esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Experimentation  
### Simulation Results  
The simulation result with the equivalent stiffness and the simulation result with the equivalent stiffness and damping coefficient are shown in Figure 4; it can be seen that the electromagnetic vibration isolation system is stable at \(5.09\times 10^{-5}\) m, which is close to 0 m. When the electromagnetic vibration isolation system is controlled by an active method with the equivalent stiffness, the rise time \(t_{r}\) is 0.05 s, the peak time \(t_{p}\) is 0.09 s, the overshoot \(\sigma\)% is 60.7%, the settling time \(t_{s}\) is 1.4 s, and the number of oscillations \(N\) is 5. Due to the fact that the range of the overshoot in Equation (14) has no solution, the value of overshoot cannot meet the requirement. However, other performance indexes of the system can satisfy the ranges.  
Figure 3: The control diagram with active control methods.  
When the electromagnetic vibration isolation system is controlled by active method with the equivalent stiffness and damping coefficient, the vibration displacement \(x\) is \(5.09\times 10^{-5}\) m, the rise time \(t_{r}\) is 0.08 s, the peak time \(t_{p}\) is 0.11 s, the overshoot \(\sigma\)% is 7.9%, the settling time \(t_{s}\) is 0.42 s, and the number of oscillations \(N\) is 1. The performance indexes of the electromagnetic vibration isolation system can meet the requirements exactly. It can be seen from the simulation results that the equivalent damping coefficient improves the response time of the electromagnetic isolation system, reduces the overshoot and oscillations, and makes the electromagnetic isolation system have the advantages of fast response and stability, which can meet the real-time requirements of the electromagnetic isolation system in practical application.
## 4 Influence of Notch Location on the Vibration Characteristics
In addition, it was found that in modes 9, 14-15, and 18-19, the minor natural frequencies were at 0.12, 0.24, 0.36, 0.48, and 0.60, respectively, and would return in the same order as in the first-period mode 12; the effect is shown in Figure 25.  
Subsequently, the workpiece experienced a particular examination using a meticulously calibrated test set. The examination revealed dimensionless proportions of 0.12, 0.36, and 0.60, respectively, based on the natural frequency in mode 1. Notably, the frequencies recorded were 10.00, 9.00, and 8.94 Hz, respectively. In contrast, the finite element (FE) model predicted natural frequencies of 9.83, 8.98, and 8.83 Hz, respectively, with a noticeable decrease in slope observed when multiplying the trendlines. Further analysis revealed that the distance (H) from the notch to the workpiece edge was critical in shaping the natural frequency (Figure 26). [PERSON] et al. (61) explored this relationship in detail and [PERSON] et al. (62) in their studies on the location and size of cracks in beams. Specifically, the first independent vibrations were studied for sheet metal with V notches, which have a singular moment bent at an acute angle due to a transverse oscillating motion. Theoretical analysis was performed using the accepted displacement function. The natural frequencies of intense and cracked beams were calculated using the finite element method, which was then validated by experimental testing. The results indicated that the position of the notch closer to the edge of the workpiece resulted in a higher natural frequency. These findings were further confirmed in both studies, thus underscoring the reliability and validity of this research approach. Figure 25: Natural frequencies of modified with one-sided V notch slide shape on different models.
# Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data
###### Abstract  
Failure mode and effect analysis (FMEA) is one of the most widely employed pre-evaluation techniques to avoid risks during the product design and manufacturing phases. Risk priority number (RPN), a risk assessment indicator used in FMEA, is widely used in the field due to its simple calculation process, but its limitations as an absolute risk assessment indicator have been pointed out. There has also been criticism of the unstructured nature and lack of systematicity in the FMEA procedures. This work proposes an expected loss-FMEA (EL-FMEA) model that organizes FMEA procedures and structures quantitative risk assessment metrics. In the EL-FMEA model, collectible maintenance record data is defined and based on this, the failure rate of components and systems and downtime and uptime of the system are calculated. Moreover, based on these calculated values, the expected economic loss is computed considering the failure detection time. It also provides an alternative coefficient to evaluate whether or not a detection system is installed to improve the expected loss of failure. Finally, a case study was conducted based on the maintenance record data, and the application procedure of the EL-FMEA model was presented in detail, and the practicality of this model was verified through the results.  
