esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.2.2 Evaluation Method  
The detection performance of the GK2A_FDA was statistically evaluated using a 2 \(\times\) 2 contingency table (Table 3). A satellite detection was considered a hit (H) if fog was detected by GK2A and observed at the ground station. A false alarm (F) was recorded when fog was detected by the satellite but not by the visibility meter, while a miss (M) indicated fog observed by the visibility meter but not detected by the satellite. A correct negative (C) was recorded when both the satellite and the visibility meter indicated no fog. The evaluation was performed using both the nearest GK2A pixel and the 3 \(\times\) 3 neighborhood pixel methods [31].  
The performance of the GK2A_FDA was evaluated using three widely adopted categorical verification metrics: the probability of detection (POD), false alarm ratio (FAR), and bias. Their formulas are provided in Equations (1)-(3):  
\[\mathrm{POD}=\mathrm{H}/(\mathrm{H}+\mathrm{M}), \tag{1}\]  
\[\mathrm{FAR}=\mathrm{F}/(\mathrm{H}+\mathrm{F}), \tag{2}\]  
\[\mathrm{Bias}=(\mathrm{H}+\mathrm{F})/(\mathrm{H}+\mathrm{M}) \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Ground Observation Fog**} \\  & & & \multicolumn{2}{c}{**(Visibility Meters)**} \\ \cline{4-6}  & & & \multicolumn{2}{c}{**Fog**} & \multicolumn{2}{c}{**Non-Fog**} \\ \hline \multirow{4}{*}{GK2A Fog} & The nearest pixel & Fog & Hits (H) & \multicolumn{2}{c}{False alarms (F)} \\  & Non-fog & Misses (M) & & Correct negative (C) \\ \cline{2-6}  & \(3\times 3\) & \multirow{2}{*}{\(\geq\)1 Neighborhood} & \multirow{2}{*}{Hits (H)} & \multirow{2}{*}{\(\geq\)5} & \multirow{2}{*}{False alarms (F)} \\  & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & \\ \cline{1-1} \cline{5-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(2\times 2\) contingency table for the evaluation of the GK2A_FDA.  
POD indicates the probability of correct fog detection, with values closer to 1 representing higher hit rates. FAR reflects the probability of false detection, with values near 1 indicating higher false alarm rates. Bias measures the tendency of the algorithm to overestimate or underestimate fog occurrence; values above 1 indicate over-detection, while values below 1 indicate under-detection.
## 2 Principles and Methods
### Attention Block U-Net Deep Learning Network
#### 2.3.4 Evaluation for Reconstructed PA Image  
Two evaluation indexes: peak signal to noise ratio (PSNR) and structural similarity (SSIM) are selected to measure the quality of the reconstructed image [73].  
1. Peak Signal to Noise Ratio (PSNR) Based on the error between corresponding pixels, PSNR is the most widely used objective evaluation index of images. It implies the small distortion at a high value, formulated as: \[PSNR=10\cdot\log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right)=20\cdot\log_{10} \left(\frac{MAX_{I}}{\sqrt{MSE}}\right)\] (6) where \(MAX_{I}\) is the maximum value of image color, and if each sampling point is represented by 8 bits, the maximum value is 255. \(MSE\) is the mean square error of a \(m\times n\) predicted image \(X\) and ground truth image \(Y\), defined as \[MSE=\frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}[X(i,j)-Y(i,j)]^{2}\] (7)
2. Structural Similarity (SSIM) Structural similarity is used to evaluate the structural information of the image object, based on the strong correlation between adjacent pixels. compared with PSNR, this SSIM index is more proper to evaluate the trabecular reconstruction of bone PA signals. Given the predicted image \(x\) and ground truth image \(y\), SSIM is computed as: \[\text{SSIM}(x,y)=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c _{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+c_{2}\right)}\] (8) where \(\mu_{x}\), \(\mu_{y}\) are local means of \(x\) and \(y\) respectively, \(\sigma_{x}\), \(\sigma_{y}\) are standard deviations, \(\sigma_{xy}\) is the cross-covariance for \(x\) and \(y\). \(c_{1}\) and \(c_{2}\) are constants with values of 6.50 and 58.52 respectively, to avoid errors when denominator is close to 0. \(SSIM(x,y)\in[0,1]\), and a high value represents small image distortion.
