esa-sceva/satcom-chunk-collection huggingface dataset:
# Definition and Determination of Fin Substitution Factors Accelerating Thermal Simulations
## 3 Results
### Case Study: Natural Convection on Vertical Rectangular Finted Arrays
#### 3.3.4 Validation  
In this item, the performance of the specified calculation path is demonstrated by using examples from the literature. Three rectangular finned arrays from different sources were considered with three constant fin base temperatures. For each of these nine cases, a CFD simulation was carried out. Because of the results of Section 3.3.3 for each case, the respective \(FSF_{A,b,Ai}\) was determined. Each \(FSF_{A,b,Ai}\) was multiplied by the heat transfer coefficient of the corresponding smooth surface. The products were specified as the heat transfer coefficients for the convection boundary condition of the corresponding adapted solid-state simulation model.  
According to Section 3.1, a smooth and a finned surface are thermally the same if the heat flows are the same at the same ambient and surface temperatures. The heat flows resulting from the CFD analyses are compared with those of the solid-state simulations to show the thermal similarity. Furthermore, the relative deviation of these heat flows is calculated in each case, with the value from the CFD simulation forming the reference value.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(FSF\)} & \multirow{2}{*}{**Overall**} & \multicolumn{6}{c}{\(T_{fb}\) (K)} & \multicolumn{6}{c}{\(H\) (mm)} & \multicolumn{6}{c}{\(L\) (mm)} & \multicolumn{6}{c}{\(b\) (mm)} \\ \cline{3-14}  & & **303** & **323** & **373** & **423** & **473** & **12.5** & **25** & **50** & **100** & **125** & **250** & **500** & **8.8** & **14.7** & **32.4** & **56.0** \\ \hline \(FSF_{A}\) & 14.12 & 21.62 & 17.74 & 12.97 & 9.83 & 8.45 & 17.28 & 13.89 & 11.97 & 13.35 & 8.17 & 12.52 & 21.68 & 23.43 & 14.06 & 9.63 & 9.37 \\ \(FSF_{A,\eta,EL}\) & 12.86 & 17.93 & 12.57 & 9.41 & 10.55 & 13.84 & 15.78 & 11.97 & 11.77 & 11.91 & 10.65 & 11.47 & 16.45 & 19.80 & 11.98 & 9.53 & 10.12 \\ \(FSF_{A,\eta,Ai}\) & 12.67 & 17.95 & 12.59 & 9.25 & 10.20 & 13.37 & 15.84 & 12.01 & 11.61 & 11.24 & 10.44 & 11.27 & 16.30 & 19.75 & 11.83 & 9.24 & 9.88 \\ \(FSF_{A,\eta,Ba}\) & 12.82 & 17.92 & 12.55 & 9.36 & 10.49 & 13.78 & 15.78 & 11.96 & 11.73 & 11.81 & 10.61 & 11.43 & 16.42 & 19.85 & 11.94 & 9.44 & 10.04 \\ \(FSF_{A,\eta,Ro1}\) & 12.35 & 18.00 & 12.64 & 9.00 & 9.59 & 12.55 & 15.94 & 12.08 & 11.32 & 10.07 & 10.06 & 10.96 & 16.04 & 19.75 & 11.55 & 8.68 & 9.42 \\ \(FSF_{A,\eta,Ro1}\) & 13.42 & 17.89 & 12.63 & 9.94 & 11.51 & 15.12 & 15.61 & 11.87 & 12.30 & 1
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 2 System Model
### Propagation Model
Thus, the channel gain \(g_{i,DT_{j}}^{u}(t)\) between the cellular user and the D2D transmitter is [27]  
\[g_{i,DT_{j}}^{u}(t)=G_{i,DT_{j}}^{u}(d_{i,DT_{j}}^{u})^{-\xi}\zeta_{i,DT_{j}}^{ u} \tag{8}\]  
where \(\xi\) is the path loss exponent and \(\zeta_{i,DT_{j}}^{u}\) represents the channel gain corresponding to the channel shadow fading between cellular user \(C_{i}\) and D2D transmitter \(DT_{j}\), obeying a lognormal distribution. Similarly, the channel gain \(g_{i,DT_{j},DR_{j,u}}^{u}(t)\) between the D2D transmitter \(DT_{j}\) and the D2D receiver \(DR_{j,u}(n=1,2)\) is  
\[\delta_{i,DT_{j},DR_{j,u}}^{u}(t)=G_{i,DT_{j},DR_{j,u}}^{u}(d_{i,DT_{j},DR_{j,u }}^{u})^{-\xi}\zeta_{i,DT_{j},DR_{j,u}}^{u} \tag{9}\]  
