esa-sceva/satcom-chunk-collection huggingface dataset:
# A Highway Pavement Crack Identification Method Based on an Improved U-Net Model
## 3 Methods
### Improving the U-NET Model Bottleneck Layer  
The encoder part and the decoder part of the U-Net network are connected by a bottleneck layer. The purpose of adding a bottleneck layer is to extract deeper image features and make a smooth transition between the down-sampling part and the up  
Figure 3: Multi-scale U-Net network structure diagram.  
sampling part. In the original U-Net network, which is the traditional U-Net network, the bottleneck layer is composed of two convolutional layers. The bottleneck layer [37] contains the deep feature information obtained in the encoder, and since these deep feature data are highly representative of their input images, these deep feature data are transmitted to the decoder part through the bottleneck layer structure, which can interfere with the deep abstract feature data in the feature map. It can be seen that the bottleneck layer structure plays an important role in the segmentation effect on the final prediction of the image. The U-Net network bottleneck layer structure is shown in Figure 4.  
This paper improved the bottleneck layer of the original U-Net network to further improve the segmentation effect of pavement crack images by improving the bottleneck layer. This paper proposed a normalized bottleneck layer design and used this bottleneck layer as a new bottleneck layer to replace the original bottleneck layer of the traditional U-Net network. The new bottleneck layer is a batch normalization (BN) layer [38] and a dropout layer [39], which were added after the original bottleneck layer, and the purpose of doing so was to perform batch normalization and regularization on the convolutional output of the original bottleneck layer. The structure diagram is shown in Figure 5.  
The new bottleneck layer contains two convolutional layers, both of which have 1024 convolutional kernels, and these two convolutional layers are the ones that expand the dimensionality of the input image. As can be seen from the above figure, two batch normalization layers were added after each of the convolutional layers for the purpose of normalizing the activation values and preventing the network model from overfitting. The regularization layer was added after the second batch normalization layer to regularize the output image, which means that the activation value of a neuron will be stopped with a certain probability, P, during the training process to improve the generalization ability and stability of the network model.
# Software Reliability Growth Model with Dependent Failures and Uncertain Operating Environments
## 3 NHPP Software Reliability Growth Model (SRGM)
### Proposed Model  
Most models assume independent failures (see Section 3.3); however, this paper assumes dependent failures. For example, if an error occurs in a class in a certain program code, it may occur in chains in other classes that refer to this class. In this case, the failures are dependent, and the model proposed in this study also assumes the same type of failure.  
We propose a model that considers an uncertain operating environment and dependent failures. We consider the second type owing to the fact that we need to assume an uncertain operating environment. When \(a(t)=N\) and \(b(t)=\frac{b^{2}t}{1+bt}\) are given, \(m(t)\) can be obtained from Equation (8) as follows:  
\[m(t)=N\bigg{(}1-\frac{\beta}{\alpha+\int b(s)ds}\bigg{)}^{\alpha}=N\Bigg{(}1- \frac{\beta}{\alpha+\int_{0}^{t}\frac{b^{2}s}{1+bs}ds}\Bigg{)}^{\alpha} \tag{11}\]  
\[=N\bigg{(}1-\frac{\beta}{\alpha+bt-\log(bt+1)}\bigg{)}^{\alpha} \tag{12}\]  
where \(b\), \(\alpha\), \(\beta\), and \(N\) are parameters of the proposed model. The parameters \(\alpha\) and \(\beta\) in Equation (12) are the same as those in Equation (9). Therefore, parameters \(\alpha\) and \(\beta\) of \(m(t)\) are dependent on the probability distribution of \(\eta\), which indicates uncertain operating environments. This relationship leads to an assumption of dependent flaws in the model.
## 2 Materials and Methods
### Phase 2: Remote Sensing with an Infrared Camera  
In the second phase of this integrated approach, thermal images (thermograms) of the model distribution system were captured using the FLIR420 thermal imaging camera, Wilsonville, Oregon, U.S. (Figure 3). The camera has a wavelength range of 7500-14,000 nm. On the right side of the camera's display, the color range represents the temperature range of the captured area. The darker colors illustrate regions with lower temperatures, while the brighter colors illustrate regions with higher temperatures.  
