esa-sceva/satcom-chunk-collection huggingface dataset:
# Microbes as Medicines: Harnessing the Power of Bacteria in Advancing Cancer Treatment
## 2 Mechanism of Tumor Suppression
### Genetically Engineered Bacteria for Anti-Cancer Therapy
#### 2.2.4 Drug Expression Strategies
Activation of these light promoters occurs at different wavelengths and its use depends on tissue penetration and safety in the body. For instance, shorter wavelengths cannot penetrate the tissue however, near infrared light and red light can penetrate the deeper tissue parts. [PERSON] et al. engineered _E. coli_ to secrete cytolysin under the influence of p Dawn, plasmid light responsive plasmid (optogenetic) and exhibited mammalian cell cytotoxicity [13]. Bacterial enzyme L-ASNase, has the capacity of inducing apoptosis by catalyzing inhibition of global protein synthesis. [PERSON] et al. conducted studies to potentiate the performance of _Salmonella_. L-ASNase was selectively expressed within solid tumors under the control of quorum sensing system. In these QS systems, auto inducer molecules such as acyl-homoserine lactone are secreted based on the local cell density during bacterial growth. After reaching a threshold concentration, the Vibrio QS expression system is activated and hence, genes encoding for L-ASNase are expressed [74]. To test the tumor specific expression of proteins using the QS systems, quorum sensing lux system was cloned in non-pathogenic _Salmonella_ with green fluorescence protein reporter gene. The protein expression was evidently seen only in the tumor tissues and not in healthy tissues and no expression was seen in case where the distance between the bacterial colonies was greater than 155 mm [75].
## 2 Literature Review
### Machine Learning Approaches  
Despite the improvements offered by these advanced models, they often require complex estimation procedures and substantial computational resources. Additionally, the interpretability of these models can be limited, especially when dealing with high-dimensional data and numerous predictors. This has led researchers to explore alternative modeling approaches, such as machine learning techniques, which can offer greater flexibility and predictive power. Machine learning models, particularly those like CatBoost, offer flexibility and improved performance in dealing with non-linear relationships in crash data. Unlike traditional statistical models, machine learning approaches do not require predefined functional forms and can handle high-dimensional data, making them suitable for complex traffic datasets [19]. Machine learning models can also incorporate various data types and interactions, providing a more comprehensive analysis of the factors influencing crash frequency [20]. The efficacy of machine learning in crash frequency analysis has been underscored through various studies; for instance, [PERSON] et al. [21] and [PERSON] et al. [22] employed methods such as Bayesian neural networks (NNs), and support vector machines(SVMs), respectively, and found these to surpass the negative binomial model in accuracy. Following this, [PERSON] and [PERSON] [23] and [PERSON] et al. [24] successfully applied multivariate adaptive regression splines (MARS) to crashes at un-signalized intersections and urban freeway interchanges, showcasing MARS's capability to capture complex, non-linear relationships and interactions. Furthermore, [PERSON] et al. [25] utilized a neural network model to delve into the non-linear dynamics between risk factors and crash frequency, incorporating techniques to enhance model interpretability and reduce overfitting. More recently, [PERSON] et al. [26] explored ensemble machine learning (EML) techniques, including random forest and Light Gradient-Boosting Machine (LightGBM), among others, for their application in crash frequency analysis. LightGBM, known for its efficiency and minimal memory demands, has shown promise in severity analysis and is now being considered for frequency modeling [27; 28].
## 4 Experimental Results and Analysis
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **Specificity** & **F1 Score** & **Params(M)** \\ \hline AlexNet & 0.8953 & 0.8860 & 0.8897 & 0.9253 & 0.8877 & 61 \\ GoogLeNet & 0.8943 & 0.8939 & 0.8940 & 0.9236 & 0.8939 & 200 \\ VGG-11 & 0.9301 & 0.9238 & 0.9266 & 0.9661 & 0.9251 & 133 \\ ResNet50 & 0.9243 & 0.9191 & 0.9215 & 0.9618 & 0.9202 & 25 \\ ViT & 0.8031 & 0.7876 & 0.7934 & 0.8379 & 0.7899 & 86 \\ SwinViT & 0.9444 & 0.9391 & 0.9418 & 0.9612 & 0.9404 & 27 \\ Our SkinSwinViT & 0.9788 & 0.9775 & 0.9783 & 0.9936 & 0.9779 & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison between the training set SkinSwinViT and other models. Table 4 unequivocally demonstrates the superior performance of the SkinSwinViT model over other models on the testing set, attaining exemplary accuracy of 0.9906, recall of 0.9906, precision of 0.9916, specificity of 0.9995, and F1 scores of 0.9910. Notably, SkinSwinViT exhibits a notable improvement of more than 1.55% across all metrics compared to SwinViT. This discernible superiority underscores the robust classification and generalization capabilities inherent in the SkinSwinViT model. In summary, the SkinSwinViT model proposed within this study emerges as the frontrunner on the evaluated dataset, boasting high-performance metrics. Moreover, the model parameters are judiciously configured, thereby consuming fewer resources. This exemplary performance underscores the model's efficacy in skin disease classification tasks, positioning it as a promising candidate for an effective CAD system for skin disease diagnosis. The experimental findings furnish compelling evidence in support of further research and application in the realms of dermatological diagnosis and treatment.
