esa-sceva/satcom-chunk-collection huggingface dataset:
# Threatening URDU Language Detection from Tweets Using Machine Learning
## 3 Materials and Methods
### Data Preparation Layer
#### 3.2.2 Feature Vector Approaches  
For feature extraction from the preprocessed text, this study uses TF-IDF and BoW approaches. A brief explanation of these features is provided here for completeness.  
Term frequency (TF) describes how frequently a term/feature occurs. In a tweet, a feature's frequency corresponds to its importance. Every feature in the tweet corpus has been transformed by term frequency into a matrix with the number of tweets in the rows and the number of unique terms in the columns. The frequency of a feature is shown by the number of samples that share that feature overall. The inverse document frequency (IDF), which reduces the weight of a feature if the feature's occurrences have distributed among all documents, has been used to determine the weight of a feature.  
BoW counts the existence of each feature in one document and in the whole corpus and constructs the feature vector with a fixed length.  
Word-based and character-based n-grams are used to extract features from the preprocessed corpus that helps the model distinguish between threatening and non-threatening tweets. In this study, we used character level n-grams ranging from unigram (n = 1) to tetragram (n = 4). To train the model on word-level n-grams, we used unigram (n = 1) and bigram (n = 2) approaches. This attribute is used because it provides structural details and helps to make the text more pertinent.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Content Presentation Layer and Content Presentation Security Layer  
The Content Presentation Layer is above the Application Layer and manages the web application's interface using web standards: HTML for page structuring, CSS for page styling, and JavaScript to add and control interactive elements [58]. In this layer, UX/UI plays a defining role. It ensures that the content and graphic elements of the application are not only functional but also visually intuitive, encouraging users to interact easily with the web application.  
The Content Presentation Security Layer aims, primarily, at protecting the Content Presentation Layer to make it safe for users from client-side attacks and protect the data and its integrity from unauthorized alteration.  
The Content Presentation Security Layer enhances the security of all the elements of the user interface by implementing techniques like Content Security Policy [59] or Cross-Site Scripting Prevention [60].  
These measures minimize the risk of content being loaded from undesired locations, and include input validation, sanitization, and output encoding. This layer ensures secure communication with the application using the HTTPS protocol, which improves user confidence by making the browsing experience safe and shielding private information from external threats, as illustrated in Figure 5:
# On Beamforming with the Single-Sideband Transform
## 2 Frequency and Time-Frequency Transforms
### Relative Frequency Responses  
Given two systems with an input \(x(t)\), each with an impulse response \(h_{1}(t)\) and \(h_{2}(t)\), we can calculate their relative frequency responses (RFRs) respective to the output of one of the systems (assumed to be the first without compromise), these being denoted \(A_{1}(f)\) and \(A_{2}(f)\). Let \(X_{1}(f)\) be the first system's output, given by  
\[X_{1}(f)=H_{1}(f)X(f)\]  
where \(\sigma_{X}^{2}(f)=\mathbb{E}\big{\{}X_{\mathcal{F}}(f)^{2}\big{\}}=\mathbb{E}\big{\{} X_{\mathcal{R}}(f)^{2}\big{\}}\). From this, our RFRs take a similar form to Equation (9), resulting in  
\[A_{2}^{\prime}(f) =\frac{\mathbb{E}\big{\{}X_{1}^{\prime}(f)X_{2}(f)\big{\}}}{ \mathbb{E}\big{\{}X_{1}^{\prime}(f)X_{1}^{\prime}(f)\big{\}}} \tag{18a}\] \[A_{2}^{\prime\prime}(f) =\frac{\mathbb{E}\big{\{}X_{1}^{\prime\prime}(f)X_{2}(f)\big{\}}} {\mathbb{E}\big{\{}X_{1}^{\prime}(f)X_{1}^{\prime}(f)\big{\}}}. \tag{18b}\]  
The RFRs in Equation (18) make intuitive sense: \(A_{2}^{\prime}(f)\) uses the correlation between \(X_{2}(f)\) and \(X_{1}^{\prime}(f)\) (same frequency), and \(A_{2}^{\prime\prime}(f)\) the correlation between \(X_{2}(f)\) and \(X_{1}^{\prime\prime}(f)=X_{1}(-f)\) (conjugate frequency), which is in line with Equation (13).  
