esa-sceva/satcom-chunk-collection huggingface dataset:
# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 3 Experiments and Results
Figure 8a shows the IMP performance of transfer experiment which transfers to FD001 target dataset. The E2 has effective performance. The IMP performance in the E2 is greater than 20% when the size of trajectories is less than or equal than 50. The E3 has a detrimental effect performance. Figure 8b shows the IMP performance of a transfer experiment which transfers to the FD002 target dataset. Both the E5 and E6 have effective performance. The IMP performance in the E5 is greater than 15% when the size of trajectories is less than or equal than 50. The IMP performance in the E6 is greater than 45% when the size of trajectories less than or equal than 50. Figure 8c shows the IMP performance of a transfer experiment which transfers to the FD0013 target dataset. The E8 has effective performance. The IMP performance in the E8 is greater than 17% when the size of trajectories is less than or equal than 70. The E9 has a detrimental effect on performance. Figure 8d shows the IMP performance of a transfer experiment which transfers to the FD004 target dataset. Both the E5 and E6 have effective performance. The IMP performance in the E5 is greater than 15% when the size of trajectories is less than or equal than 50. The IMP performance in the E6 is greater than 30% when the size of the trajectories is less than or equal than 50. From all the above results, we also found that working conditions affect transfer learning performance a lot. How do working conditions affect transfer learning performance? We will discuss this in Section 4. Figure 7: Comparison of the RUL estimation results in test data for experiments of which the training data set is generated by randomly selecting 50 trajectories. In the subfigure, the X-axis is the test unit with increasing RUL which is sorted by actual RUL values. The Y-axis is the actual and prediction RUL values. The blue points are the actual RUL values of the test unit and the red points are the prediction RUL values of the test unit. In the subfigure caption, A\(\rightarrow\)B means from the source data A transfer to the target data B.
## 3 Result and Discussion
### Assessment of Data Augmentation  
This section aims to validate the effects of data augmentation methods on model performance. It systematically analyzes how these methods influence model development and assesses their impact on improving the models' generalization and robustness. The proposed model was retrained using the non-augmented dataset with the same hyperparameter configuration as in the augmented training. The performance of the trained model was then evaluated using key metrics to ensure that data augmentation effectively enhances the learning process and contributes to better overall model accuracy and efficiency.  
Figure 11 shows the comparison between the non-augmented and augmented models, which reveals several key insights. The augmented model starts with a higher initial accuracy and lower initial loss, indicating that data augmentation provides an early advantage in the learning process. Specifically, the augmented model begins at an accuracy of 0.59 and a loss of 1.20, compared to the non-augmented model's starting accuracy of 0.53 and loss of 1.35. This initial boost helps the augmented model learn more effectively from the outset.  
As training progresses, both models exhibit similar trends of increasing accuracy and decreasing loss. Initially, data augmentation helps the model to generalize better by providing varied examples, which is evident in the early epochs where the augmented model might have a slight advantage. However, as training continues and the epoch count increases, the non-augmented model begins to catch up in performance.  
By the 50 th epoch, both models achieve comparable high levels of accuracy, just above 0.99, and low levels of loss, around 0.03. This convergence suggests that the designed model effectively learns to fit the training data, whether augmented or non-augmented. However, this similar level of performance in training does not necessarily indicate the similar effectiveness of the models in real-world applications.  
The real effectiveness between the models must be validated using a separate test dataset that the models did not encounter during training. This test dataset should reflect real-world conditions, including variations in lighting, angles, and image quality.  
Figure 11: Comparison of training accuracy and loss between augmented and non-augmented datasets.  
Only by evaluating both models on such a separate test dataset can we truly assess their generalization capabilities and effectiveness in practical scenarios.  
The results in Figure 12 consistently shows that models trained with augmented data outperformed their non-augmented counterparts in terms of validation accuracy, precision, recall, and F1-score. This reinforces the conclusion that data augmentation significantly enhances the generalization ability of machine learning models, making them more effective in handling new, unseen data.