# Visible Light Communications through Diffusive Illumination of Sculptures in a Real Museum
## 3 Measurements Overview
For the different configurations, we characterise the performance of our VLC system both in terms of SNR and PER value. We define the SNR as the ratio between the half-amplitude of the VLC signal, recorded after the photodetector with a 200-MHz, 2.5-GS/s digital oscilloscope (Tektronix MDO3024), and the corresponding Root Mean Square (RMS) noise value, which amounts to 2.2 mV. PER measurements are performed by sending up to \(5\times 10^{5}\) packets, so that the error-free communication threshold, corresponding to  
Figure 3: Layout for SNR and PER measurements (see text): (**a**) polar, (**b**) FoV, (**c**) lateral displacement. the minimum detectable PER, is \(10^{-5}\). The RX gain is set to 50, which is the maximum compatible with the electronic bandwidth required by the chosen baudrate of 28 kbaud, effectively acting as a 30 KHz analog low-pass filter on the received signal). We chose such baudrate as the best trade off between communication data rate and range of our VLC system, as higher baudrates would require lower RX gains, hence reducing the maximum reach of the VLC signal in front of the artworks [22]. Packets are spaced out by 3 ms (inter-packet delay, IPD), where no transmission is happening. In case of lower baudrates or longer inter-packet delays, IPD could also be filled with continuous modulation of the carrier in order to avoid potential flickering perception, which is, however, not appreciable with the tested baudrates. We note that the need for using a lower baudrate as compared to largest baudrates attainable by our system [26], is yielded by the nLoS configuration. This condition generally involves lower signal with respect to a direct LoS configuration, hence requiring larger gains of RX stage. This, in turn, limits the maximum attainable electronic bandwidth of the RX stage itself. It could be interesting, in future works, to address the opportunity to embed automatic gain control stages after TIA. In order to provide a further, semi-quantitative evaluation of the combined effects of channel noise, intersymbol interference, and jitter in the VLC channel, we report, in addition to PER performances and intensity SNR maps, the eye diagrams obtained for two different SNR values, which are a general feature of our system and are valid for both the _Cructifix_ and the stout.
## 3 Data and Methods
### Data Pre-Processing  
Data preprocessing can be divided into data cleaning, data integration, and data transformation. The main goal is to optimize the data quality for clustering analysis by handling the data based on its reception characteristics. For instance, the AIS, a VHF broadcast communication system, may encounter issues with duplicate data from multiple shore-based receiving stations. The NMEA 0183 transmission baud rate is typically 4800, which needs to be higher to meet the demands of large data transmission. Even the  
Figure 1: This map shows the coast of central Taiwan. There are many red lines on the map, representing the tracks of ships.  
Figure 2: Research Process Framework.  
more advanced and faster NMEA2000 can experience congestion and delays due to high transmission volumes. Some regionally active, non-international vessels do not apply for an IMO number and instead use an MMSI code as the unique identifier. However, multiple vessels may have the same MMSI in overlapping regions, necessitating debugging or trajectory partitioning. Additionally, due to the collision avoidance design of the AIS, the update frequency varies with the ship's speed according to ITU-R M.1371-1 [29]. It is a recommendation standard developed by the International Telecommunication Union (ITU) for the Automatic Identification System (AIS), in which details the reporting intervals of AIS devices under different ship dynamic conditions, as shown in Table 1. The faster the ship's speed, the higher the broadcast update frequency, ranging from 2 s to 3 min. When a vessel is anchored or moored, if its speed exceeds 3 knots, the update frequency is 10 s; if the speed is below 3 knots, the update frequency drops to 3 min. The largest gap in time intervals occurs around the speed threshold of 3 knots. Therefore, a speed of 3 knots serves as a critical reference point for distinguishing the movement speed of a vessel in an anchored or moored state.  
In Section 2, many studies related to trajectory simplification methods have been listed, and it is explained that simplified trajectories do not affect clustering analysis, such as in the work of [PERSON] et al. (2011) [3]. Therefore, the continuity of the AIS trajectory is utilized to complete simple trajectory simplification by setting the COG angle change threshold, thereby improving calculation efficiency. The AIS historical data used in this work were filtered to exclude fishing vessels and unknown ship types, leaving primarily cargo and oil tankers. The total amount of data in the region was approximately 170 million, with around 5.4 million daily records. There were 3101 unique MMSI codes, representing 3101 ships around Taiwan daily. After the selected study area was narrowed down, the remaining data amounted to 557,000 records, with 432 unique MMSI codes.
