esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Results show that the organic practices in unirrigated orchards had a noticeable yet commercially modest effect on the chemical and physical-chemical parameters and the volatile compound profile. The effect was much less strong than that of the harvesting method, which affected severely the chemical and physical-chemical parameters, including the quality parameters (which are used in the official oil grading), and the volatile compounds. Conversely, the harvesting time in real conditions was revealed to be a factor with little repercussion on the oil quality parameters, which might be due to a suitable harvesting time schedule, although it had a noticeable effect on some important volatile compounds. Previous studies have not shown a consistent effect of the organic production on the olive oil characteristics, partly because of the difficulty of controlling other sources of variation. Our results, for oil obtained in a commercial mill, reveal an increase in the total phenols in the organic oil, which was previously reported at laboratory scale and attributed to the decreased availability of nitrogen due to the lack of chemical fertilisation. This result, obtained under controlled conditions (e.g., the same mill, the same area, the same harvesting time and method) could explain the increase in oil stability and the changes in the volatile compound profile. Considering that some studies involving irrigated orchards have not shown an increase in phenols, it would be advisable to perform further studies  
Figure 2: Projection of the oil samples (**a**) and variables from Tables 1 and 3–5 (**b**) onto the space defined by the first two principal components (PC1/PC2). \(\blacklozenge\): Organic; \(\square\): Conventional; \(\square\): Ground. \(\circ\): Chemical and physical-chemical parameters; \(\times\): LOX-derived volatile compounds; \(\bullet\): Fermentation compounds; \(\blacklozenge\): Oxidation compounds.  
under controlled conditions to shed light on how irrigation may affect the effect of the organic practices, and on whether or not the organic fertilisation currently used in commercially exploited orchards causes a consistent decrease in soil nitrogen availability. That information would help understand the real defect of the organic production and how to deal with it.  
Conceptualization and methodology, A.I.C., J.S.-C., L.M. and C.d.M.; formal analysis, A.I.C.; investigation, A.I.C., A.R., J.S.-C. and M.M.-C.; resources, A.I.C., J.S.-C. and L.M.; writing--original draft preparation, A.I.C. and A.R.; writing--review and editing, A.I.C., L.M., J.S.-C., M.M.-C. and C.d.M.; supervision and project administration, A.I.C., J.S.-C. and C.d.M.; funding acquisition, A.I.C., J.S.-C. and L.M. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Junta de Extremadura and FEDER, project GR18147.  
The authors declare no conflict of interest.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 3 Methods
### Methods of Parameter Estimation
#### 3.2.10 Kappa Distribution (K3-Park)  
For estimation with MOM, parameter \(\alpha\) has the following approximate logarithmic polynomial form, depending on \(\mathrm{C_{s}}>0\):  
\[\alpha=\exp\left(\begin{array}{l}1.7822619-0.437806883\cdot\ln(\mathrm{C_{s} })+0.049586114\cdot\ln(\mathrm{C_{s}})^{2}+\\ 0.019798571\cdot\ln(\mathrm{C_{s}})^{3}+0.002017836\cdot\ln(\mathrm{C_{s}})^{4 }-\\ 0.001937961\cdot\ln(\mathrm{C_{s}})^{5}-0.000620839\cdot\ln(\mathrm{C_{s}})^{6 }+\\ 0.000097814\cdot\ln(\mathrm{C_{s}})^{7}+0.000046042\cdot\ln(\mathrm{C_{s}})^{8 }\end{array}\right) \tag{113}\]  
\[\beta=\sigma\cdot\sqrt{\frac{\Gamma\Big{(}\frac{1}{\alpha}+1\Big{)}}{\Gamma \big{(}\frac{3}{\alpha}\big{)}\cdot\alpha^{\frac{2}{\alpha}-1}\cdot\Gamma \big{(}1-\frac{2}{\alpha}\big{)}-\frac{\Gamma\big{(}\frac{2}{\alpha}\big{)} \cdot\alpha^{\frac{2}{\alpha}-2}\cdot\Gamma\big{(}1-\frac{1}{\alpha}\big{)}^{ 2}}{\Gamma\big{(}\frac{1}{\alpha}+1\big{)}}}} \tag{114}\]  
