esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Productivity Potential of Offshore Wind in Poland and Cooperation with Onshore Wind Farm
## 1 Introduction
Figure 2: Hourly onshore wind energy production for 6 March (the lowest daily sum of wind energy production) and for 21 February (the highest daily sum of wind energy production), 2022 year, Poland [14]. It depends on the nature of the macro-scale atmospheric circulation over a given region and on physiographic conditions at the local scale. For onshore locations, it is particularly strongly modified in areas with varied relief. Wind speed is decisively influenced by the height and form of the terrain, as well as the roughness of the ground and terrain obstacles. Hilltop passes with a parallel axis and ridges with an axis perpendicular to the prevailing wind direction are particularly predestined to harness wind energy. The selection of wind turbine units according to the meteorological conditions at a given location is important for the availability and capacity factor of the installed wind power capacity. The generating capacity and output of a wind turbine is not stable, regardless of whether they are sited on land or offshore. At the same time, offshore wind energy is characterized by higher energy generation and stability than that of onshore farms. Wind speed is higher offshore than onshore, and the roughness of the ground and terrain obstacles are limited. They also use other types of turbines. They have higher rated powers and larger diameters for the circle delineated by the rotor blades, and they are most often higher than shore-based turbines. Due to higher wind speeds at sea, those offshore are sometimes designed to operate at wind speeds greater than 25 m/s, which is the limit value for onshore turbines. In this study, an analysis of how much and whether there is potential for cooperation between offshore and onshore wind energy was carried out. Taking into account the issues of dynamic changes in wind speed geographically--shifting wind speeds most often occur from the northwest to the southeast and from the north to the south--prior to the study, it was assumed that the location and cooperation of onshore and offshore sources would play a role in ensuring the security of the energy supply and power during emergency periods. More and detailed information on cooperation between offshore and onshore sources and other renewable sources, including the correlation in generation between these sources, is presented by [PERSON] and [PERSON] [17].
## 4 Results
### Interpretability  
We used two techniques to confer a sense of interpretability to the classification decisions made by the model to aid clinicians' diagnoses. Inspired by the practicality of Gradient-weighted Class Activation Maps (Grad-CAM) [79] in ECG analysis presented in [80,81], it was the first technique we leveraged to shed light on the black-box operations of DL algorithms. Grad-CAM uses the gradients of a target class in terms of either logits or labels propagating to the final convolutional layer to generate a comprehensible localization map highlighting the features of the input pertinent to predicting the target class. This method allowed us to visualize the activation regions of the input that the model deemed indicative of class-discriminative patterns. As can be seen in Figure 4(a), the NSR rhythm for ECG and PPG, respectively, has periodic intervals, and these normal regions are indicated in the heat map with similar color intensities. In Figure 4(b),d, the regions with specific intensities of interest, as detected by the CNN component to make the classification decision of AF, can be observed for the ECG and PPG segments, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Sensitivity** & **Specificity** & **F1 Score** & **MCC** & **Size** \\ \hline Teacher Baseline & 97.1 \(\pm\) 1.0 & 97.8 \(\pm\) 1.0 & 96.2 \(\pm\) 1.0 & 97.3 \(\pm\) 1.0 & 94.1 \(\pm\) 1.0 & 1,239,883 \\ Teacher (TL) & 98.2 \(\pm\) 0.0 & 98.6 \(\pm\) 1.0 & 97.9 \(\pm\) 0.0 & 98.4 \(\pm\) 0.0 & 96.5 \(\pm\) 1.0 & 1,239,883 \\ Student Baseline & 95.5 \(\pm\) 2.0 & 97.4 \(\pm\) 1.0 & 93.1 \(\pm\) 4.0 & 95.9 \(\pm\) 2.0 & 90.9 \(\pm\) 3.0 & 38,771 \\ Student (TL) & 96.4 \(\pm\) 1.0 & 97.3 \(\pm\) 1.0 & 95.3 \(\pm\) 2.0 & 96.7 \(\pm\) 1.0 & 92.8 \(\pm\) 1.0 & 38,771 \\
**Distilled Student Model (TL and KD)** & **96.6 \(\pm\) 1.0** & **97.5 \(\pm\) 1.0** & **95.5 \(\pm\) 2.0** & **96.9 \(\pm\) 1.0** & **93.2 \(\pm\) 2.0** & **38,771** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative metrics reporting model performance on MIMIC-III PPG signals for KD. Best score is in bold, and second best is underlined.  
