esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Quantitation of Selected Aroma Compounds
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Concentration [mg/L]**} \\ \cline{2-7}
**Aroma Compound** & & \multicolumn{3}{c}{**Kini**} & \multicolumn{3}{c}{**Gspusi**} & \multicolumn{1}{c}{**Literature**} \\ \cline{2-7}  & **Mean a  & \(n\) b  & \(\text{\bf{Literature}}\) \\ \hline
**myrcene** & 0.82 & 3 & 0.75–0.91 & 2.71 & 4 & 2.55–2.88 & 2.28 d–11.09 e \\ limonene & 3.83 & 3 & 3.69–4.06 & 7.03 & 4 & 6.46–7.89 & 1.22–17.21 e \\
1,8-cineole & 7.53 & 5 & 6.89–8.29 & 0.26 & 5 & 0.25–0.26 & _n.a. i_ \\ nonanal & 0.61 & 4 & 0.35–0.79 & 0.22 & 4 & 0.21–0.25 & _n.a. i_ \\ linalool & 28.53 & 5 & 2.65–30.9 & 0.93 & 4 & 0.81–1.05 & 1.90 d–36.99 e \\ estragole & 2.94 & 3 & 1.19–3.46 & 0.16 & 5 & 0.15–0.17 & _n.a. i_ \\ _trans-anethole_ & 4.70 & 5 & 4.29–5.29 & 1.26 & 5 & 1.23–1.29 & _n.a. i_ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Concentrations of selected aroma-active compounds in The King Gin Kini and Gspusi gins, as determined via stir-bar sorptive extraction (SBSE) and stable isotope dilution analysis (SIDA), in comparison to values obtained from the literature. does not automatically lead to the absence of a specific constituent aroma compound in the gin, which might be imparted by another botanical ingredient. Further, in the present study, although a list of botanicals used in each gin was kindly provided by the distillery, the quantities implemented are proprietary information. As such, a detailed and comprehensive comparison of the quantitative or semi-quantitative (FD factor) differences of individual aroma compounds within each gin type or between the two gins in relation to the botanical ingredients cannot be made here.
# A Continuous Monitoring and Reallocation Method for Successful Decisions in Change Management
## 2 State of the Art
### Human Resource Allocation Problem  
The first algorithmic solution to the assignment problem was developed and published by [PERSON] [33] with the so-called Hungarian method. The name was given in honor of two Hungarian mathematicians who inspired [PERSON] [33] in the implementation of the algorithm. The original version of the assignment problem is concerned with allocating n resources (persons) to n activities (tasks) so as to minimize the cost or maximize the benefit, subject to the constraints that one resource is used in a single activity and that each activity uses only one resource. The optimal solution is the one that minimizes the total cost, as defined in Equation (1). The HRAP is an adaptation of the assignment problem when the number of tasks to be assigned is equal to human resources [18]. The mathematical model of the assignment problem is as follows:  
\[\text{Minimize}\sum_{i=1}^{n}\sum_{j=1}^{n}c_{ij}x_{ij} \tag{1}\]  
subject to the constraints  
\[\sum_{i=1}^{n}x_{ij}=1\quad\forall j=1,...,n \tag{2}\]  
\[\sum_{j=1}^{n}x_{ij}=1\quad\forall i=1,...,n \tag{3}\]  
\[x_{ij}=\begin{cases}1&\text{if worker $i$ is assigned to task $j$}\\ 0&\text{otherwise}\end{cases} \tag{4}\]  
where \(n\) is the total number of tasks and workers, \(i\) is the index of the worker (\(i=1,2,\ldots,n\)), \(j\) is the index of the task (\(j=1,2,\ldots,n\)), and \(c_{ij}\) is the cost of task \(j\) when worker \(i\) is assigned. Both indexes \(i,j\) end at \(n\) because the number of workers is equal to the number of tasks.  