risk-evaluation; EL-FMEA; expected loss; alternative coefficient; maintenance record data +
Footnote †: journal: Applied Sciences  
Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data  
1 Department of Natural Science, Korea Naval Academy, Changwon 51704, Korea; [EMAIL_ADDRESS]  
2
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 3 Inter-SubNet with New Loss Function
### Discussion of Some Other Losses
#### 3.3.1 Robust Loss  
In addition to Huber loss, Charbonnier loss [35] is another robust loss function used in machine learning, particularly in regression tasks. Given \(y\) and \(\hat{y}\) as the estimated and ground-truth values, Charbonnier loss is expressed as follows:  
\[L_{e}(y,\hat{y})=\sqrt{(y-\hat{y})^{2}+\epsilon^{2}} \tag{12}\]  
where \(\epsilon\) is a small constant. Compared to Huber loss, Charbonnier loss is fully differentiable and has a smoother transition from quadratic to linear. Charbonnier loss can be viewed as a differentiable approximation of the Huber loss. Additionally, Huber loss has an explicit parameter (\(\Delta\) in Equation (7)) that controls the transition between quadratic and linear behavior, making it easier to adjust the sensitivity to outliers. In comparison, Charbonnier loss does not have a direct parameter for controlling this transition, which might make it less flexible in specific applications. Therefore, Huber loss is preferred over Charbonnier loss when there is a need for clear control over outlier sensitivity. Conversely, Charbonnier loss might be favored in tasks that require smoother gradients.
## 5 Results and Discussion
### General Computing Overview
It can also be seen in subsequent working conditions that with the addition and setting of sensors with gross errors, the quality factor is gradually increased. Especially when adding four gross errors in Condition 10, its quality factor has been as high as 0.6 or so. As we mentioned earlier, the established data reconciliation model requires an iterative process, and its convergence criteria are determined by the residual of redundant equations. Since there are many redundant equations, the residual of equations is introduced here as the determination of iterative convergence, which is calculated as follows [37]:  
\[\parallel\textbf{F}\parallel=\sqrt{\frac{1}{m}\sum_{j=1}^{m}\textbf{F}_{j}^{2}} \tag{20}\]  
where \(m\) represents the number of equations. It is only used to judge the iteration in the whole reconciliation calculation process, and has no physical significance. The iteration of residual of redundant equations under ten working conditions is shown in Figure 3. The iterative calculation of data reconciliation can converge within three steps, and the residual of redundant equations has been reduced to the magnitude order of \(10^{-4}\) or below, which indicates that the data reconciliation model has a good convergence for the reconciliation process of system parameters, and has a good effect in reducing the residual of the redundant equations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Working Condition** & **Sensors** & **Design Value** & **Measured Value** & **Unit** \\ \hline Condition 1 & — & & & \\ Condition 2 & — & & & \\ Condition 3 (1 gross error) & TS17 & 380.330 & 383.021 & \({}^{\circ}\)C \\ Condition 4 (1 gross error) & PS05 & 24.000 & 23.282 & MPa \\ Condition 5 (1 gross error) & MFS05 & 62.640 & 73.651 & kg/s \\ Condition 6 (1 gross error) & MFS04 & 132.530 & 149.173 & kg/s \\ \hline Condition 7 (2 gross errors) & TS17 & 380.330 & 382.612 & \({}^{\circ}\)C \\ PSO5 & 24.000 & 23.195 & MPa \\ \hline Condition 8 (2 gross errors) & MFS05 & 62.640 & 73.067 & kg/s \\ MES04 & 132.530 & 149.819 & kg/s \\ \hline Condition 9 (3 gross errors) & TS17 & 380.330 & 383.332 & \({}^{\circ}\)C \\ PSO5 & 24.000 & 23.208 & MPa \\ MES05 & 62.640 & 72.613 & kg/s \\ \hline Condition 10 (4 gross errors) & TS17 & 380.330 & 382.842 & \({}^{\circ}\)C \\ PSO5 & 24.000 & 23.211 & MPa \\ MES05 & 62.640 & 72.865 & kg/s \\ MES04 & 132.530 & 150.383 & kg/s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Setting of sensors with gross errors under different working conditions.
