esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In summary, this study employed ReaxFF-MD and DFT methods to investigate the decomposition mechanism of a PMVE/N\({}_{2}\) gas mixture over a temperature range of 1600-2600 K and examined the influence of temperature on the decomposition process. In addition, the environmental and safety aspects of the PMVE/N\({}_{2}\) mixture were evaluated to assess its engineering feasibility. The following conclusions can be drawn:  
(1) The decomposition of the PMVE/N\({}_{2}\) gas mixture is an endothermic process. The decomposition rate of PMVE increases with temperature, with a marked acceleration observed at 2200 K, while the decomposition rate of N\({}_{2}\) remains relatively stable throughout the studied temperature range.  
(2) The primary decomposition products include C, C\({}_{2}\)F\({}_{2}\), CF\({}_{2}\), CN, CO, CF\({}_{2}\)O, F, O, C\({}_{2}\)F\({}_{3}\), and CF\({}_{3}\). Notably, the yields of CN, CO, and F increase linearly with time. The decomposition process involves the dynamic recombination and re-decomposition of free radicals, which leads to the formation of products such as C\({}_{2}\)F\({}_{2}\), and C\({}_{2}\)F\({}_{4}\). Several nitrogen-containing species (e.g., C\({}_{2}\)N\({}_{2}\), CFN) were detected. However, C\({}_{2}\)N\({}_{2}\) decomposes under the simulation conditions to produce significant amounts of CN and C\({}_{2}\)N radicals. Moreover, the presence of O radicals facilitates the consumption of C\({}_{2}\)F\({}_{2}\) and reacts with particulate carbon to form CO.  
(3) Three distinct decomposition pathways were identified for the PMVE/N\({}_{2}\) gas mixture. Among these, the pathways that yield C\({}_{2}\)F\({}_{3}\)O and CF\({}_{3}\) require the least energy. The decomposition process is accompanied by the generation of toxic byproducts, including C\({}_{2}\)N\({}_{2}\), CF\({}_{2}\)O, and CO. Notably, C\({}_{2}\)N\({}_{2}\) has an LC50 (1 h rat inhalation) of 330 ppm, classifying it as a highly toxic substance. The safety of PMVE/N\({}_{2}\) must be carefully evaluated before its engineering application.  
Conceptualization, H.C. and W.Y.; methodology, H.C.; software, W.Y.; validation, H.D., W.Y. and J.Y.; formal analysis, H.C.; investigation, H.C. and W.Y.; resources, H.D., W.Y. and J.Y.; data curation, H.C.; writing--original draft preparation, H.C.; writing--review and editing, W.Y.; visualization, H.C.; supervision, S.L. and Y.L.; project administration, H.C. and W.Y.; funding acquisition, H.D. and W.Y. All authors have read and agreed to the published version of the manuscript.  
This work was Funded by the Graduate Innovation Program of China University of Mining and Technology (SJCX25_1459) and the China Postdoctoral Science Foundation (2024M763542).  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors sincerely thank [PERSON] at the Beijing Kein Research Center for Natural Sciences for his help with computer simulations.  
The authors declare no conflicts of interest.
## 5 Connection To Numerical Modelling  
The primary aim of the APD system is to produce material datasets suitable for direct use in numerical analysis. This process depends on two fundamental components: the stratigraphic profile and the parameters required for constitutive modelling. Stratigraphy is defined within module 2, whereas the constitutive parameters are derived using methods selected by the user. There are no inherent restrictions on the choice of constitutive model;  
Figure 6: Comparison between APD and interpreted values at \(\mathcal{O}\)ysand test site (the blue shaded areas represent the range of values obtained from the CPT workflow): (**a**) unit weight, (**b**) void ratio, (**c**) relative density, (**d**) peak friction angle, (**e**) shear wave velocity (modified from [59]).  
users are free to adopt any model, provided that the required parameters can be obtained. Generally, the workflow involves first determining relevant soil properties, after which an appropriate constitutive model is selected based on the soil type. The corresponding model parameters are then derived from the previously determined soil properties.
# Classification and Fast Few-Shot Learning of Steel Surface Defects with Randomized Network
## 5 Experimental Results of Classification and Its Discussion
### Classification Results on the X-SSD Dataset  
In the case of X-SSD, we followed the same steps as described in refs. [1,10] regarding the division for training and testing: 70% (952) images were used for training and the remaining 30% (408) for testing. The augmentation was identical to the above described, as well as other parameters of the training process.  
