esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 WT/PV ES Optimization Decision
### An Algorithm Based on a PSO Algorithm and Power-Flow Calculation
#### 4.5.1 Method I: Single-Objective Power-Limited Mode of Gas Turbine  
Considering the working condition of traditional gas-turbine power generation, the original gas-turbine output power is reduced by some percentage so that the turbine has a reserve capacity, and renewable energy such as WT/PV ES is incorporated to supply power to the distribution network load. A single-objective algorithm, based on a PSO algorithm and a power flow calculation, is proposed. Its process is as follows.  
1. Step 1  
Initialize the particle swarm in the first stage, as shown in Figure 4. According to the upper layer constraints and the hourly power generation of WT/PV per unit capacity, initialize the particle velocity, the position, the iteration times, and the gas-turbine working condition and substitute them into the fitness function to calculate the power flow and the objective function to obtain the initial individual optimal decision and the initial global optimal decision.  
2. Step 2  
Update the particle swarm of the first stage. Update the speed and position of each particle within the constraint range, update the individual optimal decision and the initial global optimal decision, and increase the number of iterations by one.  
3. Step 3  
Check the number of iterations. If the set maximum number of iterations is reached, proceed to Step 4; otherwise, return to Step 2 to continue the iterative calculation.  
Step 4  
Select the site for ES. According to the second stage, as shown in Figure 4, voltage and loss-sensitivity indicators partition the whole distribution network into clusters and determine the locations of ES for each cluster.  
Step 5  
Initialize the particle swarm in the third stage (the sizing of ES). According to the WT/PV planning results in the first stage, initialize the particle swarm in the third stage and calculate the ES capacity.  
Step 6  
Update the Stage 3 particle swarm, as shown in Figure 4. Update the velocity and position of each particle within the constraint range and calculate the power flow and the objective function to update the ES capacity. Increase the iteration number by 1 and update the iteration number.  
Step 7  
Check the number of iterations. If the maximum number of iterations is reached, output the optimal result of WT/PV ES capacity planning; otherwise, return to Step 6 to continue the iteration.
# Guidelines towards Information-Driven Mobility Management
## Moving towards Content-Centric Mobility Management
### The Relevancy of Context-Aware Proactive Caching  
Proactive strategies for in-network caching can assist both consumer and producer mobility, as they support _make-before-break strategies_, i.e., before a handover takes place. While in-network caching approaches per se may not suffice to support mobility [7], proactive caching can be coupled with an anchor-less strategy to improve mobility support. The key aspect to consider in such approaches is to decide when and where to cache content. Furthermore, reactive caching approaches are useful in the context of host-oriented ICN mobility management approaches, as they assist in reducing packet loss while a node transits to a new location. It should be highlighted that, while in IP-based solutions caching is used in regards to the first router in the path, in ICN caching refers to the content of the moving node and/or NDN routers in between.  
Figure 5: Categorisation of mobility management approaches that provide support for producer mobility.  
Having in mind the support of data-oriented mobility management, proactive approaches assist in caching the node's content before a handover occurs (make-before-break). The data to cache and when to cache them can benefit from mobility anticipation mechanisms as well as from network context awareness, e.g., history of requests and producer neighbourhood context [27; 28].  
Measures of neighbour availability and centrality, as well as measures concerning similarity (for instance, similarity in types of requests), and mobility awareness (e.g., handover frequency and estimation of time-to-handover) can be easily provided via an external agent [29]. Such information can assist in anticipating handovers, and selecting beforehand a \"best\" neighbour to cache producer content.
## 8 Future Perspectives  
The future of dental imaging is ready to significantly improve clinical practice by making diagnostics more accurate, efficient, and accessible globally. While emerging technologies promise broad advancements, their global accessibility remains limited due to cost and infrastructure barriers. A principal direction is the development of real-time, AI-based feedback systems designed to assist clinicians during procedures. These systems can continuously process live imaging data, identify anomalies, refine scan settings, and support decision-making in the moment. Their integration into clinical workflows can potentially improve operations and reduce reliance on post-procedural diagnostics [65].  
