esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction
## 3 Results and Discussion  
### Performance of Conformer-LSTM  
The model is used to predict wave height data using a sliding window method to predict the wave height of the twenty-fifth hour with data from every 24 h of feature variables. The prediction performance of the model is analyzed using MAE, RMSE, and MAPE as evaluation indicators for each station's prediction data. When using the Conformer-LSTM algorithm for prediction, the training parameters are set to epochs = 100 and batch_size to 16, with a learning rate of 0.001.  
Table 5 provides a preliminary view in which the Conformer-LSTM model shows better performance in predicting wave heights compared to traditional models.  
In the table, we can initially observe that the Conformer-LSTM model performs better than the LSTM model at these two sites, providing preliminary evidence that the Conformer-LSTM model has improved prediction accuracy.  
To further demonstrate the predictive performance of the model, line charts are used to compare the predicted and actual values, as shown in the figures. The model shows good fitting effects.  
Observing Figures 6 and 7, we find that the Conformer-LSTM model's prediction results have a good fitting effect with the actual wave heights.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Station Name** & **Network Name** & **MAE** & **RMSE** & **MAPE (\%)** & \(\mathbf{R^{2}}\) \\ \hline
41048 & Conformer-LSTM & 0.12909 & 0.17745 & 5.4335 & 0.95961 \\  & LSTM & 0.15055 & 0.20797 & 7.1349 & 0.94629 \\
46047 & Conformer-LSTM & 0.11975 & 0.13394 & 4.829 & 0.96392 \\  & LSTM & 0.12829 & 0.14713 & 4.9998 & 0.95843 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction Results for Stations 41048 and 46047.
## 2 Error Transfer Function of INS-Aided PLLs
### Error Transfer Function of INS-Aided PLLs  
According to the principle of INS-aided PLLs introduced in Section 2.1, the mathematical structure of the INS-aided PLLs is shown in Figure 5, in which the tracking error \(\delta\theta\) is affected by all of the error sources simultaneously. As for a normal PLL, the effects of the thermal noise \(\omega_{\varphi}\) and oscillator noise \(\theta_{clk\_error}\) on the tracking error are uncorrelated with each other. Besides, the error sources from the INS branch are physically independent from the PLL branch. Since the NCO is controlled by the sum of the PLL filter output and the INS aiding information, the relation of the tracking error caused by them is additive. According to the inertial navigation principle, the INS bias-type errors \(\Delta\!f_{{}_{IMU}}\), the INS scale factor-type errors \(K_{a}\) and the INS aiding information delay \(e^{-s_{a_{b}}}\) are independent of each other. Therefore, all of the error sources of the INS-aided PLLs are independent, and their effects on the tracking error are additive. All of the known major errors in the INS branch are considered in Figure 5, which is much more complete than the errors considered in Figure 2.  
The normal PLL's error transfer function was derived from its mathematical structure [18]. The mathematical structure become more complex after INS aiding, and the INS-aided PLL's transfer function cannot be obtained based on Figure 5 directly. Since the effects of all of the error sources on  
Figure 5: Mathematical structure of INS-aided PLLs.  
the tracking error are additive, the error transfer function between each error source and the tracking error could be analyzed individually. Additionally, the error transfer function of the INS-aided PLLs could be obtained by the superposition of the effects of all of the error sources, which is written as Equation (1). This error transfer function is the precondition of the steady-state error modeling in the next section.  
\[\delta\theta(s)=\big{(}1-H(s)\big{)}\bigg{(}\theta_{cik-error}(s)-K_{a}(s) \theta_{i}(s)-\frac{\Delta f_{MLL}(s)}{s}+\big{(}1-e^{-s_{a}}\big{)}\theta_{i} (s)\bigg{)}-H(s)\omega_{\varphi}(s) \tag{1}\]  
where \(H(s)\) is the system transfer function of the normal PLL, which can be written as follows [19].  
