esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Pre- and Post-Breakage Behavior of Laminated Glass Using a Phase-Field Damage Model
## 3 Phase-Field Model
### Layered Mindlin Plate Model  
Consider a plate consisting of \(N\) layers indexed by \(j\in\{1,2,\ldots,N\}\), recall Figure 1. The indices \(j=1,N\) are therefore reserved for the glass layers. Several options are available  
Figure 5: Graphical representation of Mindlin plate theory.  
to enforce displacement continuity at common interfaces. In some applications, the use of Lagrangian multipliers proves advantageous as these are directly linked to the interface shear stresses. However, grounding the formulation of the present model on explicit dynamics calls for the reduction of the number of degrees of freedom as much as possible. Therefore, the continuity of in-plane displacement \(\mathbf{u}\) is enforced directly by substitution considering the following two kinematic constraints:  
\[w_{j}=w_{1},\quad\forall\,j\in\{1,2,\ldots N\}, \tag{24}\] \[\mathbf{u}_{j-1}+\mathbf{S}\,\mathbf{\varphi}_{j-1}\frac{h_{j-1}}{2}=\bm {u}_{j}-\mathbf{S}\,\mathbf{\varphi}_{j}\frac{h_{j}}{2},\quad\forall\,j\in\{2,3, \ldots,N\}. \tag{25}\]  
The first equation enforces the out-of-plane incompressibility of the layers and the latter one represents the in-plane displacement continuity at the interfaces. The formulation for glass layers, identified by odd indices \(j\in\{1,3,\ldots N\}\), remains intact, whereas kinematic variables of damage-free interlayers with even indices \(j\in\{2,4,\ldots N-1\}\) are substituted by expressions determined by restrictions (24) and (25) as  
\[w_{j} = w_{1},\quad\forall j\in\{1,2,\ldots N\}, \tag{26}\] \[\mathbf{u}_{j} = \frac{1}{4}\Big{(}h_{j-1}\mathbf{S}\,\mathbf{\varphi}_{j-1}-h_{j+1} \mathbf{S}\,\mathbf{\varphi}_{j+1}\Big{)}+\frac{1}{2}\big{(}\mathbf{u}_{j-1}+\mathbf{u}_{j +1}\big{)},\forall\,j\in\{2,4,\ldots N-1\},\] (27) \[\mathbf{S}\,\mathbf{\varphi}_{j} = \frac{1}{h_{j}}\bigg{(}-\frac{1}{2}\mathbf{S}\,\mathbf{\varphi}_{j-1} h_{j-1}-\frac{1}{2}\mathbf{S}\,\mathbf{\varphi}_{j+1}h_{j+1}+\mathbf{u}_{j+1}-\mathbf{u}_{j-1} \bigg{)},\forall\,j\in\{2,4,\ldots N-1\}. \tag{28}\]  
As an illustrative example, consider a 5-layer laminate (three solid glasses and two interlayers) which, after substitution, is fully described by seven unknowns: \(w_{0}\), \(u_{0}\), \(\varphi_{0}\), \(u_{2}\), \(\varphi_{2}\), \(u_{4}\), \(\varphi_{4}\).  
It should also be mentioned that the layered plate model requires solving Equation (23) for each glass layer. This allows for different crack patterns to develop in individual layers.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 6 Discussion
### Research Questions  
#### 6.1.1 RQ1--State Representation  
The state space representation is crucial to supply the agent with sufficient information so that it can make an informed decision. Of the three agent-environment interactions, the state representation shows the most format variety, as summarized in Table 3. Variables, lists, matrices and even 3D matrices can be used for very different applications. Lists of environment variables are the most popular approach. The metrics included vary greatly but often include information about the resources and entities. While a list of metrics is a simple representation, it has been shown multiple times that it is sufficient for many applications.  