## 3 Deep Learning-Based PV Power Forecasting  
The input data consists of PV power output that variates between 0 and the rated output power. Thus, when handling some large-value data with the RNN model, a gradient explosion can be occurred and negatively affects the performance of the RNN. Furthermore, the learning effectiveness of RNN will be reduced. To remedy this issue, the input data is normalized via min-max normalization within the interval \([0,\,1]\), and then used for constructing the deep learning models. The normalization of the original measurements, \(\mathbf{y}\) is defined as:  
\[\widetilde{y}=\frac{(y-y_{min})}{(y_{max}-y_{min})}, \tag{6}\]  
where \(y_{min}\) and \(y_{max}\) refer to the minimum and maximum of the raw PV power data, respectively. After getting forecasting outputs, we applied a reverse operation to ensure that the forecasted data match to the original PV power time-series data.  
\[y=\widetilde{y}*(y_{max}-y_{min})+y_{min}. \tag{7}\]  
As discussed above, the generated PV power shows a high level of variability and volatility because of its high correlation with the weather conditions. Hence, for mitigating the influence of uncertainty on the accuracy of the PV power forecasting this work presents a deep-learning framework to forecast PV power output time-series. Essentially, deep learning models are an efficient tool to learn relevant features and process nonlinearity from complex datasets. In this study, a set of eight deep learning models have been investigated and compared for one-step and multiple steps ahead forecasting of solar PV power. The overall structure of the proposed forecasting procedures is depicted in Figure 3. As shown in Figure 3, solar PV power forecasting is accomplished in two phases: training and testing. The original PV power data is split into a training sub-data and a testing sub-data. At first, the raw data is normalized to build deep learning models. Adam optimizer is used to select the values of parameters of each model by minimizing the loss function based on training data. Once the models are constructed, they are exploited for PV power output forecasting. The quality of models are quantified using several statistical indexes including the Coefficient of determination (\(R^{2}\)), explained variance (EV), mean absolute error (MAE), Root Mean Square Error (RMSE), and normalized RMSE (NRMSE).  
Essentially, the deep learning-driven forecasting methods learn the temporal correlation hidden on the PV power output data and expected to uncover and captures the sequential features in the PV power time series. The main objective of this study is to investigate the capability of learning models namely RNN, LSTM, BiLSTM, ConvLSTM, GRU, RBM, SAE, and VAE for one-step and multiple-steps ahead solar PV power forecasting.
# Evaluating the Energy-Saving Potential of Solar Dedicated Ventilation Systems Based on ADRC
### Accurate Control System  
An efficient way for an air-conditioning system to gain energy-saving potential is by using a precisely controlled system. The control method of air-conditioning systems has been extensively investigated. Traditional proportion, integration, differentiation (PID) controllers and fuzzy PID controllers were applied to air-conditioning systems with lower control accuracy. Back propagation (BP) neural network and fuzzy neural network have rarely been used for air-conditioning systems. Active disturbance rejection controller (ADRC) is a new practical digital control technology that is independent of the precise model of the controlled object and could replace the PID control technology. ADRC has six characteristics:  
1. It does not rely on mathematical models of controlled objects.
2. It has strong anti-interference to internal and external disturbances of the controlled objects.  
3. The control response speed is fast, with no overshoot, no static error, good stability, and robustness.
4. The control quality is better than the classic PID and suitable for many kinds of high-precision control systems.
5. It is easy to adjust parameters because the scope of parameter conciliation is wide.
6. The linear, nonlinear, static, and dynamic properties of controlled objects do not need to be distinguished.  
Therefore, ADRC has attracted many experts to carry out research on its applications. [PERSON] et al. [10] used ADRC instead of an outer-loop proportion, integration (PI) controller in order to remove the sluggishness to respond to external load disturbances and designed a mathematical model to describe the stable area for the ADRC parameters. The results showed that the load requirement could be switched more smoothly in a wide section (from 10 MW to 15 MW). [PERSON] et al. [11] proposed and simulated an ADRC for temperature control of a heating ventilation and air-conditioning system, for which the ADRC expressed longer time, strong disturbance rejection, and high precision.
