esa-sceva/satcom-chunk-collection huggingface dataset:
# Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
###### Abstract  
The deep learning (DL)-based approaches in tumor pathology help to overcome the limitations of subjective visual examination from pathologists and improve diagnostic accuracy and objectivity. However, it is unclear how a DL system trained to discriminate normal/tumor tissues in a specific cancer could perform on other tumor types. Herein, we cross-validated the DL-based normal/tumor classifiers separately trained on the tissue slides of cancers from bladder, lung, colon and rectum, stomach, bile duct, and liver. Furthermore, we compared the differences between the classifiers trained on the frozen or formalin-fixed paraffin-embedded (FFPE) tissues. The Area under the curve (AUC) for the receiver operating characteristic (ROC) curve ranged from 0.982 to 0.999 when the tissues were analyzed by the classifiers trained on the same tissue preparation modalities and cancer types. However, the AUCs could drop to 0.476 and 0.439 when the classifiers trained for different tissue modalities and cancer types were applied. Overall, the optimal performance could be achieved only when the tissue slides were analyzed by the classifiers trained on the same preparation modalities and cancer types.  
computational pathology; computer-aided diagnosis; convolutional neural network; digital pathology +
Footnote †: journal: Medical Imaging  
10.3390/app11020808
# Fracture Prediction in Weldox 700E Steel Subjected to High Velocity Impact Using LS-DYNA
###### Abstract  
Analyzing fracture behaviour during high-velocity impacts is critical for designing and developing structures in various scientific and technological fields. This study investigates the fracture behaviour of Weldox 700E steel plates with varying values of fracture parameters and fracture strain criteria using LS-DYNA/explicit. It also discusses the effect of varying plate thicknesses (2 to 6 mm) when impacted by a blunt-nosed projectile with varying masses and varying velocities. The fracture behaviour of flat steel target plates is analyzed with a focus on fracture variables across the top, mid, and bottom elements of the plate thickness. It is observed that the higher value of the fracture parameter results in a smooth fracture surface with Johnson-Cook (JC) fracture strain criteria. For thicker plates, both strain rate and temperature significantly affect the process, while for thinner plates, strain rate predominantly determines element deletion. The results reveal that for the top elements, equivalent plastic strain is the dominant factor causing failure at higher velocities, while temperature plays a significant role at lower velocities. For the mid elements, both plastic strain rate and temperature are critical, whereas for the bottom elements, equivalent plastic strain, plastic strain rate, and temperature are the primary factors. In all cases, shear-type fracture is consistently observed along the mid element of the target plate.  
Keywords:finite element analysis; high-velocity impact; target; projectile; fracture behaviour _Article_
## 3 Methodology
### Proposed Framework
#### 3.1.4 Convolutional and Local Transformer Blocks  
The utilized convolutional block incorporates both an MHCA and an LFFN within a residual block framework (see Figure 2b). This hybrid design aims to efficiently capture short-range and local information. The convolutional block's formulation is described by the following equations:  
\[\mathrm{z}_{1}{}^{1}=\mathrm{MHCA}(\mathrm{z}^{1-1})+\mathrm{z}^{1-1} \tag{5}\]  
\[\mathrm{z}^{1}=\mathrm{LFFN}(\mathrm{z}_{1}{}^{1})+\mathrm{z}_{1}{}^{1} \tag{6}\]  
where \(\mathrm{z}^{1-1}\) and \(\mathrm{z}^{1}\) donate the input and output of the 1 th convolution block, while \(\mathrm{z}_{1}{}^{1}\) represents the output of the 1 th MHCA. The convolutional block leverages the transformer-like token mixing capability of the MHCA module and the locality aspects of the LFFN to extract low-frequency information from the satellite data.  
