esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Particulate Matter and Carbon Dioxide on Students' Emotions in a Smart Classroom
## 1 Introduction  
Given that children spend many hours indoors at school, maintaining good indoor air quality (IAQ) is crucial for their educational experience. Numerous physical environmental factors may influence students' academic performance, but the indoor environmental quality of classrooms has been shown to significantly impact both teaching and learning outcomes [1]. Environmental factors, such as air quality, represent a direct influence on the teaching-learning process and offer significant opportunities for classroom innovation [2].  
Studies on indoor environmental quality (IEQ) in schools frequently highlight common issues such as inadequate ventilation, improper temperatures, and the presence of airborne particles. Urban schools, in particular, face higher concentrations of particulate matter (PM) compared to rural schools, with 57% of urban respondents reporting the presence of particles in classrooms. These findings emphasize the widespread problem of poor air quality in educational settings and reinforce the need for improved ventilation, as current levels often fall below minimum recommended standards [3].  
Ref. [4] further underscore the importance of addressing air quality in schools, noting that children are especially sensitive to pollutants such as VOCs, CO\({}_{2}\), NH\({}_{3}\), PM\({}_{2.5}\), and PM\({}_{10}\). Their study revealed that these factors often exceeded regulatory limits, making it clear that improved ventilation and air quality management are critical in educational settings.  
In light of these challenges, this study takes an innovative approach by examining the impact of environmental factors--specifically particulate matter (PM\({}_{1}\), PM\({}_{2.5}\), and PM\({}_{10}\)) and carbon dioxide (CO\({}_{2}\))--on students' emotions. Using low-cost sensors, which were carefully calibrated to ensure acceptable accuracy despite certain precision limitations, we monitored air quality levels in classrooms. This approach represents a first step towards affordable, scalable solutions for real-time air quality analysis, opening new pathways for research on how environmental conditions affect students' emotional well-being.
### Contributions of This Study  
In this study, we have developed a new multi-modal MRI classification network specifically designed for diagnosing Acute Bilirubin Encephalopathy (ABE), which represents a significant step forward in medical imaging and diagnostic procedures. The key scientific contributions of our research are as follows:  
Effective integration of attention mechanisms: Our model, built upon the 3D ResNet18 architecture, innovatively incorporates spatial and channel attention modules within the feature extraction networks for T1- and T2-weighted MRI images. This integration allows the network to focus precisely on crucial features within specific areas and channels of the images, thereby significantly enhancing the accuracy of ABE diagnosis. This demonstrates a marked improvement over traditional methods, showcasing the power of attention mechanisms in medical image analysis.  
CentralNet as a key feature fusion method: A central element of our architecture is CentralNet, strategically positioned between the feature extraction models of T1 and T2 modal images [PERSON] et al. (2018). CentralNet achieves effective feature fusion by calculating the weighted sum of different modal feature data and their preceding layers. This novel approach fosters mutual supervision and learning in the cross-modal feature extraction process, enhancing the model's precision in classifying ABE. The successful integration of CentralNet and its validation through several control experiments underline its efficacy, paving the way for new possibilities in the clinical application of multi-modal MRI imaging for diagnosing ABE.  
Collectively, these contributions highlight the effectiveness of our proposed model in improving the diagnostic accuracy of multi-modal MRI for ABE. Looking forward, we aim to integrate more advanced MRI technologies and larger datasets in our future research. This will enable us to further validate the effectiveness and reliability of our methodology, continuing to contribute to the field of medical imaging and diagnostics.
## 5 Conclusion  
To accurately estimate the maximum rate of low-stress GPP (called \"GPPcapacity\") based on the light-response curve, an appropriate VI was selected. The green chlorophyll index, CIgreen (PNIR/Pgreen-1), had a strong linear correlation with chlorophyll content at the leaf scale and with GPPcapacity at the canopy and satellite scales. We demonstrated that CIgreen could capture seasonal changes and variation in photosynthesis patterns in six main plant functional types. Therefore, we consider CIgreen to be suitable for GPPcapacity estimation globally, especially for GCOM-C/SGLI satellite data. Nevertheless, CIgreen should be validated further in different areas and with other plant functional types [93] to test its robustness and sensitivity over a global range of vegetation conditions.  