# Causality Analysis with Different Probabilistic Distributions Using Transfer Entropy
## 2 Mathematical Modeling
### Review of Probability Density Functions
#### 2.2.2 Robust Statistics--Huber Logistic Estimator  
The existence of outliers in data implies fat tails in their distributions [31]. This feature biases the standard estimation of normal moments. It is especially visible with the scale factor, as in the standard deviation, as it causes a significant increase that can falsify proper interpretation of the results. There are many alternative estimators for the basic moments, i.e., the mean and standard deviations. The median value is considered a simple and robust alternative to the sample mean estimator:  
\[median^{N}(x)=x_{m}^{N}=\begin{cases}\frac{x_{N}^{N}:N^{+}x_{N}^{N}+1:N}{2}& \text{if $N$ is even,}\\ x_{\frac{N+1}{2}:N}&\text{if $N$ is odd,}\end{cases} \tag{6}\]  
where \(x_{1:N}\leqslant x_{2:N}\leqslant\cdots\leqslant x_{2:N}\) are the ordered observations. Median estimation is robust against outliers, and as such should be preferred. It is possible to find others which are more complicated, for instance, M-estimators using different functions such as the Huber or \(\psi\)-function, Hodge-Lehmann, or Minimum Covariance Determinant (MCD).  
Similarly, there are various robust scale estimators. Mean Absolute Deviation (MAD) seems to be the simplest; alternatives include various M-estimators, MCD, Qn-estimator, or many others. Previous authors have used the M-estimator with the Huber \(\psi\)-function [32]. The scale M-estimator can be obtained by solving Equation (7):  
\[\frac{1}{N}\sum_{i=1}^{N}\rho\left(\frac{x_{i}-\hat{\mu}_{0}}{\sigma}\right) =\kappa, \tag{7}\]where \(0<\kappa<\rho(\infty)\), \(\rho(.)\) is even, differentiable, and non-decreasing on positive numbers for the loss function, \(\sigma\) is a scale estimator, and \(\hat{\mu_{0}}\) is a preliminary shift factor (median). When the logistic \(\psi\) function (8) is taken as \(\rho(.)\), we obtain the logistic \(\psi\) scale estimator  
\[\psi_{log}(x)=\frac{e^{x}-1}{e^{x}+1}. \tag{8}\]
## Abstract  
Full factorial experimental design with 32 runs was used to investigate the significant and interaction variable of the reaction parameters on phenol steam reforming toward hydrogen production. Effects of selected factors on the phenol conversion (\(Y_{1}\)) and hydrogen yield (\(Y_{2}\)) were evaluated. These factors were as follows: (A) temperature (500 and 800 \({}^{\circ}\)C); (B) feed flow rate (0.16-0.46 mL/min); (C) catalyst weight (0.1-0.3 g); (D) Ni-Co ratio (0-1); and (E) phenol concentration in the feed (2-10 wt %). Ni and Co over ZrO\({}_{2}\) support for catalytic performance of phenol steam reforming (SRP) was prepared by the impregnation method. The result indicated that all the main independent variables had significant influence on the dependent variable of \(Y_{1}\) and \(Y_{2}\) with a range of 2.7%-96.8% and 21.4%-72.4%, respectively. Additionally, some interaction variables like \(AE\), \(BE\), \(CE\), and \(DE\) have also influenced the \(Y_{1}\) and \(Y_{2}\) responses. This design showed that the best initial conditions that produced maximum \(Y_{1}\) and \(Y_{2}\) responses were at 800 \({}^{\circ}\)C, 0.16 mL/min feed flow rate, 0.3 g of catalyst, 0 ratio of Ni-Co (Co/ZrO\({}_{2}\)), and 10 wt % of phenol in the feed, where the phenol conversion was predicted to be 94.98% and the hydrogen yield was predicted to be 67.4%. Within the limits the variables were examined, a regression model which well-fitted the experimental data was proposed. The regression model were reduced to simplify and to get the significant regression coefficient with \(p\)-value less than 0.05.  