The best previous results were also produced by ExtVGG16 [10] (a little below 100%), and could be now beaten slightly with EfficientNet-B7. Table 3 shows, beside accuracy, the computed macro-precision, macro-recall, and macro-F1 values for 14 different methods, including EfficientNet-B7. Macro-values refer to the averaging of class averages. We also included the confusion matrix and a misclassified example for EfficientNet-B7 in Figure 4.  
We can conclude that the NEU dataset seems to be too easy since many methods reached over 99% accuracy and both ExtVGG16 and EfficientNet-B7 could result in perfect classification. There is a larger gap between the accuracy of the different methods on X-SSD, where EfficientNet-7 showed the best accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Model** & **MMCCN\({}^{1}\)[13]** & **SBF-Net** & **ResNet50** & **Rev-VGG** & **MVM** & **Rev-VGG** & **Decaf** & **VSD\({}^{1}\)[18]** & **ResNet48** & **ACELBP** & **OVERFAT** & **Classic** & **BYCC** & **ExtVCG16** & **EfficientNet-B7** \\  & & **+MFN** & **Net9** & **-VGG** & **Net94** & & & & **+MFN** & **ResNet50** & & **+MFN** & **+MFN** \\ \hline Accuracy & 99.72\% & 99.72\% & 99.67\% & 99.67\% & 99.67\% & 99.55\% & 99.33\% & 99.27\% & 89.17\% & 99.17\% & 98.93\% & 98.7\% & 98.67\% & 96.3\% & **100\%** & **100\%** \\ \hline \hline \end{tabular}  
* A quantity of 40% of images used for testing, 60% for training. 2 A total of 50 images per class used for testing, remaining images for training.  
\end{table}
Table 2: Comparison of the classification accuracy of different models on the NEU dataset. If not specified then information is based on [29]. Training/testing ratio is 80/20 in general. Best values are highlighted in bold.  
Figure 4: (**Left**) Confusion matrix of EfficientNet-B7 classification of the seven error types of the X-SSD dataset. (**Right**) An example image of the Si class and an Is defect wrongly classified as Si.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Model** & **ExpNet-2** & **ChoutNet** & **ShuffleNet** & **SuperneNet** & **Xception** & **VCG16** & **ResNet48** & **ResNet101** & **ResNet12** & **RepVCG** & **RepVCG** & **RepVCG** & **EuVCG16** & **EfficientNet-B7** \\ \hline
**Accuracy** & 89.55\% & 88.72\% & 87.50\% & 91.42\% & 90.44\% & 92.63\% & 93.57\% & 87.01\% & 92.15\% & 88.85\% & 91.67\% & 95.10\% &
# Survey of Artificial Intelligence Model Marketplace
## 4 Model Pricing and Trading
### Model Pricing Schemes
#### 4.1.1 Pricing Desiderata (_Y-Axis_)  
Pricing desiderata are the axioms or properties to be satisfied by a pricing scheme to be considered fair, effective, and applicable. Like other digital products markets, in model trading marketplaces, these desiderata ensure that pricing techniques align with both sellers' and buyers' needs and the overall market dynamics. In the following, we brief some of these axioms.  
**Arbitrage freeness:** Arbitrage-free property ensures that buyers cannot exploit pricing inconsistencies. It guarantees that models with higher accuracy or quality are always priced higher than those with lower accuracy or quality. It also prevents buyers from combining cheaper models to replicate the performance of more expensive ones. To achieve this, a pricing scheme must ensure buyers cannot take advantage of price differences among published model instances to maximize their utility at a lower cost, which would otherwise reduce the seller's revenue. A pricing function \(F(x)\) is arbitrage-free if it is error-monotone, subadditive, and non-negative [41; 87].  
**Revenue/Welfare Maximization:** A pricing scheme is revenue-maximizing when it guarantees profit to the sellers (model owners). In a centralized trading setting, the broker facilitates the transaction between the seller and the buyer in a win-win manner. i.e., by ensuring that sellers' revenue is maximized at the same time, buyers' utility is maximized as well. However, in a distributed setting like federated learning, the welfare (benefit) is evenly distributed across the participating clients based on their active contribution to model training.  