The importance of the optimal solution to the human resource allocation problem has been widely studied in the literature, and various mathematical models and algorithms have been proposed. Several approaches to solving the HRAP in different real-life scenarios are presented by [PERSON] and [PERSON] [18]. There are exact methods, branch-and-bound algorithms, dynamic programming, linear programming, and the Hungarian method [34; 35]. When the above methodologies cannot achieve solutions accurately and relatively quickly due to the high dimensions of these problems, researchers opt for heuristics or meta-heuristic algorithms [36], Tabu search [37], simulated annealing [38], algorithms inspired by nature, especially by ant colonies (ant colony optimization) [39; 40], and hybrid algorithms [41]. These algorithms are applied in various fields, such as production management [36; 42], healthcare systems for the allocation of nursing staff [43; 44], project management [45; 46], organization maintenance management [47], hotel unit management [48; 49], education [35], and the military [50]. [PERSON] et al. [51] introduced and tested a Nurse Reallocation (NuRe) method for addressing and managing nurses' resistance during Electronic Health Record (EHR) implementation. The results of the experiments show that the early stages are the critical time frame for the hospital manager to reallocate nurses [51].
###### Abstract
Furthermore, the limited communication and computing power of wireless sensor networks adds to the complexity of data collection [10]. The primary contribution made by the suggested work is to conserve energy and achieve the maximum lifetime of WSNs. The K-medoids approach is used for clustering nodes with similar data. Each cluster node has a CH chosen using the ASFO algorithm. Furthermore, computation time and CH energy consumption have been minimized because of the shorter transmission range.
## 1 Introduction
This model calculates the long-term trend of the forest carbon cycle by iterations based on the characteristics of _NPP-age_, forest age, and NPP in the reference year, and comprehensively takes the impacts of climate change, CO\({}_{2}\) concentration rise, forest disturbances, and nitrogen deposition on the carbon cycle into consideration. The InTEC model has been used extensively in research. For example, [PERSON] et al. [26] used the InTEC model to estimate the carbon balance of Canada. [PERSON] et al. [27] used the InTEC model to estimate the soil organic carbon density of the Chinese forest ecosystem. [PERSON] et al. [28] used the InTEC model to simulate the carbon storage changes of the Poyang Lake area. [PERSON] et al. [29] used the InTEC model to reconstruct the interannual variation of NEP at the site scale. [PERSON] et al. [30] used the InTEC model to estimate the carbon changes of US forests. Subtropical forest ecosystem total net ecosystem productivity in the East Asian monsoon region is about 720 million tons of carbon per year or about 8% of the global NEP; it plays an important role in the global carbon cycle and has challenged the traditional understanding that temperate forests in Europe and America are the main carbon sink functional areas [31; 32; 33]. China is an important distribution region of subtropical forests, while Zhejiang Province is located on the northern edge of the subtropical zone and plays an important role in the carbon balance of the regional forest ecosystems. In addition, the arbor forests in Zhejiang Province are still dominated by young and middle-aged forests, and changes in their age will impact the carbon cycle and the future evolution of regional forest ecosystems. Therefore, this study takes the main forest type in Zhejiang Province as our study target, based on forest resources extracted from remote sensing information, and uses the InTEC model to simulate the forest ecosystem NEP in Zhejiang Province. We analyze its spatiotemporal evolution pattern of NEP, and its response to meteorological factors such as temperature, precipitation, relative humidity, and radiation. The findings have important scientific implications for addressing climate change in subtropical forest ecosystems.
# Spatiotemporal Analysis of Web News Archives for Crime Prediction
## 5 Crime Prediction
### Supervised Learning
#### 5.1.2 Prediction Using Random Forest  
Random Forest is the most famous and powerful supervised machine learning algorithms. The algorithm creates several decision trees within a forest [43]. In general, the more trees in the forest, the more robust the prediction and high the prediction accuracy rate. It predicts the new class based on features of previous classes of old trees. When a new tree is introduced, each tree gives votes for the new tree; the forest chooses the classification of having the most votes of all the other trees in the forest [25]. We used the Weka tool for prediction of crime events by random forest on the crime dataset. Random forest algorithms can be implemented based on the following steps [44]:  
1. It Randomly selects \(k\) features from total \(m\) features on random basis where \(m\gg k\).
2. It calculates the node \(d\), among the \(k\) features, using the method of best split point.
3. It Splits the node into daughter nodes using the method of best split.
4. It repeats steps 1 to 3 until, it reaches \(l\) number of nodes.
5. It builds forest by repeating steps 1 to 4 for \(n\) number of times to create \(n\) number of trees.
6. It takes the test features and use the rules of each randomly created decision tree to predict the outcome and stores the predicted outcome (target).