# Automated Exploratory Clustering to Democratize Clustering Analysis
## 3 Materials and Methods
### Skyline Operator  
If we want to optimize the coverage, we need to include all clusterings that are somewhat interesting. A common approach for this is to determine the _skyline_ or _Pareto set_ of all clusterings, which comprises every clustering that is not dominated in every CVI by another clustering [56]. The resulting clusterings are considered to be interesting, as also visualized by means of an example of a skyline, indicated by red objects, in Figure 5. The underlying concept of domination is formalized in the following definition.  
**Definition 6** (Domination).: _Let \(c,d\in\mathbb{U}\) be two clusterings. The clustering \(c\) dominates \(d\) according to a Quality function \(Q\), if it is worse in no single dimension and superior in at least one as follows [57]:_  
\[c\succ_{Q}d\Leftrightarrow\forall i:Q(c)_{i}\geq Q(d)_{i}\wedge\exists j:Q(c)_{ j}>Q(d)_{j} \tag{7}\]  
Based on the definition of domination among clusterings, we can now define a skyline or Pareto-optimal subset as follows:  
**Definition 7** (Skyline).: _Let \(C\) be a set of objects and \(Q\) a Quality function. The Skyline \(S(C)\in\mathcal{P}(C)\) is defined as follows:_  
\[S(C)=\{c\in C|\
ot\exists d\
eq c,\;d\in C:d\succ_{Q}c\} \tag{8}\]  
It is evident that the size of the skyline is only limited by the size of the input dataset and can be independent of the number of CVIs. However, every clustering which seems to be the \"best\" according to one component of the Quality function \(Q\) or a combination of components will be part of the skyline. It is easy to construct an Interestingness function that will directly lead to the skyline set being returned.  
**Definition 8** (Skyline interestingness).: _Using the skyline defined in Definition 7, we define the skyline Interestingness function as follows:_  
\[I(C)=\begin{cases}|C|&,C=S(C)\\ -1&,else\end{cases} \tag{9}\]  
While this method gives a maximal coverage (according to the Quality function inherently utilized in the skyline \(S(C)\)), the resulting skylines can be extensive and exhibit significant redundancy wrt. to similarity of the contained clusterings. This issue is addressed via the subsequent Interestingness function.
# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 4 Exploring Future Directions
### Sign Language to Enhanced Text (Using LLM) to Speech  
Another promising application refers to the conversion of the result of the sign language recognition model into enhanced text using large generative language models, followed by TTS. This development could have a transformative impact on people with speech and hearing impairments who need to deliver speeches/presentations or participate in public speaking. By translating sign language into clear and structured spoken language, TTS systems can help users effectively communicate their messages to a wider audience without relying on intermediaries such as translators.  
In addition, integrating tools such as ChatGPT to rephrase the result can ensure that the generated speech is not only accurate but also designed to sound natural and engaging. This would allow users to retain full control over their communication and support greater independence in professional, educational, and social settings.  
Figure 8 presents a follow-up scenario, in which sign language is transposed to plain text and, using a large language model, the result is rephrased to sound better, and a text-to-speech model is then used to help the presenter to communicate effectively.
## 3 Navigation Framework  
\(M\) users move with uniform velocity in the tracking scenario. To describe the tracking system, we choose EKF among the Bayesian estimators. The state-space model comprises a _transition model_, which details how the state evolves over successive time intervals, and an _observation model_, which defines the relationship between the measurements taken and the current state of the users.  
The state vector is denoted as  
\[\mathbf{\mathrm{s}}(t)\triangleq\big{[}\mathbf{\mathrm{s}}_{1}(t),\ldots,\mathbf{\mathrm{ s}}_{m}(t),\ldots,\mathbf{\mathrm{s}}_{M}(t)\big{]}^{\mathsf{T}}, \tag{7}\]  
where  
\[\mathbf{\mathrm{s}}_{m}(t)\triangleq\big{[}\mathbf{\mathrm{p}}_{m}(t)^{\mathsf{T}}, \dot{\mathbf{\mathrm{p}}}_{m}(t)^{\mathsf{T}}\big{]}^{\mathsf{T}}. \tag{8}\]  
\(\mathbf{\mathrm{p}}_{m}(t)=\big{[}x_{m}(t),y_{m}(t),z_{m}(t)\big{]}^{\mathsf{T}}\) and \(\dot{\mathbf{\mathrm{p}}}_{m}(t)=\big{[}v_{x_{m}}(t),v_{y_{m}}(t),v_{z_{m}}(t)\big{]} ^{\mathsf{T}}\) are 3-D position and velocity parameters of the \(m\)-th user, respectively.