Figure 5: _Cont._To summarize, within the purview of interpretability, we note that the Grad-CAM highlighted areas can be further examined by a clinician to ascertain the validity of the model's internal functioning and reiterate the latter's role as a clinical support framework. For longer signal durations, measures of complexity and correlations may hold additional utility.
## 4 Approach Description
### Objective Function Formulation  
This work proposes solving the inverse kinematics (IK) problem as a global constrained optimization task. The objective function is formulated to minimize the error between the desired end-effector pose (\(\mathbf{R}_{d},\mathbf{t}_{d}\)) and the current end-effector pose (\(\mathbf{R}(\mathbf{q}),\mathbf{t}(\mathbf{q})\)), which can be computed using Equations (2) and (3).  
The objective function \(f\) is defined as  
\[f(\mathbf{q})=k_{l}\|\mathbf{t}_{d}-\mathbf{t}(\mathbf{q})\|+k_{R}\|\mathbf{R }_{d}-\mathbf{R}(\mathbf{q})\|, \tag{11}\]  
where \(\|\mathbf{t}_{d}-\mathbf{t}(\mathbf{q})\|\) represents a Euclidean norm error between the desired and the current position, and \(\|\mathbf{R}_{d}-\mathbf{R}(\mathbf{q})\|\) represents the Frobenius norm error between the desired and the current orientation. Additionally, \(k_{t}\) and \(k_{R}\) are positive weight factors.  
The optimization problem is then defined as follows:  
\[\operatorname*{arg\,min}_{\mathbf{q}}f(\mathbf{q}),\quad\text{ subject to }\ \mathbf{q}_{l}<\mathbf{q}<\mathbf{q}_{u}, \tag{12}\]  
where \(\mathbf{q}_{l}\) and \(\mathbf{q}_{u}\) are the lower and upper joint boundary, respectively. The joint boundary represents the joint limits. They are defined as  
\[\mathbf{q}_{l} =\left[q_{l,1}\quad q_{l,2}\quad\cdots\quad q_{l,n}\right]^{T},\] \[\mathbf{q}_{u} =\left[q_{u,1}\quad q_{u,2}\quad\cdots\quad q_{u,n}\right]^{T}.\]  
The constrained optimization in (12) is reformulated as an unconstrained optimization problem using quadratic penalty functions. The proposed objective function \(f^{\prime}(\mathbf{q})\) is then defined as follows:  
\[f^{\prime}(\mathbf{q})=f(\mathbf{q})+\gamma\left[\sum_{j=1}^{n}\max\left(0,\,q _{l,j}-q_{j}\right)^{2}+\sum_{j=1}^{n}\max\left(0,\,q_{j}-q_{u,j}\right)^{2} \right], \tag{13}\]  
where \(\gamma>0\) is a coefficient that scales the contribution of the penalty functions. By setting \(\gamma\) larger, constraints are penalized more severely. Moreover, when \(q_{l,j}-q_{j}\leq 0\) and \(q_{j}-q_{u,j}\leq 0\), the constraint in dimension \(j\) is satisfied.  
Finally, the proposed optimization problem is given as  
\[\operatorname*{arg\,min}_{\mathbf{q}}f^{\prime}(\mathbf{q}). \tag{14}\]  
Notice that the proposed objective function (13) incorporates the weighted error in both position and orientation, along with a penalty scheme. By minimizing the objective function, the joint configurations result in lower position and orientation errors while ensuring feasible joint solutions.