Since the data provided by SEWA was a few years old at the time of the study, conducting field tests may not be meaningful as maintenance activities might have already been carried out at the suspected locations. In addition, due to government restrictions related to field experiments at the locations identified in Phase 1, similar experiments were conducted on the model setup. The thermal images of the model water distribution system, shown in Figure 4a,b, were captured manually. This WDN model is a carefully built setup  
Figure 3: FLIRT420 IR camera.  
Figure 2: Flow Chart of phase 1 using ArcGIS Pro.  
that includes a dune-sand-filled box with four PPR pipes (th = 5.7 mm) and is designed to simulate pipe leaks in underground conditions. One of the pipes is a regular pipe without any leaks, and the three other pipes were created with simulated leaks from cracks, holes, and joints. An experiment was conducted on the pipe with a hole. These grayscaled IR images generated during laboratory-based experiments were then processed to clearly display the heat signatures. Since water has a cooling effect compared to the surrounding soil, with the help of time series images, the leakage locations can be identified.  
The aim is to capture the thermograms in grayscale RGB (red, green, and blue) format where all three layers are equal. Therefore, only one layer is selected for image processing. The grayscale image is later processed to a false color image with multiple layers for perception purposes to increase the contrast of the thermal images. Figures 5 and 6 show a sample grayscale image and its corresponding false-color thermogram from a previous study [13]. The leakage is characterized by a lower temperature than the soil. It is important to consider that the temperature of the soil will vary with time. The temperature of the surrounding soil will decrease continuously as the leak spreads throughout the soil. Therefore, the surface with the highest temperature variation is above the leakage. The next section outlines the mathematical model that will emphasize the temporal temperature variation.  
Figure 4: (a) Labeled diagram of experimental setup; (b) model water distribution system.  
Figure 5: Sample multitemporal IR images (a) 40 minutes (b) 60 minutes, adapted from [13].
## 1 Introduction
The optical methods are based on the relationship between turbidity and suspended-sediment concentrations through infrared-light scatter, and this method is widely used when direct measurement is difficult. Although this method is economically efficient and facilitates data continuity unlike traditional measurement methods, it presents certain drawbacks. In particular, turbidity is known to vary with the sizes and shapes of sediment particles, and estimating concentrations based on measurements conducted at only one point yields low spatial resolutions [19; 20]. Additionally, measurements of the turbidity and concentration of suspended sediments require considering water quality factors aside from sediment characteristics, namely, the salinity of sea water, mineral composition, and the conductivity of nitrogen- or phosphorus species [21; 22]. In terms of measuring the concentration of transported suspended sediment, no method can be considered flawless. The monitoring of suspended sediment requires the careful consideration of multiple factors, including concentration range, particle size range, method uncertainty, required reliability, site accessibility, cost, time, and the expertise of personnel involved [17]. In this study, our focus was on an indirect method that utilizes underwater images to estimate sediment concentrations. This approach addresses the limitations observed in previous methods, such as restrictions on particle size and concentration range, while also offering advantages in terms of cost, accessibility, and resolution. Unlike previous proposals for estimating sediment concentrations based on underwater images [23], we employed specially designed equipment in the form of a box to maintain consistent lighting conditions, as these conditions have the most significant impact on underwater image quality. Subsequently, we directly estimated the suspended-sedimentconcentration by analyzing color variations in these underwater images and compared the results with water quality factors. Through extensive research and experimentation, we have successfully developed a simpler, more efficient, and practical method for estimating suspended-sediment concentrations.
# Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning
## 3 Proposed Model
### Feature Fusion Module  
Numerous feature fusion techniques have been proposed, such as bilinear pooling [32] and self-attention networks [33]. However, these methods often involve additional fully connected layers with a large number of parameters, which can lead to overfitting, particularly when dealing with limited data. Considering the constraints (limited data) of our task, we chose to adopt a simpler approach. In this study, we employ a straightforward yet effective late fusion scheme based on feature concatenation, which demonstrates competitive results [33].  
Models trained separately on the ARD dataset for the ARD task are then used to extract features by removing the classification layer. The extracted features are concatenated into a single 1D feature vector of size 4096, effectively combining the complementary information from both RGB and depth data. Figure 1 shows the feature fusion process. Finally, a simple single-layer neural network is trained to process the concatenated feature vector and make the final decision regarding the presence of ARD. This approach is characterized by its simplicity and effectiveness.
