esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions
### Evaluation of Performance Metrics
#### 3.2.4 Training State for (ANN)  
The model's dynamics training at epoch 4250 helps illustrate the characteristics of the learning process, as shown in Figure (a)a. The gradient values in the first subplot suggest a great deal of initial variation, going above 0.1 before stabilizing around the 0.01 mark. This variation suggests that the learning rate may have been set a bit high initially;  
Figure 11: Regression for ANN: (a) (LUCIA Building, nZEB-HVAC), (b) (FUMEM Building-Heating).  
however, the eventual stability near the end of learning indicates that convergence was ultimately successful. The metric variations in the stability of the second subplot appear to show that the parameters of the model reached convergence, with very little updating actually occurring by the end of training. Ultimately, the validation performance subplot indicates stability improvement over the determined epoch to the limit of approximately 4000 by epoch 4250, which validates the model's learning ability to generalize based on the training data.  
Differently, the second training example shown in Figure 12b continued for only 238 epochs, which indicates equivalent training but equally informative stability patterns. The gradient values suggest that the trend smooths downwards, and then stabilizes without oscillation across epochs, indicating that the model converged stably to an optimum solution. The parameter \(\upmu\) demonstrates an astonishing consistency across training, and appears to have likely reached a steady state or possibly a local minimum. It is likely that the model's training parameters were aptly selected with regard to the learning rate that neither over-updated the parameters nor committed the learning to premature convergence to a local minimum. Overall, the various training trajectories provide some reasonable benchmarks on how generations of the model optimized and converged with distinct training characteristics.
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 1 Introduction
### Contributions  
In this paper, the main contributions are related to the development of a complete 5G-enabled IoT system for fully monitoring the performance of fishery farms. Specifically, this IoT-based system improves the state-of-the-art and contributes to the improvement in the existing systems in the following aspects:  
1. Wide-Area-Enabled communications (>1 km2--using NB-IoT, LTE-M, and LoRa/LoRaWAN);
2. The system combines a higher number of sensors, compared to the state-of-the-art, in order to better characterize the studied environment;
3. The system allows real-time monitoring and low-power consumption, as a sensor switching protocol (I2C-based) has been improved to perform more efficiently;
4. The system is cost-efficient and allows for high modularity, as it has been implemented with a Docker-based system to install and update firmware (with OTA updating) in each node and in the farmhouse.
## 4 Results and Discussion
### Spectral Analysis  
Optical emission spectrum (OES) facilitates in the understanding of the types of reactive species generated inside a low-pressure plasma sterilization chamber, under different experimental conditions. The result of the emission line spectrum shows that as the high voltage is applied, the emission spectrum of OH radicals, hydrogen, and oxidative species is obtained. The spectrum was recorded by a high-resolution spectrometer (HR-4000 CG-UV-NIR) through a fiber optic with a resolution of 0.5 nm; the wavelength range was from 200 nm to 900 nm. The OH: radical's peak is at 309 nm; the Balmer \(\alpha\) peak of the hydrogen was observed at 656 nm, the Balmer \(\beta\) peak at 484 nm, and the reactive oxygen peaks at 777 nm and 844 nm, respectively. Figure 3 represents the OES of the reactive species generated inside the sterilization chamber with and without using H\({}_{2}\)O\({}_{2}\) vaporized gas.  
The emission spectrum reveals a higher concentration of OH radicals and reactive oxidant species with the addition of vaporized H\({}_{2}\)O\({}_{2}\) gas. The spectrum represents the fact that the intensity of oxidant species generated by the low-pressure plasma discharge significantly depends upon the H\({}_{2}\)O\({}_{2}\) vaporized gas injection. The OH radicals, hydrogen (H\({}_{\alpha}\), H\({}_{\beta}\)), and oxygen species O & O\({}_{2}\) with the redox potentials (2.80 V, 0, 2.07 V and 1.23 V), respectively, interact with the _V. Cholera_ cells. The reaction of such ROS can damage its integrity as well as prevent the colony-forming units from regenerating.
## 2 Computation of the Effective Length of Type-A Grounding System According to IEC 62305-3
### Methodology Used for Obtaining the Type-A Grounding System Effective Length Dataset  
In this section, we will briefly outline the procedure developed for obtaining the effective length of each electrode for the type-A grounding system. The described methodology will be repeated a large number of times since we will modify the mentioned input parameters in order to ascertain their effect on the effective length (this will be discussed in the following section). At the foundation of our methodological approach stands a previously developed robust and accurate transient electromagnetic model of a grounding system, which was extensively tested on much more complex grounding grid systems and soil configurations [17; 21]. Its accuracy was verified both by comparison with published algorithms as well as commercial software such as XGSLab (version 9.6.1) [22] and CDEGS (version 13.0) [23]. So, for our case of a simple grounding electrode buried horizontally in homogeneous soil, this model will certainly provide results of sufficient accuracy. It is important to note here that in our computations the beneficial effect of soil ionization [18] on the impulse impedance of the grounding electrode will be disregarded in the scope of this paper. Also, as mentioned in the introductory section, the frequency dependence of soil parameters is disregarded, and a constant soil permittivity value of 10 is chosen for all simulations. It is our plan to modify some of these assumptions in our future work and to investigate the effect this produces.  
