esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Data Analysis  
Before constructing a predictive model, it is crucial to conduct a comprehensive pre-processing of the original data set. Raw data sets often contain incomplete or noisy information, and the inaccuracy and instability of these data can have a significant negative impact on the training effectiveness of the model [47; 48; 49]. Therefore, to ensure that the model can learn and predict accurately, the data must be carefully cleaned and prepared before the model-building phase [50; 51; 52].  
To eliminate these potential disadvantages, data cleaning is particularly important. Data cleansing aims to find and correct false data and noisy information in the data that is not relevant to the model's prediction goals, thus ensuring the purity of the data set. This process is essential to reduce the negative impact of data quality issues on model training results [53; 54].  
In this study in particular, we focus mainly on the relevant performance data of the applicants. Such data collection is prone to errors or data gaps, which, if not properly addressed, can greatly hinder efforts to build a model that accurately predicts student recruitment. Therefore, in the data pre-processing stage, special attention should be paid to the calibration and correction of student performance data to ensure that each datum can accurately reflect the actual academic level of students [55; 56].
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 2 Experiment Program  
The experimental procedures commenced with a sieve analysis and an assessment of the physical characteristics of clay soil, gypsum, and glass powder. Examining the physical characteristics involved forming composite samples by incorporating gypsum at varying proportions (0%, 5%, 10%, and 15%) and glass powder at different ratios (0%, 4%, 8%, and 12%). These percentages were determined concerning the dry mass of the soil.  
The compaction process for the samples entailed dry densities of 1500 and 1700 kg/m\({}^{3}\), with 1700 kg/m\({}^{3}\) representing the maximum dry density. The two intended dry densities were adopted to evaluate the effect of compaction on different blends. The samples were cured for 7, 28, and 56 days. In order to determine the specimens' strength, initial shear modulus (\(G_{0}\)), durability, uniaxial compression strength (UCS), ultrasonic pulse velocity (UPV), and wetting-drying cycle, durability tests were carried out. X-ray diffraction (XRD) analyses and scanning electron microscopy (SEM) were also performed to determine the microstructural transformations. Table 1 provides a comprehensive overview of the physical characteristics of clay soil, gypsum, and glass powder (GP) as essential additives in the experimental program.
# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 1 Introduction
Comparing the reverberation time obtained by the neural network, Sabine formula, and ODEON 2.6D has allowed researchers to evaluate the prediction ability of the trained model. [PERSON] [21] studied the acoustic indicators of a single space and constructed a ML predictive model based on different indoor characteristics (furniture size, placement, etc.). The purpose of this study is to use ML to propose a predictive model of acoustic indicators as a simple evaluation tool for the architectural design and interior decoration of small and medium-sized activity centers. First, we confirmed the compatibility of the field measurements and simulations, then used the parametric design to generate samples as the object of analysis, and finally obtained accurate solutions of the acoustic indicators using the acoustic simulation software Odeon. After the data was generated, ML was adopted, and the parameters of the space, such as the basic geometric information, material properties, and placement positions, were used to obtain a predictive model. The resulting model provided a compromise method regarding acoustic performance evaluation, which had a certain degree of accuracy and was quick and simple for practical use. The workflow is shown in Figure 1.
## 3 Results and Discussion
### UV-Vis Absorption Spectrum Analysis  
UV spectroscopy is an absorption spectroscopy method that absorbs light in the ultraviolet region via the molecule, which causes electrons to excite from the ground energy state to a high energy state. Besides, spectroscopy is the process of analyzing electromagnetic radiation received or released when the samples' molecules move from one energy level to another energy level. The significant aspect of ionanofluids is to store or transfer the ample solar energy into practical energy that allows energy to be stored during sunlight and used at night. This form of energy is known as adsorptive energy that can be calculated in various wavelengths using UV-Vis spectrometry. The studied samples containing different weight percentages of MXene/DG nanofluid incorporated with [Emim][OSO\({}_{4}\)] ionic liquid were subjected to UV-Vis Spectrometry using Perkin Elmer Lambda 750 equipment.
