esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Traditional Approaches for Enzyme Immobilization  
Enzyme immobilization improves reusability, stability, and catalytic efficiency [10]. Traditional methods include carrier-based (adsorption, affinity bonding, entrapment, encapsulation, ionic bonding) and carrier-free (covalent bonding, cross-linking) approaches, each with benefits and drawbacks. Among these, adsorption is simple and cost-effective, preserving enzyme structure but relying on weak interactions (e.g., hydrogen bonds, van der Waals forces), which may cause enzyme leakage under varying pH or temperature conditions [11]. However, its scalability for industrial applications be improved through carrier modifications. For instance, adsorbed lactose on granular activated carbon aids pollutant removal [12], while poly (methyl-methacrylate) (PMMA) microchannels improve horseradish peroxidase activity [13] and benzo[a]pyrene degradation in soil [14].  
Entrapment immobilizes enzymes within polymeric matrices, offering high enzyme loading with minimal modification. However, pore-size constraints can lead to mass transfer limitations and enzyme leakage [8]. Optimizing material design can improve enzyme retention and substrate accessibility [9]. For example alkaline phosphatase entrapped in silica retained 30% of its activity over two months [15], while \(\alpha\)-glucosidase in pHEMA maintained 90% of its activity after multiple uses [16].  
Covalent bonding attaches enzymes to solid supports via stable functional groups, improving stability and reducing leakage. While this method may reduce activity due to conformational changes [17], it often preserves enzyme structure, enhancing catalytic performance. For instance, covalently immobilized cellulase retained 73% of its activity, while lipase on magnetic nanoparticles showed a 2.1-fold activity increase [18].  
Cross-linking, often using bifunctional reagents like glutaraldehyde, enhances enzyme stability, but it may reduce activity if not controlled [19]. Cross-linked enzyme aggregates (CLEAs) offer benefits like scalability and low cost, though their small pores limit diffusion rates. For example, genipin-cross-linked lacase CLAEs show superior thermal stability and reusability compared to glutaraldehyde-based CLEAs [20], while magnetically separable CLEAs (Mp-CLEAs) further enhance catalytic efficiency and reusability [21].  
In recent years, carrier-free methods like CLEAs have become popular due to their higher enzyme concentrations, stability, and reduced costs [22; 23]. However, immobilization may decrease enzyme-substrate affinity and kcat due to diffusion barriers or conformational changes, depending on the method and materials used [24].
# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 1 Introduction
### Literature Review in Bone Metastasis Diagnosis from Breast Cancer Patients in Nuclear Medicine  
Focusing on bone scintigraphy, which is considered as the well-known imaging method of screening, concerning the diagnosis of bone metastasis, only two previous studies were reported in the literature that deal with metastatic breast cancer classification. The first study was devoted to the development of a computer-aided diagnosis system for bone scintigraphy scans (CADBOSS) [65]. CADBOSS is able to detect metastases in an accurate way, that combines an active contour segmentation algorithm for hotspots detection, an advanced method of image gridding to extract certain characteristics of metastatic regions and finally, an artificial neural network classifier for identifying possible metastases [65]. Another similar research study was conducted during a PhD thesis in [66] which deals with the investigation of CAD system to improve the diagnostic accuracy of planar whole-body bone scan interpretations. During this PhD thesis, four sub studies were accomplished sequentially. The first sub study investigated an automated method that determines the existence of metastases after bone scanning, using neural networks and image-processing techniques. The second study dealt with the discrepancies between observers as well as the performance, in terms of interpretations solely on bone scan images, regarding the existence of bone metastatic disease. The third study resulted in the development of a second CAD system based on improved image processing and artificial neural network to classify bone scans in two categories, considering a larger database of whole-body bone scans. Further investigation took place on how the proposed CAD system can benefit the physicians by reducing inter-observer variation. The final outcome of that thesis was the development of a totally automated computer-assisted diagnosis system that can identify metastases after examining bone scans, applying multi-payer perceptron artificial neural network techniques, and a small database of whole-body bone scans (135 patients). The highest sensitivity achieved from all the studies and accomplished during this thesis was approximately 89% [66]. None of these research studies provide publicly available data.  
