esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Uncertainties and Further Developments  
It is worth analyzing the uncertainties related to the obtained results. In particular, the InSAR-derived theoretical consolidation curve, as shown in Figure 6, was obtained by averaging the InSAR observations over the study area and assuming some simplified behavior for the involved phenomena. The calculation of the estimated statistical distribution of consolidation subsidence was also affected by several uncertainties, as it was  
Figure 8: Conceptual scheme of the liquefaction effects and post-liquefaction subsidence developed in the study area. (**a**) Typical stratigraphic column of the investigated area before the earthquake sequence as in Figure 2. (**b**) Effect of the earthquake shaking, with increased pore pressures (the blue ‘plus’ symbols) and the formation of sand boils at the surface, with water leaks from cracks. (**c**) Post-seismic upward seepage flow and excess pore pressure dissipation after the earthquake sequence, with an accumulation of ground subsidence.  
based on a simplified analytical approach. Finally, the dissipation time of excess pore pressure was strictly dependent on the assumed boundary conditions, which were related to the geomorphological setting of the subsoil of the study area. The main limitation in the boundary conditions adopted in this study was the hypothesis of one-dimensional consolidation. Indeed, the drainage path followed by the water was tridimensional and particularly deviated from the vertical assumption when elements working as \"vent valves\" were present, e.g., wells. Better results should be obtained in estimating consolidation values over time with more sophisticated methods, such as numerical analysis, which better models the development of liquefaction and dissipation of excess pore pressure, potentially yielding more accurate displacement estimates [12; 40]. However, there is a tradeoff between the complexity of the analysis and the effort needed to acquire input parameters. According to the spatial extent and complexity of the study area, the adopted approach provided a reasonable estimate of post-liquefaction subsidence, and it was adequate to explain the observed phenomenon at a regional scale. More detailed numerical analyses can be performed, but at a larger scale than that adopted in this study, since they require detailed knowledge of the subsoil's geometrical and lithological features.
## 3. Results  
### 3.1. Clinical Findings  
Most of the patients were from the rural area (56% versus 43%) and, at the time of diagnosis, the mean age of patients was 58.8 (range, 36 to 81 years) (Table 1). In the total group, 14/30 patients (46.6%, respectively) were women; the most frequent localization of melanoma was on the chest (16/30, 53.3%), followed by limbs (11/30, 36.6%); and another 3 patients had different melanoma localizations. Regarding the histological category, 13/30 patients (43.3%) were registered with nodular melanoma, the same percentage (43.3%) with superficial spreading melanoma and, for 4 of the samples, the histological form was unknown. Clark level (the tumor invasion through the layers of the skin) was available only for 24 samples: 2 samples had level II, 7 III level, 12 level IV and 3 had level V.  
All the statistical analyses were performed using SPSS version 24.0 software (IBM Corp., Armonk, NY, USA). The normality of data was assessed using skewness and kurtosis tests and a visual inspection of histograms. Student's \(t\)-test was applied for a comparison of the means of normally distributed series of values, depending on the number of degrees of freedom (df). All means and medians were reported with appropriately calculated 95% confidence intervals. Associations between the categorical variables were assessed by Chi-square tests which were applied in two study groups for excluded events.  
Synthesizing the demographic characteristics of the study group, the preponderance of patients over 60 years of age, male and from rural areas is noticeable. In the cases studied, the location on the chest (56.7%) and limbs (33.3%) predominates and, in three patients (10%), the origin of melanoma was different (scalp or retroauricular).
