esa-sceva/satcom-chunk-collection huggingface dataset:
# Cracking and Toughening Mechanisms in Nanoscale Metallic Multilayer Films: A Brief Review
## 4 Other Fracture Properties
### Fracture Toughness
#### 4.1.1 Length-Scale-Dependent of Fracture Behavior
The equilibrium number of dislocations at a specific load is [8; 113]:  
\[n=\frac{4\pi(1-v)}{\ln(\widetilde{h}/\widetilde{r})}(\frac{\widetilde{K}_{app }\sqrt{\widetilde{h}}}{A\sqrt{2\pi}}\sin\phi\cos\frac{\varphi}{2}-\widetilde{ \gamma}) \tag{17}\]  
where \(\varphi=45^{\circ}\) is the angle of inclination of the slip plane to the interface, \(A\approx 1\),\(\widetilde{r}=2.7r_{0}/b\), with \(r_{0}\) being the effective core radius in \(A\), the far field mode I stress intensity \(K_{app}=1.12\sigma_{app}\sqrt{\pi h}\)[55]), \(h_{\phi}=h/\sin\varphi\), and the surface energy \(\gamma\), respectively:  
\[\widetilde{K}=\frac{K_{app}}{\mu\sqrt{b}},\ \widetilde{h}=\frac{h_{\varphi}}{b}, \ \widetilde{\gamma}=\frac{\gamma}{\mu b} \tag{18}\]  
The stress at the blunted crack tip \(\widetilde{\sigma}_{tip}\) is related to \(n\) and \(K_{app}\):  
\[\widetilde{\sigma}_{tip}\sqrt{n}=2\sqrt{\frac{2}{\pi}}\widetilde{K}_{app}(1- \frac{3(\sin\varphi\cos\frac{\varphi}{2})^{2}}{\ln(\widetilde{h}/\widetilde{ r})})+\frac{12A}{\sqrt{h}\ln(\widetilde{h}/\widetilde{r})}\widetilde{\gamma}\sin \varphi\cos\frac{\varphi}{2} \tag{19}\]  
The microcrack will propagate from a channel crack when the microcrack tip stress reaches the \(\sigma_{c}\) of the materials (\(\widetilde{\sigma}_{tip}=\widetilde{\sigma}_{c}\)). The maximum number (\(n_{\max}\)) of equilibrium dislocations, which is generated from the crack tip before the cleavage in A layer can be obtained from Equations (17) and (19). Under this condition, the far-field applied stress intensity is then the fracture toughness of the multilayers, which could be obtained as \(\widetilde{K}_{IC}=\widetilde{K}_{app}\). The predicted \(n_{\max}\) in Cu/Nb (see Figure 14b) apparently shows a size effect [8]. \(n_{\max}\) increases sharply when \(h\) increases from 5 nm to 25 nm, Larger \(n_{\max}\) means a better plastic deformation capability of Cu layers. The Cu layers is more ductile and hinder the crack propagation which comes from the brittle Nb layers. Additionally, with \(h\) increasing, the far field mode I stress intensity ahead of microcrack depends on the propagation of microcracks in Nb layer mainly, which is scaling with \(h^{0.5}\)[114]. As a result, with increasing \(h\), \(K\) increases, then the ductility and \(K_{IC}\) of NMMFs decrease. Meanwhile, a large ISF means that the microcrack is easily to propagate to form an opening fracture, the transition in fracture mode, which will occur from opening to shear at \(h_{\text{cri}}\), is verified in Figure 15[8]. In conclusion, the dominant factor which controls the modulation period and modulation ratio dependent fracture mode in NMMFs is the constraint effect from ductile layer on brittle layer [46].
# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 3 Materials and Methods
### The Proposed Method  
The general structure of the proposed multi-instance partial decision tree (MIPART) method is presented in Figure 2. This method applies a Bagging ensemble technique to a multi-instance dataset, where Bagging generates multiple subsets, \(D_{1}\) to \(D_{n}\), by randomly sampling the dataset with replacement, creating diverse bags that reduce variance and improve generalization. In multi-instance learning, each bag is associated with a binary label--either 0 (negative) or 1 (positive)--where a bag is labeled as 1 if it comprises at least one positive instance, and 0 otherwise. Within each bag, MIPART employs the Simple Multi-Instance (SimpleMI) classifier as a base learner, constructing a partial decision tree (PART) to capture local decision boundaries. The PART algorithm adopts a _separate-and-conquer strategy_, incrementally generating rules without requiring global optimization. It builds a pruned decision tree for the current dataset, selects the leaf with the largest coverage, and converts it into a rule. The process is repeated recursively for the remaining instances until none are left. This simplifies the tree structure and ensures flexibility while maintaining computational efficiency [43]. Each PART tree, trained on a unique subset, produces bag-level predictions, which are then aggregated using majority voting to achieve the final class label prediction. This ensemble strategy enhances robustness and accuracy by combining perceptions from multiple independently trained bag-based classifiers.  
The PART algorithm operates with a complexity of \(O(k\times a\times n\text{log}(n))\), where \(k\) is the number of classes, \(a\) is the number of attributes, and \(n\) is the number of samples. In the context of MIPART, this complexity is scaled by the ensemble size, where we used a Bagging size of 100 in our experiments. The proposed method is computationally efficient, able to recognize complex relationships while maintaining simplicity. Although PART is a rule-set-based algorithm, which can sometimes lead to overfitting, particularly in highly noisy datasets, its performance is enhanced by the pruning process, the ensemble strategy and the cross-validation utilized in MIPART. One of PART's key advantages is its robustness in handling missing values, which is particularly useful in real-world applications where data may be incomplete. These advantages made PART an ideal choice in the MIPART method, allowing it to achieve strong predictive performance across diverse multi-instance datasets.
## 2 Experimental Setup
### Pressure Measurement Test  
The delta wing pressure measurement test was completed in the \(\varnothing\) 0.7 m wind tunnel of CARDC. The setup of the delta wing pressure measurement test is shown in Figure 5. There are two CCD cameras near the shooting place. The fast response PSP is sprayed on the upper part of the delta wing, which is excited by a UV LED and fluoresces. First, we turned off the UV LED to create a completely dark environment. Then, 10 background images were captured using the same camera settings as in the experiment and the average of these images is called I\({}_{\text{dark}}\). We collected 10 images under reference and wind-on conditions and subtracted I\({}_{\text{dark}}\) from the corresponding raw images. Then, the average values of the images collected under these two conditions are denoted as I and I\({}_{\text{ref}}\), respectively. The ANN used to predict coefficient A is denoted as N\({}_{2}\).  
We sprayed the fast response PSP on a circular thin aluminum sheet to make the sample, and calibrated coefficients A and B through the static calibration system. The true value of the pressure is obtained by an electronic-scanned pressure sensor. The thickness gauge and surface roughness measuring instrument were used to measure the paint thickness and roughness on the surface of the delta wing. Then, the paint thickness and roughness were used to predict the pressure sensitivity coefficient through N\({}_{1}\), and finally, the predicted pressure sensitivity was used to predict coefficient A through N\({}_{2}\).  
According to Equation (1), we can calculate the pressure of the delta wing by the following equation:  
\[P=\frac{[\frac{I_{ref}}{T}-B(T)]}{A(T)}\times P_{ref} \tag{2}\]  
Using the in situ calibration method [20], the calibration experiment, and the method in this paper, three groups of coefficients A and B were obtained, and Equation (2) was used to calculate the pressure. The feasibility of the method proposed in this paper is evaluated by comparing the root mean square error (RMSE) of the pressure calculated by these three methods.  
Figure 5: Experimental setup of the delta wing.
