esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Preparation and Characteristics of Concrete Modified by Construction Waste
## 3 Results
### The Influence of Admixture Dosage on Concrete Strength  
The changes in the axial compressive strength of concrete under various factor levels of modifier concentration, modification time, and recycled aggregate are shown in Figure 10. As shown in Figure 10, with the increase in modifier dosage, the overall compressive strength of recycled concrete shows an increasing trend. The modifier dosage increases  
Figure 8: The influence of various factor levels on water absorption rate.  
Figure 9: Slump test results.  
from 8% to 12%, and the axial compressive strength of concrete increases by 12.81%; with the increase in modifier dosage, the axial compressive strength of concrete also shows an increasing trend. When the modification time is increased from 30 min to 90 min, the axial compressive strength of concrete increases by 6.33 MPa, an increase of 11.24%; the axial compressive strength of concrete decreases with the increase in recycled aggregate content, increasing from 20% to 60%. The axial compressive strength of concrete decreases by 2.67 MPa, a decrease of 4.15%. The main reason is that the surface of recycled aggregate is wrapped in cement slurry, which promotes the hydrophilicity of recycled aggregate and reduces the elastic modulus between recycled aggregate and cement slurry. Due to the high strength of the interface between cement slurry and coarse aggregate and the mortar itself, the fracture often runs through the middle of coarse aggregate. Excessive recycled aggregate does not promote the compressive strength of concrete. Therefore, the dosage of recycled aggregates plays a very important role in the strength of high-strength concrete. According to the analysis of the experimental results, the 28 d axial compressive strength of recycled concrete is relatively combined as A\({}_{3}\)B\({}_{3}\)C\({}_{1}\).  
In summary, based on the comprehensive analysis of fluidity, water precipitation rate, and concrete axial compressive strength, the optimal proportion of recycled concrete A3B3C1 is determined, which is a modifier content of 12%, a modification time of 90 min, and a waste content of 20%.
## 3 Results on ML/DL-Based Channel Estimation
### Overview of Channel Estimation Approaches  
Tables 4 and 5 summarize ML/DL methods, optimizers, and training data for SC-UWA (Single-Carrier Underwater Acoustic) and MC-UWA (Multi-Carrier Underwater Acoustic) systems, respectively. SC-UWA transmits data using a single carrier and is characterized by simple, energy-efficient designs. In contrast, MC-UWA systems use multiple subcarriers to improve robustness in complex underwater environments. SC-UWA systems primarily use lightweight models such as LR (Linear Regression), LSTM, ABiGRU (Attention Bidirectional Gated Recurrent Unit), and UACC-GAN (Underwater Acoustic Communication Channel-Generative Adversarial Network). Common optimizers include Adam, Adagrad, Adadelta, and Nadam. Among these, Adam is widely preferred due to its ability to adapt the learning rate during training. MC-UWA systems utilize more complex models. These include CNNs, BiLSTMs (Bidirectional Long Short-Term Memory networks), Transformers, and GAN-based architectures. Optimizers like RMSprop (Root Mean Square Propagation) and Pelican are also used. While SC-UWA favors simplicity, MC-UWA applies DL models for greater accuracy and performance. Training examples are drawn from direct UWA measurements and synthetic datasets. These datasets may be generated using statistical models [25] or sourced from real measurements [26]. The diversity and quality of training data play a vital role in model effectiveness. To summarize, SC-UWA systems prioritize efficiency with lightweight models, while MC-UWA systems employ complex architectures and diverse data sources to enhance accuracy in challenging underwater conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Training Loss** & **Complexity** & **Accuracy** & **Precision** \\ \hline
