esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Reference Data  
To produce accurate crop maps and ensure the reliability of the results, declarations from farmers obtained from the Sistema Informativo Agricolo della Regione Lombardia (SIARL), referred to 2016, and provinces of Milan and Pavia have been requested [31; 32]. This dataset consists of the location of agricultural parcels with information regarding the crop cultivated in each parcel. This information is provided by farmers and is mandatory for CAP funding applications. It contains information about the crops cultivated in each parcel, the total area of the parcel, and the area of the parcel dedicated to each crop. The data have been divided into two sets to obtain training and validation datasets.  
A land use map has been obtained from the project \"Destinazione d'Uso dei Suoli Agricoli e Forestali\" (DUSAF) of the Lombardy region from 2015 [34]. From the Copernicus Land Cover Monitoring Service (CLCMS), a series of products have been freely downloaded: Imperviousness map (2018), Riparian zones product (2013), and EU-Hydro database (2013) [35]. These products have been used to derive a mask from excluding non-agricultural areas, such as urban areas, forests, riparian zones, and water bodies.  
Figure 1: The study area is located in the Lombardy Region (Italy) within the Po plain. The red dot represents the location of the Area of Interest.  
Figure 2: Phenological calendar for the main crops in the study area.
## 4 Results
### Supervised ML  
Table 6 presents the results of SML approaches for classifying topic relevance using features based on keyword frequency and first position of occurrence. The best-performing model for each technology domain is based on the highest AUC score. The parameter \"No. AUC90\" indicates the number of models achieving an AUC of at least 0.90, reflecting the robustness of performance across models. The parameter \"C(ANN/Best)\" represents the computational time ratio between D-ANN and the best-performing classical SML model or the next best model if D-ANN was the best.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **SSB** & **EVC** & **CV** & **eVTOL** & **LiDAR** \\ \hline C0 & 184 & 608 & 276 & 645 & 408 \\ C1 & 97 & 576 & 324 & 768 & 328 \\ R1C0 & 175 & 131 & 38 & 16 & 15 \\ R1C1 & 72 & 259 & 182 & 142 & 176 \\ CI & 0.41 & 0.51 & 0.21 & 0.11 & 0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification based on keyword frequency and first position features.  
The results highlight several key findings, as follows:  
* Classical SML models, such as NB, GB, and XGB, yielded comparable performance to the D-ANN in terms of AUC, CA, and F1 scores. For instance, GB was the best model for EVC and eVTOL, achieving an average AUC of 0.95 across all technology domains;
* D-ANN achieved the highest performance for CV (AUC = 0.994) but required significantly more computational resources, with a C(ANN/Best) ratio averaging 24.8 across the technology domains. In some cases, such as SSB, the D-ANN required 53.5 times more computational time than the best-performing model (NB), despite similar classification performance;
* The SML approach outperformed UML methods (topic modeling and t-SNE clustering) in all metrics, demonstrating superior ability to classify topic relevance in patent documents covering diverse technical domains;
* The number of models achieving AUC \(\geq\) 0.90 (\"No. AUC90\") was consistently high, particularly for CV (10) and eVTOL (9), indicating that SML methods are robust across those feature sets and technical domains.  
While D-ANNs achieved slightly higher classification accuracy in some cases, the C(ANN/Best) ratio illustrates that their computational cost is substantially higher compared to classical supervised models. This trade-off suggests that classical models remain the more practical choice when computational efficiency is a key consideration. Furthermore, the marked improvement in SML methods over UML approaches highlights their effectiveness in relevance classification tasks across a variety of technical domains.
## 6 Transport Layer
### Summary  
The transport layer manages end-to-end communication, which should take into account underlying medium conditions in the context of _IoT_. A number of adaptive transport layer protocols have been reviewed in this section. By incorporating spectrum knowledge from PHY/MAC layer, the data flow can be more accurately regulated and congestions can be better curbed. However, not a great many studies have been dedicated to this area and a considerable number of challenges are yet to be addressed. First, it is pointed out in [43] that a reliable control channel to disseminate spectrum-related parameters has a vital effect and calls for investigation. Also, the issue of security vulnerability at the transport layer is raised in [119]. For instance, malicious parties could pose themselves as sources of monitored spectral events by generating signals bearing certain features. Unguarded nodes would accordingly vacate the channel and look for a new frequency, during which the transmission is interrupted and the loss of ACK packets reduces the congestion window. Persistent attacks on multiple channels sometimes can even cut off the connection permanently [119]. Furthermore, existing literature has largely focused on TCP. Though TCP is arguably most commonly used, other transport protocols such as UDP and ATP [120] are potential candidates for low-power wireless networks and yet remain undiscovered.
