esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Degradation Experiments  
Typical adsorption and degradation experiments were carried out in 250 mL conical flasks. A certain amount of catalyst was added to a pH-adjusted 100 mL OTC solution and mixed thoroughly for 60 min in a constant temperature shaker to determine the adsorption capacity of the material. Subsequently, the peroxymonosulfate solution was introduced into the reactor. At reaction times of 0, 2, 5, 10, 20, and 40 min, 1 mL of liquid sample was extracted and filtered through a 0.22 \(\upmu\)m polyether sulfone membrane filter, followed by the addition of 0.5 mL of methanol to quench the reaction. The samples were then analyzed immediately using high-performance liquid chromatography (HPLC). Three parallel samples were prepared for each experimental condition, and the results were averaged. In this study we selected catalyst type, pollutant concentration, oxidizer dose, and catalyst dose to perform one-way reaction experiments, respectively. And this is used to judge the catalyst's performance. Detailed experimental conditions can be found in Table 1.
# Finite-Horizon Kinetic Energy Optimization of a Redundant Space Manipulator
## 2 Materials and Methods
### Parameters Description  
The main parameters involved in the PMKE method are the inverse kinematics discrete time step \(\Delta t\), the prediction step size \(\Delta T\), the horizon \(h\), and the update interval \(I\). This section explains their meaning and the value that has been chosen for the simulations performed for the purposes of this work. Inverse kinematics discrete time step \(\Delta t\): this parameter is the time step used to numerically perform the integration of the inverse kinematics equation (2). It has been set to 0.01 s for the implementation hereby presented. Prediction step size \(\Delta T\) and horizon \(h\): these altogether determine the prediction window \(T_{p}\), which is related to the ability of the method to take into account future (up to \(T_{p}\) after the current time \(t_{0}\)) issues that could undermine the quality of the solution. The two parameters must be balanced considering that, for example, for a fixed \(T_{p}\), having a higher \(h\) and a smaller \(\Delta T\) allows for better resolution of the prediction, making it less likely for the method to pursue local improvements over global ones. However, it also implies longer computational times. For the 3-DOF manipulator model used in the simulations (see Section 3), good results are reached with \(h\) = 2. Thus, this value is used to avoid the computational complexity related to higher \(h\) values. This means that \(\Delta T\ =\ T_{p}/2\), and leaves the question open about how to choose \(T_{p}\). Increasing it does not change the computational complexity, which is only influenced by \(h\), however a prediction too far in the future might feature a reduced influence on the current path point. Furthermore, it is hard to establish a proper \(T_{p}\) because a value that is good for certain trajectories does not necessarily fit other ones: some of them might benefit from a longer prediction window, while some others might benefit from a shorter one, which makes it difficult to develop a tuning strategy. The value of the prediction window is set to 20% of the total simulation time. This means that, for trajectories with simulation time of 10 s such as the ones used in the following section, the method will perform the optimization of the cost function up to 2 s, corresponding to 200 inverse kinematics time steps. This means that \(\Delta T\) is equal to 1 s.
## 3 Results and Analysis  
The experimental environment is configured as follows: a graphics processing unit (GPU) (NVIDIA GeForce GTX 3090, RAM: 24 GB), python3.8, pytorch2.1.1, cuda12.1. The optimizer utilized is Stochastic Gradient Descent (SGD) with a momentum of 0.9 and initial learning rate of 0.01, following an exponential decay strategy (gamma = 0.9). The loss function used is the cross-entropy loss function. The batch size is set to 64 images. ResNet-4, ResNet-6, and ResNet-8 underwent 100 epochs of training, while the remaining models were trained for a total of 50 epochs.  
All distress samples were resized to 224 \(\times\) 224 as input, and transfer learning was employed to fine-tune the pre-trained weights obtained from the ImageNet dataset [37] for mitigating overfitting. The training was repeated three times for each model to mitigate the impact of sample randomness.
## 4 Discussion
### Study Limitations  
Studies based on experimental trials inherently present several limitations, particularly when involving biomechanical measurements in human subjects. One primary challenge lies in the complexity of the human body, which introduces significant variability that is difficult to control. This variability affects the reliability and repeatability of measurements, as it is virtually impossible to replicate identical conditions. Factors such as slight changes in posture, muscle activation, fatigue levels, or even emotional state can influence the outcome.  