[49] & 0.01 (CCE) & Lower (153,930 P) & 95.3\% & 89\% to 100\% \\ \hline
[50] & MSE & Lower (3.48 ms) & 99.25\% & — \\ \hline
[51] & 1.946 (CE) & Lower (7.164 ms) & 99.38\% & — \\ \hline
[52] & Contrastive Loss & — & 98.6\% & 66\% to 100\% \\ \hline
[53] & \(10^{-8}\) (CE) & — & 100\% & 40\% to 100\% \\ \hline
[54] & Negative Log Likelihood & Lower (4.5 ms, \(0.5\times 10^{6}\) P, \(0.5\times 10^{8}\) FLOPs) & 93.4\% & 33.33\% to 100\% \\ \hline
[55] & CE & — & 64\% to 73\% & 58.8\% to 100\% \\ \hline
[56] & 0.10 (Hinge Loss) & Low (SVM efficiency) & 98.28\% to 99.78\% & 99.90\% to 99.94\% \\ \hline
[46] & — & Lower by 9 times (No. of parameters) & 98.5\% & 97\% to 99\% \\ \hline
[47] & Nearly zero (Not Given) & Lower (0.28 M parameters, 7.02 M FLOPs) & 99\% & — \\ \hline
[48] & CE & Medium (145.9 k parameters) & 86\% to 91.1\% & 61\% to 100\% \\ \hline
[44] & CE & High (3.10 M P, 0.58 M FLOPs) & 92.2\% & 80\% to 100\% \\ \hline
[9] & 0.2 (CE) & High (38.48 M P, 1753 s) & 94.31\% & 91.02\% \\ \hline
[45] & — & Medium (2.23 ms to 8720.94 ms) & 64\% to 83\% & 61\% to 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 18: Comparative analysis of modulation recognition techniques.
## 3 Insights into Federated Learning
### FL Architectures and Algorithms
```
each node initialize model parameters \(W_{0}\) for each training round t do for each node i in paralleled do \(W_{t+1}^{i}\leftarrow\text{ClientSite}(i,W_{t})\) endfor for each neighbour k of node i do \(W_{t+1}^{i}\leftarrow\sum_{k=1}^{k}\frac{n_{k}}{n}W_{t+1}^{ik}\) \(\triangleright\) Exchange update endfor ClientSite(\(i,W_{t}\)): Fit local model with it dataset, batch size, epochs, and learning rate to get new model update \(W_{t+1}^{i}\) Return (\(W_{t+1}^{i}\))
```  
**Algorithm 2** FedAvg Decentralized  
Another topology is the hybrid of centralized and decentralized design known as hierarchy [45], which has been developed as shown in Figure 3. This topology's goal is to work with many clients, which sometimes is a bottleneck of centralized FL, and to lower communication expenses in decentralized FL. The server will sometimes become overloaded in a centralized design with just one server. While in a decentralized network, every participant will communicate with each other during each training round, which will result in very high communication cost across the network. These issues are known to be resolved by hierarchical architecture [63; 64]. At each communication round, not all clients send model updates to the global server, but just a group of clients transfers their model updates to hierarchy servers (group or hierarchy server). The group servers then aggregate hierarchical updates and send them to the global server. Thus, this architecture significantly balances the communication load to the global  
Figure 3: Hierarchical FL [63; 64]. server. Additionally, it resembles the hierarchical organizations (silos) in real life, making applying FL simpler. The pseudocode of this training type can be found in Algorithm 3. ```
ServerSite: \(\triangleright\) Run on Server  initialize model parameters \(W_{0}\) for each training round t do for each hierarchy server h do  random select clients for each client k in parallel do \(W_{t+1}^{k}\leftarrow\textbf{ClientSite}(k,W_{t})\) endfor \(W_{t+1}^{h}\leftarrow\sum_{k=1}^{K}\frac{u_{k}}{n}W_{t+1}^{k}\) \(\triangleright\) Hierarchy update endfor \(W_{t+1}\leftarrow\sum_{h=1}^{H}\frac{u_{k}}{n}W_{t+1}^{h}\) \(\triangleright\) Global update endfor ClientSite(\(k,W_{t}\)): \(\triangleright\) Run on Clients  Fit local model with it dataset, batch size, epochs, and learning rate to get new model update \(W_{t+1}^{k}\) Return (\(W_{t+1}^{k}\)) to server
```  