Generalizing to a situation with \(M\) sensors, one can follow the same steps and achieve that for each \(m\)-th sensor, we have  
\[A_{m}^{\prime}(f) =\frac{\mathbb{E}\big{\{}X_{1}^{\prime}(f)X_{m}(f)\big{\}}}{ \mathbb{E}\big{\{}X_{1}^{2}(f)\big{\}}} \tag{19a}\] \[A_{m}^{\prime\prime}(f) =\frac{\mathbb{E}\big{\{}X_{1}^{\prime\prime}(f)X_{m}(f)\big{\}}} {\mathbb{E}\big{\{}X_{1}^{2}(f)\big{\}}}. \tag{19b}\]  
Notably, Equation (19a) reduces to \(A_{1}^{\prime}(f)=1\) and \(A_{1}^{\prime\prime}(f)=0\), using the fact that \(X_{1}(f)\) and \(X_{1}(-f)\) are uncorrelated (see Property A6).  
Observing strictly the mathematical structures of Equation (8) and Equation (11), the FT can be treated as a particular case of the RFT formulation, where \(A_{m}^{\prime}(f)=A_{m;\mathcal{F}}(f)\), and \(A_{m}^{\prime\prime}(f)=0\). Knowing this, we use the SSBT formulation of Equation (26) for both the SSBT and the STFT, as it is a more general model. The necessary considerations are taken when particularizing the equations for the STFT.
## 2 Monostatic-Bistatic Geometry for Stereo-Radargrammetric Reconstruction
### 2.2 Methods for Monostatic-Bistatic DEM Generation  
There is not a unique strategy for radargrammetric relief reconstruction. In [17] five different methods are presented, considering simplified assumptions on monostatic-bistatic geometry. The first approach is derived from optical stereo photogrammetry and is based on the definition of the parallax difference between the two observations. A stereo parallax is created when an object is viewed from two different positions. Height computation by means of the parallax method is based on relative measurements, i.e. a datum must be defined and heights are computed with respect to datum level. Hence, the absolute parallax is defined with reference to the target relief displacement in the radar image, whereas parallax differences are obtained with respect to datum, which allows definition of a reference parallax that must be calculated to derive target relative height [23]. Finally, accurate knowledge of the height of a limited number of ground control points is needed to evaluate topography [29; 30].  
The parallax method strongly relies on image quality and on capability to extract information from images, therefore the parallax method is the most sensitive to angle decorrelation and SNR degradation [17]. However, the intrinsic nature of radar, and of SAR too, allows direct access to slant range and Doppler measurements. On this basis the parallax difference can be expressed as a function of SAR measurements and there is no use in considering it as an independent parameter [17]. This statement is of fundamental importance to pass from optical derived approaches to true stereo radar methods, such as the rigorous stereo SAR problem [9] modified for monostatic/bistatic tandem acquisitions [31], or the hybrid approaches based both on the parallax and on the classic SAR range-azimuth measurements [24]. The first step of all these methods is the image matching: that is, relating a pixel of the bistatic image to the corresponding one in the monostatic image; in this way it is possible to associate both monostatic and bistatic SAR measurements (slant range, Doppler centroid frequency, ground range, azimuth...) of each observed target. For the sake of completeness it important to remark that the proposed monostatic-bistatic system is, no matter, narrow band SAR, and therefore speckle noise will be  
Figure 4: Ratio between bistatic and monostatic SNR for different observation strategies as a function of the baseline (platforms operating at 620 km altitude in parallel trajectories).  
generated in the images. Robust pattern recognition algorithms shall be used to guarantee the sub-pixel co-registration [13] that is fundamental for accurate DEM generation.  
A comprehensive analysis of methods for monostatic-bistatic DEM generation is available in [17]. In the following subsections the main relations for relief reconstruction are recalled, thus establishing the ground for performing a preliminary mission analysis and for producing an error budget of height estimation accuracy.