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 8 Lipase-Catalyzed Synthesis of Sustainable Polyesters and Polyamides from Biobased Monomers
### Biobased Polyamides  
At present, studies related to the enzymatic synthesis of biobased synthetic polyamides are scarcer. A few potentially biobased aliphatic polyamides, such as nylon 4,10, nylon 6,10, and nylon 8,10, can be synthesized via lipase-catalyzed polymerization. However, the molecular weights of the obtained polyamides were quite low.  
[PERSON] et al. [197] studied the N435-catalyzed polycondensation of diethyl sebacate and 1,8-octanedamine (Scheme 51). The enzymatic polymerization gave nylon 8,10 with \(\overline{M_{\mathrm{n}}}\)'s of around 2000-5000 g/mol.  
In our group, oligomers including nylon 4,10, nylon 6,10, and nylon 8,10 were produced via the lipase-catalyzed polymerization of diethyl sebacate with different diamines, with a \(\overline{DP_{\mathrm{max}}}\) of up to 16 (Scheme 52) [235].
## 1 Introduction
Owing to these particular advantages of multispectral imaging over HSI, a range of studies utilized multispectral imaging for quality prediction of meat where the data of meat samples were first collected using HSI. The optimal wavebands were selected by applying a variety of chemometric techniques of variable selection, and some of which are documented in [22]. However, despite being simple and effective, no study published concerning on the development of multispectral imaging system using the selected bands for meat quality analysis, and only applied in terms of data analysis by developing multispectral models to reduce the (HSI) data dimension and redundancy. Although the aforementioned spectroscopic devices (specifically, HSI technique) have great potential for product-quality management in large-scale industries, their price makes them inaccessible to small-scale industries. Small-scale or medium-sized meat-processing industries often cannot afford the expensive instruments and quality-control staff; therefore, they cannot ensure the quality of their product. Hence, they tend to seek devices that are economically reasonable and easily applicable to their industry. Thus, to fulfill the small-scale industry requirements, it is important to develop low-cost, yet accurate devices. The objectives of this work are as follows: (a) Develop low-cost and non-destructive sensing approach for monitoring meat-sample quality; (b) Investigate the feasibility of the developed technique for predicting important chemical elements, e.g., moisture, fat, and protein, in the meat; (c) Develop a multivariate analysis model to analyze the chemical components of the meat.
## 6 Simulation Results
### Communication Overhead
#### 6.2.1 Communication Overhead in Aggregator  
Compared to the hop-by-hop encryption used in DAA and the privacy homomorphic encryption in SDA-PH, our scheme uses the FHE scheme to encrypt sensor data and achieve data confidentiality. Figure 5 illustrates the performance of these three protocols under different numbers of neighboring nodes of per aggregator, including transmission delay, delivery radios and throughput. The comparison result for average delays of the aggregator is SDA-PH \(<\) FESA \(<\) DAA. Since FESA increases part of the communication overhead using FHE, the average delivery ratios and the average throughput of the aggregator in FESA are better than the other two schemes, especially when there are more than six neighboring nodes for one aggregator.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position** & **Aggregators** & **Monitoring Nodes** & **Forwarding Nodes** & **Neighboring Nodes** \\ \hline DAA & 4 & \(2T\) & \(T\) & \(T\) \\ FESA & 2 & \(T\) & \(T\) & \(T\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison for computational overhead of MACs.  
