esa-sceva/satcom-chunk-collection huggingface dataset:
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 5 Results and Discussion
### Inpainting Performance on Multi-Speaker Datasets  
Similar analytic procedures were applied to the multi-speaker datasets, where we exclusively trained a five-layer LSTM model to examine the inpainting performance.  
Table 9 shows the average MOSs of the five-layer LSTM models across the four multi-speaker datasets for different gap lengths, comparing the original and inpainted speech signals in both NB and WB scenarios. Similar results can be observed across the four multi-speaker datasets, where the average MOS decreased as the gap length increased in both NB and WB scenarios. For the LibriM1 and LibriM2 datasets, when the gap length was less than or equal to 200 ms, the fluctuation in the average MOS caused by different bandwidths for the same dataset was relatively minor. However, the fluctuation began to increase when the gap length was greater than 200 ms. For the HIFI and LibriM3 datasets, the fluctuation in the average MOS caused by different bandwidths across all gap lengths in the same dataset was subtle.  
The same frequency analysis procedure was also employed for the multi-speaker datasets. In this case, we selected a test speech signal from the HIFI dataset. The duration of the original signal was 6.38 s, and the gaps started from 2.88 s and lasted for 20 ms, 40 ms, 50 ms, 100 ms, 200 ms, 500 ms, and 1000 ms, respectively. A five-layer LSTM model was used to restore the missing speech samples. In Figures 6 and 7, the layout of each figure is the same as that used previously, with the only difference being that the corresponding zoomed-in range of the x-axis is now 2.0-4.6 s.  
In Figures 6 and 7, it can be observed that all the inpainted waveforms in the time domain follow the trend of the original signal throughout the entire gap length with acceptable accuracy. Compared with the original signal, the envelope of the inpainted speech signal follows the shape of the original signal, but with significant losses in amplitude.  
Figure 6: Frequency analysis for gap lengths less than 100 ms. The gaps start at 2.88 s and last for 20 ms, 40 ms, 50 ms, and 100 ms, respectively, corresponding to the first to fourth columns on the right side of the figure. The zoomed–in and gap areas are marked with red dashed lines.  
Figure 7: Frequency analysis for gap lengths greater than 100 ms. The gaps start at 2.88 s and last for 200 ms, 500 ms, and 1000 ms, respectively, corresponding to the first to third columns on the right side of the figure. The zoomed–in and gap areas are marked with red dashed lines.
## 6 Results and Discussion
### Evaluation of No. of Functioning/Alive Nodes  
Figure 4 reveals the total no. of active nodes during the entire simulation. Findings indicate that there were 225 nodes at the beginning of the simulation. At the completion of the simulation in AMCTD, the results are as follows:  
* 85 alive/functioning nodes without attack,
* 72 alive nodes with 4 attacker nodes,
* 58 alive nodes with 8 active nodes,
* 54 alive nodes with 12 active nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline No. of Nodes & 225 \\ No. of Sinks & 10 \\ Routing Protocol & AMCTD \\ Attack type & Active Attack \\ No. of Attacker nodes & 4, 8, 12 \\ No. of rounds & 9000 \\ Simulation volume & 500 m \(\times\) 500 m \(\times\) 500 m \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
Figure 3: Active Attack in AMCTD.  
These results demonstrate that active attack substantially influences energy usage, resulting in significantly fewer alive nodes in attack scenarios. It has been observed that by increasing the attacker nodes, significant increase has been observed in the number of dead nodes, and the number of alive/functioning nodes decreased substantially. When the number of alive/functioning nodes decreases, the overall network performance degrades.  
The number of functioning/alive nodes in various scenarios is listed in Figure 5. The data represented in Figure 5 make it abundantly evident that as the no. of attacker nodes increases in the scenario, so does the no. of dead nodes. It indicates that in the scenarios, the no. of attacker nodes is inversely proportional to the no. of alive nodes and directly proportional to the number of dead nodes. Furthermore, the scenario's increasing attacker nodes cause a 10% increase in dead nodes, which significantly reduces performance.  
