esa-sceva/satcom-chunk-collection huggingface dataset:
# Lithium-Ion Battery Prognostics through Reinforcement Learning Based on Entropy Measures
## 4 Methodology
### Predictive Models
#### 4.2.1 Lstm  
Long Short-Term Memory, known simply as LSTM, is a framework for a recurrent neural network (RNN) which avoids the problem of long-term dependency. Unlike standard feedforward neural networks, LSTM has feedback connections, and hence, it can update  
Figure 6: Entropy vs. Cycles for PL11, PL19, and PL09.  
Figure 7: Train–Test split schematic.  
and store necessary information. It has been widely utilized in time series forecasting in different fields of science in recent years [30].  
A unit LSTM cell consists of an input gate \(i_{t}\), forget gate \(f_{t}\), and an output gate \(o_{t}\). Each gate receives the current input \(x_{t}\), the previous state \(h_{t-1}\), and the state \(c_{t-1}\) of the cell's internal memory. \(x_{t}\), \(h_{t-1}\), and \(c_{t-1}\) are passed through non-linear functions, which yield the updated \(c_{t}\) and \(h_{t}\)[31]. Considering \(W_{i}\), \(W_{f}\), \(W_{o},W_{c}\) and \(U_{i}\), \(U_{f}\), \(U_{o},U_{c}\) as the correspondig weights matrices and \(b_{i},\ b_{f},\ b_{o},b_{c}\) as the bias vectors, each LSTM cell operates based on the following Equations.  
\[i_{t}=\sigma(x_{t}U_{i}+h_{t-1}W_{i}+b_{i}) \tag{5}\]  
\[\widetilde{c}_{t}=\tan h(x_{t}U_{c}+h_{t-1}W_{c}+b_{c}) \tag{6}\]  
\[f_{t}=\sigma\Big{(}x_{t}U_{f}+h_{t-1}W_{f}+b_{f}\Big{)} \tag{7}\]  
\[c_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{c}_{t} \tag{8}\]  
\[o_{t}=\sigma(x_{t}U_{o}+h_{t-1}W_{o}+b_{o}) \tag{9}\]  
\[h_{t}=\tan h(c_{t})*o_{t} \tag{10}\]  
In this study, all three gates take permutation entropy of the battery voltage at cycle \(t\) and the battery capacity at cycle \(t-1\) as their input variables, \(x_{t}\) and \(c_{t-1}\), and output the estimated battery capacity, \(\hat{y}\), for the given inputs as shown in Figure 8. Furthermore, an algorithm is presented for the proposed LSTM model.  
Figure 8: Schematic of a unit LSTM cell.
# Microalgal Proteins and Bioactives for Food, Feed, and Other Applications
## 3 Application of Microalgae as Food, Functional Foods and Feed  
Microalgae biomass is commonly used as a food supplement in the form of powder, tablets, capsules, flakes or pastes, but recently, it was incorporated into some food products including noodles, bread, pasta [136], ice-creams [137], cookies and biscuits [138], chocolate [139], gelled deserts [140], yoghurts [141] and cheeses [142]. The number of drinks and snacks containing microalgae biomass has doubled in western countries in the past few years. However, there are limitations concerning their incorporation into food products, including their bright green color, \"fishy\" aroma and the fact that they can stain surfaces due to their lipid content. Other drawbacks are legal and legislative issues, discussed in Section 4. From the techno-functional point of view, the most important properties of proteins are solubility, emulsification, nutritional quality and digestibility. Solubility is largely dependent on amino-acid composition and sequence, as well as conditions such as pH and the ionic strength of the solvent. The solubility typically increases when the pH is further away from the isoelectric point. In general, microalgae proteins have high solubility in a high pH and minimal solubility at a pH below 4. Due to its amphiphilic character, protein has a great emulsifying ability. Proteins are widely recognized as the major component influencing the rheological properties of food products and stability during storage [42]. Due to their high content of surface active proteins, various microalgae species were proved to have a great ability to stabilize proteins and foams and exhibited comparable stabilization properties in commonly used synthetic surfactants or animal-based proteins. In the future, microalgae proteins have the potential to replace surfactant and animal proteins in the food industry [143]. The techno-functional properties of selected microalgae species and their protein fractions are listed in Table 11, together with the effect of their incorporation into food. As outlined in Table 11, the emulsifying properties of selected proteins from different _Chorella_ sp. were excellent and comparable to egg protein in many instances. Microalgae proteins exhibit comparable to superior interfacial stabilization compared with animal- or plant-based proteins [143]. Their emulsions and foams exhibit minor pH-dependency due to a characteristically low isoelectric point and an extraordinary resistance to increased ionic strength.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Species** & **Fraction/Product** & **Effects** & **References** \\ \hline _Arthrospira platensis_ & & & \\ _Nannochloropsis_ & & & \\ _gaiditama_ & & & \\ _Tetraselins impellucida_ & & & \\ _Scenedesmus_ & & & \\ _dimorphus_ & & & \\ \hline _Arthrospira platensis_ & Soluble protein isolate & & \\ \multicolumn{4}{c}{All properties strongly influenced by pH} \\ \hline \hline \end{tabular}
\end{table}
Table 11: Techno-functional properties of selected microalgae species and effect of their incorporation into food.
