esa-sceva/satcom-chunk-collection huggingface dataset:
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 2 Measurement Algorithm
### Establishing a Machining Feature Model for Design
#### 2.2.2 Parsing the Point Set Generated by Arcs  
Similar to the process of drawing a straight line, the circle of a part is drawn by the radius size, starting point position, center position, and degree of arc. First, the coordinates of the starting point (\(x_{s}\), \(y_{s}\)) and the center of the circle (\(x_{o}\), \(y_{o}\)). are clarified. Then, the arctangent function is used to obtain the starting angle \(\theta\) of the arc.  
\[\theta_{i}=atan_{2}(y_{s}-y_{o},x_{s}-x_{o}) \tag{9}\]  
The starting angle must be adjusted according to the position of the starting point to ensure that it is in the correct quadrant. The coordinates (\(x_{i}\), \(y_{i}\)) of each point on the arc are determined by the following Formula (10):  
\[x_{i}=x_{o}+R\cdot cos(\theta_{i})\cdot gridsize \tag{10}\]  
\[y_{i}=y_{o}+R\cdot sin(\theta_{i})\cdot gridsize \tag{11}\]  
where _gridsize_ is the actual length represented by each pixel, obtained from the proportional relationship between the actual depth dh and the camera.
## 2 Materials and Methods
### Organization and Dependencies  
SteWI is designed as a semi-interdependent set of Python libraries (_stewi_, _chemical-matcher_, _facilitymatcher_, and _stewicombo_). Each library performs unique tasks in providing a standardized output or harmonization (Figure 1). Some libraries have dependencies on other libraries; all have a common structure and analogous Application Programming Interfaces (APIs). Like the other tools in the ecosystem, SteWI draws heavily on the _pandas_ library [18] and uses the _pandas_ dataframe as its basic structure for data storage, import, reshaping, and aggregation. _Pandas_ is an extremely powerful data manipulation framework that has enabled the rapid rise of the data science field [19; 20] The _requests_ library is used generally to pull data from APIs when they are available [21]. Source data contained in Microsoft Excel files are read using the _openyval_ engine (for.xlsx files) [22] or the _xlr_ engine (for.xls files) [23]. Outputs are stored in Apache parquet format [24], which enables efficient processing and retrieval of large datasets via the _pyarrow_ library. Configurable elements for data retrieval and processing are generally stored in relevant.yaml files. YAML is a simple text-based format that is used to store configuration data across the tool ecosystem [14]. SteWI relies on the _PyYAML_ package to parse these files [25]. Finally, SteWI relies on the USEPA LCA Ecosystem support package _esupy_ for local file management, metadata processing, and path management.
## 2 Simulation of InSAR Parameters, Power, Coherence and Phase
### Effect of Vegetation Forward Scattering Amplitude on Coherence  
\(\gamma\) is defined as follows:  
\[\gamma\equiv\frac{2\sigma_{\pi}}{cos\theta}\equiv\frac{8\pi\rho_{\mathrm{N}} Im<f>}{k\ cos\theta} \tag{16}\]  
The extinction coefficient (\(\sigma_{\mathrm{x}}\)) and incidence angle (\(\theta\)) yield a value of \(\gamma\) equal to 0.462 for the simulations conducted. This value is contingent upon the numerical density of vegetation scatterers (\(\rho_{\mathrm{N}}\)), wave number (k), and the imaginary component of the vegetation forward scattering amplitude (Im). Given that \(\rho_{\mathrm{N}}\) is defined as a constant, it was assigned a value of one for the simulations of power, phase, and coherence. Using the same \(\rho_{\mathrm{N}}\) and \(\gamma\), Im is determined to be 1.7548 from Equation (15).  