## 3 Results
### The Canopy Spectral Characteristics between Different Experimental Treatments  
The canopy spectral curves of rice at different days after heading are shown in Figure 4. There was low reflectance in the visible region (350-760 nm), and its maximum reflectance appeared at near 550 nm (the green peak). In the near-infrared region (800-1350 nm), the reflectance was high, and there were a plateau region (800-900 nm) and two absorption bands at the spectral curve. It can be inferred from Figure 4 that the reflectances of T1 and T2 in the visible region were lower than that of CK, but the reflectances of T1 and T2 in the near-infrared region were higher than that of CK. The reflectances of T1 and T2 had a significant difference, and compared with T1, the reflectance of T2 decreased in the visible region and increased in the near-infrared region, which could be because of the higher diffuse radiation fraction in T2. Moreover, the largest difference of spectral curves between T2 and T1 appeared at DAH 14d, when the green peak reflectance of T2 decreased by 16.12% (2016), 21.34% (2017), and the average reflectance of T2 at plateau region (800-900 nm) increased by 10% (2016), 11% (2017). The analysis results suggest that an increase in diffuse radiation fraction led to a decrease in reflectance in the visible region and an increase in reflectance in the near-infrared region.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Years**} & **Days after** & \multirow{2}{*}{**Treatments**} & \multirow{2}{*}{**LAI**} & \multirow{2}{*}{**Years**} & **Days after** & \multirow{2}{*}{**Treatments**} & \multirow{2}{*}{**LAI**} \\  & & & & & & & \\  & **Heading** & & & & & & \\ \hline \multirow{8}{*}{2016} & \multirow{2}{*}{DAH 14d} & CK & \(5.02\pm 0.11\) a & \multirow{8}{*}{DAH 14d} & CK & \(5.27\pm 0.09\) a \\  & & T1 & \(5.25\pm 0.10\) b & & & & T1 & \(5.61\pm 0.09\) b \\  & & T2 & \(5.65\pm 0.12\) c & & & & T2 & \(5.89\pm 0.08\) b \\  & & CK & \(4.85\pm 0.16\) a & & & & CK & \(5.09\pm 0.07\) a \\  & & T1 & \(5.01\pm 0.08\) a & \multirow{2}{*}{2017} & \multirow{2}{*}{DAH 21d} & T1 & \(5.31\pm 0.02\) ab \\  & & T2 & \(5.38\pm 0.06\) b & & & & T2 & \(5.54\pm 0.03\) b \\  & & CK & \(4.66\pm 0.06\) a & & & & CK & \(4.93\pm 0.08\) a \\  & & T1 & \(4.84\pm 0.04\) b & & & & T1 & \(5.08\pm 0.03\) b \\  & & T2 & \(5.23\pm 0.08\) c & & & & T2 & \(5.21\pm 0.07\) c \\ \hline \hline \end{tabular}
\end{table}
Table 4: The LAI values of rice under different treatments at different periods after heading, in 2016–2017.  
Figure 4: Canopy spectra curves of rice between different treatments: (**a**–**c**) are DAH 14d, DAH 21d, and DAH 28d in 2016; (**d**–**f**) are DAH 14d, DAH 21d, and DAH 28d in 2017.
# Comparative Analysis of Measurement Tools in the Cognex D900 Vision System
## 2 Background and Literature Review
### Vision Systems: Definition, History, and Evolution  
A vision system, also known as computer vision, is a field of artificial intelligence in which programs aim to identify objects depicted in digital images provided by cameras. This enables computers to \"see\". These systems can analyze large sets of visual data, detecting features and patterns that can then be applied to other images. This field relies on the use of deep learning and neural networks for processing visual information, which forms the basis for processes such as facial recognition and augmented reality [5].  
In 1963, [PERSON], considered the father of computer vision, described the process of deriving 3D information from 2D photographs. In 1966, [PERSON] tasked a student with connecting a camera to a computer so it could describe what it saw. In 1969, [PERSON] and [PERSON] demonstrated how charge could be moved across a semiconductor surface, leading to the invention of the Charge-Coupled Device (CCD) concept. In 1971, [PERSON] demonstrated how a CCD could be used as an imaging device. Three years later, [PERSON], working for Kodak, showed how color images could be captured using a CCD. In 1972, scientists at General Electric developed an X-Y array of photosensitive elements known as a charge injection device (CID), eliminating some of the drawbacks associated with CCDs. However, the success of CMOS sensors did not come until the next thirty years, partly due to the variability in the CMOS manufacturing process. In 1980, [PERSON] built the 'neocognitron', which became a precursor to modern convolutional neural networks. The first digital image scanner to convert images into grids of numbers was the device built by [PERSON] in 1974, known as the Kurzweil Reading Machine (Figure 1). This was essentially a scanner that converted written text into speech using OCR (Optical Character Recognition) to assist the visually impaired in reading printed texts [6; 7; 8; 9; 10; 11].  
