esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey of Artificial Intelligence Model Marketplace
## 4 Model Pricing and Trading
### Model Pricing Schemes
#### 4.1.2 Pricing Techniques (X-Axis)
In an attempt to prevent arbitrage pricing in marketplaces, [PERSON] et al. [41] proposed a pricing function that leverages the Gaussian noise injection mechanism to produce a range of model instances with different accuracy levels (versioning) and prices them accordingly. The function is designed to be arbitrage-free and error-monotone, ensuring fair pricing based on model accuracy. The function also includes a revenue optimization component to maximize the seller's profit while maintaining the buyer's affordability. Effective model sharing or trading in marketplaces requires a fair and transparent product-compensation swapping method between the model developers and the users. [PERSON] et al. [90] proposed to promote performance-based pricing (i.e., versioning using different datasets) in model trading marketplaces. In their method, the genetic algorithm jointly solves a bi-level optimization problem where the upper level maximizes the seller's revenue while the lower level maximizes the buyer's utility. An effective marketplace should facilitate the achievement of its stakeholder's objectives. i.e., the broker should ensure that data owners are compensated while model buyers' utility requirements are ensured. For example, [PERSON] et al. [87] proposed to integrate differential privacy and Sharpley value in the model trading marketplace by formulating compensation and pricing functions that account for the data owner's privacy and model buyer's utility while leveraging dynamic programming to optimize revenue maximization and privacy preservation jointly. The use of Shapley value and differential privacy parameter (i.e., epsilon) in these functions ensures a fair and competitive marketplace, balancing the needs of data owners, brokers, and model buyers. Also, effective marketplaces shall encourage data owners to participate actively in model training. Along this direction, [PERSON] et al. [89] proposed a structured and fair method for valuing data contributions and distributing rewards in a collaborative model development environment. The proposed approach ensures that parties (data owners) are incentivized to contribute valuable data, which are rewarded with a model in return based on the information gained from the contributed data, leading to the development of high-quality machine-learning models. Similarly, some marketplaces match model training data demand with an appropriate supply. For example, [PERSON] et al. [93] proposed to improve pricing in data-augmented model trading marketplaces. Their method leverages mixed integer linear programming (MILP) to yield a price curve that publicly assigns a price to each performance metric. Markov decision processes (MDP) were designed and solved using dynamic programming to facilitate buyers' traversal across the performance metrics and determine the optimal stopping time that balances costs and benefits.
## 6 Verification of the Proposed Protocols
### Formal Methods  
Formal methods and automated tools have been used to verify mobile network security to assess their security properties [20; 50], hence providing strong security guarantees. Security protocols properties are very challenging for most verification techniques and tools. This is due to the use of cryptographic primitive and algebraic properties that are hard for symbolic reasoning, hence certain tools and manual proof checks are not suitable [50]. However, there are automated verification tools that can be used for protocol modeling and analysis such as Automated Validation of Internet Security Protocols and Applications (AVISPA) [57] and ProVerif [58].  
ProVerif [58] is an automatic tool for analyzing security protocols, with Dolev-Yao [59] as the adversarial model and enabling the verification of security properties, by supporting equational theories defined by users. It supports the underlying theory of abstraction, focusing on unbounded sessions, and uses precise Horn clause abstraction and applied pi calculus [60] as a formal language for modeling security protocols. The syntax is paired with a formal semantics to enable reasoning about protocols. It also supports a variety of cryptographic primitives, modeled by equations and rewrite rules. Additionally, it takes the security properties to be proved, such as authentication, secrecy or observational equivalence, as input. It translates the protocol into an internal representation by Horn clauses, which makes crucial abstractions in order to support an unbounded number of sessions. Cryptographic primitives are modeled as functions, messages as terms built over an infinite set of names such as \(a,b,c,...\), variables such as \(x,y,z,...\) and a finite set of function symbols such as \(f1,...,fn\). The syntax of ProVerif process language is shown in Table 2 and a full description can be found in [58]. Therefore, ProVerif suits the type of analysis conducted in this paper. It has been used before to formally verify security protocols [38; 39; 50].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Term** & **Grammar** \\ \hline M, N := & term \\ a, b, c, k, s & name \\ x, y, z & variable \\ h(M1,...,Mn) & constructor/destructor application \\ D := & expressions \\ fail & failure \\ P, Q := & processes \\ out(N,M); P & output \\ in(N, x : T); P & input \\!P &!P replication \\
0 & nil \\ P | Q & parallel composition \\ new a : T; P & restriction \\ let x : T = D in P else Q & expression evaluation \\ if M then P else Q & conditional \\ \hline \hline \end{tabular}
\end{table}
Table 2: Core language: term and process grammar.
