esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Image Segmentation on 2D Echocardiogram
## 1 Introduction
introduced an end-to-end unsupervised segmentation method based on differentiable feature clustering, which iteratively optimizes feature representations and pixel labels to achieve high-quality segmentation results [6]. These approaches offer the potential to leverage the strengths of CNNs while avoiding the need for extensive labeled data. In the context of echocardiography, unsupervised segmentation holds significant promise. Echocardiographic images are often noisy, subject to considerable variability, and lack consistent annotations, making them ideal candidates for unsupervised techniques. By avoiding reliance on annotated data, unsupervised methods can explore anomalies and segment heart structures more effectively, particularly at the pixel level, providing a more accurate delineation of the heart's anatomy. By leveraging the rich feature representations learned by CNNs and the robust clustering capabilities of unsupervised methods, it is possible to develop effective segmentation algorithms that enhance echocardiogram analysis [5; 7]. This paper presents a novel unsupervised methodology for segmenting 2D echocardiography images by combining objective functions with CNN-based feature extraction to achieve accurate and robust segmentation of cardiac structures. The proposed method addresses the limitations of traditional unsupervised techniques by leveraging deep learning for feature refinement and spatial continuity, offering a more accurate and flexible approach to cardiac segmentation. Evaluated on a dataset of 2D echocardiograms from Stanford [7], this approach demonstrates its efficacy and potential for improving automated cardiac analysis. By integrating deep learning with clustering techniques, this study advances the field of unsupervised medical image segmentation, providing a robust framework for segmenting complex cardiac images without the need for extensive annotated data. Additionally, it offers the flexibility to handle previously unseen data, paving the way for more efficient and accurate echocardiographic analysis.
## 4 Experiments
### Implementation Details  
Models were constructed and implemented using Pytorch. Before inputting into the model, the images were resized uniformly. Considering that the original image size is not consistent across datasets, here, the image of CK+, Oulu-CASIA dataset was resized to \(224\times 224\) pixels and the image of RAF-DB, FERPlus dataset was resized to \(112\times 112\) pixels. When reading the dataset, random flip, random rotation, etc. were used for the data enhancement work. The initial value of the adaptive parameter was defaulted to \(1\). The parameters for training were set as follows: cross-entropy loss function was used, SGD optimizer, the learning rate was fixed to \(1\times 10^{-3}\), the batch size was \(24\) and epoch was set to \(100\) for CK+, Oulu-CASIA datasets, and the batch size was \(128\) and epoch was set to \(60\) for RAF-DB, FERPlus datasets. All experiments were done on a single NVIDIA A100 GPU card.
# Port-Related Shipping Gas Emissions--A Systematic Review of Research
## 3 Discussion and Results
### Analysis of the Most Commonly Applied Methodologies and Datasets--Third Layer
#### 3.3.5 EEA Research
Given this, traffic information can be compared and validated, resulting in more accurate emissions values estimates. By combining all the analysed key data and methodological factors used in selected studies, a proposition of a relevant methodology for the estimation of ship emissions in ports can be introduced. To begin with emissions estimation, data acquisition should be carried out by combining multiple sources of marine traffic information. With the widespread use of AIS, better coverage of both static and dynamic information about ships and their movements is available. Therefore, a bottom-up EB approach is proposed. However, in order to validate AIS information and to get an overview of vessels that are not required to have an AIS onboard, statistical information representing the TD of the traffic inside the interest area should be applied. As can be seen in Figure 4, all key factors in emissions estimation are classified by colour and linked inside a methodology and data diagram for port-related calculation of emissions on a ship-by-ship basis. Within the diagram, grey colouring marks the static TD on the ship and its engines; blue indicates the combination of static and dynamic data for estimating LF through the propeller law and thereby determining a ship's MA; the colour yellow represents more complex datasets defined by TD and classified through methodological aspects relevant to traffic inside the research interest area; finally, orange indicates the key elements for calculating ship emissions as an output value, which is marked in red. The interactions of all key segments outlined in this research are presented in Equation (11) for estimating ship emissions in ports. \[\text{E}=(\text{P}_{\text{ME}}\times\text{LF}\times\text{EF}_{\text{ME}}+\text {P}_{\text{AE}}\times\text{LF}\times\text{EF}_{\text{AE}})\times\text{T}\times \text{CF} \tag{11}\]  
where:  
E: Emissions quantity by mode for each ship call--in grams (g);  
\(\text{P}_{\text{ME/AE}}\): Total power of main engines/auxiliary engines--in grams per kilowatt hour (g/kWh);  
LF: Load factor expressed as actual engine work output--as a percentage of engine power (%);  
EF: Emission factors of different pollutants in regard to engine function, engine type, fuel type, installation year--in grams per kilowatt hour (g/kWh);  
T: Time spent in a certain movement activity--in hours (h);  
CF: Control factor for emission reduction technologies--constant. Figure 4: Proposal of methodological and data key factors for port-related calculation of emissions on a ship-by-ship basis.
