esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Deep Belief Network for Peak Wavelength Prediction
In order to predict the wavelength accurately in the final DBN model derived from the DBN model training, a total of 20 collections were made for each spectrum, and 780 spectral samples were finally obtained. From these samples, the dataset was split into training (80%) and test sets (20%). Once data were collected, the next phase was data preprocessing, which ensured the raw data were in a suitable form for model training. Thus, the training dataset (i.e., captured reflected spectrum) was preprocessed and normalized between 0 and 1. This involved normalizing the interference patterns to achieve consistency across the dataset. The most critical part of preprocessing is feature extraction, where relevant features indicative of peak wavelengths are extracted from the interference patterns. These features are essential, as they provide the DBN with the necessary information to learn and predict accurately. Figure 5: DBN for peak wavelength detection in FBGs and interferometric spectra. The proposed DBN model was implemented using the TensorFlow framework, along with the Keras and Sklearn libraries. The training was conducted on a PC equipped with an Intel Core i7-4790 3.60 GHz CPU and 20.0 GB of RAM. Figure 5 illustrates the architecture and training process of the proposed DBN. The preprocessed reflection spectra of the FBGs with interferences were used as inputs to train the DBN algorithm, with the corresponding peak wavelengths of the FBGs and interferences serving as target values. Each layer of the DBN was pre-trained individually using RBMs. This was done in an unsupervised manner, utilizing contrastive divergence to initialize the network weights. This step is essential in capturing the underlying structure of the data without requiring labeled information. After pre-training, the RBMs were stacked to form the complete DBN. The entire network was then fine-tuned using supervised learning with a labeled dataset of known peak wavelengths. This fine-tuning adjusts the weights and biases across all layers to optimize the network for peak wavelength detection. The mean squared error (MSE), mean absolute error (MAE), and root mean square errors (RMSE) loss function were used for measuring the difference between predicted and actual wavelengths, and the optimizer Adam was used to minimize this loss, The MSE, MAE, and RMSE are expressed in the following equations:  
\[MSE=\frac{1}{n}{\sum_{i=1}^{m}}(y_{i}-y)^{2} \tag{2}\]  
\[MAE=\frac{1}{n}{\sum_{i=1}^{m}}\left|y_{i}-y\right| \tag{3}\]  
\[RMSE=\sqrt{\frac{{\sum_{i=1}^{n}}{(y_{i}-y)}^{2}}{n}} \tag{4}\]  
where \(n\) is the number of predicted values, \(y_{i}\) is the actual value, and \(y\) is the predicted value, respectively. During the training of the DBN, various parameters such as the number of epochs, batch sizes, hidden layers, hidden units, and optimizer and activation functions were adjusted to achieve optimal values and to prevent overfitting. Tuning these parameters resulted in different training times, and MSE, MAE, and RMSE values.
# Parallel Analysis of Offshore Wind Turbine Structures under Ultimate Loads
## 5 Conclusions  
Over one thousand analyzed loading cases can be required to design OWT support structures using combinations of various loads, which uses a significant amount of computer time. In this paper, three schemes were proposed to overcome this difficulty. The first is the assumption of blade wind forces independent of the support structure, so forces can be calculated at the hub center as three forces and three moments, considering the blades without the support structure. This assumption can reduce computer simulation time significantly, since the six components are only dependent on wind profiles, which form only a limited number of different cases in the structural analyses. Moreover, the components are the same for each design cycle. The second scheme is using a few control loads to perform the design procedure between the second and second last design cycles. From the test cases, 50% of total computer time can be saved, compared to without this scheme, but the final design results are exact the same. The third scheme involves the use of parallel computational procedures, since all the loading cases can be independently executed in different CPU cores, as well as on different computers. We established an efficient procedure, so that manpower was minimal. The test cases, using 5044 loading cases, indicated that the proposed method can complete the design procedures using a number of personal computers within several days, and the design procedures are fully parallel. The DTU 10-MW OWT support structure was analyzed with 5044 loads, including IEC 61400-3, tropical cyclone, and seismic loads; however, the steel design was controlled by several loads. Without seismic and typhoon loads, DLC 2.2 may dominate the steel design for the tower and columns, and DLCs 6.1 and 7.1, the parked condition with large wind speed and wave, may dominate steel design for bracings and piles. For earthquakes which are not extreme, such as the PGA of 0.32 g with 20 m soil liquefaction, the required steel weight increases about 5%, compared to the IEC 61400-three loads. The major controlled parts are piles and bracings, while the tower and columns are still dominated by the IEC 61400-3 loads. For class-3 tropical cyclones, the required steel weight increases by about 19%, compared to the IEC 61400-3 loads, and almost all the steel design is controlled by IEC 61400-3 DLC 1.2, where yaw misalignment is important factor. If the class-3 tropical cyclone load and the seismic load with the PGA of 0.32 g and 20 m soil liquefaction are considered, the required steel weight increases about 21%, compared to the IEC 61400-3 loads. The structure is still dominated by the tropical cyclone load of DLC 1.2, but some of the bracings and piles are controlled by seismic loads. The reason for this is that the 20 m soil liquefaction causes the piles and bracings to weaken.
