esa-sceva/satcom-chunk-collection huggingface dataset:
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 5 Proof of Identity
### Proposed SRUP Implementation  
It is suggested that the mechanism described in Section 5.1, be implemented within a future release of the SRUP protocol. Assuming that the registration process has already occurred (externally to SRUP), a device wishing to become subject to control from a server would issue a SRUP_MESSAGE_TYPE_JOIN_REQ message to the server, which would consist of the device's identity and a signature to prove the authenticity of the origin. Depending on the system, the server could either directly grant membership of its control-group to the device or require the device to provide additional verification via observation. Note that since the device would not be part of an existing SRUP network at the time of the join operation, the SRUP message would need to be sent over an MQTT topic corresponding to the server's ID: reserved specifically for join requests.  
Whether or not this additional observation step will be required depends on the details of the system being implemented. However, two specific use-cases can be identified, which are representative of many likely real-world deployments of the SRUP protocol.
# Enhancing Skin Lesion Classification Performance with the ABC Ensemble Model
## 4 Results
### Category-Wise Classification Performance  
In this research, we classify skin cancer images from the HAM10000 dataset into seven classes, and the model's classification performance may differ for each class. Accordingly, we visualized the confusion matrix to understand the model's classification performance for each class on the test dataset, as shown in Figure 14. The y-axis represents the actual classes, while the x-axis indicates the predicted classes by the model. The left confusion matrix (a) shows the raw confusion matrix, displaying the actual counts of correctly and incorrectly classified instances for each class. The diagonal elements represent True Positives (TP), while the off-diagonal elements indicate misclassifications. The right confusion matrix (b) shows the normalized confusion matrix, where the values are represented as percentages, demonstrating the model's classification performance for each class. This helps in understanding the relative performance of the model across different classes, regardless of the imbalance in the number of samples for each class. As demonstrated in Figure 14b, we can see that the True Positive Rate (TPR) for the 'nv' class is the highest at 0.98, followed by the 'vasc', 'bcc', 'bkl','mel' and 'akiec' classes, with a TPR of 0.90, 0.87, 0.86, 0.85 and 0.73, respectively. However, the 'akiec' class exhibits a relatively lower TPR of 0.73. This suggests that while our ABC ensemble model did not perform as well in learning features for the 'akiec' class, it successfully captured the features for the remaining six classes. The issue of insufficient learning for the 'akiec' class will be addressed in detail in Section 5.  
Figure 14: Confusion matrices of the ABC ensemble model for each class of the skin cancer dataset. (**a**) indicates the raw confusion matrix which displays the actual counts of correctly and incorrectly classified instances for each class, while (**b**) shows normalized confusion matrix where the values are represented as percentages, showing the model’s classification performance for each class.
## 4 Discussion
### The Increase of Pore Pressure  
Pore-water pressure increases only when the test is performed under a drained condition [65], in which the water is unable to migrate freely within the testing time duration and hence the pore-water will be pressurized. The increase of pore pressure reduces the rock strength via two aspects: decreasing the effective normal compressive stress [47] and inducing microcrack damage once the pore pressure exceeds the tensile strength of rocks [40]. Whether the rock is tested in drained or un-drained condition can be identified by the fluid diffusion characteristic time (\(t_{d}\)), which is determined in a porous saturated material by the following equation [65; 66]:  
\[t_{d}=\frac{{d_{m}}^{2}}{H} \tag{3}\]  
where \(d_{m}\) is the minimum distance from the specimen center to a free surface and \(H\) is the hydraulic diffusivity of this material. The hydraulic diffusivity is estimated by [67]:  
\[H=\frac{\kappa BK}{\eta\alpha} \tag{4}\]  
where \(\kappa\) is the permeability of the material; \(K\) is the bulk modulus of the porous material; \(\eta\) is the viscosity of fluid; \(B\) and \(\alpha\) are the [PERSON]'s and Biot's coefficients, respectively. In this study, \(d_{m}\) is 25 mm according to the specimen geometry. \(\eta\) for water is about \(1\times 10^{-3}\) Pa\(\cdot\)s. \(B\) and \(\alpha\) are assumed to be of the order of unity [64; 65]. \(\kappa\) and \(K\) are listed in Table 3. Therefore, the characteristic times for water diffusion of sandstone, granite and marble are 0.35, 222 and 2440 s, respectively, which are approximated to the predicted values, as shown in Figure 9. Apparently, \(t_{d}\) for the granite and the marble are higher than their failure times, in other words, the two rocks were tested under un-drained condition. The increasing pore pressure will induce severe crack damage [40; 68] and lower the UCS of wet granite and marble specimens. In contrast, \(t_{d}\) for the sandstone is far below its average failure time (226 s), which suggests that pore pressure is limited due to the discharge of pore water. This can hereby interpret the phenomenon that the granite and the marble specimens in crack closure stage (Figure 6) generate more microcracks when they become water-saturated while the ring counts of saturated sandstone slightly decrease.  