\[\gamma=\mu-\frac{\beta\cdot\Gamma\big{(}\frac{2}{\alpha}\big{)}\cdot\alpha^{ \frac{1}{\alpha}}\cdot\Gamma\big{(}1-\frac{1}{\alpha}\big{)}}{\Gamma\Big{(} \frac{1}{\alpha}\Big{)}} \tag{115}\]  
For estimation with the L-moments, parameter \(\alpha\) has the following approximate forms depending on L-skewness:  
\[\alpha=\exp\left(\begin{array}{l}0.000753505-0.983447042\cdot\ln(|\tau_{3}| )+0.162877024\cdot\ln(|\tau_{3}|)^{2}+0.247492727\cdot\ln(|\tau_{3}|)^{3}+\\ 0.116264397\cdot\ln(|\tau_{3}|)^{4}+0.029183504\cdot\ln(|\tau_{3}|)^{5}+0.004031 622\cdot\ln(|\tau_{3}|)^{6}+\\ 0.000270699\cdot\ln(|\tau_{3}|)^{7}+0.000005628\cdot\ln(|\tau_{3}|)^{8}\end{array}\right) \tag{116}\]  
\[\beta=\frac{\mathrm{L}_{2}}{\alpha^{\frac{1}{\alpha}}\cdot\Gamma\Big{(}1- \frac{1}{\alpha}\Big{)}\cdot\Big{(}\frac{\Gamma\big{(}\frac{3}{\alpha}\big{)} }{\Gamma\big{(}\frac{3}{\alpha}\big{)}}-\frac{\Gamma\big{(}\frac{3}{\alpha} \big{)}}{\Gamma\big{(}\frac{1}{\alpha}\big{)}}\Big{)}} \tag{117}\]  
\[\gamma=\mathrm{L}_{1}-\beta\cdot\alpha^{\frac{1}{\alpha}}\cdot\frac{\Gamma \big{(}\frac{2}{\alpha}\big{)}\cdot\Gamma\Big{(}1-\frac{1}{\alpha}\Big{)}}{ \Gamma\Big{(}\frac{1}{\alpha}\Big{)}} \tag{118}\]
## 7 Security Analysis
### Informal Security Analysis
#### 7.1.1 Masquerade Attack  
According to Section 1.1, a malicious adversary \(U_{ma}\) can obtain SG device of legitimate user and can intercept transmitted data over insecure channel. If \(U_{ma}\) tries impersonate a legitimate user, \(U_{ma}\) must correctly generate an authentication request and response messages. However, \(U_{ma}\) cannot generate the authentication request message \(\{M_{1},M_{2},M_{3}\}\) and authentication message \(\{M_{4},M_{5},M_{6}\}\) without the correct random nonces \(R_{SD}\) and \(R_{UC}\). Furthermore, \(U_{ma}\) cannot generate a session key \(SK_{ij}=h(R_{SD}||R_{UC})\)because secret parameter \(X_{i}\) is not available to \(U_{ma}\). Therefore, the proposed scheme is secure against masquerade attack.
## 3 Methods
### Criteria for Evaluation  
An important task to be conducted after the modeling is to evaluate each model's accuracy and generalization ability. As mentioned previously, in this work, 80% of the data samples were assigned as training that was used to train the RF and improve the RF models, while the rest 20% of the data samples were assigned as testing data to verify the performance of the RF and improved RF models. To evaluate the performance of the built models, four metrics were used in this work, i.e., the coefficient of determination (R\({}^{2}\)), the root mean squared error (RMSE), the mean absolute error (MAE), and the variance account for (VAF). These evaluation indicators are defined below.  
The square of the correlation between the predicted and measured values is represented by R\({}^{2}\). The RMSE characterizes the standard deviation of the fitting error between the predicted values and the measured values. The MAE indicates the mean absolute error between the predicted values and measured values. The VAF describes the prediction performance by comparing the standard deviation of the fitting error with the standard deviation of the actual value. The following equations were utilized to calculate the R\({}^{2}\), RMSE, MAE, and VAF values:  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n}(y_{ i}-\overline{y})^{2}} \tag{35}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}} \tag{36}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y_{i}}\rvert \tag{37}\]  
\[\text{VAF}=\left(1-\frac{var(y_{i}-\hat{y_{i}})}{var(y_{i})}\right)\times 100 \tag{38}\]  
where \(y_{i}\), \(\hat{y_{i}}\), and \(\overline{y}\) denote the measured, predicted, and mean values of the PPV, respectively. When the predicted and measured PPV values are precisely the same, R\({}^{2}\) is 1, the RMSE is 0, the MAE is 0, and the VAF is 100 (%).
