esa-sceva/satcom-chunk-collection huggingface dataset:
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 4 Study on the Mechanism of GO Adsorption Process
### Influence of External Environment on Adsorption
#### 4.2.2 Temperature  
Temperature change will not only affect the energy of the adsorbent and the adsorbent in the stable existence, but also change the motion speed and contact probability of the particles in the solution, thus promoting or inhibiting the adsorption process of Co\({}^{2+}\) by GO. In order to explore the effect of temperature on adsorption, the temperature was calculated by the keyword \"temperature\" setting in the Gaussian software, and the stable configurations and adsorption energy changes of [GO(COO)...Co]\({}^{+}\), [GO(O)s...Co]\({}^{+}\) and [GO(O)...Co] at 20 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 50 \({}^{\circ}\)C were solved.  
The following results were found:  
(1) With the gradual increase in temperature, [GO(COO)...Co]\({}^{+}\) gradually changed from Co\({}^{2+}\) connected with hydroxyl oxygen on a carboxyl group at 20 \({}^{\circ}\)C to a ring structure connected with Co\({}^{2+}\) and two carboxyl oxygens at other temperatures, and its energy also increased correspondingly by 0.108951 Hartree; its stability decreased somewhat. The structure and energy changes of [GO(COO)...Co]\({}^{+}\) are shown in Figure 4:  
(2) With the increase in temperature, the structure of [GO(O)s...Co]\({}^{+}\) and [GO(O)...Co] also underwent similar changes, from Co\({}^{2+}\) connected to GO through hydroxyl oxygen atoms at 20 \({}^{\circ}\)C and 30 \({}^{\circ}\)C to the structure of Co\({}^{2+}\) and the hydroxyl group separated from the graphene sheet, as shown in Figure 5.  
It can be seen from the above calculation results that the influence of temperature on the adsorption capacity of different functional groups on GO is not the same. In order to accurately determine the influence of temperature on the adsorption, it is necessary to combine the analysis with experiments.
# A Near-Ultraviolet Photodetector Based on the TaC: Cu/4 H Silicon Carbide Heterostructure
## 3 Results and Discussions
### Photoreponse Characteristics
Under these conditions, with a beam diameter of 2 mm, the resulting response spectrum is depicted in Figure 9. The envelope of the pulse oscillation spans approximately 260.7 \(\upmu\)s. The response time was determined using the following formula [31]:  
\[\tau_{total}=\sqrt{\tau_{initial}^{2}+\tau_{response}^{2}} \tag{3}\]  
Consequently, the cut-off frequency can be calculated as follows:  
\[f_{cutoff}=\frac{1}{2\pi\tau_{response}} \tag{4}\]  
where \(\tau_{response}\) is the response time with a value of 260.2 \(\upmu\)s, and \(\tau_{total}\) is the total response time with a value of 260.5 \(\upmu\)s. The \(\tau_{initial}\) is the pulse duration of the exciting laser source, which is equal to 10 ns, and the cut-off frequency is 611 kHz. A comparative analysis of the proposed photodetector with previously reported devices is summarized in Table 1. ZnO-based photodetectors, as discussed in [11; 62; 63], demonstrate low and dark currents but suffer from significantly reduced responsivity and slower response times. Similarly, the Ga\({}_{2}\)O\({}_{3}\)/GaN photodetector presented in Ref. [64] achieves a responsivity of \(5\times 10^{2}\) mAW\({}^{-1}\) at 2 V, which can be markedly improved through field-enhanced exciton ionization at higher voltages. For instance, at 16 V, the responsivity increases dramatically to approximately 2.6 \(\times\) 10\({}^{6}\) mAW\({}^{-1}\). However, this enhancement comes at the cost of a very narrow response bandwidth of around 4 nm and a sluggish response time of 0.1 s due to the ionization process. Additionally, a boron-doped 3C-SiC nanobelt photodetector reported in Ref. [19] recorded a remarkable responsivity of \(6.37\times 10^{8}\) mA/W and a response time of 50 ms at a 405 nm operating wavelength. In comparison, even though the SiC/TaB-based photodetector described in Ref. [31] achieved a responsivity of 2.9 \(\times\) 10\({}^{3}\) mA/W with a faster response time of 260.5 ns, a thick layer of TaB is needed to achieve such a performance, which contributes to the higher cost of photodetector design. In contrast, the proposed TaC: Cu/4H n-SiC photodetector outperforms many of these alternatives by delivering a balanced performance. It achieves  
Figure 6:0 Time response of the fabricated device upon excitation with a 10 ns Q-switched pulse. a responsivity of 1.66 AW\({}^{-1}\), maintains a low and dark current of 1.1 mA, and demonstrates a rapid response time of 260.2 us, making it a compelling candidate for practical photodetection applications.