For implementation of GPP\({}_{\text{capacity}}\) global estimation using GCOM-C/SGLI, we should first calculate CI\({}_{\text{green}}\) from satellite data and use the empirical linear equation from Table 4 to estimate the P\({}_{\text{max\_capacity}}\)(the maximum GPP\({}_{\text{capacity}}\) when PAR = 2,000 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\)). The P\({}_{\text{max\_capacity}}\) (the maximum GPP\({}_{\text{capacity}}\) under light saturation) in Equation (1) should be calculated using P\({}_{\text{max\_capacity}}\)2000, PAR of 2,000 \(\upmu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\) and \(\alpha_{\text{slope}}\) (photosynthetic quantum efficiency) in Table 3 for each plant functional type. Then, GPP\({}_{\text{capacity}}\) should be estimated with the PAR obtained by satellite using Equation (1).  
Additionally, in the future, the global GPP\({}_{\text{capacity}}\) can be combined with vegetation stress maps to estimate global GPP. Stress maps may be calculated from environmental conditions such as the vapor pressure deficit, leaf water potential, and soil water content. We expect that our approach will be useful for improving the accuracy of global GPP estimations derived from satellite data.
# Blockchain-Based Distributed Computing Consistency Verification for IoT Mobile Applications
###### Abstract
To address the above-mentioned problems, in this paper, we propose a novel blockchain-based distributed and lightweight data consistency verification model (BDCA), which ensures the consistency of data transmission during cross-chain interactions. The BDCA model comprises four entities: IoT device (_DO_), source chain (_SC_), target chain (_TC_), and audit chain (_AC_). _AC_ is a distinct chain that audits data consistency during cross-chain interactions between _SC_ and _TC_. The audit information is recorded in _AC_, which is transparent, tamper-proof, and preserves the decentralized feature of blockchain technology. To account for the possibility of data modification in the blockchain, we design a batch verification dynamic Merkle hash tree (BV-MHT), which stores the position information of the data to facilitate data location. Additionally, we utilize the auxiliary verification information form (AVF) to implement data updating and auditing with minimal overhead. Furthermore, in order to ensure the security and privacy of the transferred data, we introduce an advanced gamma multi-signature scheme (AGMS) to encrypt the exchange data between _SC_ and _TC_. The main contributions of this paper are as follows:  
* We propose a novel blockchain-based distributed and lightweight data consistency verification model (BDCA), which provides robust data security and privacy guarantees. Furthermore, we demonstrate that BDCA can effectively achieve cross-chain data interaction consistency verification with minimal overhead through experimental evaluations. * We design a batch verification dynamic Merkle hash tree (BV-MHT), which supports batch verification and dynamic data updates. Furthermore, we introduce the concept of the auxiliary verification information form (AVF) to locate the position of the stored data and improve batch validation efficiency. * We construct random challenges and store the audit logs in the audit chain (_AC_) for future checking, enabling efficient and reliable cross-chain data consistency verification while preserving the transparency and tamper-proof feature of the blockchain. The remainder of the paper is organized as follows. First, we summarize the related work in Section 2 and introduce preliminaries of BDCA in Section 3. Second, we format BDCA in Sections 4 and 6. Third, we provide the security analysis and theoretically and experimentally analysis of BDCA in Sections 7 and 8, respectively. Lastly, we summarize the paper in Section 9.
###### Abstract  
Purpose: The main objective of this research was to develop a questionnaire for the subjective evaluation of the quantity and magnitude of impacts experienced by rugby players. Methods: Thirty-six male rugby union players (mean \(\pm\) SD, age; 23.5 \(\pm\) 3.6 years, height; 179 \(\pm\) 10.0 cm, body mass; 89.58 \(\pm\) 13.6 kg) participated in this study, comprising eighteen forwards and fourteen backs. Participants were tasked with completing self-reported questionnaires assessing their perception of the quantity and magnitude of impacts after thirteen consecutive matches. Objective data were concurrently collected using Micro-Electrical Mechanical Systems (MEMS) integrated with a GPS device (WIMU, Realtrack Systems, Almeria, Spain). Results: The statistical analysis revealed that 49.7% of players overestimated and 39.8% underestimated the number of impacts above 8G, with a mean team error of 0.017 impacts. Bayesian methods indicated a 95% high-density interval for the mean error between \(-0.46\) and 0.45, with 95.8% within the region of practical equivalence, signifying negligible bias at the team level. Positional variations were notable, with Halfbacks overestimating and Fullbacks and Second Row underestimating impacts. The errors and number of impacts displayed a non-linear relationship, better described by an exponential model. Additionally, the study identified significant correlations between the number of impacts players estimated and the actual impacts received, further underlined by position-specific trends, suggesting that players' estimation abilities are influenced by the actual exposure to impacts and their playing positions. Conclusions: The utilization of a subjective impact questionnaire is a valid tool to assess rugby players level of impacts during a match. This approach proves particularly valuable in situations where technology is unavailable, showcasing its adaptability as a practical tool in diverse sporting contexts.  