Another transformative direction is the combination of multimodal technologies, particularly the incorporation of AR, VR, and 3D modeling into routine dental procedures. These tools are expected to become the standard for surgical planning, patient education, and clinical training. As AR and VR systems evolve to be more comprehensive, they will increase surgical accuracy, promote experiential learning in education, and improve communication between clinicians and patients. In parallel, 3D printing, enabled by high-resolution imaging, will continue to advance, enabling the rapid and personalized production of surgical guides, prostheses, and anatomical models to individual patient scans [43, 66, 67].  
Equitable access to these innovations remains a priority. The development of affordable, mobile, and AI-enabled diagnostic tools is essential for reducing irregularities in access to oral healthcare. These technologies can provide reliable, high-quality diagnostics to underserved and remote communities, contributing to greater global oral health equity [68].  
To ensure the responsible and lasting impact of these innovations, collaboration among researchers, clinicians, engineers, and policymakers is essential. Priorities include establishing ethical and legal frameworks, enhancing interoperability, and ensuring AI technologies are transparent, explainable, and safely integrated into practice. Long-term validation and real-world evidence will also be crucial for adoption and scalability. Ultimately, the convergence of AI, AR/VR, and digital imaging represents an important step toward smarter, more personalized dental care. When carefully applied, these tools can improve diagnostic accuracy, surgical outcomes, and the overall patient experience.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 4 Implementation of DRX Power Savings with Adaptive SPS
### Sleep Period and Power Saving  
Figure 7 shows the time frame of the adaptive SPS transmissions along with the DRX cycle for uplink traffic. During the ON-period, at time \(t_{0}\), the UE transmits data in the PUSCH. When time \(t_{ON}\) is reached, the UE enters sleep state and remains in sleep for the duration of \(t_{Sleep}\). At the end of the sleep state, the UE takes \(t_{PU}\) time to power up from sleep to active. Then the ON-period is started again and uplink transmission is done at \(t_{0}\). The time between two successive transmission is the class-specific SPS period \(T_{SPS,S}\).  
From Figure 7 it follows:  
\[T_{SPS,S}=(t_{ON}-t_{0})+t_{Sleep}+t_{PU}+t_{0} \tag{17}\]  
From (17) we can derive the relationship in (18).  
\[t_{Sleep}=T_{SPS,S}-t_{ON}-t_{PU} \tag{18}\]  
So the sleep duration \(t_{Sleep}\) is dependent on the value of \(T_{SPS,S}\). To model the actual amount of power saving in different states of the DRX cycle, we implement the UE power consumption model presented in [31] which is also used in literature [28; 32] for DRX-based analysis. The model is shown in Figure 8.  
Figure 7: Time frame of adaptive SPS with DRX.
## 3 Thermal Buckling Analysis and Optimization  
The ITPS panels are supposed to withstand significant aerodynamic load and extreme thermal gradients. The load-carrying parts of ITPS panels are thin plates, which implies that there would be large unsupported sections subject to thermal compressive stresses and in-plane mechanical compressive loads. These plates would be highly sensitive to buckling. When local buckling occurs, it might lead to catastrophic failure indirectly. The idealized 2D cell attained from previous optimization did well in balancing insulation and mechanical performance. However, when it comes to 3D structure, the possibility of buckling still exists. In order to achieve a reliable 3D structure, finite analyses for thermal buckling and optimization were carried out in this section using COMSOL.
## 3 Model and Background
### Network Structure  
As is shown in Figure 2, the network structure is improved by forming MFN-group based on [9]. The formation of network structure is shown in Algorithm 2, details are as follows.
## 3 Experimental Results
### Effectiveness of Deep Learning with Different Activation Functions  
To build an optimized and efficient DL model based on the CNN architecture, we performed several experiments by varying the hyperparameters and activation functions to assess the DL performance using various activation functions, as shown in Table 8. From the findings, we remark that InceptionV3 with swish surpasses all other AFs and reveals a very good performance and reaches an accuracy of 0.9010 with a precision that amounts to 0.9135, a recall that comes to 0.9088. and the F1-score stands at 0.9077.  