Figure 9: Predicted water diffusion characteristic time against porosity (the predicted lines are obtained according to Equations (3) and (4), in which the permeability of rock is approximated via rock porosity (\(\phi\)): \(\kappa=31\phi+7463\phi^{2}+191(10\phi)^{10}\)); the fully filled symbols are the diffusion characteristic times for the three tested rocks).
# Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles
## 4 Methodology for the BN Integration at the Mission Manager Level
### Decision Networks (DN)
#### 4.1.1 Mission Planning Description with DN  
Figure 15 illustrates our approach in a case of a mission for an autonomous vehicle, where we combine a Health Management modules with a decision-making module. The first module is based on Bayesian Networks and has been developed from FMEA tables. According to the reference mission, the module allows, from information given by sensors, to continually monitoring the system,  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline
**UAV** & \multicolumn{4}{c}{**inc**} & \multicolumn{4}{c}{**dec**} \\
**Alt.** & & & & & & & & & & & & \\
**(U\_A)** & & & & & & & & & & & & & \\ \hline
**Battery** & **ok** & & & & **bad** & & & **ok** & & & **bad** \\ (\(H\_E\)) & & & & & & & & & & & & & \\ \hline Actions & SU & SD & EL & SU & SD & EL & SU & SD & EL & SU & SD & EL \\ (A) & & & & & & & & & & & & & \\ \hline U & 100 & 0 & 0 & 0 & 0 & 100 & 0 & 100 & 0 & 0 & 0 & 100 \\ \hline \end{tabular}
\end{table}
Table 4: Utility table for decision making with DN associated with the actions Speed Up (SU), Slow Down (SD) and Emergency Landing (EL).  
Figure 14: Decision Network (DN) example to make a decision between the different actions to choose (Speed UP, Slow Down, Emergency Landing) taking into account the state of the unmanned aerial vehicle (UAV) in altitude and the health status of the battery.  
to check the mission reliability and to be able to detect and locate a failure with BN monitors. The decision-making module is a Decision Network (defining the Influence Diagram), taking a list of possible actions and the results of the Health Management modules and gives the adequate recovery action to define the new mission plan.
# Perceptual Quality of Audio-Visual Content with Common Video and Audio Degradations
## 9 Discussion and Comparison among Experiments
### On the Internal Consistency and External Comparison  
In order to evaluate the reliability of the results of this study, the agreement among subjects on their quality perceptions responses is reported. To do so, the internal consistency of the perceptual responses from all three experiments was calculated using the [PERSON]'s alpha coefficient [66]. In addition, these results are compared with external perceptual responses gathered from the literature. To this end, perceptual data from VQEG-MM [19], UnB-2013 [25], INRS [44], and LIVE-NFLX-II [24] were considered for comparison.  
The [PERSON]'s alpha coefficients for these datasets are reported in Table 14. The interpretation of the results can be made using these criteria: coefficients between 0.00 and 0.69 are associated with a poor internal consistency, a fair internal consistency is associated with coefficients between 0.70 and 0.79, good internal consistency ranges from 0.80 and 0.89, and an excellent internal consistency is associated with coefficients between 0.90 and 1 [67; 68].  