Two specific model architectures show great success on multiple examples: recurrent and graph approaches. Both strategies create a state that aggregates information from  
\begin{table}
\begin{tabular}{l l l l} \hline
**RL Role** & **Advantages** & **Disadvantages** & **Examples** \\ \hline \multirow{3}{*}{Tabular methods} & Simple implementation. & Limited state representation. & \\  & Explainable results. & Must sufficiently explore all & [45; 166; 175; 294; 296] \\ \hline \multirow{3}{*}{Iterative list selection} & Very flexible. Widespread & Single-use actions. Often & \\  & literature adoption. & requires action masking. & [31; 40; 49; 217; 232] \\ \hline \multirow{3}{*}{Hybrid approaches} & Simplify agents’ decisions. & RL only optimizes a subset. & \\  & Enhance other methods with RL & External methods or tools might & [18; 123; 184; 254; 258] \\  & decision making. & be insufficient to optimize. & \\ \hline \multirow{3}{*}{Specific neural network models} & Variable-sized inputs and & \\  & outputs. Relative positions of & Frameworks are harder to train. & \\  & pixels, nodes and tokens provide & Scalability issues. & [141; 188; 232; 238; 271] \\ \hline \multirow{3}{*}{Multi-agent} & Agents can make simpler & More complex frameworks. & \\  & decisions. Specialized agents for & Decentralization requires & [49; 116; 217; 246; 251] \\ \cline{1-1}  & smaller tasks. & communication protocols. & \\ \hline \end{tabular}
\end{table}
Table 6: Common RL agent roles and highlighted examples.  
individually considering all its parts, while still being compatible with problems of any size. Normalizing the state also seems to be beneficial.
## 2 Materials and Methods  
### Study Area, Data Collection and Preprocessing  
The following analysis was carried out in the Google Earth Engine environment using Sentinel-2 satellite data, in particular the collection \"Harmonized Sentinel-2 MultiSpectral Instrument, Level-1C\" (S2) and the \"Sentinel-2 Cloud Probability\" (CP) collection. The term \"Harmonized\" indicates that the satellite scenes prior to the \"04.00 update\", which occurred on 25 January 2022, have been aligned with subsequent ones to ensure radiometric measurements' homogeneity. The field object of this study is located in the municipality of Conselice (RA), in Emilia-Romagna (Italy). The field has an extension of 11.144 hectares (about 1114 Sentinel-2 pixels). The centroid coordinates of the field are Lon.: 11.788; Lat.: 44.536, and it is located in a flat area in the Po River valley at an altitude of 5 m above sea level. The field is represented in Figure 1.  
The crop type examined was soft winter wheat planted in the field during the 2017/2018, 2019/2020, and 2021/2022 seasons. The sowing dates were 31 October 2017; 4 November 2019; and 2 November 2021. The field was rain-fed. In all growing seasons, the same treatments aimed at controlling weeds were applied: one before sowing and another in April. The absence of weeds and the condition of the crops were monitored through routine field inspections in November, January, and April. The annual field-level yields, as recorded by the farm during the post-harvest weighing process, are reported in Table 1.  
Through a Google Earth Engine (GEE) script, the collection of satellite images was first pre-processed to filter the spatial, temporal, and spectral intervals of interest and eliminate images of inadequate quality and duplicates (sometimes present within the collections). Spatially, the field under investigation falls within the Military Grid Reference System (MGRS) tile 32 TQQ. Temporally, a filter was applied to include the scenes falling in the November/June period of the 2017/2018, 2019/2020, and 2021/2022 seasons. Furthermore, a filter was applied to include the bands necessary for calculating the NDVI index (B4 and B8). Finally, by exploiting the collection image properties \"GENERAL_QUALITY\" and \"GEOMETRIC_QUALITY\", the tiles that did not pass the Sentinel-2 On-Line Quality Control (OLQC) [42] were eliminated.