The local transformer block combines MMHSA, MHCA, LFFN, and pointwise convolutions by using the capabilities of residual module structure (Figure 2c). This block is able to capture long-range dependencies using the MMHSA module efficiently. The formulation of the local transformer block is presented as follows:  
\[\mathrm{z}_{1}{}^{1}=\mathrm{conv}(\mathrm{z}^{1-1}) \tag{7}\]  
\[\mathrm{z}_{2}{}^{1}=\mathrm{MMHSA}(\mathrm{z}_{1}{}^{1})+\mathrm{z}_{1}{}^{1} \tag{8}\]  
\[\mathrm{z}_{3}{}^{1}=\mathrm{conv}(\mathrm{z}_{2}{}^{1}) \tag{9}\]  
\[\mathrm{z}_{4}{}^{1}=\mathrm{MHCA}(\mathrm{z}_{3}{}^{1})+\mathrm{z}_{3}{}^{1} \tag{10}\]  
\[\mathrm{z}_{5}{}^{1}=\mathrm{concatenate}(\mathrm{z}_{2}{}^{1},\mathrm{z}_{4}{}^{1}) \tag{11}\]  
Figure 4: Local feed-forward network (LFFN) module architecture.  
\[\text{z}^{1}=\text{LFFN}(\text{z}_{5}^{1})+\text{z}_{5}^{1} \tag{12}\]  
where \(\text{z}^{1-1}\) and \(\text{z}^{1}\) represent the input and output of the \(\text{l}^{\text{th}}\) transformer block, while \(\text{z}_{1}^{1}\) to \(\text{z}_{5}^{1}\) denote the outputs of the respective transformer block layers. The conv() function indicates a pointwise convolutional layer.  
The MHCA module is responsible for learning dependencies among tokens within short ranges, and the LFFN module improves the extraction of local information within the transformer block. The concatenation of MMHSA and MHCA within the transformer block combines both low and high-frequency information.
## 3 Energy Harvesting Techniques
### Solar Energy Harvesting  
Solar energy harvesting leverages sunlight to generate electrical power using photovoltaic (PV) cells [61; 62]. These cells convert light into electricity through the photovoltaic effect. The power generated by a PV cell can be expressed as follows:  
\[P=\eta\cdot A\cdot G\cdot\cos(\theta) \tag{1}\]  
where \(P\) is the electrical power output (watts), \(\eta\) is the efficiency of the PV cell, \(A\) is the surface area of the PV cell, \(G\) is the solar irradiance, and \(\theta\) is the angle of incidence of sunlight on the PV cell. Solar energy is one of the most commonly used methods in energy harvesting due to its abundance and reliability. The **advantages** of solar energy harvesting are as follows:  
* High Power Output: Solar panels can generate substantial amounts of energy, suitable for various applications.
* Mature Technology: Well-established technology with extensive research and development.
* Environmentally Friendly: Solar energy is renewable and does not produce emissions. The **limitations** of solar energy harvesting are as follows:
* Intermittent Availability: Solar energy depends on weather conditions and time of day.
* Space Requirement: Efficient solar panels require adequate surface area, which might be challenging in some applications.  
Figure 2 illustrates a typical solar energy harvesting system, including the solar panel, energy storage (battery or super-capacitor), and the sensor node.
## 3 Filtering Method  
### MFL Analysis  
The detection range of a single magnetic sensor is small because the LMF of a defect is always weak and small. Two rows of magnetic sensors are arranged along the y direction to cover the entire surface of the rail head, as shown in Figure 2. The defects and varied lift-off both change the output of the sensors.  
The surface defects are generally distributed near the edge or the center of the rail head, and usually not across the entire surface or perpendicular to the x direction. Therefore, when the sensors in a row pass through a defect, some magnetic sensors may detect the defect and their outputs change while other sensors do not detect the defect and their outputs do not change by the defect. The output of a sensor that detects the defect is affected by both the defect and the change of the lift-off, while the output of a sensor that does not detect the defect is affected only by the change of the lift-off.  