## 3 Mathematical Model for L-Shaped Uniform Array
^ {T}\) as the array observation of the L-shaped array at timestamp \(t\) which is a vector of the signal samples for each individual antenna in the L-shaped array, such that \(h_{i}(t)\) corresponds a single signal sample received from the antenna \(i\) at timestamp \(t\). The signal samples of an L-shaped array are composed of \(h_{i}(t)=x_{i}(t)\) such that \(i=1,\ldots,M-1\), the common antenna \(h_{M}(t)=x_{M}(t)=y_{1}(t)\) for both ULAs in addition to \(h_{M+j-1}(t)=y_{j}(t)\) such that \(j=2,\ldots,M\). The model of the signal samples received by the L-shaped array at a timestamp \(t\) can be expressed as Equation (8),  
\[\mathbf{h}(t)=\mathbf{A}_{h}\mathbf{s}(t)+\mathbf{n}_{h}(t), \tag{8}\]  
where \(\mathbf{n}_{h}(t)\in\mathbb{C}^{(2M-1)\times 1}\) is the additive white Gaussian noise (AWGN) and \(\mathbf{A}_{h}\in\mathbb{C}^{(2M-1)\times d}\) is the ideal steering matrix of the L-shaped array, respectively, shown in Equation (9),  
\[\mathbf{A}_{h}= \begin{bmatrix}\mathbf{a}(\alpha_{1},\beta_{1})&\mathbf{a}( \alpha_{2},\beta_{2})&\ldots&\mathbf{a}(\alpha_{d},\beta_{d})\end{bmatrix} , \tag{9}\]  
if each element of the ideal L-shaped array response, \(\mathbf{a}(\alpha,\beta)\in\mathbb{C}^{(2M-1)\times 1}\), is represented in Equation (10),  
\[a_{k_{x},k_{y}}=e^{j(k_{x}-1)\mu_{a}}e^{j(k_{y}-1)\mu_{\beta}}, \tag{10}\]  
where \(1\leq k_{x}\leq M\) and \(1\leq k_{y}\leq M\), hence the L-shaped array response can be expressed in a compact form in accordance with Equation (11),  
\[\mathbf{a}(\alpha,\beta)^{T}= \begin{bmatrix}a_{1,1}&a_{2,1}&\ldots&a_{M,1}&a_ {M,2}&a_{M,3}&\ldots&a_{M,M}\end{bmatrix} ^{T}. \tag{11}\]
# Performance Analyses of a RAIM Algorithm for Kalman Filter with GPS and NavIC Constellations
## 1 Introduction
However, WLS is not suitable for advanced navigation methods, such as multi-sensor integration and vector receivers, where KF is an integral part. Therefore, KF-based integrity monitors are essential. An important finding of the analyses is as follows. Innovation-based tests with an extended KF (EKF) navigation processor mistake slow ramp for the residual measurement error that the filter estimates, leading to missed detection. RAIM with SKF, on the other hand, can successfully detect such faults. It also completes processing in time on a low-end computer. Thus, it offers a promising solution to developing KF RAIM in the range domain. The current work does not consider constellation-wide faults. Measurements from different satellites at a particular time epoch are assumed to be uncorrelated with one another. The remaining paper is organized as follows. First, the KF RAIM extended in this paper is briefly outlined for ease of understanding of the subsequent modifications. Next, extensions of the algorithm are presented in detail. Following this, simulation studies are performed. Finally, the paper concludes with a summary of the key findings and future work.
# Image Classification of Pests with Residual Neural Network Based on Transfer Learning
## 4 Results and Discussion
### Discussion
#### 4.2.3 Transfer Learning  
As shown in Figure 9, the adoption of transfer learning can significantly improve the classification performance of the model. With the new learning method, the model cannot be fully trained due to insufficient number of samples, so the experiment results have the obvious problem of overfitting. Take the ninth group of experiment as the example, the training accuracy rate has reaches as high as 99.59%, while the test accuracy rate is only 53.78%. With the help of pre-trained \"knowledge\", the model adopting transfer learning significantly improves classification accuracy compared to the new learning model, and the time needed for the same accuracy is greatly reduced. When comparing the effects of freezing and fine-tuning on transfer learning, when the freezing model is adopted for training, the parameters in the feature extraction are not involved in the training, so the classification accuracy rate enters a relatively stable state when the parameters of the fully connected layer are fully trained. Due to the fewer parameters involved in the training, the model training time is greatly reduced compared to the new learning and fine-tuning methods. The fine-tuning model receives finer learning based on the original \"knowledge\", so it has a higher classification accuracy than the freezing model.  
In conclusion, since the dataset is relatively small compared to the model, the new learning model cannot fully learn the features of all samples, so the classification accuracy is lower than that of the transfer learning model. Due to the differences between the datasets, fine-tuning needs to be adopted for different datasets in the case of transfer learning.  