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# Trajectory Optimization with Dynamic Drivable Corridor-Based Collision Avoidance
## 2 Problem Statement
### Vehicle Kinematics Constraints  
The vehicle kinematic constraints (c.f. Figure 1) are presented as follows:  
\[\frac{d}{dt}\left[\begin{array}{c}x(t)\\ y(t)\\ v(t)\\ \phi(t)\\ \theta(t)\end{array}\right]=\left[\begin{array}{c}v(t)\cdot\cos\theta(t)\\ v(t)\cdot\sin\theta(t)\\ a(t)\\ \omega(t)\\ v(t)\cdot\tan\phi(t)/L_{w}\end{array}\right]. \tag{1}\]  
The coordinates \((x,y)\) represent the position of the vehicle's rear axle center point P, \(v\) is the velocity of P, \(\theta\) is the vehicle's heading angle, \(\phi\) is the steering angle of the front wheels, \(a\) is the vehicle's acceleration, \(\omega\) is the vehicle's angular velocity, and \(L_{w}\) is the wheelbase, which is the distance between the front and rear axle centers of the vehicle. These variables are subject to the following bounds:  
\[\left[\begin{array}{c}\mathrm{a}_{\mathrm{min}}\\ \mathrm{v}_{\mathrm{min}}\\ -\Omega_{\mathrm{max}}\\ -\Phi_{\mathrm{max}}\end{array}\right]\leq\left[\begin{array}{c}a(t)\\ v(t)\\ \omega(t)\\ \phi(t)\end{array}\right]\leq\left[\begin{array}{c}\mathrm{a}_{\mathrm{max}} \\ \mathrm{v}_{\mathrm{max}}\\ \Omega_{\mathrm{max}}\\ \Phi_{\mathrm{max}}\end{array}\right],\,t\in[0,\,t_{\mathrm{f}}]. \tag{2}\]  
\(\mathrm{a}_{\mathrm{min}}\) and \(\mathrm{a}_{\mathrm{max}}\) represent the lower and upper bounds of acceleration, \(\mathrm{v}_{\mathrm{min}}\) and \(\mathrm{v}_{\mathrm{max}}\) represent the lower and upper bounds of velocity, \(\Omega_{\mathrm{max}}\) represents the maximum magnitude of the front wheel angular velocity \(\omega(t)\), and \(\Phi_{\mathrm{max}}\) represents the maximum magnitude of the front wheel steering angle \(\phi(t)\).
# EGS-YOLO: A Fast and Reliable Safety Helmet Detection Method Modified Based on YOLOv7
## 4 Experiment
### Experiment Results
#### 4.4.4 Comparative Experiment  
To validate the advancement of EGS-YOLO, we compare it with other advanced models, including YOLOv8m, YOLOv7, YOLOv5x, YOLOv5l, YOLOv5m, and YOLOv3, as well as existing studies in terms of accuracy. The experimental results are presented in Table 6.  
From Table 6, it is evident that EGS-YOLO achieves the highest [EMAIL_ADDRESS], reaching 91.1%, which is 4%, 4.5%, 4.1%, 3.8%, 0.2%, and 1.7% higher than YOLOv3, YOLOv5m, YOLOv5l, YOLOv5x, YOLOv7, and YOLOv8m, respectively. EGS-YOLO is also more accurate than the models by [PERSON] and [PERSON] by 1.5% and 2.82%, respectively. EGS-YOLO has the fastest inference speed (RTX3090) at 3.9 ms, which is 6.3 ms, 3.1 ms, 5.6 ms, 10 ms, 0.6 ms, and 3.3 ms faster than YOLOv3, YOLOv5m, YOLOv5l, YOLOv5x, YOLOv7, and YOLOv8m, respectively. In terms of parameters and computational complexity, EGS-YOLO has 37.3% fewer parameters and 33.8% lower computational complexity than the baseline model YOLOv7. Compared to YOLOv8 of the same scale, EGS-YOLO is superior.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **[EMAIL_ADDRESS] (\%)** & **Params (M)** & **FLOPs (G)** & **Inference (ms)** & **Size (MB)** \\ \hline Baseline & 90.9 & 36.5 & 103.2 & 4.5 & 74.9 \\ +GhostModule & 90.1 & 22.6 & 68.0 & 3.9 & 47.2 \\ +GhostModule & 90.6 & 22.9 & 68.3 & 3.9 & 47.8 \\ +GhostModule & 90.7 & 22.6 & 68.0 & 3.9 & 47.2 \\ +GhostModule & 91.1 & 22.9 & 68.3 & 3.9 & 47.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Result of Ablation Experiment (Baseline is YOLOv7).  