# Improved Anomaly Detection by Using the Attention-Based Isolation Forest
## 4 Attention-Based Isolation Forest
### Loss Function with the Contamination Model  
Let us substitute the obtained expression (17) for the attention weight \(\alpha(\mathbf{x},\mathbf{A}_{k}(\mathbf{x}),\mathbf{w})\) into the objective function (14). After simplification, we get  
\[\min_{\mathbf{w}\in S(1,T)}\sum_{s=1}^{n}\max\Bigg{(}0,D_{s}(\epsilon,\omega) +y_{s}e\sum_{k=1}^{T}h_{k}(\mathbf{x}_{s})w_{k}\Bigg{)} \tag{18}\]  
where  
\[D_{s}(\epsilon,\omega)=y_{s}(1-\epsilon)\sum_{k=1}^{T}\sigma\Bigg{(}-\frac{ \left\|\mathbf{x}_{s}-\mathbf{A}_{k}(\mathbf{x}_{s})\right\|^{2}}{\omega} \Bigg{)}-\gamma T. \tag{19}\]  
Let us introduce the new variables  
\[v_{s}=\max\Bigg{(}0,D_{s}(\epsilon,\omega)+y_{s}e\sum_{k=1}^{T}h_{k}(\mathbf{x }_{s})w_{k}\Bigg{)}. \tag{20}\]  
Then, the problem (18) can be rewritten as follows:  
\[\min\sum_{s=1}^{n}v_{s}, \tag{21}\]  
subject to  
\[v_{s}\geq D_{s}(\epsilon,\omega)+y_{s}e\sum_{k=1}^{T}h_{k}(\mathbf{x}_{s})w_{ k}, \tag{22}\]  
\[v_{s}\geq 0,\;s=1,\ldots,n, \tag{23}\]  
\[w_{1}+\cdots+w_{T}=1,\;w_{k}\geq 0,\;k=1,\ldots,T. \tag{24}\]  
This is a linear optimization problem with the optimization variables \(w_{1},\ldots,w_{T}\) and \(v_{1},\ldots,v_{n}\).  
The optimization problem can be improved by adding a regularization term \(\left\|\mathbf{w}\right\|^{2}\) with the hyperparameter \(\lambda\), which controls the strength of the regularization. In this case, the optimization problem becomes  
\[\min\sum_{s=1}^{n}v_{s}+\lambda\left\|\mathbf{w}\right\|^{2}, \tag{25}\]  
subject to (22)-(24).  
We obtain a standard quadratic programming problem whose solution does not meet any difficulties.
## 3 Results  
In this section, we present the results of ECA for the interrelation between the strong (target) EQs and the order parameter fluctuation minima \(\beta_{min}\) that have been found in the regional studies of Japan [59], Eastern Mediterranean [62], and in the global seismicity [60; 61], see Figure 1. For these reasons, the time-series of target EQs will serve as event time-series A and the time-series of \(\beta_{min}\) as event time-series B, whereas the unit of time for \(\Delta T\) and \(\tau\) is one day. Following a previous study [7], which employed ECA to estimate the statistical significance of Seismic Electric Signals (SES) [72; 73] as EQ precursors in Greece and Japan, we focus our study in the cases where the precursory \(p\)-value is smaller than or equal to the trigger \(p\)-value. Moreover, we also focus on the cases where the precursor coincidence rate equals to unity, as in our studies there have been always observed \(\beta_{min}\) before the target EQs.  
Figure 1: World map depicting the areas of Japan (green rectangle) and Eastern Mediterranean (yellow rectangle) at which regional studies based on the variability \(\beta\) have been made. The locations of the target EQs are also shown for both the global (red stars) and regional studies (green and yellow stars), see Tables 1, 2, and 4, respectively. The star corresponding to the M9 Tohoku EQ that occurred on 11 March 2011 in Japan is half red and half green since it is included both in global and regional studies.
## 2 Experimental Setup
### Time-Resolved Photoelectron Spectroscopy (TRPES) Apparatus  
Both the 205-nm pump and 90-nm probe pulses were focused onto a continuous molecular beam formed from a nozzle inside a magnetic-bottle TOF photoelectron spectrometer. The probe pulse energy in the TOF chamber reaches sub-uJ levels; the maximum pulse energy of the 9 th harmonic after a double reflection by the SiC plates was measured to be \(\sim\)0.2 uJ with a power meter (12A-TEC, Ophir Optronics, Jerusalem, Israel). However, we performed TRPES experiments with reduced probe pulse energies [12; 16; 17] to suppress space-charge effects (distortions of TRPES spectra induced by mutual Coulomb repulsion between photoelectrons. See, e.g., ref. [20].). In addition, we kept the excitation probability sufficiently low (\(\lesssim\)5%; the _pump_ pulse energy was typically \(\sim\)10\({}^{2}\) nJ.) to minimize undesired multiphoton processes. The generated photoelectrons were detected using a microchannel plate at the end of a 1-m long TOF tube, and the signal was averaged using a digitizer (U1084A, Agilent Technologies, Santa Clara, CA, USA). To improve the signal-to-noise ratio (SNR) and to reduce the acquisition time, we employed the rapid-scan data-acquisition method, in which the pump-probe delay is continuously scanned and the data are recorded on the fly and in real time [21]. In our apparatus, as a motorized translation stage (FS-1020 PX, Sigma Tech, Saitama, Japan) scanned the pump-probe delay at a constant speed (300 fs/s), the digitizer acquired the photoelectron signals every 0.1 s (i.e., a delay step of 30 fs). In a single scan time of 10 s, there were 100 delay steps. To improve the SNR, the signals were arithmetically averaged over 200 scans, and thus the total acquisition time was 30-40 min (10 \(\times\) 200 = 2000 s).
