esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient and Fast Hybrid GWO-JAYA Algorithm for Design Optimization
## 3 Test Problems and Optimization Results
### 2D Path Planning
The figure compares the optimization histories of the best runs for the algorithms listed in Table 1; the average FHGWJA convergence curve is also shown in the figure. The plot is limited to the first 4500 analyses of the optimization history and to the 40.5-48.5 mm trajectory length interval for the sake of clarity. It can be seen from the figure that the best and average optimization runs' convergence curves of FHGWJA always lie below those of the other algorithms. FHGWJA started its best optimization run from the very large cost of 225.35 mm (i.e., about 5.5 times the globally optimum length of 40.811 mm found with FHGWJA) while the initial cost for all other optimizers ranged between 59.62 and 184.214 mm. However, the present algorithm immediately recovered the initial gap in cost function with respect to its competitors. The hybrid simulated annealing algorithm (HFSA) of Ref. [81] and the modified big bang-big crunch algorithm (mBBBC-UBS) implementing the \(W(X_{itr,tr}^{\rightarrow})\leq 1.1W(X_{opt}^{\rightarrow})\) elitist strategy of FHGWJA were the only optimizers to compete in convergence speed with FHGWJA, respectively, for the first 150 and 190 analyses. Table 2 presents the optimization results for the 2D path planning problem variant solved with 20 design variables (10 base points and 10 obstacles). FHGWJA again was the best optimizer overall. In fact, it designed the very short trajectory of length 40.898 mm while the best solutions found with the other algorithms correspond to longer trajectories than 40.898 mm, ranging between 40.997 mm (for hybrid HS) and 41.141 mm (for improved JAYA). Furthermore, the worst solution of FHGWJA corresponds to the path length of 40.924 mm, again shorter than the best designs obtained with all its competitors. The optimal positions of base points found with FHGWJA were (3.9165;1.7729), (7.9192;3.6484), (20.853;16.019), (25.143;21.071), (27.178;24.426), (27.185;24.444), (27.188;24.449), (27.967;26.268), (27.998;26.345), and (28.981;28.578) mm. FHGWJA was also significantly faster than the other algorithms, completing optimization runs on average within only 5153 analyses while computational cost of its competitors ranged between 7408 (for hybrid HS) and 50,000 (for standard GWO) analyses. Standard JAYA was slightly more efficient than improved JAYA while standard GWO was the worst optimizer overall. The present algorithm was the most robust optimizer, achieving the lowest standard deviation on path length and just 4.8% dispersion on the number of analyses. The \(W(\overrightarrow{X_{i,tr}})\leq 1.1W(\overrightarrow{X_{best}})\) elitist strategy implemented in FHGWJA again improved significantly the performance of the mAHS, mBBBC-UBS, and MsinDE algorithms with respect to the original AHS, BBBC-UBS, and sinDE formulations documented in [94, 95, 96]:
# A Fast, Decentralized, Self-Aligned Carrier Method for Multicellular Converters +
## 5 Experimental Result
### System Reconfiguration Speed  
To compare the reconfiguration speed between the CPSC (modified updating rule) and the proposed DSA-PSC method, the transient system responses of a reconfiguration event when removing a cell (Figure 47) or reinserting a cell (Figure 48) for both methods are shown. It can be seen that using CPSC method, the reconfiguration system response takes around 250 \(\upmu\)s, while this system response occurs within about 200 \(\upmu\)s with the proposed DSA-PSC method. These measured system reconfiguration times are also similar to the simulation results, showing that DSA-PSC method is faster than the CPSC method.
## 3 Results
### Monitoring Human Behavior  
The CB/CNT/PDA/TPU flexible strain sensor, when adhered to different parts of the human body, can serve as a wearable device for monitoring human motion. Each sample was tested five times under the same conditions, and the average value was taken. As shown in Figure 7a,b, when the flexible strain sensor is applied to the index finger, the resistance change correlates with the finger's movement during flexion. The sensor  
Figure 6: (**a**) Relative resistance changes in the CB/CNT/PDA/TPU flexible strain sensor under low strain ranges (1%, 5%, 10%, 15%, 20%); (**b**) Relative resistance changes under high strain ranges (50%, 70%, 100%, 120%, 150%); (**c**) Long-term durability of the CB/CNT/PDA/TPU flexible strain sensor under 70% strain, with an enlarged view of the local signal. The purple color box represents 700–1000 ms, and the orange color box represents 4700–5000 ms.  