**Algorithm 3** FedAvg Hierarchy
# The Montreal Experience: A Retrospective Study Part I--Basic Principles and Treatment Algorithm
## 2 Methods  
This retrospective study was conducted in accordance with the tenets of the Declaration of Helsinki and was approved by the Universite de Montreal Ethical Committee Review Board (CERC 19-036-June 2019). Data were extracted from the file of each patient, aged 5-18 who: (1) consulted CUV myopia control clinic between January 2017 and December 2018 and (2) were kept under the same myopia management (same design/concentration) during that time. Elements considered for analysis were: age, gender (male, female), ethnicity (Asian, Caucasian), spherical refractive error, photopic pupil size, strategy employed to control myopia, and their variants (4 OK lens designs, 4 soft multifocal lenses, 2 dosages of atropine 0.01% or 0.025% and axial length). Details about lens designs are described in Tables 1 and 2. Data were collected at baseline and every 6 months, for both eyes, during the study period (2 years).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Lens Type** & **Brand Name** & **Treatment Zone (mm)** & **Jessen Factor (D)** & **Overall Diameter (mm)** \\ \hline OrthoK 1 & Paragon CRT (Cooper Vision) & 6.0 & 0.50 & 10.5 \\ \hline \multirow{2}{*}{OrthoK 2} & U de M design (custom; RGP designer software) & Variable vs. pupil Between 5.4 and 6.0 & Variable. Average 0.75 & Variable. Covers 95\% of the visible cornea, average 10.6 \\ \hline \multirow{2}{*}{OrthoK 3} & \multirow{2}{*}{DRL (Precilens)} & Variable vs. refraction Between 5.0 and 6.0 & Variable. Covers 95\% of the visible cornea, average \\  & & Average 5.5 & & 10.8 \\ \hline \multirow{2}{*}{OrthoK 4} & \multirow{2}{*}{DreamLens (Bausch \& Lomb)} & 5.0 to 6.0 & \multirow{2}{*}{0.75} & \multirow{2}{*}{10.6} \\  & & Average 5.0 & & \\ \end{tabular}
\end{table}
Table 1: Lens variants description, OrthoK.  
Figure 1: (**a**) Myopia control strategy Decision tree. (**b**) Contact lens selection decision tree. SMF: Soft multifocal, distance-centered, high add. SMF-T: Soft multifocal toric, distance-centered, high add. C-OK: Custom OrthoK (or OK with smaller central zone). OK: OK regular design (4–5 curves/standard central zone). G: Regular glasses (single vision). AMG: Anti-myopia glasses (lenses designed to control myopia). Hybrids: Duette progressive, distance-centered: customizable zone + add power (>+3D). * In some countries, customized soft MF lenses may be also an option (zones + add power customizable).
# Research on a Reductive Deep Chlorine Removal Process for Breaking Through the Solid Film Barrier
## 2 Materials and Methods
### Calculations Related to Thermodynamic Parameters
#### 2.4.3 Comparison of Thermodynamic Parameters  
The calculation results of the ascorbic acid dechlorination method and copper slag dechlorination method were compared and analyzed, as shown in Table 3. According to the thermodynamic calculation results of the two methods, corresponding thermodynamic diagrams were drawn, as shown in Figure 3.  
The thermodynamic calculations show that the potential difference (\(\Delta\)E\({}_{1}\)) for ascorbic acid dechlorination is 0.091 V higher than that of copper slag dechlorination (\(\Delta\)E\({}_{2}\)), and its reaction-driving force is increased by 18.6%. The standard Gibbs free energy (\(\Delta\)G\({}_{1}\)\({}^{0}\)) for ascorbic acid dechlorination is 56.1 kJ/mol lower than that of copper slag dechlorination (\(\Delta\)G\({}_{2}\)\({}^{6}\)), suggesting that ascorbic acid dechlorination had stronger reaction spontaneity. The standard equilibrium constant (K\({}_{1}\)) for ascorbic acid dechlorination is significantly greater than that of copper slag dechlorination (K\({}_{2}\)), with K\({}_{1}\) being 6.7 \(\times\) 10\({}^{9}\) times larger than K\({}_{2}\). This indicates that the reaction tendency for ascorbic acid dechlorination is much stronger than that for copper slag dechlorination under standard conditions, and the reaction is more complete.  