**Privacy Preservation/Compensation:** A pricing scheme should preserve the sellers' privacy or compensate appropriately based on the level of sensitive information (data) disclosed and utilized during model training. Differential privacy is one of the most famous techniques to prevent/minimize the disclosure of sensitive information in model training [91; 92].  
**Truthfulness and Fairness:** Truthfulness guarantees fairness in marketplaces, which can be viewed from different perspectives. From the data owners' side, some pricing schemes incentitize the data owners to reveal data of good quality for model training. Similarly, from the buyers' side, an effective pricing scheme should facilitate or guarantee  
Figure 6: A taxonomy of pricing in model marketplaces.  
truthful valuation of model instances to the buyers while ensuring fair allocation of profit or benefit to the sellers. Sharpley value is one of the most famous techniques for guaranteeing fairness in digital commodity markets [98].
# On Modeling Eavesdropping Attacks in Underwater Acoustic Sensor Networks +
## 5 Empirical Results
### Eavesdropping Probability with an Isotropic Eavesdropper
#### 5.1.2 Eavesdropping Probability in AUSNs  
We then conduct simulations on the eavesdropping probability in AUSNs with an array eavesdropper (\(M=10\)). Figure 8 shows the results. Note that the transmission power in AUSNs can be calculated by Equation (15) and the reference transmission power is \(P_{t}^{i}=100\,\mathrm{dB}\). Similarly, we can also draw a conclusion that our analytical framework can accurately model the eavesdropping  
Figure 7: Eavesdropping probability in IUSNs with an _isotropic_ eavesdropper versus node density \(\rho\) (/km\({}^{2}\)) when \(f=10\) kHz, \(f=30\) kHz and \(f=90\) kHz under different spreading factor \(k\) and wind speed \(w\). (**a**) \(k=1,w=0\,\mathrm{m/s}\); (**b**) \(k=1,w=10\,\mathrm{m/s}\); (**c**) \(k=2,w=0\,\mathrm{m/s}\); (**d**) \(k=2,w=10\,\mathrm{m/s}\).  
probability in AUSNs as the simulation results match the analytical results. Besides, we can see that the eavesdropping probability increases with the increased node density \(\rho\) and decreases with the increased signal frequency \(f\), and eavesdropping probability decreases when wind speed \(w\) or spreading factor \(k\) increases. Moreover, if we compare the eavesdropping probability in IUSNs with that in AUSNs together (by aligning Figures 7 and 8 together), we can find AUSNs have the lower values in terms of eavesdropping probability than IUSNs, implying that the nodes have less chance of being eavesdropped upon in AUSNs than that in IUSNs.
## 1 Introduction
[15] researched the aerodynamic performance of a double VAWT system and found that the system's efficiency was significantly improved. Their findings also validate that the blade azimuth angle has little impact on the aerodynamic performance. [PERSON] et al. [16] demonstrated through their research that close spacing arrangements of VAWTs in an array formation have been proven to be superior compared to standalone VAWTs. It has been shown that the overall power output can be increased by more than 20%. [PERSON] et al. [17] studied floating double VAWT systems offshore, investigating different spacing, pitch angles, and periods of upstream turbines. They found relatively large pitch angles and relatively small pitch periods reducing the negative effects of wake interference and small spacing having better power output. [PERSON] and [PERSON] [18] investigated the effect of spacing, wind direction, and vertical height difference on the efficiency of double VAWT systems based on the Taguchi method. Their results indicate that the vertical height difference has the most significant impact on the entire unit, and with the same height, double rotors have a 26.6% higher efficiency than single rotor ones. [PERSON] et al. [19] investigated the influence of different wind conditions on the power output of dual-rotor systems. The findings revealed that wind direction, particularly when the wind frequency is high relative to the wind speed, has a significant impact on determining the optimal installation direction. Furthermore, it was determined that the maximum average power coefficient (CP) is 1.27 times higher than that of an isolated wind turbine. [PERSON] et al. [20] added diamond-shaped flow deflectors to the double VAWT system and found that the average torque coefficient could be increased by 23% under the deflectors' influence. Their study also revealed that the double VAWT system's performance is sensitive to wind direction. [PERSON] et al. [21] studied the performance of dual Savonius rotors using the Taguchi design method. The CFD and experimental results indicated that the average power coefficient of the dual-rotor configuration improved by 19% compared to standalone wind turbines without the use of deflectors. Numerous studies indicate that double VAWT configuration can significantly improve wind turbine performance. Previous studies by our research group have shown that a standalone Nautilus wind turbine has a conversion efficiency of 26%, which is quite remarkable. However, previous studies have not identified the fundamental reasons behind the enhancement effect; instead, they have merely described the phenomena in isolation. This paper investigates the flow field details and performance enhancement of double rotor systems at different spacing ratios and wind directions, explaining the nature of the performance improvement of VAWT arrays. Based on the research results, several reasonable array layouts were proposed, which represents a significant breakthrough, and the accuracy of the computational simulations was verified through wind tunnel experiments.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 4 Joint Digital Signal Processing Schemes for Carrier Recovery
### Experimental Evaluation  
In [66], we experimentally investigated master-slave carrier recovery in comb-based receivers. Using the same setup, we also investigated joint phase estimation. In the setup, two free-running EO frequency combs with 25-GHz spacing were used as transmitter and LO light sources, and two 10-GBaud PM-64 QAM subchannels were transmitted and received at the same time. This enabled us to perform proof-of-concept demonstrations for a back-to-back case.  