# Comparative Analysis of Marine Alternative Fuels for Offshore Supply Vessels
## 3 Discussion
The novelty of this research lies in several key findings that contribute new insights to the field of alternative marine fuels, specifically for offshore supply vessels (SOVs). First, this study provides a focused analysis of various alternative fuels--LNG, hydrogen, ammonia, and biofuels--tailored to the operational requirements of SOVs. This vessel-specific approach is relatively unexplored, offering new perspectives on how these fuels perform under conditions unique to SOVs, such as range, refuelling, and safety. A key result is the identification of LNG as the most viable short- to medium-term solution for reducing GHG emissions in the SOV sector. While green LNG has been discussed in previous research, this study positions it as a practical transitional fuel until more advanced technologies, such as hydrogen and ammonia, can be widely adopted. This strategic framework for managing the fuel transition process provides fresh insight into how the maritime industry can balance immediate emission reductions with long-term decarbonisation goals. Additionally, this research expands on the integration of hybrid and fully electric propulsion systems, assessing the feasibility of combining these systems with alternative fuels. The consideration of advancements in battery technology and renewable energy sources introduces a more comprehensive approach to decarbonisation, particularly for SOVs. Finally, this paper emphasises the infrastructural challenges associated with large-scale adoption of alternative fuels, particularly hydrogen and ammonia. By identifying these challenges and advocating for coordinated industry and regulatory efforts, this study contributes valuable knowledge on the practical steps required to achieve long-term decarbonisation. In conclusion, this paper makes several original contributions to the field of maritime decarbonisation, particularly through its focus on SOVs, its multi-faceted evaluation of alternative fuels, and its novel analysis of hybrid propulsion systems. The findings offer valuable guidance for both industry stakeholders and policymakers as they navigate the complex challenges of transitioning to a low-carbon future in the offshore supply vessel sector. At a conceptual level, this paper has considered factors such as spatial constraints, refuelling intervals in relation to bunkering infrastructure, and the operational profile. Nevertheless, it is worth mentioning that further investigation is needed at the detailed design stages to fully consider ship-specific restrictions such as space restrictions for fuel tanks, refuelling periods, and limitations of battery usage such as volume, weight, charging time, etc.
## 3 Materials and Methods
### Implementation of Key Technologies
#### 3.2.3 MoE Routing Gate Networks Optimization
\[L_{DPO}\Big{(}\pi_{\theta};\pi_{ref}\Big{)}=-E_{(x,y_{w},y_{l_{v}})}\sim D\left[ log\sigma\Bigg{(}\beta log\frac{\pi_{\theta}(y_{w}|x)}{\pi_{ref}(y_{w}|x)}-\beta log \frac{\pi_{\theta}(y_{l}|x)}{\pi_{ref}(y_{l}|x)}\Bigg{)}\right] \tag{5}\]  
where \(\sigma\) is the sigmoid function; \(x\) is the _Query_; \(y_{w}\) is user-selected response preference data; \(y_{l}\) is the user's choice of poor response data; \(\pi_{\theta}\) is the policy model; \(\pi_{ref}\) is the reference model; \(\pi_{\theta}(y_{w}|x)\) represents the cumulative probability of the current policy model generating a good response given the input \(x\); \(\pi_{ref}(y_{w}|x)\) represents the cumulative probability of the current reference model generating a good response given the input \(x\). \[r_{\theta}(x,y)=\beta log\frac{\pi_{\theta}(y|x)}{\pi_{ref}(y|x)} \tag{6}\]  
For the update strategy of model parameters, \(r_{\theta}(x,y)\) in Equation (6) is the implicit reward function of the DPO method. Intuitively, the gradient optimization of the loss function increases the possibility of the pre-trained model responding to preference \(y_{w}\), and reduces the possibility of non-preference \(y_{w}\). Further, by combining QLoRa and DPO, the performance of the routing gate network model is optimized through parametric efficient fine-tuning. QLoRa algorithm is used to compress the structured parameters of the routing gate network model, and the original high-dimensional parameter space is projected to the low-rank subspace through low-rank matrix decomposition. This algorithm converts the full-parameter fine-tuning task into learnable low-rank adapter optimization, significantly reducing the scale of tunable parameters while maintaining the model's representational power. The directional fine-tuning of routing a gated network is implemented by combining the DPO algorithm. Compared with the traditional RLHF paradigm, DPO restructures the preference learning problem into a classification task and directly optimizes the policy network parameters based on paired preference data, effectively avoiding the computational bottleneck in the training stage of the reward model. This end-to-end optimization  