Figure 14: Ranges of quality and content scores in Experiments 1, 2 and 3.  
From the results presented in Table 14, it can be observed that, in general, all studies presented results with a good level of internal consistency (coefficient \(\alpha\) above 0.8). In addition, the experiments conducted using the datasets VQEG-MM, LIVE-NFLX-II, and UnB-2018 (Experiment 1) presented an excellent level of internal consistency (coefficient \(\alpha\) above 0.90). This indicates that subjects agreed on the quality score when the quality levels, represented by the HRCs, were shifted. If we consider the number of different distortions included in each perceptual experiment, plus the number of test conditions they used (summary of audio-visual studies in Table 2), the quality scores gathered using the UnB-2018 dataset gain more relevance. These results support the reliability of the scores collected in this study. In addition, they validate the application of an immersive approach and encourages the execution of more experiments using this type of methodology.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs  
The network layer attempts to enable cooperative sensing, connection, and data packet routing between the sensors and communicators. However, the routing protocols must be created to meet the necessary performance criteria in UWSNs in order to promote energy efficiency. Convergence, robustness, and scalability are some of these requirements. The main goals of all these routing methods in UWSNs are to increase network lifetime and supply communicating nodes in efficient and reliable ways. The main factors affecting a routing protocol's energy efficiency in UWSNs are computing costs, communication, and neighborhood discovery.  
We offer an original categorization of E2 RPs for UWSNs in Figure 12. Current UWSN E2 RPs fall into five categories: (i) bio-inspired, (ii) cluster-based, (iii) reinforcement learning-based (RL), (iv) cooperative reliability-based, and (v) depth-based. The bulk of E2 RPs used in UWSNs uses routing protocols, based on cluster and cooperative reliability. However, due to the hasty advancement in routing protocols, which are based on artificial intelligence, bio-inspired and reinforcement learning are attracting a lot of attention. This is because they can choose the best routing protocols and are able to quickly adapt to the changing UWSNs environment. The existing E2 RPs for UWSNs are thoroughly reviewed in this section in terms of their basic operating principles.
###### Abstract  
Currently, the seismic designs of reinforced concrete (RC) bridges with tall piers are often accomplished following the ductility-based seismic design method. Though the collapses of the RC bridges with tall piers can be avoided, they are likely to experience major damage and loss of functionality when subjected to strong near-fault ground motions. The objectives of this study are to put forward an innovative design concept of a tall-pier system and its application in tall-pier bridges. The concept of the innovative tall-pier system is derived from the principle of earthquake-resilient structures, and is to improve the seismic performances of the tall-pier bridges under strong near-fault ground motions. The proposed tall-pier system has a box section and is composed of four concrete-filled steel tubular (CFST) columns and energy dissipating mild steel plates (EDMSPs). Trial design of a bridge with the new composite tall-pier system is performed based on a typical continuous rigid frame highway bridge with conventional RC box section tall piers. Both static analysis and nonlinear time history analysis of both the bridges with the new composite tall piers and conventional RC tall piers under the near-fault velocity pulse-type ground motions were conducted in Midas Civil2019 and ABAQUS. The results show that: under the design-based earthquake (DBE), the CFST columns and connecting steel beams remain elastic in the bridge with the new composite tall piers, while the damage is found in the replaceable EDMSPs which help dissipate the seismic input energy. The displacement responses of the new bridge are significantly smaller than those of the conventional bridge under DBE. It is concluded that the bridge with the new composite tall piers is seismic resilient under near-fault ground motions.  