## 2 Methods  
### Slip Length Boundary Condition  
Both theory and experiments [3; 4; 5] suggest that the physics involved in drag reduction by riblets acts through a local mechanism. Indeed, since riblets are small compared to the turbulent structures of the near-wall cycle, far enough from the wall, the turbulent flow perceives the presence of riblets only as a homogeneous effect: the upward shift \(\Delta U^{+}>0\) of the logarithmic portion of the mean velocity profile:  
\[U^{+}=\frac{1}{\kappa}\log(y^{+})+A+\Delta U^{+} \tag{1}\]  
where \(\kappa=0.392\) is the von Karman constant, and \(A=4.48\) is the near wall intercept for smooth surfaces (these constants are set after [29], but their numerical value does not affect the outcome of the study). The dimensionless vertical shift \(\Delta U^{+}\) equals the virtual shift in wall units of the non-slipping wall [4], i.e., the slip length \(\lambda^{+}\). We exploit this shift to account for the presence of riblets via a slip boundary condition, which linearly relates the wall value of the longitudinal component of the velocity \(u_{w}\) (the subscript \(w\) indicates quantities evaluated at the wall) to the wall shear rate \((\partial u/\partial y)_{w}\) through the slip length \(\lambda\):  
\[u_{w}=\lambda\bigg{(}\frac{\partial u}{\partial y}\bigg{)}_{w}, \tag{2}\]  
thus effectively recovering the no-slip condition when \(\lambda=0\). The discrete counterpart of Equation (2), where the derivative is approximated with a finite difference, reads:  
\[u_{w}=\lambda\frac{u_{1}-u_{w}}{d}, \tag{3}\]where \(u_{1}\) is the longitudinal velocity at the first inner mesh point, and \(d\) is its distance from the wall. Hence, the velocity at the wall is:  
\[u_{w}=u_{1}\frac{\lambda}{\lambda+d}. \tag{4}\]  
In this work, we always set the shift of the mean velocity profile at \(\Delta U^{+}=1\), which corresponds [15; 20; 21] to the best-performing riblets with triangular cross-section. These riblets have a square root of the cross-sectional area of \(l_{g}^{+}=10.5\), and provide a drag reduction of 7% when measured in the lab under controlled conditions and at low \(Re\). Using \(\Delta U^{+}=1\) implies setting \(\lambda^{+}=1\), whence the physical size of the riblets varies along the body with the friction velocity of the flow. In other words, riblets are assumed to be locally optimal everywhere, and the corresponding physical dimensions are computed as a result of the simulation.  
It is worth noticing that the present boundary condition can be used to simulate, besides riblets, any other drag reduction method whose effect reduces to a shift in the mean velocity profile. To this purpose, only the slip-length value must be adjusted.
## 2 MPS Method
### Mathematical Formulation
#### 2.1.2 Particle Interaction Model  
In the MPS method, functions are approximated by a weighted average approach. In order to avoid non-physical pressure oscillation in an original kernel function, a modified kernel function which was proposed by [50] is employed in this paper, that is:  
\[W(r)=\begin{cases}\frac{r_{e}}{0.85r+0.15r_{e}}-1&0\leq r<r_{e}\\ 0&r_{e}\leq r\end{cases} \tag{3}\]  
where \(r_{e}\) is the radius of particle interaction, and \(r\) is the distance between two particles.  
Summation of the weight function is called particle number density, which is used to keep the incompressibility of fluid:  
\[\langle n\rangle_{i}=\sum_{j\
eq i}W(|\mathbf{r}_{j}-\mathbf{r}_{i}|) \tag{4}\]  
The gradient model is the weighted average of a physical quantity \(\phi\) between particle \(i\) and its neighboring particles. It is mainly used to discretize the term of pressure gradient. The model proposed by [35] is adopted here, that is:  
\[\langle\
abla\phi\rangle_{i}=\frac{d}{n^{0}}\sum_{j\
eq i}\frac{\phi_{j}- \widehat{\phi_{i}}}{|\mathbf{r}_{j}-\mathbf{r}_{i}|^{2}}(\mathbf{r}_{j}-\mathbf{r}_{i})W(|\bm {r}_{j}-\mathbf{r}_{i}|) \tag{5}\]  
\[\widehat{\phi_{i}}=min(\phi_{j},\phi_{i}),\{j:W(|\mathbf{r}_{j}-\mathbf{r}_{i}|)\
eq 0\} \tag{6}\]  
where \(d\) is number of dimension, \(n^{0}\) is initial particle number density, and \(\mathbf{r}\) is position vector.  