# Optimization of Wheelchair Control via Multi-Modal Integration: Combining Webcam and EEG
## 5 Experimental Results and Discussion
### Comparison with Previous Works  
The fusion method provides higher accuracy compared to EEG signals or a webcam. The accuracy of the fusion method is always higher than that of the webcam and EEG signals, as shown in Figure 13.  
Figure 12: Confusion matric of fusion.  
Figure 13: Validation accuracy.  
For example, in epoch 11, the accuracy of fusion is up to 0.99 whereas the accuracy of EEG signals and webcam are limited to 0.95; 0.98, respectively.  
The combination of facial expressions and neural signals has the potential to improve wheelchair control systems, offering intuitive interaction and precise control. This fusion of parameters enhances responsiveness and personalization, empowering individuals to navigate their surroundings with increased ease and independence.  
A comparison of the proposed methodology with deep learning and other classification algorithms from the literature [35; 36; 37; 38; 39] is presented in Table 4. The comparison focuses on studies that were also evaluated in terms of precision, recall, accuracy, and F-score.  
We have found that our proposed algorithm based on a fusion of CNN-LSTM outperforms the other works in both accuracy and precision. These results show the advantage of our networks in predicting the four emotions based on EEG signals and video recognition.  
Our network architecture essentially represents a comprehensive method of emotion identification, seamlessly integrating the perceptive capabilities of image analysis with the nuanced insights derived from EEG signal processing. Late fusion creates a cooperative synergy that enables our model to overcome single-modality methods' constraints.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
### Contribution
The running time of our Sparse Time-Series Clustering framework is \(O(NT\log^{2}T+INk\log^{2}T)\), where the first term accounts for the time complexity of Sparse Gaussian Process Regression with \(\Theta(\log T)\) inducing points and the second term accounts for the running time of \(k\)-means with the DTW distance, running for a maximum of \(I\) iterations when applied to \(N\) sparse time series with \(\Theta(\log T)\) inducing points each. The running time of applying the \(k\)-means algorithm to the original dataset with \(N\) time series of length \(T\) each is \(O(INkT^{2})\). Therefore, the asymptotic running time of our Sparse Time-Series Clustering framework is \(\Theta(\max\{T^{2}/\log^{2}T,\,IKT/\log^{2}T\})\) times faster than the asymptotic running time of directly applying \(k\)-means to the original dataset. Intuitively, the improvedasymptotic running time of our framework is due to improved running time for computing DTW from \(\Theta(T^{2})\) in the original data to \(\Theta(\log^{2}T)\) in the sparsified data. In addition to speeding up \(k\)-means, by computing a sparse representation of the original data set, Sparse Gaussian Process Regression tends to smoothen outliers and particularly noisy parts of the original time series, thus resulting in clusterings that are more robust to noisy observations and of higher quality. We conduct an extensive experimental evaluation of our Sparse Time-Series Clustering framework on the datasets of the University of California (UCR) Time-Series Classification Archive [20] (see Section 5). The main finding is that Sparse Time-Series Clustering, with a logarithmic number of inducing points, computes clusterings with Adjusted Rand Index (ARI, see Section 2.3) comparable to the ARI of a baseline clustering, computed by applying the standard \(k\)-means algorithm to the original datasets (see Table 3). As for the running time, \(k\)-means runs significantly faster when applied to the sparsified dataset (see Figure 6). The most computationally demanding step of our framework is the modeling step, where we apply Sparse Gaussian Process Regression. For large datasets, the total running time of our framework is faster than applying \(k\)-means to the original dataset (see Table 5). Moreover, there are datasets for which we did not manage to run the standard \(k\)-means algorithm in our computational infrastructure, while our Sparse Time-Series Clustering framework produces good quality clusterings in reasonable running time (see Table 4). We should also note that the modeling step may run offline, once per time series, with its sparse approximation stored for any future use, and is perfectly parallelizable.
