esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accurate Probabilistic Model for TVWS Identification
## 4 Proposed Probabilistic Model
### Calculation of Threshold \(T\)  
In light of the previous discussion, it is evident that \(T\) is the most important parameter for accurate sensing, and hence it must be meticulously calculated to permit the balance of \(P_{fa}\) and \(P_{d}\). It is possible to define a minimum limit \(M_{min}\) and a maximum limit \(M_{max}\), with \(M\) remaining between these values, as follows.  
\[M_{min}\leq M\leq M_{max} \tag{8}\]  
where \(M_{min}\) is obtained by calculating the intersection point of two plotted pdf,  
\[M_{min}=W(p)\cap X(p) \tag{9}\]  
Note that this is a complex operation considering Equations (6) and (7). The maximum value of \(M\) is calculated using  
\[M_{max}=\sigma z \tag{10}\]  
where \(\sigma\) is the standard deviation of the distribution and \(z\) is obtained from the \(F(z)\) function, which defines the area under the curve in terms of the standard deviation [32].  
To calculate \(T\), the standard deviation and the value of \(z\) must be considered. The expression for the noise is given by  
\[T=\overline{\text{W}}(p)+\sigma_{W}z_{W} \tag{11}\]  
whereas for the composite signal consisting of signal and noise it is obtained with  
\[T=\overline{X}(p)+\sigma_{X}z_{X} \tag{12}\]  
Figure 9: Representation of the \(P_{m}\), \(P_{fa}\), and \(P_{d}\), generated by the \(T\) and the pdf curves for \(W(p)\) and \(X(p)\).  
To obtain \(T_{min}\), it is necessary to solve the equation system (6) and (7) and to eliminate \(x\). The result is expressed in dBm.  
To calculate \(T_{max}\), a \(P_{fa}\) of 1% is considered according to [13]. Once \(T\) is calculated in terms of \(\overline{\text{W}}(p)\), it is possible to define \(P_{fa}\) as follows,  
\[P_{fa}=F^{-1}(z_{W}) \tag{13}\]  
With the statistical and probabilistic information of \(W(p)\) and \(X(p)\), and for a given \(T\), that is common for both pdf curves, the expression for \(z_{X}\) is given by  
\[\overline{\text{W}}(p)+\sigma_{W}\,z_{W}=\overline{\text{X}}(p)+\sigma_{X}\,z _{X} \tag{14}\]  
\[z_{X}=\frac{\overline{\text{W}}(p)-\overline{\text{X}}(p)+\sigma_{W}z_{W}}{ \sigma_{X}} \tag{15}\]  
and \(P_{d}\), related to \(X(p)\), is obtained with  
\[P_{d}=F^{-1}(z_{X}) \tag{16}\]  
The information provided by \(P_{fa}\) vs \(P_{d}\), permits to understand the relation between these two probabilities, which defines the optimal value for the proposed adaptive \(T\), as observed in Figure 5b. For both RFE and SA devices, the adaptive \(T\) proposed in this research is closer to the \(\overline{\text{W}}(p)\). In this manner, it avoids missed detections, and observes enough separation, reducing the \(P_{fa}\) to a value of 3%. The most important factor is that the proposed adaptive \(T\) maintains the same behaviour even if the noise floor or the signal strength experiences any variation.
# Modular Robotic Design and Reconfiguring Path Planning
## 4 Simulation and Analysis of Path Planning for Module's Reconfiguration
### Collaborative Collision Analysis of Modules  
Take the simulation results of the improved ant colony algorithm under environment 1 as an example. As shown in Figure 15, four collision avoidance behaviors occur in the path during the collaborative path planning of the module. The first time the two modules have the third type of collision avoidance behavior, the direction of the clamping module is unchanged and the angle of motion is 90\({}^{\circ}\), the two modules pass the waiting area and move in the same direction respectively, the clamping module represented by the moving path \"\(L_{1}^{*}\)\" moves to the waiting area (4,9), the interstitial module represented by the moving path \"\(L_{2}^{*}\)\" retreats to the grid (3,9), and continues to move in the original direction after the clamping module passes. The second time the second type of collision avoidance behavior occurs, the direction of motion of the module remains unchanged and the angle of motion is 90\({}^{\circ}\), the interstitial module represented by the moving path \"\(L_{2}^{*}\)\" waits for a grid distance at the position of the grid (6,11), and the interstitial module continues to move after the clamping module passes the waiting area (6,12). The third and fourth time the second type of collision avoidance behavior occurs again, the interstitial module represented by the movement path \"\(L_{2}^{*}\)\" waits for a grid distance at the position of the grid (9,18) and (10,19), after the clamping module passes the waiting area (10,18), (11,19) the interstitial functional module continues to move.  