## 1 Introduction
These features were used as inputs to a Support Vector Machine (SVM) model to classify normal and abnormal heart sounds, obtaining an accuracy of 97%. \begin{table}
\begin{tabular}{c c c} \hline
**Author** & **Dataset** & **Method** \\ \hline
[9] & 37 recordings of heart sounds & Normalized Average Shannon Energy \\ \hline
[10] & 80 recording of heart sounds & S-transform and Shannon Energy \\ \hline
[11] & 77 recording of heart sounds & Wavelet decomposition and reconstruction (normalized average Shannon energy (NASE)) \\ \hline
[12] & Physionet [20] and Pascal [21] & Hidden Markov model (HMM) \\ \hline
[13] & Physionet database [20] & Logistic regression and hidden semi-Markov model (HSMM) \\ \hline
[15] & \multirow{2}{*}{Pascal database [21]} & Wavelet Decomposition and Spectrogram \\ \cline{3-3}
[16] & &
\begin{tabular}{c} \\ \end{tabular} & Butterworth band pass filter with order 2 and \\ \cline{3-3}
[18] & &
\begin{tabular}{c} Chebyshev type I low-pass filter and \\ Shannon envelope \\ \end{tabular} \\ \hline
[19] & &
\begin{tabular}{c} Band-pass Butterworth sixth-order filter and \\ Shannon envelope \\ \end{tabular} \\ \hline
[22] & Physionet database [20] & HSMM–convolutional neural network (CNN) \\ \hline
[23] & Pascal database [21] & Discrete wavelet transform and Hilbert transformation \\ \hline
[24] & Physionet [20] and Pascal [21] & Adaptive sojourn time HSMM \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of previous work in the automatic segmentation of heart sounds. In [26], the authors extracted a total of 27 features: 13 MFCC features, 10 statistical features and four frequency features. In that work, they used an XgBoost algorithm, obtaining an accuracy of 92.9%. In [27], six linear prediction coefficients (LPC) and 14 MFCCs were extracted for each segment of a cardiac cycle (S1, S2, systole and diastole). These features became the input to three classifiers
## 3 Materials and Methods
### Long Short-Term Memory (LSTM)  
LSTM networks are a type of RNN designed to effectively capture long-term dependencies in sequential data [17]. Unlike traditional RNNs, LSTMs are capable of learning and remembering information over extended periods, making them particularly useful for tasks involving time series data, natural language processing, and speech recognition [37; 38; 39]. The core component of an LSTM network is its memory cell, which maintains its state over time. Each memory cell is equipped with three gates: the input gate, the forget gate, and the output gate. These gates regulate the flow of information into and out of the cell, allowing the network to selectively remember or forget information. The input gate controls the extent to which new information is added to the cell state, the forget gate determines the degree to which existing information is discarded, and the output gate manages the information that is passed to the next time step. For fall detection, LSTMs are effective because they can process sequential radar data, capturing the temporal evolution of human motion patterns. This allows them to identify subtle changes over time that are indicative of a fall, distinguishing them from normal activities.
# Simulating and Evaluating a Real-World ElasticSearch System Using the RECAP DES Simulator
## 4 Modelling and Simulation of Opening.io  
The Opening.io virtual infrastructure consists of an ES cluster composed of one ES node and three data nodes. The ES node is also called the coordinator (master node) and is responsible for the following: (i) distributing the queries among the data nodes, (ii) managing and coordinating the query search results of different data nodes and (iii) returning the query results to the user.  
Three virtual data nodes are used for distributed data storage. The data nodes are responsible for storing and processing old and fresh data. The four nodes (ES and data nodes) are hosted in three virtual machines; VM0, VM1 and VM2, respectively. The three VMs are hosted on the premises in the same physical machine. Note that both the ES node and one of the data nodes (data node 1) are hosted by the same VM (VM0), as shown in Figure 1.  
