esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion
### Impact of the Proposed System  
The qualitative analysis and interpretability of the models may be supplemented with an estimate of the savings that may result from the application of the proposed system. The cost reduction can be associated with the genetic testing of individuals showing symptoms of the disease. According to Table 1, the mean value of the daily number of tests performed was 23,178 in July 2020. Most of the tested people had symptoms of the disease because, in accordance with the policy of the Polish government, doctors directed only such persons for tests. The percentage of positive tests at that time in Poland ([[https://ourworldindata.org/grapher/covid19-positive-rate-since-5](https://ourworldindata.org/grapher/covid19-positive-rate-since-5) th-death?tab]([https://ourworldindata.org/grapher/covid19-positive-rate-since-5](https://ourworldindata.org/grapher/covid19-positive-rate-since-5) th-death?tab) =table&time=2020-07-01..2020-07-31, accessed on 10 September 2021) was a maximum of 2.5%, which leads to the conclusion that, everyday, 22,599 people uninfected with SARS-CoV-2 were tested. According to the pessimistically rounded results of the generated classifiers, their specificity (Tables 7, 9, 12 and 14) was 0.3 and 0.1 for XGBoost and the logistic regression model, respectively. This means that, at that time, these models would not recommend testing for 6779 and 2259 people daily, respectively, if applied to the screening of the patients directed to testing. Thus, the XGBoost and logistic regression models would save, in this way, over USD 13 million and over USD 4 million in July 2020, respectively (assuming USD 63 per test, as stated in Table 1).
## 2 Fundamentals
### Multi-Level Thresholding Image Segmentation
#### 2.3.1. Otsu Method  
[PERSON]'s multi-threshold image segmentation is a statistical, unsupervised, and non-parametric method used to determine the optimal threshold values in image processing [4]. The goal of this method is to maximize the between-class variance, ensuring that pixels within the same segment have similar intensity values while also achieving strong separation between different segments. Assume an image is to be divided into N classes. To achieve this segmentation, N \(-\) 1 thresholds \(T_{1},T_{2},\ldots,T_{N-1}\) must be determined. Multi-thresholding separates pixels based on their greyscale intensity values using these thresholds. The normalized histogram of the image is defined below in Equation (15)  
\[p_{i}=\frac{h_{i}}{NP},\ \sum_{i=0}^{L-1}\ p(i)=1 \tag{15}\]  
Here, \(h_{i}\) is the histogram count for intensity level i, NP is the total number of pixels in the image, and \(L\) is the number of greyscale levels. For a single threshold \(T\), the probability of occurrence (weights) for the two classes separated by T are given by:  
\[w_{0}(T)=\sum_{i=0}^{T-1}p(i)\ \ \ \ \ \ w_{1}(T)=\sum_{i=T}^{L-1}p(i) \tag{16}\]  
The class means are:  
\[\mu_{0}(T)=\frac{1}{w_{0}(T)}\sum_{i=0}^{T-1}i.p(i)\ \ \ \ \mu_{1}(T)=\frac{1}{w_{1}(T)}\sum_{i=T}^{L-1}i.p(i) \tag{17}\]  
The total mean intensity of the image is:  
\[\mu(T)=\sum_{i=0}^{L-1}i.p(i) \tag{18}\]  
[PERSON]'s method selects the threshold T* that maximizes the between-class variance:  
\[\begin{array}{c}\sigma_{B}^{2}(T)=w_{0}(T).w_{1}(T).\left[\mu_{0}(T)-\mu_{1 }(T)\right]^{2}\\ T^{*}=argmax_{B}^{2}(T)\end{array} \tag{19}\]  
For multi-threshold segmentation (i.e., more than two classes), the principle is extended to maximize the sum of between-class variances for all N classes. This typically involves evaluating combinations of thresholds to find the set that yields the highest overall between-class variance.