The comparison of [EMAIL_ADDRESS] between YOLOv7 and EGS-YOLO is shown in Figure 16, with the horizontal axis representing the number of iterations. As shown in Figure 16, after 125 iterations, the [EMAIL_ADDRESS] of EGS-YOLO gradually approaches and slightly surpasses that of YOLOv7. This indicates that EGS-YOLO maintains high accuracy while effectively improving inference time, the number of parameters, and computational complexity. EGS-YOLO has excellent performance high accuracy and high real-time performance. The test of EGS-YOLO on individual images is shown in Figure 17. From Figure 17, it can be seen that the detection accuracy basically reaches more than 90%, and
# The Diagonal Arch Bridge, a Particular Case of Spatial Arch Bridges
## 3 Definition and Typological Classification
### Main Geometrical Variables  
The arch is the structural element that characterizes the bridge. Its structural performance consists of supporting the loads applied to the deck (through cable hangers), transmitting them to the foundations. The most common geometries used to define the centerline of the arch are the parabola, the catenary, and the circular arc. The arch is defined by the following parameters (Figure 21c):  
* \(L_{A}\): Span of the arch, defined as the length between the springs of the arch.
* \(f_{A}\): Rise of the arch in the vertical plane. Despite the plan of the arch is diagonal, its elevation has a vertex at the crown of the arch (\(0,f_{A}\)) and passes through its springs (-\(L_{A}/2\), \(0\)) and (\(L_{A}/2\), \(0\)).
* \(\theta_{A}\): Arch rotation angle, or angle between the horizontal projection of the centerline of the arch and the deck. \(\theta_{A}=\text{atan}(b/L)\) when the arch crosses the deck diagonally.
* \(\omega\): Inclination of the plan of the arch with respect to the vertical plane.  
Figure 19: (**a**) Slodowa island Footbridge (photo, Metamorfa_Studio, pixabay.com (accessed on 17 December 2020)), (**b**) Pont de l’Observatoire (photo, [PERSON], Wikimedia Commons), and (**c**) Ripshorst Footbridge (photo: [PERSON], structure.net (accessed on 17 December 2020)).  
Figure 21: Geometrical parameters and classification of the diagonal arch bridge (DAB). (**a**) Geometrical classification of the DAB, (**b**) Geometrical classification of the PAB, (**c**) Variables of the diagonal arch bridge, (**d**) Inferior deck, (**e**) Intermediate deck, (**f**) Superior deck, (**g**) Straight deck, (**h**) Skew deck, (**i**) Curved deck, (**j**) S-Shaped deck, (**k**) Crossed arches, (**l**) Parallel arches, (**m**) Alternate crossed arches, (**n**) Radial arches.  
The geometry of the deck, including horizontally curved decks, is defined by the following parameters (Figure 21c):  
* \(L_{D}\): Span of the deck.
* \(b_{D}\): Width of the deck.
* \(\theta_{S}\): Angle of skew of the deck. Angle between the axis of the deck and the line of supports.
* \(g_{D}\): Horizontal sagitta of the horizontally curved deck (Figure 21). For S-shaped decks, \(g_{D}\) changes its sign when the curvature of the deck changes.
* \(R\): Radius in the deck axis, for curved and S-shaped decks (Figure 21).  
The hangers are the structural elements that link the arch and the deck, transferring the loads from the deck to the arch. Hangers can be defined by the following parameters (Figure 21c):  
* \(s_{hD}\): Distance between hangers measured in the deck plan.
* \(s_{hA}\): Distance between hangers measured over the developed length of the arch. The relative arch-deck position, as it happens in all the arch bridges, can be (Figure 21d-f):
* \(Z_{D}=0\), inferior deck.