The methodology of obtaining the effective length of each electrode for the type-A grounding system is relatively simple, although time-consuming Figure 2. In the first step of every simulation, a 0.5 m long type-A grounding system is buried at a certain depth parallel to the Earth's surface, and a lightning current is injected into its central point. Note here that this corresponds to two segments each of length \(\ell_{1}\) = 0.25 m as seen in Figure 1. Next, the computation of impulse impedance is performed and the impulse impedance for that grounding rod length is noted. Then, the grounding electrode is extended in each direction by 0.25 m, and the computation procedure is repeated. If the change of the impulse impedance is considered insignificant according to the previously defined condition, then it is the effective length is considered found. If not, the process is repeated by extending the electrode by an additional 0.25 m in each direction until the effective length is found. This iterative process is extremely time-consuming since it needs to be repeated for every input parameter combination. Note again that three input parameters are modified: the soil resistivity, the rise time of the lightning current injected into the grounding system, and the burial depth of the grounding system. More about the selection of their ranges and chosen sample values is provided in the following section.
## 4 Controller Design
### Control Strategy  
In the 1-DOF robotic lower limb system, the controller is developed by the traditional PID control and the FSMC. The PID controller is used in this paper to compare the results with FSMC. The FSMC combines the traditional fuzzy theory and the sliding theory to reduce the input variables of the fuzzy controller by reducing the large fuzzy rule base of the traditional fuzzy rules, and combined with the sliding surface system can effectively suppress the system response of the non-linear term and improve the stability of system control.  
In order to effectively simplify the fuzzy rule base without sacrificing control performance, this paper uses FSMC. The general fuzzy system judges the system response by two input variables including the error \(e\) and the error rate \(\dot{e}\), when establishing the fuzzy rule base. The fuzzy sliding surface system combines the input variable error \(e\) and the error rate \(\dot{e}\) into a sliding surface \(\sigma=\alpha\cdot e+\dot{e}\), which replaces the error \(e\) and the error rate \(\dot{e}\) into a new input variable. Thus, the original two-dimensional fuzzy rule base is changed to the one-dimensional fuzzy sliding surface rule base, as shown in Figure 7.Where \(X_{set}\) is the desired angle, \(G_{u}\) is the scale factor, the control amount of the fuzzy controller after defuzzification can be given a proportional gain according to the system requirements to improve the actual response of the controller, U is the control output after defuzzification, s is a differentiator, and x is the actual output angle.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Value** & **Unity** \\ \hline Wire diameter & 3.5 & mm \\ Center diameter & 26.5 & mm \\ Number of laps & 4 & loop \\ Installation angle & \(-10\) & degree \\ Installation torque & 794 & N\(\cdot\)mm \\ Free angle & 0 & degree \\ Working angle & 54 & degree \\ Working torque & 4280 & N\(\cdot\)mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the torsion spring.  
Figure 7: Block diagram of fuzzy sliding mode controller (FSMC).
## 3 Results and Discussion  
### Structural Analysis  
As can be seen in Figure 2, the morphology of the phosphate layer depends on the phosphate bath chemical solution. The microstructural analysis of the I-Zn (Figure 2a) and II-Zn/Fe (Figure 2b) samples shows multiple zinc phosphate tetrahydrate crystals of different sizes that are interwoven throughout the steel surface. Also, the layer shows some pores (black dots) and intercrystalline zones, which are related to the phosphate layer specific porosity [24]. The phosphate crystals present on the steel surface are fine, compact, and have the form of flowers or plates, this form being specific to the hopeite. The main difference between I-Zn and II-Zn/Fe samples is the size of the phosphate crystal, which is much smaller in the case of the II-Zn/Fe samples.  
The manganese phosphate layer (Figure 2c) has a different morphology compared to the zinc phosphate and zinc/iron phosphate layers. Crystals corresponding to manganese phosphate are smaller compared to those of zinc phosphate. Due to the nickel and iron presence in the solution, the size of the phosphate crystals decreases, consequently, large surfaces being uniformly coated as a result of the overlapping of the crystals at the microscopic level. The layer morphology can be attributed to the accelerating effect of the galvanic couples formed between the iron and nickel ions, which act as crystallization centers in the formation process, increasing their number exponentially and resulting in a layer with a high number of small crystals.