## 1 Introduction
However, these multiple-shot technologies also inevitably sacrificed time resolution, restricting the investigation of dynamic events with high frame rates. To solve the problem, FPM using sparse sampling [33] and multiple coded illumination [34; 35] was also proposed to decrease the exposure times. Recently, FPM approaches of multiplexing by FOV [36; 37] or wavelength [38] have been proposed to achieve single-shot imaging through the compromise of FOV or requiring the sample to have the same absorption for different wavelengths. Apart from multiplexing, algorithms like compressed sensing [39; 40; 41] have been developed to enhance the resolution; unfortunately, compress sensing relies on a measurement satisfying the Restricted Isometry Property (RIP), which is not suitable for Fourier measurements in a traditional band-limited imaging system. In this work, we propose a single-shot super-resolution phase imaging method using a band-limited image and its Fourier transform constraints via analytic extrapolation. Based on the single-shot phase capability of the BIFT imaging system, analyticity is used to increase the SBP and achieve single-shot super-resolution phase imaging. The analyticity arises from the inherent finite FOV constraints in the BIFT system, where the optical field is an analytic function as diffracted, and Fourier transformed from the object within the finite FOV. This property can be used to infer the high spatial frequencies that cannot be collected by the band-limited imaging system, a process known as analytic extrapolation [42; 43]. We used mathematical theory and noise-free numerical simulation to demonstrate that analytic extrapolation can increase the SBP of the system by 2.5 times without using any prior object knowledge or redundant measurements. Additionally, in the presence of noise, it is theoretically shown that the SBP can be improved by about 1.3 to 1.8 times through analytic extrapolation, enabling super-resolution phase imaging in a single exposure. Through super-resolution imaging with a single exposure, our method potentially provides a fast and high-throughput phase imaging technique for observing complex optical fields with full-wave information and dynamic events with high frame rates and high resolution.
## Result and Discussion
### EIS and Tafel Plot Studies  
In order to investigate the nature of the catalytic process, electrochemical impedance spectroscopy (EIS) and Tafel plot studies were undertaken. EIS was conducted potentiostatically at 1.00 V (vs. Ag/AgCl) under the same conditions as the amperometric studies, with the same light source. Figure 6a,b shows the Nyquist and Bode plots, respectively, of the irregular-shaped particles/PPy film, the nanowires/PPy, and the control PPy electrodes without CaMn\({}_{2}\)O\({}_{4}\), in the dark and under light illumination. Following previous work in our laboratory [17], the EIS data were modelled with the equivalent circuit shown in Figure 6c. The modelled results are depicted as the solid lines in Figure 6a. As can be seen, the modelled data yielded an excellent match with the measured data. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Electrode**} & **S** & **I\({}_{\text{D}}\)** & **I\({}_{\text{L}}\)** & **\(\Delta\)I\({}_{\text{D}}\)** & **\(\Delta\)I\({}_{\text{L}}\)** & **\(\Delta\)I\({}_{\text{D}}\)/S** & **\(\Delta\)I\({}_{\text{L}}\)/S** \\  & **m\({}^{2}\) g\({}^{-1}\)** & **\(\mu\)A cm\({}^{-2}\)** & **\(\mu\)A cm\({}^{-2}\)** & **\(\mu\)A cm\({}^{-2}\)** & **\(\mu\)A cm\({}^{-2}\)** & **\(\mu\)A cm\({}^{-2}\)** & **\(\Delta\)I\({}_{\text{D}}\)/S** & **\(\Delta\)I\({}_{\text{L}}\)/S** \\ \hline Irregular-shaped & & & & & & & & \\ particles/PPy & 4.7 & 326 & 701 & 96 & 254 & 20 & 54 \\ nanowires/PPy & 5.4 & 380 & 769 & 150 & 322 & 28 & 60 \\ PPy alone & N/A & 230 & 447 & N/A & N/A & N/A & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between irregular-shaped CaMn\({}_{2}\)O\({}_{4}\) powder and CaMn\({}_{2}\)O\({}_{4}\) nanowires for Brunauer-Emmett-Teller (BET) specific surface area, increase in dark- and photo-current with regard to control electrode; BET specific surface area (S), dark current (I\({}_{\text{D}}\)), photo-current (I\({}_{\text{L}}\)), increase in dark current with respect to control (\(\Delta\)I\({}_{\text{D}}\)), increase in photo-current with respect to control (\(\Delta\)I\({}_{\text{L}}\)). Table 2 shows the results derived from the modelling. It is evident from Table 2 that C\({}_{\rm dl}\) was the highest for the CaMn\({}_{2}\)O\({}_{4}\) nanowires/PPy sample. The capacitance C\({}_{\rm dl}\) is a comparative measure of the active area of the