Figure 3 shows the x and z plane near a defect. The defect width and depth are denoted as 2a and b, respectively. The distance between the front and rear sensors rows is denoted as l, and the lift-off is denoted as d. The magnetic field intensity of the point P(x,z)  
Figure 2: Array sensors layout.  
above the defect is \(H(x,z)\) and its x and y component are \(H_{x}(x,z)\) and \(H_{z}(x,z)\), respectively. \(H_{x}(x,z)\) and \(H_{z}(x,z)\) can be obtained by Equations (1) and (2), respectively [19].  
\[H_{x}=H_{x}(x,z)=\frac{\sigma_{ms}}{2\pi}\left[tan^{-1}\frac{b+z}{a-x}+tan^{-1} \frac{b+z}{a+x}-tan^{-1}\frac{z}{a-x}-tan^{-1}\frac{z}{a+x}\right] \tag{1}\]  
\[H_{z}(x,z)=\frac{\sigma_{ms}}{4\pi}\ln\frac{\left[\left(x+a\right)^{2}+\left(z +b\right)^{2}\right]\left[\left(x-a\right)^{2}+z^{2}\right]}{\left[\left(x-a \right)^{2}+\left(z+b\right)^{2}\right]\left[\left(x+a\right)^{2}+z^{2}\right]} \tag{2}\]  
where \(\sigma_{ms}\) is the magnetic charge density of the defect side, which can be calculated by Equation (3):  
\[\sigma_{ms}=5.3\left(\frac{b/a+1}{b/(a\mu)+1}\right)H \tag{3}\]  
where \(\mu\) is the magnetic permeability of the material, and \(H\) is the applied magnetic field strength.  
According to Equations (1) and (2), if \(z=1\) mm, \(a=1\) mm, \(b=1\) mm, and \(\sigma_{ms}=1\), the magnetic field distribution in x and z directions of the defect is shown in Figure 4.  
Figure 4: Magnetic field distribution in the x and z directions.  
Figure 3: The x and z plane near a defect.
## 2 Characterization of Cloud Types and Their Impairments
### Classification of the Clouds  
When studying the time-averaged characteristics of clouds, one should keep in mind that they refer only to that part of the period during which cloudiness was observed. Frequencies for general cloud cover, low-level clouds, and vertical extent clouds over continents and oceans can be found in climate guides and atlases. By including the results of aviation soundings, it is further possible to estimate the frequency of cloudiness at the upper and middle levels. For large regions, the total cloud cover and the amount of any type of cloud present can be estimated using satellite measurements. The frequency of cloudiness is closely related to the general circulation of the atmosphere, the nature of the underlying surface, and insolation. In addition, the height of the site, orography, and other factors affect the frequency of cloudiness [37].  
According to the World Meteorological Organization (WMO), a cloud is defined as a collection of very small drops of water (above 0 \({}^{\circ}\)C), ice crystals (between \(-20\,^{\circ}\)C and \(-40\,^{\circ}\)C), or their mixture, the presence of which is predominantly observed in the troposphere at different heights above ground level. This organization also gives a generally accepted classification of the various types of clouds. It is worth considering the fact that all clouds have a non-static vertical thickness, and this can vary depending on the particular type of layer and type of cloud. In Table 2, averaged values are provided for most types of clouds.  
The radii \(r\) of water particles inside clouds range from 1 \(\upmu\)m to approximately 30 \(\upmu\)m [39]. A standard value of around 200 \(\upmu\)m is also commonly suggested as the limiting liquid particle diameter above which droplets can cause drizzle or rainfall [38]. As for ice crystals, their presence is observed in clouds located in the upper troposphere, which are also known as cirrus clouds. These solid hydmeteors take various forms, including bullets, rosettes, thick plates, short pillars, etc., the maximum dimensions of which vary greatly depending on the type of ice crystal and range from 100 \(\upmu\)m to 1500 \(\upmu\)m [40].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cloud
## 3 System Model and Problem Description
### Network Model  
To achieve discretization of continuous UAV trajectories, the UAV flight service period is \(T_{f}\) divided into \(N\) equally long time slots, with each slot being longer \(\delta_{t}=\frac{T_{f}}{N}\).  
