esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Profile  
Figure 9 compares the cooling rates measured in the centre of the fusion zone for three different welding speeds (3 mm/s, 4 mm/s, and 5 mm/s). Over the solidification range for AZ31, a welding speed of 5 mm/s demonstrates a faster cooling rate (72 \({}^{\circ}\)C/s) than welding speeds of 4 mm/s (87 \({}^{\circ}\)C/s) and 3 mm/s (109 \({}^{\circ}\)C/s). Higher welding speeds lower the heat input and increase the cooling rates, leading to fine grain structure in the fusion zone. The thermal gradient (cooling rate (G.R)/travel speed (R)) calculated for the different welding speeds of 3, 4, and 5 mm/s are 36 \({}^{\circ}\)C/mm, 21 \({}^{\circ}\)C/mm, and 14 \({}^{\circ}\)C/mm, respectively. The growth rate (R) along the welding direction varies for welding speeds from 3 to 5 mm/s. Thus, the G/R ratio calculated along the welding direction is reduced from 12 \({}^{\circ}\)C s/mm\({}^{2}\) for a 3 mm/s welding speed, to 2.8 \({}^{\circ}\)C s/mm\({}^{2}\) for a 5 mm/s welding speed. As discussed earlier, increasing the welding speed significantly increases constitutional supercooling, by lowering the G/R ratio [33]. This results in the larger supercooled liquid regions ahead of the growth front. With the increasing weld travel speed, a lower temperature gradient in the weld pool results in higher constitutional supercooling in the liquid, and promotes the formation of equiaxed grains. The equiaxed grains observed in the current investigation of welds prepared with 5 mm/s may be attributed to a combination of high welding speed (low heat input), and low G/R ratio, by increasing constitutional supercooling.
## 3 Result and Discussion
### Property of Polymer Micelles
These results suggest that the polymers in the solution would behave as they are widely acknowledged, i.e., CMC become low, owing to a low acid-value, thus verifying the fact that the polymers would be dissolved in the aqueous phase, preserving their amphiphilic nature. It should be further noted that the \(I_{1}/I_{3}\) values of BL-01, BL-03, and BL-05, which cannot be directly dissolved in aqueous solutions, seemed to plateau at polymer concentrations over 0.005 wt%, whereas those of BL-02 and BL-04 continued to decrease until the concentration was over 0.01 wt%. An increase in polymer concentration above CMC would increase the number of micelles in the solutions. Considering that the pyrene concentrations were the same in all samples, pyrene molecules would be effectively distributed inside the micelle of BL-01, BL-03, and BL-05, compared to BL-02 and BL04.This suggests that loading capacities of hydrophobic molecules such as pyrene are higher per polymer weight in BL-01, BL-03, and BL-05 than in BL-02 and BL-04. From these results, the dispersant polymer which did not easily dissolve in water would have a high loading-capacity for hydrophobic coloring materials, validating our proposed process of preparing a pre-solution of the polymers in DEG as a water-soluble organic solvent. Figure 4: Micelle-size-distribution of polymers; BL-01D) 0.1% BL-01 aqueous solution from DEG solution; BL-02) 0.1% BL-02 aqueous solution from polymer powder; BL-02D) 0.1% BL-02 aqueous solution from DEG solution; BL-03D) BL-03 aqueous solution from DEG solution; BL-04) BL-04 aqueous solution from polymer powder; BL-05D) BL-05 aqueous solution from DEG solution.
# Queue Spillover Management in a Connected Vehicle Environment
## 2 Method Principle  
In a connected vehicle environment, the vehicle is equipped with an intelligent terminal. It can sense the information on the vehicle and the surrounding environment information. When passing through the intersection, it can communicate with the intersection signal controller through wireless communication. The controller can obtain the real-time position, speed, and other information on the vehicle from V2I. The interaction between the intersection signals controller and vehicle information, is shown in the Figure 1.  
This paper presents a method to optimize the overflow problem of intersections with V2I. First, the vehicle traveling model is established, and the maximum control distance between the intersection is calculated. Then, the road intersection overflow trend is judged according to the real-time vehicle information data. Finally, the controller optimizes the green time of the intersection and completes the phase assignment. The control flow of the system, is shown in Figure 2.