* \(f_{A}\geq Z_{D}>0\), intermediate deck.
* \(Z_{D}\geq f_{A}\), superior deck.
# Real-Time Penetration Recognition Based on Voltage Signal in K-TIG Welding
###### Abstract  
This paper focuses on discerning the melting state of keyhole tungsten inert gas (K-TIG) welding, a promising welding technique still considered a novelty. In order to achieve this goal, a sensing system is established to collect voltage signals during the welding process of 304 stainless steel. Analysis of the collected signals reveals that the characteristic signal power spectrum between 36 kHz and 37 kHz is closely related to whether the welding has complete joint penetration. The experimental findings confirm that the spectral power density of the characteristic signal varies significantly with the alteration of welding quality. Moreover, it is observed that when the welding melting state remains unchanged, the change in welding parameters is not noteworthy. When adjusting welding parameters to maintain stable feature signals during the welding process, the corresponding penetration state of the welding does change. Therefore, it is possible to ensure the stability of welding quality by altering welding parameters in real time. These research results provide valuable insights into the real-time quality monitoring of the K-TIG welding process.  
K-TIG welding; welding quality; arc voltage; power spectrum; real-time monitoring +
Footnote †: journal: Applied  
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Footnote 0: 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 5 The Proposed Scheme
### Construction of ABE-RS
#### 5.1.4. Revocable Encryption  
\(\mathsf{EncML}(GP,M,\mathbb{M},\mathcal{T})\to CT_{\mathbb{M},\mathcal{T}}\). The scheme \(\mathbb{M}=(A,\zeta)\) represents an LSSS where \(A\in\mathbb{Z}_{p}^{l\times n}\) is the share-generating matrix and \(\zeta\) is a function mapping from \([l]\) to \(S_{A}\). A random vector \(\mathbf{v}=(s,v_{2},\ldots,v_{n})\) is chosen from \(\mathbb{Z}_{p}^{n}\), and a random value \(r_{j}\in\mathbb{Z}_{p}\) is selected for each row \(A_{j}\) of matrix \(A\). The following computations are performed:  
\[\begin{split}& C_{0}=M\cdot e(g,g)^{\sigma s},C_{1}=g^{s},C_{2}=g^{ \beta s},\\ & C_{3,Root}=(g^{\gamma H_{1}(Root)})^{s},\\ &\bigg{\{}C_{\mathbf{4},j}=\frac{(g^{\alpha})^{A_{j}\cdot \mathbf{v}}}{Q_{\zeta(j)}^{r_{j}}},C_{5,j}=g^{r_{j}}\bigg{\}}_{j\in[l]}.\end{split} \tag{7}\]  
The resulting ciphertext is given by \(CT_{\mathbb{M},\mathcal{T}}=(\mathbb{M},\mathcal{T},C_{0},C_{1},C_{2},C_{3, Root},\{C_{4},j},C_{5,j\}_{j\in[l]})\).  
The re-encrypted ciphertext is updated to \(CT_{\mathbb{M},L}^{(up)}=(\mathbb{M},L,C_{0},C_{1},C_{2},\{C_{3,yx}\}_{y_{x} \in cover(L)},\{C_{4,j},C_{5,j}\}_{j\in[l]})\), where \(L\) represents the user list of revocable attributes. The updated ciphertext for \(C_{3,yx}\) is calculated as follows:  
\[\bigg{\{}C_{3,yx}=(g^{\gamma H_{1}(y_{x})})^{s}\bigg{\}}_{y_{x}\in cover(L)}. \tag{8}\]
## 4 Experiment and Results
### Modified BertGCN and Citation Graph  
The simplified BertGCN model, in contrast to the original BertGCN model, requires no \(\lambda\) variation setting and produces a single-valued performance. The longer case yielded an average validation accuracy of 89.52% (the test accuracy of the best model was 84.33%), outperforming most statistical conditions. Conversely, the shorter case achieved an average validation accuracy of only 85.09% (the test accuracy of the best model was 86.04%), which is the lowest compared to any statistical condition. These results demonstrate a clear contrast in the model performance, with the shorter case approaching the lowest validation accuracy observed in the original BertGCN experiment, and the longer case approaching the highest. On the other hand, the citation-based GCN model achieved a mean validation accuracy of 78.83% under the cloud condition with 280-token input sentences in 20 trials.