Additionally, it is noticed that the InceptionV3 with LeakyReLU model shows its lowest validation accuracy amounting to 87.06, so according to Figure 19, it is remarkable in the loss curve that the InceptionV3 with LeakyReLU model marks a high loss value compared to the other AF curves.  
Figure 19: Curve represents the loss and accuracy value of different activation function applied for training the InceptionV3 model.  
Figure 18: Curve represents the loss and accuracy value of different DL optimizers applied for training the InceptionV3 model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Activation Function** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Tanh & 89.28\% & 91.12\% & 91.11 \% & 91.08\% \\ Sigmoid & 88.95\% & 90.94\% & 90.86\% & 90.88\% \\ Softmax & 87.27\% & 88.52\% & 87.75\% & 87.57\% \\ Softsign & 89.50\% & 91.30\% & 91.28\% & 91.29\% \\ LeakyReLU & 87.06\% & 91.00\% & 90.95\% & 90.96\% \\ Swish & 90.10\% & 91.35\% & 91.88\% & 90.77\% \\ Elu & 89.25\% & 86.57\% & 83.48\% & 83.31\% \\ ReLu & 88.73\% & 90.50\% & 89.87\% & 89.72\% \\ Softplus & 89.80\% & 91.44\% & 91.00\% & 90.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of the different activation functions applied to train the InceptionV3 architecture.
## 4 Empirical Research
### Measurement Model Test  
This study tests the measurement model by assessing composite reliability (CR), average variance extracted (AVE), discriminant validity, and outer loading. As shown in Table 2 and Figure 2, each variable's CR value is over 0.7, and Cronbach's a is likewise greater than 0.7, indicating that the internal consistency of the research data is qualified. Besides, each structure's AVE value is above 0.5 and outer loading is greater than 0.7 as well, indicating the standard for convergent validity is met [96]. The R square of technology, organization, and environmental factors on the intention to adopt collaborative robots is 0.516.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Latent Variable** & **Item** & **Loading** & **Mean (SD)** & **Cronbach’s \(\alpha\)** & **CR** & **AVE** \\ \hline \multirow{4}{*}{RA} & RA1 & 0.719 & \multirow{4}{*}{3.044 (0.970)} & \multirow{4}{*}{0.850} & \multirow{4}{*}{0.898} & \multirow{4}{*}{0.690} \\ \cline{2-2} \cline{5-8}  & RA2 & 0.891 & & & & \\ \cline{2-2} \cline{5-8}  & RA3 & 0.876 & & & & \\ \cline{2-2} \cline{5-8}  & RA4 & 0.824 & & & & \\ \hline \multirow{4}{*}{CB} & CB1 & 0.799 & \multirow{4}{*}{3.120 (0.980)} & \multirow{4}{*}{0.834} & \multirow{4}{*}{0.889} & \multirow{4}{*}{0.669} \\ \cline{2-2} \cline{5-8}  & CB2 & 0.861 & & & & \\ \cline{2-2} \cline{5-8}  & CB3 & 0.871 & & & & \\ \cline{2-2} \cline{5-8}  & CB4 & 0.733 & & & & \\ \hline \multirow{4}{*}{CX} & CX1 & 0.869 & \multirow{4}{*}{2.870 (0.791)} & \multirow{4}{*}{0.802} & \multirow{4}{*}{0.876} & \multirow{4}{*}{0.704} \\ \cline{2-2} \cline{5-8}  & CX2 & 0.889 & & & & \\ \cline{2-2} \cline{5-8}  & CX3 & 0.752 & & & & \\ \hline \multirow{4}{*}{OB} & OB1 & 0.811 & \multirow{4}{*}{2.924 (1.030)} & \multirow{4}{*}{0.856} & \multirow{4}{*}{0.903} & \multirow{4}{*}{0.699} \\ \cline{2-2} \cline{5-8}  & OB2 & 0.881 & & & & \\ \cline{2-2} \cline{5-8}  & OB3 & 0.867 & & & & \\ \cline{2-2} \cline{5-8}  & OB4 & 0.782 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reliability and Validity Coefficients for Constructs.  