# Prediction Models for Diabetes in Children and Adolescents: A Review
## 5 Machine Learning and Deep Learning Models to Predict Diabetes in Children and Adolescents
### ML Based Ensemble Model  
As a promising approach for predicting diabetes in various medical settings [97], the ML-based Ensemble Model, with preprocessing as a key component, is developed. The preprocessing techniques improved dataset quality through feature selection and imputation of missing values, with their effectiveness rigorously evaluated through ablative analysis. The study achieved superior predictive accuracy compared to prior research, even with the use of just four key features: BMI, age, average systolic, and diastolic blood pressure. These features were chosen for their clinical relevance and interpretability. The framework employs a weighted ensemble of ML classifiers, assigning probabilities to outcomes generated by individual models to enhance classification performance.
## 3 Data Processing and Methodology
### Graph Construction  
To predict the speed of a road network, integrating the taxi movement data into the road link data is necessary. We used the road link data from the Ministry of Land, Infrastructure, and Transport, Republic of Korea. Figure 2 shows the road link data from Cheongna Hill Station to Jukjeon Station. Using a buffer operation, a spatial join was conducted to merge the taxi point data and road link data.  
A spatial join was a crucial step in the data processing for training the model. We utilized a 3 m buffer length because the width of general roads is 3 m. The 3 m buffer length accurately reflects the standard road width, which yielded reliable and meaningful results. This approach allowed for the correct association of various data points with the corresponding road links. However, one interesting challenge arose in the vicinity of intersections, where multiple road links could be accessed within the same buffer area.  
To address this, a prioritization strategy was implemented, giving precedence to the links closest to the point of interest. Furthermore, this research used trimmed means of speed per link. It served as a valuable technique to reduce errors that may occur during the aggregation of numerous point data through the spatial join process. By carefully selecting and processing the data within the defined buffer, this approach ensured that the resulting dataset was highly accurate. Figure 3 depicts the spatial join process linking the point data with the road link data. Each point data can obtain a link index through the spatial join. This data fusion was essential for creating the foundation for further deep learning techniques employed in this study, such as STGCN and CNN.  
Figure 2: Visualization of urban road network data.  
In summary, the spatial join process played a pivotal role in integrating various data sources and facilitating the creation of graph and image data, which were then harnessed for training deep learning models in this study. This approach enriched the dataset and laid the foundation for the successful exploration of spatio-temporal patterns in the context of road networks and taxi location information.
## 3 Numerical Modeling and Validation  
### Finite-Element Model and Boundary Conditions  
To simulate the circular excavation supported by the PRSS, the finite element software Plaxis 3D version 2023.1 was adopted. In the simulation, the soil and piles were represented using 10-noded tetrahedral volume elements. The steel plates were modeled using six-noded triangular plate elements, while the crown beams and walings were modeled using beam elements. In the model, the lateral displacement along the vertical boundaries was constrained (roller support), whereas both the lateral and vertical displacements were constrained at the bottom boundary (pin support). The vertical model boundaries of the YZ-plane and XZ-plane corresponded to the plane of symmetry along the centerline of the excavation. As presented in Figure 4, only one-quarter of the excavation geometry was modeled, taking advantage of the symmetry to perform analysis more efficiently without compromising accuracy. To minimize the boundary effects, the dimensions of the model were selected as 75 m in length, 75 m in width, and 60 m in height. The soil mesh surrounding the PRSS was refined near the excavation and coarsened further away, based on a sensitivity study that determined an optimized mesh size with negligible differences in computed results after further refinement.  
Figure 4: Finite element model of the excavation: (**a**) geometry; (**b**) mesh and (**c**) earth Derm.  
Figure 3: Construction processes of PRSS: (**a**) fabrication of the supporting piles; (**b**) bore holes for the supporting piles; (**c**) installation of supporting piles, and backfilling of pile holes; (**d**) installation of steel plates; (**e**) excavation of soil layers; (**f**) installation of walings; (**g**) grouting polymer behind the steel plates.