## 3 Proposed Chaos-Based Stream Ciphers Based on Secure PRNGs of Chaotic Sequences
### Architectures Description of the Proposed PRNGs-CS
#### 3.2.1 The Proposed LSP-PRNG
\(XL(n)\), \(XS(n)\), and \(XP(n)\) are denoted as the maps output values at instant \(n\) of the: Logistic, Skew-Tent, and PWLCM map, respectively, defined as follows:  
The first three outputs of the matrix M, \(XLC(1)\), \(XSC(1)\), \(XPC(1)\) are given by the following equation:  
\[\left[ \begin{array}{c}XLC(1)\\ XSC(1)\\ XPC(1)\end{array} \right]\!\!=\!\!M\times\left[ \begin{array}{c}XL(1)\\ XS(1)\\ XP(1)\end{array} \right] \tag{17}\]  
with:  
\[XL(1)= Logistic\!\left\{\mbox{mod}\!\left(XL(0),\!2^{N}\right)\right\} \tag{18}\]  
\[XS(1)= STM\!\left\{\mbox{mod}\!\left(XS(0),\!2^{N}\right),P_{s}\right\} \tag{19}\]  
\[XP(1)= PWLCM\!\left\{\mbox{mod}\!\left(XP(0),\!2^{N}\right),P_{p}\right\} \tag{20}\]  
Then, if \(2\leq n\leq N_{s}\), (\(N_{s}\) represents the number of samples desired), the three outputs of the matrix M are governed by Equation (15), with:  
\[XL(n)= Logistic\!\left\{\mbox{mod}\!\left(XLC(n-1),\!2^{N}\right)\right\} \tag{21}\]  
\[XS(n)= STM\!\left\{\mbox{mod}\!\left(XSC(n-1),\!2^{N}\right),\ P_{s}\right\} \tag{22}\]  
\[XP(n)= PWLCM\!\left\{\mbox{mod}\!\left(XPC(n-1),\!2^{N}\right),\ P_{p}\right\} \tag{23}\]  
The obtained multiplexed samples of the sequence \(X(n)\) are controlled by the chaotic sample \(X_{th}(n)\) and a threshold \(T\):  
\[X(n)\!=\!\left\{ \begin{array}{ll}mod\!\left((XPC(n)+XLC(n)),2^{N}\right)&if \ 0<\!X_{th}(n)\!<\!T\\ \\ XSC(n)&otherwise\end{array} \right. \tag{24}\]  
where \(X_{th}(n)\!\!=\!\!XPC(n)\oplus XSC(n)\) and \(T\!\!=0.8\times 2^{N}\).
## 4 Experiments
### Parameter Selection Experiment  
In this algorithm, the value of pixel size increment \(\delta\) directly affects the success rate of screening, thus affecting the accuracy of spike center positioning. Therefore, the values of \(\delta\) are 0, 10, 20, 30, and 40, respectively, in this paper, and the number of wrong samples under different values is tested to determine the value of \(\delta\) through experiments. The experimental results for various values of \(\delta\) are shown in Figure 14.  
It can be seen in Figure 14 that when \(\delta\) is 20, the screening error rate of the four types of samples is the lowest, which is universal. Therefore, the pixel size increment \(\delta\) in this paper is 20.  
Figure 13: The success rate of road nail area screening for different algorithms.
# Design and Development of an SVM-Powered Underwater Acoustic Modem
## 2 Materials and Methods
### Software Tools  
Signal processing and classification were carried out using MATLAB, with custom scripts developed for data decimation, feature extraction using the CWT, and SVM classification.  
For the feature extraction, the CWT was utilized by leveraging the \"bump\" wavelet, which is preferred for its robust response to non-stationary signals in noisy environments. The CWT's ability to localize signal characteristics in both the time and frequency domains makes it particularly suitable for analyzing the complex signals encountered in underwater acoustic communication. The mathematical details of the CWT and the reasons for choosing the \"bump\" wavelet are expounded in Sections 3 and 4, respectively, with comparative analyses highlighting its advantages over the FFT and STFT in extracting meaningful features from acoustic signals.  
The SVM classifier was configured with a radial basis function (RBF) kernel, which is ideal for handling the nonlinearities in the feature space created by the CWT. Parameter tuning, particularly for the \(\mathbf{C}\) and \(\gamma\) parameters of the SVM, was conducted through a series of validation tests to enhance the classification accuracy.  