## 3 Results and Discussion
### Model Performance
However, improved performance in the predictability of the model was obtained using MLPNN-3, MLPNN-4, and MLPNN-5, as indicated by the R\({}^{2}\) values of 0.969, 0.954, and 0.939, respectively, and low RMSE values. This implied that the nature of the activation function used in the hidden and outer layers significantly influenced the model's performance [32]. Using the scaled conjugate gradient optimization algorithm, the MLPNN-3 with hyperbolic tangent as the hidden layer activation function and sigmoid as the outer layer activation function had the best predictive performance, as indicated by an R\({}^{2}\) of 0.969. It is obvious that using hyperbolic tangent as an activation function at the hidden and outer layers did not result in good predictability by the model, as indicated by an R\({}^{2}\) of 0.433. The performance of the MLPNN models with various configurations optimized using gradient descent algorithms is depicted in Figure 6. It  
produced per liter of waste glycerol were in close proximity with the predicted values, as shown in Figure 6a-e. The training and testing of the experimental data in the models resulted in low errors, as indicated by the low SSE values. Figure 5: Comparison between the actual and predicted biohydrogen using (**a**) MLPNN-1 (**b**) MLPNN-2 (**c**) MLPNN-3 (**d**) MLPNN-4 (**e**) MLPNN-5 (**f**) MLPNN-6 with a scaled conjugate gradient optimization algorithm. The level of importance of the analysis of how the input variables influence the predicted values is depicted in Figure 7. It can be seen that all the input variables, namely waste glycerol, Urea, Endo-nutrient, and Na\({}_{2}\)PO\({}_{4}\), had varying levels of influence on the predicted biohydrogen production. However, waste glycerol with the highest level of importance value of 0.542 had the most significant influence on the predicted biohydrogen production. Figure 6: Comparison between actual and predicted biohydrogen using (**a**) MLPNN-7 (**b**) MLPNN-8 (**c**) MLPNN-9 (**d**) MLPNN-10 (**e**) MLPNN-11 (**f**) MLPNN-12 with a gradient descent optimization algorithm.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
###### Abstract  
The present study aims to describe the characteristics of the hydraulic transport of manganese nodules in a vertical pipe. The solid-liquid two-phase flows were simulated using a numerical technique that combines the computational fluid dynamics (CFD) method and the discrete element method (DEM). Manganese nodules with diameters of 5.0 mm, 15.0 mm, and 30.0 mm were selected. The effects of the initial solid volume fraction and the initial mixture velocity were investigated. The results show that with increasing initial solid volume fraction, the liquid and solid velocities decrease but the total pressure drop over the pipe increases. Small particles are responsible for high particle collision frequency, which causes decreases in both the liquid velocity and the total pressure drop. Energy loss is aggravated by increasing the initial mixture velocity, manifesting in the increase of the total pressure drop. The retention ratio of manganese nodules varies inversely with the initial mixture velocity. A formula is proposed to describe the pressure drop due to the presence of solid particles and collisions.  
manganese nodules; hydraulic lift; vertical pipe; CFD-DEM coupling; pressure drop; slip velocity +
Footnote †: journal: Applied Sciences
## 3 Method
### Adaptive Map and Compass Factor Strategy  
The selection of parameters significantly has an impact on the result. Conventional methods of choosing appropriate parameters can be roughly divided into two categories [39]: parameter adjustment and parameter control. Parameter adjustment is used for parameters preset before the beginning, and the parameters that are kept constant throughout the execution of the algorithm. Parameter control means that parameters run with the initial value at the beginning of the algorithm. Then, they can be adaptively updated according to the operation of the algorithm.  