## 2 Materials and Methods  
### Plant Material and Chemicals  
The native pigmented corn kernel (_Zea mays_) was donated by a local producer in San Marcos, Jalisco, Mexico (latitude: 20\({}^{\circ}\)46\({}^{\prime}\)21.6\({}^{\prime\prime}\), longitude: W 104\({}^{\circ}\)17\({}^{\prime}\)8.36\({}^{\prime\prime}\), and altitude: 1524 m.a.s.l.). The pigmented corn kernel was chosen for its regular size, avoiding material with injuries (Figure 1a). The corn kernel was dried (50 \({}^{\circ}\)C/24 h) and ground in a rotatory impact mill (Retsch SR300, Haan, Germany). Then, the powder was sievedto a particle size of 500 \(\upmu\)m (stainless-steel mesh no. 35; Humboldt, AASHTO M92, IL, USA), as shown in Figure 1b. The pigmented corn kernel powder was protected from light and kept at 25 \({}^{\circ}\)C until analysis. All reagents and chemicals were analytical- or HPLC-grade. Methanol, Folin-Ciocalteu reagent, sodium acetate, gallic acid, 2,2-Azinobis-(3-ethylbenzothazoline-6-sulfonic acid) (ABTS), potassium persulfate, catechin, 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), ferric chloride hexahydrate, 2,2-Diphenyl-1-picrylhydrazyl (DPPH), hydrochloric acid, 2,4,6-tripyridyl-s-triazine (TPTZ), and water were acquired from Sigma-Aldrich Co. (St. Louis, MO, USA) and aluminum chloride (AlCl\({}_{3}\)) and sodium nitrite (NaNO\({}_{3}\)) from Golden Bell (Guanajuato, Mexico).
## 5 Working Principle of Volumetric Verification  
Volumetric verification aims to reduce the influence of geometric results based on volumetric error from LT measurement. This is obtained using the kinematic model of the machine including the measurement system on it (as shown in Section 2).  
Figure 5: Scheme of multilateration optimization.  
The relationship that provides this equation generates a non-linear equation system. Using the Levenberg-Marquardt algorithm in a non-lineal optimization process, the approximation functions of geometric errors are obtained reducing the volumetric error.  
The working principle is shown in Figure 6. Nominal points introduced in the machine kinematic model are influenced by the geometric errors of the machine, modifying the point coordinates. As the error values are not known, regression functions provide the values of each error at each point to approximate the geometric errors of the kinematic model.  
To reduce the volumetric error of the machine, the values of the coefficients that define the regression functions change during the optimization process. When the verification process is complete, one of the convergence criteria is fulfilled. Then, the regression functions obtained define the approximation function of each geometric error.
## 3 Results and Discussion
### Analysis of Deposited Zinc Mass and Zinc Coating Quality
#### 3.2.2 Hot-Dip Zinc Coating
In contrast, the synthetic corundum-blasted substrate exhibited more pronounced inhomogeneities, with zeta-phase outgrowths largely detached from their lower portion and completely surrounded by the upper eta phase. Additionally, defects within the delta phase were more prominent in these samples compared to other categories. Similar changes in intermetallic phases have been reported in previous studies [43; 44] and can be explained by the sequential nucleation of the zeta phase, followed by the delta phase [46]. The high frequency of microcracks within the coating may accelerate corrosion degradation [48]. In this respect, the leveling effect of hot-dip zinc can be considered detrimental. However, from an aesthetic perspective and in terms of reducing surface contamination, a smoother surface may contribute to enhanced corrosion resistance [49]. Figure 12: Mass increase of hot-dip zinc coating on different substrate types as a function of immersion time in the zinc bath. Figure 14: Appearance of metallographic specimens with hot-dip zinc coating (immersion time: 210 s): (**a**) blasted with synthetic corundum, (**b**) blasted with steel grit, (**c**) ground, and (**d**) reference sample. Figure 13: Surface roughness parameters Ra and Rz before and after hot-dip zinc coating (arithmetic mean \(\pm\) standard deviation; immersion time: 210 s).