About the Pedar(r) in-shoe pressure measurement system, additional technical limitations arise. Over time, the system's sensors may experience sensitivity degradation due to repeated use and the mechanical stress on the insoles. This wear can lead to subtle calibration or response accuracy shifts, potentially affecting data consistency. Furthermore, while the Pedar(r) system is designed to be portable and optimized for dynamic conditions, such as walking or running, its performance may be compromised when used in static or quasi-static trials. In these scenarios, minimal movement or inconsistent pressure application can lead to less reliable readings, as the system may struggle to capture stable data outside its optimal operational context.  
Although efforts were made to mitigate variability by requiring each subject to repeat the measurement process five times per device, perfect repeatability could not be guaranteed. Human movement naturally involves slight variations, and even with consistent instructions, intra-subject variability is expected. These fluctuations likely contributed to some of the discrepancies observed in the results.  
Moreover, a significant constraint of this study was the limited sample size. A small participant group reduces statistical power and increases the influence of individual variability, making it more difficult to draw generalized conclusions. This restricts the findings' applicability to broader populations and highlights the need for further research with larger and more diverse cohorts to validate and extend the results.
# Energy Management for a Power-Split Plug-In Hybrid Electric Vehicle Based on Reinforcement Learning
## 2 PHEV Powertrain Model
### Power Request Model  
Given a certain driving cycle, the power required to drive the vehicle powertrain can be calculated as,  
\[P_{req}=(F_{f}+F_{w}+F_{i})v \tag{3}\]  
where \(P_{req}\) is the vehicle request power, \(F_{f}\), \(F_{w}\), and \(F_{i}\) represent the resistance derived from the road, air drag and vehicle inertial, respectively. \(v\) denotes the driving velocity. The resistances, that merely associated with vehicle and environment parameters, can be expressed as,  
\[\left\{\begin{array}{l}F_{f}=mgf\\ F_{w}=C_{d}Av^{2}/21.15\\ F_{i}=\delta mg\end{array}\right. \tag{4}\]  
where \(m\) is the total mass, \(f\) denotes the road resistance coefficient, \(g\) is the gravity coefficient, \(A\) is the frontal area of the vehicle, \(C_{d}\) is the aerodynamic drag coefficient, and \(\delta\) is the rotational mass coefficient. As shown in Figure 1, the power flow equations can be formulated to describe the corresponding power flow, as:  
\[\left\{\begin{array}{l}P_{req}=P_{final}\cdot\eta_{final}\\ P_{final}=(P_{mot1}+P_{mot2}+P_{eng})\cdot\eta_{gear}\\ P_{bat}=(P_{mot1}/\eta_{c1}+P_{mot2}/\eta_{c2})+P_{acc}\\ P_{eng}=f_{eng}(T_{eng},\omega_{eng})\end{array}\right. \tag{5}\]  
where \(P_{final}\) is the driveline power, \(P_{mot1}\), \(P_{mot2}\), and \(P_{eng}\) are the output power of motor 1, motor 2 and engine, respectively. \(P_{acc}\) denotes the power of electric accessories and is assumed to be a constant value, i.e., 220 W. \(\eta_{gear}\), \(\eta_{final}\) and \(\eta_{c}\) are the transmission efficiency factor of gear, final drive and electric convertor, respectively. As seen in Figure 1, the planetary gear set works as the coupling device that connects the engine and the motors, and the corresponding dynamic equations are expressed as follows:  
\[\left\{\begin{array}{l}\omega_{eng}=\frac{1}{1+igar}\omega_{mot2}+\frac{igar }{1+igar}\omega_{mot1}\\ T_{eng}=-(1+igar)T_{mot2}=-\frac{1+igar}{igar}T_{mot1}\\ \omega_{ring}=\omega_{mot1}=\frac{v}{v_{inl}}r_{final}\end{array}\right. \tag{6}\]  
where \(i_{gear}\) is the transmission ratio of the planetary gear, \(\omega_{mot1}\), \(\omega_{mot2}\), and \(\omega_{ring}\) are the speed of motor 1, motor 2 and ring gear, respectively; \(T_{mot1}\) and \(T_{mot2}\) are the torque of two motors; \(r_{inl}\) denotes the radius of the wheel and \(r_{final}\) is the final driveline ratio. In this article, we choose to ignore the inertial of planet gear, sun gear and ring gear for ease of managing the energy distribution.  