The sensitivity analysis of scattering amplitude involves calculating and plotting coherence for various imaginary components. Coarse comparisons for Im values ranging from 0.175 to 2.65 are illustrated in Figures 10 and 11. Various observations can be made based on these figures. Figure 10 indicates that, as the amplitude of the imaginary component ranges from 0.175 to 1.7548, InSAR coherence appears to decline with decreasing imaginary component values. Furthermore, as the component diminishes, saturation occurs beyond the point at which the actual component saturates (exceeding 10 m). The forward scattering amplitude influences both the saturation point and the coherence magnitude. The imaginary component ranges from 1.7548 to 2.65 in Figure 11. The saturation point stabilizes at approximately 15 m of tree height.  
Figure 9: Vertical component of the reflected–direct bistatic field’s effect on coherence vs. tree height for \(\varepsilon_{-}g=12+\mathrm{j}3\).
# Study on the Influence of Moisture Content and Void Ratio on the Disintegration of Red Clay
## 3 Microstructure Analysis of Red Clay
### Nuclear Magnetic Resonance Test
This indicates that there is a critical moisture content for the volume of large pores, and the volume of large pores is the smallest near the critical moisture content, with the soil particles in the closest contact. The total pore volume followsthe same trend as the volume of large pores, decreasing first and then increasing with the moisture content. Compared with e = 1.1, the main peak area of the T\({}_{2}\) spectrum in soils with the same moisture content decreases slightly, while the secondary peak area increases significantly. When the moisture content remains constant, the soil undergoes a transition from a three-peak structure to a double-peak structure as the void ratio increases. The starting T\({}_{2}\) value of the main peak in the nuclear magnetic attenuation curve shifts to the right as void ratio increases, and the area of the main peak gradually decreases. The volume of small pores decreases slightly as void ratio increases, and the contact between soil particles becomes more compact. The starting T\({}_{2}\) value of the secondary peak in the nuclear magnetic attenuation curve also shifts to the right as void ratio increases, and the area of the secondary peak increases significantly. The volume of large pores increases significantly, resulting in a gradual increase in pore volume and looser contact between soil particles. Therefore, there is a strong correlation between the red clay structure and the distribution of pore sizes, as well as the void ratio and initial moisture content. When analyzing soil structure, it is important to take into account not only void ratio or moisture content, but also other structural characteristics. Focusing solely on these factors can result in an incomplete understanding of the soil's properties and may lead to errors in explaining the relationship between disintegration rate and structural characteristics.
# Dynamic Phase Comparison Planar Direction-Finding Algorithm on Satellite Radio Receiver
## 3 Simulation Results and Analysis  
In this section, several simulations of DF algorithms are conducted to compare and analyze their accuracy under various influencing factors. The simulations employed plain numerical methods and generated normally distributed random numbers to add additive Gaussian noise to the signal.  
Initially, a planar simulation of the dynamic phase comparison algorithm proposed in the previous section is carried out, which is based on a satellite receiver equipped with a dipole antenna. Next, based on the receiver equipped with dual orthogonal dipole antennas, the dynamic phase comparison algorithm is simulated alongside the planar amplitude comparison algorithm to analyze the DF accuracy under different expected angles. Subsequently, by integrating the amplitude comparison algorithm into the dynamic phase comparison algorithm, a dynamic fusion DF method is proposed. Additionally, by incorporating digital signal processing techniques, including band-pass filtering and coherent accumulation, a dynamic fusion DF system for space-borne digital receivers is designed. Finally, considering three influencing factors: SNR, sampling frequency, and coherent accumulation, the performance of the DF system is analyzed through three simulations.
## 5 Research Progress on Membranes for Redox Flow Batteries
### Nafion (Perfluorinated Membrane)
#### 5.3.3 Polymer Blending with Polyvinylidene Fluoride (PVDF)  
Polymer blending is an effective method for polymer modification. Earlier, Nafion/PVDF blends were utilized to prepare cation exchange membranes for VRB systems [90]. The addition of the highly crystalline and hydrophobic PVDF seems to effectively control the swelling of Nafion. In VRB single cell tests, the Nafion/PVDF blend membranes exhibit higher columbic efficiency than recast Nafion at various current densities. The blend membrane with 20 wt% of PVDF shows an energy efficiency of 85% at 80 mA\(\cdot\)cm\({}^{2}\), which is superior to that of recast Nafion. This membrane also exhibits a longer (twice) duration in OCV decay test and much lower permeation of V\({}^{4+}\) when compared to recast Nafion. However, the mechanism behind the improvement in ion selectivity is still unclear [90].