produced stable and repeatable response signals, with the resistance change when the finger was fully bent being significantly higher than in the semi-bent state. Specifically, when fully bent, the \(\Delta\)R/R value was 12.5, while in the semi-bent state, it was 3.6. Similarly, Figure 7c,d show the sensor's response to various bending states of the elbow, where the resistance change was proportional to the degree of elbow flexion. For the elbow in the semi-bent position, the \(\Delta\)R/R value was 2.9, and in the fully bent position, it was 21.1. These results demonstrate that the flexible strain sensor generates varying resistance signals of different intensities, depending on the type of motion, highlighting its ability to distinguish between different movements. Figure 7e illustrates the wrist's swinging motion, which also results in a change in resistance, showcasing the sensor's capability to monitor subtle human movements. Figure 7f shows the extension and bending of the knee, indicating that the sensor can also monitor larger-scale human movements. These experiments confirm that the CB/CNT/PDA/TPU sensor is well suited for detecting a wide range of human body movements, from fine gestures to more substantial motions, making it an effective tool for wearable health and motion monitoring applications.  
Figure 7: Applications of the CCPT flexible strain sensor in monitoring various human motions: (**a**,**b**) Sensor response to finger bending and releasing; (**c**,**d**) Sensor response to different elbow bending angles; (**e**) Sensor response to continuous wrist flexion movements; (**f**) Sensor response to continuous knee bending and straightening motions.
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
Inclusion and exclusion criteria were made and implemented to control eligibility and relevancy of primary selected studies. Keeping in view these criteria, research studies from the above-mentioned sources published in journals were included from the year 2015 to June 2023. Non-English articles and those lacking RTM applications for yield modeling and crop traits retrieval were excluded from this SLR. Similarly, posters, abstracts, conference publications, blog information and Wikipedia data were also excluded. Only published research papers having any evaluation or accuracy criteria were included in this SLR. Research studies (9) having other accuracy criteria, like RMSE, RRMSE, BIAS, or percentage variations, were included in SLR. Meta-analysis, however, was carried out only for those articles (82) with a coefficient of determination (R\({}^{2}\)). This resulted in the exclusion of 9 included articles during the meta-analysis about accuracy assessment.
## 3 Results and Discussion
For _trans_-TMC molecules, the oscillator strengths of the S\({}_{0}\)\(\rightarrow\) S\({}_{1}\) transition at fields on the order of 4.3 \(\times\) 10\({}^{-2}\) V/nm increases to 0.2. Therefore, a voltage difference in the order of a few hundreds of mV for an inter-electrode distance (nanogap) adapted to the molecular size, should be sufficiently strong to induce a mixing between the S\({}_{1}\) and S\({}_{2}\) states, suggesting that the S\({}_{1}\) state participates in the current-induced excited-state dynamics and promotes the isomerisation. In our case, DFT calculations indicate that a voltage bias is likely to enhance the contribution of the S\({}_{1}\) state to the electron dynamics, promoting the isomerisation process. The applied bias voltage does not need to overcome the double-bond rotation barrier to promote conformational switching. Travelling electrons will activate isomerisation pathways by populating the \(\pi^{\bullet}\) orbitals connected with photo switching processes, as evidenced by the dramatic EEF- induced mixing of the key S\({}_{1}\) state. Figure 4: Oscillator strength for the S\({}_{0}\)\(\rightarrow\) S\({}_{1}\) transition. Black, blue, and red colours correspond to external electric fields (EEF) = 0, +2.3 \(\times\) 10\({}^{-4}\) a.u. and \(-\)2.3 \(\times\) 10\({}^{-4}\) a.u., respectively. Note the strength difference in the enhancement of the oscillator between trans-TMC and cis-TMC conformers (a factor of 12 and 600, respectively). To summarize, we can rationalize all observed experimental results with the assignment of HC and LC values to _trans_-TMC and _cis_-TMC isomers, respectively:  
**Conductance. ** The _cis_ isomer has a lower conductance value than the _trans_ isomer. As previously reported [22], the breaking of the \(\pi\)-conjugation due to isomerisation lowers the transport efficiency along the polyene backbone [32]. **Lifetime. ** The difference in lifetime between the HC blinks and the LC blinks reflects the different stability of each isomer in the molecular junction. The higher lifetimes of the LC blinks and the irreversibility of the _trans_-to-_cis_ isomerisation can be attributed to a relatively higher stability of the _cis_-TMC isomer in the junction due to a more constrained configuration involving the terminal phenyl rings in the molecular anchoring to the electrodes. **Population vs. Vbias. ** The ratio of LC/HC blinks increases with increasing Vbias. DFT calculations suggest that the \(S_{1}\) state remains spectroscopically dark at low fields, and therefore cannot promote electron transport. Thus, only high conductance associated with the _trans_-_TMC_ isomer is observed. At high biases, the EEF induces an excited state mixing and hence promotes the _trans_-_cis_ isomerisation, leading to an increasing number of (less conductive) cis conformers.