samples tested. Polypyrrole films exhibited the lowest values for C\({}_{\rm dl}\). The higher catalytic performance of the CaMn\({}_{2}\)O\({}_{4}\) nanowires/PPy sample could, therefore, be attributed, at least in part, to a larger catalytically active area of nanowires. In oxygen evolution (photo)electrocatalysis at pH 12, the catalytic performance may be controlled by reactant adsorption to the catalyst (with an associated resistance due to adsorption, R\({}_{\rm ad}\)) or by the fundamental catalytic turnover process (with an associated charge transfer resistance, R\({}_{\rm CT}\)) [17]. In the present study, operating at 1.00 V vs Ag/AgCl (compared to 0.8 V in previous studies [17]) appears to
## 1 Introduction
[PERSON] et al. [20] used the method of ABAQUS and FRANC3D to perform the numerical simulation of crack growth and fatigue life of an axial fan blade. The location of the crack was identified to be at the blade-root conjunction. Stress intensity factors for three modes were presented, and the fatigue life was evaluated using the Forman de Koning model. [PERSON] et al. [21] conducted a numerical simulation of the fatigue crack propagation of an airfoil blade operating under aerodynamic and centrifugal loading. The prediction correlated well with the experimental data and the literature. It was revealed that the crack propagates towards the trailing edge (TE) and leading edge (LE) under both aerodynamic and centrifugal loading. Despite the vast number of simulation and investigation methods for crack propagation that have been presented, there is still a lack of reliable and efficient simulation approaches for crack propagation, especially for the case of axial fan-blade crack propagation subjected to real loads. Taking account of the literature analysis provided, a few studies have been performed regarding the prediction of cracks propagating in blades subjected to centrifugal and aerodynamic loads, rarely for the case of axial underground mine fans. To compensate for the deficiencies of existing studies, this research contributes to the field by innovatively studying the crack propagation of an axial underground mine-fan blade under a centrifugal load, aerodynamic load, and a combination of both loads, showcasing original approaches and addressing neglected or simplified aspects of previous research. In this paper, the primary research contents and strategy implemented are as follows: fundamental theories and methods for crack propagation are introduced in Section 2, particularly the theory governing the stress intensity factor. Considering these principles, the proposed method is applied to the center double slant crack with a hole specimen and PMMA specimen and validated through analytical and experimental comparison in Section 3. The crack growth path of the axial underground mine fan blade is accurately predicted in Section 4. Section 5 provides a summary of the study's main findings and comments.
## 3 Results  
### DSC Analysis Results  
The results of the DSC analyses for both of the commercial and developed feedstocks are plotted in Figure 3. It was found that the first part of the binder for the commercial feedstock had a higher peak melting temperature of about 64 \({}^{\circ}\)C compared to that used in the current study (PEG 1500), which was approximately 45 \({}^{\circ}\)C, whereas the second part of the binder for commercial feedstock had a lower peak melting temperature of about 126 \({}^{\circ}\)C than that of the current feedstock, which was about 136 \({}^{\circ}\)C. A small third peak was also noted at about 158 \({}^{\circ}\)C in the commercial feedstock, which might indicate that there is a third polymeric component in the commercial feedstock. It should be noted that the DSC test for the commercial feedstock has been repeated three times to make sure that consistent data were obtained. [PERSON] et al. [17] investigated the use of different commercial feedstocks including 316L stainless steel PolyMIM and reported that the feedstock had a similar infrared spectrum to polypropylene, though they have not reported their findings in detail. One might argue that the second part of the binder for commercial feedstock is actually high density polyethylene, as the peak melting temperature lies in the peak melting temperature range for high density polyethylene, which is about (125.8-138 \({}^{\circ}\)C) [18]. In addition, the small third peak could correspond to the peak melting temperature of the isotactic polypropylene which ranges from 160 to 166 \({}^{\circ}\)C [19].  
Figure 3: DSC thermograms for the (**a**) commercial feedstock and (**b**) current study feedstock.  