## 5 Working Face Abandoned Roadway Filling Technology Process
### Field Test of Inorganic Cementitious Material for Abandoned Roadway  
As shown in Figure 15, the transport roadway of the 3122 working face of Jingxin Coal Industry is a trapezoidal section, with an upper width of 3700 mm, a lower width of 4300 mm, and a height of 2750 mm, and the abandoned roadway of the 3122 working faces is rectangular, with a width of 4500 mm, and the height of 5000 mm, considering that there is a height difference of 2250 mm between the transport roadway of 3122 and the abandoned roadway, the grouting pipe leads out from the transport roadway of 3122 and injects grout into the abandoned roadway diagonally upwards, leaving a 0.5 m distance between the end position of the pipe and the top plate of the abandoned roadway. To facilitate the operation, the slurry pipe can be designed to be installed on the wall of the roadway and fixed with wire. In the underground filling test, due to the limitations of the site construction conditions, the pipeline system has not been perfected, and it is necessary to lift the pipe manually, spray slurry in the bucket in advance, and observe the foaming and solidification phenomenon, to ensure that the slurry can be foamed normally before carrying out the filling test on the abandoned roadway.  
Through the initial test downhole, the filling equipment operated effectively. The slurry is prepared according to the material proportion in Program 5. In the underground environment, the slurry displays excellent foaming properties with a stable internal structure. Initially, a few bubbles may escape from the surface as the slurry begins to foam. Once sufficiently foamed, the slurry fills the container with a concave-convex shape on the contact surface, indicating a good foaming effect. As shown in Figure 16, after the slurry is prepared and shaped, it enters the test site to be filled through the conveying system, and the filling material can rely on its fluid pressure to be completely paved and covered inside the abandoned roadway, the surface is even and flat after filling, and no accumulation of slurry is found.  
The slurry began to foam and expand once it was filled into the abandoned roadway. Initially, air bubbles escaped from the middle of the filled section, causing the slurry to bulge and form stripes and unevenness. Gradually, it spread to fill the entire space. Through the underground filling practice, it was observed that using the new type of inorganic cementitious filling material for grouting and filling the abandoned roadway resulted in good slurry foaming, meeting the filling requirements. Further observation of mine pressure during later mining stages will help understand the distribution of the surrounding rock stress and deformation, ensuring smooth passage through the abandoned roadway and enabling safe and efficient mine production.  
Figure 15: Position of abandoned roadway section.
## 3 Algorithms for Managing Complex Traffic Scenarios and Violations
### Types of Complex Traffic Scenarios and Violations
#### 3.1.1 Unpredictable Pedestrian Behavior  
One of the most challenging aspects of urban driving is dealing with unpredictable pedestrian behavior, particularly jaywalking. [PERSON] et al. [47] highlighted this challenge in their study on human-like autonomous car-following models, noting that pedestrians often make sudden decisions that deviate from expected patterns, requiring autonomous vehicles to have rapid detection and response capabilities.  
Figure 4 illustrates the algorithmic framework for managing complex traffic scenarios in autonomous driving systems. This flowchart outlines four key components: complex scenarios encountered on the road, methods for detection and prediction, response algorithms, and ongoing challenges. The diagram shows how autonomous systems progress from identifying challenging situations to executing appropriate responses while highlighting  
the continuous need for improvement in areas such as real-time processing and ethical decision-making. This visual representation provides a concise overview of the intricate processes of navigating complex traffic environments, emphasizing the interconnected nature of perception, prediction, and action in autonomous driving technology. Numerous factors, including individual psychology, cultural norms, and environmental conditions, influence pedestrian behavior. [PERSON] and [PERSON] (2018) also analyzed pedestrian behavior at crosswalks and found that factors such as group size, age, and even parked vehicles significantly affect crossing decisions. This complexity makes it difficult for autonomous systems to predict pedestrian intentions accurately.