## 3 Stanag 5066
If no ACK is received, a uniformly distributed back-off period ranging from 250 ms to 4 s is waited before the next retransmission. The maximum back-off period is doubled after each try. NONARQ transmission mode uses CRC and segmentation to detect whether the data have been received without errors. Using SIS protocol metadata, transmitter nodes can set NONARQ segments to be retransmitted from 1 to 15 times. During reception, NONARQ data can be reassembled using error-free segments in first place, segments with errors, or even not received ones (padded with binary-zero data) using SIS protocol metadata flags that inform the application about which data parts have present errors. Figure 3: ARQ packet state flow from queued to received or lost. Figure 2: STANAG 5066 link state diagram. STNAAG 5066 sets an upper boundary for both ARQ and NONARQ transmission modes, limiting the maximum transmission window to 60 s. During this time, each node is allowed to transmit all the queued messages that fit into the aforementioned window. After the transmission window, it is mandatory for STNAAG 5066 nodes to stop during an implementation-dependent period before transmitting more data. In this work, the implemented STNAAG 5066 protocol stack has been configured for waiting during an uniformly-distributed random time ranging from 250 ms to 4 s (as the ARQ back-off time). The developed executable binary was developed and compiled to be run under GNU/Linux operating systems, and both _to-phy_ (or its software driver) and _to-application_ communications are driven through a TCP/IP socket. Thus, applications that pretend to use the implemented STNAAG 5066 stack will have to implement SIS protocol over a TCP/IP socket. The STNAAG 5066 stack implementation has already defined some implementation-dependant characteristics, such as segment sizes or internal timing. The use of STNAAG 5066 standard fits the scenario under consideration in this work. This protocol was designed for low-throughput and high-latency scenarios, and the channel capacity associated with EM underwater propagation, as well as the hardware of the implemented physical devices, fit the characteristics that STNAAG 5066 standard has been designed for.
## 4 Analysis of the Inversion Results
### Influence of Different Sample Numbers on the Inversion Results  
The final calculation results of the second comparison group are exhibited in Figure 8. With the increase in the number of training samples, the RMSE of the validation samples decreased gradually. Figure 8 shows that when the number of training samples was 950, the RMSE of the validation samples reached the minimum value of 0.0417, and the validation accuracy of the CNN model reached about 96%. To further illustrate the accuracy of this algorithm for the inversion of the dynamic modulus of the pavement structure, the evaluation metrics (RMSE, R\({}^{2}\) value, MAPE (mean absolute percentage error), and MAE) of the algorithm in this paper are shown in Table 7 when the training set samples are taken as 950.
###### Abstract
Recently, deep convolutional GAN [22], information maximizing GAN [23], Wasserstein GAN [24], and CycleGAN [25] have been developed as derivative technologies for GANs. Among them, CycleGAN can transform images without paired training data; it can convert MR images to CT images [26] and reduce noise [27]. Recently, several studies on the mutual conversion of images using CycleGAN have been performed. However, to the best of our knowledge, only a few studies have reported using CycleGAN-generated images for the extensive training of deep learning models. If CycleGAN generates brain images from normal brain MR images, it is expected to improve the accuracy of extraction and segmentation by increasing the variety of data. In this study, we aimed to develop a novel method for virtually generating cerebral infarction images from healthy images using CycleGAN and applying them to the U-Net training model to improve the cerebral infarction automatic extraction accuracy by U-Net. Hereafter, U-Net with CycleGAN represents an approach that uses CycleGAN-generated images for training; U-Net without CycleGAN represents an approach that uses only real images.