QPIO is simple to be implemented as its parameter number is limited. However, it still poses a great challenge to adjusting the parameters in QPIO. As mentioned in a previous section, the turning of map and compass factor \(R\) can adjust the algorithm on better global or local searching capability. If \(R\) is set to some constant values, the algorithm is not capable of coordinating the global and local search synchronously during the optimization procedure. Thus, we aim to find a function with the following ideal characteristics. The initial value of \(R\) is small with gradual changes; as the independent variable increases, it rapidly reaches the preset values [40].  
\[R(\tau)=\frac{1}{a+b*e^{-\tau}} \tag{18}\]  
\[\tau=-10+\frac{N_{c}*20}{N_{c1\mathrm{max}}} \tag{19}\]  
Apparently, \(R(\tau)\) belongs to \([0,1/a]\); \(\tau\in[-10,10]\) is determined by the number of iteration and its maximum; \(N_{c}\) is the number of iteration; parameter \(b\) can adjust the rising speed. Specifically, the value of \(R\) is coincident with conventional QPIO algorithm when the value of \(a\) is set to 1.  
By using the dynamic parameters tuning strategy (18) and (19), \(R\) is small at the beginning and it gradually increases after fully global search. The local search is better completed accordingly. With the consideration on the value range of \(R\) and the balance of global-local search, we set \(a=1\) and \(b=100\) in this paper.
## 4 Mixed-Flow Load-Balanced Scheduling (MFLBS)
### Static Routing Network
#### 4.2.1 Bandwidth Allocation  
For different flows on the same link, we allocate bandwidth according to the max-min fairness algorithm. In order to avoid starvation caused by the delay in obtaining the corresponding bandwidth for the flow with low priority, some literature provides a method to adjust the priority to adapt to the new network conditions.  
Here, we give the right to fair competition for bandwidth to all flows entering the static routing network.  
\[p^{f_{k^{\prime}}}=p^{f_{k}}\,k^{\prime}=k\in(1,2,\ldots,K)\ \forall<i,j>\in f_{k}\{ \ldots\}\cap<i,j>\in f_{k^{\prime}}\{\ldots\} \tag{6}\]  
where, if the flow \(f_{k}\) and flow \(f_{k^{\prime}}\) have the same link segment in their respective routes, their priorities will be the same.  
Therefore, the two newly arrived flows \(f_{k}\) and \(f_{k^{\prime}}\) on the same link \(l_{i,j}\) in Figure 3 will equally divide the link bandwidth \(C_{i,j}\) with the flow \(f_{1}\) that originally exists on the link \(l_{i,j}\). The three flows each obtain \(10\) Gb/s at this time, but the required bandwidth of \(f_{k}\) 8 Gb/s is less than the obtained bandwidth \(10\) Gb/s, and the bandwidth obtained by \(f_{k}\) and \(f_{1}\) has not reached the corresponding required bandwidth \(20\) Gb/s, so the links \(l_{i,j}\) will take back the excess \(2\) Gb/s of \(f_{k}\) and distribute it equally to \(f_{k^{\prime}}\) and \(f_{1}\) again. Finally, \(f_{1}\), \(f_{k}\), and \(f_{k^{\prime}}\) on \(l_{i,j}\) obtain actual bandwidths of \(11\) Gb/s, \(8\) Gb/s, and \(11\) Gb/s, respectively.
# Effect of Variable Conditions of Exposure on the Physical and Mechanical Properties of Blockboards
## 3 Results and Discussion
### Surface Soundness  
Surface soundness (SS) testing results are presented in Figure 11. EN standards do not present any requirements for blockboards, but, generally, the industry requirement for MDF flooring is 1.2 N/mm\({}^{2}\), and, for particleboard (PB) designed for interior use, this requested limit is 0.80 N/mm\({}^{2}\) (the green line from Figure 11) [36].  