## 6 Refined Authenticated Key Exchange Protocol with Point Halving Optimization
### Revised Protocol Steps  
Step 1--Initiator \(\mathcal{A}\) (Request Generation):  
1. Generate a session-specific random scalar \(\kappa_{\mathcal{A}}\in\mathbb{Z}_{n}\) and compute its DBC representation using bases \(\frac{1}{2}\) and 3:\[\kappa_{\mathcal{A}}=\sum_{i=1}^{\ell}\sigma_{i}\Big{(}\tfrac{1}{2}\Big{)}^{\beta_ {i}}\cdot 3^{\tau_{i}},\quad\sigma_{i}\in\{-1,+1\}\]
2. Compute pseudorandom public component: \(\Gamma_{1}=[\kappa_{\mathcal{A}}]\Phi\) using point halving and tripling.
3. Derive authentication hash: \(\Gamma_{2}=H(\mathsf{pk}_{\mathcal{A}}\parallel\Gamma_{1})\).
4. Generate commitment: \(\Gamma_{3}=\mathsf{Enc}_{\mathsf{sk}_{\mathcal{A}}}(\Gamma_{2})\).
5. Transmit the pair \(\{\Gamma_{1},\Gamma_{3}\}\) to \(\mathcal{B}\).  
Step 2--Responder \(\mathcal{B}\) (Response Computation and Verification):  
1. Recompute hash: \(\widehat{\Gamma}_{2}=H(\mathsf{pk}_{\mathcal{A}}\parallel\Gamma_{1})\).
2. Verify authenticity: \(\mathsf{Dec}_{\mathsf{pk}_{\mathcal{A}}}(\Gamma_{3})\stackrel{{ \rightarrow}}{{=}}\widehat{\Gamma}_{2}\).
3. Generate independent scalar \(\kappa_{\mathcal{B}}\in\mathbb{Z}_{n}\) and derive its DBC representation.
4. Compute response point: \(\Psi_{1}=[\kappa_{\mathcal{B}}]\Phi\) via DBC-based scalar multiplication.
5. Compute confirmation hash: \(\Psi_{2}=H(\mathsf{pk}_{\mathcal{B}}\parallel\Psi_{1})\).
6. Encrypt confirmation: \(\Psi_{3}=\mathsf{Enc}_{\mathsf{sk}_{\mathcal{B}}}(\Psi_{2})\).
7. Derive session key: \(K_{\mathcal{B}}=[\kappa_{\mathcal{B}}]\Gamma_{1}\).
8. Send \(\{\Psi_{1},\Psi_{3}\}\) to \(\mathcal{A}\).  
Step 3--Initiator \(\mathcal{A}\) (Final Verification and Key Derivation):  
1. Recompute \(\widehat{\Psi}_{2}=H(\mathsf{pk}_{\mathcal{B}}\parallel\Psi_{1})\).
2. Verify: \(\mathsf{Dec}_{\mathsf{pk}_{\mathcal{B}}}(\Psi_{3})\stackrel{{ \rightarrow}}{{=}}\widehat{\Psi}_{2}\).
3. Derive session key: \(K_{\mathcal{A}}=[\kappa_{\mathcal{A}}]\Psi_{1}\).
## 3 Results
### Effects of Transient FaHMGR and FaHMGRi Expression on Strawberry coloration and Aroma
The results showed that the expression of key anthocyanin metallic pathway genes, including _FaC4H_, _FaCHI_, _FaCHS_, _FaF3H_, _FaANS_, _FaUFGT_, _FaMYB10_, _FabHLH143_, _FaWD40_, and _FaPAL2_, was lowest in FaHMGROE strawberries, whereas _Fa4 CL_, _Fa4 CL2_, and _FaDFR_ increased. In contrast, in FaHMGRR fruits, levels of _FaC4H_, _FaCHS_, _FaANS_, _FaUFGT_, _FaMYB10_, _FabHLH143_, _FaWD40_, and _FaPAL2_ were higher than in both CK and FaHMGROE strawberries (Figure 4). This result suggests a negative correlation between FaHMGR level and the anthocyanin accumulation. For aroma synthesis genes, _FaCNL_, _FaPIN_, and _FaNCED3_ exhibited the highest expression in FaHMGROE fruits, while _FaDAHPS1_, _FaDAHPS2_, _FaNES1_, _FaOMT_, _FaCHP1_, _FaAAT_, _FaQR_, and _FaNCED1_ showed the lowest expression. When _FaHMRR_ was inhibited, genes _FaNES1_, _FaOMT_, _FaCHP1_, _FaAAT_, _FaQR_, and _FaNCED1_ were significantly upregulated. Figure 4: Effects of changes in FaHMGR levels on the expression of genes associated with anthocyanin and aroma metabolic pathways following transient overexpression of _FaHMGR_ or suppression via _FaHMGRi_ in strawberries. The pCAMBIA1302 empty vector was used as a control. Different letters represent significant differences between groups.