Figure 5: _Cont._
## 3 Results and Discussion
### Fatigue Lives Predictions
Therefore, based on the reported imperfections data [27]: the number of undercuts was 38 in 100 mm and the undercut average length size was 1.11 mm; four undercuts were located evenly spaced across the width (12.5 mm) of the specimen, this allowed the estimation of the 2c\({}_{\text{f}}\) value as 12.5/4 mm and assuming the same a/c relationship as in Table 5, the a\({}_{\text{f}}\) = 0.96 value was obtained. Due to the fact that fatigue lives are calculated by means of the parameters A and n of the da/dN-\(\Delta\)K curves and initial crack sizes a\({}_{\text{i}}\) in Tables 5 and 6 or, with a\({}_{\text{i}}\) = 0.1 mm (for all specimens) according to the recommendation in [8] or also used in [13], this generally underestimated the fatigue lives of the specimens. The initial crack size was slightly decreased to 0.07 mm (for all cases) and the parameters A\({}^{\star}\), n\({}^{\star}\) were determined only with the first fifteen points using the same data of da/dN-AK curves. The previous adjustments are in line with the practical procedure that establishes the initial crack size, adapted from the S-N data, for its application with the da/dN-\(\Delta\)K curve, as indicated in [26], where it is also pointed out that the initial crack size, rather than a physical size, is a model parameter and this could lead to limits of transferability to other component geometries. The adjustments can be justified due to the small sizes of the fatigue starts and growing cracks, and the thin thickness of the plate in the present work. Due to the sensitivity of the initial crack value a\({}_{\text{i}}\), for a better estimation of the fatigue life to final crack size a\({}_{\text{f}}\), ten steps were used in the integration between a\({}_{\text{i}}\) and a\({}_{\text{f}}\), where the first steps were of less crack increase than the last steps and the effect of the variation of crack aspect ratios a/c and a/t was also considered, assuming a linear relationship between these two. The previous assumption considers roughly what was reported in [41] for the growth of cracks with a large initial aspect ratio a/c as was the case of the crack aspect ratios found in the present work. The Y values and Mk factors displayed in Tables 5 and 6 correspond to the average of the first five steps of the integration and were calculated with \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Series-Crack Type**} & \multicolumn{2}{c}{**Crack Sizes**} & \multicolumn{4}{c}{**Fatigue Life at 600 MPa**} \\ \cline{2-9}  & **a\({}_{\text{f}}\) c\({}_{\text{i}}\)** (mm)** & **a\({}_{\text{f}}\) c\({}_{\text{f}}\) (mm)** & **M\({}_{\text{k}}\) Factor** & **Y Value** & **Experimental** & **Predicted** & **Ratio** \\ \hline S1-semi-elliptical crack & 0.04, 0.56 & 0.96, 1.56 & 1.12 & 1.10 & 29,264 & 23,239 & 1.26 \\ S2-extended crack & 0.02, 12.5 & 1.5, 12.5 & 1.06 & 1.15 & 26,225 & 21,487 & 1.22 \\ S3-corner crack & 0.15, 1.58 & 3.00, 4.81 & 1.00 & 1.21 & 33,762 & 29,904 & 1.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Data and parameters for the predictions of the fatigue life.
## 6 Proposed System Model and Machine Learning Prediction Models
### Machine Learning Classifiers  
We are going to build our model using supervised ML techniques, as the dataset is labeled data. The model predicts whether the ongoing call failed or succeeded (only two values), which is considered as a binary classification. Therefore, we choose four different classification techniques that fit the binary classification: the decision tree, naive Bayes, KNN and SVM.  
1. **Decision Tree**: The decision tree classifier is considered as a simple classifier and requires a few parameters. Moreover, the famous advantage of decision tree over other classifiers is its high ability to perform causality assessment (understanding the relation between feature and result). This is on the contrary to black-box modeling techniques, where their internal logic can be challenging to understand.
2. **Naive Bayes**: The naive Bayes classifier is based on Bayes theorem, which measures the probability of an event occurring given another event. \[P(c|x)=\frac{P(x|c)P(c)}{P(x)}\] (1) where \(x\) is the feature vector; \(c\) is the classification variable. It is easy and fast to predict the class of test data set. When the assumption of independence holds, a naive Bayes classifier performs better than other models, such as logistic regression, and it also needs less training data. It performs well in categorical input variables compared to a numerical variable(s), and the data present in CDRs are a categorical data type.
3. **K-Nearest Neighbor**: We apply KNN classifier since the dataset is sufficiently large and the number of observations is very high compared to the number of features;thus, we went for a low bias and high variance algorithm. Moreover, KNN represents a good option in terms of computational time.
4. **SVM**: We apply a SVM classifier since they are suitable for binary classification tasks, which is related to and contains elements of non-parametric applied statistics, neural networks and machine learning. It is more efficient in spaces which are high dimensional. It makes adequate use of memory.
# Review on the Recent Numerical Studies of Liquid Atomization
## 2 General Overview of Liquid Atomization
### Non-Dimensional Groups  
The breakup patterns in a spray can be highly complicated due to multiple entities, re-entrant, and other topologically complex interfaces, plus multiple spatial and time scales. In addition, multiple physical properties and fluid properties influence liquid atomization, which makes mathematical and numerical description very challenging, especially in the early days of this field. Fortunately, predecessors described their findings in terms of a number of non-dimensional groups. They are still in use today, and most authors make use of one or more of those listed in Table 1. The logic behind their choice is as follows.  