Figure 2: R square of the Collaborative Robots Innovation Adoption.  
As shown in Table 3, [PERSON]'s Test and Heterotrait-Monotrait ratio (HTMT) test were used to measure the discriminant validity of this study. The HTMT value between variables was lower than the threshold of 0.85, and the square root of AVE of each variable was also greater than the correlation with its variables [96].
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 4 Model Setup and Validation
### Model Validation
#### 4.2.3 Significant Wave Height Validation  
Figure 7 shows a comparison between the simulated and measured data of significant wave heights at each verification point of Typhoon Mekkhala. The absolute error at the Gulei Buoy was 0.1 m, with a relative error of 4%. For Buoy No. 1, the absolute error was 0.07 m, resulting in a relative error of 3%. At Buoy No. 2, the absolute error reached 0.1 m, with a relative error of 4%. Lastly, at No. 2 Strait, the absolute error was 0.05 m, resulting in a relative error of 2%. It is worth noting that the maximum significant wave height measured at the No. 1 marker is 5.9 m, the maximum value without considering the tide level is 5.26 m, and the maximum value after considering the tide level is 5.83 m.  
Figure 5: Comparison of calculated and measured wind speeds at each verification point of Typhoon Mekkhala. (**a**) Gulei buoy. (**b**) No. 1 buoy. (**c**) No. 2 buoy. (**d**) No. 2 Strait buoy.  
These findings indicate that the simulation results are more accurate after considering the influence of the tidal level. However, it is evident from the figure that the optimization effect varies among the verification points when considering the tide level. Particularly, the optimization effect appears to be minimal at the Gulei buoy. This inconsistency could be attributed to the limited influence of tide levels at this specific location, resulting in a lesser impact on the optimization outcome.  
The figure indicates that the significant wave height and wind speed at each point exhibit variations corresponding to the movement of the typhoon center. These variations follow a trend of initially increasing and then decreasing, with the peak time occurring simultaneously with the maximum intensity of the typhoon at the verification point. However, there exists a discrepancy between the measured and simulated values of significant wave height, which is attributed to the rapid movement of the typhoon center. Nonetheless, after considering the influence of the tide level, the errors in significant wave height and wind speed are reduced within a certain range. This adjustment brings the simulated results closer to the actual situation, making the trend more consistent with the observed data compared to the simulation results without considering the tide level. In conclusion, considering the tide level leads to more accurate simulation results for waves, which can facilitate the investigation of the impact of a typhoon landing at different tide levels.  
Figure 6: Comparison of simulated and measured tide levels at each verification point of Typhoon Mekkhala. (**a**) Sansha buoy. (**b**) Pingtan buoy. (**c**) Xiamen buoy. (**d**) Jiangjunao buoy.
###### Abstract  
Flip chip technology has been widely used in various fields. As the density of the solder balls in flip chip technology is increasing, the pitch among solder balls is narrowing, and the size effect is more significant. Therefore, the micro defects of the solder balls are more difficult to detect. In order to ensure the reliability of the flip chip, it is very important to detect and evaluate the micro defects of solder balls. High-frequency ultrasonic testing technology is an effective micro-defect detection method. In this paper, the interaction mechanism between high-frequency ultrasonic pulse and micro defects is analyzed by finite element simulation. A transient simulation model for the whole process of ultrasonic scanning of micro defects is established to simulate scanning in acoustic microscopy imaging. The acoustic propagation path map is obtained for analyzing acoustic energy transmission during detection, and the edge blurring effect in micro-defect imaging detection is clarified. The processing method of the time-domain signal and cross-section image signal of micro defects based on sparse reconstruction is studied, which can effectively improve the accuracy of detection and the signal-to-noise ratio.  