where \(d_{i,DT_{j},DR_{j,u}}^{u}\) is the distance between D2D transmitter \(DT_{j}\) and D2D receiver \(DR_{j,n}\) at time \(t\), and \(\zeta_{i,DT_{j},DR_{j,u}}^{u}\) represents the channel gain corresponding to the channel shadow fading between D2D transmitter \(DT_{j}\) and D2D receiver \(DR_{j,n}\), obeying a lognormal distribution. The channel gain \(g_{i,DR_{j,u}}^{u}(t)\) between the cellular user and the D2D receiver is  
\[g_{i,DR_{j,u}}^{u}(t)=G_{i,DR_{j,u}}^{u}(d_{i,DR_{j,u}}^{u})^{-\xi}\zeta_{i,DR_ {j,u}}^{u} \tag{10}\]  
where \(d_{i,DR_{j,u}}^{u}\) is the distance between the cellular user and the D2D receiver \(DR_{j,n}\) at the moment \(t\), and \(\zeta_{i,DR_{j,u}}^{u}\) represents the channel gain corresponding to the channel shadow fading between the cellular user and the D2D receiver \(DR_{j,n}\), obeying a lognormal distribution.
## 7 RNN-Based Optimization of Mirror Placement for Maximizing SNR in ADRs
### RNN Methodology
#### 7.2.2 RNN Hidden State  
The RNN hidden state \(\mathbf{h}(t)\) encodes temporal dependencies and is updated as  
\[\mathbf{h}(t)=f_{h}(W_{h}\mathbf{P}(t)+U_{h}\mathbf{h}(t-1)+\mathbf{b}_{h}), \tag{46}\]  
where \(W_{h}\) is the weight matrix for the input signals, \(U_{h}\) is the weight matrix for the previous hidden state, \(\mathbf{b}_{h}\) is the Bias term, and \(f_{h}\) is an activation function (e.g., tanh or ReLU).
## 5 Performance Analysis of MSD Algorithm Based on Hybrid SC
### Hardware Resource Overhead of MSD Algorithm
This is primarily due to the fact that hybrid SC stores random bit sequences of local signals, which mitigates the computing resource overhead for numeric domain conversion at the cost of additional storage resources. Considering the ample storage capacity in modern hardware systems, this increased storage requirement can be considered an acceptable compromise.
# MFCDR: A Meta-Learning-Based Model for Federated Personalized Cross-Domain Recommendation
## 2 Related Work
### Meta-Learning  
Meta-learning, often known as learning to learn, is the act of enhancing learning algorithms across many learning stages with the goal of rapidly acquiring new concepts or abilities with limited sample data. The meta-learning paradigm may be generalized to a new task following training on many tasks. In the base learning process, the learning algorithm solves a task defined by the dataset and the goal. Optimizer-based meta-learning [2], model-based meta-learning [3], metric-based meta-learning [4], data augmentation-based meta-learning, and reinforcement learning based meta-learning [5] are the five current classifications of meta-learning. Meta-learning has been shown to be effective in image classification [6], recommendation systems, and small sample learning [7].  
[PERSON] et al. [8] suggested metric scaling to enhance the performance of meta-learning in handling few-sample classification problems, but pairwise comparisons can be computationally expensive when the task becomes large. [PERSON] et al. [9] proposed separating the gradient descent process from the optimized loss function task data to improve the generalization capability of meta-learning, but this suffers from overfitting problems. [PERSON] et al. [10] proposed combining meta. [PERSON] et al. [11] gave a probabilistic interpretation of meta-learning and proposed new algorithmic examples. Few of them target recommendation systems, which are a complex task.  