# FPSO Hull Structures with Sandwich Plate System in Cargo Tanks
## 6 Ultimate Strength Analysis
### Static Loads  
Before the calculation of the ultimate strength, the static load from one load condition was applied (draught equal to 26.8 m) for each structure. There are two objectives for this step. The first one is to check if the model is well-calibrated. This is done by comparing the results with the ones expected by the Euler-Bernoulli beam theory and by analyzing how much the reaction force at the spring is related to the resultant forces applied. If the model is well-calibrated, the stress at the structures should be similar to the beam theory, and the reaction forces at the springs should be lower than 0.3%. The other reason for applying the static load first is because the ultimate strength loads are obtained using the static load as a  
Figure 8: Boundary loads to be applied.  
reference. For example, the input for the ultimate strength analyses can be two times the load applied at the static load case.  
The first result to compare between structures is the vertical displacement in Table 10. As can be seen, the steel structure has a bigger deflection than the SPS structure, approximately, 19% bigger. This can be explained because the SPS structure has a higher moment of inertia (2391 m4) rather than the steel structure midship section (2191 m4), indicating a greater force required.  
The second result is a comparison between the FEM and beam theory presented in Table 11. The points taken to be analyzed are at the midship section of tank 2, where the shear stress is zero according to the shear force distribution from HydroD, to minimize the boundary effects on the forces by the extremities. At first, it can be seen that the model's loads are calibrated because the spring reaction is lower than the reference. Furthermore, the normal stress and neutral axis are similar to the ones expected by the beam theory for both models. The difference between them can be explained by the slight change of the neutral axis, and the beam theory does not consider the plate's thickness, which can change the stresses.  
As can be noticed in Figure 9, there are some differences in the normal stress between parts of the structure for the same height (side, side longitudinal bulkhead and central longitudinal bulkhead). This happens because of two reasons. The first one is the difference in thickness between plates and joints of structure (for example the side and bottom) that can distribute the stresses. The second reason can be explained by a phenomenon called warping [22], and it is related to the distortion of the longitudinal stresses in the cross-section due to the transverse shear or torsion.
# Design of a Dual-Path Speech Enhancement Model
## 4 Experimental Setup  
### Dataset  
We evaluated our proposed model using the VoiceBank + DEMAND (VBD) [26] dataset, a commonly used open dataset in recent SE research. The VBD dataset contains 11,572 training utterances spoken by 28 speakers and 824 test utterances spoken by two speakers. During the training, ten types of noise, including eight from the DEMAND database and two artificially generated ones, were added to the utterances at SNRs of 0, 5, 10, and 15 dB. For evaluation, we added five types of unseen noise from the DEMAND database to the test utterances at SNRs of 2.5, 7.5, 12.5, and 17.5 dB.
## 2 Methods
### Evaluation Index  
In this paper, loss function, accuracy, and confusion matrix are used to evaluate the recognition results. The loss function curve and test accuracy curve obtained by machine learning training can intuitively understand the recognition accuracy of the model. The confusion matrix compares the real value and predicted value of the fusion sample and visually presents the result through the matrix, so as to measure the robustness of the diagnostic model more comprehensively.  
Figure 7: Neural network structure based on multi-sensor information fusion.  
The cross entropy loss function is the loss function employed in this study, and minimizing it is the CNN model's training target:  
\[Loss=-\frac{1}{N}\sum_{i=0}^{N-1}\sum_{K=0}^{K-1}Y_{i,k}\ln P_{i,k} \tag{9}\]  
where \(y_{i,k}\) denotes the actual label of the \(i\)th sample as \(k\), with a total of \(K\) label values and \(N\) samples, and \(P_{i,k}\) denotes the concept of the \(i\)th sample predicted as the kth label value. By fitting this loss function, the inter-class distance is also increased.  
In the field of machine learning, a confusion matrix is a visual tool that is specifically used for supervised learning. In the evaluation of image recognition accuracy, it is mainly used to compare the classification results, as shown in Figure 8. True positive (_TP_) represents the sample that is actually positive and predicted to be positive; false negative (_FN_) represents the sample that is actually positive but predicted to be negative; false positive (_FP_) represents the sample that is actually negative but predicted to be positive; and true negative (_TN_) represents the sample that is actually negative and predicted to be negative.  