In the 1990s, multiple video recording devices and video surveillance systems for ATMs emerged. In 2001, the first real-time face detection system (Viola-Jones) was developed at the MIT. In 2009, Google began testing robot cars [5; 12].  
The development of vision systems spanned various decades. In the 1970s, the focus was on edge extraction from images, line labeling, 3D modeling, and representing objects as connections of smaller structures, optical flow, and motion estimation. The 1980s brought advancements in scale-space theory, shape inference from different cues such as shadow, texture, and focus, as well as contour models known as \"snakes\". The 1990s concentrated on camera calibration, 3D scene reconstruction from multiple images, the dense stereo correspondence problem, and multi-view stereo techniques, as well as image segmentation using graph cuts. Since the 2000s, computer vision has advanced in areas such as autonomous vehicles, face recognition, healthcare, and many other fields [12].
# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 2 Methodology
### Applied Algorithms for Predicting Lung Cancer
#### 2.1.2 Logistic Regression  
A technique of mathematical forecasting called logistic regression predicts the possibility that a result belongs to some category--typically denoted as \"1\" (e.g., whether a patient has a disease or not). This method is frequently applied to binary classification issues, where estimating the probability of an event occurring is the aim [33].  
1. Model Formulation: Let the binary outcome variable be \(y\), with \(y=0\) indicating no lung cancer and \(y=1\) indicating the presence of lung cancer. \[P(y=1|X)=\sigma(z)=\frac{1}{1+e^{-z}}\] (2) Here, z represents the sigmoid function that maps a linear combination of the input.
2. Model Training: The training of the logistic regression model was performed using the maximum likelihood estimation methodology. We defined the data's likelihood function \(L\) as follows: \[L(\beta)=\prod_{i=1}^{N}P(y_{i}|X_{i})=\prod_{i=1}^{N}\sigma(z_{i})^{y_{i}}(1- \sigma(z_{i}))^{1-y_{i}}\]where \(N\) denotes the number of observations, and \(z_{i}=\beta_{0}+\beta_{1}x_{i1}+\cdots+\beta_{n}x_{in}\).  
The natural logarithm of the likelihood function then yields the log-likelihood function:  
\[\ell(\beta)=\sum_{i=1}^{N}[y_{i}\log(\sigma(z_{i}))+(1-y_{i})\log(1-\sigma(z_{ i}))]\]  
To find the optimal parameters \(\beta\), we maximized the log-likelihood function, often using optimization algorithms such as gradient ascent or solvers like lbfgs in Python libraries such as scikit-learn.
## 2 Results and Discussion
### Water Activity Analysis  
Water activity \(a_{w}\) measures the degree of water that is bound to a food product and predicts the product's safety and stability with regard to microbial growth and the rates of chemical and biochemical reaction rates. The \(a_{w}\) of fresh and dried _C. alata_ leaves and the final moisture content of _C. alata_ samples dried with CD, VMD and CPD-VMFD are shown in Table 1. The \(a_{w}\) of the dried samples ranged from 0.1291 to 0.2836. The CD at 60 \({}^{\circ}\)C samples had the lowest final moisture content and significantly lower \(a_{w}\) than other dried samples, which could be attributed to the high hot-air temperature applied and the longer drying duration than VMD and CPD-VMFD, thereby ensuring low residual moisture in the sample. Increasing temperature in CD resulted in lower \(a_{w}\) and final moisture content. However, the application of different levels of microwave wattage showed no evident trends. Preferably, dried products should attain a low \(a_{w}\) ranging from 0.60 to 0.80 because food spoilage bacteria and most moulds are inhibited below the \(a_{w}\) of 0.91 and 0.80, respectively [18]. In the present study, all of the drying treatments ensured microbiologically safe products. The determination of \(a_{w}\) is also important in the prediction of biochemical reactions that may occur, knowing that Maillard reaction takes place at an optimum \(a_{w}\) of 0.65 [19].