In the 3D Cartesian coordinate system, the flight altitude of the UAV is denoted as \(h_{(t)}\) in a time slot \(t\in\{1,2,\ldots,N\}\), and its projection coordinates \(q_{(t)}\) in the horizontal plane are represented as \(\left[x_{(t)},y_{(t)}\right]\), while its position coordinates \(c_{(t)}\) in 3D space are represented as \(\left[q_{(t)},h_{(t)}\right]\).  
Due to the need for the UAV to return to the starting point after the service period ends, its trajectory must meet periodic constraints:  
\[c_{(1)}=c_{(n)} \tag{1}\]  
In addition, during the flight of a UAV, the flight altitude \(h_{(t)}\), flight speed \(v_{(t)}\), and flight acceleration \(a_{(t)}\) are, respectively, constrained by its maximum flight altitude \(h_{max}\), maximum flight speed \(v_{max}\), and maximum flight acceleration \(a_{max}\).  
\[h_{(t)}\leq h_{max} \tag{2}\]  
\[v_{(t)}=\frac{|c(t+1)-c(t)|}{\delta_{t}}\leq v_{max} \tag{3}\]  
\[a_{(t)}=\frac{|v(t+1)-v(t)|}{\delta_{t}}\leq a_{max} \tag{4}\]  
If the 3D coordinates \(w_{u}\) of user device \(u\) are \(\left[x_{u},y_{u},z_{u}\right]\), then the distance between the UAV and the \(u\)-th device is \(d_{u}(t)\), represented as \(\left\|w_{u}-c_{(t)}\right\|_{2}\).  
To simplify the analysis, this paper models obstacles using a cylinder. The center coordinates \(o_{k}\) of the \(k\)-th obstacle are \(\left[x_{k},y_{k}\right]\), the radius is \(r_{k}\), and the height is \(l_{k}\). To ensure the safety of UAV flight, obstacle avoidance constraints can be decomposed into horizontal and vertical constraints:  
\[\left|q_{(t)}-o_{k}\right|\geq r_{k}+d_{safe} \tag{5}\]  
\[h(t)\geq l_{k}+d_{safe} \tag{6}\]  
Figure 1: Model of UAV-assisted MEC system.  
where \(d_{safe}\) is the preset safety margin. In a 3D environment, UAVs only need to choose between vertical crossing and horizontal circling to smoothly avoid obstacles. Therefore, combining the above two constraints produces  
\[\Big{[}|q_{\,(t)}-o_{k}\Big{|}\geq r_{k}+d_{safe}\Big{]}\Big{|}\Big{|}\Big{[}h(t )\geq l_{k}+d_{safe}\Big{]} \tag{7}\]
## 2 Materials and Methods  
### Study Sites  
Small Mammal Sampling Was Conducted in Two Areas: Bougouni and Faladje  
The rural village of Faladje (Kati District/Koulikoro Region) and its rural surroundings (13\({}^{\circ}\)00\({}^{\prime}\) N/8\({}^{\circ}\)20\({}^{\prime}\) W and 13\({}^{\circ}\)08\({}^{\prime}\) N/8\({}^{\circ}\)20\({}^{\prime}\) W) is located 80 km northwest of Bamako. The village has a Catholic mission health center that serves 23,000 inhabitants living in the village and surrounding areas.  
The semi-urban locality of Bougouni is located in the Sikasso Region (11\({}^{\circ}\)25\({}^{\prime}\) N/7\({}^{\circ}\)29\({}^{\prime}\) W and 11\({}^{\circ}\)25\({}^{\prime}\) N/7\({}^{\circ}\)28\({}^{\prime}\) W), 160 km from Bamako.  