## 5 Conclusions  
In this paper, a measuring device was proposed and tested that can be sufficiently accurately and reliably used to determine the pigment and nitrogen content of plant leaves.  
A performance test was performed, and the measurement accuracy of the device was evaluated against existing methods and technical means of measuring pigment content in plant leaves. The measuring device was found to provide sufficiently accurate and reliable measurements of pigment content in plant leaves compared with existing methods. It has been shown that the device can measure a wider range of pigments than previous methods.  
The measurement accuracy of the proposed device ranges from 72 to 97% compared with the reference method, and compared with the reference, the technical means' accuracy ranges from 87 to 90%. This accuracy depends on the type of plant being measured.  
The proposed measuring device has the potential to save time and resources in the measurement of pigment content in plant leaves, which may have practical applications in areas such as crop monitoring.  
The developed measurement device could help improve the quality of data collected on the pigment content of plant leaves, which could positively impact research in a number of fields, including plant biology, agriculture, and environmental science.  
The results of this work may provide new knowledge about the physiology of plant pigments, including their function, distribution, and regulation. Also, new methods for measuring pigment content in plant leaves can be developed to be applied to other areas of research and potentially lead to further advances in scientific knowledge.  
In summary, modern non-contact methods and means are increasingly important in the quality assurance of various products and processes and are crucial for the safety and reliability of various engineering systems.  
This work should be continued with research aimed at generalizing the models for determining both the pigments in plants and the nitrogen in them.  
Conceptualization, Z.Z. and V.S.; methodology, Z.Z.; software, Z.Z.; validation, G.S., M.V. and Z.Z.; formal analysis, V.S.; investigation, Z.Z. and G.S.; resources, M.V.; data curation, Z.Z. and M.V.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z. and V.S.; visualization, Z.Z.; supervision, Z.Z. and V.S.; project administration, Z.Z.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are available upon request.  
This work was administrative and technically supported by the Bulgarian national program \"Development of scientific research and innovation at Trakia University in the service of health and sustainable well-being\"--BG-RRP-2.004-006-C02.
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 4 Architecture of ColluDroid
### Training Phase
#### 4.1.2 Secure Learning Method  
As the developers have incentives to manipulate features to evade detection, we have to consider a more secure SVM classifier to make it more difficult for attackers to evade. Here, we take Sec-SVM [26] as an example and test it under our ColluDroid framework.  
Sec-SVM [26] is based upon the rationale that a robust classifier should not change its decision on a sample if only a small subset of feature values are modified. For linear classifiers, this can be easily quantified by measuring whether the classifier's weights are evenly distributed among features since  
Figure 3: Architecture of ColluDroid.  
more evenly distributed feature weights should require the adversary to manipulate a higher number of features to evade detection. According to [26], the Sec-SVM problem can be defined as:  
\[\begin{array}{ll}\min_{w,b}&\frac{1}{2}\|w\|_{2}^{2}+C\sum_{i=1}^{n}max(0,1-y_ {i}f(x_{i})),\\ \text{s.t.}&w_{k}^{lb}\leq w_{k}\leq w_{k}^{ub}.\end{array} \tag{7}\]  
Note that this optimization problem is identical to standard SVM, except for the presence of a box constraint on \(w\). The lower and upper bounds on \(w\) are defined by the vectors \(w^{lb}=(w_{1}^{lb},...,w_{d}^{lb})\) and \(w^{ub}=(w_{1}^{ub},...,w_{d}^{ub})\), which is selected with a procedure. It is a trade-off considering both accuracy and security.