When a group of modules completes the reconstruction path planning, the next priority module of the robot performs the inter-module collaborative path planning until the overall reconstruction tasks of the robot are completed. If there are remaining modules in the platform after completing the reconstruction tasks, the modules stop moving and wait for the following reconstruction tasks. Finally, the two modules work together to reach the reconfiguration target location and always maintain the collision-free optimal reconfiguration path.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 6 Dynamic Simulation Results
### Case 2: Wind Speed = 8.0 m/s
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Control Strategies** & **Case 1** & **Case 2** \\ \hline System frequency nadir (Hz) & MPT & 59.391 & 59.391 \\  & Conventional method & 59.574 & 59.506 \\  & Proposed method & 59.574 & 59.490 \\ \hline Second frequency nadir (Hz) & Conventional method & 59.693 & 59.720 \\  & Proposed method & 59.693 & 59.720 \\ \hline Nadir-based frequency response (MW/Hz) & Conventional method & 164.3 & 141.7 \\  & Proposed method & 164.3 & 137.3 \\ \hline Released rotating kinetic energy (s) & Conventional method & 2.698 & 1.388 \\  & Proposed method & 1.491 & 0.730 \\ \hline Captured mechanical power (MWh) & Conventional method & 6.67 & 3.51 \\  & Proposed method & 103.9 & 93.0 \\ \hline Rotor speed recovery duration (s) & Conventional method & 103.9 & 93.0 \\  & Proposed method & 62.2 & 59.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of simulation results for two cases. captures more mechanical input power than in the conventional method. Hence, the proposed restoration effectively employs the rotating masses of DFIGs for STFS.
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
## Appendix A Detailed Calculations
### PCC Vivace  
PCC Vivace separates time into consecutive monitor intervals (MIs) [14; 15; 37]. Each MI is responsible for transforming performance statistics presented at that MI to a numerical utility value.  
\[U=s_{i}-(bs_{i})\frac{d(\tau_{i})}{dT}-cs_{i}L_{i} \tag{11}\]  
where \(\frac{dRTT_{i}}{dT}=\frac{d\tau_{1}}{dT}\) is the RTT gradient in an MI, \(s_{i}\) is sender \(i\)'s the sending rate, \(L_{i}\) is the loss rate, \(T\) is the time unit, and \(b\) and \(c\) are constants \((b\geq 0,c<1)\).  
Using Equation (11) with a fixed-point approach along the lines of [18], we obtain the equilibrium \(U^{\star}\) (utilityfunction) as  
\[U^{\star}=\frac{W_{1}}{\tau_{i}}-b\frac{W_{1}}{\tau_{i}}\frac{d\tau_{i}}{dT}- c\frac{W_{1}}{\tau_{i}}L_{i}\]  
\[U^{\star}=1-b\frac{d\tau_{i}}{dT}-cL_{i} \tag{12}\]  
We consider two cases to explain the causes of unfairness and starvation.  
Case i: Considering two PCC flows with different RTTs, \(\tau_{1}\) and \(\tau_{2}\), and experiencing the same loss, \(p\), we can evaluate how the congestion windows \(U_{1}^{\star}\) and \(U_{2}^{\star}\) evolve as  
\[\frac{U_{1}^{\star}}{U_{2}^{\star}}=\frac{1-b\frac{d\tau_{1}}{dT}-cL_{i}}{1-b \frac{d\tau_{2}}{dT}-cL_{i}} \tag{13}\]  
If \(\tau_{1}=2\tau_{2}\), then \(\frac{U_{1}^{\star}}{U_{2}^{\star}}=\frac{1-2b\frac{d\tau_{2}}{dT}-cL_{i}}{1-b \frac{d\tau_{2}}{dT}-cL_{i}}\)  
Case ii: Considering two PCC flows with same RTTs, \(\tau\), and experiencing different losses \(p_{1}\) and \(p_{2}\), we can evaluate how the congestion windows \(U_{1}^{\star}\) and \(U_{2}^{\star}\) evolve:  
\[U_{1}^{\star}=1-b\frac{d\tau}{dT}-cL_{i}\]  
\[U_{2}^{\star}=1-b\frac{d\tau}{dT}-cL_{i}\]  
\[\frac{U_{1}^{\star}}{U_{2}^{\star}}=\frac{(1-b\frac{d\tau}{dT}-cL_{i})(1-p_{2} )}{(1-b\frac{d\tau}{dT}-cL_{i})(1-p_{1})}=\frac{1-p_{2}}{1-p_{1}} \tag{14}\]  
If \(p_{1}=2p_{2}\), then \(\frac{U_{1}^{\star}}{U_{2}^{\star}}=\frac{1-p_{2}}{1-2p_{2}}\).  