## 4 Analysis of Security and Performance
### Performance Analysis  
This section will discuss the possibilities of obtaining pairing values and authenticating messages. The proposed scheme will be compared to DCS [7], PASS [8], CH-IBC [9] and BDH [11] with respect to functions and performance in message authentication.
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
## 5 Experiment
### Traditional Vehicle Cross-Regional Authentication Simulation
#### 5.1.1 Simulation Parameter Setting  
In this simulation, nodes use IEEE 802.11p to communicate, and the data rate is 6 Mbps. The frequency of the car signal is 5.89 GHz. The receiving gain of the car antenna is set to 2.0 dBi, \(-\)4.0 dBi, 0.9 dBi, 1.1 dBi in the order of front, back, left, and right, and the receiving sensitivity is  
Figure 6: Simulation platform diagram.  
\(-\)89 dBm. There are two types of simulation scenarios, using a simulated virtual rectangular map and a real map with actual road conditions. The virtual map is shown in Figure 7, where the RSUs are equidistant, and the communication range of a single RSU is set to 1 km, which ensures that the RSUs can interact while covering the entire road. The channel fading model chooses multipath Rayleigh fading. The real map is shown in Figure 7, adding actual road condition information and obstacle shadow occlusion, and testing network parameters by randomly generated traffic to communicate with the SM node. This experiment introduces the sending source and destination to distinguish the SM node and the vehicle node and adds the vehicle information table to find the illegal vehicle. According to the above settings, we analyze the average value of the results of multiple experiments. Last but not least, in this section, we use the vehicle, cross-regional information, and timestamp to generate record information as the transaction content, so that subsequent hash-chain simulation results can be analyzed for authentication delay.
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 3 Dynamic Buckling Criteria
### Relationship between Static Force and Dynamic Force
#### 3.3.3 Parametric Effect Due to Thickness  
The simulations are carried out using the identical parameters with the radius of 2.0 m, the elastic modulus of 210 GPa, a Poisson ratio of 0.15, the external pressure of 15 MPa, and the different thickness parameters of 0.03 m, 0.04 m, 0.05 m, and 0.06 m, respectively. For the case of a small thickness of 0.03 m, the impact velocities are set to be 1.0 m/s and 2.0 m/s, whilst in the other cases, the velocities are set to be 4.0 m/s and 8.0 m/s.  
Table 6 presents the relationship between the contact force and the thickness. It should be noted that the impact duration is different in each case of thickness, since the shell thickness can change the contact stiffness. To compare the effect of the shell thickness, the contact forces due to an impact velocity of 1.0 m/s are visualized over the data derived from an impact velocity of 4.0 m/s, as illustrated by Equation (6) and Table 4 (previously discussed in Section 3.3.2).  
Figure 14a shows the relationship between force versus impulse and the shell thickness considering the velocity of 4.0 m/s. Figure 14b shows the same for the velocity of 8.0 m/s. It can be seen that the impulse is almost the same, despite the differences in impact duration. It can be found that the relationship can be expressed as a linear relation as written in Equation (9), considering the results from the various conditions shown in Table 7, where, \(k_{t}\) and \(km_{t}\) are the coefficients of maximum force and mean force, and \(C_{t}\) and \(Cm_{t}\) are a constant. When the impulses are almost identical, it is noted that the higher the max force, the shorter the time duration.  