## 3 Simulation Setup  
### Parameter Setup  
The numerical model applied in this paper was modified based on sprayFoam solver in open source C++ code library OpenFOAM-2.4.0. The initial parameters including temperature, pressure, and nozzle diameter for the numerical study as shown in Table 1 were all based on our previous experiment conditions. To exhibit the influences of inject pressures and superheat degrees on iso-pentane flashing spray, four initial injection pressures of 1.2, 1.5, 1.8, and 2.1 MPa and three initial injection temperatures of 50, 60, 70, and 80 \({}^{\circ}\)C were chosen in the simulation (16 cases in total). Each case experienced a flashing spray process due to the rapid pressure drop from the initial high pressure to the low atmospheric pressure (below the saturation pressure corresponding to the initial injection temperature).  
An adjustable run time step algorithm was used to improve computing performance while maintaining temporal accuracy and numerical stability. The thermoproperties of iso-pentane, such as density, viscosity, surface tension, etc., obtained from the NIST-REFPROP 9.1 database were added into the OpenFOAM material library. Furthermore, the calculation method of the thermophysical properties in OpenFOAM was recompiled by the interpolation method rather than the original regression method.  
In addition, as mentioned in Section 2.2.1, the spray cone angles and diameter distribution parameters acquired from the experiment under different inject conditions are shown in Table 2.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & \multicolumn{1}{c}{**Value**} \\ \hline Ambient temperature (K) & 300 \\ Ambient pressure (MPa) & 0.1 \\ Iso-pentane initial temperature (\({}^{\circ}\)C) & 50, 60, 70, and 80 \\ Iso-pentane inject pressure (MPa) & 1,2, 1.5, 1.8, and 2.1 \\ Diameter of the nozzle (mm) & 0.6 \\ Duration of injection (ms) & 10 \\ Maximum droplet diameter (um) & 60 \\ Minimum droplet diameter (um) & 0.5 \\ Pareels per second & 20,000,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial parameters for numerical study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inject Pressure (MPa)** & \multicolumn{1}{c}{**Inject Temperature (\({}^{\circ}\)C)**} & \multicolumn{1}{c}{**Spray Cone Angle (\({}^{\circ}\))**} & \multicolumn{1}{c}{**Distribution Parameter \(d\)**} & \multicolumn{1}{c}{**Distribution Parameter \(n\)**} \\ \hline
1.2 & 50 & 8.82 & 10.96 & 1.021 \\
1.5 & 50 & 11.46 & 10.53 & 1.032 \\
1.8 & 50 & 18.23 & 10.33 & 1.039 \\
2.1 & 50 & 21.56 & 9.84 & 1.045 \\
1.2 & 60 & 16.07 & 9.55 & 1.071 \\
1.5 & 60 & 26.66 & 9.38 & 1.059 \\
1.8 & 60 & 27.45 & 8.52 & 1.033 \\
2.1 & 60 & 31.38 & 8.56 & 0.995 \\
1.2 & 70 & 32.16 & 8.48 & 1.045 \\
1.5 & 70 & 36.37 & 8.49 & 1.014 \\
1.8 & 70 & 41.28 & 8.48 & 1.045 \\
2.1 & 70 & 42.49 & 8.96 & 1.030 \\
1.2 & 80 & 45.21 & 8.52 & 1.023 \\
1.5 & 80 & 46.52 & 8.35 & 1.015 \\
1.8 & 80 & 47.20 & 8.42 & 1.009 \\
2.1 & 80 & 47.95 & 8.38 & 1.012 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spray cone angles and diameter distribution parameters for injection model.