7. For each predicted class, the votes are calculated.
8. Final predictions are made based on the high voted predicted class using the random forest algorithm.
## 4 Materials and Methods
### Assessment of Performance of SPAD Prediction Models  
Three metric scores, namely the root mean square error (RMSE), adjusted determination coefficient (R\({}^{2}\)), and the mean absolute error (MAE), were used to comparatively assess the performance of the regression models. RMSE and MAE were used to evaluate the accuracy of the models, using the ground-truth SPAD data, while R\({}^{2}\) evaluated the performance of the models based on the k-fold cross-validation [61]. The segmentation methods were labelled as MLP-FR, SVM-FR, RF-FR, ExG-FR, and ExGR-FR for field images and MLP-GR, SVM-GR, RF-GR, ExG-GR, and ExGR-GR for glasshouse data.  
The normalized color vegetation indices (CVIs), which are commonly used in estimating plant chlorophyll and nitrogen, were extracted from the segmented images, and correlated (Pearson correlation) with the SPAD values. The dataset included 180 segmented cowpea images and 240 segmented wheat images, which were analyzed separately. Each dataset was divided into three portions in a 70:15:15 ratio for model training, and validation and testing. The training data were used to fit the model, while the validation dataset reduced overfitting during the fine-tuning of the hyperparameters [62]. The testing dataset provided an unbiased evaluation of the final model. The top five indices of each segmentation method were used as input features to train the respective regression models.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 3 Derivation of the BECPB for Rayleigh Channel
### BECPB for Battery with IDC-Based BUF  
After plugging Equation (16) into Equation (3), the power consumption for battery with IDC-based BUF is  
\[P_{\text{bat}}(t)=\left\{\begin{array}{ll}\frac{f_{\text{ray}}(B,d)+P_{\text{ a}}+P_{\text{d}}}{\eta-\gamma[f_{\text{ray}}(B,d)+P_{\text{a}}+P_{\text{d}}]}& \text{when emitting symbol `1'}\\ \frac{P_{\text{d}}}{\eta-\gamma P_{\text{d}}}&\text{when emitting symbol `0',}\end{array}\right. \tag{18}\]  
As a symbol duration is \(\frac{1}{B}\) and symbol '1' and '0' are transmitted with the same probability, the BECPB for the battery with IDC-based BUF in Rayleigh channel is  
\[E_{\text{bat,I}}=\frac{1}{2B}\left[\frac{f_{\text{ray}}(B,d)+P_{\text{a}}+P_{ \text{d}}}{\eta-\gamma[f_{\text{ray}}(B,d)+P_{\text{a}}+P_{\text{d}}]}+\frac {P_{\text{d}}}{\eta-\gamma P_{\text{d}}}\right] \tag{19}\]
# Interpolation-Filtering Method for Image Improvement in Digital Holography
## 3 Proposed Method
### Interpolation  
The simplest interpolation is the nearest neighbor [100]. Each pixel in the source image becomes larger according to the desired final size. Therefore, no fundamentally new signal values appear since, for a pixel in the obtained image, the known value of the nearest pixel in the original image to the pixel in the obtained image is chosen. The nearest neighbor method is one of the fastest interpolation methods.  
A more complex algorithm is bilinear interpolation [100]. Weighted values in the four closest pixels of the original image are used, with the weights inversely proportional to the distance between the original pixels and the pixel under consideration. Since the value in the pixel under consideration is a linear combination of the weighted signals, the values  
Figure 2: Schematic of the proposed method.  
in the obtained image pixels may differ from the values in the initial pixels. Now, signals appear that were not present in the original image. Because of this, bilinear interpolation gives a smoother image than the nearest neighbor method.  
Bicubic interpolation is a complicated version of bilinear interpolation [100]. It uses 16 nearest pixels to determine the value in the considered pixel of the obtained image. The essential difference from bilinear interpolation is the higher sharpness of the obtained image.
