esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Shortlisting of Seismic Attributes
### Curvature Attributes  
Here, curvature measured the extent of the bending of a surface at a specific point using volumetric methods and analyzed the geometry distortion of the seismic volume instead of analyzing the variations in the seismic amplitude. Curvature is an effective tool to detect small scale faults and fractures with low displacement that would otherwise not be possible to identify with other fault attributes such as coherency. Compared to coherency, the curvature attribute draws the discontinuous features using minimum and maximum anomalies. Thus, it does not depict the precise position of a fault [83]. The surface of a reflector is supposed to be a quadratic surface to compute the curvature attribute [28].  
\[z\left(x,y\right)=ax^{2}+by^{2}+cxy+dx+ey+f \tag{3}\]  
whereas,  
\[a=\frac{1}{2}\;\frac{\partial p}{\partial x}\]  
\[b=\frac{1}{2}\;\frac{\partial q}{\partial y}\]  
\[c=\frac{1}{2}\left(\frac{\partial q}{\partial x}+\frac{\partial p}{\partial y}\right)\]  
\[d=p\]  
\[e=q\]  
where \(p\) is the volumetric estimation of the inline-dip, q is the crossline-dip, and the coefficients become \(x=y=0\)[25].  
Multiple curvature attributes were applied in the current study; these were maximum curvature, minimum curvature, the most positive and the most negative curvature attributes. Each of these curvature attributes had different parameters regardless of their signs to detect the fault and fracture discontinuities [11]. The results showed that the maximum curvature attribute was the most beneficial in terms of delineating the fractures in the study area (Figure 5a-d).  
Figure 4: Time slices at 1780 ms of (**a**) inline-dip, (**b**) polar-dip, (**c**) azimuth, and (**d**) crossline-dip attributes. The yellow arrows show differences in discontinuities, while the red arrow shows a small scale faults (SSF). The results of the polar-dip were more effective in highlighting the discontinuities.
# Improvement of [PERSON]'s Stripe Noise Suppression Method for Side-Scan Sonar Images
## 3 Experiments
### Evaluation Metrics  
This study employed the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) [31] as evaluation metrics to assess the effectiveness of striped noise elimination.  
The PSNR is a vital measure in image quality assessment, and it is widely used in image processing. In image denoising, the noise power is determined by computing the mean squared error (MSE) between the denoised image and the original image. For an original image (\(I\)) and its denoised version (\(R\)), the \(MSE\) is computed as follows:  
\[MSE=\frac{1}{MN}\sum_{i=1}^{M-1}\sum_{j=1}^{N-1}\Big{[}I(i,j)-R(i,j)\Big{]}^{2}\]  
Figure 3: Denoising effect based on feature similarity matching criteria (the red box indicates the fringe noise area). (**a**) Noisy image. (**b**) Denoised image.  
The \(PSNR\) is defined as follows:  
\[PSNR=10 log_{10}\Bigg{(}\frac{Max_{1}^{2}}{MSE}\Bigg{)}\]  
where \(Max_{1}^{2}\) represents the maximum pixel value of the original image. In image denoising research, a higher \(PSNR\) indicates that the denoised image R contains less noise, thus implying better denoising effectiveness.  
\(SSIM\) is a metric used to gauge image similarity, and it is also applicable for assessing compressed image quality. This study utilized \(SSIM\) to evaluate denoised image quality by computing the structural similarity between the denoised and original images. \(SSIM\) assesses image similarity based on three factors: luminance, contrast, and structure.  