# Time Series Classification with Shapelet and Canonical Features
## 5 Experimental Analysis
### Parameter Settings  
RSFCF has a total of six parameters to set (see Table 1). Since the length range of the pattern in the learning task cannot be known in advance, we simply set the minimum and maximum lengths of Shapelet to 3 and \(l\), respectively (\(l\) is the time series length in the learning task). The number of Shapelets \(k\) and the Shapelet maximum offset _shift_ randomly selected per tree can be used to flexibly control the efficiency of the algorithm, and larger parameter values can theoretically obtain higher accuracy with a decline in efficiency. Parameter \(k\) is set to \(\sqrt{l}\) to seek a compromise between accuracy and efficiency. We conducted specialized experiments to verify the effect of the Shapelet maximum offset _shift_, the number of features \(a\) used in the data transformation and the number of trees \(r\) in the RSFCF classification model on RSFCF performance to guide parameter value setting. **Effect of the number of trees \(r\) on the performance of RSFCF (\(a\) is set to 8, _shift_ is set to \(l/10\)). For a reasonable assessment and to fully account for experimental efficiency, we selected all 60 datasets with a total number of instances less than 2000 and time series length less than 600 in the 112 datasets shown in Table 2 (i.e., the datasets with names bolded in Table 2, which we will abbreviate as Small60 datasets later). Since RSFCF is essentially a random algorithm, we repeated the experiment 10 times on each dataset of Small60. ** \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline _min_ & Shapelet minimum length & 3 \\ _max_ & Shapelet maximum length & \(l\) \\ \(k\) & The number of randomly selected Shapelets for each tree & \(\sqrt{l}\) \\ \(a\) & The number of features used in the data transformation & 8 \\ _shift_ & The maximum offset of Shapelet & \(l/10\) \\ \(r\) & The number of trees in the RSFCF classification model & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter setting of RSFCF.
## 3 Application Characteristics
In many instances, an application with the massive number of devices requires very low cost devices, for example, smart metering, whereas others like smart homes may be able to absorb reasonable cost. This is hence captured as of low relevance for such applications. The major attributes for these characteristics [4] are summarized in Figure 1.
# Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images
## 2 Materials and Methods
### Dataset Description
#### 2.1.3 BCDR Dataset  
The breast cancer digital repository (BCDR) project has two main objectives: (1) establishing a reference to explore computer-aided detection and diagnosis techniques, and (2) offering teaching opportunities to medical-related students. The BCDR has been publicly available since 2012 and it is still under development. BCDR provides comprehensive patients cases of breast cancer including mammography lesions outlines, prevalent anomalies, pre-computed features, and related clinical data. Patient cases are BIRADS classified, biopsy proven, and annotated by specialized radiologists. The bit depth is 14 bits per pixel and the images are saved in the TIFF format [21]. In this research, a total of 159 of abnormal images have been used, consisting of 80 benign and 79 malignant.  
It is worth to be noted that all images have been converted into png and resized into 224 \(\times\) 224 and 227 \(\times\) 227 to fit every pre-trained network. Figure 2 shows some samples of the CBIS-DDSM, MIAS, and BCDR datasets including benign and malignant findings.  
Figure 1: Examples of noisy and corrupted images. (**a**,**b**) contain some black arrow marks made by doctors or radiologists indicating the location of the lesion. (**c**) is a result of insufficient illumination or incorrect device adjustment.  
Figure 2: Datasets examples. Benign cases are shown in (**a**–**c**). Malignant cases are shown in (**d**–**f**). (**a**,**d**) represent samples from the curated breast imaging subset of digital database screening mammography (CBIS-DDSM) dataset, (**b**,**e**) represent samples from the mammographic image analysis society (MIAS) dataset, and (**c**,**f**) represent samples from the breast cancer digital repository (BCDR) dataset.
## 3 Experiment Results and Analysis
### Effects of Controlling Factors on Seepage-Erosion Process  
Previously, the progress of water-sand inrush was introduced in detail, as well as the nonlinear features along with the whole seepage-erosion process. In this section, the variation characteristics of water-outflow rate, sands erosion rate and sands mass concentration of each test is monitored and further analyzed to quantitatively demonstrate the impacts of controlling factors, soil compactness, hydraulic pressure and confining pressure on the instability process of sandy soil under karst water. The occurrence time variation of monitor objects' characteristic value with relative density, hydraulic pressure and confining pressure is listed in Table 6.