Based on the above descriptions, the instantaneous fuel consumption \(F_{rate}\) can be redefined as:  
\[F_{rate}=f(T_{eng},\omega_{eng})=f(P_{bat},P_{req},v) \tag{7}\]  
Now we can find that \(F_{rate}\) can be directly determined by \(P_{bat}\), thus it is necessary to model the battery and analyze its power relationship.
# Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study
## 4 Results
### Model Assessment
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Items** & \(Q^{2}_{predict}\) & **PLS-SEM\_RMSE** & **PLS-SEM\_MAE** & **LM\_RMSE** & **LM\_MAE** \\ \hline BEH1 & 0.108 & 0.436 & 0.241 & 0.482 & 0.299 \\ \hline BEH2 & 0.183 & 0.552 & 0.387 & 0.580 & 0.403 \\ \hline BEH3 & 0.108 & 0.586 & 0.384 & 0.664 & 0.415 \\ \hline BEH4 & 0.093 & 0.633 & 0.432 & 0.666 & 0.433 \\ \hline EASE2 & 0.199 & 0.630 & 0.453 & 0.646 & 0.462 \\ \hline EASE3 & 0.274 & **0.487** & 0.339 & 0.480 & 0.340 \\ \hline EASE4 & 0.247 & 0.515 & 0.414 & 0.570 & 0.431 \\ \hline EASE5 & 0.150 & 0.405 & 0.245 & 0.437 & 0.281 \\ \hline EFF1 & 0.094 & 0.380 & 0.241 & 0.384 & 0.253 \\ \hline EFF3 & 0.130 & 0.660 & **0.532** & 0.669 & 0.504 \\ \hline EFF5 & 0.161 & **0.406** & **0.292** & 0.384 & 0.270 \\ \hline SAT1 & 0.357 & **0.454** & **0.368** & 0.444 & 0.309 \\ \hline SAT2 & 0.388 & **0.422** & **0.335** & 0.395 & 0.282 \\ \hline SAT3 & 0.299 & 0.577 & **0.491** & 0.626 & 0.490 \\ \hline SAT4 & 0.298 & 0.535 & **0.423** & 0.552 & 0.403 \\ \hline USE1 & 0.178 & 0.603 & **0.503** & 0.641 & 0.470 \\ \hline USE2 & 0.229 & 0.459 & **0.366** & 0.471 & 0.348 \\ \hline USE4 & 0.168 & 0.620 & **0.502** & 0.632 & 0.469 \\ \hline USE5 & 0.212 & 0.457 & **0.378** & 0.458 & 0.354 \\ \hline \hline \end{tabular} Note that bold values in the rows indicate that endogenous construct items have higher prediction errors in terms of RMSE or MAE when compared to the naive LM benchmark.  
\end{table} 
Table 9: Results of testing the predictive power of the research model. is a moderate predictor, feedback quality (\(q^{2}=0.075\)) appeared to be a weak predictor of ease of use in the context of mobile banking applications. Responsiveness (\(q^{2}=0.224\)) was also found to have moderate relevance in predicting the efficiency of mobile banking applications. While ease of use (\(q^{2}=0.242\)) has moderate relevance and security of personal data (\(q^{2}=0.062\)) has weak relevance, usefulness (\(q^{2}=0.013\)) does not have sufficient relevance in predicting customer satisfaction with mobile banking applications. Finally, ease of use appeared to have weak relevance (\(q^{2}=0.119\)) in predicting the usefulness of mobile banking applications while efficiency is not relevant enough (\(q^{2}=0.005\)) in that respect.
## 2 Definition of Vulnerability Indices Based on the Proposed Network
### The Proposed Network Formulation  
In recent years, providing effective and new strategies in compliance with the modern power system standards to collapse the vulnerability indices is required. Technically, employing a security policy in the power system reduces destructive threats such as blackouts in post contingency situations. Hence, the main goal of this section concentrates on getting to an efficient framework of energy exchange between the power grid and a microgrid including some distributed energy resources with the aim of mitigating the power system vulnerability. To this end, there is a need to describe the power grid modeling in the first place. In our suggested model, the power grid is formulated to charge the electrical loads in order to minimize the total cost of generation [36; 37]. For more clarification, the objective function and limits of the smart grid are literally represented in (14)-(26). Equation (14) shows the objective function including the operation cost, start-up/shot-down costs, and cost of power exchange all of which need to be minimized. Besides this, each generation unit should keep the generation capacity limits for the active/reactive powers for the sake of the fuel limit. To clarify this problem, the power generation constraints of units are formulated in Equations (18) and (19) in which the binary variable \(zx_{g,t}\) is modeled for the start-up/shot-down (\(zx_{g,t}=1\) or \(0\)) rates of generation units. It is significant to mention that the reserve power produced by generators can be employed to keep the power balance of the grid in the contingency condition. Hence, Equations (20) and (21) illustrate the limits of the generator's reserve power at \(t\). The utility constraint for the power flow of feeders may cause positive/undesirable impacts on the procedure of optimizing the power following the objective function. So, the feeder power flow is computed and modeled by (22) and (23). The bus angles of the grid are indicated by \(\eta_{b}^{max}\), which should be restricted between the max/min values as shown in (24). Equations (25)and (26) represent the active/reactive power flow restrictions needed for the power grid feeders, respectively.