## 2 Materials and Methods
### Vector Beams and Their Propagation Dynamics
Let us remark that Equations (9) and (11) are mere approximations of the exact situation described in Equation (8); however, they provide a useful physical insight of the polarization transformation caused by the Gouy phase difference of the RCP and LCP components [33]. Such an approximation can be only considered when the difference \(|\ell_{R}|-|\ell_{L}|\) is small, so the amplitudes of the two LG beams are similar. Nevertheless, the polarization rotation can be considered exact at the radial coordinate where \(u_{\ell_{R}}(z)=u_{\ell_{L}}(z)\).
## 1 Introduction
In 2010, an output power of 53 W with a peak power of 40 kW from a hybrid amplifier laser system using Nd:YVO\({}_{4}\) was reported, corresponding to an optical efficiency of 42.4% [22]. In 2012, using gratings to stretch and compress the pulses, a multi-pulse picosecond laser with 14 mJ and a pulse duration of 28 ps operating at a repetition rate of 1 kHz from a Nd:YVO\({}_{4}\) regenerative amplifier was reported [23]. In 2013, a laser beam with an average power of 4.7 W running at the repetition rate from 1 kHz to 10 kHz was delivered from a diode dual-end-pumped Nd:YVO\({}_{4}\) regenerative amplifier [24]. Most recently, 10.5 W, 14.2 ps pulses at 1064 nm with a repetition rate of 10 kHz from a Nd:YVO\({}_{4}\) amplifier system was obtained [25] and an average power of 44.5 W, a pulse duration of 8.8 ps at 100 MHz with an optical efficiency of 56% was achieved from a sapphire face-cooled Nd:YVO\({}_{4}\) slab amplifier [26]. However, Nd:YVO\({}_{4}\) has low mechanical fracture so the pump intensity must be kept under a threshold. Considering the characteristics of the Nd:YVO\({}_{4}\) crystal, we employed quasi-continuous wave laser diodes as end-pumping sources for Nd:YVO\({}_{4}\) crystals and a side-pumped Nd:YAG module as a power amplifier. At a repetition rate of 1 kHz, an output energy of 64.8 mJ centered at 1064.4 nm was achieved. An average power of 37.5 W at a repetition rate of 100 kHz pumped by continuous wave (CW) laser diodes was obtained. Compact, stable, and high power DPSS laser amplifier systems with good beam qualities are excellent picosecond sources for high power OPCPA and high-efficiency laser processing.
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 4 Experiments
### A Comparison of Our Algorithm APSO with Other Metaheuristic Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Algorithm** & \(V_{avg}\) & \(V_{std}\) & \(V_{opt}\) \\ \hline \multirow{6}{*}{\(F_{1}\)} & APSO & 3.41E-80 & 1.84E-79 & 1.06E-155 \\  & GWO & 6.32E-09 & 5.32E-09 & 6.31E-10 \\  & WOA & 3.62E-06 & 8.47E-06 & 5.44E-09 \\  & SSA & 1.45E-10 & 4.04E-10 & 0.00E+00 \\  & BOA & 3.19E+03 & 3.99E+02 & 2.16E+03 \\ \hline \multirow{6}{*}{\(F_{2}\)} & APSO & 9.69E-75 & 2.35E-74 & 2.26E-77 \\  & GWO & 9.65E-05 & 4.81E-05 & 3.24E-05 \\  & WOA & 2.05E-04 & 2.54E-04 & 9.35E-06 \\  & SSA & 1.58E-05 & 4.53E-05 & 1.86E-118 \\  & BOA & 2.46E+43 & 5.36E+43 & 2.06E+39 \\ \hline \multirow{6}{*}{\(F_{3}\)} & APSO & 9.13E-109 & 4.83E-108 & 4.06E-154 \\  & GWO & 3.47E+00 & 3.80E+00 & 2.11E-01 \\  & WOA & 1.99E+02 & 2.61E+02 & 1.56E+01 \\  & SSA & 6.15E-08 & 2.81E-07 & 0.00E+00 \\  & BOA & 3.49E+03 & 6.21E+02 & 2.17E+03 \\ \hline \multirow{6}{*}{\(F_{4}\)} & APSO & 3.45E-73 & 1.26E-72 & 3.36E-78 \\  & GWO & 3.75E-02 & 2.09E-02 & 8.00E-03 \\ \cline{1-1}  & WOA & 3.73E-01 & 4.05E-01 & 4.68E-02 \\ \cline{1-1}  & SSA & 1.14E-06 & 3.15E-06 & 0.00E+00 \\ \cline{1-1}  & BOA & 3.55E+01 & 1.56E+00 & 3.33E+01 \\ \hline \multirow{6}{*}{\(F_{6}\)} & APSO & \(-2.57\)E+52 & 1.38E+53 & \(-7.68\)E+53 \\  & GWO & \(-1.77\)E+02 & 1.64E+01 & \(-2.47\)E+02 \\ \cline{1-1}  & WOA & \(-1.77\)E+02 & 3.11E+01 & \(-2.47\)E+02 \\ \cline{1-1}  & SSA & \(-1.76\)E+02 & 4.57E+00 & \(-2.41\)E+02 \\ \cline{1-1}  & BOA & \(-1.77\)E+02 & 2.91E+01 & \(-2.47\)E+02 \\ \hline \multirow{6}{*}{\(F_{9}\)} & APSO & \(-1.64\)E+03 & 9.11E+00 & \(-1.68\)E+03 \\  & GWO & 2.19E+01 & 9.88E+00 & 2.62E+00 \\ \cline{1-1}  & WOA & \(-9.78\)E+50 & 3.61E+51 & \(-1.59\)E+52 \\ \cline{1-1}  & SSA & 4.15E-06 & 1.66E-05 & 0.00E+00 \\ \cline{1-1}  & BOA & 3.64E+03 & 4.14E+02 & 2.89E+03 \\ \hline \multirow{6}{*}{\(F_{11}\)} & APSO & 2.36E-31 & 1.52E-33 & 2.36E-31 \\ \cline{1-1}  & GWO & 1.64E-07 & 1.79E-07 & 1.16E-10 \\ \cline{1-1}  & WOA & 1.22E-06 & 2.69E-06 & 4.95E-09 \\ \cline{1-1}  & SSA & 5.45E-07 & 6.81E-07 & 1.27E-11 \\ \cline{1-1}  & BOA & 3.48E+00 & 2.92E+00 & 2.22E-04 \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of our improved algorithm APSO and other metaheuristic algorithms on ten functions, where \(V_{avg}\), \(V_{std}\) and \(V_{opt}\) denote the average, standard deviation and optimal values of the solution, respectively.
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 4 High-Speed Train Noise Control Methods  
### Traditional Noise Control Methods for High-Speed Trains  
#### 4.1.1 Train Vibration Reduction  
High-speed trains will inevitably impose a greater impulse on railroad infrastructure, causing an increase in the vibration intensity of the infrastructure and the environment along the railroad line [66]. Under the action of the cyclic force caused by high-speed trains, excessive vibration amplitudes can damage the infrastructure, while this vibration also generates much noise, affecting the safety, comfort, and stability of train operation [67].  
Figure 5: (**a**) Turbulent kinetic energy distribution of the pantograph [55]; (**b**) instantaneous isosurface plots of the Q-criterion, colored by vorticity magnitude (Q = 10,000) [55]; (**c**) bogie speed flow chart [58]; (**d**) turbulent kinetic energy distribution of high-speed trains.  
At present, the main active isolation measures and passive isolation are adopted to reduce high-speed train vibration and thus control high-speed train noise. Active isolation measures include floating slabs [68], highly resilient rail pads [69], and high-performance wheels [70]. All these measures can directly reduce the vibration of the train to a large extent. Passive isolation measures include open trenches and soft-filled barriers [71]. Although the above measures show a good damping effect, damping to reduce the actual engineering problems related to noise still faces certain difficulties due to the huge mass and fast speed of high-speed trains.