# Improved SE-ResNet Acoustic-Vibration Fusion for Rolling Bearing Composite Fault Diagnosis
## 2 Methods
### Improved SE-ResNet  
The SE attention module is a representation of channel attention. It focuses on the problem of interdependence between model channels. The convolution operation first convolves each channel of the input, then sums the results of the convolution for each channel. This allows spatial features to be fused with channel features, resulting in a particularly mixed set of features. The SE module abstracts this confounding and allows the deep learning model to learn directly on the channel features. SE brings out the interdependencies between feature channels, obtaining the importance of each channel. Features on each channel are weighted, accentuating important features and suppressing secondary ones. The SE attention mechanism module can be easily ported to other network architectures. As depicted in Figure 2, the SE module has three main operations: compression, excitation, and scale. W stands for width, H stands for height, and C stands for channels.  
Squeeze employs global pooling to condense the spatial characteristics of each channel into a singular global feature, effectively integrating the information from each channel feature. Subsequently, a fully connected (FC) layer is incorporated to assess the significance of each channel based on the compressed global features obtained. Figure 3 shows the excitation operation. The weight values for each channel, determined by the SE module, are individually multiplied with the matrix corresponding to the respective channel in the original feature map.  
Figure 3: Excitation operation.  
Figure 2: Squeeze and Extraction Module.  
Microsoft Labs introduced ResNet in 2015. Currently, ResNet has gained widespread application in feature extraction across diverse fields. ResNet's residual module can better utilize the shallow features of the data to obtain more critical features. In the task of feature recognition and classification, the residual module is used as the main feature extraction structure.  
After several experimental comparisons, it was decided that the ResNet50 network and the SE attention module would be used in this paper, as shown in Table 1:
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
## 4 Experiments
### Ablation Study
#### 4.4.3 Contribution of Non-Autoregressive Prediction  
The effectiveness of the non-autoregressive pattern of the transformer decoder was also evaluated. For comparison, we constructed an autoregressive version of our model, named NaST-auto. In NaST, the decoder receives a query sequence generated in advance by using the last observed time step \(X_{obs}\) (see Figure 1). Nevertheless, NaST-auto does not receive the query sequence from the same inputs as NaST. NaST-auto predicts one future location each time. Once a prediction is made, it will be added to the historical trajectory sequence and then sent to the transformer encoder for subsequent processing. Hence, each next location is predicted based on the trajectories of previous time steps. The results are shown in Table 4.  
Figure 4: Spatial interaction matrices of different spatial sparsity (\(\gamma\)). (**a**) Spatial interaction matrix for \(\gamma=0\); (**b**) Spares spatial interaction matrix for \(\gamma=0.1\); (**c**) Spares spatial interaction matrix for \(\gamma=0.5\).  
Figure 5: Temporal interaction matrices of different temporal sparsity (\(\delta\)). (**a**) Temporal interaction matrix for \(\delta=0\); (**b**) Spares temporal interaction matrix for \(\delta=0.5\); (**c**) Spares temporal interaction matrix for \(\delta=0.7\).  
The original non-autoregressive model NaST exhibited lower errors than its counterpart. The results of the autoregressive version NaST-auto showed a 17% error increase in ADE and 22% in FDE, and it worked even worse on FDE metrics in particular. The reason could be attributed to the fact that the autoregressive inference procedure was prone to error accumulation during prediction. Apart from that, the use of the last time step observation as a query sequence likely helps to predict the future locations and significantly reduces the error. The contribution of the non-autoregressive pattern to the final performance of the pedestrian trajectory prediction is clearly validated.
## 5 Method and Design
### Modular Approach
#### 5.2.2 Multiprocessor  
The model depicted in Figure 5 is designed for a single CPU and functions effectively in that context. To simulate scheduling algorithms in a multiprocessor environment, the original model must be replicated to include as many instances as there are CPUs in the system. For example, if there are four CPUs, there should be four models, each with its own local queue module and CPU module. Figure 6 illustrates the model configuration with four CPUs. Duplicating the pGlobalQueue and tJobGenerator components is unnecessary, as these should be shared among the CPUs for job distribution.  
