with coherence \(\geq\) 0.45 are characterized by spectral amplitude higher than the background noise. On the basis of this observation, we set as a coherence threshold the value of 0.45.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 1 Introduction
### Automated Land Use Mapping  
Most studies that have used deep learning to automatically map land use features have a constrained geographical extent and are limited to a standard set of training images, for example the University of California Merced Land Use Dataset [38] and Banja-Luka [39]. Although this is advantageous in benchmarking different methodologies, no studies have operationalized the applications for real-world land use mapping over a large geographical area [23; 40].  
Previous work in Queensland has focused on land cover. [PERSON] et al. [41] developed a time-series based method to operationally map summer and winter crops, and [PERSON] et al. [42] used U-Net to map land cover, specifically the extent of woody vegetation cover, to a resolution of 1 m. However, there is an absence of studies that classify land use in Australia using earth observation data with a resolution of less than 10 m which is required for detailed land use mapping [43; 44] and the resolution in which CNNs have the most success [23].  
The aim of this study is to demonstrate that using a convolutional neural network and high-resolution imagery (<1 m) to automate land use mapping is more rapid and accurate than existing manual methods. This would be of benefit to the on-going response to Panama TR4, future biosecurity incidents, and other events requiring a rapid response (e.g., natural disasters). Additionally, the improved land use data would better inform natural resource planning and monitoring, biodiversity conservation, and the monitoring and modelling of the effects of land management practices on water quality.
## 2 Background and Literature Study
### Necessity of Data Security and Privacy
Phishing: A type of cyber-attack in which attackers deceive individuals into providing sensitive information such as usernames, passwords, credit card numbers, or other personal details. This is typically achieved by masquerading as a trustworthy entity in electronic communications. Password Attacks: Such attacks are conducted to gain unauthorised access to systems and data by cracking or bypassing passwords. These attacks exploit weak or compromised passwords to infiltrate accounts and networks, leading to potential data breaches, identity theft, and other malicious activities.
## 4 Model  
The overall architecture of RGISQL adopts an encoder-decoder structure, as illustrated in Figure 3. The input for RGISQL consists of natural language questions and database schemas. Initially, the model extracts schema encodings, schema linking, question relationships, and relationships among segments, tables, and columns obtained through segmentation processing, which are represented as an interaction graph. In the input module, BERT is used to process the interaction graph, generating node embeddings X and edge embeddings R. The encoder employs a relation graph attention network (RGAT) architecture, which includes both node-centric and edge-centric graphs. During the encoder's output stage, multiple relation-aware transformer (RAT) layers integrate the updated embeddings to form a unified representation. Finally, a decoder based on an abstract syntax tree performs recursive decoding on the unified representation to generate the final SQL query.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 3 FLMPC: Proposed Protocols
### Network Architecture  
Before describing architecture, few terms need to be defined here: _Relay nodes_ are the sensor nodes which not only forward their sensed data but also forward data of higher depth nodes. While, _Cross nodes_ are the sensor nodes lying near or at the layers.  
Both LMPC and FLMPC consist of relay nodes, cross nodes, surface gateways and a sink. The sink works as an embedded system to manage the node battery in an efficient way. All the sensor nodes are randomly distributed in the network. Initially, all the nodes are sleeping and become active when they receive data packet. If they are not taking part in communication, they get back to sleep mode. Binary tree is established in both schemes for multiple-path transmission of a data packet. Let's assume a scenario depicted in Figure 1 in which LMPC network field be distributed into homogeneous layers. From every node, two copies of the data packet are generated irrespective of the layer, until it reaches the destination. Now, it is obvious that noise at the bottom of the sea is very low, where we can afford to generate one data packet, however, the noise increases as we approach the surface of the water.  
Therefore, we need to generate multiple copies closer to the water surface. In this regard, we have divided the network field into unequal layers as illustrated in Figure 2. In addition, the source node  
Figure 1: Network Architecture of layered multipath power control (LMPC).  
generates only one copy of the data packet at the lower layers, however, as it propagates towards the destination, it starts transmitting two copies through cross nodes deployed over the layer. If, cross node is not present, then only single copy is passed on through a relay node. The source and relay node unicast data packet upon reception. Contrary to source node, a cross node uses IP multicast technology to transmit data packet. Both existing and proposed schemes direct multiple copies of a data packet towards gateways through different paths in order to reduce the retransmissions. All the copies of a data packet are relayed to the sink where they are combined to generate original data packet. Unlike LMPC, FLMPC generates binary tree from cross nodes only with the aim of reducing collisions. Acoustic modem is installed in gateways to receive data from sensor nodes while a radio modem is used to direct collected data towards the sink.