# Improving the Maritime Traffic Evaluation with the Course and Speed Model
###### Abstract  
Recent projections from marine transportation experts highlight an uptick in maritime traffic, attributed to the fourth industrial revolution's technological strides and global economic rebound. This trend underscores the need for enhanced systems for maritime accident prediction and traffic management. In this study, to analyze the flow of maritime traffic macroscopically, spatiality and continuity reflecting the output of ships are considered. The course-speed (CS) model used in this study involved analyzing COG, ROT, speed, and acceleration, which can be obtained from the ship's AIS data, and calculating the deviation from the standard plan. In addition, spatiality and continuity were quantitatively analyzed to evaluate the smoothness of maritime traffic flow. A notable finding is that, in the target sea area, the outbound and inbound CS indices are measured at 0.7613 and 0.7501, suggesting that the outbound ship flows are more affected than inbound ship flows to the liquidity of maritime traffic flow. Using the CS model, a detailed quantitative evaluation of the spatiality and continuity of maritime traffic is presented. This approach facilitates robust comparisons over diverse scales and periods. Moreover, the research advances our understanding of factors dictating maritime traffic flow based on ship attributes. The study insights can catalyze the development of a novel index for maritime traffic management, enhancing safety and efficiency.  
spatiality; continuity; CS model; maritime traffic flow +
Footnote †: journal: _sciences_
## 3 Results
### Dependence of Degradation Rate of TMP on pH Value  
The natural pH of the 10 ppm TMP solution in ultra-pure water was 5.59 at 25 \({}^{\circ}\)C. Figure 8 illustrates the relationship between the degradation rate and pH values for photocatalytic degradation. The pH values chosen for the experiments are representative of those found in environmental waters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Trendline and \(R^{2}\)** & \(k_{\mathrm{r}}\)**min\({}^{-1}\)** & \(t_{1/2}\)**, min** \\ \hline TMP 10 ppm & y = \(-\)0.0253x + 0.2271 & & 0.0253 & 27.4 \\ \(R^{2}\) = 0.9822 & & & \\ TMP 10 ppm + 13 mM isopropanol & y = \(-\)0.0023x + 0.018 & & \\ \(R^{2}\) = 0.9649 & & & \\ TMP 10 ppm + 13 mM ammonium oxalate & y = \(-\)0.0374x + 0.0717 & & \\ \(R^{2}\) = 0.9928 & & & \\ TMP 10 ppm + 3.4 mM triethanolamine & y = \(-\)0.0013x + 0.0328 & & \\ \(R^{2}\) = 0.9697 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Photocatalytic degradation rate constant, \(R^{2}\) and half-life.  
Figure 8: Photocatalytic degradation with and without buffers pH 8.1 and 6.3.  
Figure 8 shows that the addition of buffers slightly increases the degradation rate of TMP. The total degradation of TMP can be expected in 2 h of irradiation with the addition of buffer 6.3.  
The pH has a significant impact on the photocatalytic degradation process, which is influenced by the ionization state of both the compound and catalyst. The TiO\({}_{2}\) point of the zero charge is 6.25 [45] and plays a crucial role in this process. In buffered TMP solutions, a slightly slower degradation rate was observed at pH 8.1 (\(k\) = 0.0306 min\({}^{-1}\)) compared to pH 6.3 (\(k\) = 0.0372 min\({}^{-1}\)). The pK\({}_{\text{a}}\) value of TMP is 7.10 \(\pm\) 0.02 [46], meaning that at pH \(<\) pK\({}_{\text{a}}\), TMP will be mostly in its cationic form, while at pH \(>\) pK\({}_{\text{a}}\), the dominant form of TMP will be neutral. At both investigated pH values, the catalyst surface is negatively charged and TMP is present mostly in its neutral form (90%) at pH 8.1 and in its positively charged form (90%) at pH 6.3 [46], leading to the attraction between the catalyst surface and TMP molecules. The slightly faster degradation at pH 6.3 can be explained by the stronger attraction between cationic TMP and the catalyst surface, compared to neutral TMP at pH 8.1.  