The divergence model is used to discrete the velocity divergence in PPE and is similar to the gradient model. It is described as follows:  
\[\langle\
abla\cdot\mathbf{u}\rangle_{i}=\frac{d}{n^{0}}\sum_{j\
eq i}\frac{(\mathbf{r }_{j}-\mathbf{r}_{i})\cdot(\mathbf{u}_{j}-\mathbf{u}_{i})}{|\mathbf{r}_{j}-\mathbf{r}_{i}|^{2}}W(| \mathbf{r}_{j}-\mathbf{r}_{i}|) \tag{7}\]  
The Laplacian model is the weighted average of the distribution of a physical quantity \(\phi\) from particle \(i\) to its neighboring particles. This model is used to discretize the viscosity term of PPE and is described as follows:  
\[\langle\
abla^{2}\phi\rangle_{i}=\frac{2d}{n^{0}\lambda}\sum_{j\
eq i}(\phi_{ j}-\phi_{i})W(|\mathbf{r}_{j}-\mathbf{r}_{i}|) \tag{8}\]  
where \(\lambda\) is a parameter which is used to compensate for a finite range of kernel function, and can be obtained by:\[\lambda=\frac{\sum\limits_{j\
eq i}|\mathbf{r}_{j}-\mathbf{r}_{i}|^{2}W(|\mathbf{r}_{j}-\mathbf{r} _{i}|)}{\sum\limits_{j\
eq i}W(|\mathbf{r}_{j}-\mathbf{r}_{i}|)} \tag{9}\]
## 1 Introduction
### Degradation Mechanisms  
The degradation process in the MAPbI\({}_{3}\) layer occurs when the methylamine group (MAI) is lost via sublimation and PbI\({}_{2}\) is formed [3; 24], according to Equations (1) and (2). MAPbI\({}_{3-\times}\)Cl\({}_{x}\) and MAPbI\({}_{3}\) present a similar moisture assisted degradation where methylamine group is lost via sublimation and PbI\({}_{2}\) is formed. The highly hydrophilic property of perovskite allows the absorption of the water from the surrounding environment [3]. As a result, the perovskite layer, which is originally of a brownish color, gradually turns yellow [4]. Oxygen and ultraviolet radiation can further affect the stability of PSCs, as can be seen in Equations (3) and (4) [3; 8], albeit to a lesser extent than humidity [25].  
MAPbI\({}_{3}\) is one of the most common forms in which the perovskite layer is manufactured. The degradation equations of the perovskite layer under moisture, oxygen and UV light are depicted below [25]:  
\[\mathrm{MAPbI_{3}(s)}\ +\ \mathrm{H_{2}O}\framebox{$\leftrightarrow$}\ \mathrm{MAI \,(aq.)}\ +\ \mathrm{PbI_{2}(s)} \tag{1}\]  
\[\mathrm{MAI(aq.)}\ \framebox{$\leftrightarrow$}\ \mathrm{MA(aq.)}\ +\ \mathrm{HI(aq.)} \tag{2}\]  
\[\mathrm{4 HI(aq.)}\ +\ \mathrm{O_{2}}\framebox{$\leftrightarrow$}\ \mathrm{2I_{2}(s)}\ +\ \mathrm{H_{2}O} \tag{3}\]  
\[\mathrm{2 HI(aq.)}\ +\ \mathrm{h\
u}\framebox{$\leftrightarrow$}\ \mathrm{H_{2}(g)}\ +\ \mathrm{2I_{2}(s)} \tag{4}\]  
Additionally, under UV irradiation, the presence of oxygen and water molecules at the heterojunction between the electron transport layer (ETL) and the perovskite layer results in a redox reaction within their comprised elements, which leads to the decomposition of the perovskite, reducing the performance of the PSCs [8; 26]. The characteristics of the ETL are related to the optoelectronic performance of the device. The long-term stability of the device is influenced by trivalent Ti ions, and oxygen vacancies are generated by the irradiation of TiO\({}_{2}\) valence band electrons under ultraviolet (UV) light. The oxygen vacancies on the surface of TiO\({}_{2}\) are able to adsorb oxygen and water molecules, accelerating the degradation of the perovskite layer [8].