The overall flow of the multiprocessor model remains consistent with the single CPU model, with two transitions requiring modification: tCpuQueue and tSteaLQ. In a single CPU environment, tCpuQueue can always fire, and tSteaLQ may not need to fire at all. However, these transitions are crucial for distributing and redistributing jobs in a multiprocessor environment. The transition tCpuQueue for a CPU should only fire if its local queue has the fewest jobs. This ensures a fair distribution of jobs, minimizing idle times for the system. In scenarios where job running times vary, one CPU can accumulate multiple jobs while another remains jobless. In such a scenario, the tSteaLQ transition should take a job from PLQ and return it to PGDolabQueue, enabling redistribution to an idle CPU.  
Figure 5: Utilizing the built-in resource in GPenSIM to simplify the model.
# Rectification for Stitched Images with Deformable Meshes and Residual Networks
## 3 Materials and Methods
### Deformable Mesh  
This paper introduces deformable meshes to meet the application demands in different scenarios. Traditional rectangles exhibit weaker generalization capabilities when dealing with complex scenes. Therefore, the paper introduces two additional mesh models: hexagonal and triangular meshes. Hexagonal meshes have more uniform relationships between adjacent pixels, with each hexagon having six neighbors with equal adjacency properties. This provides better spatial consistency for image rectification. For example, during the image interpolation process, hexagonal meshes can offer smoother and more natural transitions. Moreover, hexagonal meshes closely resemble the shapes of many objects and structures found in the natural world, such as beehives and crystal structures [39]. Therefore, they may provide a more natural representation of images related to natural landscapes. Triangular meshes, on the other hand, excel in realistically reconstructing the shapes in images, especially when the images contain curves and surfaces [40]. The use of triangular meshes allows for better adaptation to irregular image regions, enabling more flexible shape approximation and, consequently, a more accurate capture of details in the images.  
Simultaneously, this paper proposes two operational modes for the deformable meshes: speed-oriented and quality-oriented. In the speed-oriented mode, the input image undergoes the detection of rigid structures within irregular images using a simple recognition network. Based on the detected structure count and orientation, the mesh shape that best fits the threshold is directly selected. Currently available mesh shapes include triangles, rectangles, hexagons, and more. In the quality-oriented mode, each mesh shape conducts residual regression predictions on the input image, generating a rectified image. Ultimately, the optimal output is selected based on the loss values of the rectified images, choosing the one with a relatively superior rectification effect.
## 3 FSS Analysis
### Parametric Variation  
Selecting reasonable cell structure parameters can control the distribution of the induced current, thereby obtaining the required transmission characteristics. The unit structure parameters not only affect the operating frequency of the FSS, but also affect the operating bandwidth, insertion loss, polarization stability, and oblique incidence stability. Therefore, it is necessary to examine the influence of the changes of \(G\) and \(W_{1}\) parameters on the frequency response characteristics of the proposed FSS. Figure 8a,b shows the reflection coefficient curves with different parameter values for \(W_{1}\) and \(G\), respectively. Table 2 counts the 3 dB bandwidth of stopbands with different \(W_{1}\) and \(G\).  
Figure 8 shows that the proposed FSS has flat top characteristics in the stopband without significant fluctuations, indicating a high degree of stability in the stopband. Also, there are steep edges on both sides of the stopband, indicating that it has good frequency selective characteristics. As shown in Table 2, with the increase of \(G\) and \(W_{1}\), the four frequency bands shift towards the low frequency direction as a whole, but the shift of the high frequency band is greater. When \(W_{1}\) and \(G\) have the same amount of change, the change of the \(W_{1}\) value has more effect on the transmission characteristic curve. Therefore, \(W_{1}\) can be adjusted to determine the general position of the stopband, and \(G\) can be adjusted to determine the precise position of the stopband. In actual application scenarios, by reasonably adjusting \(W_{1}\) and \(G\) of the unit cell, multiband requirements can be satisfied.  
Figure 7: Comparison of reflection response of the proposed FSS calculated by HFSS and ADS.