esa-sceva/satcom-chunk-collection huggingface dataset:
In this context, we seek to suggest, implement, and validate a crop mapping scheme of methods, enabling the identification of nine different crop types and thus provide information on farmer's compliance, with respect to their CAP requirements. A parcel-based Sentinel-2 MSI time-series approach is used for the construction of the feature space. The utilized imagery captures the growing stages of the various crop types in order to successfully discriminate between them. The local Land-Parcel Identification System (LPIs), which provides the geospatial input for crop delineation and local farmers' declarations, as part of their CAP subsidy applications, are employed for the object partitioning of the images and the supervised classifiers' training, respectively. Thematic maps are produced for three different levels of crop nomenclature (crop type, crop family, and season of cultivation), employing separately a quadratic kernel SVM and a RF classifier. SVM and RF are two of the most popular non parametric classifiers in land cover mapping and their comparison has been an increasingly interesting topic in recent publications [16]. However, there is limited literature on their performance comparison under a Sentinel-2 based scheme, and even more under computational efficiency considerations that the Sentinel-induced big data shift demands. Differences between the two classifiers are evaluated in terms of execution time, classification accuracy, and relevance to the crop identification issue. Further, the size of the variable space and the importance of individual variables are quantified and accordingly evaluated. The proposed methodology is finally applied to Landsat-8 OLI and down-sampled Sentinel-2 MSI equivalent variable spaces, for the comparison of spatial, temporal, and spectral characteristics between the two sensors. Lastly, we performed a compliance check analysis for the CAP Crop Diversification requirement to showcase the scheme's capacity for effective decision making within the context of the control of CAP subsidies.
## 6 Methodology for the Proposed Approach  
In this study, the BWO methodology has been used to determine the appropriate size of DGs, and strategic planning as well as scaling of EV loads are performed in the distribution networks. The step-by-step procedure of approach in the statement of the problem is outlined as follows:  
* The bus data and line data of the test systems are initialised.
* Bus injections to branch currents and branch current to bus voltage matrices are formed to obtain the DLF method.
* The P-Index is formulated using Equation (6), by estimating VSF at all buses given in Equation (7).
* To determine the most suitable place for DGs and their appropriate size, the BWO is used, which utilises the P-Index as an objective function.
* The size of WTGS as DGs to be connected are decided as candidates for the initialisation of BWO.
* The collection of candidates produces the search agents that explore the search space by updating their position vectors using Equation (11). The model for the matrix is framed by minimum and maximum DG size for the first phase of optimisation.
* The sizing (candidate solutions) are employed at specified buses to determine appropriate sizing.
* The best solutions are identified, depending upon the intricacy of the approach.
* The WTGS as DGs are installed at the determined buses to enhance the voltage stability of the system.
* The bus data and line data of the system are updated for the incorporation of EV loads.  
Figure 4: Pseudo-code of BWO algorithm.  
* The EV loads are modelled as ZIP loads and are placed on high-voltage buses to ensure the voltage stability of the system. The BWO algorithm is used to restrict the charging and discharging modes of EVs, utilising the voltage deviation at buses as an objective function.
* The EV loads are integrated at best candidate solutions.
* After the participation of EV loads, the bus and line data of the integrated system are updated to assess the voltage profile and losses of the system.
# Attention-Guided Wireless Channel Modeling and Generating
## 3 Modeling and Generating
### Se-Gan
#### 3.2.2 Feature Channel Recalibration  
After obtaining the global statistics of each channel, SEL recalibrates the importance of each feature channel through a fully connected network (a simple MLP). This consists of two fully connected layers:  
The first fully connected layer reduces the number of features from the original channel size to a smaller size, controlled by the parameter reduction. This reduction step reduces computational overhead and encourages the model to learn more efficient representations. The number of channels is reduced by a factor determined by the reduction parameter, typically set to 16 or other values depending on the task. The ReLU activation is applied to introduce nonlinearity and enhance the model's expressiveness, enabling it to learn complex relationships in the data.  
The second fully connected layer restores the channel dimension to its original size. This transformation concludes with the Sigmoid activation function, which outputs a set of scalar values between 0 and 1, one per channel. These scalar values represent the attention weight for each feature channel, indicating its importance in the final decision-making process.  
In short, the compressed features are processed through a network consisting of two fully connected layers. The first layer, a linear layer, reduces the dimensionality of the  
Figure 3: Structure of SEL.  
features, decreasing parameter count and computational complexity while enhancing the model's expressiveness through a ReLU activation function. The second linear layer restores the dimensions and concludes with a Sigmoid activation function that outputs the weight coefficients for each channel.