As there are no other research studies for breast cancer bone metastasis classification in nuclear medicine (and no research paper has been published, either in journals or conferences, which concerns bone scintigraphy analysis and classification, using deep learning), we briefly report some recent studies in nuclear medicine for bone metastasis diagnosis in men suffering from prostate cancer. Bone scintigraphy is a well-recognized image modality for metastatic prostate cancer diagnosis in bones.
# Multi-Agent Deep-Q Network-Based Cache Replacement Policy for Content Delivery Networks
## 4 Formulation of Markov Decision Process Problem
### Action Space  
Next, we define the action space for the \(i\)th edge server at the \(t\)th decision epoch. In order to restrict the action space \(\mathcal{A}\), we assume that at most one cached content is replaced in each decision epoch. This reduces the computational complexity in each decision epoch [17]. Let \(a_{t}\in\mathcal{A}\) be the action selected at decision epoch \(t\), transitioning states from \(s_{t}\) to \(s_{t+1}\). If the current content in the cache is replaced with the requested content, then \(a_{t}=1\); otherwise, \(a_{t}=0\).
## 3 Computational Results
Algorithm 5 started from a random initial solution and could search for a more diverse solution space within a short CPU time limit. When the CPU time limit was changed to 600 s, the MST2 algorithm and Algorithm 5 produced a better-quality solution in terms of the relative standard deviation [29]. The relative standard deviation (RSD) in Table 3 in parenthesis was measured by \(RSD=100\frac{\sigma}{\mu}\), \(\sigma=\sqrt{\frac{\sum\left(f(x)-\overline{f(x)}\right)^{2}}{n}}\), and \(\mu=\overline{f(x)}\), where \(f(x)\) is the OFV of each run and \(\overline{f(x)}\) is the mean value of the OFV out of \(n=10\) runs. For some instances, the relative standard deviation (RSD) was less than 5.0 \(\times\)\(10^{-4}\) even though not all runs found the same OFV. We used 0.000 as the value of RSD when the value was rounded up to three decimal points. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & & & \multicolumn{2}{c}{**Algorithm 3**} & \multicolumn{2}{c}{**Algorithm 4**} & \multicolumn{2}{c}{**Algorithm 5**} \\ \cline{3-8}  & **ID** & **Size** & **Density** & **OFV** & **TB (s)** & **OFV** & **TB (s)** & **OFV** & **TB (s)** \\ \hline p30000\_1 & 30,000 & 0.5 & 127,239,168 & 591 & 127,292,467 & 591 & 127,336,719 & 592 \\ p30000\_2 & 30,000 & 0.8 & 158,439,036 & 572 & 158,472,098 & 555 & 158,526,518 & 571 \\ p30000\_3 & 30,000 & 1 & 179,192,241 & 584 & 179,219,781 & 587 & 179,261,723 & 590 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of Algorithms 3–5 on p30000 instances with a CPU time limit of 600 s and \(r=2\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & & \multicolumn{2}{c}{**MST2**} & \multicolumn{2}{c}{**_r-Flip with 60 s**} & \multicolumn{2}{c}{**MST2**} & \multicolumn{2}{c}{_r-Flip with 600 s_} \\ \cline{3-8}  & **ID** & **Size** & **Density** & **with 60 s** & \(r=1\) & \(r=2\) & **with 600 s** & \(r=1\) & \(r=2\) \\ \hline p3000\_1 & 3000 & 0.5 & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) \\ p3000\_2 & 3000 & 0.8 & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) \\ p3000\_3 & 3000 & 0.8 & 4 (0.01) & 7 (0.007) & 10 (0) & 10 (0) & 10 (0) & 10 (0) \\ p3000\_4 & 3000 & 1 & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) & 10 (0) \\ p3000\_5 & 3000 & 1 & 10 (0) & 9 (0.003) & 7 (0.002) & 9 (0.001) & 10 (0) & 10 (0) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The solution quality of the MST2 algorithm and Algorithm 5 with CPU time limits of 600 and 600 s out of 10 runs. Table 4 reports the computational results of the CPU time limit of 60 s, and Table 5 reports the computational results of the CPU time limit of 600 s. In Table 4, it can be seen that the MST2 algorithm matched 5 out of 27 of the best solutions within the CPU time limit. The 1-flip strategy in Algorithm 5 matched 26 out of 27 of the best solutions, whereas the 2-flip strategy in Algorithm 5 matched 18 out of 27 of the best solutions. For the MST2 algorithm, the computing time to find the initial solution exceeded the CPU time limit of 60 s for two large instances.