A number of recent studies have introduced meta-learning to recommendation systems. [PERSON] et al. [12] propose a sequential meta-learning approach for any differentiable model, which solves the problem of overfitting and forgetting due to the small size of new data containing little information about long-term user preferences, but it does not learn the preferences of new users well. [PERSON] et al. [13] offer the CLOVER meta-learning framework to ensure that meta-learning recommendation models are thorough and fair. Mecos is a meta-learning-based cold-start sequential recommendation framework proposed by [PERSON] et al. [14] to alleviate the item cold-start problem in sequential recommendation. Mecos can extract user preferences from limited interactions and match target cold-start items with potential users, but it tends to ignore cold items. [PERSON] et al. [15] proposed MetaKRes, a collaborative meta-knowledge augmented recommendation system that uses convolutional encoders to transfer knowledge obtained from knowledge graphs and user preferences, but is unable to personalize recommendations for users.  
In contrast to these approaches, we personalize recommendations to cold-start users by learning personalized transferable embeddings of cold-start users through a meta-network in the meta-recommendation module on the server.
# Gradient Agreement Hinders the Memorization of Noisy Labels
## 3 Proposed Method
### Gradient Agreement Updating
```
Input:\(\theta_{A}\), \(\theta_{B}\), \(\theta_{C}\), \(param\ update\ threshold\ \tau\), learning rate \(\eta\), batch size bs, training dataset \(\mathcal{S}=(\mathcal{X},\mathcal{Y})=\{(x_{i},y_{i})\}_{i=1}^{N}\) for\(e=0\) to MaxEpochdo
2 /*warm up \(\theta_{A}\) and \(\theta_{B}\) for certain epochs*/
3 if\(e<\ WarmUpEpoch\)then
4\(\theta_{A}=\) WarmUp(\(\mathcal{S},\theta_{A}\))
5\(\theta_{B}=\) WarmUp(\(\mathcal{S},\theta_{B}\))
6 else
7 foriter = 1 to IterNumdo
8 Draw two mini-batches \(\{(x_{n}^{a},y_{n}^{a})\}_{n=1}^{bs}\) and \(\{(x_{n}^{b},y_{n}^{b})\}_{n=1}^{bs}\) from \(\mathcal{S}\) using Class Distribution Agreement Sampling strategy
9 /*performing a forward propagation step*/
10\(\mathcal{L}_{a}=\frac{1}{bs}CE(\mathcal{P}_{model}(x_{n}^{a};\theta_{A}),y_{n }^{a})_{n=1}^{bs}\)
11\(\mathcal{L}_{b}=\frac{1}{bs}CE(\mathcal{P}_{model}(x_{n}^{b};\theta_{B}),y_{n }^{b})_{n=1}^{bs}\)
12 /*propagating the loss back to obtain gradients of parameters*/
13 Backward(\(\mathcal{L}_{a}\))
14 Backward(\(\mathcal{L}_{b}\))
15 for\(\mathcal{P}_{i}^{A},\mathcal{P}_{i}^{B}\ in\ (\theta_{A},\theta_{B}\))do
16 /*add gradients to parameters*/
17\(\hat{\mathcal{P}}_{i}^{A}=\mathcal{P}_{i}^{A}+\eta\
abla\mathcal{P}_{i}^{A}\)
18\(\hat{\mathcal{P}}_{i}^{B}=\mathcal{P}_{i}^{B}+\eta\
abla\mathcal{P}_{i}^{B}\)
19 /* compute gradient agreement coefficient \(g_{i}\)*/
20\(g_{i}=\Phi(Reshape(\hat{\mathcal{P}}_{i}^{A}),Reshape(\hat{\mathcal{P}}_{i}^{ B}))\)
21 /* compare \(g_{i}\) to threshold to determine whether to update the parameters*/
22\(\tilde{\mathcal{P}}^{A_{i}}=\mathcal{P}^{A_{i}}+\mathbb{1}_{g_{i}\geq\tau}* \eta\
abla\mathcal{P}^{A_{i}}\)
23\(\tilde{\mathcal{P}}^{B_{i}}=\mathcal{P}^{B_{i}}+\mathbb{1}_{g_{i}\geq\tau}* \eta\
abla\mathcal{P}^{B_{i}}\)