## 6 Security Concerns  
One of the most important challenges for communications is how to ensure the patient security and privacy during transmission of data to avoid the threat from attacker [33]. The authors in [34] analyzed privacy threat types based on the wireless health monitoring system architecture and built the key system model for identity threat and context privacy preservation, based on traffic analysis threat. The authors mention that to resist these threats, the integrated message encryption, identity authentications and traffic context privacy preservation, based on identity-based cryptography (IBC) and identity-based signature (IBS), is carried out at one time during the process of sending, receiving and accessing the patients' health information. However, the authors in [35] argue that the results carried out for [34] lack context privacy and do not provide sufficient security for physicians. They propose a new platform they call the \"u-healthcare system\", which provides security considerations for their future platform. In addition, authors in [35], mention that for Body Area Networks (BANs) to monitor and control a large variety of physical parameters in different contexts, it is necessary to tolerate a high degree of change and possibly even permit temporary privacy violations in order to meet functional safety or performance requirements. For example, an individual wearing an EKG might experience a heart arrhythmia and the real-time reporting of this incident takes precedence over already existing privacy requirements, such as is presented in our scenario.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**End Device**} & \multicolumn{2}{c}{**ECG**} \\ \hline
**SD/BO** & **Percentages** & **SD/BO** & **Percentages** \\ \hline
0.25 & 44.39\% & 0.25 & 50.02\% \\
0.5 & 58.70\% & 0.5 & 67.57\% \\
1 & 45.13\% & 1 & 51.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Percentage of Packages that received ACK on the first attempt BO = 7.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**End Device**} & \multicolumn{2}{c}{**ECG**} \\ \hline
**SD/BO** & **Percentages** & **SD/BO** & **Percentages** \\ \hline
0.25 & 54.26\% & 0.25 & 38.88\% \\
0.5 & 41.36\% & 0.5 & 60.02\% \\
1 & 52.45\% & 1 & 50.86\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Percentage of packets that received ACKs on the first attempt for BO = 9.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reinforcement Learning Energy-Efficient Routing Protocols (RL-Based E2 RPs)
#### 5.4.4 Comparison of Reinforcement Learning Energy-Efficient Routing Protocols  
Table 11 shows, the comparison between the existing RL-based E2 RPs. RL-based techniques are becoming more popular because they have high adaptability in underwater environment. Although the RL-based concepts had been used from many years. There are no routing protocols that are proposed on the basis of Q-learning techniques in terms of energy efficiency for UWSNs. In dynamic environment the energy efficient Q-learning techniques achieves great performance by using error and trial. Most energy efficient routing protocols based on Q-learning techniques are designed as Markov problems decisions. The network reward function, action space and state space architecture are challenging to utilize and relies entirely keeping in view the application objectives and requirements shows that, the majority of current E2 RPs are considers in each individual data packet as a network state. Furthermore, the network reward function has a strong correlation with both the remaining energy and the distance from the repeater node to sink node. In comparison with other categories of E2 RPs. E2 RPs that are based on RL are still consider static and single sink nodes. Therefore, a large scope of research exists based on multiple sink node for routing protocols on the basis of energy efficient Q-learning techniques. The main disadvantages of the E2 RPs based on Q-learning techniques are that they need to store and change its Q-value with each interaction, which makes the network denser. Considering this limitation, present E2 RPs do not contemplate a huge network, and simulation results are performed only with less than 125 communicating nodes. Hence, the performance of E2 RPs, which are based on Q-learning techniques are still questionable, since they need to be improved in terms of how they behave on large scale UWSNs.