The MATLAB environment was also used for the initial data processing and visualization, including the generation of scalograms and the plotting of signal characteristics.  
For the development and synthesis of the FPGA's Verilog code, the iCEcube2 Version 2020.12 software (Lattice Semiconductors, Hillsboro, OH, USA) suite was utilized, which allowed for precise control over the FPGA's functionality and signal-processing capabilities.
# A Novel Multi-User Codebook Design for 5G in 3D-MIMO Heterogeneous Networks
## 4 System Performance Analysis Based on Random Geometric Finite Feedback
The greater the feedback overhead, the greater the amount of information received by the base-stations from the user feedback, the smaller the quantization error, the more accurate the beam vector, the smaller the throughput loss, and the better the system performance. This article studies the improved 3D-MIMO precoding scheme.
# Threatening URDU Language Detection from Tweets Using Machine Learning
## 4 Experiments and Results
### Results Using Deep Learning Models  
This study also employs deep learning models for threatening text in the Urdu language, which include LSTM, GRU, CNN, and FCN. The experimental results for these models are provided in Table 13. The results suggest that the performance of deep learning models is poor as compared to machine learning models. The best accuracy of 65.40% is obtained by the LSTM model. Similarly, LSTM also shows the best F1 score of 65.40% and the best recall of 67.90%. The performance of CNN is the best regarding precision, which is 68.10% for threatening text detection. Deep learning models tend to show better performance when trained using a large dataset. However, the dataset used in this study is comparatively small and insufficient for deep learning models to obtain a good fit.  
Figure 8 shows the ROC-AUC curve for deep learning models. It confirms that the performance of LSTM is superior to other models. It has an AUC of 0.7304, while GRU and CNN perform marginally poor performance as compared to LSTM. FCN shows the worst performance with an AUC of 0.5449 only.  
Figure 9 shows the confusion matrices for deep learning models.  
LSTM has 700 correct predictions with 344 correct predictions for threatening tweets and 356 correct predictions for non-threatening tweets. FCN shows poor performance and has only 574 correct predictions. The performance of CNN and GRU is marginally poor than the LSTM. CNN has 698 correct predictions, 375 for threatening and 323 for non-threatening text, while GRU has a total of 697 correct predictions.
## 2 Results and Discussion
### Impact of Cluster-Zone Leaf Removal on Grape Phenolic Profiles
#### 2.2.3 Flavan-3-Ols  
A total of six free flavan-3-ols were identified and the composition of proanthocyanidins was investigated. The results showed that LR treatment had no impact on the total concentration of free flavan-3-ols, which is consistent with previous studies on the Cabernet Sauvignon [18], Pinot Noir [11] and Sangiovese [21] grapes. Moreover, no consistent trend in the impact of LR treatment on total flavon-3-ols across two distinct varieties over two years (Table S2). In 2021, the total proanthocyanidins and flavon-3-ols in Cabernet Sauvignon grapes treated with LR ware found to be higher than in the control group, with LR2 exhibiting the highest content in both extension units and terminal units. In 2022, a significant difference was observed in the concentrations of total proanthocyanidins and flavon-3-ols between LR2 and LR3 in Cabernet Sauvignon grapes. However, the differences between the LR treatment groups and the control group were not statistically significant. Furthermore, no significant differences in total proanthocyanidins and flavon-3-ols were observed between the different groups of Marselan grapes in 2022. In a previous study, the impact of consecutive three-year LR treatments on the proanthocyanidins in Cabernet Sauvignon grapes did not exhibit a consistent trend [18]. This indicates that the impact of LR treatments on the composition of proanthocyanidins may be relatively insignificant and is likely to be influenced by other factors.