## 5 Conclusions  
This paper presents the outcomes of a recent investigation focusing on the development of an offset cylinder reflector antenna, specifically designed to fulfill the needs of low-Earth orbit science missions, with a primary focus on monitoring Arctic snow and sea ice. The primary aim of this work is to devise, construct, and evaluate an offset cylindrical reflector antenna designed to operate at a Ku-band frequency of 17.2 GHz. The reflector features a compact aperture size of 6.3\(\lambda\)\(\times\) 8.5\(\lambda\) and a focal length of \(F\) = 98 mm (\(F/D\) = 0.65). Employing a lightweight 2 \(\times\) 8 element patch array feed to illuminate the reflector, the antenna achieves a gain of 25 dBi and an aperture efficiency of 52% at 17.2 GHz. The mechanism for reflector deployment was proposed, allowing the antenna to be stored within a 1U space. The array feed has been fabricated and tested, demonstrating favorable agreement across all performance metrics. Furthermore, the reflector system has been manufactured and subjected to testing, yielding commendable gain measurements and radiation pattern characteristics.  
Conceptualization, methodology, and software, B.A.F.E.; validation, B.A.F.E. and D.I.; formal analysis and investigation, B.A.F.E.; resources, D.I.; writing--original draft preparation, B.A.F.E.; writing--review and editing, D.I. and L.S.; supervision, D.I. and L.S.; project administration, D.I.; funding acquisition, D.I. All authors have read and agreed to the published version of the manuscript.  
We acknowledge the support of the Canadian Space Agency (CSA) [21 FAMANB10].  
Not applicable.  
All data are available in the manuscript.  
The authors declare no conflicts of interest.
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
## 2 Development and Implementation of the 3D Model
### Generation of Random Mechanical Properties  
#### 2.2.1 Tensile Strength Random Generation  
The Weibull distribution, established by [PERSON] (1954, 1955), characterizes the heterogeneity of the tensile strength of the material. The probability density function \(f_{w}(x,b,c)\), cumulative function \(F(x,b,c)\), and inverse cumulative function \(F^{-1}\) for a random variable \(x\geq 0\), are outlined in Equations (4)-(6). Here, the terms \(b>0\) and \(c>0\) denote the shape and scale parameters governing this distribution.  
\[f_{w}(x,b,c)=\frac{b}{c}\Big{(}\frac{x}{c}\Big{)}^{b-1}e^{-\left(\frac{x}{c} \right)^{b}} \tag{4}\]  
\[F(x,b,c)=1-e^{-\left(\frac{x}{c}\right)^{b}} \tag{5}\]\[F^{-1}(u,b,c)=c(-\ln{(1-u)})^{1/b} \tag{6}\]  
The mean \(\mu_{w}\) and standard deviation \(\sigma_{w}\) of the distribution can be evaluated, respectively, by Equations (7) and (8).  
\[\mu_{w}=c\Gamma\left(1+\frac{1}{b}\right) \tag{7}\]  
\[\sigma_{w}=\sqrt{c^{2}\Gamma\left(1+\frac{2}{b}\right)-\mu^{2}} \tag{8}\]  
where \(\Gamma\) is the gamma function, given by Equation (9),  
\[\Gamma(\eta)=\int_{0}^{\infty}t^{\eta-1}e^{-t}dt;\qquad\eta>0 \tag{9}\]  
For positive integer values of \(\eta\), this function can be evaluated as \(\Gamma(n)=(n-1)!\).  
The numerical generation of random values for tensile strength relies on the inverse transformation method. The essential steps of this procedure are summarized in Algorithm 1. In this process, random numbers \((u)\), derived from the uniform distribution \((U(0,1))\) within the range of 0 to 1, are generated. Subsequently, these generated values are applied to Equation (6), providing random numbers following the Weibull distribution.  
```
1:for each finite element do
2: Generate a random number \(u\) from \(U(0,1)\);
3: Given the parameters \((b,c)\), evaluate \(f_{t}=F^{-1}(u)=c(-\ln{(1-u)})^{1/b}\);
4: Return \(f_{t}\);
5:endfor
```  
**Algorithm 1** Generation of random tensile strength by inversion method
## 2 Related Work  
### Image Segmentation Method  
There are many methods of image segmentation, which can be divided into the following categories [16; 17].  