# An Enhanced Mechanism for Fault Tolerance in Agricultural Wireless Sensor Networks
## 3 WSN Topology
### WSN Routing and Data Transmission
#### 3.2.1 Routing Mechanism for Sensor Nodes and the CH  
The routing mechanism is divided into two main stages. The first step is data communication between sensor nodes and the CH (see Figure 2), while the second step consists of data aggregation and data transmission from the CHs to the BS.  
After the deployment of the network devices on the agricultural field, the group leader (e.g., CHs) broadcasts an announcement with its identification number (ID) over the cluster. When the sensor nodes receive the CH's announcement, they reply with a _join request_ to their leader. After that, the CH sends the TDMA scheduling, allowing each node to have a time slot for the transmission of its data. Once the node receives its TDMA, it enters a passive state (e.g., sleeping state) while waiting for the _data request_ sent by the leader CH. Once the cluster establishment is completed, the data transfer can take place.  
Figure 1: WSN hierarchical topology.  
When the time slot arrives, the CH sends a data request to the associated sensor. At the moment of receiving the data request, the sensor node enters the active state (e.g., awake state) and it collects information from its environment and sends it to CH. After that, it re-enters the passive state. In this way, the node saves energy and therefore, extends its lifetime. After having received all the environmental information from the sensor nodes, the leader CH can aggregate the data and put them into a single packet in order to send it to the BS. The cluster construction process is presented in Algorithm 1.  
```
Input: Wireless sensor network Output: WSN hierarchical structure based on clusters
```  
1. Cluster Head initialization 0
2. Broadcast the cluster ID (\(CH_{ID}\)) over the cluster;
3. For loop = 1 to \(m\) sensor
4. If CH receives the join request from \(node_{id}\)then
5. Send TDMA to \(node_{id}\);
6. Thread \(Node_{id}\) in the node file;
7. End If
8. End loop
9. Initiate Node_TimeOut as node data reception deadline;
10. End Cluster Head initialization0
11. Sensor node initialization0
12. If the sensor node receives the \(CH_{ID}\) from the CH then
13. Send a join request with the node ID to the CH;
14. If the sensor node receives the TDMA from the CH then
15. Go the passive state0 //Waiting for a data request from the CH;
16. End If
17. End If
18. End Sensor node initialization0 ```
**Algorithm 1**Cluster Construction in a WSN
# Feasibility of Developing Sustainable Concrete Using Environmentally Friendly Coarse Aggregate
## 3 Experimental Procedure
### Mixing, Casting, and Curing of Concrete  
Dry materials (i.e., cement, sand, and coarse aggregates) were added to the 120 L pan type mixer and mixed for 4 min. Once the dry materials are mixed adequately, water was added to the pan mixer and mixed for another 6 min until a homogeneous mix was obtained. A standard slump test was performed to measure the workability of the concrete in accordance with AS 1012.3.1 [52]. The mix was poured into steel moulds in two approximate layers while providing mechanical vibration for each layer using a vibratory table to remove air bubbles and to attain a smooth and glossy finish. Test specimens comprised 100(\(\phi\)) \(\times\) 200(h) mm\({}^{3}\) cylindrical specimens for density [53], compressive strength [54], elastic modulus [55] and Poisson's ratio [55] tests, 100(\(\phi\)) \(\times\) 50(h) mm\({}^{3}\) cylindrical specimens for water absorption tests [56], 100 \(\times\) 100 \(\times\) 350 mm\({}^{3}\) beams for flexural strength [57] and 75 \(\times\) 75 \(\times\) 280 mm\({}^{3}\) prisms for drying shrinkage [58]. The steel moulds were kept at 24 \(\pm\) 2 \({}^{\circ}\)C of temperature and 70% relative humidity for 24 h to provide initial curing to the specimens. After the initial curing period, the specimens were stripped from the moulds and kept in lime saturated water (i.e., to reduce calcium hydroxide leaching from concrete specimens) at room temperature (24 \(\pm\) 2 \({}^{\circ}\)C) to provide moist curing in accordance with AS 1012.8 [59].