Initial (before exposure) SS for all samples was around 1.7 N/mm\({}^{2}\) for V blockboards and below 0.8 N/mm\({}^{2}\) for HDF blockboards. The higher the density of material, the higher SS expected. In the present research, the blockboards with HDF face sheets (with high density) had the lowest SS values compared to blockboards with lower density of the faces (V blockboards). This unusual behaviour was found also by other researchers for HDF [37]. As can be seen in Figure 11, the SS decreased as the MC increased. At the highest uptake of humidity, which was obtained in the climatic chamber, the tested boards had the lowest SS after three months of exposure: 1.29 N/mm\({}^{2}\) for V blockboards and 0.38 N/mm\({}^{2}\) for HDF blockboards. The climate conditions in the kitchen had a slight influence on SS, which decreased by approximately 6% for V blockboards and 12% for HDF blockboards. The effect of high values of RH occurred in bathroom space and in climatic chamber, and it was more pronounced after three months of exposure, determining the increase of SS by 25% for Ba-V board, 28% for Ba-HDF board, 25% for CC-V board and 51% for CC-HDF board. All surfaces of the face sheets were visibly affected by the SS test after three months of exposure to RH variations. The surface layer was affected more by the SS test when the moisture absorption increased, without affecting the entire structure.  
Figure 11: Surface soundness results.
# A High-Sensitivity Magnetic Field Sensor Based on PDMS Flexible Resonator
## 2 Materials and Methods  
### PDMS Flexible Resonator Magnetic Sensing Structure  
Resonant wavelength can be derived from the resonance condition of WGM:\[\lambda=\frac{2\pi rn_{r}}{m} \tag{1}\]  
where \(\lambda\) is the resonant wavelength in vacuum, \(r\) is the radius of the resonator, \(m\) is the mode number and \(n_{r}\) is the effective refractive index. Since \(m\) is a constant for a specific mode, the magnetic field applied to the sandwich structure will cause changes in the radius and refractive index of the PDMS flexible resonator, resulting in a shift in the optical resonant wavelength:  
\[\frac{\Delta\lambda}{\lambda}=\frac{\Delta R}{R}+\frac{\Delta n}{n} \tag{2}\]  
The principle of the magnetic field sensor based on the PDMS flexible resonator is shown in Figure 1. The PDMS flexible resonator can be optically injected and output through a tapered optical fiber. The electromagnetic coil generates a magnetic field that acts on the sandwich structure, which in turn causes the magnetic iron layer to act on the PDMS flexible resonator, resulting in the drift in the resonant wavelength of the resonance spectrum. The change in resonant wavelength under different magnetic fields is obtained, which is the sensitivity of the magnetic field sensor.  
In order to verify the response of the designed magnetic sensing structure, a numerical simulation based on COMSOL 6.0 simulation software is used. The simulation calculation of the sensitivity of this magnetic sensor device is achieved by using a two-dimensional axisymmetric model with a magnetic field and a solid module.
## 4 Discussion
Direct UVC radiation exposure to the skin and eyes from some UVC lamps can result in painful eye injury and burn-like skin reactions [22]. Since some UVC lamps contain mercury, which is toxic even in small amounts, considerable caution is required when cleaning and discarding a broken lamp. It is important to mention that if a virus or bacterium is not directly exposed to UVC, it will not be inactivated. For example, if the virus or bacteria is covered in dust or dirt, lodged in a porous surface, or on the underside of a surface, it will not be inactivated. It is evident that SARS-CoV-2 is mostly transmitted by contacting infected people at close range through inhalation of a \"droplet\" or \"aerosol\" [23]. Hence, it is necessary to have improved indoor air quality through proper ventilation and safer indoor environments to protect people from the infection. UVC can be used in disinfection surfaces and indoor spaces as ultraviolet light has been increasingly used in applications outside of medical purposes. For years, hospitals have used UVC light-emitting robots to disinfect patient rooms and operating rooms, as well as medical equipment or instruments [24]. UVC LEDs can also be used to disinfect vehicles such as cars, trains, and aircrafts in a way that robots or human-controlled machines with UVC emission can move though the vehicles to disinfect the surfaces where light can reach. It is also possible to use UVC LEDs to disinfect air in indoor spaces like schools, restaurants, shops, and even housing spaces where UVC LEDs can be installed in air flow systems overhead and aimed at the ceiling to disinfect air as it circulates [25; 26]. Similarly, UVC LED light sources can also be installed in heating, ventilation, and air conditioning (HVAC) systems to disinfect air as it flows via ductwork. Researchers and industries are developing novel UVC light disinfection technologies such as the automation of the disinfection process using robots [27]. Novel UVC air and surface treatment technologies will provide new tools to control the current and future pandemics.