Figure 8: Accuracy of some species with different data enhancement. Note: A, B and C are data enhancement methods, which are the same as those in Table 2. Experimental group 1–5 represent the subsets of the IP102
## 6 Limitations and Use Cases
### Limitations and Threats to Validity  
In terms of the design of our method, the written language may affect the categorization accuracy. For example, we used MeCab [37] to separate the term units. Since MeCab cannot distinguish proper nouns, which are specific to the software development project, it may separate proper nouns into separate terms. Furthermore, our method assumes that each ticket is related to only one feature of the software system. In real developments, tickets can be related to multiple features. Thus, our assumption may cause information loss. Other unsupervised learning techniques may link one ticket to multiple features. On the other hand, because multiple links make it difficult to understand the category features, they may have the opposite effect on understanding the implemented software features.  
As a threat to the internal validity of the case study and the experiment, the ticket content and parameters (such as the weights of important and unimportant terms) affect the result of our categorization method. For example, the task description impacted the categorization accuracy in our case study. The requirement engineer of the project was concerned that our method could not identify implementation and specification omissions because the tickets are written for implemented features. In the future, we plan to enhance the ability to handle less descriptive tickets. For example, we will consider available information associated with tickets other than the ticket content. Furthermore, to mitigate the threat of manually setting the parameters, we recommend employing a group that includes domain experts to identify appropriate parameters when applying our method to a particular domain for the very first time. After the initial setting, the parameters can be reused for the same domain. Another threat to the internal validity ofthe experiment is that we assumed each subject had a similar capability of software system development project comprehension. In the future, we plan to conduct further experiments with more subjects possessing similar backgrounds such as practitioners with comparable development experiences.  
A threat to the external validity of the case study and the experiment is that we used data for a single project using a dataset with more than 100 tickets in one organization. The same project has been used for case studies in previous requirements engineering studies [6; 7]. However, the total number of tickets may exceed 10,000 in large-scale projects [29]. Furthermore, the granularity of features addressed by tickets may differ according to the software project and domain. Thus, our method's scalability and domain generalizability should be further examined using larger datasets in multiple domains. In the future, we plan to conduct more case studies and experiments using other larger development projects and discuss the scalability and the generalization of our method.
## 4 Results
### Data Visualization  
Multiple types of data have been collected, processed, and/or generated, particularly photographs and 3D building models [309]. To enable users to explore and interact with these data, two applications have been developed, targeting different user groups and scenarios: the 4D City application for mobile devices and the 4D browser for desktop use. Nowadays, all modern devices and browsers are capable of displaying 3D content via WebGL ([[https://caniuse.com/?search=webgl](https://caniuse.com/?search=webgl)]([https://caniuse.com/?search=webgl](https://caniuse.com/?search=webgl)) (accessed on 29 September 2023)). This includes extensive 3D city models due to the increasing computing power of mobile devices [310]. Three-dimensional asset formats like gITF [311] with Draco 3D compression [312] ensure that the data loaded for transmitted 3D content are kept to a minimum.  
To put the spatialized photographs and generated 3D models into context, other data sources were incorporated. Digital elevation models (DEMs) on the basis of the SRTM or ALOS World 3D satellite data have a resolution of up to 30 m, are publicly available, and can be queried by Google Elevation API or OpenTopography. With the DEM-Net Elevation API ([[https://elevationapi.com](https://elevationapi.com)]([https://elevationapi.com](https://elevationapi.com)) (accessed on 29 September 2023)), it is possible to query ready-meshed digital terrain models that are optionally textured with a map. DEMs with a higher fidelity (up to 1 m resolution) can be retrieved from national land survey offices. However, they have to be downloaded and processed manually.  
The 3D building models, gained from various sources and processed as described above, cover only small areas at very specific places. Hence, there are many void areas. OpenStreetMap (OSM) offers detailed geodata from around the globe, including building footprints. The Overpass API was utilized to query these building footprints that are used to generate 3D geometries [313] during runtime to complement these void areas. Additionally, the 3D city model can be enriched with in-depth information on buildings, places, statues, etc. In this regard, open knowledge graphs such as Wikidata are queried via SPARQL [314] to find and collect points of interest in the vicinity.  
Figure 18: Example of a word cloud consisting of architectural terms of a scholarly text (**left**) and the corresponding 4D view (**right**) within the 4D browser application (websited in this figure accessed on 3 December 2023).
## 4 Results and Discussion  
As previously mentioned, four input variables, namely temperature, relative humidity, chloride contamination level, and rebar surface corrosion condition were utilized to predict the amplitude of the signal from the rebar of concrete slabs. The entire database was arbitrarily divided into training and testing datasets at a ratio of 80/20. That means the testing dataset consisted of 244 instances while 44 data points were in the testing dataset. The k-fold cross-validation method was used for hyperparameter tuning of the predictive DBN model. The performance of the predictive model was evaluated based on \(R^{2}\) and _RMSE_ meters. Finally, the parameter analysis was also conducted. Details of mentioned work are presented in the following sections.