Figure 4: AMCTD: No. of Functioning/Alive Nodes Without attack and with 4, 8 and 12 Attacker Nodes.  
Figure 5: No. of Alive/Functional Nodes of AMCTD without Attack, AMCTD with 4, 8 and 12 Attacker nodes.
# Prediction of Walnut Deterioration Using Kernel Oxidative Stability
###### Abstract  
Monitoring walnut oxidation is essential to control walnut quality during storage. An accelerated oxidation method for differentiating the oxidative stability index (OSI) of walnut kernels was examined and the effects of instrument operational parameters such as temperature and airflow were evaluated. Four cultivars, Chandler, Solano, Durham, and Howard were analyzed at 110, 120, and 130 \({}^{\circ}\)C with 15, 20, and 25 L h\({}^{-1}\) airflow. Analysis using 110 \({}^{\circ}\)C with 25 L h\({}^{-1}\) yielded the lowest coefficients of variance (4.4) than other operational parameters; analysis using the same temperature at lower airflow, 15 L h\({}^{-1}\), yield the highest coefficient of variance (10.5). Kernel OSI values were independent of airflow, however, dependence of temperature coefficient and Q\({}_{10}\) were demonstrated. The results from selected parameters were correlated with fat and moisture content, peroxide value, UV absorbances, oil oxidative stability, hexanal, and rancidity to establish the relationships between OSI values and quality changes during storage. Using 0.5 g of ground kernels, at 110 \({}^{\circ}\)C with 25 L h\({}^{-1}\) airflow gave a lower coefficient of variance and higher correlation with kernel quality and oxidative markers comparing to other combinations of operating parameters.  
**Keywords:** peroxide value; storage; Rancimat; hexanal; rancidity; sensory evaluation +
Footnote †: journal: _foods_
## 3 Model-Free Control
### Ultra Local Model  
The fundamental concept of MFC comes from the assumption that mathematical model of a dynamic single-input-single-output (SISO) system with input \(u\) and output \(y\) can be approximated on a short time interval \([t-T,t]\) by an ultra-local model equation [36].  
\[y^{(n)}=F+\alpha u \tag{10}\]  
where \(y\) represents the system output, \(n\) is the order of time derivation selected by the operator, \(u\) is the control input, \(F\) refers to a time-varying quantity that includes all exogenous disturbances and un-modeled dynamics such as nonlinearities and uncertainties, and \(\alpha\) is a normalization factor chosen such as the quantities \(y^{(n)},F\) and \(\alpha u\) have a similar order of magnitude.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 2 Foundations and Evolution  
### Overview of Collaborative Robotics (Cobots)  
Collaborative robots (cobots) are robotic systems explicitly designed for direct physical interaction with humans within a shared workspace, without the need for conventional safety barriers [16; 17]. Unlike traditional industrial robots, which operate in isolated environments, cobots integrate intrinsic safety mechanisms--such as compliant actuators, force-torque sensing, and intelligent motion planning--to enable close and safe collaboration [18]. Their flexibility, rapid deployment, and capacity for programming by demonstration have driven their adoption across manufacturing, logistics, healthcare, and education sectors [19]. Cobots represent a paradigmatic shift towards hybrid workspaces where human creativity is synergistically augmented by robotic precision, fostering higher productivity, enhanced ergonomics, and agile production systems.