Figure 3: Comparison of thermodynamic parameters between ascorbic acid dechlorination and copper slag dechlorination in standard state: (**a**) reaction potential difference, (**b**) standard Gibbs free energy, (**c**) standard equilibrium constant.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Thermodynamic Parameter**} \\ \cline{2-3}
**Dechlorination Process** & **Reaction Potential** & **Standard Gibbs Free Energy (\(\Delta\)G\({}^{0}\)m)** & **Standard Equilibrium** \\  & **Difference (\(\Delta\)E)** & **Standard Gibbs Free Energy (\(\Delta\)G\({}^{0}\)m)** & **Constant (K)** \\ \hline \
\begin{tabular}{c} Ascorbic acid \\ dechlorination \\ Copper slag \\ dechlorination \\ Process comparison \\ \end{tabular} & 0.49 V & \(-\)94.6 kJ/mol & 3.733 \(\times\) 10\({}^{16}\) \\ \hline
\begin{tabular}{c} Ascorbic acid \\ dechlorination \\ Process comparison \\ \end{tabular} & 0.399 V & \(-\)38.5 kJ/mol & 5.58 \(\times\) 10\({}^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermodynamic parameter comparison of ascorbic acid dechlorination and copper slag dechlorination methods.
# High-Precision Light Spot Position Detection in Low SNR Condition Based on Quadrant Detector
## 4 Improved Detection Method  
The improved detection method can be described as seen in Figure 3.  
In Figure 3 we can see, firstly, modulation is added before the beacon light is illustrated on QD. Secondly, low pass filters (LPF) are replaced with band pass filters (BPF) because the modulating signal pulls the frequency spectrum from base frequency to medium frequency. Finally, we use Kalman filtering to eliminate the effect of white noise.
## 6 Result  
A total of 32 subjects were enrolled in this study and were assigned to either the CSG or the CTG. No statistically significant differences were observed between the general characteristics of the two groups (Table 1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CSG** & **CTG** & **t (\(p\))** & **LCI, UCI** \\ \hline Age (year) & 23.25 \(\pm\) 1.65 & 23.50 \(\pm\) 3.18 & \(-\)0.279 (0.782) & \(-\)2.1, 1.6 \\ Sex (female/male) & 9/7 & 8/8 & \(-\)0.344 (0.733) & \(-\)0.4, 0.3 \\ Height (cm) & 171.81 \(\pm\) 7.18 & 167.75 \(\pm\) 7.30 & 1.588 (0.123) & \(-\)1.2, 9.3 \\ Weight (kg) & 65.50 \(\pm\) 11.47 & 62.69 \(\pm\) 11.23 & 0.701 (0.489) & \(-\)5.4, 11.0 \\ BMI (kg/m\({}^{2}\)) & 22.10 \(\pm\) 2.88 & 22.32 \(\pm\) 4.03 & \(-\)0.177 (0.861) & \(-\)2.7, 2.3 \\ \hline \hline \end{tabular} Values are expressed as means \(\pm\) SD. CSG: cervical stabilization and scapular stabilization exercises group; CTG: cervical stabilization and thoracic exercises group; BMI: body mass index; LCI and UCI: lower and upper (95%) confidence interval.  