\[f_{p}=k_{t}\cdot t+C_{t},f_{m}=km_{t}\cdot t+Cm_{t} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Case** & _t/v_ & \(\Delta t\) & \(f_{p}\) & \(f_{m}\) & **Im** & **Case** & _t/v_ & \(\Delta t\) & \(f_{p}\) & \(f_{m}\) & **Im** \\ \hline \(R=2.0\) & 0.03/1 & 0.063 & 3.07 & 1.91 & 0.12 & \(R=2.0\) & 0.03/2 & 0.062 & 5.7 & 3.5 & 0.22 \\ \(E=210\) & 0.04/4 & 0.042 & 18.3 & 10.4 & 0.44 & \(E=210\) & 0.04/4 & 0.042 & 18 & 10 & 0.42 \\ \(v=0.15\According to the results shown in Table 7, it can be found that the ratio of \(k_{t}/km_{t}\) to \(C_{t}/Cm_{t}\) can be considered as a fixed value at 1.70, which is the average value of 1.72, 1.69, 1.67 and 1.72; thereby, the maximum force and the mean force can be expressed using an identical formulation, \(f_{p}=k_{s}\cdot f_{m}\). Furthermore, it can be established that the contact forces during collisions have an identical shape function, despite the differences in shell thickness. The ratios of \(k_{t}\) to \(km_{t}\) under different cases of \(v\) = 4.0 and 8.0 m/s are 0.47 and 0.48, respectively, which are almost identical to the ratio of the corresponding velocity. Thus, it can be evident that Equation (9) is independent of the other parameters.
## 4 Comparison between Two Models
### Acceleration Responses of the Sites  
#### 4.1.1 Acceleration Time Histories of Ground Surfaces  
Two acceleration measurement points, AS1 and AS2, at the top of the models were selected to analyze the acceleration response under different harmonic excitations. Figure 6 shows the acceleration time histories under transverse excitation and longitudinal excitation for a specific time period, and it can be found that the waveforms of the two measurement points are in good agreement. The acceleration error is defined as the difference between the peak acceleration of AS1 and AS2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Case** & **Input Motions** & **Peak Acceleration (g)** & **Input Direction** \\ \hline WN1 & white noise & 0.05 & xy \\ \hline S12-x & Sine-12 Hz & & \\ S17-x & Sine-17 Hz & & \\ S19-x & Sine-19 Hz & 0.1 & x \\ S21-x & Sine-21 Hz & & \\ S23-x & Sine-23 Hz & & \\ \hline S12-y & Sine-12 Hz & & \\ S17-y & Sine-17 Hz & & \\ S19-y & Sine-19 Hz & 0.1 & y \\ S21-y & Sine-21 Hz & & \\ S23-y & Sine-23 Hz & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test cases for the shaking table tests.  
Figure 6: _Cont._By calculation, the acceleration errors were within 3% for the HRM model and 10% for the JRM model under a transverse excitation, and the JRM model also showed good agreement at the excitation frequencies of 21 Hz and 23 Hz.  
Under longitudinal excitation, the acceleration errors were within 5% for the HRM model and 12% for the JRM model. Because there are many joint sets in the JRM model compared to the HRM model, under harmonic excitation, the seismic motions were reflected and refracted between the joint sets, which increased the dynamic response at the model boundary, resulting in a more pronounced boundary effect in the JRM model than in the HRM model.  
With the above analysis, the boundary effects of the rock models were small and the AS2 measurement point can be considered as in \"free field\", which serves as the basis for the power spectral density analysis in Section 4.2.2.
## 5 The Proposed Routing Method
### Distributed and Lightweight Trust Mechanism (W-Trust)
#### 5.1.3 Total Trust  
It represents the sum of direct trust and indirect trust and is obtained from Equation (17).  
\[T_{ij}^{total}=\alpha T_{ij}^{Weighted-direct}+(1-\alpha)T_{ij}^{Weighted- indirect} \tag{17}\]  
where \(\alpha\in[0,1]\) is a weight coefficient.  
Algorithm 1 offers the pseudo-code related to the trust mechanism presented in CTSRD.  