As we know, **Starvation** is an extreme case of unfairness; the two cases discussed above will have the following consequences:  
Case i: If \(p_{1}=np_{2}\), where \(n=\{1,2,3,\ldots,20\}\)  
\[\frac{U_{1}^{\star}}{U_{2}^{\star}}=\frac{1-p_{2}}{1-np_{2}}\]  
Case ii: If \(\tau_{1}=n\tau_{2}\), where \(n=1,\ldots,20\)  
\[\frac{U_{1}^{\star}}{U_{2}^{\star}}=\frac{1-bn\frac{d\tau_{1}}{dT}-cL_{i}}{1- b\frac{d\tau_{2}}{dT}-cL_{i}}\]
# A New Method for Finding the Shape of the Main Cable in the Special Cable Plane
## 5 Analysis of the Main Cable Curve of Spatial Special-Shaped Suspension Bridge  
In order to study the differences between different calculation models and different algorithms in the calculation process of spatial special-shaped main cables, this paper takes a single cable plane curved suspension bridge as an example for calculation. This bridge is a double-tower, three-span steel truss spatial special-shaped suspension bridge, and the bridge span is 61 m + 168 m + 61 m. The midspan vertical rise span ratio is 1/8, and the transverse rise span ratio is 1/14. There are 48 suspenders in the whole bridge. The distance between the two suspenders along the bridge is 6m, and the vertical force at the lower end of the suspender on the middle span of the main cable is 135 KN-267 KN. The stiffening beam is a steel truss beam with a height of 2.4 m, a width of 5 m and a longitudinal slope of 2.2%. The elevation and plan of the bridge are shown in Figure 2, and the material properties of the main cable and suspender are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & \
\begin{tabular}{c} **No Stress** \\ **Concentration/(N/m)** \\ \end{tabular} &
\begin{tabular}{c} **Area/(m\({}^{2}\))** \\ **Modulus/Gpa** \\ \end{tabular} \\ \hline Main cable & 1361.2376 & \(1.7671\times 10^{-2}\) & 195 \\ Sling & 96.6672 & \(1.2566\times 10^{-3}\) & 195 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material Characteristics.
# Survey of Artificial Intelligence Model Marketplace
## 2 AI Marketplace Overview
### Current AI Marketplaces Review
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Name** & **Exiting** & **Customized** & **Cooperation** & **Pricing Service** & **Customization** & **Utilization** & **Privacy and** & **Asset** \\  & **Model** & **Model** & **Cooperation** & **Pricing Service** & **Customization** & **Utilization** & **Policy and** & **Management** \\ \hline Workday & Yes & Yes & No & \begin{tabular}{c} disclosed only if personal \\ information is provided \\ to the sellers \\ \end{tabular} & No & No & Yes & Yes \\  & & & & & & & \\ \hline DataStax [64] & Yes & Yes & Yes & No & Yes & Yes (Nvidia) & No & Yes \\ \hline Al Marketplace & Yes & Yes & Yes & No & Maybe & No & No & Yes \\ by Info-Tech [75] & Yes & Yes & Yes & No & Maybe & No & No & Yes \\ \hline Instructure [76] & Yes & No & No & Free & No & No & \begin{tabular}{c} Canvas Education \\ System only \\ \end{tabular} & Yes \\ \hline Al Marketplace & Yes & No & No & Yes & No & No & Yes \\ AlFT [77] & Yes & & & & & & \\ \hline Gravity AI [63] & Yes & \begin{tabular}{c} Under \\ development \\ \end{tabular} & No & Yes & No & No & No & Yes \\ \hline Al Marketplace & Yes & Yes & Yes & Free Trial with & & & \\ by AI Planet [65] & Yes & Yes & Yes & membership & Maybe & No & No & Yes \\ \hline AKIRA-ai [66] & Yes & No & No & \begin{tabular}{c} Free Trial, but most of \\ them are not available \\ \end{tabular} & No & No & No & No & \begin{tabular}{c} Poor maintenance. \\ Some AI models not \\ longer exist \\ \end{tabular} \\ \hline Amazon & Yes & No & No & \begin{tabular}{c} 2 months free trial, then \\ pay by usage \\ \end{tabular} & No & No & No & Yes \\ \hline Google AI & Yes & Yes & No & \begin{tabular}{c} Yes (type in questions \\ to receive \\ by usage \\ \end{tabular} & Yes (integrate & & \\ \begin{tabular}{c} multiple \\ models) \\ \end{tabular} & No & Yes (based on user \\ \