## 1 Introduction
These enriched features significantly enhance the model's ability to capture complex nonlinear relationships, which are critical for accurately predicting natural gas demand. (2) Model selection. To enhance predictive accuracy from various perspectives, three prediction models with distinct characteristics were selected: Informer, MLR, and SVR. Each model leverages unique strengths--for example, Informer excels at processing long-term time series data, while MLR and SVR effectively model linear and nonlinear relationships. By combining these models with different feature sets, 12 basic models were constructed, enabling the comprehensive exploration of feature contributions. (3) Ensemble modeling. A stacking ensemble method was developed using the 12 basic models as base learners. This method aggregates predictions from multiple models, capturing hidden feature interactions and improving the overall performance. By leveraging the strengths of different models and features, the stacking ensemble method effectively addresses complex nonlinear relationships and minimizes prediction errors. An analysis of actual data from a specific region in Chengdu, China, was conducted to compare the prediction results of individual models with those of the stacked ensemble models. The stacked ensemble method demonstrated superior performance and strong generalization capabilities. The main contributions are as follows:  
* **Pioneering application of stacking ensemble:** This study marks the first application of the stacking ensemble method to natural gas procurement forecasting. By integrating the strengths of 12 base models derived from Informer, MLR, and SVR backbone models, this approach significantly improved the \(R^{2}\) value by at least four percentage points. * **Fine-grained feature integration:** Segmenting natural gas demand by regional and usage characteristics enhanced the \(R^{2}\) value of the three backbone models by approximately 1-15 percentage points, providing more accurate inputs for the stacking ensemble model. * **Enhanced seasonality modeling:** Adding weather factors (e.g., maximum and minimum temperatures) significantly improved the model's response to seasonal changes and medium-term demand fluctuations, resulting in an \(R^{2}\) improvement of approximately 5-10 percentage points. These innovations not only improve prediction accuracy but also hold significant implications for optimizing natural gas procurement processes and minimizing operational costs. The rest of this study is organized as follows: Section 2 reviews related literature. Section 3 describes the datasets, as well as the construction of the backbone models and the stacking ensemble model. Section 4 presents and discusses the experimental results and conducts future trend predictions. Section 5 concludes the research findings and outlines directions for future research.
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
###### Abstract  
This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).  
Keywords: indoor positioning; PDR; geomagnetic positioning; particle filter; genetic algorithm +
Footnote †: journal: Applied sciences
## 5 Simulation and Analysis
### INCM Beamforming Performance
#### 5.3.4 Relationship between Output SINR and Number of Snapshots  
Figure 10 shows the output SINR of each algorithm with the number of snapshots at an input SNR of 20 dB, with 100 Monte Carlo experiments for each group, and it can be seen that most of the algorithms have a stable performance with varying numbers of snapshots. Although the proposed algorithm's performance at fewer snapshots is slightly lower than the INCM-subspace algorithm, it approaches the optimal algorithm most closely when the number of snapshots reaches 170 and above.
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 4 Core Based Multicast Tree
### Security Analysis
#### 4.2.3 Replay Attack  
Whenever a device is decommissioned, its profile is removed from the entire corporate database. However, in a large organization, host profiles are stored in multiple locations and may be difficult to remove all at the same time. This deficiency may be exploited by the attackers. They may reconnect the device to the corporate network and launch a replay attack using its old profile.  
In our proposed system, as soon as a new system is installed, we create a new set of public and private key pairs, without changing its publicly known identity. Our system will also distribute the new hash-polynomial securely to all nodes. Whenever a decommissioned system uses its publicly known identity, only its new keys are used by other hosts. Whenever it initiates a _CBT-join_, the response to this _CBT-join_ by means of a challenge will be encrypted using its new public-key. Since the decommissioned node has no knowledge of its new private key, it may not be able to join the network using its old profile. Any unsolicited message sent by the decommissioned node will be ignored, as its digital signature may not be valid.