```
0:\(n_{i}\), \(n_{j}\), \(n_{c}\): IoT nodes
0:\(T_{ij}^{Total}\): Total trust between \(n_{i}\) and \(n_{j}\)
0: Begin
1:\(\mathbf{n_{i}}\): Calculate the direct trust between \(n_{i}\) and \(n_{j}\) based on PSR and PRR using Equation (7);
2:\(\mathbf{n_{i}}\): Standarclae \(T_{ij}^{direct}\) using Equation (10);
3:\(\mathbf{n_{i}}\): Compute a weight coefficient based on \(T_{ij}^{standard}\) using Equation (13);
4:\(\mathbf{n_{i}}\): Update \(T_{ij}^{direct}\) using Equation (14);
5:if\(T_{ij}^{standard}(t)<0\)then
6:\(\mathbf{n_{i}}\): Penalize \(T_{ij}^{direct}\) based on Equation (15);
7:else if\(T_{ij}^{standard}(t)\geq 0\)then
8:\(\mathbf{n_{i}}\): Reward \(T_{ij}^{direct}\) based on Equation (15);
9:endif
10:\(\mathbf{n_{i}}\): Calculate \(T_{ij}^{Weighted-indirect}\) using Equation (16);
11:\(\mathbf{n_{i}}\): Compute \(T_{ij}^{total}\) using Equation (17);  End
```  
**Algorithm 1** W-Trust mechanism
# Linear Algorithms for Radioelectric Spectrum Forecast
## 5 Conclusions  
The SARIMA and GARCH algorithms have been evaluated in this paper in order to forecast the reception power in channels of a GSM band. Even though GARCH algorithm presented lower prediction errors than SARIMA algorithm, the use of a SARIMA algorithm is more convenient for a CR system, because it has higher precisions with respect to availability and occupancy times, with which the use of spectrum efficiency is improved and the interference level and collisions between PUs and SUs will be reduced. Additionally, the SARIMA algorithm employs lower observation times than the GARCH algorithm. As noted, for the best of cases, observation times lower than four seconds could be obtained for the three GSM channels, which is an advantage in practical CR systems.  
For a CR system, the forecast developed in the GSM band could help to improve the use of spectral efficiency, since it would allow CR users to share channels and avoid collisions with PUs in the found opportunities.  
The SARIMA and GARCH algorithms forecast not only the reception power; but the occupation and availability times for GSM channels. It would also be feasible to use the training data from one day for the forecast of a CR user's received power from a primary BS.  
The significance of the forecast of received power is that CR users can save energy in the process of detecting the spectrum and take advantage of spectral opportunities, thereby increasing the rate of successful transmission and transmission opportunities, reducing the time to find an available channel, and adjusting transmission power levels to protect against collisions and interference with the PUs.  
**Acknowledgments: This study was supported by Colciencias (Administrative Department of Science, Technology and Innovation of Colombia), Universidad Distrial Francisco Jose de Caldas and Universidad Nacional de Colombia.**  
**Author Contributions: The SARIMA algorithm was developed by [PERSON], GARCH algorithm was implemented by [PERSON], [PERSON] designed the presentation of results, [PERSON] developed the results and [PERSON] analyzed the results. All authors read and approved the final manuscript.**  
**Conflicts of Interest: The authors declare no conflict of interest.**
# Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine
## 3 The Proposed Adaptive Weight Online Sequential Extreme Learning Machine
### Integrated Structure  
The block diagram of AWOS-ELM algorithm is given in Figure 1. When the new sequential sample \(\aleph_{k+1}=\{(\mathbf{x}_{i},\mathbf{t}_{i})\}_{i=(\sum\limits_{j=0}^{k+1}N _{j})+1}^{k+1}\) arrives, the weight estimator accesses the confidence coefficient of \(\aleph_{k+1}\) and determines the corresponding weight \(\lambda_{k+1}\). Then the training module of AWOS-ELM algorithm utilizes \(\lambda_{k+1}\) and \(\aleph_{k+1}\) to train the network. The weight estimator includes an AWOS-ELM testing module, residual generator and sigmoid function. The testing module produces the prediction value \(\{\mathbf{\hat{t}}_{i}\}_{i=(\sum\limits_{j=0}^{k+1}N_{j})+1}^{k+1}\) according to the input vector \(\{\mathbf{x}_{i}\}_{i=(\sum\limits_{j=0}^{k+1}N_{j})+1}^{\sum\limits_{j=0}^{k+ 1}N_{j}}\). The residual \(r_{k+1}\) is produced by the residual generator according to the comparison between the prediction value \(\{\mathbf{\hat{t}}_{i}\}_{i=(\sum\limits_{j=0}^{k+1}N_{j})+1}^{k+1}\) and the target value \(\{\mathbf{t}_{i}\}_{i=(\sum\limits_{j=0}^{k+1}N_{j})+1}^{k+1}\), where the residual \(r_{k+1}\) is defined as the following:  
\[r_{k+1}=\sqrt{\frac{\sum\limits_{i=1}^{N_{k+1}}\left\|\mathbf{\hat{t}}_{i+\sum \limits_{j=0}^{k}N_{j}}-\mathbf{t}_{i+\sum\limits_{j=0}^{k}N_{j}}\right\|_{F} ^{2}}{N_{k+1}\times m}} \tag{17}\]  