\begin{tabular}{c} case or data type) \\ \end{tabular} \\ \hline H\({}_{2}\)Oai [69] & Yes & No & No &
\begin{tabular}{c} free trial, cloud service is \\ \(\$50,000 per unit per year \\ \end{tabular} & No & No & Yes & Yes \\ \hline Hugging Face [70] & Yes & Yes & Yes & free, pro member needs to pay & & Yes & Yes \\  & & & & monthly subscription fee & Yes & Yes & No & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Current AI marketplace review. Following the detailed review conducted on the existing AI marketplaces, we have identified a gap in the availability of dedicated comprehensive pricing services. Moving forward, this research will examine the pricing services within the AI marketplace. This component is crucial for AI model developers, as it provides essential guidance for setting competitive and appropriate market prices, which are mostly less considered by the existing AI marketplaces.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 3 Fabrication of Hydrogels
### Chemical Crosslinking
#### 3.3.2 Free Radical Polymerization  
Free radical polymerization is one of the most widely used methods for synthetic hydrogels. Radicals play a central role in this technique by initiating the polymerization reaction between initially stable monomers upon activation [125]. The mechanism of this reaction consists of three steps: initiation, propagation, and termination. Radicals can be generated from chemical or thermal initiators, which produce reactive radical species when certain criteria are met (initiation) [124, 125, 163]. Radicals, with their unpaired electrons, are highly reactive and extremely unstable chemical species. In this process, the radical from the initiator attacks a monomer, which makes the monomer radical and causes it to then immediately react with another monomer, as if a monomer is added to a growing polymer chain (propagation). When a monomer has more than two reactive sites or a crosslinker is used, this process creates a three-dimensional polymer network. Since the hydrogel synthesis via radical polymerization commonly occurs within a liquid phase, two polymeric chains with radical ends could collide with each other and immediately react to form a covalent bond between two ends, or one sufficiently long polymeric chain with two or more radical sites could undergo the same process, resulting in the elimination of radicals (termination) [164, 165]. The chance of termination naturally increases as the monomer concentration decreases, or the size of the polymeric chain in the propagation stage increases as the reaction progresses.  
Synthetic hydrogels such as polyacrylamide, polyacrylic acid, and polyvinyl alcohol are commonly fabricated using this method, and this technique is widely compatible with other monomers with a carbon-carbon double bond or a vinyl group [18, 110, 166, 167]. While it provides cost-effective scalability and a broad range of monomer compatibility, controlling the uniformity of the network and minimizing free radical side reactions remain challenging.  
A significant concern in free radical polymerization, especially in biomedical applications, is the toxicity of chemical initiators or their residual byproducts [106, 167, 168].  
Benefiting from the high permeability and liquid contents of hydrogels, residues could be further removed via simply extensive rinsing or dialysis. Recently, more \"bio-tolerable\" initiators like lithium phenyl-2,4,6-trimethylbenzoyll phosphinate (LAP) have been increasingly used in various hydrogel or biopolymer systems as a more fundamental solution [169; 170; 171].