## Appendix C Qualitative Analysis
### Impact of Temperature on Prompt Techniques
#### 3.1.2 Few-Shot Prompting  
Few-shot prompting at low temperatures excels in producing focused, precise, and methodically structured diagnostics in all models. This approach tightly links clinical data with diagnoses, ensuring clarity and relevance. Gemini and Medichat Llama 3 both demonstrate strong clarity and precise hierarchies in their outputs, though occasional redundancy or dense terminology can arise (e.g., Mistral). Overall, the diagnostics generated are highly actionable and grounded in evidence, making them particularly useful for clinical decision-making.  
At higher temperatures, few-shot prompting introduces greater variability and a broader range of diagnoses. However, this comes at the cost of precision and clarity. In Medichat Llama 3, the quality drops significantly, with errors in grammar, imprecision, and disorganization. In Gemini and Mistral, while core diagnoses remain clear, speculative hypotheses and overlapping conditions reduce the overall focus, leading to increased verbosity and occasional redundancy. The higher temperature is useful in complex cases requiring broader diagnostic exploration but risks reducing the actionable utility of the output.
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
###### Abstract  
The study of functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) has gained traction for uncovering FC patterns related to autism spectrum disorder (ASD). It is believed that the neurodynamic components of neuroimaging data enhance the measurement of the FC of brain nodes. Hence, methods based on linear correlations of rs-fMRI may not accurately represent the FC patterns of brain nodes in ASD patients. In this study, we proposed a new biomarker for ASD detection based on wavelet coherence and singular value decomposition. In essence, the proposed method provides a novel feature-vector based on extraction of the principal component of the neuronal dynamic FC patterns of rs-fMRI BOLD signals. The method, known as principal wavelet coherence (PWC), is implemented by applying singular value decomposition (SVD) on wavelet coherence (WC) and extracting the first principal component. ASD biomarkers are selected by analyzing the relationship between ASD severity scores and the amplitude of wavelet coherence fluctuation (WCF). The experimental rs-fMRI dataset is obtained from the publicly available Autism Brain Image Data Exchange (ABIDE), and includes 505 ASD patients and 530 normal control subjects. The data are randomly divided into 90% for training and cross-validation and the remaining 10% unseen data used for testing the performance of the trained network. With 95.2% accuracy on the ABIDE database, our ASD classification technique has better performance than previous methods. The results of this study illustrate the potential of PWC in representing FC dynamics between brain nodes and opens up possibilities for its clinical application in diagnosis of other neuropsychiatric disorders.  
Keywords: autism spectrum disorder; resting state fMRI; BOLD signal; dynamic functional connectivity; SVD; principal component; oriented energy +
Footnote †: journal: Medical Imaging
# Risk Assessment of UAV Cyber Range Based on Bayesian-Nash Equilibrium
## 2 Related Research Work
### Risk Assessment of UAV
In addition to cyber security, in the research work exploring UAV Physical Layer Security (PLS), such as the rate-split multiple access technology that supports IoT in satellite and aerial integrated cyber communication proposed in [32], we can explore how to apply this access method to the UAV cyber range to improve the data transmission rate and reliability. At the same time, [33] provides a secure energy efficient hybrid beamforming technique for satellite-integrated Earth cyber range, which provides us with the possibility to achieve simultaneous transmission and confidentiality of signals in UAV communications. In addition, the self-powered absorbable reconfigurable smart surface technology proposed by [34] provides a new perspective for enhancing the physical layer security of the UAV cyber range, especially in countering malicious interference and cyber-attacks. Finally, the covert mmwave communication technology in [35] can maintain the concealment of communication even in the face of space random monitors under the condition of finite block length, which is of great significance for covert communication of UAVs in complex environments. Taken together, our research efforts will integrate these advanced technologies to develop a comprehensive set of UAV physical layer security solutions designed to improve the communication performance and security of the UAV cyber range while ensuring robustness in the face of a variety of threats. Although the existing studies have discussed the security of physical layer and wireless control mechanism of UAVs, the risk of the UAV cyber range is often ignored, which provides opportunities for potential attackers. Existing assessment methods and technologies may not be sufficient to fully cover the security risks of drones.
