esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 2 Acquisition System Influencing Factors
### Laser Spot Location Error
Therefore, we can choose \(\Delta\) to evaluate the spot center positioning error, and the calculation of the fixed measurement deviation \(\Delta\) is shown below ([PERSON] et al., 2014):  
\[\Delta=\frac{N\mu}{N\mu+N_{s}}\Delta x_{w}, \tag{9}\]  
where \(\Delta x_{w}\) is the offset between \(x_{0}\) and the center of the calculation window. After continuous iteration of the improved centroid method, the max offset will be no more than 0.5 pixels. \(N\) represents the number of pixels within the windows for calculation, provided by \(N=N_{spot}^{2}\), and \(\mu\) is the mean value of noise electrons in a single pixel of the camera. According to Equation (9), and assuming \(\Delta=0.1\) pixels, we can calculate the minimum value \(N_{s,min}\) required for the spot's center-of-mass positioning accuracy to meet the requirements within a frame:  
\[N_{s,min}=4N\mu. \tag{10}\]  
From this, we can calculate the threshold necessary for successful acquisition:  
\[S_{sig}\geq N_{s,min}. \tag{11}\]
# Adaptive Binaural Cue-Based Amplitude Panning in Irregular Loudspeaker Configurations
## 2 Methods
### Magnitude Compensation Stage
#### 2.1.2 Spectral Smoothing  
Due to the inherent complexity and measurement inaccuracies of the RIR, a direct solution using Equation (3) may result in an unstable filter. To mitigate this issue, spectral smoothing is applied to the RIRs using the methodology from [31], which simultaneously smooths both the magnitude and phase spectra. When considering only the magnitude during the inverse filtering process, the smoothed magnitude \(|H_{\mathrm{ts}}(k)|\) is obtained as  
\[|H_{\mathrm{ts}}(k)|=\sqrt{\sum_{i=k-m(k)}^{k+m(k)}\left\{[H_{\mathrm{R}}(i)]^{ 2}+[H_{\mathrm{I}}(i)]^{2}\right\}W_{\mathrm{sm}}[(i-k+m(k))]}, \tag{4}\]  
where \(H_{\mathrm{R}}(i)\) and \(H_{\mathrm{I}}(i)\) are the real and imaginary parts of the RIR in the frequency domain, and \(m(k)\) is the smoothing index corresponding to the length of the smoothing window \(W_{\mathrm{sm}}\). This method simulates the auditory system's property, where frequency resolution decreases at higher frequencies. By incorporating non-uniform resolution, the equalization becomes less precise at these frequencies. Moreover, this approach is less affected by positional changes and avoids the processing artifacts of traditional ideal inversion methods [32].
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 2 Related Work
### Prior Knowledge for Detection SAR Images  
Before the popularity of deep learning, constant false alarm rate (CFAR) was one of the most seminal feature extraction methods [56] for object detection in SAR images. CFAR is a statistical detection method based on hypothesis testing theory, which ensures a constant false alarm probability by selecting a suitable threshold, and then determines whether the target signal exists under this condition. The MS-CFAR [57], which takes into account the statistical characteristics of the tested cell, has a more stable performance compared to other algorithms based on CFAR. The statistical clutter edge selector designed in [58] can obtain a uniform clutter field before CFAR, improving the detection performance of the classical CFAR detector. Moreover, the idea of improving the algorithm by using clutter boundary statistical features is also reflected in [59]. The bi-parametric clutter map CFAR detection method proposed by [PERSON] et al. [60] estimates the local threshold by following the standard deviation and mean values of the new clutter map, which has better applicability and target detection capability compared to the mono-parametric method. [PERSON] et al. [61] chose to use the half-sided Gaussian distribution for modeling and proposed a new method calledHG-CFAR. This method has higher efficiency relative to the CFAR method based on a Gaussian distribution.  