## 3 Capacitive Sensing with Electronic Detection  
### Principle of Operation  
The essential part of the electronic detection measurement system is a modified differential capacitive sensor, shown in Figure 7. It is composed of two planar capacitors with inter-digitated  
Figure 6: Measured responses of CSC12 cantilevers functionalized with APS molecules (red) on switching between pure N\({}_{2}\) and a mixture of N\({}_{2}\) and 50% vapour pressure of TNT at room temperature. In this measurement, the temperature changes in the system (blue) due to switching between the two gases are deliberately set to approximately 30 mK.  
electrodes with comb-like structure (COMB), covered with a thin layer of silicon dioxide: \(C_{p}\) is chemically modified, while \(C_{n}\) is not. Both capacitors are near each other, so on average, an equal number of target molecules is present in the atmosphere above capacitors. As one of the sensors is chemically modified with a molecular monolayer enhancing the surface adsorption of target molecules when compared to the non-functionalized capacitor, the average surface coverage of targeted molecules is increased on the modified capacitor.  
The time-average capacitance of the capacitor \(C_{p}\) (Figure 7) is increased because of adsorbed molecules to the functionalized plates of that capacitor; the plates are now partially covered with an extra monolayer of targeted molecules, which have a dielectric constant larger than that of the air. The capacitances of the two capacitors in a differential pair are now slightly different, which can in principle be measured using appropriate low noise electronics. A rather straightforward calculation using Equations (2) and (3) shows that the detection of small number of adsorbed target molecules is possible if the measurements are performed with extremely low noise electronics.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion
### Research Gaps and Future Directions
#### 4.2.2 Industrial Flexibility Potential  
Compared to the residential and commercial sectors, demand flexibility potential for the industrial sector has been relatively unexplored and few researchers have focused on quantifying the flexibility potential of industrial activities [18; 21; 26; 31; 43; 49; 67]. In fact, of the studies reviewed, only nine papers quantified industrial flexibility potential on its own.  
Often, researchers presented methods designed to evaluate the flexibility potential of a particular industrial activity or employed simplifying assumptions to use regional statistics of industrial production [18]. This is in part due to the significant differences like industrial demand compared to that of residential and/or commercial sectors. Demand reduction in an industrial facility may impact the plant's production levels, pre- and post-process inventory buildup, and the work schedule of the crew, to name a few. These factors complicate the analysis and limit the generalizability of different solutions since each industrial process is different, not just in characteristics but also in terms of operation, objectives, and requirements. There have only been a few examples focused on the detailed flexibility potential of the industrial sector. For example, [26] provided a tool for industrial process operators to evaluate the costs and benefits of participating in DR; the tool analyzes the facility to determine how an industrial customer should proceed before a DR event. In [67], the authors developed an optimization model to maximize demand reduction potential subject to various operational constraints such as inventory constraints, cost constraints, crew constraints, and workstation interdependencies.  
The oversimplified analysis of industrial activities leads to inaccurate estimations of the sector's flexibility potential. It also ignores the inherent variability in energy use and operation characteristics of this sector, which could otherwise be used to improve the reliability and security of the power grid. There is both a need and an opportunity for developing generalized demand flexibility models for various industrial facilities or sectors where common themes and patterns are identified, characterized, and modeled without overlooking unique features and operation aspects.
# Forward Computational Modeling of Respiratory Airflow
## 5 Ventilation and Perfusion  
Thus far, the focus of this report has been on how airflow distribution can be modeled in the airway tree, since airflow distribution defines ventilation distribution. However, pulmonary pathophysiology also affects the distribution of perfusion, resulting in potential mismatch of ventilation-to-perfusion. Note that such mismatch also exists when perfusion is perturbed. Alveolar ventilation \(\dot{V}_{\text{A}}\) is the movement of air to and from the blood-gas barrier, while pulmonary perfusion \(\hat{Q}\) is blood flow in pulmonary blood vessels. For effective oxygenation of a living organism, both \(\dot{V}_{\text{A}}\) and \(\hat{Q}\) must be appropriately matched. Regional ventilation with poor perfusion results in physiologic dead space, which leads to wasted ventilation, while poor regional ventilation with normal perfusion results in shunting. Both conditions impair the uptake and elimination of gases by the lung [34; 43].  