Given two input images, x and y, the definition of \(SSIM\) is as follows:  
\[SSIM(x,y)=[l(x,y)]^{a}[c(x,y)]^{b}[s(x,y)]^{\gamma}\qquad(\alpha,\beta,\gamma>0)\]  
with  
\[l(x,y)=\frac{2\mu_{x}\mu_{y}+c_{1}}{\mu_{x}{}^{2}+\mu_{y}{}^{2}+c_{1}}\]  
\[c(x,y)=\frac{2\sigma_{xy}+c_{2}}{\sigma_{x}{}^{2}+\sigma_{y}{}^{2}+c_{2}}\]  
\[s(x,y)=\frac{\sigma_{xy}+c_{3}}{\sigma_{x}+\sigma_{y}+c_{3}}\]  
where \(l(x,y)\) is the brightness comparison, \(c(x,y)\) is the contrast comparison, and \(s(x,y)\) is the structure comparison. \(\mu_{x}\) and \(\mu_{y}\) denote the mean values of \(x\) and \(y\), respectively, while \(\sigma_{x}\) and \(\sigma_{y}\) represent the standard deviations of \(x\) and \(y\), respectively. \(\sigma_{xy}\) denotes the covariance between \(x\) and \(y\), and \(c_{1}\), \(c_{2}\), and \(c_{3}\) are constants used to avoid division by zero errors.  
In practical calculations, it is common to set \(\alpha=\beta=\gamma=1\) and c_3 = c_2/2. This simplifies the definition of \(SSIM\) as follows:  
\[SSIM(x,y)=\frac{\big{(}2\mu_{x}\mu_{y}+c_{1}\big{)}\big{(}\sigma_{xy}+c_{2} \big{)}}{\big{(}\mu_{x}{}^{2}+\mu_{y}{}^{2}+c_{1}\big{)}\big{(}\sigma_{x}{}^{ 2}+\sigma_{y}{}^{2}+c_{2}\big{)}}\]  
\(SSIM\) ranges from 0 to 1, with a higher value indicating less disparity between the output image and the undistorted image, indicating superior image quality. When two images are identical, \(SSIM\) equals 1.
# Nonlinear Stability Analysis of Shallow-Buried Bias Tunnel Based on Failure Mode Improvement
## 1 Introduction
[31] compared the maximum surface settlement due to tunnel excavation obtained from the Mohr-Coulomb model with the results of the hardened soil model and showed that as the depth of the tunnel increases, the settlements on the ground surface decrease, according to Mohr-Coulomb model, and approach the real values. [PERSON], [PERSON] et al. [32] proposed a generalized perturbation solution for elastic brittle plastic rocks around deeply buried non-circular tunnels, based on the Mohr-Coulomb criterion. [PERSON], [PERSON] et al. [33] derived the solution procedure for the radius of the plastic zone and the elastic-plastic stress expression for a given liner internal pressure based on the Mohr-Coulomb criterion. A large amount of experimental data and engineering practice show that most geotechnical materials follow the nonlinear damage criterion [34; 35; 36; 37; 38; 39; 40; 41; 42]. However, the research on the arc failure mode of shallow-buried biased tunnel under the comprehensive action of surface inclination and slope top load needs to be further improved [13; 43; 44; 45]. Therefore, in this paper, the circular arc failure mode of shallow-buried tunnel is constructed, considering the surface tilt and slope top load, combined with the nonlinear failure criterion of the tangent method, using the SQP algorithm in MATLAB (R2022a) software, to obtain the upper limit solution of the surrounding rock pressure limit of the circular arc failure mode under surface tilt and slope top load. Combined with the example analysis, the influence of slope top load, buried depth ratio, cohesion, and axial tensile stress on the surrounding rock pressure and damage mode is studied, which will provide useful guidance for tunnel engineering practice.
## 4 Results
### Analysis Implementation--Adoption of GPP Criteria  
This study identified and analyzed the GPP criteria at European level and the national criteria adopted by the Member States. Figure 5 shows the mapping of the GPP criteria adopted at national level by the Member States of the European Union, with particular attention to those relevant for settlement and urban transformation. The analysis was based on the identification and evaluation of GPP criteria both at European and national levels, selecting those most relevant to the construction sector on an urban scale.  
The selection was made by identifying, for each Member State, the product categories to which GPP criteria were applied. Taking Italy as an example, GPP criteria have been defined for the following categories: interior furnishings, incontinence aids, work shoes and accessories in leather, paper, cartridges, construction, cultural events, public lighting, heating and cooling of buildings, industrial washing and rental of textiles and mattresses, urban waste and street sweeping, collective catering, disinfection, printers, textiles, vehicles, public green spaces and street furniture.  