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 4 Results and Discussion
### Simulation Results
#### 4.4.2 Effect of Mobility  
The effect of speed or node mobility on the performance of both schemes within a terrain size of 100 m\({}^{2}\) is explained as follows.  
* Performance in the presence of type-I attackers: Figure 4a represents end-to-end delay; it can be seen that our scheme produces greater delay than OCEAN because OCEAN uses former information for route selection, i.e., OCEAN avoids paths through misbehavior nodes; however, our scheme assesses new neighbors from the start. This problem will be further investigated in our future work. The packet drop of both schemes is shown in Figure 4b; it can be observed that OCEAN drops fewer packets with low speed, but as mobility increases, OCEAN drops more packets as compared to our scheme. The reason behind the scene is the second-chance mechanism of OCEAN, as a malicious node joins the network, it again initiates packet dropping attack. Evil throughput is presented in Figure 4c; it can be seen that with low mobility, evil throughput of OCEAN is small, but with medium speed, both schemes are the same; however, with high-speed, evil throughput of our scheme decreases. Good throughput is shown in Figure 4d, which shows that our scheme provides high throughput to good nodes. Normalized routing load is shown in Figure 4e; it can be concluded that NRL of our scheme is less than OCEAN from low to high mobility. PDF is shown in Figure 4f; our scheme delivered more packets than OCEAN.
* Performance in the presence of type-II attackers: Effect of mobility in the presence of type-II attackers against different mobility on the performance of both schemes in terms of delay, packet drop, evil throughput, good throughput, normalized routing load and packet delivery fraction is presented in Figure 5a-f. Delay is given in Figure 5a; our scheme results in higher delay as compared to OCEAN. Figure 5b represents drop packets; it can be found that our scheme leads to a slightly greater packet drop because of a bad/hard location problem. The throughput available to evil nodes is presented in Figure 5c; a zero evil throughput of both schemes can be observed, because selfish attackers discard RREQ packets. Good throughput is shown in Figure 5d; our scheme provides more throughput to good nodes as compared to OCEAN. Figure 5e represents NRL; it can be observed that our scheme's NRL is small compared to OCEAN. PDF is depicted in Figure 5f; our scheme performs well compared to OCEAN.
# SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
## 4 Time Series Stream Synthesis  
Generating independent test data through symbolic execution is not enough to test DSP programs because they do not take temporal features into account, as the test data from symbolic execution represents only the \"what\" that we will compute in a dataflow model [41]. Temporal features of stream data are equally critical because the behavior of SUT (i.e., Software Under Test ) is decided by the value of the input and the sequence of test data set in some specific order. In this paper, we will consider not only the values of test data (i.e., the \"what\" part of stream data) but also the temporal orders and time intervals between them (i.e., the \"when\" part of stream data).  
Some anomalies may happen in real stream processing scenarios, but we do not consider them this time. Such anomaly includes data loss, tampering, and duplication. In this paper, we only consider the out-of-order and the lag of the stream. In other words, we will build the test stream basing on the data sequence without losses, duplication, or value variation.
## Materials and Methods
### Test Apparatus and Test Procedure  
In this research, the fixed free resonant column manufactured by GDS Instruments Ltd. was employed as the most reliable tool for the determination of the damping ratio of soil specimens in the shear strain range of \(10^{-4}\)%-\(10^{-2}\)%. A more detailed description with technical features and the blueprint of the devices can be found in [38; 39].  