Figure 2: Schematic of the Plasma Electrolytic Oxidation (PEO) setup.  
In order to investigate this further, an FTIR test (Fourier Transform Infrared Spectroscopy) was carried out in the wavelength range from 4000 to 400 cm\({}^{-1}\) with a sampling number of 16 times. The result of the test is shown in Figure 4.  
The obtained data were compared with the results in the literature, and it was found that several peaks coincide with the high density polyethylene peaks reported by [PERSON] and [PERSON], as shown in Table 1[20] from work on the recovery of hydrocarbons from high density polyethylene waste. Other peaks could not be explained due to the complexity of the binder system used, which makes the analysis process more challenging.
## 2 Key Factors Contributing to Spatial Resolution in PET Imaging  
Positron emission tomography (PET) is based on the detection of coincident 511-keV photon pairs resulting from positron-electron (\(e^{+}\)-\(e^{-}\)) annihilation [12; 13]. A \(e^{+}\), emitted by a radioactive tracer, travels a short distance in tissue before interacting with a nearby \(e^{-}\), leading to an annihilation event. This process produces two 511-keV photons emitted in nearly opposite directions (\(\approx\)180\({}^{\circ}\) apart). These photons are detected in temporal coincidence by detectors, and each event is assigned to the corresponding line of response (LOR). The accumulation of a large number of such events positioned on the system LORs enables tomographic reconstruction of the tracer distribution in three dimensions. The physical and technical characteristics involved in \(e^{+}\) emission, annihilation, and photon detection contribute to the overall spatial resolution of the reconstructed PET image.  
The spatial resolution of the PET system is further determined by the design and performance of the PET detection system. A typical PET scanner consists of detector modules comprising of scintillation crystals optically coupled to photodetectors. Upon interaction with a 511-keV photon, the scintillator emits visible light, which is converted into electrical signals by the photodetector [12; 14; 15]. Common scintillator materials include lutetium oxyorthosilicate (ISO), bismuth germanate (BGO), and gadolinium oxyorthosilicate (GSO), selected based on properties such as light yield, decay time, and stopping power [16; 17]. The intrinsic resolution, energy and timing performance, and depth of interaction (DOI) handling of these detection elements significantly influence the PET system's spatial resolution.  
One metric to determine a PET scanner's spatial resolution is its ability to distinguish two closely placed positron-emitting point sources. This resolution is influenced by several factors, both inherent and design-dependent. The inherent, unavoidable factors include the positron range [18], determined by the characteristics of the radionuclide used and tissue traversed, and the non-collinearity of annihilation photons [18], whose impact is influenced by the scanner's geometry. Design-dependent factors, which can be mitigated through proper design and operation, include the detector dimensions and architecture, variation in photon penetration depth in the detector crystals (parallax error) [19; 20], Comptonscattering of photons between detector crystals, patient movement, the event position decoding method used, and the image reconstruction process, which can introduce further blurring due to factors such as noise level, sampling rate, and smoothing [18; 21].