## 6 Limitations and Future Work  
In this paper, we conducted a systematic analysis to evaluate the research articles and peer-reviewed papers published in the field of security and privacy of the Dark Net. We collected papers from six different digital repositories and limited our search to papers published in English. Therefore, we might have missed some papers outside of these digital libraries. However, our extensive literature review provides a detailed overview of the current research on Dark Net privacy and security, focusing on the user side. Additionally, the first author of the paper primarily performed the thematic evaluation. We addressed this by having all papers considered by the second and third authors in the last discussion. As for user studies, we only found 9 papers that included any user study analysis, despite our best efforts. We understand it is challenging to conduct user studies on such sensitive topics; however, even those papers are focused primarily on Tor than Dark Net.  
This shows the research gap, and thus in the future extension of our work, we plan to conduct a literature review of user studies of privacy and security of the Dark Net using a specific keyword-based search. We also plan to contribute to the field by conducting user studies to evaluate the user perceptions and experiences of Dark Net usage while focusing primarily on a privacy and security perspective. In such a user study, we would like to compare the differences in perceptions and experiences of users based on their age group and technical abilities. This would also be an opportunity to review the privacy and security threats users face on the Dark Net and measure their awareness of these risks. The Dark Net's privacy and security risks are an apparent concern for the users, as shown through the different user study evaluations. As we acknowledge the difficulty in obtaining such user participation, we emphasize the criticality of understanding the perception of the Dark Net for any user, given the secretive yet vulnerable nature of the platform.
# TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks
## 5 Discussion
### Limitations and Implications
This exposure essentially discloses the approximate locations of GSs, potentially including the residences of individuals operating these stations;
2. Data availability: For organizations developing satellites with confidential data and utilizing these projects due to limited financial resources for establishing a network of ground stations, it is imperative to recognize that the data will be made public. Even if the decoder is not readily accessible, it is crucial to understand that encrypted data becomes more susceptible to individuals attempting decryption efforts;
3. Open-Source Code and Increased Vulnerability: The open-source nature of the code powering these projects introduces a unique set of security considerations. Although open-source projects often benefit from community scrutiny, making it easier to detect and fix vulnerabilities, they can also be more susceptible to attacks. The transparency of the code may expose potential weaknesses, requiring constant vigilance and rapid response to address identified vulnerabilities.
# EAIA: An Efficient and Anonymous Identity-Authentication Scheme in 5G-V2V
## 4 System Model
### Attack Model  
In our research, the network attack model is the Dolev-Yao model, which is a widely used attack model in the field of information security for analyzing and verifying the security of communication protocols. This model assumes that the attacker has full control over the network, enabling them to intercept, modify, replay, and inject messages. The  
Figure 1: System model.  
strength of the Dolev-Yao model lies in its provision of extensive capabilities to the attacker, and thus any protocol that can ensure security under this model is considered to be highly secure.  
In the context of 5G vehicular networks, the Dolev-Yao model is employed to evaluate and enhance the security of communication protocols. For instance, in communications between vehicles and infrastructure as well as between vehicles themselves, attackers might attempt to intercept and alter data to carry out man-in-the-middle attacks or identity forgery. Specifically, the following scenarios can be expressed:  
1. Eavesdropping, Interception, Modification, or Deletion of Messages: An adversary can eavesdrop on, intercept, modify, or delete publicly transmitted messages, potentially compromising the confidentiality and integrity of the communication. Passive attacks may also be used to gather sensitive information.
2. Replay Attacks: an adversary might capture and resend previously transmitted data packets to deceive other vehicles or infrastructure, leading to the propagation of duplicate or misleading information.
3. Message or credential forgery: an adversary might forge false messages or credentials to impersonate legitimate vehicles or road infrastructure, causing the dissemination of incorrect traffic information or false warning messages, thereby impacting road safety.
4. Man-in-the-middle attacks: an adversary could position themselves between communicating parties, establishing normal connections with both sides and deceiving them into exchanging data through the attacker.  
Meanwhile, it is assumed that the connections between the 5G core network and gNB, as well as between gNB and RSUs, are secure due to their wired nature. Additionally, the connections between vehicles and RSUs or gNBs are assumed to be insecure. Furthermore, all network functions within the 5G core network are assumed to be trusted since they are located in secure environments (such as hardware security modules).
# A New Understanding and Modelling of TSP and BP Indices Compared to Safety IMO Ship Requirements
## 3 Results
### Modelling
Finally, it is interesting to comment on the validity range of the previous models and their variables, as it shown in Table 3.  
In escort towing operations, as two ships are involved (the escort tug and assisted vessel), it is necessary to know the required TSP of the assisted vessel sailing at different speeds to be counteracted in the event of an emergency situation. Therefore, in this case, it is necessary to know the TSP needed by the escorted ship and the TSP developed effectively by the tug, in order to be sure that escort operations are being carried out with a sufficient margin of safety. Then, taking into consideration the rudder forces of different tankers registered in previous studies with the rudder blocked at various angles [28], we developed a mathematical model that allows us to calculate the TSP required by a ship relating the following independent variables: Deadweight; Ship Speed; and Rudder Angle. This data collection is included in Table 4.  