# A Survey of Deep Learning for Data Caching in Edge Network
## 5 Research Challenges and Future Directions
### Deep Learning for Cache Dimensioning  
As introduced in Section 2, cache dimensioning explores the appropriate cache size allocation for content hosts, such as CRs and BSs. Disappointingly, there is rarely a paper that applies DL on cache dimensioning decisions. One proper reason is lack of training data set in contrast to content popular prediction, where we have historical user request log to train a DNN. In addition, the caching size allocation affects the network performance and economic investment. Recently, network slicing has been identified as an important tool to enable 5G to provide multi-services with diverse characteristics. The slice is established on physical infrastructure including network storage. Therefore, it is a very interesting topic to consider the allocation of the memory space to support content caching and other storage services, which guarantees QoE and satisfies task requirements. Furthermore, for the case lacking a training data set, DRL can be viewed as a promising technology to configure slicing settings, as well as cache dimensioning. For the action space designing, it can be either discrete by setting storage levels, or continuous which allocates the memory space directly. However, there are requirements to design a caching-enabled network slicing model especially for dynamic allocation, as well as associated DRL framework, including state space, detailed action space, reward function, and agent structure.
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 3 Stealth Communication
### Future Perspectives
#### 3.3.2 Coexistence of Stealth Channel and Public Channel  
Another future perspective for stealth communication is to hide the stealth channel in the public channel with noise and recovering the stealth signal by the hybrid system. Compared with the digital ways of separation, the hybrid system has two major advantages: (1) The privacy of the public channel is protected. In traditional digital-based method, to separate the stealth channel from the public channel requires the information from the public channel to be digitized at the stealth receiver. Since public channel has a higher data rate than the stealth channel. The digitization process not only introduces a considerate amount of workload to the stealth receiver but also expose the public channel data. By using the analog system discussed in Section 3.2, the stealth signal is separated from the public channel by the analog method without knowing the information from the public channel. (2) With the hybrid system, the public channel distributes keys in a secure way between the stealth transmitter and the receiver. Traditional methods share the public keys for the stealth channel with a public channel. Since the public channel is not protected, sharing digital keys directly with public channel exposes the existence of the stealth channel. Instead of sending the digital keys with unprotected public channels, the public keys are distributed through the statistical information of the public channels. The statistical information of public channels always exists whether the stealth channel is turned on or off.  
The statistical information is measured by the hybrid system discussed in Section 2 with under-sampled signals. The public keys are shared between the stealth receiver and the stealth transmitter. By using Rivest-Shamir-Adleman (RSA) algorithm, the stealth transmitter uses the public key to encrypt the stealth signal, and the stealth use private to decrypt the stealth signal.  
This perspective recovers the stealth signal by using hybrid photonic signal processing and blind source separation methods. The photonic method can mix and separate signal with hundreds of GHz to THz bandwidth in real-time, which is far beyond the ADC limit and enable completely stealth transmission. The blind source separation device measures the under-sampled pattern of the mixed signal, which is the public key of the stealth channel, and with the private keys, the signal can be recovered. The private key has two components (Figure 8): (1) the trained parameters of the neuron network for edge device; (2) the physical parameter of the photonic system. An eavesdropper can also measure the statistical pattern of the public channel and find out the public key; however, without the private key, the existence of the signal cannot be detected.  
Figure 7: Wireless stealth communication system.
# Methodology for the Prediction of the Thermal Conductivity of Concrete by Using Neural Networks
## 3 Methodology
### Development of the Model
#### 3.2.1 Importing the Database  
Normalizing the dataset is vital in any deep learning model. Since the database may present the parameters in a different and wide range of values, it is necessary to normalize them by putting all features on a standard scale to avoid the data values affecting the weights and biases during the training of the model. Generally, the normalization process transforms the input and output parameters, putting them on a standardized scale, i.e., transforming them to have a mean of zero and a standard deviation of one. This step guarantees that each parameter will contribute equally during the learning process and prevents any parameter from dominating this process due to its more extensive scale. Another advantage is that it enhances the generalization capabilities of the models, improving the prediction of new data. The normalization known as z-score is commonly used to bring the numerical features to the same scale. The mathematical equation of this process is presented in Equation (1).  
\[\text{z}=(\text{x}-\mu)/\sigma, \tag{1}\]  
where z is the normalized value, x is the number we would like to normalize, \(\mu\) is the mean, and \(\sigma\) is the standard deviation of this parameter. Therefore, in order to achieve the normalized values for each numeric column, the mean and the standard deviation are calculated. Each value in the numeric column is subtracted by the column's mean and then divided by the column's standard deviation.