\[H(s)=\frac{\frac{K_{d}K_{o}F(s)}{s}}{1+\frac{K_{d}K_{o}F(s)}{s}} \tag{2}\]
# Structural Performance of a Precast Concrete Modular Beam Using Bolted Connecting Plates
## 4 Experimental Results and Analysis  
### Crack Propagation and Failure Modes  
The cracks that appeared during the flexural tests are shown in Figure 9. In the MCF specimen, initial flexural cracks were observed when the load reached 80% of the maximum load. These cracks extended vertically toward the compression zone. In the PCF specimen, flexural cracks developed, and the cracks occurred at the bottom of the connection when the flexural load reached 90% of maximum load. After this, these cracks extended vertically into the compression zone of the beam, and failure occurred. In both specimens, tension failure was observed which initiates by yielding of rebars followed by crushing of concrete at the compression zone. The strain distribution of rebar will be introduced in detail in the load-strain relationship.  
Figure 8: Location of gauges (mm): (**a**) the locations of gauges for the flexural test; (**b**) the locations of gauges for the shear test.  
Figure 7: Test setup: (**a**) schematic of the test setup for flexural test; (**b**) schematic of the test setup for shear test.  
The cracks and failure modes of specimens for the shear tests are shown in Figure 10. In every specimen, the shear cracks extended from the support to the loading point, and shear failure occurred. The initial flexural cracks were observed in the monolithic specimen and the PC modular specimen when the loads reached 30% and 40% of the maximum load, respectively. As the shear load increased, shear cracks developed, and cracks were formed in the connection region of PCS. Afterward, when shear loading was 90% of the maximum load, shear cracks progressed to the compression zone of the beam, and then flexural-shear failure was observed in both specimens. According to much research on PC modular members, cracks generally progress along with the joint configuration [32; 33]. In PCF and PCS specimens, the initial propagation of cracks was similar to that of monolithic beams, and the general structural behavior of a PC beam was observed. This indicated that the proposed method can be applied in construction sites. In addition, there was no large deformation and partial detachment on the connection region, and failure did not occur in a brittle manner. The similar crack pattern between the PC modular specimens and the monolithic specimens indicates that the stress can transfer between each module effectively [34; 35]. In the PCF under flexural load, flexural cracks were observed in the tension zone of the midspan. However, In the PCS under shear load, the cracks were extended vertically along the connection region. This crack occurred at the interfaces between the precast concrete and the mortar. It was judged that these cracks did not affect the structural capacity, because they were not accompanied by a decrease in load or increase in displacement [36; 37]. In addition, it was confirmed that the connection played a role in transferring the shear stresses based on the shear cracks observed in each module.
## 3 Active Receiving Wearable Body Area Network with Loop Antenna  
Figure 1a presents a basic receiver block diagram with an active antenna. In Figure 1a, the LNA, low noise amplifier, is an integral part of the antenna. A commercial E PHEMT LNA, low noise amplifier, is connected to the printed loop antenna. The loop antenna is etched on an FR4 substrate with a thickness of 0.005 mm. The loop antenna diameter is 50 mm. The active antenna configuration is shown in Figure 1b. The receiving active antenna block diagram is presented in Figure 1c. The radiating element is connected to the LNA via an input matching network. An output matching network connects the amplifier output port to the receiver. A compact DC bias network supplies the required voltages to  
the amplifiers. The amplifier specifications are given in Table 1. The amplifier complex S parameters are listed in Table 2. The amplifier noise parameters are listed in Table 2. The amplifier gain is around 25 dB at 100 MHz, and decreases to 8 dB at 2 GHz. The loop antenna S11 parameter on a human body is presented in Figure 1d. A textile sleeve covers the loop antenna to match the loop to the antenna environment. The radiating loop antenna and the textile sleeve are attached to the patient body. The antenna bandwidth, computed and measured, is around 20% for VSWR; better than 3:1. The active loop antenna S21 parameters, gain, on human body, are shown in Figure 1e. The active antenna gain is 25 \(\pm\) 2.5 dB for frequencies from 350 to 580 MHz. The active loop antenna noise figure is 0.7 \(\pm\) 0.2 dB for frequencies from 400 to 900 MHz. There is a good agreement between computed and measured.  