Specifically, there is a group of works which are for ship detection only. [PERSON] et al. [62] found that the grayscale values on SAR images fluctuate due to the influence of speckle noise. Such fluctuations take a regular form in uniform regions and an irregular form in non-homogeneous regions. The ship region is a homogeneous region while the ship boundary region and the artificial target are non-homogeneous regions. Based on this phenomenon, they designed a new texture feature IUDF for helping ship detection in SAR images. The proposed algorithm using IUDF has high accuracy and processing speed. The ship, as a scatterer, has a strong coherent backscattering signal, and this feature can be formed by evaluating the sub-resolution electromagnetic field, in which [PERSON] et al. [63] considered the Rice factor as a parameter that is sensitive to a dominant scatterer such as a ship. Therefore, they designed a filtering technique based on the Rice factor, by which the accuracy and efficiency of the ship detection algorithm can be effectively improved. [PERSON] et al. [64] also designed a filtering technique for SAR ships, but their technique was based on the strong double-bounced scattering phenomenon occurring from ships. In [65], the authors found that the azimuth ambiguities caused by the undersampling of the echo signal during SAR imaging can lead to false alarms of ships. As a result, they located and eliminated the ambiguous points in SAR images in advance according to the imaging coefficients of SAR, as a way to reduce the interference of orientation-ambiguous points to SAR ship detection.
# A Versatile Shared-Aperture Antenna for Vehicle Communications
## 3 Results
### S Parameters  
The antenna's port-matching performance is displayed in Figure 14, and upon comparison, we can observe that the measured reflection coefficient closely aligns with the simulation. The microstrip antenna for GPS covers the commonly used L1 band (1575.42 \(\pm\) 1.023 MHz), the impedance bandwidth of the S-band cross-dipole circularly polarized antenna is approximately 53.3% (2.2-3.8 GHz), and the pattern reconfigurable antenna's operational bandwidth is sufficient to meet the application requirements of V2V (5.855-5.925 GHz).  
Furthermore, based on the data in Figure 15, it is evident that across various frequency bands, the influence of other antennas on the antenna operating in that frequency band  
Figure 14: Simulated and measured reflection coefficient of (**a**) microstrip antenna; (**b**) cross-dipole antenna; (**c**) pattern reconfigurable antenna.  
Figure 12: The prototype, the test environment, and the port definition.  
Figure 13: The antenna test architecture.  
is minimal, resulting in high isolation. In the GPS L1 band, the isolation exceeds 25 dB, in the S-band, it surpasses 20 dB, and in the V2V band, the isolation is close to 40 dB. The poorest isolation in the S-band occurs at around 3 GHz, likely due to the microstrip antenna resonating at its second harmonic. Circularly polarized antennas can receive electromagnetic waves of various polarizations, and their proximity may lead to a decrease in isolation. Nevertheless, the isolation remains above 15 dB, which is acceptable.
# Performance Analysis of a Zero-Energy Building Using Photovoltaics and Hydrogen Storage
## 3 Results and Discussion
### Results for the Building Energy Needs  
The first step of this work regards the presentation of the building's thermal loads which are depicted in Figure 5. The heating and cooling loads are given in this depiction and it is clear that the studied building has both significant heating and cooling loads. These loads are covered by a heat pump which is a reversible one. In addition, the electrical consumption of the heat pump is given in Figure 6. The maximum heating load was found  
Figure 4: The solar irradiation variation on the tilted surface and the cumulative solar energy quantity per area for the examined location (Athens, Greece).  
on 15 January and is 8.38 kW, while the maximum cooling load was found on 19 August and is 8.29 kW. It is useful to state that the heating period starts on the 1 of November and it lasts up to the end of April. On the other hand, the cooling period starts on 25 May and lasts up to the middle of October. So, the heating period lasts for six months and the cooling period for 4 months and 20 days. The reported electrical consumption of the heat pump has significantly lower values than the loads because the used heat pump is an efficient one. So, it is remarkable to refer that the maximum electrical demand of the heat pump is found at 2.1 kW.  
The cumulative energy demands are depicted in Figure 7 for heating, cooling, and DHW. On a yearly basis, the heating energy demand was found at 9777 kWh and the cooling energy demand at 7237 kWh. The load for DHW demand was found at 3452 kWh.  
Figure 5: Heating and cooling thermal loads of the studied building.  
Figure 6: Electricity demand for covering the heating and cooling needs of the studied building.  