## 5 Simulation Results and Discussions
### Tracking Control with Acoustic Localization
#### 5.1.3 Tracking Control Results with Prediction Biased Errors  
When there are biased errors in the predicted ocean current information, due to the inherent robustness of the MPC feedback control by using the updated states from acoustic localization, the AUV can still follow the reference path, as shown in Figure 9. However, compared to Figure 5 where there are no prediction biased errors, the AUV trajectory sees larger fluctuations. The root-mean-square-error for the reference tracking increases to 6.17 m compared to 5.59 m in the case of no biased errors. The larger tracking errors are partly due to the modeling errors, i.e., the error between the model used for the controller design and the model used for simulations. Furthermore, we also run a simulation where no ocean current information is used in the controller design, i.e., the predicted mean current speed, angle, and their covariances are all set to zero in the controller. In this case, infeasibilities occur and the simulation could not continue. Therefore, a proper consideration of the influence of ocean currents is necessary.  
Figure 8: Solver times of the MPC online optimization problems.  
Figure 7: Input trajectories and constraints.
## 2 Relevant Theory  
### Characterization of Coal-Rock Images  
Adverse conditions in underground coal mines--such as darkness, reflections, uneven illumination, occlusions, shadows, water drenching, coal dust, and mechanical vibrations--degrade the visual quality of coal-rock images. These factors result in low illumination, weak edges, poor contrast, and non-uniform brightness, all of which hinder effective feature extraction and accurate recognition. Therefore, enhancing image quality and emphasizing target object features (e.g., coal or rock) is essential for resolving these technical challenges.  
The multidimensional image-enhancement network proposed in this paper improves both reflectivity and illumination, while effectively mitigating common defects such as halo artifacts and overexposure, thereby optimizing the overall image quality. One of the core difficulties in automated coal-rock recognition lies in designing a deep learning model capable of robustly capturing the semantic differences between coal and rock, as well as effectively modeling their stacked spatial relationships and enabling stable error backpropagation.  
To address this, the proposed model adopts an encoder-decoder architecture augmented with a full-scale connectivity structure. To support precise segmentation under challenging conditions, the framework incorporates a multi-channel attention module with dilation to focus on informative features, a Res2 Block structure for capturing multi-scale patterns, and a comprehensive loss function that facilitates pixel-level guidance during training on low-quality underground coal-rock images.  
Figure 1: Flow chart for coal-rock identification. The coal–rock image dataset is first fed into the image decomposition module to obtain the enhanced coal–rock image and then the pixel-level segmentation is realized by the image segmentation module.
# Determination of Factors Influencing Road Transport Demand: Evidence from Croatia
## 2 Methods  
The research process was divided into three distinct phases: data collection, data processing and statistical analysis, as well as interpretation of results. Each phase was designed to ensure a comprehensive examination of the factors influencing road transport demand while maintaining methodological rigor.  
In the first phase, historical data relevant to road transport demand were collected from various official sources, including infrastructure management authorities, the Croatian Bureau of Statistics, and local government databases. The data set spans the period from 2004 to 2016, covering key economic, demographic, and traffic and transport-related indicators. The selection of this time frame was intentional, as it captures periods of economic growth, financial crisis, and recovery, thereby providing a well-rounded basis for analysis.  
The second phase focused on data processing and statistical analysis. Descriptive statistics were used to systematically interpret and categorize the data collected. Additionally, inferential statistical methods were applied to identify relationships between variables. The Pearson correlation coefficient was utilized to measure the strength and direction of the linear relationships between transport demand and influencing factors. Its application was justified based on the assumption of linear relationships in transport demand forecasting.  