As previously mentioned, [PERSON] et al. [16] demonstrated the RECAP DES using real-world ES trace data from Linknovate.com, one of the RECAP partners. However, despite having the same goal of offering an ES service, Opening.io has its own system specificity, and therefore this study validates the applicability of the RECAP DES on a discrete system that is independent of the RECAP project and the extensibility of the RECAP DES in order to accommodate new idiosyncratic requirements. Indeed, they differ in terms of where the data reside, the number of nodes that can be hosted in a VM at any one time and the nature of their query traffic. In the Linknovate model, the ES node collects partial query results from all data nodes in parallel then aggregate the results. In contrast, with Opening.io's model, the entire query result resides in only one data node; therefore, the ES node forwards the query and gets the results from a unique data node. Furthermore, in the Linknovate model, VMs can only host one node--an ES node or data node--at one time. In the Opening.io model, the VMs can host one or two types of nodes--data nodes only, or a data node and an ES node. Furthermore, Linknovate is characterised by only one type of traffic query, (read, queries), whereas the Opening.io model has two types of query traffic, (read, queries) and (write, queries). Finally, the Opening.io model is characterised by three types of read queries that differ from each other with respect to their service times; the Linknovate model only has one type of read query.  
Figure 1: A high-level overview of the Opening.io architecture. VM: virtual machine; ES: ElasticSearch.  
Table 1 summarises the capacities of the different VMs comprising the architecture of Opening.io.
## 3 Results  
The data are shown in Table 2. Comparing the data before and after the maxillary expansion, both programs showed the following statistically significant differences (\(p<0.05\)): 6 RIM-6 LIM and 6 RABM-6 LABM regarding the transversal analysis, N-Ans, S-Go and N-Ans+Ans-Me concerning the vertical analysis, while the skeletal class remained unaltered during the entire observation period. The posterior facial height values varied from T0 to T1.  
Delta-Dent reported relevant discrepancies of pre- and post-expansion linear distances RIf-RMf and LIf-LMf, although these differences were not considered statistically significant by Simplant (\(p>0.05\)). Delta-Dent provided higher values of standard deviation compared to Simplant, which can be considered more accurate because of its lower standard deviation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variables** & \multicolumn{2}{c}{**Simplant**} & \(\ddagger\) & **Deltadent** & \(\ddagger\) & **v** \\ \hline  & **Pre-Exp** & **Post-Exp** & & **Pre-Exp** & **Post-Exp** & \\ \hline
6 RABM-6 LABM & 36.18 (2.88) a & 40.85 (2.59) b & \(p<0.0001\) & 36.47 (2.77) a & 41.25 (2.74) b & \(p<0.0001\) & NS \\ \hline
6 RIM-6 LIM & 37.40 (2.79) a & 41.71 (2.85) b & \(p<0.0001\) & 39.49 (6.58) a & 43.97 (6.71) b & \(p<0.0001\) & NS \\ \hline RIf-RMf & 54.63 (4.48) a & 56.24 (4.93) a & NS & 52.83 (5.29) a & 56.54 (4.84) b & \(p=0.0211\) & NS \\ \hline LIf-LMf & 54.10 (4.63) a & 57.17 (4.96) a & NS & 52.73 (5.39) a & 56.28 (4.23) b & \(p=0.0039\) & NS \\ \hline N-Ans+Ans-Me & 104.42 (11.09) a & 107.00 (10.29) b & \(p=0.0436\) & 103.94 (10.05) a & 107.50 (9.84) b & \(p=0.0078\) & NS \\ \hline N-Ans & 46.39 (5.69) a & 48.62 (5.62) b & \(p=0.0126\) & 46.28 (5.83) a & 48.50 (5.94) b & \(p=0.0015\) & NS \\ \hline Ans-Me & 58.03 (6.75) a & 58.38 (6.14) a & NS & 57.68 (5.84) a & 59.00 (5.54) a & NS & NS \\ \hline Sup/inf facial H & 80.09 (9.85) a & 83.27 (8.78) a & NS & 80.36 (9.55) a & 81.64 (10.48) a & NS & NS \\ \hline S-Go & 63.90 (6.16) a & 66.62 (6.31) b & \(p=0.0024\) & 62.72 (5.42) a & 65.05 (6.02) b & \(p=0.0279\) & NS \\ \hline N-Me & 100.58 (10.35) a & 103.47 (9.92) a & NS & 101.07 (9.45) a & 104.64 (9.50) a & NS & NS \\ \hline S-Go/N-Me (\%) & 63.82 (4.98) a & 64.64 (5.30) a & NS & 62.28 (3.58) a & 62.36 (3.72) a & NS & NS \\ \hline SNA & 83.12 (1.88) a & 83.02 (1.83) a & NS & 83.27 (1.64) a & 82.75 (1.73) a & NS & NS \\ \hline SNB & 78.48 (2.93) a & 78.44 (1.91) a & NS & 78.53 (2.71) a & 78.08 (1.71) a & NS & NS \\ \hline ANB & 4.71 (2.16) a & 4.67 (1.79) a & NS & 4.72 (2.22) a & 4.73 (1.75) a & NS & NS \\ \hline WITS & 2.27 (0.85) a & 2.83 (1.28) a & NS & 3.21 (1.49) a & 2.53 (1.35) a & NS & NS \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean values (standard deviation) of the 3D cephalometric analysis from the two programs.  