Figure 3: Different secondary breakup regimes. Reprinted from [PERSON] et al. [34].  
No matter in primary or secondary atomization, the aerodynamic forces act to deform a liquid surface, causing it to become unstable or fragment. This deformation is resisted by the surface tension force, which tends to restore the surface to be flattened and smooth. As a result, the Weber number, We, defined as the ratio of the disrupting aerodynamic forces to the restorative surface tension force, is the most important parameter when describing atomization. The We can be defined as the gas We using gas properties, liquid We using liquid properties, or relative We using the relative velocity between phases. A larger We indicates a higher tendency toward fragmentation.  
Viscosity hinders deformation and also dissipates kinetic energy supplied by aerodynamic forces. Both factors reduce the likelihood of fragmentation. To account for this, many authors make use of the Ohnesorge number, Oh, which represents the ratio of viscous force to surface tension force. A higher Oh indicates a lower tendency toward fragmentation. The Ohnesorge number is usually defined using the properties of liquid phase.  
Other important dimensionless groups are the density ratio of liquid to gas \(\varepsilon\), the viscosity ratio of liquid to gas N and the Reynolds number, which is not independent because it can be derived from We and Oh. The momentum flux ratio of the liquid to gas q is an indicator of the jet trajectory and how far the liquid jet can penetrate into the atmosphere.  
These dimensionless groups are not only related to the atomization regimes but also influence the choice of numerical methods. Their definitions are summarized in Table 1.
# Student Dropout Prediction for University with High Precision and Recall
## 3 Data Characteristics
### Primitive Statistics on Data
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dropout Reason** & **Count** & **Ratio** \\ \hline Admitted to Other University & 158 & 54\% \\ Personal Issues (Health, Family Affairs) & 57 & 20\% \\ Voluntary Dropout & 39 & 13\% \\ Did Not Register & 22 & 8\% \\ Misc. & 16 & 5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Freshmen’s Reasons for Dropping Out of the University (1 March 2020). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Academical** & **Total No. of** & **Female/Male** & **No. of** & **Female/Male** & **Dropout** \\
**Year** & **Students** & **Ratio of** & **Dropouts** & **Ratio of** & **Ratio** \\ \hline Freshman & 3481 & \begin{tabular}{c} 85.4\% \\ (F:1604/M:1877) \\ 76.5\% \\ (F:1532/M:2001) \\ 81.6\% \\ (F:1612/M:1975) \\ 81.7\% \\ \end{tabular} & 252 & \begin{tabular}{c} 68\% \\ (F:102/M:150) \\ 29.7\% \\ (F:55/M:185) \\ 40.8\% \\ (F:47/M:115) \\ 29.4\% \\ (F:20/M:68) \\ \end{tabular} & 7.2\% \\ \end{tabular} \\ Sophomore & 3533 & \begin{tabular}{c} 76.5\% \\ (F:1532/M:2001) \\ 81.6\% \\ (F:1612/M:1975) \\ 88 \\ \end{tabular} & 240 & \begin{tabular}{c} 29.7\% \\ (F:55/M:185) \\ 40.8\% \\ (F:47/M:115) \\ 29.4\% \\ (F:20/M:68) \\ \end{tabular} & 6.7\% \\ \end{tabular} \\ Junior & 3587 & \begin{tabular}{c} 81.6\% \\ (F:47/M:115) \\ 88 \\ \end{tabular} & 162 & \
\begin{tabular}{c} 40.8\% \\ (F:47/M:615) \\ 29.4\% \\ (F:20/M:68) \\ \end{tabular} & 4.5\% \\ Senior & 4170 &
\begin{tabular}{c} 29.4\% \\ (F:1876/M:2294) \\ \end{tabular} & 2.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Total Number of Students in Each Academical Year and The Number of Students Dropped Out in 1 March 2020. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Academical** & **Dropouts** & **Same Province** & **Other Province** & **Ratio** \\ \hline Freshman & 252 & 39 & 213 & 18\% \\ Sophomore & 240 & 35 & 205 & 17\% \\ Junior & 162 & 43 & 119 & 36\% \\ Senior & 88 & 36 & 52 & 69\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rate of Dropout with Respect to Born Region in 1 March 2020.
# Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology
## 3 Microgrid Modeling and Design
### PV Array  
PV Array is typically modeled based on a combination of a number of solar cells connected in series to form a string (\(N_{S}\)), in addition to a number of strings connected in parallel (\(N_{P}\)) [50]. The building block of the array is the single-diode equivalent circuit of solar cells. The performance of this circuit mainly depends on a five-parameter model including diode ideality factor (\(n\)), light-induced current (\(I_{L}\)), diode reverse saturation current (\(I_{o}\)), series resistance (\(R_{s}\)), and shunt resistance (\(R_{sh}\)). Therefore, the irradiance- and temperature-dependent current-voltage (\(I\)-\(V\)) characteristics of a solar cell can be computed using the equation [51]:  
\[I=I_{L}-I_{o}\left[\exp\left(q\frac{V+IR_{s}}{nKT}\right)-1\right]-\frac{V+IR_{ s}}{R_{sh}} \tag{13}\]  
where \(K\) denotes the Boltzmann constant and \(T\) is the cell temperature in Kelvin. The PV array current (\(I_{PV}\)) and voltage (\(V_{PV}\)) with \(N_{S}\) series cells/string and \(N_{P}\) parallel strings are calculated by [51]:  
\[I_{PV}=N_{P}I_{L}-N_{P}I_{o}\left[\exp\left(q\frac{V_{PV}+I_{PV}\frac{N_{S}}{N _{P}}R_{s}}{nN_{s}KT}\right)-1\right]-\frac{V_{PV}+I_{PV}\frac{N_{S}}{N_{P}}R _{s}}{\frac{N_{S}}{N_{P}}R_{sh}} \tag{14}\]  
In this regard, the input model parameters for a 100-kW PV array were set based on a 315 W module (Sun Power; SPR-315E-WHT-D). The module specifications, evaluated at the stranded test conditions (STC), are listed in Table 1. The number of PV modules used to implement this array was set to 320 modules, each module was composed of 96 cells. The modules were arranged in 64 strings; each string consisted of 5 modules. Therefore, in Equation (14), \(N_{S}\) is set to be 480 cells/string (96 cells \(\times\) 5 modules) and \(N_{P}\) is 64 strings. The \(I\)-\(V\) and power-voltage (\(P\)-\(V\)) characteristics of one module and the whole array, are plotted as shown in Figure 5.  
Figure 4: Microgrid Architecture.  
Weather fluctuations have a great impact on the PV panels' yield. Referring to the power yield measured at the STC, the PV maximum output power (\(P_{PVm}\)) is calculated using [52]:  
\[P_{PVm}=\frac{G}{1000}P_{ref}(1+\lambda(T_{C}-25)) \tag{15}\]  
where \(G\) represents incoming solar irradiance in W/m\({}^{2}\), \(P_{ref}\) represents the maximum output power measured by the module manufacture at the STC, \(\lambda\) is a correction factor, and \(T_{C}\) represents the temperature in degrees Celsius.
## 3 Results and Discussion
### Thermal Activation Process Test Results
#### 3.3.2 Effect of High Temperature Calcination on Activity
However, as the calcination temperature continued to rise, the thermal energy obtained by amorphous SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) adjusted or eliminated the lattice defects, and the activity decreased sharply [66; 67]. Some of the oxides in the specimen after water quenching were dissolved in water, thereby reducing the oxide content. By determining the Ka activity rate [51] as 5.7%, it was shown that the Ka values after high-temperature calcination were between 5.1% and 6.3%, which indicated low activity. The contents of soluble SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) decreased with increasing the calcination temperature, mainly because a portion of SiO\({}_{2}\) was involved in chemical transformations. In summary, the low-temperature heat treatment from 300 \({}^{\circ}\)C to 700 \({}^{\circ}\)C effectively promoted the opening of microscopic pores of volcanic rock powders and induced partial dehydroxylation reactions of internal minerals [68; 69]. The structure of amorphous or metastable mineral phases underwent rearrangement or transformation, producing new microcrystalline minerals with higher surface energy and reactivity. However, the maximum value of the 28-day activity index was only 64% for heat treatment temperatures between 800 \({}^{\circ}\)C and 1400 \({}^{\circ}\)C. In this high temperature interval, the mineral phase underwent melting and recrystallisation reactions, with the transformation of crystalline minerals to amorphous glassy phases, which was usually accompanied by a remodeling of the pore structure to produce a more stable crystalline structure, resulting in a decrease in activity. Although the heat treatment temperature had a significant effect on the structure and properties of volcanic rock powders, factors such as the initial composition of the volcanic rock powders, the particle size, and the time of heat treatment were equally important.