## 6 Results and Evaluation
### Results

Table 11: Summary of average f1-scores of 30 tests for classifiers using training dataset #2.  
prediction improvement by the DT and three ensemble learning classifiers also increased by 6%-8.5%. Among those clash types predicted by machine learning classifiers, errors and pseudo clashes gained the greatest increase in two experiments, as shown in Figure 12.  
From the results of these two experiments, we could preliminarily prove that under the condition of a small training dataset, the hybrid method proposed by this study combining rule-based reasoning and supervised machine learning can improve the predictive performance compared with using machine learning approach alone. A more in-depth evaluation will be discussed next.
# _WebTrackingScore:_ A Combined Web Tracking Risk Score System for Websites
## 4 Web TrackingScore: Definition and Evaluation
### Tracking Analysis of the Top 50 Websites
#### 4.2.1 Data Related to Cookies  
In this analysis, a total of 551 cookies were extracted. Based on their origin, 360 were first-party cookies and 191 were third-party cookies, representing 65.34% and 34.66% of the total, respectively (Figure 3a). Regarding their lifespan, 161 were session cookies and 390 were persistent cookies, resulting in a distribution of 29.22% for session cookies and 70.78% for persistent cookies (Figure 3b). These two classifications are not mutually exclusive; a session cookie can be either first-party or third-party, just like a persistent cookie. _iPrivacyScanner_ represents this classification by combining both categories.  
Figure 2: Research process followed for _WebTrackingScore_.  
Of the total 551 cookies, 124 are classified as first-party and session cookies, 236 as first-party and persistent cookies, 37 as third-party and session cookies, and 154 as both third-party and persistent cookies, resulting in a percentage distribution of 22.5%, 42.83%, 6.72%, and 27.95%, respectively (Figure 4). Among these four categories, the most concerning for privacy are third-party persistent cookies. However, contrary to expectations, the 154 cookies of this type are not evenly distributed across the websites. In Figure 5, we represent the number of websites that are using a specific number of cookies. In this figure, the number of cookies represented ranges from 0 to the maximum number detected on a single website. In this case, the maximum is 43 cookies, used by _Yandex.ru_. As shown in Figure 5, most of the websites use between 0 and 4 third-party cookies. Namely, 28 websites have zero cookies of this type, while the majority are found on three websites (Yandex.ru, amazon.com, and Weibo.com, accessed on 19 November 2024), with one having 32 cookies, another 41, and the third 43. These three websites account for a total of 116 cookies of this type, representing 75.32% of the total.  
Figure 4: Percentage distribution according to the combination of types of cookies.  
Figure 5: Distribution of third-party persistent cookies (number of websites using a specific number of third-party cookies).  
Figure 3: Percentages of the type of cookies according to their classification.
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 5 Bicep Modules' Design
### Main Orchestration (main.bicep)  
In the architectural design of this automated infrastructure provisioning solution, the primary focus is on the Bicep file, which serves as the orchestration layer that binds individual modules into a cohesive whole. Rather than defining every Azure resource directly, the main file is responsible for specifying deployment parameters, setting environment-level configurations, and invoking modules with appropriate inputs.  
The main orchestration module (main.bicep) coordinates the deployment of all individual infrastructure components. It imports and links reusable Bicep modules for networking, identity, search services, container hosting, databases, and DNS configuration.  
This central module manages parameter injection, output propagation, and inter-module dependencies. The full Bicep orchestration file is available in Appendix G.  
This orchestration strategy enables a high degree of flexibility. Environment-specific parameters, such as region, address space, and SKU selection, are defined at the orchestration level, rather than being hard-coded within modules. This parameterization ensures that the same modules can be deployed to different environments, with differences handled through external parameter files. Consequently, teams can maintain a single source of truth for infrastructure design while customizing deployments to match the cost, performance, and compliance needs of each environment.  
The orchestration layer enhances the visibility and traceability of the overall deployment topology. By defining explicit module invocations and their dependencies, the main module.bicep serves as living documentation of the system's architecture, improving maintainability and supporting onboarding for new team members [24; 25].