Those not pertinent to the construction sector and settlement transformation processes have been excluded from these categories. In this way, it was possible to identify macro-categories common to all Member States, including only the products relevant to the construction and settlement transformation sector. The categories identified are building construction, civil engineering, real estate and maintenance, public space maintenance, air conditioning, lamps, energy, electricity, garden, transport and vehicles. For each of these, the application by countries with different levels of relevance was investigated (Figure 6).  
The symbology adopted allows us to distinguish two levels of relevance:  
* Green triangle: Indicates the criteria with a direct link to building interventions and urban transformation processes (e.g., building materials, transport, vehicles, air conditioning systems).
* Green ellipse: Represents criteria with an indirect link, i.e., relevant to urban sustainability but not directly related to building activity (e.g., green management, maintenance, energy, etc.).  
The analysis shows, in Figure 6, considerable heterogeneity between Member States in terms of the adoption of GPP criteria.  
In general, the map shows that some countries (e.g., Italy, Romania, Cyprus) have integrated a wide range of GPP criteria into their public procurement systems, while others have a more limited or selective application.
## 4 Data Aggregation
### Temporal
Then, the sink node estimates the sensed data of each node at the current period using the received data from previous data sensing period and updates it by receiving new values. At the other end, each sensor node does the same prediction using the same data sequence and saves the predicted values in a queue. Therefore, in the next sensing round, each node compares its sensing value with its predicted value in queue and sends its sensed value only if the error between the predicted value and sensed value exceeds the prediction error threshold. Meanwhile, the sink node by not receiving any sensed value from the sensor node uses its predicted value for the sensing value of this round. However, the continuous of successful predictions faces the protocol with two challenges: distinguishing the difference between not sending data due to successful prediction and node failure, and excessive cumulative error. To address the issues, the protocol exploits another threshold value for the number of continuous and successful predictions, which forces the sensor node to send its sensor readings after exceeding the threshold value. The protocol eliminates redundant data transmission tremendously, while it does not impose any overhead transmission for the algorithm implementation. However, to achieve acceptable level of accuracy, it seems essential to consider a wise trade-off between communication overhead and reducing concurrent error.
# InoT Sensor Network for Wild-Animal Detection near Roads
## 2 Materials and Methods  
### IoT Detection System  
The prototype animal detection system must be able to detect animals moving in the roadside area and inform the drivers of passing cars about them. The system consists of separate animal detection and signaling IoT nodes that have the possibility of mutual  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **LoRaWAN** & **Sigfox** & **NB-IoT** \\ \hline \multirow{3}{*}{Range} & \textless{5 km (Urban),} & \textless{10 km (Urban),} & \textless{15 km} \\  & \textless{15 km (Rural)} & \textless{50 km (Rural)} & \textless{15 km} \\ \cline{1-1}  & Sub-GHz ISM: EU & Sub-GHz ISM: EU & \\ \multirow{3}{*}{Spectrum} & (433, 868 MHz) US & (868 MHz) US & \multirow{3}{*}{Licensed (700–900 MHz)} \\  & (915 MHz) & (902 MHz) & \\ \cline{1-1} Bandwidth & 125–500 kHz & 100 Hz & 180 kHz \\ \cline{1-1} Data Rate & 0.3–37.5 kbps (LoRa) & \textless{100 bps (uplink)} & \textless{150 kbps} \\ \cline{1-1}  & 50 kbps (FSK) & \textless{600 bps (downlink)} & \\ \hline \hline \end{tabular}
\end{table}
Table 1: LPWAN technology parameters.  
communication. For testing purposes, the system gateway and thermal vision camera were added.  