# Flooding Conceptual Review: Sustainability-Focalized Best Practices in Nigeria
## 5 Conceptual Framework
### Causes of Flooding  
Flood is a result of many conditions working singly and in synergy. These conditions are mainly natural and anthropogenic. Natural causes of flooding are generally a result of heavy rain and downpour. Anthropogenic causes of flooding are enhanced by human activities [33; 34]; that is, flooding in terms of environmental hazard is not totally a physical phenomenon. Floods only become a hazard when they impinge unfavorably upon human activity, mostly due to built-infrastructure along floodplains and coastlines. Based on this understanding, flood hazards also create socioeconomic phenomena and socio-psychological conditions of stress. Major causes of flooding have been linked to human interaction with the environment (e.g., urbanization and agricultural activity). As urbanization intensifies, natural surfaces are replaced by buildings, paved roads and concrete surfaces, which do not readily allow water to percolate into the ground. The effect is, therefore, a large proportion of rainfall, which normally should infiltrate into soil or be intercepted by vegetation, is immediately converted into surface runoff. Attempts to harness available water resources have resulted in the construction of dams and other water control structures. However, the failure of these structures, infrequent as they may be, have also resulted in floods (e.g., the collapse of the Bagauda Dam near Kano in 1988).  
The encroachment of buildings on floodplains through towns and cities and the depositing of waste materials creating blockage can facilitate flooding. In combination, poor city planning and  
Figure 3: Four key components of urban flooding: climate and meteorological events, poor urban planning, urbanization, and anthropogenic activities (Adapted from [PERSON] et al. [27]).  
management, in addition to natural rain-induced causes, can be detrimental in an urban setting. Six general causes of flooding specific to Nigeria, include: (1) heavy rainfall (i.e., a tropical climate combined with a relating wet season); (2) soil nature (i.e., poor infiltration of rainwater flow and soil percolation); (3) deforestation (i.e., increased forest and vegetation removal, especially within lowlands and valley beds); (4) climate change (i.e., attributed by NEMA, including the worst flood disaster in 2012 and indirect aggravation of flood patterns in flood prone areas [27]); (5) poor waste disposal (i.e., blockage of drainage channels, especially in urban centers); and (6) poor land use policy planning and management (i.e., improper sitting of buildings, structures, road and drainage construction and land use ordering [35], as well as available control tools to oversee development standards). It is unfortunate Nigerian authorities lack proper planning and orderly conduct when it comes to sound promotion of flood prevention [36].
## 4 Results and Discussion of Explainable AI (XAI) for VGG19 Model
### Comparison of LIME and Grad-CAM Explanations for VGG19
#### 4.1.3 Discrepancies in Explanations  
The discrepancies between LIME and Grad-CAM in the case shown in Figures 6c and 12c can be attributed to several factors:  
* Local vs. global interpretations--LIME provides local interpretations by focusing on small perturbed regions, while Grad-CAM offers a global view based on gradients. The difference in their approach can lead to highlighting different aspects of the image.
* Sensitivity to perturbations--LIME's reliance on perturbations can sometimes make it sensitive to noise and local variations, which might not significantly affect the gradient-based approach of Grad-CAM.
* Model uncertainty--the model's lower confidence in its prediction for this particular case likely caused the divergence. When the model is uncertain, the explanations from different methods can vary more significantly, reflecting the underlying ambiguity in the model's decision process.
* Feature importance--different algorithms might prioritize different features. LIME might highlight specific small regions that it considers crucial, while Grad-CAM might emphasize broader regions that overall influence the decision.