24 end if
25
26 end for
27
28 end for
```  
**Algorithm 1**Gradient Agreement Updating.
# Transmission Lines Insulator State Detection Method Based on Deep Learning
###### Abstract  
Aerial images are commonly used for detecting insulators in transmission lines to ensure their safe operation. However, each capture session generates thousands of insulator images, requiring manual collection, organization, and analysis. Therefore, to achieve automation in insulator state detection, this paper proposes a method based on deep learning for insulator state detection in transmission lines. Firstly, an insulator state detection model is built based on YOLOv7, and the model is improved using a bi-level routing attention mechanism and a content-aware up-sampling operator. Then, combined with dataset augmentation, including cropping, flipping, rotating, scaling, and splicing and a bounding box loss function incorporating a dynamic non-monotonic focus mechanism, 4000 visible images from different voltage levels of transmission lines are used for training. Finally, using a confusion matrix combined with comparative and ablation experiments, the results of insulator state detection are analyzed. Experimental results show that the proposed method achieves a detection accuracy of 97.1%. The detection accuracies for insulators exhibiting self-explosion, damage, flashover, and insulator strings are 93.5%, 98.6%, 97.5%, and 98.9%, respectively. Analysis results demonstrate that the proposed method can effectively realize insulator status detection.  
insulator; YOLOv7; state detection; deep learning; aerial image +
Footnote †: journal: Applied Sciences
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 6 Applications of Microbial Biopolymers
### Microbial Biopolymers as Coagulants and Flocculants
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Polymer** & **Pollutant** & **Removal Rates (\%)** & **References** \\ \hline \multicolumn{4}{c}{Inorganic contaminants} \\ Chitosan/Poly(acrylamide-acryloyloxyethyl) & Zn2\({}^{\text{+}}\) & 99.3 & [329] \\ trimethylammonium chloride & \(\text{Sodium alginate}/\text{triethylenetetramine nanofoculant}\) & \(\text{Pb}^{\text{2+}}\) & 97 & [330] \\ Chitosan/acrylamide/titaconic acid/3-acrylamide propyl & & & \\ trimethylammonium chloride & & & \\ Cationic Pullulan Derivatives & \(\text{FeO}\) & 95 & \\ TiO\({}_{2}\) & 75 & [332] \\ \multicolumn{4}{c}{Organic contaminants} \\ Chitosan/poly (acrylamide-itaconic acid) & Crystal violet & 81.6 & [333] \\ Polyaluminium & Congo red & 93.81 & [334] \\ chloride/ xanthan gum & Novadim & 90 & \\ Cationic pullulan derivatives & Bordeaux mixture & 98 & [335] \\  & Karate Zeon & 80 & \\ Methylene Blue & 99 & \\  & Duasyn Direct Red & 95 & \\ Cationic cellulose/bentonite & Acid Black 2 & 100 & [336] \\ Crystal Violet & 100 & \\  & Basic Green 1 & 99 & \\ Carboxymethyl cellulose/titaconic acid/sodium alginate & Crystal violet & 92.2 & [337] \\ Cellulose nanocrystals & Reactive blue 19 & 80 & [338] \\ Sodium alginate/methacryloxymethylammonium chloride & Dissolved Organic Carbon (DOC) & 35.42 & [339] \\ Other contaminants (Water Quality Parameters) & & & \\ Sodium alginate/methacryloxymethyltrimethyl ammonium chloride & UV\({}_{254}\) & 32.42 & [339] \\ Turbidity & 93.95 & \\ Xanthan Gum/Polyacrylamide & Total Solids (TS) & 76.97 & [340] \\ /SiO\({}_{2}\) & Total Dissolved Solids (TDS) & 48.21 & \\  & Total Suspended (TSS) & 93.75 & \\ xanthan gum/polyacrylamide & Turbidity & 52.63 & [341] \\ Pullulan/p(N-isopropylacrylamide) & Turbidity & 88 & [332; 342] \\ Poly-\(\gamma\)glutamic acid & Turbidity & 86.6 & [343] \\ Chitosan/Poly(acrylamide-acryloyloxyethyl) & Total Phosphorus (TP) & 98.8 & [329] \\ trimethylammonium chloride & COD & 44.8 & \\ Chitosan/poly-glutamic acid & Total Nitrogen (TN) & 53.4 & [344] \\  & TP & 28.1 & [344] \\ Turbidity & 98.3 & \\  & COD & 86.2 & \\ Bentonite/chitosan/poly-glutamic acid & TN & 80.3 & [285] \\  & TP & 52.3 & \\  & Turbidity & 98.2 & \\ Sodium alginate/methacryloxymethyltrimethyl ammonium chloride & Turbidity & 95.96 & [339] \\ Sodium alginate/polysilicate aluminum calcium & Turbidity & 97.2 & [345] \\ Chitosan/Poly(acrylamide-acryloyloxyethyl) & COD & 72.5 & [329] \\ trimethylammonium chloride & & & \\ Cellulose Nanocrystals & Turbidity & 99.7 & [346] \\  & Turbidity & 80 & [347] \\ Dicarboxylic cellulose & COD & 60 & [348] \\ \multicolumn{4}{c}{} \\ \end{tabular}
\end{table} 
Table 6: Studies related to the use of biopolymers as coagulants flocculants to treat wastewater.
## 4 Discussion  
### Passive Form of Signal Source and Circuit System Model  
Considering the specimen as a system, the circuit shown in Figure 3 or Figure 4 is transformed into the circuit system (\(H(t)\)) shown in Figure 7, which is called a piezoelectric conversion system. In Figure 7, the compressive load (\(P\)) applied to the specimen is used as the excitation source of the circuit system (\(H(t)\)), which will cause the generation of an electric potential (\(E_{\sigma}(t)\)) within the specimen. The input excitation \(E_{\sigma}(t)\) of the circuit system (\(H(t)\)) is not generated by measurement instruments, so it is called a passive source. Analyzing the input excitation \(E_{\sigma}(t)\) and solving the output response of the circuit system (\(H(t)\)) is a very worthwhile issue. According to the experimental results, the existence of electric signals can be detected at very low load levels. This means that when a very low load is applied, the particle organization structure of the specimen is reset and electromagnetic radiation occurs. At a certain level of load, the typical waveform of the specimen is shown in Figure 8, which is the voltage waveform of 8\(\#\) granite specimen under a load of 2 kN. This shows that the signal in the circuit system (\(H(t)\)) is attenuating and oscillating. Below is an analysis of this.  
At \(t=0\), a compressive load (\(P\)) is applied, which leads to the excitation of the electric potential (\(E_{\sigma}(t)\)) and the beginning of charging the capacitance (\(C_{\sigma}\)). As time (\(t\)) increases and the compressive load (\(P\)) continues to act, the voltage (\(U_{C\sigma}(t)\)) across the capacitor's terminals will also gradually increase. The voltage (\(U_{C\sigma}(t)\)) can be expressed as:  
\[U_{C\sigma}(t)=E_{\sigma}(t)\left(1-e^{-t/\tau}\right) \tag{3}\]  
In the formula, \(\tau=R_{\sigma}C_{\sigma}\). When the load (\(P\)) rises to a predetermined value, \(U_{C\sigma}(t)\) increases to its maximum value, i.e., \(U_{C\sigma}(t)=E_{\sigma}(t)\). According to Formula (3), the charging curve exhibits an exponential monotonic increasing trend. Once the compressive load (\(P\)) is stopped, the capacitor begins to discharge, and the discharge pattern can be expressed as:  
\[U_{C\sigma}(t)=E_{\sigma}(t)e^{-t/\tau} \tag{4}\]  
Figure 8: Voltage waveform of granite sample 8\(\#\).  
Figure 7: Piezoelectric conversion system.  