# End to End Delay and Energy Consumption in a Two Tier Cluster Hierarchical Wireless Sensor Networks
## 5 The Intra-Cluster Communication  
### Traffic Model  
For each non CHs (mote or sensor) we assume a Poisson process with rate \(\lambda\). Then, the probability \(p_{act}\) that a sensor becomes active during one mini slot of duration \(T_{ms}=T_{msC}=T_{msT}\) is given by,  
\[p_{act}=1-e^{-\lambda T_{ms}} \tag{2}\]  
Please note that Equation (2) is the probability that at least one data packet is generated during a Combi-Frame. Following [8] we assume that each sensor is equipped with a unit-size buffer. Then, taking into account the structure of the Combi-Frame, the traffic model is a Bernoulli process and following the FSA protocol, each sensor can successfully transmit to its CH no more than one data packet by Combi-Frame.
# The Progress of Cobalt-Based Anode Materials for Lithium Ion Batteries and Sodium Ion Batteries
## 2 Li\({}^{+}\)/Na\({}^{+}\) Storage Mechanism
### Li\({}^{+}\)/Na\({}^{+}\) Storage Mechanism in Co-Based Alloys  
For cobalt-based alloys, lithium/sodium ion storage properties are decided by metal components that make up the alloy with cobalt [40; 41; 42; 43]. Even so, the presence of cobalt components in the alloys is essential, which disperses into the alloys to improve the conductivity of alloys, as well as the retard volume expansion during cycles [44; 45]. Many types of research on alloying storage mechanisms of cobalt-based materials have been published. For example, [PERSON] et al. briefly pointed out the lithium ion storage mechanisms of Sn-Co@polymethylmethacrylate (PMMA) nanospheres [40]. In Figure 2a, an obvious slope between 1.2 and 0.4 V during the first discharge can be ascribed to the formation of SEI film. Then, a plat plateau at 0.3 V is attributed to the initial lithiation of the Sn component of the alloy, including the isolation of Co and the formation of Li\({}_{x}\)Sn. Subsequently, the platform centered at about 0.5 V during the charge can be ascribed to the delithiation of Li\({}_{x}\)Sn and the formation of Sn. The entire lithium storage process of the alloy can be described by following equations:  
\[\mathrm{Co_{y}Sn+xLi^{+}+xe^{-}\to Li_{x}Sn+yCo(0<x<4.4) Discharge} \tag{1}\]  
\[\mathrm{Li_{x}Sn+yCo\to Sn+yCo+xLi^{+}+xe^{-}Charge} \tag{2}\]  
Figure 1: Li\({}^{+}\)/Na\({}^{+}\) storage mechanisms for different Co-based materials.  
As displayed in Figure 2b, the sodium storage mechanism of cobalt alloy can be simply demonstrated by cyclic voltammetry (CV) curves of SnCo@C in SIBs for the initial three cycles [41]. During the first discharge process, peaks centered at around 0.75 and 0.15 V can be ascribed to the first sodiation of the alloy, which indicated the formation of Na\({}_{x}\)Sn and Co. The peaks at 0.3, 0.6 and 0.7 V upon the charge process represented a desodiation process from Na\({}_{x}\)Sn to Sn. Similar to the lithium storage mechanism, the sodium storage mechanism of SnCo@C can be concluded by following equations:  
\[\text{SnCo}+\text{xNa}^{+}+\text{xe}^{-}\rightarrow\text{Na}_{x}\text{Sn}+ \text{Co}(0<\text{x}<4.4)\text{ Discharge} \tag{3}\]  
\[\text{Na}_{x}\text{Sn}+\text{Co}\rightarrow\text{Sn}+\text{Co}+\text{xNa}^{+ }+\text{xe}^{-}\text{ Charge} \tag{4}\]  
In Figure 2c,d, the charge-discharge profiles of Sn@C and SnCo@C displayed similar voltage platforms at different cycles, indicating the decisive role of the metal component alloyed with cobalt. Furthermore, the presence of cobalt improves the capacity retention ratio during cycles, suggesting the importance of cobalt.