where \(m\) is the dimension of the output vector \(\mathbf{t}_{i}\). Then the sigmoid mapper produce the accessed weight \(\lambda_{k+1}\) according to the difference between \(\tau_{r}\) and \(r_{k+1}\) as the following:  
\[\lambda_{k+1}=\frac{1}{1+e^{-\theta(\tau_{r}-\tau_{k+1})}} \tag{18}\]where \(\tau_{r}\) is the threshold value and \(\varphi\) is a scaling factor which can represent the gradient of the sigmoid function at the threshold point. For the samples with the residuals closer to the threshold \(\tau_{r}\), the greater the \(\varphi\) is, the greater the difference between calculated weights will be. Moreover, \(\varphi\) is manually selected to be 500 according to the performance in this paper. As observed from Equation (18), the greater the residual \(r_{k+1}\) is, the less the accessed weight \(\lambda_{k+1}\) is, and \(0<\lambda_{k}<1\). The network of the testing module should be updated according to the training module before next sequential data chunk is incoming. The abnormal samples which cannot match the normal model well will have low accessed weight, and their negative impact to the subsequent learning process will be reduced. Thus, the AWOS-ELM algorithm is able to properly handle the varying confidence coefficients of each data chunk.
# A Systematic Literature Review on Military Software Defined Networks
## 5 Additional Contributions
### Unmanned Aerial Vehicles' Applications  
A survey of [PERSON] et al. [23] focused on the research potential of Unmanned Aerial Vehicles (UAV), where SDN can contribute to lowering cost and increasing availability in both public and military UAVs. The UAV networks need to support dynamic nodes with frequent change of network topologies, where nodes have a high network outage rate. Due to the need for OpenFlow network controller availability in dynamic networks, this requires a distribution of network controllers where balance and cooperation between network controllers must be addressed. However, the authors state that hybrid SDN with delegated packet processing is the most promising SDN technology for UAV. The current protocols for network routing in UAV have high latency, while the fast, flexible routing and multipath selection in SDN are assumed to out-perform current UAV routing protocols. Correspondingly, [PERSON] et al. [145] presented an architecture based on SDN, where UAV sensors controlled by OpenFlow are expected to increase reusability, scalability and modularity in the UAV network. The UAV papers refer to hardware security, management and orchestration as research challenges.
# Joint Energy Supply and Routing Path Selection for
## 4 Simulation Results and Analysis
### Comparision with Two Other Strategies  
In this section, we compare our proposed algorithm with two other strategies, that is, the proportional distribution strategy and greedy strategy. The results are show in Figures 12 and 13, and the average travel time, which reflects the additional charging costs, represents the average time per charging scheme spent on traveling.  
The proportional distribution strategy adopts the same routing algorithm as this article, while its charging strategy is only the first stage of our charging strategy, which means that this strategy needs to recharge the whole network. At the same time, the routing metric of the greedy strategy is the minimum transmission energy consumption, and its charging strategy is as follows: each sensor node sends a request message to the sink for charging, and then the WCD supplements the nodes with full energy from low to high according to the remaining lifetime until all nodes have been charged or the WCD's energy has been exhausted. The greedy strategy method is adopted as the energy supply strategy in some references, such as [49; 50; 51]. By this way, we explore the contrastive result under different routing algorithms and energy supply strategies. It can be seen that although the average travel time of the algorithm in this paper is slightly higher than in the greedy strategy, it has the highest network lifetime, and the lifetime increases significantly with the increasing of charging duration, what's more, the average travel time gradually decreased. Besides, in order to explore the influence of node task size, we test the network lifetime under two packet sizes, and the results are shown in Figure 12a,b. From the results, although the network lifetime is reduced due to the increased energy consumption, they have almost the same trend and we can conclude that the proposed algorithm is also suitable for different packet sizes under different task sizes.  
Figure 11: Network lifetime with varying network size.