Figure 3: Representation of tugs’ TSP real and obtained with Model 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Min** & **Max** & **Unit** \\ \hline Length & 24.4 & 47.0 & m \\ Beam & 9.42 & 16.00 & m \\ Depth & 4.0 & 7.5 & m \\ Draft & 4.60 & 8.25 & m \\ Speed & 10 & 16 & Knots \\ BP & 32 & 110 & tons \\ TSP (10 kt) & 30 & 110 & tons \\ BHP & 1624 & 10,400 & HP \\ Built & 1981 & 2017 & \\ Model 1 (BP) & 35.23 & 106.80 & tons \\ Model 2 (BP) & 31.50 & 181.90 & tons \\ Model 3 (BP) & 150.01 & 35.40 & tons \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Validity range of Models 1, 2, and 3. Equation (4) includes the result of Model 4 obtained:  
\[ \begin{array}{l}\text{TSP}=-37.8-23.74\text{-}\text{SS}-0.000074\text{-}\text{ DWT}+11.80\text{-}\text{RA}+1.406\text{-}\text{SS}^{2}-\\ -0.3196\text{-}\text{RA}^{2}+0.000069\text{-}\text{SS-DWT}+0.3847\text{-}\text{SS-} \text{RA}+0.000005\text{-}\text{DWT}\text{-}\text{RA}\end{array}  \tag{4}\]  
where SS is ship speed in knots; DWT is deadweight in tons; and RA is rudder angle in degrees, being all variables referred to as tanker vessels. With this mathematical model, it we calculate the TSP (tons) required by a tanker with a precision of R-square of 96.69%, as can be seen from Figure 4.  
Once again, the validity range of Model 4 and its variables are shown in Table 5.
## 4 Experimental Results
### Baselines
#### 4.3.2 Convolution Neural Network (CNN)  
CNN is known for its capability to effectively capture local patterns within data and generate high-level representations of features. By learning the structural information of the malware stream, CNN can determine whether a given sequence is malicious. We opted for this model due to its demonstrated performance in previous studies [18; 19]. The model architecture comprises an embedding layer, three convolution layers, and the GlobalMaxPooling layer for downsampling operations. The fully connected (FC) layer gathers essential patterns from the pooling layer's output, while the final layer uses a softmax activation function to represent the likelihood of the given input containing malicious actions.
## 4 Parametric Study
### Temperature Effect  
In this section, the influence of temperature rise load on the lateral buckling structure and typical lateral buckling behavior of submarine pipeline is discussed. The initial imperfection amplitude, initial half-wavelength, pipeline design parameters, pipeline appropriate relationships, soil characteristics, and those considered in Table 1 remain unchanged. The effects of temperature difference inside and outside the pipeline on the pipe buckling amplitude and bending stress distribution are shown in Figure 12a. In the constant imperfection condition, the buckling amplitude increases with the rise of pipeline temperature. The buckling state still shows a symmetric distribution at the imperfection. However, the feeding-in length decreases with increasing temperature, which reduces the suppression of the anchor point.  
In Figure 12b, when the temperature difference increases, both the maximum axial compression stresses \(\sigma_{\mathrm{Mm}}\) increase significantly. At the same time, an increase in the temperature difference causes the bending stress extreme point to move in the positive direction of \(L_{\mathrm{S}}\), and the value of \(d\sigma_{\mathrm{Mm}}\) becomes smaller.  
As can be seen from Figure 13, \(\lambda^{2}\) increases linearly with the anchor point length as the temperature increases. At the same time, the effective axial force will grow faster with the increase of temperature, which risks the stability of the pipeline.  
As shown in Figure 14, in the thermal vibration model, the vibration frequency of the pipeline increases with increasing temperature, and the thermal expansion force will cause the thermal vibration effect of the pipeline material. At the same time, the increase rate of the linear natural frequency becomes faster with the increase in the pipeline diameter-thick ratio.
## 4 Discussion
### Practical Applications  
Our findings indicate that the H-station is a reliable tool for measuring relative strength changes in quadriceps, hamstring, and H/Q-ratio. Moreover, the H-station is relatively cheap, enabling the device to be placed in public and private clinics to, e.g., monitor patients after an anterior cruciate ligament injury, as well as in professional or semi-professional sport clubs. This has important implications for practitioners, coaches, and clinicians, as the device will enable monitoring of the strength of the lower limb of athletes over a season or after an injury for handball or football players. In that respect, SEM and MDC values are very useful to ensure that gains in quadriceps and hamstring strength are above measurement errors and clinically relevant.