# Model Test on Grouting Properties of Alluvial Filler Soil
## 8 Grouting Model Test Results and Discussion
### Comparative Analysis of Physical and Mechanical Properties of Grouting Soil  
It can be seen from Table 3 that the index of soil masses is improved; the average density of soil increased by 5.53%; the water content decreased by 18.11%; the cohesion of soil increased by 52.4%; the internal friction angle increased by 2.1%; the compressive coefficient decreased by 35.0% after grouting; and the compressive modulus increased by 54.6%. It indicates that grouting can effectively improve the mechanical properties of alluvium mixtures.
## 4 Validation of SRSVD  
### Case Study I  
In this section, the feasibility of the SRSVD algorithm is tested by building a signal acquisition system. The test system is shown in Figure 3, and consists of five parts. The signal generator generates useful signals for testing; the amplifier analog circuit board attenuates the useful signal output of the signal generator with large capacitance and outputs the amplification result of the amplifier chip; the signal acquisition instrument (DN8509N) is used to collect the output signal of the amplifier chip; and the shielding box blocks external interference and the PC is used to record the chip output signal. We set the sampling frequency to 128 kHz and the signal frequency to be measured to 123 Hz. We randomly intercept a segment of signal with 6400 sampling points for analysis. Figure 4 shows the signal waveform and its frequency spectrum. As can be seen from Figure 4, the signal generated by the signal generator cannot be effectively identified due to the adverse effects of noise in the circuit. Figure 4: (**a**) The original signal waveform, (**b**) its frequency spectrum, and (**c**) partial enlargement. Figure 3: Experiment platform system and analog amplifier circuit board. (**a**) The test system and (**b**) the analog amplifier circuit. We used SRSVD to denoise this signal and try to extract the signal from the signal generator. First, we used the dictionary learning method to train a dictionary. It should be noted here that although the data in this experiment are generated by the signal generator, we often do not know the types and features of the collected signals in practical situations. Therefore, the prior of the signal generator was ignored in this experiment. K-SVD algorithm was used for training, and the algorithm procedure is shown in Algorithm 2. K-SVD algorithm is a dictionary learning algorithm proposed by [PERSON] et al. [1] in 2006 and has gradually become the most widely used dictionary learning algorithm. K-SVD updates the dictionary columns one by one, reducing the computational complexity of updating the dictionary. The initial dictionary is set to a Gaussian random dictionary, \(n=6400\), \(m=10\), and the number of samples is set to 50. Some dictionary atoms obtained by K-SVD are shown in Figure 5. It can be seen that the learned atom has a certain harmonic performance, which has a certain degree of matching with the signal generated by the signal generator in the internal structure. The original signal is denoised by SR through the learned dictionary, and the result is shown in Figure 6.  
SVD is used to perform secondary filtering on the denoised signal. After being filtrated by PMI, the final result is shown in Figure 7. It can be seen that by SRSVD denoising, the dynamic range of the frequency of the irrelevant components is smaller. Moreover, the frequency of 123 Hz is more obvious. Figure 5: Learned dictionary atoms. Figure 6: (**a**) The denoised signal waveform by SR and (**b**) its frequency spectrum.
## 5 Experimental Results  
Building upon the system design architecture introduction, this section begins by conducting a performance analysis of the data stream pipeline with hardware processing. It then elaborates on the integration of the micro-processor and hardware accelerators within the FPGA. The experimental results first highlight individual software and hardware computations and subsequently engage in a discussion regarding the performance of software and hardware co-design.