According to Expression (4), the discharge curve exhibits an exponential monotonic decay. Therefore, in the circuit system (\(H(t)\)), in addition to the energy-consuming element, resistance (\(R_{\sigma}\)), there are two different energy storage elements--inductance (\(L_{\sigma}\)) and capacitance (\(C_{\sigma}\)). Only when \(L_{\sigma}\), \(C_{\sigma}\), and \(R_{\sigma}\) all exist in the circuit system (\(H(t)\)) will the attenuation oscillatory discharge process occur. This shows that it is not comprehensive to study electromagnetic radiation by analyzing pure capacitor energy storage discharge (i.e., a single cause is the capacitor).
## 5 Discussion
### Limitations  
Despite significant progress, previous and current approaches to AI-based DTs for Industry 4.0/5.0 still face several limitations. First, the high computational requirements of AI-based DTs pose a challenge for real-time processing, especially in resource-constrained environments. Many existing DT implementations struggle with data silos, which makes seamless integration across industrial systems difficult. The reliance on large volumes of real-time data raises concerns about data security and privacy, especially when processing sensitive industrial information. Interoperability remains a major challenge, as different industries and manufacturers use different standards, preventing seamless communication between DTs. The high cost of developing and deploying AI-based DTs limits accessibility, especially for small- and medium-sized enterprises (SMEs). Current AI models are often not explainable, making it difficult for human operators to trust and interpret the recommendations generated by DTs. Many DTs are heavily dependent on cloud computing, which can cause latency and dependency on stable internet connections. The complexity of integrating AI-based DTs into existing industrial workflows often requires the extensive reconfiguration and retraining of personnel. Limited generalizability means that DTs optimized for one industrial process may not be easily adapted to others without significant retraining. While predictive maintenance is a key application, false alarms and inaccurate failure predictions can still occur, leading to unnecessary interventions or missed issues. The scalability of current DT frameworks remains a challenge, especially in large industrial ecosystems with many interactive DTs. AI models used in DTs require continuous updates and retraining, which can be time-consuming and computationally expensive. The lack of standardized validation methods for AI-based DTs makes it difficult to assess their reliability and performance across industries. In Industry 5.0 applications, the human-AI collaboration aspect is still underdeveloped, as most DTs focus primarily on automation rather than improving human decision making. Finally, current approaches often struggle to effectively integrate sustainability indicators, which limits their ability to optimize energy consumption and reduce the environmental impact of industrial processes [87; 88; 89; 90; 91].
# Method to Measure Light Spot Position in Beacon-Less Inter-Satellite Communication System
## 6 Conclusions  
In conclusion, we proposed a new method to measure spot position under multi-beams irradiation by using a single quadrant detector (QD). We modulate received light and transmitted light at different frequencies, use cyclic cross-correlation (CCR) to detect the amplitude of the weak beacon light's photocurrent output from QD, and finally calculate the received light spot position. This method can work well in the case of very large light intensity difference. Experiment results show that even when the two beams differ in intensity by 91.7 times, the CCR method can accurately detect the weak light spot position, with that the maximum error is 0.026 mrad and the root mean square error is as low as 0.012 mrad.  
The proposed method only needs a small amount of data to participate in the calculation, which greatly improves the calculation efficiency and costs little hardware resource. Therefore, this method is especially suitable for real-time spot position detection. Furthermore, this improved method does not need to consider the synchronization of the signals, which means that this method is of great practical value in Is-OWC communication.  
Conceptualization, Q.L. and S.X.; methodology, Q.L. and S.X.; software, Y.X. and D.H.; validation, Y.H.; formal analysis, Q.L.; investigation, Y.H. and Z.P.; resources, Q.L.; data curation, H.G.; writing--original draft preparation, Q.L. and S.X.; writing--review and editing, Q.W.; visualization, Y.H. and Z.P.; supervision, Y.H.; project administration, Q.L. National Natural Science Foundation of China (NSFC) (U1738204, 61571096, 61775030); Sichuan Science and Technology Program (2019Y0167).  
The authors declare no conflict of interest.