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods
### Evaluation Metrics
#### 4.3.1 F1 Score  
F1 score was applied to evaluate the detection performance of all change detection algorithms. We used this score as the primary metric to assess each algorithm for all datasets with different types of concept drifts. F1 score, as presented in Equations (2)-(4), is derived from two other metrics, namely precision and recall [31]. Precision measures the ability of the assessed algorithm to avoid false positives. Recall expresses the capacity of the assessed algorithm to detect as many drifts as possible. By combining these two metrics, F1 score provides a broader picture of the algorithm's accuracy.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1=2\cdot\frac{Precision\cdot Recall}{Precision+Recall} \tag{4}\]
## 3 Stability Analysis
### Time-Delay Independent Stability
The stability of the system does not change, so the stability of the system depends on the stability of the system when \(\tau^{*}=0\). If \(l-2k>0\), it means that the polynomial \(F(\omega_{0})\) has real roots, i.e., the stability of the system falls outside the time-delay independent stability region, and the stability of the whole system switches a finite number of times with the increase in time delay. In the regions s\({}_{1}\), s\({}_{2}\) and s\({}_{3}\), the number of non-zero terms in the discrimination sequence is 12 and the number of sign changes is 6, so the polynomial has no real roots. In the regions of s\({}_{4}\)\(\sim\) s\({}_{15}\), the number of non-zero terms in the discrimination sequence is 12 and the number of sign changes is 4, so there are two positive real roots of the polynomial. From the above analysis, it can be concluded that the blue region part of Figure 3 does not meet condition 1, so it is not Routh-Hurwitz stable at \(\tau^{*}=0\), and this region is unstable. There are real roots in the orange region, so it is necessary to determine the stability interval according to the stability switches. The green region is the time-delay independent stability. It can be found from Figure 3 that the time-delay independent stability region is V-shaped. We find that that the time-delay independent stability region expands with the increase in \(\zeta_{2}\), which indicates a close relationship between damping and time-delay independent stability. The larger the damping, the larger the time-delay independent stability region.
## 4 Materials and Methods
### Pre-Processing and Re-Scaling  
Prior to feeding the dataset into the deep learning model, a series of pre-processing steps were meticulously applied to the samples. These pre-processing procedures included the elimination of null and duplicate rows, thus ensuring the integrity of the dataset. Furthermore, encoding techniques were adeptly employed to transform categorical data into numerical representations. In our dataset, a binary classification was established with only two categorical data classes, namely \(Authentic\) and \(Spoofed\). To facilitate this categorization, we encoded authentic signals as 0 and spoofed signals as 1, thus effectively converting them into numerical values. Additionally, the ToA values underwent a scaling transformation, whereby they were multiplied by a factor of \(10^{7}\). This scaling operation was undertaken to alleviate the presence of extensive fractional values within the dataset, thereby enhancing its suitability for deep learning analysis [39]. Furthermore, the datasets underwent a standardization process using the min-max scaling method. This technique effectively centers the features to have a mean of 0 and a STD of 1, and this achieved using the following equation:  
\[Z=\frac{x-x_{min}}{x_{max}-x_{min}} \tag{24}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{S_{AP}}\) & \(\mathbf{SNR_{NAP}}\) & \(\boldsymbol{\beta}\) & \(\mathbf{T_{p}(NAP)}\) & \(\mathbf{d_{NAP}}\) & \(\mathbf{d_{SE}}\) & \(\mathbf{d_{EAP}}\) & \(\mathbf{T_{p}(SAP)}\) & **Status** \\ \hline \(-70.858\) & 18.860 & 0.202 & 1.163 & 34.908 & 11.110 & 43.688 & 1.826 & Authentic \\ \(-70.859\) & 18.820 & 0.203 & 1.163 & 34.910 & 11.111 & 43.442 & 1.818 & Authentic \\ \(-70.856\) & 18.805 & 0.204 & 1.163 & 34.901 & 11.109 & 43.334 & 1.814 & Authentic \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\ \(-69.213\) & 3.195 & 1.231 & 0.962 & 28.884 & 11.108 & 2.084 & 0.300 & Spoofed \\ \(-69.158\) & 3.024 & 1.255 & 0.956 & 28.702 & 11.110 & 2.260 & 0.295 & Spoofed \\ \(-68.662\) & 1.674 & 1.466 & 0.903 & 27.108 & 11.111 & 3.534 & 0.252 & Spoofed \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample data from the developed dataset.  
where \(x\) represents the original data feature, \(x_{min}\) corresponds to the feature's minimum value, and \(x_{max}\) denotes the feature's maximum value. This transformation effectively re-scaled the features within the dataset to span a normalized range of \([0,1]\). Consequently, the minimum and maximum values of each feature or variable were precisely adjusted to 0 and 1, respectively, as illustrated in Table 3. The dataset features are described in Table 4.