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 5 Android Vulnerability Detection
### Methods Based on Code Analysis  
TinyDroid and DroidMoSS [7] are based on a static malware detection technique. The input of TinyDroid is the application/.apk files, and machine learning is used for parameter learning. By using the Apktool, first.apk file is decompiled into Smali code. Smali is a higher-level explanation of the Dalvik bytecode, which may be further abstracted into instruction representation. TinyDroid finds n-grams from the instructions and uses them for classification. These n-grams are matched with benign and malicious applications' n-grams. Based on this technique, classification takes place. The authors claimed that some antivirus performances are below fifty percent, but TinyDroid's classification rate is 95.6%. The clone detection technique is another approach for Android malware. The process first uses dex2 jar for the conversion of the Dalvik VM (virtual machine) bytecode into the JVM bytecode (Java VM). In the next step, the Java bytecode was decompiled using the JD-CORE java decompiler. Decompilation yields higher-level code, which is used for clone detection. The limitation of this approach is that it can detect the malware from defined classes and similar malicious files. DroidMOSS is presented to measure the similarity between applications to find the repackaged applications. To do that, applications are considered from a third-party marketplace, their distinguishable features extracted, and application-specific fingerprints are generated. Fingerprint generation is performed by the fuzzy hashing technique. The third-party application marketplace chooses from 6296 apps in USA, 12,595 apps in China, and4015 applications in Europe considered for experiments. These applications are measured against 68,187 apps from the Android market. The author concludes that 5-10% of applications are repackaged, and 13-30% of applications are just redistributed on the Android marketplace. These repackaged applications not only harm the economic interest of a developer/nation, but repackaged applications are also more vulnerable to attacks such as backdoors and Trojans. The benefit of this approach is that it operates directly on Dalvik code without source code requirements.
# uDMA: An Efficient User-Level DMA for NVMe SSDs +
## 4 Design of uDMA  
uDMA is an efficient and dynamically adaptive user-level DMA mechanism for different I/O sizes for NVMe SSDs. It can lighten the I/O software stack by amortizing per-request latency. Different from the latest user-level DMA, uDMA integrates three different pinned memory management strategies. For small data requests, it provides a statically pinned memory management method. It builds a pinned memory pool for medium data requests to alleviate frequently allocating and pinning memory pages. It adopts the strategy of dynamic allocating and pinning memory pages for big data requests.
# Enhancing the Photocatalytic Activity of Immobilized TiO2 Using Laser-Micropatterned Surfaces
## 2 Materials and Methods
### Characterization of Photocatalytic Surfaces  
The structural, morphological, and optical properties of the photocatalytic materials underwent comprehensive characterization. X-ray diffraction (XRD) analysis was conducted using a Rigaku SmartLab instrument, employing Cu-K\({}_{\alpha 1}\) (\(\lambda\) = 1.540562 A) and Cu-K\({}_{\alpha 2}\) radiation (\(\lambda\) = 1.54439 A) at an intensity ratio of 2:1. Measurements were taken in the 2\(\theta\) range of 20\({}^{\circ}\) to 80\({}^{\circ}\) with a step size of 0.03\({}^{\circ}\) and an exposure time of 3 s. Raman spectra were acquired using a Renishaw inVia Raman Microscope, employing backscattering geometry and a 2400 lines mm\({}^{-1}\) diffraction grating. The experimental setup includes an argon-ion laser (514.5 nm) for excitation. All measurements were conducted with a \(\times\)50 magnification lens at RT, achieving a spectral resolution of 2 cm\({}^{-1}\). For the determination of chemical composition and morphology, a Schottky field-emission ultra-high resolution scanning electron microscope (FE-SEM, JEOL F7610F, Tokyo, Japan) equipped with an energy-dispersive X-ray spectrometer (EDX), was utilized. The thickness of the TiO\({}_{2}\) films deposited on flat silicon substrates was measured using a surface profilometer (Alpha Step IQ, KLA-Tencor, Mlipitas, CA, USA). Furthermore, optical transmittance spectra were obtained on TiO\({}_{2}\) films deposited on glass substrates at RT, across the spectral range of 300-800 nm, using a Perkin Elmer UV-vis-NIR Lambda 19 spectrophotometer with a scanning speed of 240 nm min\({}^{-1}\).