Figure 1: _Cont._  
Figure 1: Receiving active antenna assembly. (**a**) Receiver block diagram with active antenna; (**b**) Active printed loop antenna layout; (**c**) Receiving active antenna block diagram; (**d**) Loop antenna S11 parameters on human body; (**e**) Active loop antenna S21 parameter, gain, on human body.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Specification** & **Remarks** \\ \hline Frequency range & 0.4–3 GHz & \\ \hline Gain & 26 dB @0.4 GHz & Vds = 3 V; Ids = 60 mA \\ \hline N.F & 0.4 dB @0.4 GHz & Vds = 3 V; Ids = 60 mA \\ \hline P1 dB & 18.9 dBm @0.4 GHz & Vds = 3 V; Ids = 60 mA \\ \hline OIP3 & 32.1 dBm @0.4 GHz & Vds = 3 V; Ids = 60 mA \\ \hline Max Input power & 17 dBm & \\ \hline Vgs & 0.48 V & Vds = 3V; Ids = 60 mA \\ \hline Vds & 3 V & \\ \hline Ids & 60 mA & \\ \hline Supply voltage & \(\pm 5\) V & \\ \hline Package & Surface Mount & \\ \hline Operating Temperature & \(-40\)\({}^{\circ}\)C-80 \({}^{\circ}\)C & \\ \hline Storage Temperature & \(-50\)\({}^{\circ}\)C-8100 \({}^{\circ}\)C & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Low noise amplifier (LNA) specification.
## 4 Automatic Classification of Polygonal Wheels
### Data Fusion  
Following feature normalization, a data fusion is performed to enhance the sensitivity of the features to abnormal cases.  
To merge the features obtained through the PCA and CWT techniques, the Mahalanobis Distance (_MD_) is applied for each sensor as follows:  
\[MD=\sqrt{(x_{i}-\overline{x})\cdot C^{-1}\cdot(x_{i}-\overline{x})^{T}} \tag{6}\]  
in which \(x_{i}\) is the matrix of extracted features, \(\overline{x}\) is a mean vector of the features from the baseline simulation, and the inverse covariance matrix of the baseline simulation is defined by \(C^{-1}\). In the first step, all features are merged (Figure 10a) and later, all sensors are merged (Figure 10b). In the first fusion step, a 115 \(\times\) 1 vector is obtained for each of the four sensors and 115 train passages. Then, in the second fusion stage, a single 115 \(\times\) 1 vector is obtained, in which the information from all four sensors is combined for each train passage. Figure 10, based on the PCA technique, shows that the second step of the merger considerably increases the damage sensitivity. This approach groups the polygonal wheel profiles according to the dominant harmonic.  
On the other hand, as depicted in Figure 11, an approach using the CWT technique ensures a classification concerning the defect amplitude in each of the profiles. The first-order fusion is very sensitive to categorization by amplitude level; however, the second-order fusion promotes less dispersion in the results for the damage scenarios. The CWT model gains sensitivity for the categorization of the different wear amplitudes when all sensor information is merged. It is concluded that for a categorization by defect amplitude, the second fusion level will be adopted in this approach.  
Figure 10: Data fusion for PCA features: (**a**) 1 st step fusion features for accelerometer Acc2 and (**b**) 2 nd step fusion of all sensors.
# AI-Driven Innovations in Software Engineering: A Review of Current Practices and Future Directions
## 3 AI Applications in Software Engineering
### Design and Architecture
#### 3.2.2 Intelligent System Modeling  
System modeling is the process of creating abstract representations of software systems to comprehend and communicate their structure and behavior. AI augments system modeling by providing intelligent tools capable of generating models from requirements specifications or existing codebases.  
Employing techniques such as graph analysis, machine learning, and pattern recognition, AI tools can automatically generate Unified Modeling Language (UML) diagrams or other architectural representations. These models assist in visualizing complex systems, identifying potential bottlenecks, and enhancing communication among development team members.  