tall-pier bridge; seismic resilience; composite box pier; energy dissipating mild steel plate; replaceable component; velocity pulse +
Footnote †: journal: Applied Soft. 2020, 10, 7377; doi:10.3390/app10207377
## 3 Results and Discussion
### Effects Graphs
#### 3.3.2 Effect of Pumpkin seed  
Extract Dose  
The removal efficiency of pollutants strongly depends on the coagulant dosage, which can be categorized as insufficient (under-dosage), optimal, or excessive (over-dosage). Optimizing the coagulant dosage is essential for improving wastewater treatment efficiency [28; 38], as shown in Figure 4a,b,d. A dose of 2 mL/L of PSE is insufficient to adsorb all pollutants (turbidity, COD, and phosphate removal), leading to incomplete coagulation. Consequently, a higher dosage is required to achieve effective treatment. At 11 mL/L, which represents the optimal dosage, the removal of turbidity, COD, and phosphate is maximized. This is primarily due to the high protein content in PSE, which enhances floc formation through charge neutralization (by interacting with oppositely charged ions) and bridging mechanisms (by forming particle-polymer complexes during adsorption onto polymer chains) [39]. However, exceeding this optimal point, such as with a dose of 20 mL/L, leads to over-dosage. Additionally, the excess coagulant saturates the colloidal surfaces, causing particle restabilization, which hinders floc formation and ultimately reduces treatment efficiency [38].  
Figure 4c shows that the residual UV 254 increased with increasing coagulant dosage, indicating that the optimal dosage for achieving the maximum reduction in UV 254 is 2 mL/L. This phenomenon can be attributed to the organic nature of the PSE coagulant used in the study. Its composition imposes a limitation on its effectiveness for this parameter, as excessive doses may introduce additional organic matter into the treated water [40].
## 2 Materials and Methods
### Measurements
#### 2.3.2 Measurement of Muscle Activity  
To quantify the muscle activity of the individual's VMO and VL, data were gathered using wireless surface electromyography (EMG) (TeleMyo DTS; Noraxon Inc., Scottsdale, AZ, USA). The sampling rate of the EMG signal was set to 1000 Hz, and the amplified waveform was filtered using a 40-400 Hz band pass filter and a notch filter to remove 60 Hz noise. To quantify all of the collected EMG signals, the root mean square (RMS) was processed, and the collected signals of each muscle were normalized as a percentage (% maximal voluntary isometric contraction; %MVIC) of the maximal voluntary isometric contraction. For each movement, electromyography signals were collected for five seconds. The initial and final seconds of the data were removed, leaving only a three-second analysis. Electromyography was recorded for three trials and the results were averaged. A one-minute break was permitted between each recording. The acquired data were expressed as a percentage of the root mean square of the maximum voluntary isometric contraction (%MVIC). A maximal voluntary isometric contraction (MVIC) test was performed before the participant assumed each of the three positions to ensure consistent measurement of the EMG signals from the postures. The knees were positioned against the edge of the platform, with the knees flexed at 90\({}^{\circ}\). The participants were instructed to sit on an elevated platform without allowing their feet to touch the floor. Then, they were instructed to elevate their affected legs flexed to a knee angle of 60\({}^{\circ}\) and subsequently extend them to their fullest extent. Measurement of the maximum voluntary isometric contraction value of each muscle was performed 3 times for 5 s, and then the initial and final seconds were removed from the dataset. The average signal amount for these seconds was used. To prevent any movement and to facilitate a maximal isometric contraction of the knee extension, the examiner placed their hand on the ankle of the test leg. To avoid fatiguing the muscles, the evaluation was performed 3 times, resting for 2 min between each trial [28]. An electrode pad was situated 10 cm away from the patella using the connection line between the anterior superior pelvic spine and the outer edge of the patella for VL measurement. In addition, an electrode was positioned at a 50\({}^{\circ}\) angle along a straight line with respect to the parallel line running between the external patellar margin and anterior superior pelvic spines, 4 cm from the patella [29]. After completing the MVIC test, the participants were given a 5 min break before undergoing training in the 3 specific movements. The CoP and muscle activity measurements were performed simultaneously (Figure 2). Each position was repeated three times. There was a rest period of 1 min after each movement. The participants took a 5 min break after each position trial to minimize muscle fatigue.