Accuracy rate is the most commonly used classification performance index, which can be used to represent the accuracy of a model. The specific expression of accuracy is:  
\[A=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]
## 5 High-Rise Building Optimal Evacuation Efficiency
### Optimal Evacuation Efficiency for High Floors in the Building  
When considering the efficiency of using elevators on the fourth and fifth floors in staircase-determined evacuation, we found that time savings are roughly equivalent. To further determine the optimal evacuation methods for the fourth and fifth floors, we modified the evacuation methods on the fourth floor while keeping the fifth floor as an unlimited method within the C27: 2L3L4L5L scheme. A comparison of the evacuation times for different methods on the fourth floor is presented in Figure 9. From Figure 9, we can observe that, when other floors are using stairs and the fourth floor uses an unlimited method, the evacuation efficiency is the highest. Similarly, by comparing C26 and C27, it can be deduced that, when other floors use stairs, the fifth floor using an elevator results in the shortest evacuation time, but it is still longer than the evacuation time compared to the scenario where other floors use stairs and the fourth floor uses an unlimited method. Therefore, to avoid any reduction in evacuation efficiency that can occur when the fourth and fifth floors simultaneously use elevators, in accordance with the stair-determined evacuation scheme, the recommended evacuation methods for the fourth and fifth floors are 4W5L.  
Figure 8: Comparison of reduced times for C15 and C17.  
1. Analysis of evacuation efficiency on the fifth floor. During the simulation process, it was found that there was more severe congestion among differently-abled people in stairwells compared to non-differently-abled individuals, especially on lower floors where congestion was more pronounced. When upper floors use stairs for evacuation, middle floors can make greater use of elevators. This approach has two benefits: it effectively alleviates the congestion that occurs in stairwells on middle floors during short periods; and it allows individuals on the highest floor to flexibly choose their evacuation method based on the situation in the middle floor stairwells. Therefore, it is recommended for upper-level buildings to use stairs for evacuation.
2. Analysis of evacuation efficiency on the fourth floor. Regarding evacuation methods for the fifth floor, when using stairs for the fourth floor, in the case of using an elevator-determined evacuation scheme, ample elevator resources make it effective for the fourth floor to entirely use elevators, which can compensate for the slower evacuation of differently-abled people. It also prevents congestion with fifth-floor occupants at the stairwells on the fourth floor. In the case of a stair-determined evacuation scheme, where elevator resources are limited, using the unrestricted evacuation method on the fourth floor has multiple advantages. First, it helps distribute the flow of people on the fourth floor, alleviating congestion at the elevator doors. It also fully leverages the evacuation capabilities of evacuees and allows high-floor occupants to choose their path based on the crowding status in the stairwells, easing congestion on the lower- and middle-floor stairwells.
## 5 Performance Evaluation
### Energy Efficiency of Nodes and PC
Figure 13: Cumulated energy consumption amount of both coordinator and node according to the traffic amount compared with IEEE 802.15.6 protocol. narrower on aSymMAC than IEEE 802.15.6. As a result, we can recognize that the main goal of this research has been accomplished. From understanding these results from the overall figures in this section, we could find that the proposed MAC could achieve the enhancement of previous mechanisms even though some points might not be improved (e.g., the latency of normal data transmission and the energy efficiency of PC). However, in this case, the latency of normal data transmission and the energy efficiency of PC are not critically significant in the real environment.
###### Abstract  
This study introduces a new metro train demand model that simultaneously captures both aggregate ridership from automated fare collection (ARC) data and disaggregate consideration propensities, using individual survey data from Chennai, India. This joint framework produces more accurate aggregate demand estimates than traditional OLS (R\({}^{2}\) improves from 0.67 to 0.75), as it is able to capture the complex and non-linear relationship between disaggregate consideration probability, reflecting potential demand, and aggregate football, reflecting realized demand. It is observed that increasing the consideration probability enhances the football overall. However, some locations exhibit an opposing trend between consideration and football (low consideration but high football, or vice versa). Also, the sets of influential factors vary across these two dimensions. For instance, individual-level variables (income and out-of-vehicle travel time) and multi-modal connectivity features (presence of an airport and multimodal hubs near the metro) play a key role in football. In contrast, consideration probability is primarily influenced by access time, cost, and egress distance. Furthermore, factors influencing consideration probability (walkability, train service quality, and first-last-mile connectivity) vary across segments (based on vehicle unavailability, exclusive vehicle availability, and limited vehicle availability). Evidence of selection bias among metro riders, non-normality, and intra-person variability effects in football is observed. From a policy perspective, neglecting the disaggregate consideration effects on realized aggregate demand, i.e., football models, can overestimate the role of metro costs and out-of-vehicle travel time. In addition, the ridership levels of the metro are overestimated at higher metro fare levels. The new model illustrates that applying location-specific and dimension-specific policy interventions can be more effective than uniform area-wide policies for enhancing the user base and realized ridership.  
Keywords:distinct stages of metro usage; consideration probability; realized football; self-selection; multimodal connectivity; public transportation; potential demand +
Footnote †: journal: Applied sciences