\end{table} 
Table 1: Subject characteristics (\(n\) = 32). All groups showed significant differences in the SCM and UT muscle tone, stiffness, and elasticity; the PPT; and the cervical ROM (\(p\) < 0.001) (Tables 2-4). There were significant time interactions for muscle tone, stiffness, elasticity, and PPT in both the SCM and UT muscles. For the SCM muscle, muscle tone showed a significant reduction (F = 43.74, \(p\) < 0.001, \(\eta^{2}\) = 0.593), stiffness decreased significantly (F = 85.13, \(p\) < 0.001, \(\eta^{2}\) = 0.739), and elasticity improved (F = 29.16, \(p\) < 0.001, \(\eta^{2}\) = 0.493). The PPT also showed a significant increase (F = 119.57, \(p\) < 0.001, \(\eta^{2}\) = 0.799). Similarly, in the UT muscle, muscle tone decreased significantly (F = 58.18, \(p\) < 0.001, \(\eta^{2}\) = 0.660), stiffness reduced (F = 51.88, \(p\) < 0.001, \(\eta^{2}\) = 0.634), and elasticity improved (F = 75.67, \(p\) < 0.001, \(\eta^{2}\) = 0.711). The PPT showed a significant increase (F = 136.21, \(p\) < 0.001, \(\eta^{2}\) = 0.820) (Tables 2 and 3). Additionally, the cervical ROM demonstrated significant improvements in extension (F = 153.80, \(p\) < 0.001, \(\eta^{2}\) = 0.836), flexion (F = 89.45, \(p\) < 0.001, \(\eta^{2}\) = 0.749), right rotation (F = 127.96, \(p\) < 0.001, \(\eta^{2}\) = 0.810), and left rotation (F = 121.54, \(p\) <
## 3 Problem Optimization  
### Problem Formulation  
From the square root law theorem, the covert rate is no more than \(\mathcal{O}(\sqrt{n})\) bits for the CCS with \(n\) covert channels, when \(n\rightarrow\infty\). For the CCS with finite channels, we cannot use the square root theorem to calculate the covert rate, since the DEP of the system with finite covert channels cannot be negligent [30; 31]. In this paper, the covert rate of the CCS is considered as the objective function of the CCS with finite LTMB. Thus, we can obtain the following expression:  
\[\eta=nR(1-\delta), \tag{15}\]  
where \(\eta\) is the average number of bits transmitted from UAV to U2 with the \(n\) covert channel.  
To maximize the covert rate, we formulate the joint optimization problem:  
\[(P1)\max_{n,P_{\mathrm{zz}},P_{\mathrm{zz}},H}\eta, \tag{16}\] \[s.t.\] \[n\leq N,\] (17) \[P_{u_{1}}+P_{u_{2}}\leq P_{\mathrm{max}},\] (18) \[P_{u_{1}}>P_{u_{2}},\] (19) \[\mathcal{D}(P_{F},P_{M})\leq 2\epsilon^{2},\] (20) \[H_{\mathrm{min}}\leq H\leq H_{\mathrm{max}}. \tag{21}\]  
where \(P_{\mathrm{max}}\) is the maximum transmit power of the UAV. To maximize the covert rate, we optimize the UAV-assisted NOMA CCS under several constraints, including LTMB, maximum transmit power of the UAV, covertness, and flying altitude. This is because the design of \(n\) and \(P\) affects both the covert rate of the channel from UAV to U2 and the detection performance of Willie. Also, due to the mobility of the UAV, the flight altitude constraint is adopted to maximize the covert with guaranteed covertness. In the following, we first find the optimal values of \(n\) and \(P\) that maximize the effective throughput \(\eta\) under the \(\mathcal{D}(P_{F},P_{M})\leq 2\epsilon^{2}\) and \(n\leq N\) constraints. Secondly, due to the nonconvexity of problem P1, we need to focus on converting the covertness constraint Equation (20). Finally, we design the SGPA algorithm.
## 2 Electro-Thermal Modeling and Analysis of Composite Materials Under Lightning Strike  
### Mathematical Models  
The electro-thermal coupling model is widely utilized for analyzing the electro-thermal effects induced by lightning currents passing through materials. This model primarily focuses on the transient heat transfer process following the impact of lightning currents on the laminate. It describes the phenomena of Joule heating, internal heat transfer within the material, and thermal exchange with the surroundings by controlling the governing equations, energy conservation equation, and boundary condition equation. Thus, it provides a comprehensive analytical framework for simulating and analyzing the electro-thermal effects of lightning strike on the laminate. The mathematical model reveals the laws of current path and heat transfer during lightning strike through a fully coupled electro-thermal process. Under specific thermal boundary conditions, the transient heat transfer analysis was conducted by applying the generated Joule heat to individual finite elements, ultimately obtaining the temperature field distribution of the laminate to evaluate the damage area. Specifically, three main behaviors occur during the lightning strike: (i) current conduction behavior; (ii) thermal conduction behavior; (iii) thermal exchange behavior. These are detailed as follows.