Automating system modeling through AI not only accelerates the modeling process but also ensures that documentation remains current, which is crucial for onboarding new team members and facilitating future system enhancements. Moreover, AI-driven modeling tools can simulate system behavior under various scenarios, enabling predictive analysis and the early detection of design flaws.  
Advanced AI methodologies, such as model-driven engineering (MDE) augmented with AI, enable the generation of code directly from models, thereby bridging the gap between design and implementation [49]. This integration fosters a seamless transition from system models to executable code, enhancing development efficiency and consistency.
# Frequency Selective Surfaces: A Review
###### Abstract  
The intent of this paper is to provide an overview of basic concepts, types, techniques, and experimental studies of the current state-of-the-art Frequency Selective Surfaces (FSSs). FSS is a periodic surface with identical two-dimensional arrays of elements arranged on a dielectric substrate. An incoming plane wave will either be transmitted (passband) or reflected back (stopband), completely or partially, depending on the nature of array element. This occurs when the frequency of electromagnetic (EM) wave matches with the resonant frequency of the FSS elements. Therefore, an FSS is capable of passing or blocking the EM waves of certain range of frequencies in the free space; consequently, identified as spatial filters. Nowadays, FSSs have been studied comprehensively and huge growth is perceived in the field of its designing and implementation for different practical applications at frequency ranges of microwave to optical. In this review article, we illustrate the recent researches on different categories of FSSs based on structure design, array element used, and applications. We also focus on theoretical breakthroughs with fabrication techniques, experimental verifications of design examples as well as prospects and challenges, especially in the microwave regime. We emphasize their significant performance parameters, particularly focusing on how advancement in this field could facilitate innovation in advanced electromagnetics.  
frequency selective surfaces; microwave frequency; spatial filters; electromagnetic waves; periodic structures +
Footnote †: journal: Applied Sciences
## 3 Results
### Tensile Strength vs. Ductility in DED Components  
The layering strategy in DED plays an essential role in determining mechanical properties. Lower layers closer to the substrate (base material) may experience enhanced tensile strength due to rapid cooling, grain refinement, and dislocation density increase. However, this rapid cooling might compromise ductility by inducing micro-fissures or a non-uniform microstructure [25]. Extending our analysis to additional case studies, we consider the example of DED-produced turbine blades [26, 27]. For this type of component, the outer layers, subjected to higher stress, demand increased strength to withstand forces during operation. Therefore, these outer layers could be designed with a focus on higher tensile strength, achieved by optimising cooling rates, controlling deposition parameters, and sacrificing some ductility. Similarly, inner layers could prioritise ductility to withstand dynamic loading conditions. Control parameters like laser power, deposition speed, and layer thickness significantly impact this balance. A higher laser power might enhance strength but at the expense of ductility due to rapid solidification, while slower cooling rates could improve ductility but might reduce overall strength.  
The selection of alloy compositions in DED also influences this balance. For instance, nickel-based superalloys might prioritise strength over ductility due to their high-temperature applications, whereas certain steel alloys might aim for a balance between the two properties.  
This expanded discussion demonstrates the compromises and strategic considerations involved in balancing tensile strength and ductility in DED-produced components across various industries and applications.
# Scalable OneM2M IoT Open-Source Platform Evaluated in an SDN Optical Network Controller Scenario +
## 3 The OneM2M IoT Platform and Proposed Implementation for Optical Network Control
### The OneM2M Platform for an Optical Network
#### 3.3.1 Implementation of the First Use Case  
In the first experiment, the controller receives periodic updates on the monitored parameters from the network elements (both real and emulated), including the measured input/output power on an ROADM port and pre-Forward Error Connection (pre-FEC) Bit Error Rate (BER) on a transponder. In particular, leveraging on specifically designed Python scripts, \(N\) ROADM agents send updates to the network controller (implemented through the OneM2M platform) at a configurable pace. In this experiment, we measured the rate \(R_{\text{POST}}\) of POST messages per second that are successfully received and elaborated by the Mobius platform.  