## 4 Performance Evaluation
### Outage Probability
#### 4.1.4 Outage Analysis of Dual-Distributed-Select Relaying  
In case 4, the signal transmitted through the relay \(i=I_{a}\) is referred to as path 1, and its SNR is \(\gamma_{SR_{I_{a}D}}\).  
\[\gamma_{SR_{I_{a}}D}=\min\bigl{\{}\gamma_{SR_{I_{a}}},\gamma_{R_{I_{a}}D}\bigr{\}}, \tag{44}\]  
where \(\gamma_{SR_{I_{a}}}=\max\limits_{i=1,2,\cdots N_{a}}\Bigl{\{}\gamma_{SR_{I_{a} }}^{FSO},\gamma_{SR_{I_{a}}}^{RF}\Bigr{\}}\) is the instantaneous SNR of the first hop, and \(\gamma_{R_{I_{a}}D}=\max\Bigl{\{}\gamma_{R_{I_{a}}D}^{FSO},\gamma_{R_{I_{a}}D}^{ RF}\Bigr{\}}\) is the instantaneous SNR of the second hop.  
Similarly, the signal transmitted through the relay \(i=I_{b}\) is referred to as path 2, and its SNR is \(\gamma_{SR_{I_{a}}D}\).  
\[\gamma_{SR_{I_{a}}D}=\min\bigl{\{}\gamma_{SR_{I_{a}}},\gamma_{R_{I_{a}}D}\bigr{\}}, \tag{45}\]  
where \(\gamma_{SR_{I_{a}}}=\max\Bigl{\{}\gamma_{SR_{I_{a}}}^{FSO},\gamma_{SR_{I_{a}}} ^{RF}\Bigr{\}}\) and \(\gamma_{R_{I}D}=\max\limits_{i=1,2,\cdots N_{a}}\Bigl{\{}\gamma_{RD}^{FSO}, \gamma_{R_{I}D}^{RF}\Bigr{\}}\), respectively.  
The two path signals arrive at the GS, and the SC technique is adopted. The end-to-end instantaneous SNR of case 4 is  
\[\gamma_{SD4}=\max\bigl{\{}\gamma_{SR_{I_{a}}D},\gamma_{SR_{I_{a}}D}\bigr{\}} \tag{46}\]  
The outage probability of case 4 can be given as (47):  
\[\begin{split}& P_{out4}=\text{Pr}\bigl{(}\gamma_{SR_{I_{a}}D}< \gamma_{th}\bigr{)}\text{Pr}\bigl{(}\gamma_{SR_{I_{a}}D}<\gamma_{th}\bigr{)}= F_{\gamma_{SR_{I_{a}}D}}(\gamma)F_{\gamma_{SR_{I_{a}}D}}(\gamma)\\ &=\Bigl{[}1-(1-F_{\gamma_{SR_{I_{a}}}}(\gamma))(1-F_{\gamma_{R_{ I_{a}}D}}(\gamma))\Bigr{]}\Bigl{[}1-(1-F_{\gamma_{SR_{I_{a}}}}(\gamma))(1-F_{\gamma_{R_{ I_{a}}D}}(\gamma))\Bigr{]}\\ &=\Bigl{[}\prod_{i=1}^{N_{a}}\Bigl{(}F_{\gamma_{SR_{i}}^{FSO}}( \gamma)F_{\gamma_{SR_{I_{a}}^{RF}}}(\gamma)\Bigr{)}+F_{\gamma_{SR_{I_{a}}D}}( \gamma)F_{\gamma_{R_{I_{a}}D}^{RF}}(\gamma)-\prod_{i=1}^{N_{a}}\Bigl{(}F_{ \gamma_{SR_{i}}^{FSO}}(\gamma)F_{\gamma_{SR_{i}}^{RF}}(\gamma)\Bigr{)}F_{\gamma _{R_{I_{a}}D}^{RF}}(\gamma)F_{\gamma_{R_{I_{a}}D}^{RF}}(\gamma)\Bigr{]}\\ &\times\Biggl{[}F_{\gamma_{SR_{I_{a}}^{FSO}}}(\gamma)F_{\gamma_{ SR_{I_{a}}^{RF}}}(\gamma)+\prod_{i=1}^{N_{b}}\Bigl{(}F_{\gamma_{SR_{i}}^{FSO}}( \gamma)F_{\gamma_{SR_{i}}^{RF}}(\gamma)\Bigr{)}-\prod_{i=1}^{N_{b}}\Bigl{(}F_{ \gamma_{R_{i}}^{FSO}}(\gamma)F_{\gamma_{SR_{i}}^{RF}}(\gamma)\Bigr{)}F_{\gamma _{SR_{I_{a}}^{RF}}}(\gamma)F_{\gamma_{SR_{I_{a}}^{RF}}}(\gamma)\Biggr{]}\\ \end{split} \tag{47}\]
## 4 Simulation Analysis
### Simulation Results
#### 4.2.3 Energy Evaluation  
Table 4 shows the total number of dead nodes, total energy consumptions and total residual energy of the protocols after 1,000 rounds of operation. This table clearly shows that R-EERP has a much more desirable energy expenditure performance than those of LEACH and S-EERP. On average, R-EERP exhibits a reduction in energy consumption of 40 and 45 percent over S-EERP and LEACH, respectively.