## 3 Results Analysis and Discussion
### The Effect of Relative Density on Soil Behavior  
In the study, unreinforced and reinforced tests were carried out in loose and medium-dense sand with Dr = 30% and Dr = 50%. Tests to examine the effect of relative density on bearing capacity were evaluated. In all tests, increases in ultimate bearing capacity occurred when Dr = 30% increased to 50%. Different relative densities mean different dry densities. In previous studies on fiber-reinforced sand with different dry densities, it was stated that the stiffness and bearing capacity of the fiber-reinforced soil with higher dry density were higher [65]. In addition, the interfacial movement between the fiber and the soil increases significantly with increasing relative stiffness [54]. A larger relative density means smaller porosity and smaller porosity means increased cohesion and friction as a result of increased contact area between fiber and soil particles. Similar observations were made by [PERSON] et al. (2016) on fiber-reinforced soil. The authors reported that a high relative density causes a further increase in the effective contact area between soil particles and fiber. [PERSON] et al. (2017) and [PERSON] et al. (2018) indicated that increasing the relative density and increased fiber deformation increased the mechanical interaction between fiber and soil. Thus, it can be noted that the relative density positively affects the soil's mechanical behavior. In the reinforced tests, the bearing capacity ratios obtained in the tests performed at Dr = 30% relative density at all fiber types, fiber length, fiber percentage, and reinforcement depths were higher than the bearing capacity ratios obtained from the tests performed at Dr = 50% relative density. Maximum bearing capacity ratios according to relative density were: BCR = 6.7 at Dr = 30%, BCR = 5.5 at Dr = 50% for glass fiber; BCR = 7.5 at Dr = 30%, BCR = 5.8 at Dr = 50% for basalt fiber; BCR = 10.4 at Dr = 30%, and BCR = 8.5 at Dr = 50% for macromesh fiber. It is seen that the effect of reinforcement is more effective in loose soils (2018). The highest increase in ultimate bearing capacity was determined as 280% in glass fiber-reinforced tests with Fl = 24 mm Fc = 1% reinforcement depth 2B; as 277% in the tests with macromesh fiber reinforcement with Fl = 48 mm Fc = 1% reinforcement depth 2B, and as 286% from the tests with macromesh fiber reinforcement with Fl = 24 mm Fc = 1% reinforcement depth B. Especially in soils reinforced with 2B and 3B depths, as the relative density increased, the ultimate bearing capacities increased significantly.
## 4 WSN Clustering
### Energy-Aware Distributed Unequal Clustering  
The Energy-Aware Distributed Unequal Clustering (EADUC) handled the issue of energy clusters [86]. For example, to fill up an energy gap, nodes with differing energy resources are examined and clusters of varying sizes are formed. The outcomes were greater in comparison to LEACH for the purpose of increasing network lifespan and reducing energy costs. EADUC creates uneven cluster structures to improve energy efficiency. Although sparse data is taken into consideration, over redundancy of dense causes excessive energy consumption. Consequently, this will negatively impact the network lifespan.