First, we studied the phase correlation between the channels. This was done by performing separate phase tracking on the two simultaneously-received channels and comparing the resulting phase traces. In Figure 8, the phase traces for two frequency spacings, 25 GHz and 275 GHz, are plotted together with their difference. In both cases, the phases are similar enough to be nearly indistinguishable when plotted on top of each other. However, when the phase difference is plotted separately, a small, but noticeable difference can be seen for the 275-GHz spacing case. Indeed, as seen in Figure 8c, the standard deviation of the phase difference increases with the frequency spacing. With the addition of some measurement noise, the increase is approximately linear with frequency spacing, in agreement with Equation (1).
## 5 Results
### Synthesis  
In each case, attempting to maximize two of the three properties inherently leads to the degradation of the third. In Case 1, maximizing decentralization and security limits scalability due to increased communication complexity and latency. In Case 2, maximizing security and scalability reduces decentralization as node participation becomes restricted or hierarchical structures emerge. In Case 3, maximizing decentralization and scalability compromises security by simplifying consensus mechanisms and lowering resilience against attacks.  
**Theorem 1**.: _It is impossible for a blockchain system to simultaneously achieve the following:_  
* _Maximum Decentralization:_ \(D=D_{max}=1\)__
* _Maximum Security:_ \(S(f)=1\) _for_ \(f<f_{max}\)__
* _Maximum Scalability:_ \(C=1\) _for the desired throughput (T) and latency (L)_  
**Proof of Theorem 1.** Assuming all three properties can be maximized leads to contradictions in each case analyzed. Therefore, our initial assumption must be false. \(\square\)  
Our proof demonstrates that the Blockchain Trilemma is not just a theoretical concept, but a fundamental limitation grounded in the principles of distributed systems and consensus mechanisms. The interdependencies among decentralization, security, and scalability create inherent trade-offs that cannot be eliminated.  
While our proof focuses on the impossibility of simultaneously achieving maximum levels of all three properties, practical systems may achieve acceptable levels by balancing the trade-offs. For example, a blockchain might accept slightly increased latency to enhance security without fully compromising scalability.  
Optimizations such as improvements in network infrastructure and consensus algorithms can shift the boundaries but cannot eliminate the inherent trade-offs. Future technologies like quantum computing and new protocols may offer new approaches, but they would still need to address the fundamental limitations outlined in our analysis.
## 3 Theory and Method  
### RoBERTa Layer  
RoBERTa aims to learn a common language representation by pre-training large-scale unlabeled text data for fine-tuning and application on various downstream natural language processing tasks. Figure 1 is an architecture diagram of RoBERTa's model.  