reaction parameters; phenol steam reforming; Ni/Co on ZrO\({}_{2}\); full factorial experimental design +
Footnote †: journal: Applied Chemistry
## 4 Shannon Entropy and Kullback-Leibler Divergence  
The general definition of Shannon entropy for the probability distribution \(P\) of **X** is  
\[\mathrm{H}(P)=\mathrm{E}_{P}(-\log P(\textbf{X}))=-\int_{Val(\textbf{x})}P( \textbf{x})\log P(\textbf{x})\,d\textbf{x}. \tag{6}\]  
The Kullback-Leibler divergence between two distributions \(P\) and \(Q\) for the same random variables **X** is defined as  
\[\mathrm{KL}(P\,\|\,Q)=\mathrm{E}_{P(\textbf{X})}\bigg{(}-\log\frac{P(\textbf{ X})}{Q(\textbf{X})}\bigg{)}=-\int_{Val(\textbf{x})}P(\textbf{x})\log\frac{P( \textbf{x})}{Q(\textbf{x})}\,d\textbf{x}. \tag{7}\]  
They are linked as follows:  
\[\underbrace{\mathrm{E}_{P(\textbf{X})}\bigg{(}-\log\frac{P(\textbf{X})}{Q( \textbf{X})}\bigg{)}}_{\mathrm{KL}(P(\textbf{X})\,\|\,Q(\textbf{X}))}= \underbrace{\mathrm{E}_{P(\textbf{X})}(-\log P(\textbf{X}))}_{\mathrm{H}(P( \textbf{X}))}+\underbrace{\mathrm{E}_{P(\textbf{X})}(\log Q(\textbf{X}))}_{ \mathrm{H}(P(\textbf{X}),Q(\textbf{X}))} \tag{8}\]  
where \(\mathrm{H}(P(\textbf{X}),Q(\textbf{X}))\) is the cross-entropy between \(P(\textbf{X})\) and \(Q(\textbf{X})\). For the many properties of these quantities, we refer the reader to [PERSON] and [PERSON] [48] and [PERSON] [49]. Their use and interpretation are covered in depth (and breadth!) in [PERSON] [19; 20] for general machine learning and in [PERSON] and [PERSON] [5] for BNs.  
For a BN \(\mathcal{B}\) encoding the probability distribution of **X**, (6) decomposes into  
\[\mathrm{H}(\mathcal{B})=\sum_{i=1}^{N}\mathrm{H}\Big{(}X_{i}\mid\Pi_{X_{i}}^{ \mathcal{B}}\Big{)}\]where \(\Pi^{\mathcal{B}}_{X_{i}}\) are the parents of \(X_{i}\) in \(\mathcal{B}\). While this decomposition looks similar to (1), we see that its terms are not necessarily orthogonal, unlike the local distributions.  
As for (7), we cannot simply write  
\[\operatorname{KL}\left(\mathcal{B}\left\|\ \mathcal{B}^{\prime}\right)=\sum_{i=1}^{N }\operatorname{KL}\left(X_{i}\ |\ \Pi^{\mathcal{B}}_{X_{i}}\left\|\ X_{i}\ |\ \Pi^{\mathcal{B}^{\prime}}_{X_{i}}\right)\right.\]  
because, in the general case, the nodes \(X_{i}\) have different parents in \(\mathcal{B}\) and \(\mathcal{B}^{\prime}\). This issue impacts the complexity of computing Kullback-Leibler divergences in different ways depending on the type of BN.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 5 Challenges and Future Directions
### Challenges
#### 5.1.2 Earth Pressure During Sinking
[158] observed that the ratio of end resistance to side friction resistance has progressively become the key component to safe sinking of the caisson foundation as the dimension of caisson foundation continually increases. The behavior of lateral earth pressure on circular shafts was studied using the shaft sinking technique [159]. In this model study, a miniature model caisson equipped with a cutting edge advanced at a recess distance from the lining surface, which was intended to represent induced soil movement during construction. To replicate soil displacement throughout the excavation process, [PERSON] and [PERSON] [160] employed a mechanical device to move the rigid shaft lining to explore the earth pressure distribution on cylindrical shafts. The results revealed that with the minor wall movement, the lateral earth pressure decreased rapidly, and the shaft movement required to achieve the full active state was determined to be 1.5% and 2.5% of the shaft radius, respectively, aligning well with the findings of [161; 162]. The presented assumptions and simplifications have had a considerable impact on outcomes in theoretical approaches, whereas the choice of constitutive models and parameters in numerical methods has significantly influenced the results. Physical modeling provides more advantages than theoretical and numerical approaches in establishing real conditions to examine ground pressure distribution in open caisson shafts. Many existing studies have adopted small-scale models in 1-g and centrifuge modeling to determine the distribution of earth pressure and the induced surface and subsurface soil displacements, which will be explained in the upcoming sections.
Optimal Timing of Cholesterectomy in Secondary Choledocholithiasis Patients Who Underwent Preoperative Endoscopic Retrograde Cholangiopancretagraphy  
[PERSON]  
1 Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, [EMAIL_ADDRESS]  
[PERSON]  
2 Division of Traumatology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, Taiwan, shantel14  
In Taiwan, the present-day treatment of bile duct stones is endoscopic retrograde cholangiocarcinoma-creatography (ERCP), or, in some cases, a laparoscopic cholecystectomy with a bile duct exploration.  
Many studies have shown that the advantages of having surgery over not having surgery have been well established. Choledocholithiasis mostly results from gallstones passing through the cystic duct into the common bile duct. After the resolution of acute inflammation with the appropriate medical treatment and after internal drainage with therapeutic endoscopic retrograde cholangiocarcinoma-creatography (ERCP) stone removal, a cholecystectomy should be performed to prevent recurrence [2, 6, 7, 8].  