Keywords:flip chip; solder balls; micro defects; high-frequency ultrasound; sparse reconstruction +
Footnote †: journal: Applied Sciences
## 2 Experimental Procedure and Design
### Experimental Setup for Schlieren Method  
Gas flow through the nozzle into the fabricated cut kerf slot was observed using the Schlieren method. A [PERSON]'s Z-Schlieren assembly with two concave mirrors was configured as shown in Figure 3a for this experiment. This assembly is compact and avoids errors due to chromatic aberration caused by the optical lenses with the use of a condenser lens which focuses the light source into a point source that passes through a slit of 2 mm diameter. A gas supply with compressed air was connected to the nozzle via a pressure regulator. A constant pressure of 6 atm was maintained at the inlet of the nozzle for this experiment and was derived from the plasma arc cutting process. For the Schlieren setup, a 120 W high-power white LED lamp was used as the light source. The concave mirrors used in this setup were axially parabolic mirrors with a diameter of 200 mm and a focal length of 2030 mm and made of Pyrex material coated with aluminum. The reflectance of the mirror was more than 90%.  
The light emitted by the high-power LED lamp is reflected by the first concave mirror and directed horizontally toward the second concave mirror. Parallel light is generated in the region between the two mirrors. In this region, the fabricated cut kerf assembly with nozzle is placed and the gas flow through the fabricated cut kerf is observed. The reflected light from the second mirror is focused and directed toward the high-speed camera. A knife edge is placed at the focal point of the second concave mirror to improve the contrast by blocking the deflected light. The experimental setup for the Schlieren method is given in Figure 3b.  
The gas flow from the nozzle induces a change in gas pressure and the temperature in the test region, and causes a change in the refractive index of the region. These variations in pressure and temperature gradient can be captured with the help of the Schlieren setup. For imaging, Photron's FASTCAM mini UX100 camera was used. No separate ND filter or band pass filter was used in the Schlieren configuration in this study. A shutter speed (exposure time) of 1/25,600 s and a frame rate of 1000 FPS were used to capture the images.  
Figure 2: Process of fabricating cut kerf shape assembly for Schlieren experiment. (**a**) Plasma cut sample; (**b**) kerf geometry design for Schlieren experiment; (**c**) fabricated kerf assembly for Schlieren experiment.
## 2 Materials and Methods
### Data Preprocessing
It estimates influences (e.g., light scattering) for each sample relative to the entire mean spectrum. Scatter correction by MSC was calculated as follows:  
\[\mathrm{x}_{\mathrm{org}}=\mathrm{b}_{0}+\mathrm{b}_{\mathrm{ref,1}}\cdot \mathrm{x}_{\mathrm{ref}}+\mathrm{e}\, \tag{10}\]  
\[\mathrm{x}_{\mathrm{corr}}=\ \frac{\mathrm{x}_{\mathrm{org}}\ -\ \mathrm{b}_{0}}{ \mathrm{b}_{\mathrm{ref,1}}}, \tag{11}\]  
where \(\mathrm{x}_{\mathrm{org}}\) is one original sample spectra measured by the HSI camera, \(\mathrm{x}_{\mathrm{ref}}\) is a reference spectrum used for preprocessing of the entire dataset, \(\mathrm{e}\) is the unmodeled part of \(\mathrm{x}_{\mathrm{org}}\), \(\mathrm{x}_{\mathrm{corr}}\) is the corrected spectra, and \(\mathrm{b}_{0}\) and \(\mathrm{b}_{\mathrm{ref,1}}\) are scalar parameters, which differ for each sample [45]. The SNV method, a related method to MSC, is the second most applied method for scatter correction of NIR data and is given in the following equation:  
\[\mathrm{x}_{\mathrm{corr}}=\ \frac{\mathrm{x}_{\mathrm{org}}\ -\ \mathrm{a}_{0}}{ \mathrm{a}_{1}}, \tag{12}\]  
where \(\mathrm{a}_{0}\) is the average value of the sample spectrum to be corrected, and \(\mathrm{a}_{1}\) is the standard deviation of the sample-spectrum [45]. SG for spectral derivation, which includes a smoothing step, was applied with MATLAB's _Sgolawfit_ function. The used filter worked with a polynomial order of 3 and a frame length of 11.