## 2 Materials and Methods
### Dominance-Based Rough Set Approach
#### 2.1.1 Lower and Upper Approximations of Class Unions  
The initial step involves data structuring that allows for identifying potential inconsistencies. For this purpose, the lower and upper approximations of class unions are established. In the case of binary classification with \(Cl_{0}\) (negative class) and \(Cl_{1}\) (positive class), we can directly consider decision classes instead of unions. The lower approximation \(\underline{P}\) includes all alternatives that definitively belong to a specific class. When all criteria are potentially non-monotonic, \(\underline{P}(Cl_{r})\) contains all alternatives in \(Cl_{r}\) that are not indifferent to any alternative from \(Cl_{1-r}\), i.e., \(\underline{P}(Cl_{r})=\{a_{i}\in A^{R}:\
exists a_{k}\in Cl_{1-r},\forall g_{j }\in G:g_{j}(a_{i})=g_{j}(a_{k})\}\). In turn, the upper approximation \(\overline{P}\) contains alternatives that possibly belong to the class. In our setting, \(\overline{P}(Cl_{r})\) contains all alternatives that are indifferent to any alternative from \(Cl_{r}\), i.e., \(\overline{P}(Cl_{r})=\{a_{i}\in A^{R}:\exists a_{k}\in Cl_{r},\forall g_{j} \in G:g_{j}(a_{i})=g_{j}(a_{k})\}\). Hence, if a pair of alternatives shared the same performances on all criteria while being assigned to different classes, they would belong only to an upper approximation while not being contained in the lower one.  
To explain the structuring step of DRSA, we will refer to the decision matrix from Table 1. Three of five alternatives (\(a_{1}\), \(a_{2}\), and \(a_{3}\)) are assigned to class \(Cl_{0}\), and the remaining two are assigned to class \(Cl_{1}\). Let us first focus on the lower and upper approximations of class \(Cl_{0}\). The performances of \(a_{1}\) on all criteria match those of \(a_{4}\), which is assigned to \(Cl_{1}\). Therefore, \(a_{1}\) cannot be included in \(\underline{P}(Cl_{0})\). However, both alternatives belong to \(\overline{P}(Cl_{0})\). Next, \(a_{1}\) has the same performances as \(a_{2}\), but since both of them are assigned to \(Cl_{0}\), there is no inconsistency, and the alternatives belong to \(\underline{P}(Cl_{0})\) and \(\overline{P}(Cl_{0})\). Overall, \(\underline{P}(Cl_{0})\) is equal to \(\{a_{2},a_{3}\}\), whereas \(\overline{P}(Cl_{0})\) is \(\{a_{1},a_{2},a_{3},a_{4}\}\). Analogously, the lower and upper approximations of \(Cl_{1}\) are \(\underline{P}(Cl_{1})=\{a_{5}\}\) and \(\overline{P}(Cl_{1})=\{a_{1},a_{4},a_{5}\}\).