Faladje and Bougouni are two sites with different bioclimatic patterns located northwest and southeast of Bamako, respectively (Figure 1). The village of Faladje and the town of Bougouni are located in the Sudano-Saharan and Sudano-Sahelian zones, respectively, 77 km and 160 km from Bamako (Figure 1), and are research sites of the Malaria Research Training Center (MRTC) program. Housing is essentially traditional and rural in Faladje, composed of mud-made houses with a few modern houses. In contrast, in the town of Bougouni, there are two contrasting types of housing: modern semi-urban and traditional rural. The populations of these two sites, who are mainly of Bambara ethnicity, practice rain-fed agriculture, family livestock raising, and market gardening in the dry season.  
In Faladje, the average temperatures in the dry season (December-April) and in the rainy season (May-August) are 41 \({}^{\circ}\)C and 32 \({}^{\circ}\)C, respectively, and the average rainfall in the dry season (December-April) is 8 mm compared to 134 mm in the rainy season (May-August). In Bougouni, the average temperature in the coldest month (December) is 26.0 \({}^{\circ}\)C, and in the hottest month (April) it is 32.5\({}^{\circ}\)C. During the least rainy months (December, January, February), the rainfall recorded was 1 mm, and in the wettest month (August) it reached 280 mm.  
This study was implemented to investigate reservoirs of emerging pathogens in Mali; Faladje and Bougouni were selected to initiate the survey because the two areas have been reported to be potential endemic areas of re-emerging viruses' diseases. The Lassa virus is endemic in the southern area of Mali, with higher LASV IgG seroprevalence ranging from 14.5% to 44% per village in the Bougouni District, Sikasso region [17]. Faladje is located in the Kita District, Koulikoro region. Both Koulikoro and Sikasso regions have recently reported cases of yellow fever.
## 3 Results and Discussion
### Adsorption of BPA onto MRGO@ZIF7-MIP
#### 3.2.3 Sorption Isotherm  
The adsorption capacity is an important parameter that reflects the performance of the adsorbent and determines the amount of adsorbent required during the adsorption experiment. The adsorption capacity of the adsorbent on the template molecules can be analyzed by the adsorption isotherm. The adsorption isotherms of BPA by MRGO@ZIF7-MIP, MRGO-MIP and MRGO@ZIF7-NIP are presented in Figure 7. The Langmuir isotherm  
Figure 5: Adsorption effect of different factors: adsorbent dosage (**a**), temperature (**b**), pH (**c**). Other conditions: 10 mL sample solution of 0.08 mmol L\({}^{-1}\) BPA.  
Figure 6: Adsorption kinetic curve of MRGO@ZIF7-MIP, MRGO-MIP, and MRGO@ZIF7-NIP for BPA. Other conditions: 20 mg sorbent, 20 mL sample solution of 0.08 mmol L\({}^{-1}\) BPA, temperature 25 \({}^{\circ}\)C.  
model was adopted to fit the experimental data. The \(Q_{\rm e}\) are correlated with the isotherm model of [PERSON] to Equation (2):  
\[1/Q_{\rm e}=1/Q_{\rm max}+C_{\rm e}/bQ_{\rm max} \tag{2}\]  
where \(Q_{\rm e}\) (mg/g) is the adsorbed amount of BPA, \(Q_{\rm max}\) (mg/g) is the maximum adsorbing capacity, \(b\) is the adsorption equilibrium constant, and \(C_{\rm e}\) (mg/mL) is the equilibrium concentration [67].  
As shown in Figure 7, the adsorption of BPA by MRGO@ZIF7-MIP and MRGO-MIP were both better than that by MRGO@ZIF7-NIP and increased significantly with the increase in the initial concentration of BPA. Calculated with the isotherm model of Langmuir, the maximum adsorption amount of MRGO@ZIF7-MIP, MRGO-MIP, and MRGO@ZIF7-NIP for BPA was 24.32, 15.46 and 6.34 mg/g, respectively. MRGO@ZIF7-MIP showed the highest adsorption amount, about 1.6 times that of MRGO-MIP and about 4 times that of MRGO@ZIF7-NIP. The results of adsorption isotherm analysis again indicate that the adsorption sites present on the surface of molecularly imprinted polymers have good molecular recognition of BPA.