1. Rule-based methods: These methods segment images by applying certain rules, including threshold-based, edge-based, and region-based rules [18; 19; 20]. These methods are simple and easy to implement, and their advantages and disadvantages are derived from their simplicity. Because of the simplicity of rule-based approaches, segmentation rules are difficult to design for complex tasks.
2. Shape-based methods [21; 22]: These methods summarize the information of segmented objects or images to outline a general shape and use this information as a prior model to segment the image. These segmentation methods include active-shape model (ASM) and active appearance model algorithms. Shape-based methods have obvious effects in the segmentation of images with clear edges and regular shapes, and are slightly less effective in segmenting images with weak borders and irregular graphics.
3. Atlas-based methods: These methods use atlases (i.e., previously segmented images) for target image registration [23]. Atlas-based algorithms rely on prior knowledge, and their rules are not particularly suitable for segmenting targets with large differences.
4. Graph-based methods: The image is mainly divided into discrete components, and then these components are connected in a certain form based on conditional random fields to form an overall result [24; 25]. This type of clustering algorithm is fast and simple to implement. Moreover, these algorithms are suitable for tasks that do not require high precision.
5. Machine learning-based methods [26]: Traditional image segmentation methods are usually based on handcrafted features (e.g., SIFT) and classifiers (i.e., k-nearest neighbor (k-NN) or artificial neural networks). However, with the development of convolutional neural networks (CNNs), the paradigm has shifted to end-to-end methods [27; 28]. Usually, large networks such as CNNs contain many nodes and parameters between the input image to be segmented and the output target segmentation result. By training and adjusting the structure and parameters of these nodes, an image segmentation network with strong recognition ability can be obtained.  
Although great progress has been made in automatically segmenting the heart in CXR images, some limitations still exist, such as the effectiveness of the segmentation method decreasing when the angle and displacement of CXRs change. Furthermore, the need to use downsampling algorithms leads to irregularities and inaccuracies in the segmented edges in CXRs, which reduces the applicability of these methods in clinical settings [29; 30].
## 2 Data Preprocessing
### Data Normalization  
Normalization usually is the final step for data pre-treatment. In terms of data characteristics, this step can be divided into local normalization and global normalization. A typical local normalization process is unit scaling, which is widely used for spectral data. The similarity of two spectral samples is represented by the dot product of their unit spectral vectors. Global normalization aims to make sensor variables comparable, preventing some variables with lower magnitudes from being overwhelmed by others with higher magnitudes. However, in some cases, global optimization can amplify the noise or the information of irrelevant sensor variables, which might be harmful for modeling.
## 2 Background
### Summary Statistics  
In 2016, the questionnaire was sent to 1721 municipalities, and there were more than 1664 responses, representing a response rate of 97%. We focus on the responses of municipalities by excluding designated cities. That is, the size of our sample is 1644, representing almost all the municipalities in Japan.  
The size of the municipalities measured with a standard financial scale or by total population tend to have right-skewed distributions (Figure 1). The standard financial scale is the sum of the municipality's standard tax revenue and ordinary local allocation tax by the national government (2016) (p. 23). This scale is an index of the size of a municipality's budget, which indicates how much money they can use for their own purposes. This index rages from 2.75 to 1726.80 million dollars, with a mean of 135.61 million dollars. In addition, the total population in municipalities ranges from 160 to 892,535, with a mean of 60,257.32 persons. These indices imply that the size of the municipalities is widely dispersed, including a large number of small- and medium-scale municipalities and several large-scale municipalities.  
From the survey, we found that 400 of the 1644 municipalities had adopted a GPP by 2016. This indicates that almost 24% of municipalities in Japan have a GPP. Therefore, we have enough variation in the implementation of GPPs by municipalities to examine the relationships between the implementation of a GPP and the progress of green procurement.  
Figure 1: The distributions of population and standard financial scale.  
Finally, the right and left sides of Figure 2 show box plots of the natural log of standard financial scale and total population in municipalities with and without GPPs, respectively. Each box area implies between the 25 th and 75 th percentiles of each of the size indices, while an upper line implies an upper adjacent value, and a lower line implies a lower adjacent value. Moreover, the points outside of both the upper and lower lines indicate outliers. We can see that in terms of size, municipalities with GPPs tend to be larger than those without GPPs. However, the outliers in Figure 2 imply that several large-scale municipalities, such as Kagoshima City or Suginami district, have not adopted a GPP.