In the case of non-buffered TMP solution, the initial pH of 5.59 decreases during oxidation, resulting in a final pH of 3.86, at which TMP is present only in its cationic form. This situation results in both the catalyst surface and TMP being positively charged, leading to repulsion between them and the slower degradation of TMP.
## 1 Introduction
In fact, with the arrival of Sentinel-1 (S1 SAR data at 10-m spatial resolution and a six-day revisit period) and Sentinel-2 (S2 optical data at 10-m spatial resolution and a five-day revisit period) satellites, an operational algorithm has been developed for soil moisture mapping over agricultural areas with six days' revisit time and high spatial resolutions (up to plot scale) [16]. The objective of this paper is to analyze the variation in surface soil moisture values obtained from the very high spatial resolution Sentinel-1 surface soil moisture maps (S1-SSM) during one year over the Occitanie region in the south of France. In this study, the variation of the SSM is analyzed only according to the variation of rainfall derived from GPM data. Although the water cycle components, such as evaporation rate and snow melt, play a critical role in SSM variation, these components were not considered in this study. This study will allow us to investigate the opportunity to detect heavy rainfall using Sentinel-1 surface soil moisture products obtained with a revisit time of six days at high spatial resolution in the south of France. After a detailed description of the study site and the data used in Section 2, Section 3 includes a comparison between S1-SSM and precipitation, as well as an analysis of the possibility of detecting heavy rainfall using S1-SSM with a revisit time of six days in the south of France (Occitanie region). A discussion is presented in Section 4, including the effect of S1 temporal resolution on detecting heavy rainfall and investigating the potential of SMAP soil moisture product with a revisit time of one day to detect heavy rainfall. Finally, Section 5 presents the main conclusions.
## 3 Materials and Methods
### Data Analysis Approach  
#### 3.5.1 Data Preparation  
To ensure the robustness of the analysis, both datasets, which were susceptible to outliers due to their nature of collection, were initially prepared for further processing [68; 71]. Gaussian-distributed features were standardized using scikit-learn to ensure they had a mean of zero and a standard deviation of one [72]. Scikit-learn is a versatile library for machine learning in Python for efficient implementations of a wide range of algorithms for classification, regression, clustering, and general data preprocessing. Non-Gaussian features underwent robust scaling, which scales the data based on percentiles rather than the mean and standard deviation, making it less sensitive to outliers. Outliers, defined as values with a z-score greater than 3, were replaced with the median value of the respective feature.  
Additionally, to facilitate seamless integration of data from facial analysis and audio analysis, a unique mapping was created to match the IDs between the two output files. After this process, columns deemed irrelevant to the analysis, such as those relating to time or IDs, were removed. For the regression of Pareto scores, only values between the 10 th and 85 th percentile were considered, aiming to mitigate the influence of outliers in the subsequent analysis, see Figure 4.
# Decentralized Identity Authentication with Auditability and Privacy
## 2 Background and Related Works  
### Blockchain  
A blockchain system consists of distrusting nodes that implement a replicated, tamper-evident, append-only log data structure called a ledger. The ledger comprises blocks, each block contains transactions that modify some global states, and the blocks are linked through cryptographic hash pointers. The nodes keep the global chain of blocks consistent through a consensus protocol. In addition, most blockchains support smart contracts, which are user-defined functions executed by all nodes in the network.  
Existing blockchains can be broadly classified into permissionless and permissioned based on their consensus protocol [11]. Examples of the former include Bitcoin [12] and Ethereum [13], in which any nodes can join and leave. Examples of the latter include Hyperledger Fabric [14] and Quorum [15], in which membership is strictly enforced. Permissionless blockchains use variants of the proof-of-work (PoW) consensus protocol, whereas permissioned ones use variants of Byzantine fault-tolerant (BFT) protocols [16].  