The applications of nanostructured mesoporous magnesium carbonate include pharmaceutical ones such as drug stabilizer [7,8] and anticoagulant [9]. Additionally, a mesoporous structure could make these materials suitable as adsorbents for pollutants and other substances. An interesting example of this is a report on mesoporous magnesium carbonate, synthesized with methanol, and treated with additives, which showed good adsorption of \(\text{CO}_{2}\), due to the high surface area (over 500 m\({}^{2}\)/g), with the inorganic additives increasing \(\text{CO}_{2}\) uptake and selectivity [10]. Another potential use of high surface area carbonates is the adsorption of contaminants. [PERSON] et al. report that \(\text{MgCO}_{3}\) is a better adsorbent of \(\text{Pb}^{2+}\) and \(\text{Zn}^{2+}\) ions than clay minerals [11]. Mesoporous calcium carbonates have also been reported as good adsorbents for heavy metal ions (Pb\({}^{2+}\) and Cd\({}^{2+}\)); the removal efficiency depended on the crystal structure [12]. [PERSON] et al. showed that synthetic mesquehonite is a highly efficient agent for removing Cu\({}^{2+}\), which was precipitated as a mixture of hydroxides and carbonates [13]. A novel application of hydromagnesite is in the directional displacement of fluids in porous media [14]. This shows the importance of studying the synthesis of magnesium carbonates. Many of the synthesis methods for magnesium carbonate, including those using supercritical carbon dioxide (\(\text{scCO}_{2}\)), with ethanol as a cosolvent [15] and mineral carbonation in aqueous solution [16] usually do not yield mesoporous materials (pores of 2-50 nm). Another option for cosolvent is methanol, which can increase the solvation power of the main solvent, to control the morphology of carbonates and has been used as a control factor of the pore size according to traditional synthesis works but can result in amorphous carbonates [17]. Reaction temperature is also an important factor that affects pore size [6,7]. However, organic solvents can contribute to emission of pollutants [18]. There are some examples of works exploring mineral carbonation with magnesium, such as that of [PERSON] et al. [19] in which magnesium comes from seawater desalination waste and is precipitated as carbonates using alkanolamines. In CCS, \(\text{CO}_{2}\) is usually delivered and transported in supercritical state for geological storage [20]. Therefore, it is important to study mineral carbonation by reaction with \(\text{scCO}_{2}\), which is by itself a solvent as well as a reactant, therefore, these reactions may not require the use of organic solvents such as methanol and ethanol, making it a greener method. Here we compare \(\text{scCO}_{2}\) and aqueous synthesis methods with the objective of producing mesoporous magnesium carbonates, via environmentally friendly methods.
## 3 Proposed Workflow for the Tool Design
While a formal kinematic calibration against a specific real wheelchair model was outside the scope of this study's initial development phase, the movement dynamics (forward, backward, and rotation speed via joystick control) were iteratively adjusted during pilot testing to ensure intuitive control and representative maneuverability for basic navigation tasks. The fact that participants were seated in a physical wheelchair during the VR experience also aimed to enhance the perceived authenticity of the interaction. * Stage 4--Physical Properties and Immersive Interactions. At this stage, the physical properties of the virtual environment and the key interactions for evaluation are defined. Collider Collision detection was managed using Unreal Engine's built-in physics engine and standard collider components attached to the virtual wheelchair and relevant environmental elements. The engine's capability to detect geometric intersections between these colliders is a core and generally reliable function for identifying contact events. Our implementation focused on registering these detected collision events for data collection. The specific response to a collision (e.g., stopping movement, audio feedback) was then programmed based on these detected events. The user's movement is controlled via the VR headset joysticks: the left joystick moves the user forward and backward, while the right joystick controls wheelchair rotation. The main camera is adjusted to eye level to maximize immersion. Additionally, prefabricated camera movement and synthetic hand motion are incorporated, both responding to the real position of the controllers. Users can interact with elements such as doors (which open 90\({}^{\circ}\) using animations triggered by the synthetic hands) and light switches, all programmed through Blueprints. These virtual actions enhance realism and enable functional evaluation of access and circulation areas under controlled conditions. * Stage 5--Evaluation Method for Access and Circulation Areas in UAH. Finally, with all elements integrated (movements, collisions, and interactions), an evaluation system is established to guide users through the environment naturally and intuitively. During the experience, key data are collected, including the number of collisions between the wheelchair and environmental elements, time spent in specific areas (measured in three-second intervals), and interactions with virtual objects. These data are automatically recorded by Unreal Engine at the end of the experience, generating a report used to construct heat maps from the floor plan view. These maps reveal areas with the highest levels of interaction and permanence, as well as critical collision points. The information obtained forms the basis for evaluating the accessibility parametersdefined in this study and for proposing design adjustments that better respond to users' real needs.