Figure 8 illustrates the respective electrical energy demand for the year, also including the appliances and lighting demand. More specifically, the yearly electrical consumption for the heat pump for heating was found at 2444 kWh and for cooling at 1809 kWh. The electrical consumption for covering the domestic hot water was found at 3634 kWh, while the consumption for appliances and lighting was found at 15,769 kWh. The cumulative curve for the DHW presents a reduced slope in the summer because the load is lower in this period. More specifically, the grid water is warmer in the summer period and this is the reason for the reduced DHW needs in the summer period. Finally, the sum of the aforementioned electrical consumption is 23,656 kWh which corresponds to a yearly demand of 59.14 kWh/m\({}^{2}\).
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Dataset Collection
#### 3.1.2 Preprocessing Steps (Tokenization, Stopword Removal, Lemmatization)  
Data preprocessing is crucial to convert raw textual data into a structured format appropriate for machine learning and deep learning models. The following preprocessing steps were applied:  
* Tokenization: The text was broken down into individual words or subwords to allow models to process linguistic patterns.
* Stopword Removal: Common but unimportant words (e.g., \"aur\", \"ka\", \"ke\") were removed as they do not contribute to classification.
* Lemmatization: Words were converted to their root form to standardize variations (e.g., \"likhma\" \(\rightarrow\) \"likh\").
* Case Normalization: All text was converted to lowercase to prevent duplicate word variations due to casing.
* Spelling Normalization: Roman Urdu lacks a standardized spelling system, so different spellings of the same word were mapped to a common representation.
* Data Splitting and Validation: We used an 80/20 data split to divide our dataset into training and testing. To improve the generalization and reliability of the models, 5-fold cross-validation was performed at the model training stage. Performance results reported are means over all folds and final testing was performed on the held-out test set. We took this approach to allow for our findings to be both statistically reliable and generalizable.
# Origin and Composition of Exosomes as Crucial Factors in Designing Drug Delivery Systems
## 1 Introduction  
Exosomes can serve as a drug delivery platform for macromolecules (DNA, RNA, proteins, and lipids) and drugs in hastily developing pharmaceutical units. Currently, they are under extensive research to adjust their properties to overcome specific drug delivery obstacles [1; 2].  
For over the last two decades, exosomes have been isolated from many cell types including normal and cancer cells, and their impact on other cells has been studied. It is a well-known fact that their main properties come from the cargo that can vary between distinct cells. These cargoes are cell-derived particles, namely nucleic acids, proteins, and lipids, which can influence other cells' metabolism and functionality [3; 4]. Due to the similarity of exosomes to artificial lipid microvesicles, researchers started to incorporate drugs, macromolecules, and other substances, which, for clarity, are altogether stated as drugs in this article [5; 6]. This attitude resulted in an improvement in therapy efficiency employing increased cell-drug response, a decrease in necessary drug concentration, and an unwanted systemic organism response. The possibility of additional targeted delivery is another advantage of exosomes [7]. Some researchers proposed advanced combined delivery strategies that improve therapeutics efficiency by additional cell expression pattern manipulation with macromolecules such as RNA or DNA [8; 9; 10].  
In this review, we present current strategies for preparing exosomal drug delivery systems including all necessary steps and examples of exosomes used as drug delivery vehicles.
## 4 Experimental Results
### Dataset Description
For each sub-model, 2D cross-sections at various locations along the XYZ directions were used as conditioning data within the 3D reservoir domain, which serves as inputs for the generator. These cross-sections, obtained through core sampling, well log analysis, and seismic imaging, provide rich geological information, ensuring that the generated model aligns with prior geological knowledge. Additionally, in cases of extreme data scarcity, adaptive spatial sampling and machine learning-based data augmentation techniques can further assist in acquiring the cross-sections [42; 43].