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 2 Related Work
#### Analysis of Existing Remote IoT Sensors Systems  
According to one of our studies, the current state-of-the-art in the market shows a wide and diverse panorama of systems. At first glance, there is a clear market separation between self-contained devices which provide monitoring information on the screen of the device about the sensors attached to such devices and devices with wireless capabilities that are sending information to a central system. In the self-contained device category, there is a significant market clearly differentiated between those that support the measurement of concrete ions (e.g., \(NH_{4}^{+}\), \(NO_{2}^{-}\), \(NO_{3}^{-}\)) and those that provide more standardised values (\(pH\), \(ORP\), \(Temp\), \(EC\)). Table 1 is a comparison table of the existing sensors under this category and their market price. Note that the DO feature pushed the prices up significantly.  
In the wireless category, the state-of-the-art is significantly reduced, as shown in Table 2 below. From the state-of-the-art analysis, it can be concluded that there is no water monitoring solution from the analysed ones above specifically designed for aquaponics, and prices are completely prohibitive when wireless connectivity is required for centralised monitoring. In addition, the number of solutions for detecting selective ions are incredibly scarce (Libelium Smart Water Ions) if we consider those with wireless capabilities.  
The implementation of this research work using cost-off-the-shelf solutions to demonstrate the concept would require a price per sensor unit of GBP 3800 for the minimally viable solution, provided by Libelium Smart Water, and GBP 9918 for the optimal solution provided by the combination of Libelium Smart Water and Smart Water Ions. The prices available in Tables 1 and 2 have been collected in June 2022.  
This high cost has motivated the efforts of this work. The efforts in terms of research, development, and innovation have been focused on providing a new, cost-effective, competitive solution:  
1. Specifically designed for aquaponics;
2. Designed with flexibility to accommodate different market ranges and use cases (cheaper but less accurate versions and more expensive but more accurate versions);
3. More cost-effective than any of the existing competitors.  
\begin{table}
\begin{tabular}{l l  
Meanwhile, ready-to-buy sensors have been acquired to deliver the demonstration part of the project while efforts have been put into developing an alternative to those sensors. In terms of design and demonstration purposes for the health monitoring system, only one of the plant-growing beds and fish-growing areas has been instrumented in order to deal with complexity and costing. The final cost of our proposal for the complete node is GBP 1500 (considering all the sensor probes, the electronics, and the communications).
## 4 Test Results and Analysis  
### UCT and BST  
Figure 6 shows the complete stress-strain curves and the load-displacement curves of the mudstone and sandstone specimens in the natural state under the UCT and BST. The main mechanical properties of the samples are obtained, which are listed in Table 2. It can be seen that the stress-strain curves all have five stages: crack closure stage, elastic deformation stage, crack initiation and stable crack growth stage, unstable crack growth to failure stage and post-peak stage. Meanwhile, the ultimate deformation of sandstone is much greater than that of mudstone. Although the mudstone all drills from the project area, the strength of the samples varies greatly from the different boreholes. The uniaxial compressive strength (UCS) of mudstone ranges between 4.43-42.43 MPa, with an average UCS of 18.60 MPa, and the Brazilian tensile strength (BTS) ranges between 0.66-4.30 MPa, with an average value of 1.51 MPa. This indicates that the mechanical properties of the rock mass in the project area are highly scattered. After a statistical analysis of the results, as shown in Figure 7, it can be found that the distribution of the UCS and BTS of mudstone basically conforms to a lognormal distribution, and the ratio of the UCS to BTS is approximately 10. Meanwhile, the average UCS of sandstone is 65.31 MPa, and the BTS is 3.06 MPa. The former is approximately 21.34 times greater than the latter one.  
Figure 4: Instron 1346 servo controlling test system and rock samples of saturated and dried groups.  
Figure 5: AE test system and in situ stress measurement samples.  