# Simplified Plastic Hinge Model for Reinforced Concrete Beam-Column Joints with Eccentric Beams
## 3 Joint Shear Strength Degradation Model
### Plastic Hinge Model
#### 3.2.3 Summary of Parameters for Plastic Hinge Model  
For modeling of the load-displacement relationship, the lateral load and drift ratios at point EY are determined from Equations (5) and (6) for interior beam-column joints (or Equations (8) and (9) for exterior beam-column joints), respectively. The drift ratios at points EU and EF are determined from Equations (15) and (16), respectively, and the lateral load at point EU is the same as that at point EY. The parameters for the plastic rotation angles used in Equations (15) and (16) are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Interior Beam–Column Joint** & **Exterior Beam–Column Joint** \\ \hline \hline \begin{tabular}{c} Plastic rotation angles \\ \end{tabular} & \begin{tabular}{c} \(a_{p}=a_{j}+a_{f}\) \\ \(b_{p}=b_{j}+b_{f}\) \\ \end{tabular} \\ \hline \begin{tabular}{c} Plastic shear angle of the joint \\ \end{tabular} & \begin{tabular}{c} \(a_{j}=\frac{1}{1050}\Bigg{[}\frac{1.1\alpha_{c}(\beta_{c}+0.2)\Big{(}6-\beta_{j} \Big{)}f^{\prime}{}_{c}(b_{s}h_{c})}{V_{u}-V_{T}}-16\Bigg{]}\) \\ \(\leq a_{mj}\) \\ \end{tabular} & \begin{tabular}{c} \(a_{j}=\frac{1}{1050}\Bigg{[}\frac{2.8\alpha_{c}(\beta_{c}+0.1)\Big{(}3-\beta_{j} \Big{)}f^{\prime}{}_{c}(b_{s}h_{c})}{V_{u}-V_{T}}-8\Bigg{]}\) \\ \(b_{j}=a_{j}+0.01\) \\ \end{tabular} \\ \hline \begin{tabular}{c} Maximum shear angle \\ \end{tabular} & \begin{tabular}{c} \(a_{mj}=0.03\Bigg{(}1-0.1\frac{a_{j}}{a_{jf}}\Bigg{)}>0.01\) \\ \end{tabular} \\ \hline \begin{tabular}{c} Partial bond failure \\ \end{tabular} & \begin{tabular}{c} \(a_{ff}=\alpha_{f}y\frac{1-0.45h_{p}/L_{s}}{3400}(\kappa-0.13)(3-5\kappa)\) \\ \end{tabular} & \begin{tabular}{c} \(a_{ff}=\alpha_{f}y\frac{1-0.45h_{p}/L_{s}}{3000}(\kappa-0.12)(3-5\kappa)\) \\ \end{tabular} \\ \hline \begin{tabular}{c} Plastic rotation angle of the \\ beam end \\ \end{tabular} & \
\begin{tabular}{c} \(a_{f}=\frac{3+5\kappa}{3-5\kappa}a_{f}\leq a_{mf}\) \\ \(b_{f}=\frac{3+5\kappa}{3-5\kappa}b_{f}\leq b_{mf}\) \\ \end{tabular} \\ \hline
\begin{tabular}{c} Note: \(a_{z}=2.5\) for interior joints with two cross-beams, 2.0 for exterior joints with two cross-beams, or 1.0 otherwise; \(b_{s}\) = effective joint width in Equations (1)-(4); \(V_{u}=(A_{s}+A^{\prime}{}_{s})f_{y}-P_{u}\) for interior beam–column joint; \(V_{u}=A_{s}f_{y}-P_{u}\) for exterior beam–column joint; \(V_{T}=\min\left(A_{s}f_{yh},0.65A_{s}f_{y}\right);\beta_{j}=h_{b}/h_{c}\geq 1 ;\beta_{c}=(A_{s}+A^{\prime}{}_{s})f_{y}/(0.88f^{\prime}{}_{c}\;b_{y}h_{b})\) for interior beam–column joint; and \(\beta_{c}=A_{s}f_{y}/(0.88f^{\prime}{}_{c}\;b_{y}h_{b})\) for exterior beam–column joint. Note that when calculating \(V_{u}\) and \(V_{T}\) in exterior beam–column joints, tension bars in a beam section are used under positive or negative moment. \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for plastic rotation angles.
## 3 Instrument Control
### Back-End Configuration
#### 3.2.2 I/Q Measurements  
The I/Q acquisition mode allows to retrieve directly the in-phase and quadrature components of the radiometric signal. This mode is needed for advanced RFI detection and mitigation techniques in the host computer. In this mode, the radiometric signal is conditioned as in power mode until it reaches the ADC (see Figure 5). Once the signal is digitized, the signal is downconverted to its baseband I/Q components, which are stored and sent to the host computer for further processing. Figure 9 shows a diagram of the digital downconversion structure.  