## 3 Results and Discussion
### Survival Rate of Patients by Emergency Case  
#### 3.2.1 Survival Rate by Time Slot in Urban and Rural Areas  
We estimated the survival rate of emergency patients by emergency case considering first aid status and dispatching EMT-paramedics (Figure 6). In Case 1 (Figure 6a), the average survival rate in urban areas was about 31.7% higher than those in rural areas: 36.6% in urban areas and 4.9% in rural areas. In Case 2 (Figure 6b), the average survival rate was 59.4% in urban areas and 22.2% in rural areas, which is about 22.9% higher for urban areas and 17.3% higher in rural areas than Case 1. As the survival rate in rural areas in Case 2 was about 4.5 times higher than those of Case 1, immediate CPR action can remarkably increase the survival rate of emergency patients in Case 2.  
Figure 4: Vulnerable areas depending on the road traffic flow speed. The yellow and gray areas indicate urban and rural areas, respectively. The red areas indicate vulnerable areas of EMS, where an emergency patient could not arrive at an emergency medical center within 30 min. (**a**) shows 659 vulnerable areas (4 urban areas and 655 rural areas). (**b**) shows 3618 vulnerable areas (48 urban areas and 3570 rural areas).  
in urban areas in Case 3 was about 5% lower than that in Case 1, indicating that the survival rate of patients decreases because of the delayed arrival time of an EMT-P to assist the patient and the longer transfer time to an emergency medical center due to low speed flow in urban areas. In contrast, the lower survival rate in rural areas was because of the long distance to an emergency medical facility. Despite these negative impact factors, dispatching an EMT-P can positively affect the overall survival rates of emergency patients.  
In Case 4, the survival rate in urban areas also generally decreased to the same as Case 3, since the dispatch time of an EMT or healthcare provider has a negative effect on improving patient survival. In rural areas with Case 4, however, the survival rate was about two times as high as that in Case 1. Because rapid first aid can increase the survival rate of patients in rural areas, where there is a longer distance to a fire station or an emergency center, the survival rate could double when personnel at the nearest community health center are used. When personnel from a community health center are combined with first aid provided promptly by a person near the patient, the survival rate can increase by more than four times.
## 5 Experiments and Results
### Implementation Details
#### 5.3.2 Training  
The attention-based spatial-temporal fusion graph module consists of three AST-GCN blocks and one Enc-GRU block. Each AST-GCN block contains one spatial GCN and one temporal GCN to implement fast state propagation with 64 channels. To prevent overfitting, each GCN is normalized by BN and randomly drops information with a probability of 0.5. In the case of the Enc-GRU block, the encoder employs a one-layer GRU network with 30 hidden units. The input of the encoder is consistent with the output of the AST-GCN block, so there are 64 channels in the encoder. We execute the proposed method in PyTorch as introduced in [42]. The Adam optimizer is leveraged with a learning rate of 0.01, which is reduced by multiplying 0.1 after every 50 epochs. For each training session, we set the batch size to be 64, and the total number of epochs for training is set to 100. The data used for the experiment are split into 7:1:2 for training, validating, and testing.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 8 Performance Evaluation of the Proposed FL-IDS
### Remarks  
The performance analysis carried out using the ToN-IoT dataset provides the following results.  
* **Decentralization impact:** The presented simulation analysis reveals that the decentralization introduced by FL does not significantly affect the performance of centralized intrusion detection systems. FL is an effective method for preserving client data privacy by design while facilitating the collaborative development of a common ML model.
* **Client data distribution:** The distribution of client data influences the number of rounds required for the model to converge, but it does not negatively impact the effectiveness of the overall model. This underscores FL's ability to accommodate diverse data environments while achieving robust detection capabilities. In summary, FL seems to be a feasible and efficient approach for developing IDSs in the smart building sector. It achieves the double goals of protecting customer privacy and ensuring collaborative model development without compromising detection capabilities.