## 3 Results
### Adjacent Grade Thresholds for Sensitive RISIs  
After RIRSI (R732, R878, R712) was eventually selected as the most sensitive spectral index band combination from six types of RISIs, the thresholds of the adjacent levels could be calculated according to Equation (11). Furthermore, the thresholds of adjacent grades for the other five types of RISIs including two-band indices, RDSI (R702, R715), RNDSI (R643, R656), RRSI (R656, R643) as well as three-band indices, RIDSI (R1051, R1049, R747) and RPSI (R519, R438, R706), were also calculated, as shown in Table 2, where \(\theta_{1},\theta_{2},\theta_{3},\theta_{4}\) represents the threshold between G0 and G1, G1 and G2, G2 and G3, G3 and G4, respectively. For each threshold \(\theta_{i}(1\leq i\leq 4)\), the threshold value increased with the increase in i. Therefore, through the reconstruction of the spectral index, it had obvious variation characteristics in the sensitive band combination, which made it easy to identify the disease level using adjacent grade thresholds.
## 2 Materials and Methods
### Hyperspectral Analysis  
Hyperspectral profiles of tablets in the spectral range from 400 to 1030 nm were obtained using a Specim IQ hyperspectral camera (Spectral Imaging Ltd., Oulu, Finland). The spectral resolution of the camera was 5 nm. The spatial resolution was 512 \(\times\) 512 with a pixel size of 17.58 \(\upmu\)m \(\times\) 17.58 \(\upmu\)m. Reflectance measurement was performed approximately every 3 nm, giving 204 values for each tablet. Analysis of the obtained hyperspectral data was performed using MATLAB software version 7.11.0.584 (R2010b). For each tablet, for each wavelength point, the maximum, mean, and minimum reflectance values were obtained. The difference between the maximum and minimum reflectance values allowed for the tablet homogeneity assessment because the greater the difference, the lower the homogeneity.
## 2 Related Work
### A Brief Review of the Geodesic DGGS
#### Base Regular Polyhedron  
The first step in the construction of a Geodesic DGGS is to establish an initial partition on the globe. Since multi-resolution discrete grids are obtained by subdividing the initial grid, the cells of the initial grid should preferably be the same regular spherical polygons. A geometrically rigorous solution to the problem of covering a spherical surface with tiles shows that there are only five classes of inscribed polyhedrons: the tetrahedron, the cube, the octahedron, the dodecahedron, and the icosahedron. All these polyhedrons produce a covering that is both regular and homogeneous. The covering can be obtained by a central projection of the sides and corners of the inscribed polyhedron on the spherical surface (Figure 3). These five platonic solids represent the only ways in which the sphere can be partitioned into cells, each consisting of the same regular spherical polygon, with the same number of polygons meeting at each vertex. Platonic solids have thus been commonly used to construct Geodesic DGGSs. In general, platonic solids with smaller faces reduce the distortion introduced when transforming between a face of the polyhedron and the corresponding spherical surface. The icosahedron is thus the most commonly chosen base regular polyhedron.
## 3 Results and Discussion
### Sub-Diffraction Raman Imaging on Single Cells of E. coli
Furthermore, in the freeze-dried sample, the less intense but specific peak at 780 cm\({}^{-1}\) of nucleic acids is nearly undetectable, whereas it can be mapped in the air-dried cell. These findings are supported by the observation of many other samples of air-dried and freeze-dried cells that were found to follow the same behavior; examples of these are shown in SI (Figure S2). In order to further investigate the differences in the spatial distribution of the macromolecules in bacterial cells, we decided to apply multivariate analysis to the chemical Raman maps to consider the entire vibrational fingerprint of different biomolecules instead of the intensity of one peak at a time, which is not always very specific.