## 3 Methods
### Investigation of Missing MODIS FRP Due to Sampling Limitations
#### 3.2.4 Adjustment of Grid-Level MODIS FRP  
We also explored the potential relationship between MODIS and VIIRS grid FRP across MODIS VZA. For a given grid size, mean MODIS VZA in a grid was calculated based on VZA values of MODIS fire detections. Then, the grid FRP ratio was grouped for every 1\({}^{\circ}\) VZA, and for each group the median grid FRP ratio was obtained. Subsequently, the median grid FRP ratio was fitted as a function of MODIS VZA using a second-order polynomial function:  
\[\varphi_{med}=\beta_{0}+\beta_{1}\theta+\beta_{2}\theta^{2} \tag{3}\]  
where \(\varphi_{med}\) is the median of the grid-level FRP ratio (\(\varphi\)), \(\theta\) is MODIS VZA (in units of radian), and \(\beta_{0}\), \(\beta_{1}\), and \(\beta_{2}\) are three model parameters. Note that the use of the median instead of the mean grid FRP ratio was to avoid the effects of potential extreme FRP ratio values.  
A set of polynomial functions was calculated for the seven different grid sizes from 0.05\({}^{\circ}\) to 5\({}^{\circ}\). These functions were considered as empirical models for adjusting MODIS FRP.
## 3 Methods
### Study on the Seasonal Variation Characteristics of CI
#### 3.1.2 Discrete Fourier Transform  
Owing to the large amounts of noise in vegetation index time series caused by cloud cover, atmospheric aerosols, the solar irradiation angle, and the bidirectional reflectance of features [57; 58], selecting a fitting method that suits the characteristics of the time series when estimating phenological parameters is crucial. The Fourier transform treats time series as a fit of a series of sine and cosine functions with different frequencies. Given that vegetation growth exhibits periodic variation over time, the Fourier transform has been widely used in the fields of phenological parameter estimation and vegetation index time series reconstruction [59; 60; 61; 62]. The CI and NDVI values of typical pixels are 8-day discrete datasets suitable for the use of the discrete Fourier transform method. Owing to the limitations of the inversion process (e.g., lack of hotspot observations, especially in a cross principal plane (CPP)), the CI time series contains more potential noise. The discrete Fourier transform, as a finite time series algorithm, is theoretically significant and can more effectively mitigate noise. Therefore, the discrete Fourier transform (DFT) is chosen for the fast and effective fitting of the CI and NDVI time series. The DFT is given by Equation (1):  
\[F_{(u)}=\frac{1}{N}\sum_{x=0}^{N-1}f_{x}\times e^{-2\Pi Lux/T}, \tag{1}\]  
which can be decomposed into a real part [Equation (2)] and an imaginary part [Equation (3)]:  
\[F_{\mathrm{C}(u)}=\frac{1}{N}\sum_{x=0}^{N-1}\Bigl{[}f(x)\times\cos\Bigl{(}2 \pi\frac{ux}{T}\Bigr{)}\Bigr{]}, \tag{2}\]  
\[F_{\mathrm{S}(u)}=\frac{1}{N}\sum_{x=0}^{N-1}\Bigl{[}f(x)\times\sin\Bigl{(}2 \pi\frac{ux}{T}\Bigr{)}\Bigr{]}, \tag{3}\]  
with the magnitude calculated as:  
\[F_{\mathrm{(Magnitude)}u}=\sqrt{F_{\mathrm{C}(u)}^{2}+F_{\mathrm{S}(u)}^{2}}, \tag{4}\]  
and the phase is calculated as:  
\[F_{\mathrm{(Phase)}u}=\tan\Biggl{(}\frac{F_{\mathrm{C}(u)}}{F_{\mathrm{S}(u)} }\Biggr{)}. \tag{5}\]  
where \(f(x)\) is the \(x\)th sample value, \(u\) is the number of Fourier components or harmonics, \(x\) is an index representing the current sample number, and \(T\) is the length of the period covered.