The proposed detection-signaling nodes (DSNs) are installed on the roadside-marker posts (Figure 1). Roadside-marker posts, according to the standard, must be installed every 50 m on trunk and country roads. In the cases of straight sections of the roads, the distance between the marker posts can be increased to 100 m due to economical and technical reasons [40]. Roadside-marker posts are anchored to the roadside at least 0.5 m depth.  
In order to ensure flexibility and to simplify the installation of the system on the road section and its expansion, the DSNs are designed to be fully autonomous. As an additional feature, low-power radio modules were provided in each DSN. Radio signals were used to communicate between DSNs to activate signaling beacons in several adjacent DSNs and thereby extend the warning-area distance. Figure 1 shows the animal detection IoT network with the possible placement of the DSN nodes near the road using already existing roadside-marker posts. The system can be made up of DSN units installed next to each other in a row (at max. 50 m). Sensitive areas of operation of adjacent DSN nodes overlap. As the animal moves toward the DSN nodes, several of them are likely to detect and start signaling. The network consists of several DSN nodes that detect animal presence near the road, a photo camera (CAM) that can be triggered using LoRa messages, and a gateway (GW) that logs triggering data received from the DSN. The gateway also can be accessed using a mobile 4G connection.
# A Maturity Model for Trustworthy AI Software Development
## 2 Related Works
### Trustworth AI  
To prevent AI issues, such as ethical discrimination (e.g., discrimination based on gender, race, and/or age) and safety problems (e.g., autonomous driving accidents), a quality assurance process related to several aspects, such as fairness and safety, is required. Research coming out of the EU [13; 17] presents several dimensions of trustworthy AI software, including Safety & Robustness, Non-discrimination & Fairness, Transparency (Explainability, Traceability), Privacy, and Accountability.  
Specifically, bias (unfairness) refers to a property that is not neutral towards a certain stimulus. There are various types of biases, such as cognitive bias and statistical bias. The types of bias can also be classified depending on the characteristics of the data. [PERSON] et al. [18] classify 23 types of bias, including \"historical bias\" caused by the influence of historical data and \"representative bias\" resulting from how we sample from a population during data collection. They also introduce 10 widely used definitions of fairness, including \"equalized odds\" and \"equal opportunity,\" to try and prevent various biases. In addition, they explain bias-reduction techniques in specific AI domains, such as translation, classification, and prediction. For example, a bias in translation can be alleviated by learning additional sentences that exchange words for men and women to solve gender-specific bias that translates programmers into men and housemakers into women. In addition, [PERSON] et al. [19] benchmark ChatGPT using several datasets such as BBQ and BOLD to find AI ethical risks.  
On the topic of enhancing transparency in AI software development, there have been a few research works. For example, [PERSON] et al. [20] propose an \"AI model card\" for describing detailed characteristics (e.g., purpose, model architecture, training and evaluation dataset, evaluation metric) of AI models, and [PERSON] et al. [21] review existing tools, practices, and data models for traceability of code, data, AI models, people, and environment.  
[PERSON] et al. [22] explain the importance of data for trustworthy AI. Specifically, they introduce some critical questions about data collection, data preprocessing, and evaluation data that developers should consider. However, these techniques are still in the early stages of research, and there is a lack of research to test and prevent fairness issues, especially at organizational process maturity levels. Our work focuses on AI software development processes in the form of a maturity model to develop trustworthy AI software, rather than developing testing and evaluation methods of AI products.
# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
###### Abstract  
In the case of strong noise, when the damage occurs at different locations of the frame structure, the fault vibration signals generated are relatively close. It is difficult to accurately diagnose the specific location of the damage by using the traditional convolution neural network method. In order to solve this problem, this paper proposes a novel convolutional neural network. The method first uses wavelet decomposition and reconstruction to filter out the noise signal in the original vibration signal, then uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Analysis) to decompose the filtered signal to highlight the feature information in the filtered signal. Finally, a convolution neural network combined with WDCNN (First Layer Wide Convolution Kernel Deep Convolution Neural Network) and LSTM (Long Short-Term Memory Network) is used to achieve the accurate classification of the signal, so as to achieve the accurate diagnosis of the damage location of the frame structure. Taking the four-story steel structure frame of Columbia University as the research object, the fault diagnosis method proposed in this paper is used to carry out experimental research under strong noise conditions. The experimental results show that the accuracy of the fault diagnosis method proposed in this paper can reach 99.97% when the signal-to-noise ratio is \(-4\) dB and the objective function value is reduced to \(10^{-4}\). Therefore, the fault diagnosis method proposed in this paper has a high accuracy in the strong noise interference environment; it can realize a high precision diagnosis of the damage location of the frame structure under a strong noise environment. The contribution and innovation of this paper is to propose a novel fault diagnosis method based on the convolutional neural network, which solves the problem of accurate damage location diagnosis of frame structures under strong noise environment.  
CEEMDAN; WDCNN; LSTM; frame structure; fault diagnosis
# An Estimation of Speech Privacy Class Based on ISO Parameter
## 4 Results and Discussion
### SPC Confidence Intervals  
Figure 5a displays the confidence intervals calculated for all measurement locations. The maximum values of the 95% confidence intervals for the levels in the transmitting and receiving room are below 1 dB. The confidence interval for the ambient noise level in the receiving room is below 2.1 dB. Based on these results, it is concluded that the ambient noise level is the dominant factor contributing to the total measurement uncertainty [42]. The ambient noise level varies in repeated measurements due to changes in ambient conditions.  
Figure 5b shows the confidence intervals for the \(SPC\) parameter at all measurement locations. The 95% confidence interval for the \(SPC\) is in the range between 0.3 and 2.4 dB. Values of \(\Delta SPC\) are most closely related to the confidence interval values for the ambient noise level. It can be observed that large values were obtained for measurement locations 5, 6, 7, 38, and 40. It is noted that the same locations also exhibit the highest confidence interval values for the ambient noise level.
# Low-Cost Assessment Method for Existing Adjacent Beam Bridges
## 6 Conclusions  
In this paper, a joint damage-based assessment indicator for assessing the degradation degree of lateral load transmission performance of ARHB bridges is proposed.  
This paper modifies the traditional hinge connected beam method by introducing relative displacement in grouted joints. The relationship between joint position and LLDFs is analyzed by a modified hinge connected beam method, and summarized as that the influencing extent of joint-\(i\) on beam-\(j\) decreases approximately exponentially with an increasing number of beams between joint-\(i\) and beam-\(j\).  
This paper proposes a low-cost assessment method that can be combined with visual inspection. This method uses a new assessment indicator, namely, lateral load transmission performance rating number \(LDN\), to assess the degradation degree of load-bearing capacity of ARHB bridge superstructure. The assessment result can be used to determine the maintenance sequence. The accuracy and feasibility of the proposed method have been verified by static load test results of three concrete ARHB bridges.  
Further developments are expected considering the influence of material deterioration of beams on LLDFs of ARHB bridges in order to be more suitable for the needs of actual projects.  
Methodology, S.W.; validation, J.D.; investigation, S.W.; resources, J.D.; data curation, J.D.; writing--original draft preparation, S.W.; writing--review and editing, H.S.; supervision, J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Project of National Engineering Laboratory of Bridge Structure Safety Technology (Beijing), grant number 2020-GJKFKT-6, Transportation Science and Technology Project of Shaanxi Transportation Department of China, grant number 20-10K, and the National Natural Science Foundation of China--National Project for Research and Development of Major Scientific Instruments, grant number 51727813.  
Not applicable.  
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.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Bridge** & \(\mathbf{VD_{ldf}}\) \\ \hline Changhong Tunnel K1 + 812 Bridge & \(VD_{ldf,1}=0.012\) \\ \hline Baidong No. 2 Bridge & \(VD_{ldf,2}=0.016\) \\ \hline Yunhe Bridge & \(VD_{ldf,3}=0.0055\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: \(VD_{ldf}\) of Changhong Tunnel K1 + 812 Bridge, Baidong No. 2 Bridge, and Yunhe Bridge.