## 3 Example Analysis
### Simulation Analysis of Scenario 1
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Parameter** & **Cost (CNY)** &
\begin{tabular}{c} **Emissions** \\ **(Kg)** \\ \end{tabular} \\ \hline \multirow{4}{*}{EDNSGA-II [23]} & Minimum cost & 1164.01 & 1230.66 \\  & Minimum emissions & 1283.80 & 1175.99 \\  & BCS & 1243.40 & 1212.7 \\  & standard deviation & 22.71 & 5.08 \\  & Minimum cost & 1295.70 & 1236.5 \\  & Minimum emissions & 173.77 & 1199.08 \\  & BCS & 1317.47 & 1216.54 \\  & standard deviation & 24.66 & 7.08 \\  & Minimum cost & 1316.53 & 1241.29 \\  & Minimum emissions & 1378.31 & 1203.12 \\  & BCS & 1341.82 & 1220.07 \\  & standard deviation & 33.51 & 8.14 \\  & Minimum cost & 1310.50 & 1239.86 \\  & Minimum emissions & 1373.45 & 1202.18 \\  & BCS & 1334.62 & 1219.14 \\  & standard deviation & 31.79 & 7.99 \\  & Minimum cost & 1261.65 & 1234.08 \\  & Minimum emissions & 1337.12 & 1195.64 \\  & BCS & 1286.07 & 1214.31 \\  & standard deviation & 24.19 & 6.87 \\  & Minimum cost & 1153.99 & 1228.14 \\  & Minimum emissions & 1268.69 & 1173.67 \\  & BCS & 1230.25 & 1212.03 \\  & standard deviation & 21.22 & 4.88 \\  & Minimum cost & 1146.55 & 1226.71 \\  & Minimum emissions & 1262.74 & 1172.48 \\  & BCS & 1223.83 & 1211.28 \\  & standard deviation & 19.97 & 4.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the simulation results in Scenario \(1\). Figure 3: Cost convergence properties of Scenario 1. Figure 5 displays the Pareto fronts for the trade-off objectives achieved by different methods, facilitating comparison and the identification of optimal compromise solutions. The illustration suggests that the introduced algorithm is proficient in addressing nonlinear multi-objective optimization issues, as the Pareto optimal frontier of non-dominated solutions is evenly distributed. Consequently, the proposed algorithm expends less computational effort in diminishing emissions and operational expenses. The optimization outcomes robustly validate the algorithm's capability to tackle equality and inequality concerns prevalent in energy management scenarios. The experimental results show that the cost deviation and emission deviation of the OGGWO algorithm proposed in this paper in Scenario 1 are CNY 21.22 and 4.88 kg, respectively. The minimum emission is 1172.48 kg and the minimum cost is CNY 1146.55, both of which are lower than other optimization algorithms.
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 4 The Proposed Method
### Vector Representation of Alarms Using DK-Word2 vec
#### 4.2.1 The Category Information of Alarms  
Based on the previous description, the alarm code serves as a unique identifier for each alarm. Additionally, alarm records can offer multiple pieces of information, such as the alarm types (displayed in the third row of Table 2) and descriptions. Moreover, relying on wind turbine domain knowledge, alarms can be further classified based on various criteria. For instance, based on the monitored sub_assembles, alarms can be classified into 16 categories, which are displayed in the third row of Table 2 (This classification is identical to the one presented in Section 2.2). Based on the fault mechanisms of alarms, alarms can be classified as temperature-failure alarms, voltage-failure alarms, current-failure alarms, and control-failure alarms, which are displayed in the fourth row of Table 2. Furthermore, certain alarms can be further classified based on the specific focus of the study. For instance, as indicated by the earlier statistics, the alarming rate of the pitch and blades is relatively high. Therefore, it is necessary to conduct further studies on alarms related to the pitch and blades. The pitch system consists of three pitch actuator units, each controlling its corresponding blade. As a result, alarms related to pitch and blades can be classified according to their respective unit, as shown in the last row of Table 2.  
These pieces of category information are attributes of the alarm codes. These attributes are correlated with the fault patterns indicated by alarms, as provided by domain knowledge. We will utilize the category information to intervene and guide the process of alarm vector generation. How to reflect these attributes in the vectors of alarms is the next problem to solve.  
It should be noted that an alarm may have multiple categorical attributes. For example, alarm A155, with the description \"low coolant pressure\", can be classified as an information alarm under the first classification criterion, a cooling alarm under the second classification criterion, and a temperature failure alarm under the third classification criterion. However, it does not possess the relevant attributes for the fourth classification criterion.