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 2 Literature Review
### Convolutional Neural Network-Based Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Images** & \begin{tabular}{c} **Data** \\ **Balanced?** \\ \end{tabular} & \begin{tabular}{c} **No.** \\ **of Classes** \\ \end{tabular} & \begin{tabular}{c} **Train–Test** \\ **Split (\%)** \\ \end{tabular} & **CNN Model** & **Testing Results (\%)** \\ \hline Al-antari [16] & 600, 103 & Yes & 2 & 70/20/10 & \begin{tabular}{c} ResNet-50, \\ Inception- \\ ResNet-V2 \\ \end{tabular} & 97.50 (Acc, DDSM) \\ \hline [PERSON] [32] & 322, 1592, 387 & No & 2 & NR & Custom & 95.30 (Acc, MIAS) & 91.20 (Acc, DDSM) \\  & & & & & & 96.52 (Acc, INbreast) \\ \hline [PERSON] [33] & 410 & No & 2 & NR & \begin{tabular}{c} AlexNet, \\ DenseNet, \\ ShuffleNet \\ \end{tabular} & 99.72 (Acc, INbreast) \\ \hline [PERSON] [34] & 10,480, 410 & No & 2 & 80/20 & MSANet & 94.2 (AUC, DDSM) & 92.85 (AUC, INbreast) \\ \hline Diaz-Escobar & 322, 410, 2620, 10,020 & Yes & 2 & NR & ResNet50 & 82.20 (Acc, MIAS, INbreast, DDSM, CSAW-M) \\ \hline [PERSON] [18] & 53, 2188, 106 & No & 2 & 80/20 & Custom & 95.95 (Acc, MIAS) & 99.39 (Acc, DDSM) & 96.53 (Acc, INbreast) \\ \hline [PERSON] [36] & 3103 & No & 2 & 79.21/20.79 & \
\begin{tabular}{c} EfficientNet- \\ B4 \\ \end{tabular} & 93.44 (AUC, DDSM) \\ \hline [PERSON] [20] & 3103 & No & 2 & 85/15 & MorphHR & 83.13 (AUC, DDSM) \\ \hline [PERSON] [17] & 410 & No & 2 &
\begin{tabular}{c} ECA-Net50, \\ ResNet50 \\ \end{tabular} & NR & 92.29\% (Acc, INbreast) \\ \hline [PERSON] [19] & 378, 378 & No & 2 & 80/10/10 & VGG16 & 91.28 (Acc, LES, DES) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Literature review of recent convolutional neural network-based methods for breast cancer classification. “NR” = Not reported; other acronyms are listed in the acronym list. Another approach replaced the fully connected layers of EfficientNet models with MobileNetV2 block convolution (MBConv) layers. This model was designed to classify craniocaudal (CC) and mediolateral oblique (MLO) mammography views. It achieved 93.44% accuracy on the CBIS-DDSM dataset using data augmentation [36]. Also, a ResNet18-based model using function-preserving transformations was designed and tested on the CBIS-DDSM dataset, achieving an AUC of 83.13% with an 85/15 train-test split [20]. Finally, the multitask information bottleneck network (MIB-Net), based on VGG-16 and UNet, was proposed for simultaneous segmentation and classification of mammography and ultrasound images [19]. Using the Local Enhanced Set (LES), Denoised Enhanced Set (DES), and Breast Ultrasound Image (BUSI) datasets, the model achieved an accuracy of 91.28% with a training/validation/testing split of 80/10/10. Overall, CNN-based methods have shown superior performance compared with traditional machine learning approaches. However, their effectiveness depends on factors such as data quality, network architecture, and dataset size and balance.
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 4 Glycosylated Proteins of Pathogens Act as Weapons to Attack the Host
### Cell Surface Glycoprotein  
Host cell adhesion is important for infection initiation, and such a process is mediated by pathogen cell surface glycoproteins [100; 101; 102; 103]. One example is the cell surface glycoprotein HMW1 in _Haemophilus influenzae_. It can mediate bacterial adhesion by interacting with a specific receptor on the human cell surface [104]. The activity of HMW1 relies on the other two proteins HMW1B and HMW1C, the former is inserted into the bacterial outer membrane, mediating the HMW1 to display on the cell surface [105], while the latter with glycosyltransferase activity can catalyse glycosylation of HMW1 in the cytoplasm [20]. The HMW1 is tethered to the bacterial surface by the glycans. However, deglycosylation HMW1 sheds from the bacterial cell membrane. The deficiency of HMW1 inhibits bacterial adhesion to host epithelial cells, suggesting HMW1 is bound to the bacterial surface only after glycosylation [101].