Figure 3: DPO training process. approach not only reduces the video memory footprint, but also improves the stability of policy updates through the implicit reward modeling mechanism.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 5 Research Gaps and Future Direction  
The research conducted showed that the ADR algorithms that have been analysed prioritize different performance metrics and hence provide various solutions. Data packet collision and transmission duty cycle are issues that are common in many of the ADR schemes. Most approaches implement their ADR schemes using a single gateway. Research gaps and future work that were identified in the literature reviewed are as follows:
# Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users'
## 6 Conclusions and Plans for Future Research  
This study examined users' willingness to self-disclose different types of personal information to an IoT-CA in different smart spaces (a living space and workspace) and user contexts (one user or two users). The results found that users were most willing to disclose information related to tastes and interests to help an IoT-CA learn their preferences and then provide personalized services. Users were least willing to disclose money information to an IoT-CA owing to their concerns related to information security. Moreover, users were willing to disclose more information in the living space than in the workspace (one-user context: 44% more information; two-user context: 28% more information). However, high self-disclosure in the living space was mediated by users' expectations for reciprocal services; users had more expectations of services provided by the IoT-CA in the living space than in the office. When interacting with an IoT-CA, users had high private self-awareness and low public self-awareness. Thus, IoT-CAs can help direct users' attention to themselves and their life rather than external factors. In future research, users' actual use behaviors in a real smart space could be collected, and users' self-disclosure behaviors could be analyzed (e.g., amount and intimacy of disclosed information) during a period such as a week or a month. Further, the effects of users' self-disclosure to an IoT-CA on further human-IoT-CA interaction, such as users' trust of IoT-CAs and collaborative task completion, could be investigated.  
Conceptualization, Z.L., P.-L.P.R. and D.H.; data curation, Z.L.; formal analysis, Z.L.; funding acquisition, P.-L.P.R.; investigation, Z.L.; methodology, Z.L. and P.-L.P.R.; project administration, Z.L. and P.-L.P.R.; resources, Z.L., P.-L.P.R. and D.H.; software, Z.L. and D.H.; supervision, P.-L.P.R.; validation, Z.L. and D.H.; visualization, D.H.; writing--original draft, Z.L.; writing--review and editing, Z.L. and P.-L.P.R.  
This research was funded by the National Natural Science Foundation of China, grant number 71661167006.  
Thanks to all the participants in this study. We would like to thank Editage (www.editage.com) for English language editing.  
The authors declare no conflict of interest.
## 5 Methodology for Reliability and Availability Evaluation
### Device Failure
During the recursion, a device only joins to the current path if it is not already part of that path (line 8). For the sake of understanding Algorithm 1, considers the example of the output flow produced when the paths from device \(Fd_{0}\) to the gateway are being searched for the scenario presented in Figure 5. The output flow for this example is shown in Figure 9 (note: only two paths are described (dark gray circles) due to lack of space, however the others paths can be easily deduced.). The output flow starts at device \(Fd_{0}\) and explores as far as possible each branch (neighbor) until reaching the gateway. After that, a backtracking procedure is conducted and another branch will be evaluated. Figure 9 is composed by 5 levels. The first level is composed only by the target device (\(Fd_{0}\)). The second level is composed by the neighbors of device \(Fd_{0}\) (\(R_{0}\) and \(Fd_{1}\)). The other levels follow the same procedure taking into account the restrictions imposed by Algorithm 1 (lines 5 and 8). All paths generated by Algorithm 1 are stored in a data structure based on a fault tree. This data structure will be used to generate the minimum cut sets in the next section. Each path \(i\) belonging to device \(j\) is mapped for an \(and\) gate. The inputs of each \(and\) gate are composed by the devices of the respective path. An \(or\) gate is used to connect all the paths (\(and\) gates) from a device to the gateway.