###### Abstract
They suggest a more transparent iterative process to perform the element-state determination at each step of the imposed displacements. According to this formulation, the element's equilibrium is always satisfied (based on computational limits), and the model can converge after a few iterations, satisfying the constitutive laws of the fibers of the materials. It does not require a large number of control sections along the length of the member. It also proves to be relatively stable compared to previous models mentioned above, especially for RC sections subjected to very high inelastic stress-strain conditions. [PERSON] and [PERSON] [9] extended the model of [PERSON] et al. [5] to take into account the bond-slip phenomenon. [PERSON] and [PERSON] [10] proposed a model based on the stiffness-method formulation, which uses a triaxial constitutive law for concrete. [PERSON] and [PERSON] [11] developed another formulation of the fiber model that takes into account shear and torsion phenomena. In this formulation, the displacement field is extended by adding a torsional field. At the same time, the fiber state is calculated in the three-dimensional space using a three-dimensional constitutive law for concrete. Finally, [PERSON] et al. [12] presented a fiber model that takes into account the buckling of reinforcing bars for the circular cross-sections of bridge pillars. Coming to the proposed model [13], it belongs to the general category of the distributed inelasticity models for the inelastic response of slender RC-beam elements subjected to cyclic bending with axial force. The main target of the model is its applicability for the inelastic response of RC beams and columns, regardless of their cross-sectional shape, the position of longitudinal reinforcement bars, the possible presence of holes or exterior concrete-strengthening jacket, etc. Special computational care is given for the accurate description of the 3D shape of the concrete compressive-stress shell that, in general, acts in a random cross-section shape under a specific deformation profile. The detailed and accurate description of these stresses leads to the best approximation of the experimental results. On the other hand, it proves to contribute significantly to the numerical stability of the model, as will be described in the following. A model is considered stable if convergence is achieved in each loading step, meaning that the applied forces are balanced by the internal resisting forces. Many models suffer from numerical instabilities as described in Section 6.1.4, thus reducing their practical applicability. For the present model, the limitations mentioned in the abstract are applied, while the influence of the speed of deformation (strain rate effect) during dynamic loading of shock type is not taken into consideration. The concepts described in the following sections are as follows: the concrete and steel constitutive laws used in the model, the general FEM-formulation that is adopted, the cross-sectional-discretization process, and the overall numerical algorithm of the model along with its significant computational characteristics.
## 4 Conclusions  
This study investigates the effects of partition coatings and pin configurations on flow uniformity in a three-dimensional single-channel GPF model. Key findings are summarized as follows:  
A porosity of 0.497 in the high coated zone (second part) of the porous medium achieves superior flow uniformity under average partitioning conditions. Adjusting the length ratio (X) between the first and second parts to 0.4 and 0.6, respectively, while maintaining constant porosity in the shorter part, significantly improves flow distribution. Optimal variance is achieved at X = 0.6-0.7, resulting in a 65% improvement uniformity.  
Symmetrically and alternately arranged pins on the upper and lower sides of the inlet channel enhance flow homogeneity. Increasing the number of pins reduces velocity variance, with symmetrically arranged pins showing gradual variance reduction and alternately arranged pins demonstrating continuous improvement. Varying pin heights from 0.1 to 0.4 mm while maintaining four groups consistently decreases variance, further enhancing flow uniformity. Permeation velocity near the inlet surface increases with the number of pins, attributed to pressure and eddy effects within the pin regions.  
Combining partition-coated zones with pin configurations improves permeation velocity in the first 80% of the porous medium. The integrated approach enhances flow uniformity by approximately 75%, demonstrating the synergistic effect of these two methods. These results provide valuable insights for optimizing porous medium designs to achieve enhanced flow uniformity in GPF applications.  
Y.H.: formal analysis, software, methodology, investigation, writing--original draft. F.Z.: conceptualization, writing--review and editing, funding acquisition. Y.P.: funding acquisition, conceptualization, writing--review and editing, project administration. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific and Technological Project of Yunnan Precious Metals Laboratory (No.:YPML-2023050263).  
Not applicable.  
The raw data presented in this study are included in the article. Further inquiries can be directed at the corresponding author.  
The authors would like to express their gratitude to [PERSON], [PERSON], [PERSON], and [PERSON] for their valuable contributions to the investigation, resources, methodology, and supervision of this work.  
The authors declare no conflict of interest.