# Numerical Integration-Based Performance Analysis of Cross-Eye Jamming Algorithm
## 3 Analytic Expression of the MSD Based on Numerical Integration  
As shown in Equation (7), \(a_{\text{real}}\) and \(\phi_{\text{real}}\) are random variables due to perturbation. Because \(a_{\text{real}}\) and \(\phi_{\text{real}}\) are random variables, the expression in Equation (10) is also a random variable. Thus, the MSD can be calculated by using joint PDF, \(f_{a,\phi}(a,\phi)\)[19]. The MSD, in terms of the joint PDF, can be written as,  
\[\begin{split}\text{MSD}&=\text{E}\Big{[}(GC_{\text{ real}}-GC_{\text{nominal}})^{2}\Big{]}\\ &=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\big{(}GC_{\text{ real}}-GC_{\text{nominal}}\big{)}^{2}f_{a,\phi}(a,\phi)dad\phi,\end{split} \tag{17}\]  
where \(a\) and \(\phi\) are Gaussian random variables with means \(a_{0}\) and \(\phi_{0}\) and variance \(\sigma_{a}\) and \(\sigma_{\phi}\), respectively. Therefore, the two variables \(a\) and \(\phi\) have Gaussian PDF:  
\[f_{a}(a)=\frac{1}{\sigma_{a}\sqrt{2\pi}}\exp\left(\frac{-(a-a_{0})^{2}}{2\sigma_ {a}^{2}}\right) \tag{18}\]\[f_{\phi}(\phi)=\frac{1}{\sigma_{\phi}\sqrt{2\pi}}\exp\left(\frac{-(\phi-\phi_{0})^{2 }}{2\sigma_{\phi}^{2}}\right). \tag{19}\]  
In general, the joint PDF is not equal to the product of the PDF of each random variable. However, because the two random variables are uncorrelated, joint PDF is identical to the product of the PDF of each random variable:  
\[f_{a,\phi}=f_{a}\times f_{\phi}=\frac{1}{\sigma_{a}\sqrt{2\pi}}\exp\left(\frac {-(a-a_{0})^{2}}{2\sigma_{a}^{2}}\right)\times\frac{1}{\sigma_{\phi}\sqrt{2\pi }}\exp\left(\frac{-(\phi-\phi_{0})^{2}}{2\sigma_{\phi}^{2}}\right). \tag{20}\]  
Using Equations (17) and (20), numerical integration-based MSD can be written as  
\[\begin{array}{l}\mathrm{E}[(\mathrm{GC}_{\mathrm{real}}-\mathrm{GC}_{ \mathrm{nominal}})^{2}]=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}{( \mathrm{GC}_{\mathrm{real}}-\mathrm{GC}_{\mathrm{nominal}})^{2}f_{a,\phi}(a, \phi)dad\phi}\\ =\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}{(\mathrm{GC}_{\mathrm{real}}- \mathrm{GC}_{\mathrm{nominal}})^{2}f_{a}(a)f_{\phi}(\phi)dad\phi}.\end{array} \tag{21}\]
## 2 Materials and Methods
### Material Identification and Degree of Conversion  
Fourier Transform Infrared Spectroscopy (FTIR) coupled with the Attenuated Total Reflectance (ATR) technique was used to identify the resins in both liquid and polymerized forms, and changes in their spectra were used to determine the polymerization effect or the degree of conversion. Aligner samples were measured at 10 positions of the outer layers and 5 in the inner layer and then directly analyzed on a Bruker ALPHA ATR-FTIR spectrometer (Bruker, Ettlingen, Germany) with a diamond as the single reflection element under standard analysis conditions (wavenumber range 4000-400 cm\({}^{-1}\), resolution 4 cm\({}^{-1}\), 10 scans per spectrum). The Savitzky-Golay algorithm was used to process the spectra and improve the signal-to-noise ratio as needed. The degree of conversion (DC) is calculated from the following equation:  
\[\text{DC}(\%)=(\text{A}_{\text{LR}}-\text{A}_{\text{P}})/\text{A}_{\text{LR}} \times 100\]  
where A\({}_{\text{LR}}\) and A\({}_{\text{P}}\) are the absorbance intensities in liquid resins and after polymerization, respectively. Absorbance intensities are automatically generated from derivative spectra by the Bruker OPUS 8.8 software.