# Study on the Leakage and Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
## 5 Results and Discussion
### Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal
#### 5.2.2 Factors Affecting the Inter-Stage Pressure Drop Characteristics of Two-Stage Finger Seal  
When the radial displacement excitation of rotor is 30 \(\upmu\)m, the RMS roughness of finger element is 1 \(\upmu\)m, and the rotor speed is 10,000 r/min. Figure 17 depicts the effect of the axial pressure difference on the inter-stage pressure drop characteristics of 2 SFS. As shown Figure 17, the pressure drop of the 1 st finger seal accounts for 36.9-45.1% of the axial pressure difference of 2 SFS, the pressure drop of the 2 nd finger seal accounts for 54.9-63.1%. The pressure drop percentage of the 1 st finger seal is smaller than that of the 2 nd finger seal, and the difference increases with increasing axial pressure difference of 2 SFS, so it can be concluded that larger axial pressure difference will aggravate the imbalance of pressure drop between stages of 2 SFS, which is the same as the two-stage brush seal [23].  
When the radial displacement excitation of rotor is 30 \(\upmu\)m, the axial pressure difference is 0.6 MPa, and the rotor speed is 10,000 r/min, the effect of side leakage flow on the inter-stage pressure drop characteristics of 2 SFS is presented in Figure 18. Compared with not considering the side leakage flow, the percentage of pressure drop of the 1 st finger seal is lower when the side leakage flow is considered, and the percentage of pressure drop of the 1 st finger seal decreased by 5% when the RMS roughness of finger element is  
Figure 16: Pressure contour of 2 SFS.  
Figure 17: Effect of axial pressure difference on the inter-stage pressure drop characteristics.  
1 \(\upmu\)m. Moreover, as the RMS roughness of finger element increases, the difference between considering side leakage flow and not considering side leakage flow gradually increases. Therefore, it is necessary to consider the side leakage flow when predicting the inter-stage pressure drop characteristics of 2 SFS.  
When the axial pressure difference is 0.6 MPa, the RMS roughness of finger element is 1 \(\upmu\)m, and the rotor speed is 10,000 r/min, Figure 19 shows the effect of radial displacement excitation of rotor on the inter-stage pressure drop characteristics of 2 SFS. It is clear to see that the effect of radial displacement excitation of rotor on the inter-stage pressure drop characteristics of 2 SFS can be ignored. It indicates that the rotor's excitation does not affect the inter-stage pressure drop characteristics of 2 SFS.
# Deep Learning-Based Bird's Nest Detection on Transmission Lines Using UAV Imagery
## 5 Simulation Results and Analysis
### Evaluation Index  
In this work, we evaluate the faster RCNN, faster RCNN with focal loss, cascade RCNN and ROI mining faster RCNN models in terms of detection accuracy.  
Figure 7: Schematic diagram of bird’s nest samples after data enhancement.  
We use four evaluation parameters to evaluate the proposed work namely, precision, recall, F1 score and mean average precision (mAP). In the object detection task, we use the ratio of the area of the intersection between the final detection boxes and the sample annotation boxes to the area of their union to represent whether the final detection results are correct. The detected objects are divided into two categories, i.e., positive and negative. There are four cases for each category, i.e., true positive (TP), which correspond to correctly categorized samples in positive samples, i.e., the detector correctly detects the birds' nests as birds' nests; false positive (FP), samples that are incorrectly categorized into positive samples, i.e., the detector incorrectly detects the backgrounds as birds' nests; false negative (FN), negative examples that are incorrectly categorized into negative examples, i.e., the detector incorrectly detects the birds' nests as backgrounds; true negative (TN), negative examples correctly categorized, i.e., the detector correctly detects the backgrounds as backgrounds.  
Based on this information, we define precision as:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
Recall is defined as:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
F1 score is defined as:  
\[\text{F1}=\frac{2\times\text{precision}\times\text{recall}}{\text{precision}+ \text{recall}} \tag{6}\]  
Since the detection object in this paper is only the bird's nest category, the AP value is the mAP value. It is expressed as:  
\[\text{AP}=\int_{0}^{1}\text{p}(\text{r})\text{d}\text{r} \tag{7}\]  
where, p and r present the precision and recall, respectively.