# Transformer Self-Attention Change Detection Network with Frozen Parameters
## 5 Discussion  
This study proposes a Transformer self-attention change detection network (ZAQNet) with frozen parameters, aiming to improve the accuracy and efficiency of change detection in remote sensing images. The model combines the advantages of convolutional neural networks (CNNs) and Transformers, extracting both local and global features while reducing the computational burden of standard Transformer architectures. Additionally, the introduction of frozen parameters stabilizes feature extraction, reduces overfitting, and enhances the generalization ability of the model.  
Experimental results show that ZAQNet outperformed existing advanced change detection methods on the BTRS-CD and LEVIR-CD datasets. The model achieved superior performance in multiple metrics, including precision, recall, _IoU_, and \(F\)1 score, particularly excelling in detecting fine-grained and spatially complex changes. The GIAU (Global Information Aggregation Unit) ensures effective fusion of dual-temporal features, the GSAU (Global Spatial Attention Unit) enhances spatial awareness, and the GSCU (Global Channel Attention Unit) improves channel-wise feature discrimination. Additionally, the PRU (Pooling Refinement Unit) further optimizes spatial features, leading to more precise change localization.  
Despite these advancements, some limitations remain. First, relying on frozen parameters may limit the model's adaptability to new datasets with significantly different distributions. Future research could explore adaptive parameter freezing mechanisms to enhance model transferability. Second, while ZAQNet achieved excellent performance, its real-time inference speed and computational efficiency still require optimization. Lightweight Transformer modules or knowledge distillation techniques could be investigated to improve deployment efficiency. Finally, the current model primarily focuses on optical remote sensing images. Future work could explore multi-modal fusion strategies, integrating SAR or LiDAR data to improve robustness under varying environmental conditions such as cloud cover or seasonal changes.
## 3 Experiment and Result  
The field experiment was performed at BDIA, China (39\({}^{\circ}\)29\({}^{\prime}\)N, 116\({}^{\circ}\)24\({}^{\prime}\)E), from 1 April to 1 September 2021. BDIA is the second international airport of Beijing along with Beijing Capital International Airport. For the time being, the mega-airport has four runways, as shown in Figure 2a. The runways are marked as 17R/35L, 17L/35R, 11L/29R, and 01L/19R, respectively. As flights continue to grow, windshear alerting services are becoming increasingly important.  
A compact and integrated micro-pulse CDWL was applied at the airport, as shown in Figure 2b. It has worked continuously in different weather conditions since 1 April 2021. The CDWL is operated at an eye-safe wavelength of 1.5 \(\upmu\)m. The spatial and temporal resolution are 30 m and 0.2 s of each beam, respectively. The key parameters are listed in Table 1. The CDWL supports multiple scanning patterns, including plan position indicator (PPI), range height indicator (RHI), velocity azimuth display (VAD), glide path (GP), and custom defined pattern. The updated CDWL system has made a lot of improvements and optimization, and its reliability has been verified in previous works [2; 12; 13]. During this experiment, the lidar mainly operated in the PPI scanning mode with a period of about 40 s. The scanning range of azimuth was set as 150\({}^{\circ}\) to 360\({}^{\circ}\) due to obstacles behind it. Elevation angles were set as 0\({}^{\circ}\) to 7\({}^{\circ}\) with steps of 1\({}^{\circ}\), to cover the runway and glide path. Continuous verification of windshear alerting was conducted from 1 April to 6 June--a total of 66 days--until 10 pilot reports were accumulated. Continuous verification of wind retrieval was conducted for 87 days, from 6 June to 31 August.