CT imaging plays a tremendous role in elucidating lung perfusion and its distribution throughout the vascular tree. Maps of ventilation and perfusion in the lungs can be studied concurrently, to analyze \(\dot{V}_{A}/\dot{Q}\) relationships at different lung regions, volumes, and times during inspiration. It is assumed that for regions in the lung with little or no ventilation, the constriction of blood vessels in poorly ventilated regions and redistribution of blood to better-ventilated regions of the lung (i.e., hypoxic pulmonary vasoconstriction) maintains appropriate \(\dot{V}_{A}/\dot{Q}\) matching. But this is not always the case. [PERSON] et al. [69; 70] noted that lobes with early pulmonary diseases were hyperperfused, while lobes with late pulmonary diseases were hypoperfused. [PERSON] et al. [70; 71] reported hyperperfusion in both early and late-stage pulmonary diseases, and [PERSON] et al. [70; 72] reported a mixture of both hyperperfusion and hypoperfusion. These contradictory findings make it extremely important for pulmonary ventilation-perfusion relationships to be studied on a patient-specific basis, and for computational models to be developed to enhance knowledge in this field.
## 4 Simulation Results  
This section reports the simulation findings of the proposed PSs sitting and timing approach. For simplicity, only a few selected results are detailed, giving a comprehensive comparison of all the simulations carried out. We increased the number of new PSs from 1 to 12. As a background, Figure 18 shows the current PS service areas. The Voronoi diagram takes the PSs owned by Unareti, the red points, and the PSs owned by a competitor DSO, the yellow points. The current average distance between the squares and the related PSs is 1.82 km, while the weighted distance is 1.48 km.
## 5 Discussion
In conclusion, this study not only theoretically enriches the body of knowledge related to sports biomechanics, but also provides a scientific basis for actual sports training and injury prevention, which has an important application value and can positively contribute to athlete safety and performance enhancement by optimizing the side-cutting technique and foot progression angle. It is essential to acknowledge certain limitations in this study. First, only one healthy subject was included, precluding the consideration of the biomechanical properties of the knee joint across different genders, ages, or pathological conditions; thus, expanding the sample size is necessary for enhancing result generalizability. Second, the finite element model did not fully account for the knee joint's complexity and its structures, such as the non-linear characteristics of the medial and lateral ligaments and anatomical variations of the anterior and posterior cruciate ligaments, which may impact the model's accuracy in simulating stress and load distributions. Finally, the CT and MRI data utilized were captured at a single point in time, neglecting the dynamic changes in the knee joint during exercise, thereby limiting the model's capacity to reflect actual physiological conditions. Future research should address these limitations to improve the accuracy and clinical applicability of the model. In conclusion, the study's findings suggest that larger foot external rotation angles may elevate the risk of medial tibial attachment zone tears of the ACL during cutting maneuvers, with additional implications for the surrounding knee tissues. The increased stress on the meniscus and femoral cartilage associated with greater foot external rotation angles during side-cutting indicates that the knee endures greater impact loads, heightening the risk of knee-related injuries. To mitigate this risk, especially concerning ACL injuries, it is advisable to consciously adopt an internal rotation side-cutting strategy at initial contact. Future studies should further explore these dynamic changes in different populations to deepen our understanding of knee biomechanics in sports.
# Automatic 3D MRI-Ultrasound Registration for Image Guided Arthroscopy
## 3 Methods
### Registration
#### 3.2.4 Registration  
For the registration of the intra-operative US volumes to pre-operative MRI volumes, we use LC\({}^{2}\), a multi-modal similarity metric [11; 16]. It is a powerful measure which is invariant to local changes such as brightness and contrast and is proven to work for US-MRI registration [11]. LC\({}^{2}\) correlates both the MRI intensity values and the spatial gradient magnitude with the US. We use this cost function along with optimal patch size to perform the registration. During the registration, it is necessary to look out for and eliminate the unwanted parameter updates in order to avoid unstable results. Therefore, we optimize all the parameters of the obtained rigid transform after every evaluation. It is difficult to compute the analytic derivative of LC\({}^{2}\) which is of least square fitting and hence, as in [11], we use Bound Optimization by Quadratic Approximation (BOBYQA) [26] optimization,which is capable of internally creating its own derivative approximations resulting in fewer evaluations than any other direct search methods.
## 4 Results and Analysis
### Performance Comparison: TF-IDF + XGBoost vs. Baseline Models
Ultimately, this study demonstrates that the combination of simple feature extraction (TF-IDF) and a powerful classification model (XGBoost) effectively enhances data quality and learning performance. These findings validate the practical applicability of automated educational data analysis systems and provide insights into future research directions for scalability and continuous improvement.