# A Lightweight AI-Based Approach for Drone Jamming Detection
## 3 Topic Overview
### Jamming Attacks Against Drone Networks  
Jamming attacks are particularly effective against drone networks, as drones usually rely on external input to navigate and operate correctly. If a jammer were able to completely block the communication between the drone and the Base Station (BS), the drone would be left without any indication on how to behave and would need to activate an internal failsafe mechanism. This usually comes in the form of either a _return to base_ procedure, a _hover in place_ procedure or a _land in place_ procedure. All of these approaches leave the drone in a vulnerable position, as a bad actor could potentially capture the drone and use it for malicious purposes. This is especially true when jamming attacks are used in combination with other types of attacks, such as spoofing attacks. A jamming attack can be initiated against a drone, making it lose connection with the central server. The drone would be forced to start a failsafe procedure, like _return to base_. The attacker could then employ a GPS spoofing attack to make the drone believe that the Base Station is in a different location, leading to the drone landing in a spot designated by the attacker [23].
## 5 Case Studies
### Case Study One: Khan Academy
#### 5.1.4 Reflective Outcomes from Khan Academy Case Study  
The implementation of the Dynamic Feedback-Driven Learning Optimization Framework in Khan Academy represents a significant stride in the realm of online education. The results from this case study conclusively demonstrate that machine learning can play a pivotal role in creating personalized, adaptive, and efficient learning environments. The success of DFDLOF in Khan Academy showcases the potential of such frameworks to enhance learner engagement, improve learning outcomes, and provide equitable access to quality education.  
This case study also sets a precedent for the future integration of advanced machine-learning techniques in educational platforms. The insights garnered from this implementation provide a roadmap for other educational institutions and platforms aiming to leverage machine learning for personalized learning experiences. It highlights the need for continual investment in technological advancements and a data-driven approach to education.  
In conclusion, the application of DFDLOF in Khan Academy is not just a testament to the efficacy of machine learning in education but also a beacon for future innovations in this field. It underscores the transformative potential of integrating sophisticated machine learning algorithms in educational settings, paving the way for a more personalized, engaging, and effective learning journey for learners worldwide.
# Effect of Heat Treatment on Microstructure and Creep Behavior of Fe-40 Ni-24 Cr Alloy
###### Abstract  
Iron-nickel-chromium (Fe-Ni-Cr) alloy Haynes HR120 is an iron-nickel-based superalloy, which is extensively used in gas turbines. Hence, the materials for the fabrication of steam turbine blades should present great mechanical characteristics and creep properties. In this study, Fe-40 Ni-24 Cr was heat-treated at temperatures from 950 to 1250 \({}^{\circ}\)C. High temperature creep behavior and microstructure evolution of the selected heat-treated (1050 \({}^{\circ}\)C, 1200 \({}^{\circ}\)C, 1225 \({}^{\circ}\)C and 1250 \({}^{\circ}\)C) Fe-40 Ni-24 Cr alloy were assessed at temperatures of 800 \({}^{\circ}\)C and 900 \({}^{\circ}\)C under 100 MPa stress. The alloy consisted of titanium and niobium rich precipitates, namely NbC, (Nb,Ti)C, TiN and Ti(C,N) distributed in the matrix grain boundaries, which enhance the creep properties of the alloy. The hardness of heat-treated Fe-40 Ni-24 Cr alloy decreased with increasing temperature and grain size. The creep strain of the Fe-40 Ni-24 Cr alloy increased with escalation in the creep time and the temperature being under constant applied stress. Fe-40 Ni-24 Cr alloy shows a decrease in steady-state creep rate with an increase in grain size from 62 \(\upmu\)m to 183 \(\upmu\)m due to the grain boundary sliding mechanism and 183 \(\upmu\)m to 312 \(\upmu\)m due to the occurrence of dislocation climb. This result exhibited that grain size has a significant influence on the alloys' high temperature creep properties.  