# Combination of Chemo- and Biocatalysis: Conversion of Biomethane to Methanol and Formic Acid
## 3 Results and Discussion
### Product Separation
#### 3.4.1 Pervaporation of Methanol  
Due to the low methanol concentration in the solution a direct distillation is rather uneconomic. Therefore, pervaporation was investigated since it offers recovery of FDM and enrichment of methanol in one step which can be complemented by further distillation. In this regard we tested different commercially available polydimethylsiloxane membranes. M1 and M2 are different ultrafiltration membranes which are further optimized for either high selectivity (M11, M21) or high flux (M12, M22). The selectivity obtained at different concentrations of methanol is given in Figure 5a. All membranes show a high selectivity at low methanol concentrations. By increasing methanol concentration, selectivity drops. Membrane M12 performed best over the whole concentration range and was therefore chosen for further experiments.  
Figure 5b illustrates the methanol concentration in the retenate using membrane M12. After 22 h the methanol concentration decreased from 5 to 2.1 g\(\cdot\)L\({}^{-1}\) at 25 \({}^{\circ}\)C and 20 mbar at the permeate side and reached a minimum concentration of 0.1 g\(\cdot\)L\({}^{-1}\) at the end of the experiment.  
Figure 4: Methane conversion via combination of chemocatalysis (CH\({}_{4}\) oxidation over V/SBA-15) and biocatalysis (CH\({}_{2}\)O disproportionation over FDM). (**a**) Formation of methanol from formerly produced formaldehyde by freely dissolved FDM. The solution was enriched with formaldehyde until the addition of FDM at t = 0. (**b**) Formation of methanol from formaldehyde by immobilized FDM.
# Recent Advances in Optical Detection of Aminoglycosodies
## 2 Fluorescent Assays
### Discussion  
Although quantitative analysis can be achieved by using all of the established FL analysis strategies, each method has its advantages and disadvantages. DNA-binding dyes and MIPs are commonly employed for the development of turn-off FL sensing strategies. Both of them give responses to the presence of target AG rapidly and specifically. However, both methods suffer from a high background, which leads to relatively poor sensitivity. Compared with turn-off FL sensing methods, turn-on FL strategies showed a lower background and higher detection sensitivity. The graphene-based FRET aptasensor exhibited the highest sensitivity for the detection of kanamycin (1 pM) [44]. The high sensitivity may be attributed to the efficient quench ability of graphene, which leads to a low background, and a good binding ability of kanamycin to the aptamer. In terms of ratio FL analysis methods, compared with the turn-off FL sensing method, the ratio FL analysis methods exhibit moderate sensitivity, but better reliability because of the in-built correction. However, ratio FL analysis methods commonly need two kinds of FL molecules/nanomaterials, leading to a high experiment cost.
# Grain Size Effect in Elution Test of Electric Arc Furnace Slag
## 2 Materials and Methods  
### Slag Samples  
Seven different steel makers located in Brescia provided the carbon steel slags samples. The main differences among their slagging practices are the cooling rate [27] and the addition/no addition of SiO\({}_{2}\) for slag stabilization [30]. The slag samples were divided into three categories depending on the treatment: slagging into a hole and cooling by water jet (slow cooling); slagging on the chute and cooling by water jet (fast cooling); slagging on chute after addiction of SiO\({}_{2}\) and cooling by water jet (fast cooling and modified slag). The samples are classified in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Steel Type**} & \multirow{2}{*}{**Furnace Temperature I\({}^{\text{*}}\)CI**} & \multirow{2}{*}{**Slagging Procedure**} & \multicolumn{2}{c}{**Grinding Size**} \\ \cline{6-7}  & & & & & **4 mm** & **10 mm** \\ \hline A & Carbon steel 0.167\% \(<\) C \(<\) 0.185\% & 1550–1620 & Fast cooling & X & \\ \hline B & Carbon steel 0.04\% \(<\) C \(<\) 0.08\% & 1670–1680 & Fast cooling & X & X \\ \hline C & Carbon steel & 1600 & Fast cooling & X & X \\ \hline D & D1 & Carbon steel 0.12\% \(<\) C \(<\) 0.18\% & 1600 & Fast cooling and & X & X \\  & D2 & Carbon steel 0.12\% \(<\) C \(<\) 0.18\% & 1600 & Modified slag & X & \\ \hline E & E1 & Carbon steel C \(<\) 0.25\% & 1600 & Fast cooling and & X & X \\  & E2 & Carbon steel C \(<\) 0.25\% & 1600 & Modified slag & X & \\ \hline F & Carbon steel: 0.05\% \(<\) C \(<\) 0.8\% & 1650 & Fast cooling & X & \\ \hline G & Carbon steel 0.17 \(<\) C \(<\) 0.19\% & 1550 & Slow cooling & X & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of samples, details of some steel making process parameters (2 nd and 3 rd column) and slagging procedure (4 th column) and grinding size (5 th column).