Figure 3: (**A**) 6 RIM-6 LIM measure; (**B**) 6 RABM-6 LABM.  
None of the other parameters showed significant differences between the two programs (\(p>0.05\)).
## 3 Materials and Methods
### Traffic Direction
#### 3.3.3 Visualization Process of Traffic Direction  
The process of clustering vessel azimuth data to represent the direction of vessel traffic in a sea area involves a four-step process, as shown in Figure 4. In Step 1, data preprocessing and traffic density visualization were performed. AIS data were deduplicated to increase reliability and consistency, and vessel voyages were organized by vessel identification numbers. To obtain the representative azimuths, the Haversine formula was applied to calculate the azimuths between the data points in each ship's voyage. In Step 2, an appropriate grid cell size for water was selected. In Step 3, the azimuths within each grid cell were clustered. Given that azimuth data can be unstructured and vary in distribution, the DBSCAN algorithm was applied to identify representative azimuths. This method allowed the determination of the main orientation of ships traveling in a similar direction within each grid cell. In Step 4, the clustered azimuth data were visually represented. The representative azimuth in each grid cell was represented using a direction arrow, where the arrow length indicated the frequency of ships traveling in that direction. A long arrow indicated a major path with frequent ship movements, whereas a short arrow indicated a path with infrequent ship movements. This visualization facilitated an intuitive understanding of ship traffic patterns.  
sum (CUSUM) algorithm to analyze changes in ship direction and speed, and compared the performance of the B-tree and quadtree used for waypoint extraction. [PERSON] et al. [30] and [PERSON] et al. [49] used the Hausdorff distance to calculate the shape difference between trajectories and applied a density clustering algorithm to cluster these trajectories. [PERSON] et al. [50] proposed a hybrid clustering method that integrated the static and dynamic features of trajectories to better capture global and local movement patterns. [PERSON] et al. [51] developed a method based on the geometric structure of trajectories, integrating an improved FOP-OPTICS algorithm for clustering. [PERSON] et al. [52] introduced the DBSCAN clustering algorithm, which uses the Hausdorff distance to measure similarity and effectively clusters trajectories of different lengths. In this study, we utilized ship position data extracted from the AIS to cluster ship voyages, identifying and extracting ship traffic patterns from the clustering results. This process consists of four key stages.
## 3 Results and Discussion  
Figure 2 shows the comparison of the Geant4-simulated and experimental PDD data for a 6 MV photon beam, \(3\times 3\) cm\({}^{2}\) field size and an 100 cm source-to-surface distance (SSD) through water medium. With a statistical error related to the simulation of about 2% and an experimental error of about 2%, we can observe the good agreement of both distributions. Moreover, we confirm the ability of Geant4 to reproduce a PDD distribution for similar situations, as already carried out in the literature [22; 23; 24]. Such good agreement allowed us to safely proceed with the present method of the PDD simulation for other materials than water medium. Table 2 lists the radiological properties of the studied tissues, including \(Z_{eq}\), \(\mu/\rho\), \(\mu_{en}/\rho\), \(Z_{eff}\) and the GP-fitting parameters of the BUF for a photon energy between 0.15 and 15 MeV.  
Figure 2: Comparison of Geant4-simulated (based on IAEA phase space file) and experimental PDD data for a 6 MV photon beam (field size of \(3\times 3\) cm\({}^{2}\)) within water.