After closing the resonant column chamber, every specimen was saturated to Skempton parameter \(B\) at least 0.95. After that, the consolidation process took place. At every stage of consolidation, dynamic tests were conducted. First, the resonant frequency at torsional mode excitation had to be found. Next, the sinusoidal wave at resonant frequency was applied on top of the soil sample by the drive system, consisting of four coils and magnets. After 2 seconds of excitation, the coils were switched off, and the specimen was left to vibrate freely. The damping of the soil sample movement was recorded by accelerometer and displayed on the screen as a free vibration decay curve. Subsequently, based on the free vibration decay curve, the scientist carefully chose the right number of damping cycles at which specimen motions were utterly damped. Knowing the number of cycles, software calculated the logarithmic decrement \(\delta\) from the following equation:  
\[\delta=\frac{\sum_{1}^{n}\ln\left(\frac{Z_{n+1}}{Z_{n}}\right)+\ln\left(\frac{Z _{n+2}}{Z_{n+1}}\right)+\cdots+\ln\left(\frac{Z_{n+i}}{Z_{n+(i-1)}}\right)}{n} \tag{3}\]  
where: \(n\)--number of damping cycles and \(Z_{n+i}\)--amplitude of the respective cycle. With the value of logarithmic decrement \(\delta\), damping ratio \(D\) was calculated by the program from Equation (4).  
\[D=\sqrt{\frac{\delta^{2}}{4\pi^{2}+\delta^{2}}} \tag{4}\]  
In order to obtain a damping ratio curve dependent on shear strain (\(\gamma\)), the researcher increased the amplitude of applied voltage on the drive system and repeated all processes described above. Damping tests were conducted according to [40]. Moreover, every damping test was repeated ten times at each shear strain. Next, from ten results of the damping ratio, the average value was calculated and used in further interpretation.
# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 6 Conclusions  
In this paper, we have applied machine learning and deep learning to analyze the trends in forecasting of COVID-19 cases in the UAE. We have used different combinations of the input attributes such as Vaccinations in addition to event-related attributes, with univariate and multivariate analysis. The dataset was initially analyzed and preprocessed, then the models were trained and compared. The dataset used was taken from the Federal Competitiveness and Statistics Centre website and is updated daily. Five different models were experimented on, including Exponential Smoothing, LASSO Regression, and LSTM, which were selected as they were the most commonly used models, along LSTM-AE and Bidirectional LSTM-AE. Upon evaluation, it was found that univariate LSTM performed the best with an RMSE of 275.85, which was an improvement of more than 30% from the current state of the art related to the UAE [28]. This model had the lowest RMSE,which proves its feasibility as a model for predicting the COVID-19 trends in the UAE. The model was able to learn the trend and the seasonality of the data. It was also seen that the bidirectional LSTM was also very promising.  
Although, the Vaccination attribute and the Event attributes did not perform as we expected, which was that the attributes would show a direct effect on the trend. However, there was only approximately a 10% decrease in RMSE as compared to the best model. This can be attributed to the amount of information these attributes are providing to the model in terms of trend and seasonality. From this we learned that the attributes are useful but require some additional attributes/biases to help them.  
Through further research and the incorporation of granular informative attributes, more efficient models can be implemented. This can facilitate the simulation of various situations by the government and help policy making in terms of reducing the burden of pandemics on the nation.
## 2 Results and Discussion
### Odor Analysis
#### 2.4.2 Green and Fresh Odor  
Grass odor is sometimes referred to as a fresh note, and the chemicals with this descriptor are predominantly aldehydes with six to nine carbons and C6 alcohols [36; 37]. In _Rbloodendron_ flowers, hexanal was the major such compound in all samples, mainly contributing to the green and grassy odor. Besides this, 2-hexanal, heptanal, octanal, and benzeneacetaldehyde was also found to contribute to the green and fresh odor [34]. Among them, 2-hexanal and heptanal accounted for relatively high proportions in \(R\). _anne_ and \(R\). _agastum_. On the other hand, C6 alcohols such as (\(E\))-3-hexen-1-ol and 1-hexanol also yielded a green, fresh, and herbal odor [32]. In addition, \(\beta\)-cadinene, 2-pentyl-furan and formic acid, 2-phenylethyl ester are also related to a green odor.