# Design of a Predictive RBF Compensation Fuzzy PID Controller for 3D Laser Scanning System
###### Abstract  
A new proportional integral derivative (PID) control method is proposed for the 3D laser scanning system converted from 2D Lidar with a pitching motion device. It combines the advantages of a fuzzy algorithm, a radial basis function (RBF) neural network and a predictive algorithm to control the pitching motion of 2D Lidar quickly and accurately. The proposed method adopts the RBF neural network and feedback compensation to eliminate the unknown nonlinear part in the Lidar pitching motion, adaptively adjusting the PID parameter by a fuzzy algorithm. Then, the predictive control algorithm is adopted to optimize the overall controller output in real time. Finally, the simulation results show that the step response time of the Lidar pitching motion system using the control method is reduced from 15.298 s to 1.957 s with a steady-state error of 0.07\({}^{\circ}\). Meanwhile, the system still has favorable response performance for the sinusoidal and step inputs under model mismatch and large disturbance. Therefore, the control method proposed above can improve the system performance and control the pitching motion of the 2D Lidar effectively.  
predictive control; fuzzy algorithm; RBF; PID control; Lidar pitching motion +
Footnote †: journal: Applied Sciences
# Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application
## 4 Conclusions  
Since the increasing development and exploration of the ocean, underwater acoustic transmission and reception have been paid significant attention. In this paper, a multi-function hydrophone for underwater acoustic application was proposed and designed in terms of hardware and software. Particularly, in the hardware part, we added a pre-amplifier with gain control and a high-performance ADC chip, and the MCU was introduced. Moreover, the connection and control among different components were demonstrated. In the software part, the functions of different threads were introduced. Finally, three experiments were conducted to verify the proposed multi-function hydrophone. High-quality underwater signal reception and storage were verified using mode (1). Clear picture recovery was achieved by mode (2). Extremely low BER was obtained via mode (3). The proposed multi-function hydrophone gives a new solution to receive and process the underwater acoustic signals, having potential to build an air-land-ocean Internet of Things network.  
Figure 16: BER when \(f_{c}=15.5\) kHz. (**a**) Average BER of each hydrophone. (**b**) BER of every frame before decoding.  
Figure 15: Doppler estimation results. (**a**) Doppler estimation result when \(f_{c}=9\) kHz. (**b**) Doppler estimation result when \(f_{c}=15.5\) kHz.  
**Author Contributions:** Conceptualization, formal analysis, and writing--review and editing, Y.Z. and R.W.; investigation and writing--original draft preparation, Y.Z. and R.W.; investigation and data curation, X.Y., F.T. and J.W.; Project administration, F.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the fundamental Research Funds for the Central Universities (No. 20720210078), and in part by Marine Industry Funding of Xiamen city (No. 22 CZB012 HJ13).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 6 Discussion  
UAV cluster technology has recently gained significant attention, especially in the defense industry, due to its ability to perform a variety of tasks (surveillance, rescue, monitoring, and attacking), without the need to expose military troops to harsh environments. The idea of integrating blockchain technology into these systems in order to enhance security is not new. However, a novel scheme is proposed in the context of this work.  
To the best of our knowledge, the idea of using blockchain technology for mission creation and formation control in such systems has so far not been considered in the literature. The method examined in this paper is unique, at least in terms of the published works, as indicated by the lack of available smart contracts with this capability. In contrast to the findings of related studies, the proposed smart contract can be used for decentralized decision making in relation to missions, the formation of the swarm, and the election of a new leader when necessary. Since drones are, as already indicated, vehicles with constrained capabilities, the purpose of such Internet of Things applications is not to necessitate complicated and expensive calculations. Because of this, the smart contract's design was made to be as lightweight and user-friendly as possible.  
The proposed model's functionality and security were tested and simulated, and the results were highly encouraging. The presented simulation provides us with a good idea of how these systems would operate under actual circumstances. Such efforts will undoubtedly speed up the development of these technologies and address a number of security-related problems. However, there are still some restrictions that we should be aware of, which we outline below.