It improves BERT in the following ways:  
1. Pre-training dataset: RoBERTa conducts pre-training with large amounts of unlabeled text data, using more data than BERT and not limiting the maximum length of each sentence;
2. Pre-training tasks: RoBERTa employs the same two pre-training tasks as BERT, namely Masked Language Modeling (MLM) and Next Sentence Prediction (NSP). In the MLM task, the model needs to predict the obscured words based on the context. For a given input text \(T=\{W_{1},W_{2},\dots,W_{n}\}\), RoBERTa first masks some random words and replaces them with the special MASK token \"[MASK]\" to obtain the mask text \(M=\{m_{1},m_{2},\dots,m_{n}\}\). Then, RoBERTa encodes through the Transformer model to obtain the output \(H=\{h_{1},h_{2},\dots,h_{n}\}\) of the encoder layer. For a mask position \(m_{i}\), RoBERTa maps \(d_{model}\) to a dimensional vector \(z=W_{out}h_{i}\) by linear transformation and obtains the final predicted distribution \(P(z|T)\) by softmax operation. In the NSP task, the model needs to determine whether two sentences are continuous.  
Figure 1: Schematic diagram of the structure of the RoBERTa model.  
For a pair of input sentences \(S=(s_{1},s_{2})\), RoBERTa represents them as a special sequence \([\text{CLS}]_{\text{s}_{1}}[\text{SEP}]_{\text{s}_{2}}[\text{SEP}]\). RoBERTa then passes this composite sequence into the Transformer model for encoding, yielding the output \(H=\{h_{1},h_{2},\dots,h_{n}\}\). Here, \(h_{1}\) represents the output of the composite sequence's first token \"[CLS]\". RoBERTa then takes the vector \(h_{1}\) representing the entire sequence as input and runs it through a binary classifier to determine whether the two sentences occur in succession.  
1. Training parameters: RoBERTa has improved some training hyperparameters. It uses larger batch sizes, longer training sequences, and more training steps to enhance the model's performance;
2. Dynamic mask: RoBERTa introduced the concept of dynamic mask. Each training instance randomly generates a mask during each training step. This can improve the model's generalization ability and reduce the dependence on location information;
3. Randomization during training: RoBERTa used more randomization methods in the pre-training process, including random removal and dynamic masking, which helped the model better adapt to various natural language processing tasks.
# CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
## 3 Methods
### SVM Loss Function  
Hinge loss is often used as the objective function of SVM. Standard hinge losses are focused on dichotomous problems. For example, given a training dataset \(x_{i}\in R^{D}\), \(i\in\{1,2,\cdots,N\}\), \(y_{i}\in\{-1,+1\}\), suppose there are \(N\) samples, each with dimension D, and \(xw\) is the predicted value of a linearly separable SVM, \(Y\) is the category of correct classification, and \(w\) is a parameter that a classifier can learn from the samples. Then its hinge loss can be expressed as  
\[L_{i}=max(0,1-x_{i}w^{T}y_{i}) \tag{1}\]  
for the objective function of a binary classification problem, the loss value is 0 when \(x_{i}w^{T}y_{i}\) is greater than \(1\), i.e., the loss function prediction is accurate.  
Since SVM can filter samples at long distances from the hyperplane by choosing a threshold for the distance from the sample to the hyperplane, these samples are often the ones that can be easily and correctly classified, thus extending the loss function to a multi-classification SVM. Given \(y_{i}\in\{1\cdots K\}\), the multi-classification hinge loss for a single sample can be expressed as  
\[L_{i}=\sum_{j\
eq y_{i}}max(0,1+w_{j}^{T}x_{i}-w_{y_{i}}^{T}x_{i}) \tag{2}\]  
where \(w_{j}\) is the \(j\)-th component in parameter vector \(w\). The multi-classification SVM is to set the correctly classified prediction score at least one threshold higher than the other incorrectly classified prediction scores. If the other classification prediction scores are below the threshold, the loss value is calculated. Due to the uniqueness of \(w\), a regularization penalty is introduced to remove ambiguity. Most commonly used is the L2 paradigm. Hence, the multi-classification SVM loss function contains the regularization penalty, and the loss function of k-classification linear SVM can be expressed as  
\[\underset{w}{min}\sum_{i=1}^{N}\sum_{j\
eq y_{i}}max(0,1+w_{j}^{T}x_{i}-w_{y_ {i}}^{T}x_{i})+\lambda\sum_{k}\sum_{n}w_{k,n}^{2} \tag{3}\]  
Equation (3) can be regarded as the loss function of multi-classification SVM by data loss plus regularization loss, where hinge loss is data loss, and the L2 normal form is regularization loss. Since the penalty in L2 regularization is biased toward smaller, relatively dispersed weight vectors, the L2 paradigm can improve the generalization performance of the classifier and reduce overfitting.