Fe-Ni-Cr alloy; high temperature creep tests; microstructure evolution; precipitation; creep property +
Footnote †: journal: Applied Sciences  
1 Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; [EMAIL_ADDRESS]  
2 Advanced Materials Research Center, Department of Materials Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran  
3 Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway  
4 Correspondence: [EMAIL_ADDRESS] (E.H.); [EMAIL_ADDRESS] (H.R.B.-R.); [EMAIL_ADDRESS] (F.B)
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Model Development
#### 2.3.6 Mathematical Model Decision Trees  
Decision Tree algorithms, which use a tree-like structure to translate input information to projected outcomes, are useful tools for energy demand forecasting. The mathematical basis for how they operate, by recursively dividing the dataset according to the characteristic that yields the most information gain, is provided in Equations (37)-(41). This process considers variables like temperature, time of day, and past usage trends. The result is a set of comprehensible rules that highlight the main causes of energy usage. Decision Trees are capable of handling both continuous and categorical variables, and their use in ensemble techniques such as Random Forests improves the forecasting of energy demand's predictive accuracy and resilience. As demonstrated in Table 7, these equations are fundamental to their operation [49].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Equation** & **NO. Equation** \\ \hline Input Variables & \(X=[X_{1},X_{2},X_{3},X_{4},X_{5}]\) & (33) \\ Encoding Process & \(\mathbf{z}=f(\mathbf{x})=\sigma(\mathbf{W}_{\mathbf{c}}\mathbf{x}+\mathbf{b}_{ \mathbf{c}})\) & (34) \\ Decoding Process & \(\hat{\mathbf{x}}=g(\mathbf{z})=\sigma(\mathbf{W}_{d}\mathbf{z}+\mathbf{b}_{d})\) & (35) \\ Loss Function & \(L=\frac{1}{N}\sum\limits_{i=1}^{N}\ \ \parallel\mathbf{x}_{i}-\hat{\mathbf{x}}_{i} \parallel^{2}\) & (36) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mathematical model Autoencoder.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Equation** & **NO. Equation** \\ \hline Input variables & \(X=[X_{1},X_{2},X_{3},X_{4},X_{5}]\) & (37) \\ Gini Impurity & \(\text{Gini}(\text{D})=1-\sum\limits_{k=1}^{K}\text{p}_{k}^{2}\) & (38) \\ Entropy & \(\text{Entropy}(\text{D})=-\sum\limits_{k=1}^{K}\text{p}_{k}\text{log}_{2}( \text{p}_{k})\) & (39) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mathematical model Decision Trees.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Equation** & **NO. Equation** \\ \hline Input variables & \(X=[X_{1},X_{2},X_{3},X_{4},X_{5}]\) & (29) \\ Activation of the \(j\)-th & \(\phi_{j}(\mathbf{x})=e^{-\frac{\left\|x_{j}\right\|^{2}}{2\hat{\gamma}_{j}^{2}}}\) & (30) \\ Output of the RBF & \(y(\mathbf{x})=\sum\limits_{j=1}^{N}w_{j}\phi_{j}(\mathbf{x})\) & (31) \\ Error can be computed & \(E=\frac{1}{2}\sum\limits_{i=1}^{M}\ (y_{i}-\hat{y}_{i})^{2}\) & (32) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mathematical model (RBF).
## 2 Description of the Methodology
### Input Data  
Before the execution of any prediction, some input data are required. If the prediction is not going to start for new or ground rails, the last measured or calculated RCF status of rails is taken as input. Regarding the MBS model, the required parameters to configure the databases for a vehicle, track and their interaction are also needed. These parameters are:  
* Vehicle load condition.
* Profile and degradation level of rails.
* The number of vehicles per day and the number of bogies of a unit.
* Track quality, i.e., irregularities.
* Vehicle speed and traction/braking effort.
* Wheel/rail friction coefficient.  
The characteristics of MBS models are associated with different files (irregularities, rail profiles, etc.), which can be updated at the start of the process from databases. If the prediction of cracks due to RCF runs in parallel with other degradation modules, such as track quality or rail wear, these files would be created during the calculation. These changes are necessary to consider the aging of the track system, which modifies the necessary inputs for crack models.  
In addition, two time-variables have to be defined. These are the total prediction time \(T_{\text{total}}\) and the prediction time-step \(\Delta t\) that will be used in small increments to update the parameters for a new simulation. The value of \(\Delta t\) should be defined carefully without compromising the computational cost as the results in \(T_{\text{total}}\) could vary. If neither track  
Figure 1: Flowchart of the proposed methodology.  
irregularities nor rail profiles are updated, \(\Delta t\) and \(T_{\text{total}}\) can take the same value as MBS results will not change.