Using the I/Q mode, the SA configuration parameters take a slightly different meaning as compared to the power mode:  
* The _central frequency_ configures the frequency of the local oscillator (external mixer in the W-band), which is fixed in this mode.  
* The _reference level_ determines the maximum amplitude (or power) of the dynamic range of the ADC.
* The _ADC filter_ sets the bandwidth of the anti-aliasing filter before the ADC. It is equivalent to the RBW, but its possible values are 10 MHz, 3 MHz, 1 MHz, and 300 kHz.
* The _sample rate_ sets the decimation value after the digital downconversion, and then the output sample rate. The decimation value increases in powers of 2 from 1 to 2048.
* The _number of samples_ determines the size of each data acquisition. The available buffer can store is up to 128 k samples.
## 2 Materials and Methods  
The striped PSS was fabricated by femtosecond laser ablation with a peak wavelength of 780 nm, a repetition rate of 1000 Hz, a pulse duration of 150 fs and a pulse energy of 2 mJ. The femtosecond laser beam was focused on the sapphire substrate by a 75 mm spherical lens, with a spot size of 40 \(\upmu\)m [18]. The Figure 1 illustrates the In\({}_{\mathrm{x}}\)Ga\({}_{1-\mathrm{x}}\)N/GaN LED structures grown on striped PSS. The In\({}_{\mathrm{x}}\)Ga\({}_{1-\mathrm{x}}\)N/GaN LED was fabricated by the low-pressure vertical MOCVD (metal-organic chemical vapor deposition) method. In the process of MOCVD, we used trimethylindium (TMIn), trimethylgallium (TMGa) and ammonia (NH\({}_{3}\)) as the source materials of In, Ga and N. Bicyclopentadienyl magnesium (Cp\({}_{2}\)Mg) and silane (SiH\({}_{4}\)) were chosen as the p-type and _n_-type doping sources. High-purity N\({}_{2}\) is employed as the carrier gas. The substrates are firstly heated to 1050 \({}^{\circ}\)C within the ambient H\({}_{2}\), followed by the 6-min baking process to purge away oxides from substrates. Nitrogen-treatment is then carried out within NH\({}_{3}\) at 530 \({}^{\circ}\)C followed by the 4-min growth of GaN buffer layers. Afterwards, the chamber temperature is raised to 1020 \({}^{\circ}\)C for the recrystallization of GaN buffer layers. The GaN 3D islands nucleation lasts for 55 min at 1020 \({}^{\circ}\)C subsequently. Finally, the 28-min GaN recovery layers are grown at 1105 \({}^{\circ}\)C, respectively. The LED structure was made up with 70 nm Mg-doped _p_-type GaN, 25 nm _p_-type AlGaN (aluminum gallium nitride), seven period multi quantum wells (MQWs), 2 \(\upmu\)m Si-doped _n_-type layer, 30 nm buffer layer of GaN, striped PSS and two electrodes from top to bottom. To be specific, the _p_- and _n_-electrodes are made by Ni/Au and Ti/Al, respectively.  
Scanning electron microscopy (SEM) images were taken on a Hitachi S-3400N field emission scanning electron microscope (Hitachi, Tokyo, Japan). The crystal structures of GaN epitaxial layers were confirmed by X-ray diffraction (XRD) method (Rigaku, Ultima IV, Japan) with Cu K\(\alpha\) radiation (k = 0.154178 nm) over a 20 range from 20\({}^{\circ}\) to 80\({}^{\circ}\) at a scanning rate of 0.02\({}^{\circ}\)/step and 4\({}^{\circ}\)/min. To investigate the stress in GaN epitaxial layers grown on both planar substrate and striped PSS, micro-Raman scattering experiments, performed with multichannel modular triple Raman systems (Horiba Jobin-Yvon LabRAM Aramis, Paris, France), are carried out in backscattering geometry at room temperature. The Raman spectra was measured by focusing the 514.5 nm excitation line of Ar\({}^{+}\) laser on the sample and detected the backscattered light. The optical absorption spectrum was examined with an Agilent Cary 5000 UV/VIS/NIR spectrophotometer (Agilent Technologies, Santa Clara, CA 95051, United States).  
Figure 1: The schematic diagram of GaN-based LED on striped PSS. GaN (gallium nitride), light emitting diode (LEDs), PSS (patterned sapphire substrate), and AlGaN (aluminum gallium nitride).