# Reliability-Based Design of Driven Piles Considering Setup Effects
## 5 Conclusions  
There are the following conclusions to be obtained.  
1. \(\rho=Q_{\text{D}}/Q_{\text{L}}\) has basically no effect on the resistance factor of driven piles in clay and sand, so \(\rho\) can be regarded as a constant. Meanwhile, the resistance factor of driven piles in clay is greater than the resistance factor of driven piles in sand under the same ratio of dead load to live load level. It shows that the uncertainty of driven piles considering setup effects in sand is greater than the uncertainty of driven piles considering setup effects in clay.
2. Through the analysis of parametric \(\beta_{\text{T}}\) it is found that the smaller the resistance of the driven piles is, the higher the factor of safety for the driven piles, and the more reliable the driven piles are.
3. Through the analysis of parametric \(M_{\text{setup}}\), it is found that the soil type has a great influence on the reliability-based design of driven piles. Meanwhile, whichever value of \(M_{\text{setup}}\) is selected, the resistance factor of driven piles considering setup effects in sand and clay are basically \(0.25\) and \(0.32\), respectively, and the factor of safety for driven piles considering setup effects in clay and sand are basically \(1.7\) and \(2.2\), respectively.
4. The analysis of the influence of \(\rho_{\text{R0,Rsetup}}\) on the resistance factor and factor of safety for driven piles indicates that the negative correlation between \(R_{0}\) and \(R_{\text{setup}}\) has a significant influence on the resistance factor of driven piles, while the positive correlation between \(R_{0}\) and \(R_{\text{setup}}\) has a slight influence on the resistance factor of driven piles. However, the factor of safety is just the opposite. Therefore, the correlation between \(R_{0}\) and \(R_{\text{setup}}\) should be paid attention to in engineering applications.  
This paper proposes a methodology to calculate the resistance factor and factor of safety for driven piles considering the setup effects. Through parametric analysis, it is concluded that the total ultimate resistance is significantly improved when driven piles consider the setup effects. The length of the pile or the number of piles could be reduced, and an economic design of driven piles could be achieved.  
Conceptualization, X.B.; methodology, X.B., X.C.; validation, X.B., X.C., J.C. and Z.X.; writing--original draft preparation, X.B. and J.C.; writing--review and editing, X.B., J.C. and Z.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (52078396, 51978247, 51708428).  
The authors declare no conflict of interest.
## 4 Circuit Simulations
To validate the stability of each constructed filter circuit, the transient responses were collected for both \((1+\alpha)=1.2\) and 1.8 order filters when applying a 800 Hz square wave input signal. Both the input (solid) and output (dashed) waveforms during this transient test are given in Figure 15a,b for the 1.2 and 1.8 order filters, respectively. In both cases, the output waveforms confirm that the circuits are stable and validate the previous stability analyses. The oscillations in the transient response of the 1.8 order filter in Figure 15b confirm the results in the stability analysis, that is, that the filters with higher \(\alpha\) have less stability margin. Both simulation and experimental results serve to validate the proposed \((1+\alpha)\) fractional-order elliptic filter responses and that both topologies are appropriate in realizing these designs. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(\alpha\) & \(R_{0}\) (f) & \(R_{a}\) (f) & \(R_{b}\) (f) & \(R_{c}\) (f) & \(R_{d}\) (f) & \(R_{e}\) (f) & \(C_{a}\) (nF) & \(C_{b}\) (nF) & \(C_{c}\) (nF) & \(C_{d}\) (nF) & \(C_{e}\) (nF) \\ \hline
0.8 & 58.8 & 65.1 & 326.4 & 1.47k & 7.06k & 84.23k & 12.8 & 16.2 & 22.6 & 29.7 & 15.7 \\
0.2 & 931.5 & 374.9 & 573.6 & 837.2 & 1.23k & 1.93k & 1.28 & 5.29 & 22.85 & 98.5 & 393.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Component values to realize fractional-order capacitors with \(\alpha=0.8\) and 0.2 using fifth order Foster I topology centered at 10 kHz. Figure 14: Theoretical (solid), SPICE simulated (dashed), and experimental (dashed-dotted) magnitude responses of (**a**) \((1+\alpha)=1.8\) and (**b**) 1.2 order approximated elliptic filter responses.