## 1 Introduction
Moreover, during aqueous oxidation reactions, ferrite (FeO\({}_{4}\)\({}^{2-}\)) is reduced to Fe (III) ion (Fe\({}^{3+}\)) and hydrolyzed to form insoluble iron (III) hydroxide (Fe(OH)\({}_{2}\)). This works as a conventional coagulant, depending on the pH and dosage. Therefore, K\({}_{2}\)FeO\({}_{4}\) can function as an oxidant and a coagulant in a single-step treatment. This paper hypothesized that K\({}_{2}\)FeO\({}_{4}\) can effectively remove different contaminants by multiple treatment mechanisms, namely, the oxidation and coagulation of azo dye, thus proving to be a new, reliable, and low-cost water reuse technology. It could also be ideal for small communities, given its compactly designed operational unit, economical capital cost, and less-demanding management than a conventional two-step unit process. This study aimed to investigate the treatment efficiency of K\({}_{2}\)FeO\({}_{4}\) for dye wastewater treatment with an emphasis on removing orange II azo dye (OD) and lissamine green B dye (LGB). The treatment efficiency of K\({}_{2}\)FeO\({}_{4}\) in removing OD and LGB was evaluated by comparing its efficiency with that of the conventional NaOCI oxidant and FeCl\({}_{3}\cdot\)6H\({}_{2}\)O coagulant.
## 4 Discussion
### Employing the LRU to Constrain the Uncertainties in COS-Based GPP Simulations  
A potential improvement to the partitioning of NEE [152] is based on the function relating F\({}_{\text{C,COS}}\), the GPP and the LRU [43,122]. The quantum cascade is an advanced technique employed in the continuous measurement of F\({}_{\text{C,COS}}\); in this system, R\({}_{d}\) is much smaller than in other systems [54,56,153]. However, during the COS-based simulation of the GPP, COS emissions also occur in dehydrating leaves [85], which would lead to systematic noise. Moreover, the partial nocturnal closure of stomata, which is the reason for the depletion of the nighttime C\({}_{\text{a,COS}}\)[114,129,154,155,113,114,129,156,157], was also observed in certain species [101,113]. Overall, at night, even though COS hydrodinal assimilation on the water surface and in oxic soils, as well as oxidative release in anoxic soils [141], participate in COS circulation, the terrestrial nighttime F\({}_{\text{C,COS}}\) is mostly influenced by the plant canopy [113,129,156,73,113]. It is also worth quantifying the influence of COS nighttime flux on the LRU at the diurnal scale. Recent studies have drawn attention to the fluctuation of F\({}_{\text{C,COS}}\)[73,114]. These studies noted that even though F\({}_{\text{C,COS}}\) was considered to be regulated by the activity of the enzyme CA and the LAI of the plant canopy, both diurnal and seasonal fluctuations in C\({}_{\text{a,COS}}\) and C\({}_{\text{a,CO}_{2}}\) were correlated with F\({}_{\text{C,COS}}\)[73,114]. Additionally, C\({}_{\text{a,COS}}\) and C\({}_{\text{a,CO}_{2}}\) peaked at night when F\({}_{\text{C,COS}}\) was low [157,158]. During the heavy turbulence at dawn, as COS and CO\({}_{2}\) were replenished in the canopy and subcanopy airspace, the canopy storage effect also influenced the simulated F\({}_{\text{C,COS}}\) and F\({}_{\text{C,CO}_{2}}\)[73]. It is also worth exploring whether this process influences the LRU.  
Further studies should be performed to identify the additional key factors that regulate LRU variation [159] and the influence of NEE partitioning. Layered observations led to a proposal to partition \(\mathrm{F_{C,COS}}\) into leaf assimilation and soil exchange components [42]. Moreover, soil COS consumption, which is linearly correlated with soil temperature and makes up 34-40% of the nighttime \(\mathrm{F_{C,COS}}\) in the boreal forest, reached its saturation point at 20% water content [118; 156]. It is worth further exploring the considerable differences between the environmental conditions regulation mechanism for COS leaf assimilation and that for COS soil exchange.
## 3 Circuit Models
### Differential Microstrip Lines Loaded with CSRRs
The dimensions of the active (slot) region are 23 mm \(\times\) 7.6 mm, that is 0.28 \(\lambda\times\) 0.09 \(\lambda\) (where \(\lambda\) is the guided wavelength at the central frequency). The device is therefore very small, although bandwidth has not been optimized in this structure. It is clear that the differential signal is not altered by the presence of the CSRRs (see also Figure 8).