## 3 Breakpoint-Based Crack Annotation
### Annotation Method Comparative Analysis  
Based on previous analysis, we summarize the features of the aforementioned three annotation methods in Table 1, including the subjective consciousness effect, mechanical rationality and model training efficiency. Since the sample labelling is a human-interacting task, primary judgement should be conducted based on the visual subjective perception of a human. Therefore, an indicator named \"human subjective intervention\" is proposed to assess the interference of human consciousness in the process of creating samples for model training. Secondly, cracks are the manifestation of structural components under various loading conditions; hence, the labeled crack objects should conform to its mechanical rationality. For instance, two cracks that intersect should not be regarded as one crack because it does not conform to the mechanical analysis. Therefore, the second index proposed and compared is the mechanical rationality of three annotation methods. Thirdly, computation cost is taken into consideration by judging whether the annotation methods are conducive to model training, which is presented as the model training efficiency.  
In addition, since the proposed method focuses on the cracks of the concrete bridge beam in the actual engineering detection, the analysis of the three methods will not consider factors other than the actual detection process, and the qualitative analysis is carried out on the normal collected images.  
Comparative results are summarized in Table 1, according to our previous discussion based on the crack features and deep learning model structures. Generally, the continuous crack annotation should be the most objective method, with minimum human intervention effect; however, the high computation cost is expected during the model training with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Annotation Methods** & \begin{tabular}{c} **Human Subjective** \\ **Intervention** \\ \end{tabular} & \
\begin{tabular}{c} **Mechanical** \\ **Rationality** \\ \end{tabular} &
\begin{tabular}{c} **Model Training** \\ **Efficiency** \\ \end{tabular} \\ \hline Continuous crack & Minimum & / & Inefficient \\ annotation & & & \\ Absolute breakpoint & & & \\ crack annotation & & & \\ Relative breakpoint & & & \\ crack annotation & & & Highly efficient \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assessment of three crack annotation methods.  
those continuously annotated crack samples. As for the relative breakpoint annotation, it is subjectively influenced by humans, and its annotation results are consistent with both human subjective perception and mechanical analysis results. It also provides relatively balanced feature maps of different scales, which should be conducive to model training convergence. To verify our conjectures, an experiment is then conducted to verify the specific performance in model training of these three annotation methods, as described in Section 4.
## 3 Results
### Cadmium Concentration in Root and Shoot  
It was observed that Cd-stress significantly enhanced the Cd concentration in root of _Brassica juncea_ (L.) plants (Figure 3B). Inoculation with Cd-tolerant, IAA-producing rhizobacterial strains reduced the Cd concentration in root of _Brassica juncea_ (L.) seedlings under Cd\({}^{2+}\) stress. Maximum Cd concentration (35.3 \(\upmu\)g g\({}^{-1}\)) was observed in the roots of plants in the uninoculated control. Minimum Cd concentration was observed in the roots of plants inoculated with rhizobacterial strain FN13 where significantly less Cd was observed than in control. The strains FN14 and FN16 also decreased the Cd concentration in roots of _Brassica juncea_ (L.) plants but this decrease was non-significant in the case of FN16 when compared with the respective uninoculated control. Overall, rhizobacterial strains gave up to 11.7 to 26.9% decrease in Cd concentration in roots when compared with respective Cd-stressed control plants. Unstressed plants showed negligible concentration of Cd as compared to Cd-stressed plants.  
As observed in the case of Cd concentration in roots, Cd-stress also significantly increased the Cd concentration in shoots of _Brassica juncea_ (L.) plants (Figure 3C). The Cd concentration in shoot was decreased due to inoculation with Cd-tolerant, IAA-producing rhizobacterial strains. It was observed that Cd-concentration in shoots was less as compared to Cd in roots of _Brassica juncea_ (L.) plants. Under Cd-stress, the maximum Cd concentration (26.6 mg kg\({}^{-1}\)) was observed in the shoot of plants under uninoculated control treatment. The strain FN13 was the most effective treatment in reducing the Cd concentration in shoot of _Brassica juncea_ (L.) plants which reduced the Cd concentration up to 29% less than control. The results of FN13 were non-significant with those of FN14 and FN16 but significantly better than the control. The strains FN14 and FN16 also decreased the Cd concentration in the shoot of _Brassica juncea_ (L.) plants but this decrease was not significantly different than the control. Under unstressed conditions, the Cd concentration was up to 1.33 mg kg\({}^{-1}\) in the shoot of _Brassica juncea_ (L.) plants under uninoculated treatment. The inoculation showed Cd concentration of shoots ranging from 1.08 to 1.14 mg kg\({}^{-1}\).