Figure 6: Typical stress–strain curves (**a**) and the load–displacement curves (**b**) of the mudstone and sandstone specimens under UCT and the BST.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Borehole No.** & **Lithology** & **UCS (MPa)** & **BTS (MPa)** &
\begin{tabular}{c} **Young’s Modulus** \\ **(GPa)** \\ \end{tabular} \\ \hline  
andstone & 65.31 & 3.06 & 8.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of rock specimens tested in a natural state.  
Figure 7: Distribution of UCS (**a**) and BTS (**b**) of mudstone specimens from the project area.
## 4 Discussion
### Comparison of Skin Wrinkles Between Muscle Compression and Skin Compression  
Most existing models of skin wrinkle formation focus solely on the skin layer while neglecting the influence of underlying muscles [36]. In these models, uniaxial compression is directly applied to the skin to simulate muscle contraction, without the complex strain transfer mechanism of each layer. To address this limitation, this study compares two different loading methods: muscle compression (MC) and skin compression (SC), to accurately simulate the role of muscle contraction in skin wrinkling.  
Figure 8a presents the FE results of skin wrinkling due to SC, with hypodermis thicknesses of 0.2 mm and 1.0 mm. The differences in wrinkle formation between models with varying hypodermis thicknesses are minimal. This is because the SC method does not account for the strain transfer effects between skin layers or between the skin and muscle. Furthermore, at higher compression levels (20%), the wrinkle patterns produced by SC and MC diverge significantly. As shown in Figure 9a, SC is more likely to generate  
Figure 7: **(a)** The strain of skin surface under muscle contraction, and (b) distribution of hypodermis thickness in the face [35].  
wrinkles with pronounced tensile strain at the peak (Point A), whereas the MC method better replicates real-world observations of wrinkle formation.  
These findings underscore the importance of incorporating muscle compression into computational models of skin wrinkling. By considering the strain transfer between skin and muscle, this study provides a more realistic representation of wrinkle development, offering valuable insights into the biomechanical processes underlying facial aging.
## 1 Introduction
This analysis employs frequency-domain characteristics and utilizes finite-time technology. In reference [19], the parameters of the controller are optimized using an active disturbance rejection algorithm based on a backpropagation (BP) neural network. This optimization enhances the accuracy of the disturbance estimations from the extended state observer, leading to an improved dynamic performance and robustness of the control system. Reference [20] further incorporates an artificial intelligence algorithm into the parameter optimization of the active disturbance rejection controller, resulting in noticeable performance improvements. Currently, the optimization of the active disturbance rejection controller (ADRC) parameters primarily focuses on the bandwidth or the parameter combination. Among these methods, the latter presents distinct advantages and offers greater research opportunities [21]. In summary, various control strategies each have distinct characteristics, but they are not universally applicable. To enhance the control performance, the current trend is to develop composite control strategies that integrate other methods with active disturbance rejection control (ADRC) [22]. Table 1 compares the characteristics of ADRC, RBF-ADRC, and feedforward-RBFNN-ADRC. Feedforward-RBFNN-ADRC demonstrates the most effective performance. Consequently, this paper will focus on the RBF neural network ADRC strategy with a feedforward approach, aiming to further improve the positional control. This paper presents a compound control method that combines active disturbance rejection control (ADRC) with a radial basis function (RBF) neural network for position control of permanent magnet synchronous motors (PMSMs). This method harnesses the fast convergence and strong approximation capabilities of the RBF neural network. ADRC is integrated into the control strategy for the PMSM servo system. Specifically, a second-order nonlinear ADRC is employed to achieve combined control of both the position and speed, while the RBF neural network is utilized to compensate for and adjust the extended state observer within the controller. Additionally, to minimize dynamic tracking errors in terms of the system's position, a differential feedforward link is introduced between the reference signal and the speed output. This approach simplifies the control structure to some extent and enhances the dynamic response speed.