To assess statistically significant differences between transport demand variations across different road categories and time periods, the Tukey-Kramer test was employed. the Tukey-Kramer test is a multiple comparisons test that can be used to find means which are significantly different [28]). The test has several assumptions that need to be met in order for the test results to be considered valid: Independence, Normality, Equal variances, Paired data, and Equal sample size. This test is a commonly utilized method within the traffic field and was therefore applied in the research for the purpose of comparing means across multiple groups or treatments.  
The third phase involved interpretation of the results. Road transport demand trends were analyzed in relation to economic and demographic variables, and statistical significance testing was conducted to validate the findings. A total of 9350 transport demand measurements were analyzed from 475 road traffic counters across Croatia. These measurements provided a representative overview of road transport demand fluctuations across different road types and geographic locations. Statistical methods were used to determine the relationship between annual rates of change in the intensity of road transportation demand (traffic volume) and the collected economic, demographic, as well as traffic and transport factors.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 2 Feature Engineering Based on Circle Model
### Circle Model-Based Feature Engineering  
Circle modeling was adopted to collect the necessary features for the classifier. Take the circuit of a Leapfrog filter as an example, with a 5V, 1 kHz sine wave as the excitation signal by using MATLAB calling PSPICE. The settings of circuit parameters are shown in Figure 2. The circuit features are collected at the second-stage filtered output point (out).  
Figure 2: Diagram of Leapfrog circuit.
## 3 Results
### Sorption Tests
#### 3.2.4. Sorption Isotherms
The main advantage of the magnetite-supported sorbent is related to the relatively fast uptake kinetics, due the micron size of the sorbent particles (made possible by the readily magnetic separation of composite). It is important to report that the magnetite content is close to 50%; this means that the sorption capacities for the polymer coating are expected to be doubled, and more consistent with the values reported for alternative sorbents. It is noteworthy that for some outstanding sorbents, selected experimental conditions make the sorption properties doubtful because of the possible occurrence of precipitation phenomena. Figure S11 compares the surface characterization of the sorbent after Ni(II), Zn(II) and Hg(II) sorption with pristine sorbent. It is apparent that the irregular surface of raw sorbent is smoothed after metal binding, especially for Ni(II) and Hg(II); small agglomerates are observed in these case with flaky structures. Similar surface modifications were observed with lead accumulation at the surface of Taro crop biosorbent [74]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Parameter** & **Ni(II)** & **Zn(II)** & **Hg(II)** \\ \hline Experimental & q\({}_{\rm m,exp.}\) (mmol g\({}^{-1}\)) & 0.465 & 0.449 & 0.332 \\ \hline Langmuir & q\({}_{\rm m,L}\) (mmol g\({}^{-1}\)) & 0.497 & 0.457 & 0.347 \\  & b\({}_{\rm L}\) (L mmol\({}^{-1}\)) & 4.94 & 6.02 & 18.5 \\  & R\({}^{2}\) & 0.938 & 0.959 & 0.888 \\  & AIC & \(-\)58 & \(-\)56 & \(-\)90 \\ \hline Freundlich & k\({}_{\rm F}\) & 0.370 & 0.348 & 0.308 \\  & n\({}_{\rm F}\) & 4.04 & 3.75 & 4.84 \\  & R\({}^{2}\) & 0.857 & 0.917 & 0.762 \\  & AIC & \(-\)50 & \(-\)49 & \(-\)79 \\ \hline  & q\({}_{\rm m,S}\) (mmol g\({}^{-1}\)) & 0.459 & 0.466 & 0.323 \\  & bs (L mmol\({}^{-1}\)) & 13.7 & 5.10 & \(\infty\) * \\ Sips & n\({}_{\rm F}\) & 0.682 & 1.06 & 0.203 \\  & R\({}^{2}\) & 0.947 & 0.959 & 0.993 \\  & AIC & \(-\)55 & \(-\)51 & \(-\)129 \\ \hline \hline \end{tabular}  
* * Quasi irreversible/rectangular isotherm (\(\infty\): \(6.9\cdot 10^{6}\)).  
\end{table} 
Table 3: Ni(II), Zn(II) and Hg(II) sorption isotherms using G@MChs—parameters of the models.