## Appendix A Programming to Obtain Total Process Parameters  
% Programa para Calcular la Profundidad de Corte en el proceso de  
% mecanizado por arranque de viruta  
% [PERSON]  
% 22 de mayo de 2025  
clear all  
clc  
% DEFINICION DE CARACTERISTICAS DESEABLES PARA EL DISENO DEL PROCESO  
Fv=60; % Velocidad de avance de la herramienta de corte [mm/min]  
Ks=1700; % Presion especifica de corte del material de trabajo [MPa]  
Sy=250; % Resistencia a la fluencia acero dulce [MPa]  
Pe=746; % Potencia consumida por la Maquina Herramienta [W]  
Rho\({}_{T}\)=0.7; % Factor de eficiencia del proceso de mecanizado [Adim]  
Ra=0.002; % Rugosidad superficial para superficies mecanizadas en general [mm]  
Vc=1500; % Velocidad de corte minima [m/min]  
% Vcmax=30; % Velocidad de corte maxima [m/min]  
% DVc=1; % Increrento de la velocidad de corte [m/min]  
DeltaAcc=0.2; % Parametro de disminucion del tamano de corte [mm]% DEFINICION DE PARAMETTROS GEOMETRICS DE LA PIEZA DE TRABAJO  
Dc=25.4; % Diametro mayor de la pieza de trabajo [mm]  
dm=10.0; % Diametro menor, deseado [mm]  
lmec=70.0; % Longitud del mecanizado [mm]  
IT=110.0; % Longitud total de la pieza a tornear medida desde la punta de mor-  
dazas [mm]  
% DEFINICION DE CONSTANTES K1, K2  
K1=(pi*Dc*Fv*Ks)/(6000*Rho_T);  
K2=(0.008/Ra)*((pi*Dc*Fv)/1000)^2;  
Rc2=0.005*dm; %Redondo de la nariz de HTA para evitar asintotas  
Acsup=Pe/K1; % Cota superior para la profundidad de corte [mm]  
Acini=Acsup; % Almacenamiento de profundidad de corte inicial [mm]  
KM=sqrt(((dm/1000)^2)+((IT/3000)^2)+(((dm/5000)-(IT/3000))^2)));  
Kf=0.001073; % Parametro de proporcionalidad de coeficientes  
% ANALIZAR COMPORTAMIENTO DE VELOCIDADES  
Rc1=K2/(Vc^2); % Analisis de redondo de la nariz de Hta.  
if Rc1 > Rc2  
Rc= Rc1;  
elseif Rc1 < Rc2  
Rc = Rc2;  
end  
Ksigma=-10*(Rc/dm)+3.5; % Definicion de funcion de concentrador de esfuerzo Ksigma  
% ESTIMACION DE PROFUNDIDAD DE CORTE ac  
P1=(Dc*Acsup*Ks*Fv)/((Vc)*(dm+2*Acsup)^2);  
P2=(4*Kf)+((32*KM)/(dm+2*Acsup));  
Sac=Ksigma*P1*P2;  
while Sac > Sy  
Acsup=Acsup-DeltaAc;  
P1=(Dc*Acsup*Ks*Fv)/((Vc)*(dm+2*Acsup)^2);  
P2=(4*Kf)+((32*KM)/(dm+2*Acsup));  
Sac=Ksigma*P1*P2;  
end  
Np=ceil((Dc-dm)/(2*Acsup)); % Numero de pasadas por corte (enteras)  
Acajus=(Dc-dm)/(2*Np); % Ajuste de la profundidad de corte al tamano de la pasada  
% CALCULO DE TIEMPO DE MECANIZADO  
Tm=((3*lmec)/(2*Fv))*Np;  
% CALCULO DE LA POTENCIA CONSUMIDA OR EL MOTOR ELECTRICO  
Pec=Acajus*K1;  
% CALCULO DE LA ENERGIA PROMEDIO CONSUMIDA OR EL PROCESO  
DE MECANIZADO  
Eprom=Pec*Tm;
# Study on the Mechanical Criterion of Ice Lens Formation Based on Pore Size Distribution
## 2 Theoretical Model
### Determination of Pore Size Distribution from the Soil Freezing Characteristic Curve
#### 2.4.2 Determination of Pore Size Distribution from the Unfrozen Water Content  
The soil freezing characteristic curve is used to describe the relationship between unfrozen water content and temperature [31]. As shown in Figure 5, unfrozen water content decreases with dropping temperature.  
When \(T\) is higher than \(T_{0}\), the unfrozen water content \(w_{\rm u}\) equals the initial unfrozen water content \(w_{0}\) at the temperature \(T_{0}\) and ice is absent in the system. When \(T\) is lower than \(T_{0}\), water phase change results in decreasing unfrozen water content and increasing ice content. According to the conservation of mass, the increment of ice content is equal to the reduction of unfrozen water content. Therefore, the following formula can be obtained:  
\[\Delta V_{\rm i}=\frac{\Delta w_{\rm u}}{\rho_{\rm i}}=\frac{w_{\rm u,n}(T_{ \rm n})-w_{\rm u,n+1}(T_{\rm n+1})}{\rho_{\rm i}} \tag{27}\]  
where \(\Delta V_{\rm i}\) is the change of the ice volume and \(\Delta w_{\rm u}\) is the change of the unfrozen water content.  