###### Abstract  
This study investigated advancements in atmospheric forecasting by integrating real-time observational data into the Weather Research and Forecasting (WRF) model through the WRF-Data Assimilation (WRF-DA) framework. By refining atmospheric models, we aimed to improve regional high-resolution wave and hydrodynamic forecasts essential for environmental management. Focused on southern Greece, including Crete, the study applied a 3D-Var assimilation technique within WRF, downscaling forecasting data from the Global Forecast System (GFS) to resolutions of 9 km and 3 km. The results showed a 4.7% improvement in wind speed predictions, with significant gains during forecast hours 26-72, enhancing model accuracy across METAR validation locations. These results underscore the positive impact of the integration of additional observational data on model accuracy. This study also highlights the utility of refined atmospheric models for real-world applications through their use in forcing ocean circulation and wave models and subsequent Digital Twin of the Ocean applications. Two such applications--optimal ship routing to minimize CO2 emissions and oil spill trajectory forecasting to mitigate marine pollution--demonstrate the practical utility of improved models through what-if scenarios in easily deployable, containerized formats.  
data assimilation; digital twin; dynamic downscaling; oil spill forecasting; optimal ship routing; WRF-DA; NEMO; Wavewatch III +
Footnote †: journal: _algorithms_
## 1 Introduction
Thereafter, 8-band Worldview-3 satellite imagery (400-1040 nm) and decision tree techniques were used to discriminate between nitrogen-saturated and -unsaturated grassland plots and assess their influence on plant growth.
## 3 Results  
### Total Energy and Structural Stability  
For the LiMgPdSn-type EQH compounds FeRhCrSi and FePdCrSi, due to the different atomic arrangements of them, there are three possibly different crystal structures (here, named as type-1, type-2, and type-3), shown in Figure 1a-c. The atom positions in Wychoff coordinates are also shown in Table 1. In order to get the ground state properties of FeRhCrSi and FePdCrSi, the geometry optimization was performed by calculating the total energy of the FeRhCrSi and FePdCrSi compounds at different lattice constants for the three possible crystal structures. The calculated total energy (E\({}_{\rm t}\)) as the function of lattice constants (a) for FeRhCrSi and FePdCrSi compounds (E\({}_{\rm r}\)-a curves) are shown in Figure 2. It can be clearly seen that both FeRhCrSi and FePdCrSi compounds of type-1 structure have the lowest total energy, which means the type-1 structure is the most stable configuration. Considering the different magnetic states of these compounds, the total energy as a function of the lattice constants for the type-1 structure FeRhCrSi and FePdCrSi compounds in ferromagnetic (FM), ferrimagnetic (FIM), and nonmagnetic (NM) states has been calculated (as shown in Figure 3. From Figure 3, it can be found that both FeRhCrSi and FePdCrSi compounds in the FIM state show the lowest total energy. Therefore, for both FeRhCrSi and FePdCrSi compounds, the type-1 structure in the FIM state was the most stable state. Thus, we will focus on discussing the physical properties of these compounds with a type-1 structure and FIM state in the following parts of this paper. And the obtained equilibrium lattice constants at their ground states for FeRhCrSi and FePdCrSi are 5.82 A and 5.87 A, respectively, and the results have been shown in Table 2. The small difference of the optimized lattice constants between FeRhCrSi and FePdCrSi compounds derive from the close ionic radii between Rh and Pd atoms.
# Optimal Robust LQI Controller Design for Z-Source Inverters
###### Abstract  
This paper investigates the linear quadratic integral (LQI)-based control of Z-source inverters in the presence of uncertainties such as parameter perturbation, unmodeled dynamics, and load disturbances. These uncertainties, which are naturally available in any power system, have a profound impact on the performance of power inverters and may lead to a performance degradation or even an instability of the system. A novel robust LQI-based design procedure is presented to preserve the performance of the inverter against uncertainties while a proper level of disturbance rejection is satisfied. The stability robustness of the system is also studied on the basis of the maximum sensitivity specification. Moreover, the bat algorithm is adopted to optimize the weighting matrices. Simulation results confirm the effectiveness of the proposed controller in terms of performance and robustness.  
Keywords:ZSI; non-minimum phase; perturbation; robust LQI controller; bat algorithm +
Footnote †: journal: Applied Sciences