# ALPINE: Active Link Prediction Using Network Embedding
## 4 Query Strategies for ALPINE
### Variance Reduction
#### 4.3.1 The Fisher Information for the Modified CNE  
In ALPINE, the modified CNE finds a locally optimal embedding \(\mathbf{X}^{*}\) as the Maximum Likelihood Estimator (MLE) for the given PON \(\mathcal{G}=(V,E,D)\), i.e., \(\mathbf{X}^{*}\) maximizes \(P(\mathcal{G}|\mathbf{X})\) in Equation (3) w.r.t. \(\mathbf{X}\). The variance of an MLE can be quantified using the Fisher information [35]. More precisely, the Cramer-Rao bound [36] provides a lower bound on the variance of an MLE by the inverse of the Fisher information: \(\text{Var}(\mathbf{X}^{*})\succeq\mathcal{I}(\mathbf{X}^{*})^{-1}\). Although the Fisher information can often not be computed exactly (as it requires knowledge of the data distribution), it can be effectively approximated by the _observed_ information [37]. For the modified CNE, this observed information _for the MLE \(\mathbf{x}^{*}_{i}\)_, the embedding of node \(i\), is given by (proof in Appendix A):  
\[\mathcal{I}(\mathbf{x}^{*}_{i})=\gamma^{2}\sum_{\{i,j\}\
otin U}P^{*}_{ij}(1-P^{* }_{ij})(\mathbf{x}^{*}_{i}-\mathbf{x}^{*}_{j})(\mathbf{x}^{*}_{i}-\mathbf{x}^{*}_{j})^{T}, \tag{4}\]  
where \(\gamma\) is a CNE parameter. Thus, the variance of node \(i\)'s MLE embedding \(\mathbf{x}^{*}_{i}\) is bounded: \(\text{Var}(\mathbf{x}^{*}_{i})\succeq\mathcal{I}(\mathbf{x}^{*}_{i})^{-1}\).
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 4 Privacy-Preserved Trustworthy Seamless Service Agility
### Third-Party Service Porting  
The last step in our proposed authentication mechanism is to facilitate the actual porting between the third-party eNB/gNB and the UE requesting the service.  
* In this part, the UAV sends an attachment request to the eNB/gNB guest network on behalf of the UE requesting the service that includes the encrypted UE concealed ID, \(E_{x_{i}}(CID)\).
* At the same time, the UAV announces the \(E_{x_{i}}(eNBID)\) (encrypted by its private key) to the UE requesting the service.
* Once the UE obtains the service provider's ID, the UE then sends a guest RRC connection request to the third-party eNB/gNB and waits for the eNB/gNB guest RRC response, which establishes the service connection.
## 4 Conclusions and Future Study  
The scanning strategies' effects on the D-PBSLP of SiC were studied experimentally and numerically. Four scanning strategies, which are available in the commercial Phoenix 3D printer were considered in this study. These scanning strategies are concentric in-out, hexagonal, linear, and zigzag. The following points are concluded from this study:  
1. D-PBSLP OF SiC is possible through the optimization of the process parameters.
2. The numerical model effectively predicts the process parameters even for hard to print materials such as SiC.
3. For scanning strategies' effects, the concentric in-out and hexagonal strategies are ineffective in the D-PBSLP of SiC due to the characteristic pattern on the layer's top surface. Additionally, the linear strategy gave a low relative density value compared to other scanning strategies.
4. The Zigzag strategy can be considered the best suitable strategy to be used with SiC, as it succeeds in giving a flat top surface without any characteristic pattern and a good relative density of more than 80%. Printing of SiC complex shapes using D-PBSLP  
Figure 18: Triple periodic minimum surface (Gyroid) printed using the optimized process parameters.  
was achieved using the proper process parameters obtained from the developed numerical model.  
For future study, evaluating the mechanical properties should be covered.  
Conceptualization, M.A., G.K., A.M.Z., F.M., D.G. and M.F.; investigation, M.A. and A.M.Z.; writing--original draft preparation, M.A., A.M.Z. and G.K.; writing--review and editing M.A. and G.K.; supervision, G.K., D.G., F.M. and M.F.; project administration, G.K. and D.G. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Framework Program for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie Grant Agreement No. (764935).  
Not applicable.  