A blockchain executes and stores user transactions in the ledger. The user can ask for proof that their transaction is included in the ledger without downloading and re-executing the entire ledger. In a permissioned blockchain, this proof consists of a statement (that the transaction is included) signed by \(f+1\) nodes (\(f\) is the maximum number of Byzantine nodes that can be tolerated). In a permissionless blockchain, all transactions are represented by a Merkle root hash included in the block; thus, the proof consists of the valid Merkle path to the root hash of the latest block.
# Research on the Reliability of Bridge Structure Construction Process System Based on Copula Theory
## 2 Basic Theory of the Copula Function
### Metrics of Copula Function Correlation
#### 2.2.1 Pearson Linear Correlation Coefficient  
The Pearson linear correlation coefficient \(\rho\) is a measure of the strength of the linear correlation between two random variables. We expand it to the field of reliability analysis. The Pearson linear correlation coefficient \(\rho\) is calculated for the correlation between two functions as follows [10]\[\rho_{Z_{i}Z_{j}}=\frac{\operatorname{cov}\big{(}Z_{i},Z_{j}\big{)}}{\sigma_{Z_{i} }\sigma_{Z_{j}}} \tag{5}\]  
where \(Z_{i}\) and \(Z_{j}\) are the function of two failure modes, respectively; \(\operatorname{cov}\big{(}Z_{i},Z_{j}\big{)}\) is the covariance of two failure functions \(Z_{i}\) and \(Z_{j}\); \(\sigma_{Z_{1}}\) and \(\sigma_{Z_{2}}\) are the standard deviation of two failure functions \(Z_{i}\) and \(Z_{j}\), respectively.  
The Pearson linear correlation coefficient \(\rho\) varies in the range of \([-1,1]\). The larger the \(|\rho|\), the stronger the correlation between the two failure modes. \(|\rho|=1\) indicates that the two failure modes are completely linearly correlated, and \(|\rho|=0\) indicates that there is no linear correlation between the two failure modes. Since the Pearson linear correlation coefficient can only describe the linear correlation between the failure modes, the Pearson linear correlation coefficient \(\rho\) will be transformed when there is a nonlinear transformation between the failure modes [30]. Therefore, the applicability in engineering practice is weak.
# Accurate Phase Calibration of Multistatic Imaging System for Medical and Industrial Applications
## 4 Calibration Result  
Figure 7 shows the transmission coefficients measured at the antenna array. The transmission coefficients were measured at ten pairs of opposite Tx and Rx antennas in the antenna array. To reduce multipath reflections within the antenna array, the cylindrical cavity in the center of the antenna array is filled with the same resin as the antenna array. Figure 7a shows the coefficients among the calibration planes before phase calibration, while Figure 7b shows the coefficients among measurement reference planes after phase calibration. These graphs have frequency on the horizontal axis and phase on the vertical axis. In Figure 7a, the slopes of the phase waveforms for frequency sweeps are different  
Figure 6: The reflection coefficients of the BVA: (**a**) the phase of the reflection coefficient in the frequency domain; (**b**) the reflection coefficient in the time domain for five different BSL widths, \(d\).  
for each channel. This phase is measured between calibration planes, and the reference planes are different from each channel. Therefore, the slopes of the phase waveforms are different from each channel. In Figure 7b, where phase correction is applied, the slopes of the phase waveforms match. The slope of the phase waveform decreased at a constant rate with frequency sweep. Around 3.0 GHz and 6.0 GHz, the phase increased and then decreased again. There were also fringe jumps in phase around 3.0 GHz in some channels. These were thought to be due to multiple reflections between transmitting and receiving antennas in the central cavity of the antenna array.  
The phase difference between channels of the calibrated phase was less than 8\({}^{\circ}\) at 1.5-6.5 GHz. The phase difference between channels was obtained by unwrapping the calibrated phase and calculating the standard deviation from the difference with one channel. As the phase waveform contains ripples, the difference was calculated using the 100 MHz wide moving average phase, and the phase difference around 3 GHz was large, but this was due to fringe jumps. The standard deviation was calculated by subtracting the transmission coefficients of 10 pairs of transmitting and receiving antennas at each frequency. The standard deviation at 1.5-6.5 GHz was about 8 degrees. The phase difference between channels was approximately 1/45 of the wavelength, confirming a highly accurate phase calibration.