However, the optimal timing of cholecystectomy in secondary choledocholithiasis patients has rarely been directly studied. There has been minimal previous evidence provided for this issue, and since blinding cannot be achieved in these comparisons and the outcomes have been subjective, all of the trials were deemed to be at a high risk of bias [9, 10, 11].  
The Tokyo 2013 guidelines have proposed that the appropriate treatment of acute cholangitis should be performed in accordance with the severity grade, and a frequent reassessment with reclassification is indicated based on the response to the initial conservative medical treatment. The two most important aspects of treatment are biliary drainage and antimicrobial therapy. Acute cholecystitis can occur alongside acute cholangitis in some patients. The treatment strategy for patients with acute cholangitis and acute cholecystitis should be determined based on the severity of the disease, as well as the patient's surgical risk [12].  
In this study, we attempted to employ a big data analysis of a health insurance database to investigate the most suitable surgical time for patients who underwent cholecystectomy following ERCP based on patients' disease severity and according to 2013 Tokyo guidelines (cut-off during the year 2013 after the announcement of the Tokyo guidelines in 2013) and further categorized the patients into early cholecyst (ELC), intermediate cholecyst (ILC), and delayed cholecystectomy (DLC) groups.  
This study aimed to determine the optimal timing for laparoscopic cholecystectomy in patients with secondary choledocholithiasis.
## 1 Introduction
### Research Question  
What is the potential of transfer learning with 3D U-Net models for enhancing the classification of ductal carcinoma in breast cancer imaging, and how can its performance be compared to traditional ML methods and other DL techniques?  
1. Explore the impact of different pre-training datasets and hyperparameter settings on the performance of transfer learning with 3D U-Net models, as well as its potential application in the detection and diagnosis of other types of breast cancer and medical imaging applications.
2. Consider the potential challenges and opportunities associated with integrating transfer learning with 3D U-Net models into existing clinical workflows for breast cancer imaging and computer-aided diagnosis.
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 3 Methods  
### Ensemble Empirical Mode Decomposition  
The ensemble empirical mode decomposition (EEMD) algorithm [23; 24; 25], is a data analysis technique that decomposes a complex time series into a set of simpler intrinsic mode functions (IMFs) and a residue. The decomposition process is based on the sifting of the original data into a series of progressively higher frequency components that are referred to as IMFs. Each IMF represents a narrowband oscillatory mode, and the sum of all the IMFs and the residue is equal to the original time series. The decomposition process is repeated multiple times using different random noise samples to obtain an ensemble of IMFs, which are then averaged to reduce the noise and improve the accuracy of the decomposition.  
Assuming that the original time series is \(s(t)\), the specific decomposition process of EEMD is as follows:  
1. Introduce Gaussian white noise \(\omega_{i}(t)\) into the original time series \(s(t)\) to obtain a new sequence \(X_{i}(t)\) with added Gaussian white noise, as shown in Equation (1): \[X_{i}(t)=s(t)+\omega_{i}(t)\] (1)2. Decompose the new sequence \(X_{i}(t)\) using EMD to obtain IMF components and a trend item \(r_{i}(t)\), as shown in Formula (2): \[X_{i}(t)=\sum_{j=1}^{m}imf_{ij}(t)+r_{i}(t)\] (2)
3. After introducing different Gaussian white noises to the original time series \(s(t)\) in step (1) and performing the EMD decomposition on the resulting sequence \(X_{i}(t)\) in step (2), the process is repeated M times. According to the property of irrelevant random sequences having an average value of zero, the obtained IMF components can be averaged to eliminate the interference caused by the added white noise, while maintaining the natural dyadic filtering window of each IMF and trend term. This ensures that the averaging also preserves this property, thereby avoiding the influence of multiple additions of Gaussian white noise on the decomposition of the original sequence's IMF and resolving mode mixing. All the IMF components and trend terms obtained after EEMD decomposition are shown in Equations (3) and (4): \[imf_{j}(t)=\frac{1}{N}\sum_{i=1}^{N}imf_{ij}(t)\] (3) \[r(t)=\frac{1}{N}\sum_{i=1}^{N}r_{i}(t)\] (4)
4. Using EEMD decomposition, \(m\) IMF components and a trend item are obtained. They are expressed as follows: \[s(t)=\sum_{j=1}^{m}imf_{j}(t)+r(t)\] (5)  
By adding multiple sets of Gaussian white noise and averaging the decomposition results, the white noise can cancel each other out, making the results closer to the true sequence and meeting practical needs, based on the characteristic that the mean of Gaussian white noise is zero.