## 2 Results and Discussion
### Examples
#### 2.2.1 Plant samples
\begin{table}
\begin{tabular}{|l|l|l|l|} \hline Rank & 1\({}^{\#}\)run & 2\({}^{\text{nd}}\)run & 3\({}^{\text{rd}}\)run \\ \hline \multicolumn{4}{|c|}{_Name (Frequency)_} \\
**1** & raffinose (1649) & raffinose (1654) & 200401 (1948) \\
2 & 200401 (1252) & citric acid (1522) & citric acid (1723) \\
3 & citric acid (1083) & 212649 (1023) & raffinose (1562) \\
4 & tocopherol (1082) & 200401 (963) & tocopherol (1131) \\
5 & 201009 (918) & tocopherol (698) & 199177 (824) \\
6 & sucrose (683) & glucoheptulose (563) & fructose (654) \\
7 & 212649 (631) & 199177 (509) & 199216 (281) \\
8 & 199225 (527) & 199225 (303) & palmitic acid (189) \\
9 & palmitic acid (399) & fructose (258) & 199225 (219) \\
10 & glucoheptulose (170) & sucrose (245) & succin acid (189) \\
10 & negative & 832/923 & 252 \\ \hline \end{tabular}
\end{table} 
Table 1: Top 10 classifiers from three identical independent runs on GC-TOF-MS data (Bin Base annotation). \begin{table}
\begin{tabular}{l l l l l} \multicolumn{4}{l}{**Table 2.** Top 10 classifiers from HILIC-LC-ESI-MS and RP-LC-ESI-MS datasets (XCMS peak lists).} \\ \multicolumn{4}{l}{**Rank**} & \multicolumn{1}{l}{ions} & \multicolumn{1}{l}{m/z (Dalton)/RT (sec)} & \multicolumn{1}{l}{frequency} \\ \multicolumn{4}{l}{_HILIC-LC-ESI-MS_} \\ \multicolumn{4}{l}{**1**} & \multicolumn{1}{l}{negative} & 549/220 & 794 \\ \multicolumn{4}{l}{2} & \multicolumn{1}{l}{positive} & 1048/372 & 690 \\ \multicolumn{4}{l}{3} & \multicolumn{1}{l}{positive} & 405/205 & 621 \\ \multicolumn{4}{l}{4} & \multicolumn{1}{l}{positive} & 875/96 & 478 \\ \multicolumn{4}{l}{5} & \multicolumn{1}{l}{negative} & 1003/385 & 440 \\ \multicolumn{4}{l}{6} & \multicolumn{1}{l}{negative} & 747/487 & 347 \\ \multicolumn{4}{l}{7} & \multicolumn{1}{l}{negative} & 743/162 & 307 \\ \multicolumn{4}{l}{8} & \multicolumn{1}{l}{positive} & 995/325 & 307 \\ \multicolumn{4}{l}{9} & \multicolumn{1}{l}{positive} & 709/457 & 269 \\ \multicolumn{4}{l}{**10**} & \multicolumn{1}{l}{negative} & 832/923 & 252 \\ \multicolumn{4}{l}{_RP-LC-ESI-MS_} \\ \multicolumn{4}{l}{**1**} & \multicolumn{1}{l}{positive} & 1341/1375 & 716 \\ \multicolumn{4}{l}{2} & \multicolumn{1}{l}{negative} & 508/609 & 457 \\ \multicolumn{4}{l}{3} & \multicolumn{1}{l}{positive} & 1237/1460 & 397 \\ \multicolumn{4}{l}{4} & \multicolumn{1}{l}{positive} & 324/533 & 370 \\ \multicolumn{4}{l}{5} & \multicolumn{1}{l}{negative} & 1005/965 & 350 \\ \multicolumn{4}{l}{6} & \multicolumn{1}{l}{positive} & 304/549 & 303 \\ \multicolumn{4}{l}{7} & \multicolumn{1}{l}{negative} & 741/711 & 247 \\ \multicolumn{4}{l}{8} & \multicolumn{1}{l}{positive} & 305/547 & 196 \\ \multicolumn{4}{l}{9} & \multicolumn{1}{l}{negative} & 661/965 & 156 \\ \multicolumn{4}{l}{10} & \multicolumn{1}{l}{negative} & 153/139 & 148 \\ \end{tabular}
\end{table} 
Table 2: Top 10 classifiers from HILIC-LC-ESI-MS and RP-LC-ESI-MS datasets (XCMS peak lists).