The usage of an OneM2M server requires building a model of the real devices it has to handle. The model for the ROADM devices has been automatically derived from real devices and saved into a JSON file. Figure 3 shows an example schema representing the internal structure of an ROADM device and its connections. In Figure 4, it is possible to see a piece of the device configuration file representing that schema.  
The OneM2M server has been organized such that every container contains all the updates regarding one emulated ROADM device. Every update represents a new content instance in that container. In this way, the controller has a complete overview of the status of the network.  
Python scripts emulate the behaviour of the ROADM agents. Listing \(1\) shows their pseudo-code. The ROADM agents periodically send updates regarding the status of the  
Figure 4: A piece of JSON message representing a model of an ROADM device.  
Figure 3: Example schema of an ROADM device.  
device they control. In particular, the agents detect the actual transmitting power or receiving power (depending on the port direction) of the devices' ports into the circuit packs and communicate it to the controller. The controller can take decisions accordingly.  
We repeated the experiment 30 times per parameters configuration, varying the sleep time between consecutive update submissions, the maximum number of messages each ROADM agent sends, and the number of sending agents. The objective was to observe the behaviour with different network loads and study how efficient was the solution in every condition, increasing the confidence of the produced results.  
```
while(True): foreverycircuitpack: foreveryport: ifport-direction==\"tx\": <readtxpowerfromdeviceport> elseifport-direction==\"rx\": <readrxpowerfromdeviceport> <preparemessagefortheOneM2M> <sendmessage> <sleep(sleeping-time)>
```  
Listing 1: Pseudo-code of one of the \(N\) ROADM agents.
## 5 Experimental Results
### Comparative Analysis  
The proposed coverage hole detection and recovery mechanism for better QoSensing is validated in this sub-section using various performance metrics. The efficiency of the proposed mechanism is proven by comparing it with several existing works. The performance metrics such as coverage rate, number of dead nodes, average energy consumption and throughput are considered for the evaluation.
## 12 Evolving Fuzzy Techniques for EEG Artifact Removal
### Fuzzy Intuitionistic Approaches for the Removal of Internal/External Artifacts: Case Studies  
The paper [149] examines an innovative approach for removing artifacts in EEG signals, employing intuitionistic fuzzy sets. The procedure begins with extracting features from EEG signals using Fourier transformations, which transform the signals from the time domain to the frequency domain. This step is crucial to improve the identification of significant features needed for subsequent classification. Subsequently, PCA analysis is used to reduce the dimensionality of the data, focusing the most relevant information in a smaller number of variables. The core of the proposed method concerns the fuzzification of this reduced data, followed by its processing through a classifier based on fuzzy decision trees. This classifier benefits significantly from the ability of intuitionistic fuzzy sets to handle uncertainty. The results demonstrate that the proposed approach not only improves the classification accuracy of EEG signals, but also offers greater robustness in handling artifacts that are often present in abundance in EEG data. The ability to effectively reduce the impact of artifacts, while maintaining high classification accuracy, represents a clear advantage over conventional methods, signaling a significant breakthrough in EEG monitoring technology. These innovative aspects highlight the potential of intuitionistic fuzzy sets in improving the diagnosis and treatment of neurological conditions, facilitating more informed and accurate clinical decisions. The paper [150] discusses an innovative extension of intuitionistic fuzzy sets applied to the classification of EEG signals. The authors introduce a degree of negative hesitation that helps to better manage uncertainty in decision-making situations and pattern classification. The procedure described in the article is based on a construction of degrees of membership, non-membership, and negative hesitation through the analysis of the area of overlap between the projections of the elements and classes in a two-dimensional space. This method allows the uncertainty associated with the elements to be expressed more flexibly and improves the discrimination between different classes of patterns, which is particularly useful in the recognition of complex patterns such as those of EEG signals. The results highlight the effectiveness of NHFS in classifying EEG signals, with a significant improvement compared to traditional methods based on intuitive fuzzy sets. The ability