Figure 4: Relationship between pore radius and freezing point.  
From Equation (26), the variation of ice crystal radius can be calculated using the following equation:  
\[\Delta r_{\text{\small{pore}}}=(r(T_{\text{n}})+\delta)-(r(T_{\text{n+1}})+\delta )=r(T_{\text{n}})-r(T_{\text{n+1}})=\frac{T_{0}\gamma_{\text{\small{wi}}}}{L \rho_{\text{i}}}(\frac{\Delta T}{(T_{0}-T_{\text{n}})(T_{0}-T_{\text{n+1}})}) \tag{28}\]  
where \(\Delta T=T_{\text{n}}-T_{\text{n+1}}\) and \(\delta\) is the thickness of the water layer adsorbed on the pore surface. When \(\Delta r_{\text{\small{pore}}}\) is very small, \(r_{\text{m}}\) is selected to represent the average radius in the temperature range (\(T_{\text{n}}-T_{\text{n+1}}\)),  
\[r_{\text{m}}=r_{\text{n}}-\frac{\Delta r_{\text{\small{pore}}}}{2}=r_{\text{n} }-\frac{T_{0}\gamma_{\text{\small{wi}}}}{2L\rho_{\text{i}}}\bigg{(}\frac{ \Delta T}{(T_{0}-T_{\text{n}})(T_{0}-T_{\text{n+1}})}\bigg{)} \tag{29}\]  
Therefore, in the temperature range (\(T_{\text{n}}-T_{\text{n+1}}\)), the number (\(N_{\text{\small{rm}}}\)) of pores with radius (\(r_{\text{m}}\)) can be obtained by the following equation,  
\[N_{\text{\small{rm}}}=\frac{\Delta V_{\text{i}}}{V_{\text{\small{rm}}}}= \frac{\frac{w_{\text{\small{n}},n}(T_{\text{n}})-w_{\text{\small{n}},n+1}(T_{ \text{n+1}})}{\rho_{\text{i}}}}{\frac{4}{3}\pi(r_{\text{m}})^{3}}=\frac{3[w_{ \text{\small{u}},n}(T_{\text{n}})-w_{\text{\small{u}},n+1}(T_{\text{n+1}})]} {4\pi\rho_{\text{i}}\cdot\bigg{\{}r_{\text{n}}-\frac{T_{0}\gamma_{\text{ \small{wi}}}}{2L\rho_{\text{i}}}\bigg{(}\frac{\Delta T}{(T_{0}-T_{\text{n}})(T _{0}-T_{\text{n+1}})}\bigg{)}\bigg{\}}^{3}} \tag{30}\]
###### Abstract  
Micron-sized metal-organic framework (MOF) sheets were prepared using organic molecules with aggregation-induced emission (AIE) properties as ligands. The intermolecular ligand-to-metal charge transfer (LMCT) process occurs in MOF structures, resulting in the disappearance of the matrix coordination-induced emission (MCIE) effect and emergence of the aggregation-caused quenching (ACQ) effect. Here, we demonstrate that molecules with electron donors can compete with the LMCT process in MOF structures, thereby changing the transfer path of the excitation energy and returning it to the ground state, mainly in the form of fluorescence. Organic molecules with amino or sulfhydryl groups can act as effective electron donors, reducing the LMCT process and causing the MCIE effect of the MOF sheet. The coexistence of amino and sulfhydryl groups will strongly inhibit the LMCT process of the MOF sheet, thereby greatly enhancing the MCIE effect. Therefore, these types of molecules can be used to regulate the photoluminescence intensity of AIE-based MOF materials. In addition, there are some organic molecules with multiple carboxyl or hydroxyl groups which can produce similar effects. Finally, it was confirmed that the intermolecular LMCT process is highly sensitive, and the MOF sheet showed distinguishable fluorescence results even with the addition of small molecules in the amount of \(10^{-9}\) M. Thus, it is a feasible idea to use the fluorescence changes induced by the LMCT process as a sensitive sensing method for small molecules.  
metal-organic framework; aggregation-induced emission; ligand-to-metal charge transfer; matrix coordination-induced emission; sensitive Article