## 3 Recognition Methods and Their Implementation
### Regression Analysis Algorithm  
The main reason for using regression analysis in this article is based on the significant linear characteristics exhibited by the selected variables. Meanwhile, the dependent variable'regional growth threshold' is presented in a concise and clear numerical form, making the application of regression analysis highly feasible. In addition, the calculation process of regression analysis is relatively simple and does not require additional GPU acceleration processing. In the previous step, the growth threshold regulates the results of regional growth. Different growth thresholds should be selected for pictures with different characteristics. In this part, the regression method is used in machine learning to obtain the relationship between image features and growth threshold. Firstly, it is assumed that the relationship between a growth threshold y and image feature x is \[y=\sum_{i=1}^{N}f(\omega x_{i}+b)\] (1) in which case the MSE loss function is \[L(\omega,b)=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-f(\omega x_{i}+b))^{2}\] (2) where L is the loss function and \(\omega\) is the parameter matrix of the relationship. At this point, the gradient descent algorithm is used to find the gradient of a certain point \(\frac{dL}{d\omega}\), and let \(\omega\) move in the descending direction of the gradient; the update of the gradient can be expressed as \[\omega_{i+1}=\omega_{i}-\alpha\frac{dL}{d\omega}\] (3) where \(\alpha\) is the learning rate. Equation (3) will loop until the loss function converges. Deviation b can be handled in a similar way.
## 2 Modeling of IES
### HCNG-CCS-P2G IES Mathematical Model
#### 2.1.9 Cost Calculation  
1. Carbon Trading Cost  
When the actual carbon emissions of the IES exceed their allocated quotas, the system is required to purchase carbon credits from the market, increasing operational costs. To enhance the environmental performance of the IES, a stepped carbon trading mechanism is implemented to regulate actual carbon emissions. The specific model governing this mechanism is detailed as follows:  
\[\Omega=\Omega_{\text{a}}-\Omega_{\text{q}} \tag{19}\]  
\[f_{\text{co}_{2}}=\begin{cases}0,&\Omega\leq 0\\ \lambda\Omega,&0<\Omega\leq l\\ \lambda(1+\alpha)(\Omega-l)+\lambda l,&l<\Omega\leq 2l\\ \lambda(1+2\alpha)(\Omega-2l)+\lambda(2+\alpha)l,&2l<\Omega\leq 3l\\ \lambda(1+3\alpha)(\Omega-3l)+\lambda(3+3\alpha)l,&3l<\Omega\leq 4l\\ \lambda(1+4\alpha)(\Omega-4l)+\lambda(4+6\alpha)l,&\Omega>4l\end{cases} \tag{20}\]
2. Integrated Demand Response Cost  
Interruptions and load demand transfers inevitably affect user comfort and typically require compensation. For the electricity demand response, the compensation costs are expressed as follows:  
\[f_{\text{e,dir}}=\sum_{t=1}^{T}[\gamma_{\text{e,int}}P_{\text{load,int}}(t)+ \gamma_{\text{e,tra}}P_{\text{load,tra}}(t)] \tag{21}\]  
\[f_{\text{idr}}=f_{\text{e,idr}}+f_{\text{h,idr}}+f_{\text{c,idr}}+f_{\text{ g,idr}}+f_{\text{t,idr}} \tag{22}\]
3. Gas and Electricity Purchase Cost  
\[f_{\text{buy}}=\sum_{t=1}^{T}\Bigl{[}p_{\text{gas}}G_{\text{gas}}(t)+p_{\text {grid}}(t)P_{\text{grid}}(t)\Bigr{]} \tag{23}\]
4. Equipment Operation Cost  
For power generation equipment, such as PV and WTs, the operation cost is calculated by multiplying the unit power output by the corresponding operational cost coefficient. For energy coupling devices, such as CHP, EC, AC, EL, MR, HFC, and CCS, their operation cost is calculated by multiplying the corresponding unit energy absorption by the operation cost coefficient. For the three types of energy storage devices, the operation cost is calculated by multiplying the total energy discharged from the storage device by the corresponding operating cost coefficient. The comprehensive expression is as follows:  
\[f_{\text{op}}=\sum_{t=1}^{T}\sum_{i=1}^{N}c_{i}E_{i}(t) \tag{24}\]  
where, \(N=12\), \(f_{\text{op}}\) is the total operation cost, \(c_{i}\) is the operation cost coefficient for the device \(i\).  
In summary, the total cost can be expressed as follows:  
\[F=f_{\text{co}_{2}}+f_{\text{idr}}+f_{\text{buy}}+f_{\text{op}} \tag{25}\]  
where, \(F\) is the total cost of the integrated energy system.