# The Role of Data-Driven Methodologies in Weather Index Insurance
###### Abstract  
There are several index insurance methodologies. Most of them rely on linear piece-wise methods. Recently, there has been studies promoting the potential of data-driven methodologies in construction index insurance models due to their ability to capture intricate non-linear structures. However, these types of frameworks have mainly been implemented in high-income countries due to the large amounts of data and high-frequency requirements. This paper adapts a data-driven methodology based on high-frequency satellite-based climate indices to explain flood risk and agricultural losses in the Antioquia area (Colombia). We used flood records as a proxy of crop losses, while satellite data comprises run-off, soil moisture, and precipitation variables. We analyse the period between 3 June 2000 and 31 December 2021. We used a logistic regression model as a reference point to assess the performance of a deep neural network. The results show that a neural network performs better than traditional logistic regression models for the available loss event data on the selected performance metrics. Additionally, we obtained a utility measure to derive the costs associated for both parts involved including the policyholder and the insurance provider. When using neural networks, costs associated with the policyholder are lower for the majority of the range of cut-off values. This approach contributes to the future construction of weather insurance indexes for the region where a decrease in the base risk would be expected, thus, resulting in a reduction in insurance costs.  
index insurance; crop insurance; machine learning; neural networks; satellite data; google earth engine +
Footnote †: journal: Applied Statistics  
1 Industrial Engineering Department, Universidad de los Andes, Bogota 111711, Colombia 13 Group for Optimization and Applied Probability (COPA), Industrial Engineering Department, Universidad de los Andes, Bogota 111711, Colombia 3
## 3 Soil Features as a Consequence of Reduced Fertilization
### Soil Chemistry
#### 3.1.2 Soil Aeration  
The impact of reduced N fertilization on soil aeration is primarily indirect and related to changes in soil structure and microbial activity. Reduced fertilization often encourages better soil aggregation due to increased OM content [48]. OM contributes to the formation of stable soil aggregates, which improve soil porosity [49]. This enhanced porosity allows for better air circulation within the soil, leading to improved oxygen availability in the root zone [50]. Aeration enhances root growth, which can further improve oxygen availability  
Figure 4: Nitrogen fertilization requirements and nutrient retention characteristics of different soil types: sandy, chernozem, and clay (own work by [PERSON]).  
in the soil profile. Studies show that increased aeration supports better nutrient uptake and overall plant health by optimizing root morphology and respiration [51]. Subsurface drip irrigation, combined with reduced N levels, has been shown to improve soil oxygenation [51] and enhance root system development in crops like cucumbers, which are sensitive to hypoxia [50]. Improved aeration benefits both plant roots and aerobic microorganisms, such as those involved in the breakdown of OM and the cycling of nutrients [52].
## 5 Discussion
### The Fitness of PCA Model Integrate with AHP-QFD Datasets across Study Industries  
PCA consistently demonstrated effectiveness in reducing the complexity of AHP-QFD matrices, yet variations were observed in its suitability across different industrial sectors and datasets. For instance:  
1. Logistics Industry (Case Study A: Airport Cargo Logistic Center Site, Dataset X1): The scree plot for Dataset X1 revealed a dominant first component that explained a significant portion of the dataset's variance. The first two principal components accounted for 83.4% of the variance, and the first five components collectively explained 99.2% of the variance. This indicates that PCA is highly suitable for dimensionality reduction in this dataset.
2. Personal/Consumer Electronics Industry (Case Study B: New Earphone Development, Dataset X3): The scree plot for Dataset X3 showed that approximately 30% of the variance was explained by the first principal component, with a noticeable decline in variance explained by subsequent components. The first four components collectively elucidated 86.9% of the variance, making PCA appropriate for this dataset.
3. Healthcare/Medical Industry (Dataset X2: Temperature-Controlled Medicine Bags): The scree plot for Dataset X2 exhibited a gradual decline in variance explained by principal components, with no clear \"elbow\" point. The first component explained 27.3% of the variance, and the first seven components together explained 90.6% of the variance. This even distribution of information suggests that PCA might not be as beneficial for this dataset. This observation is consistent with the findings of construction automation industry, which noted that PCA might not be suitable for datasets with less dominant principal components [48]. This underscores the importance of considering dataset characteristics when applying PCA.