The authors give the Publisher the permission to publish the work.  
The authors declare that they have no conflicts of interest/competing interests.
## Appendix A Criteria Matrices for Components Selection  
### Sensor Selection  
The factors considered for the choice of the sensor that suits the needs of the research are (which are weighted according to a scale from 0 (lowest score) to 2 (best score)):  
* Measuring range: This is the range of values (in ppm) capable of being detected by the sensor.
* 2: Sensor capable of detecting any concentration of CO\({}_{2}\), even exceeding by far the limit values established by the standards and regulations in force. Upper limit value of the measuring range greater than 5000 ppm.
* 1: Sensor capable of detecting CO\({}_{2}\) concentrations below the limit values established by the standards and regulations in force. Upper limit value of the measuring range greater than 2000 ppm and smaller than 5000 ppm.  
* 0: Sensor capable of detecting CO\({}_{2}\) concentration values of no more than 2000 ppm.
* Accuracy: This is the error of the sensor. This value is shown in the sensor datasheet, defining the accuracy with which the sensor performs the measurements. A higher precision will result in higher accuracy in the data collected.
* 2: Accuracy \(\leq\pm\)0.1 (percent)
* 1: \(\pm\)0.1 < Accuracy \(\leq\pm\)1
* 0: Accuracy \(\geq\pm\)1
* Number of parameters: Monitoring more than one parameter is not desirable. In multi-gas or multi-parameter sensors, a laboratory calibration must be performed (although self-calibration is mentioned in the datasheet, this is obtained with a regression line, which does not necessarily will coincide with measurements by conventional devices already calibrated), because of the presence of several gases or different parameters in the same enclosure may influence data acquisition and interpretation.
* 2: Sensor capable of measuring a single gas.
* 1: Sensor capable of measuring between 2 and 4 different gases.
* 0: Sensor capable of measuring more than 5 gases.
* Response speed: This is the capacity to display changes in the values of the sensor output without delays depending on the variations of the input. It is interesting that the sensitivity is high, as it will show greater flexibility to measure sudden changes in CO\({}_{2}\).
* 2: Response speed \(\leq\) 1 min.
* 1: 1 min < Response rate \(\leq\) 2 min.
* 0: Response speed > 2 min.
* Warm-up time: This is the period of time during which the sensor does not take reliable measurements and adapts to the surrounding atmosphere. This is for ensuring the sensor takes measurements in accordance with its calibration line, waiting until the start of data acquisition. The shorter the warm-up time, the greater the autonomy, reliability and accuracy of the data collected.
* 2: The warm-up time is several minutes. Maximum 1 h.
* 1: The warm-up time is between 1 h and 8 h.
* 0: The warm-up time is longer than 8 h and can last at least 1 day.
* Price: This is one of the main objectives for creating an affordable device with the highest possible number of features.
* 2: Sensor priced between EUR 0-EUR 30.
* 1: Sensor with price between EUR 30-EUR 60.
* 0: Sensor priced over EUR 60.  
The results are shown in Table 11:
# Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models
## 3 Statistical Models for Forecasting Traffic
### Preliminary Checks  
Before applying the SARIMA and Holt-Winters models, some preliminary checks should be conducted [49]. Both models require that the data series exhibit seasonality. For this purpose, the STL (seasonal and trend decomposition using Loess) decomposition [50] can be employed. The seasonal period is 24 h and is associated with user activity. The seasonal variation around each level appears to increase proportionally with the current levels. Therefore, seasonality may be multiplicative. Regarding the trend, there are no significant changes in the lines, and their slopes are close to zero. Therefore, we may consider the trend as additive.  
To verify the stationarity of the time series, we employed the ADF (Augmented Dickey-Fuller) test [51] and the KPSS (Kwiatkowski-Phillips-Schmidt-Shin) test. In both tests, the test statistic should be less than the significance level of \(\alpha=0.05\). The ADF test assesses the null hypothesis that the time series is not stationary. For both upload and download traffic, the \(p\)-values are less than 0.05, with values of 0.00063973 and 0.000000725, respectively. The null hypothesis for the KPSS test is opposite to that of the ADF test. The \(p\)-value for the KPSS test is 0.1 in both cases, which exceeds \(\alpha=0.05\). Therefore, we can conclude that the time series is stationary.