esa-sceva/satcom-chunk-collection huggingface dataset:
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 4 Experimental Results
### Performance Evaluation of Feature Extraction and Cancer Detection on Mammography Datasets
#### 4.2.1 Evaluation of EfficientNetV2 Variants on the Combined Dataset (Mini-DDSM, MIAS, and CSAW-M)  
To evaluate the performance of the proposed model, we consider the four variants of EfficientNetV2: S, M, L and XL. The first experiment consists of testing the proposed methodology with the three combined datasets. We utilized 5164 images from mini-DDSM, 322 images from MIAS, and 10,020 images from CSAW-M. A total of 15,506 images were used, of which 10,855 were employed for training and 4651 for testing. The images are preprocessed as explained in Section 3.2 and converted to a monogenic signal space as described in Section 3.3. From each image, an ROI of size \(256\times 256\) pixels is extracted.  
Table 6 shows the results with variants of EfficientNetV2S to EfficientNetV2 XL. For all ensemble classifiers, the four performance measures described in Section 4.1 are considered by computing the average of 100 repetitions.  
Note that when using the voting KNN classifier, the EfficientNetV2S produced the best results with 86.17% accuracy, 77.58% precision, 86.17% recall, and 79.86% F1-score. The stacking classifier with EfficientNetV2M produced the best results with 86.28% accuracy, 78.75% precision, 86.14% recall, and 80.09% F1-score. The bagging classifier with EfficientNetV2S model yields 86.24% accuracy, 76.54% precision, 85.24% recall, and 79.80% F1-score. For the boosting classifier, both EfficientNetV2M and EfficientNetV2L produced an accuracy of 86.25%. EfficientNetV2L yielded a precision of 84.12%, a recall of 85.20%, and an F1-score of 84.65%, whereas EfficientNetV2L yielded a precision of 84.00%, a recall of 85.05%, and an F1-score of 84.50%.  
Although accuracy is often prioritized in classification tasks, the overall best performance in this experiment was achieved using the EfficientNetV2M model with the stacking classifier, with an accuracy of 86.28%, a precision of 78.75%, a recall of 86.14%, and an F1-score of 80.09%, as illustrated in Figure 5a.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 4 Monitoring Element
### Research Activity  
In the ALLIANCE project, we have investigated the design of efficient traffic measurement tools in the context of KDN. Network monitoring becomes a more and more complex task considering that the state information collected from the data-plane involves an ever-increasing massive amount of data (i.e., Big Data) in real network deployments. We have analyzed the main aspects to consider when measuring and classifying traffic in KDN/SDN environments (e.g., scalability, accuracy, cost). As a result, we propose a practical solution that generates flow-level traffic measurement reports in SDN environments, similar to those of NetFlow/IPFIX [34] in traditional networks. The proposed system uses only functions supported by OpenFlow, which is among the most popular standards in SDN, and allows the efficient maintenance of traffic statistics on network devices with basic characteristics (e.g., switches, routers), to finally send them to the control plane in an asynchronous way. In [35] we propose a monitoring platform that integrates the previous measurement system with a novel traffic classification module, also proposed as part of the ALLIANCE project, combining DPI and ML (decision trees), to efficiently identify the applications that generate traffic in the network.  
Likewise, in [36] we have investigated the use of ML-based techniques (Support Vector Machines, Decision Trees) for detecting cryptocurrency mining activity in the network, using only basic information from the measurement reports offered by Network/IPFIX, which is a widely deployed standard in real-world networks presently. This can be especially useful to detect potential cryptojacking attacks, which are becoming more and more common presently. In particular, this type of attack consists of compromising machines within a network to exploit their resources for cryptocurrency mining, which is often a high power-consuming process.
## 2 Design and Simulation Results
### Parametric Analysis for the Optimal Width of the Resonators (\(W_{s}\))  
Due to the compact size of the proposed antenna with quintuple rejection bands, the reflection coefficient performance of the antenna is sensitive to geometrical parameters and requires appropriate optimizations and tuning. The parametric analysis has been carried out to illustrate the critical geometrical parameters of the proposed antenna that affect the notched frequency bands. The effect of \(W_{s}\) (width of the R-CSRR and RSRR) is observed by varying its value, and the corresponding response is shown in Figure 7 by keeping all other design parameters to the fixed values. It is shown that by slightly varying \(W_{s}\), the corresponding notch frequency is shifted to the upper and lower frequency band, which is quite critical. After performing parametric tuning, the optimal resonator width has been selected as \(W_{s}\) = 0.3 mm.
# Software Integration of Power System Measurement Devices with AI Capabilities
## 4 Laboratory and Field Tests
### PV Plant Integration Testbed and Results
Synchronization was achieved even when both PV plants were not in close proximity as in Section 4.1. It is confirmed that the delay depends on the equipment, regardless of the software. Having the measurements synchronized allows us to analyze the events recorded simultaneously. Figure 13 shows the voltage values in each phase at plant 1 (Cruz del Doctor) versus the values recorded at plant 2 (Cabeza Oliva). It can be observed that at 16:41, a voltage surge was recorded at plant 2 but not at plant 1. In contrast, at 16:44, a similar event was recorded simultaneously at both plants. This suggests that the first event was likely originated at or near plant 2, while the second event occurred further out in the grid.
## 3 The Optimization of the Decision Criterion in the EDBA Algorithm
A number of wavelengths allocated to the ONU unit is equal to zero (\(W=0\)) at the start of the allocation process. The \(GT\) parameter is set as a default to 1 \(\upmu\)s at the 25 Gbit/s transmission rate corresponding to the 3.1250 KB data space value. Then, the iteration begins if a condition of the EDBA algorithm is satisfied:  
\[Rh.GT<TW=\frac{RsB}{W+1} \tag{3}\]  
If the \(TW\) size allocated to the next wavelength is \(Rh\)-times larger than the \(GT\) data space, then this concrete wavelength will be allocated (\(Rh=1\) at the start of the allocation \begin{table}
\begin{tabular}{c c} \hline \hline
**Basic Parameter** & **Symbol** \\ \hline a number of ONU units & \(N\) \\ a number of actual wavelengths assigned to ONU units & \(W\) \\ index of ONU units & \(K\) \\ index of wavelengths & \(I\) \\ starting cycle times & \(SC_{i}\) \\ a guard time interval & GT \\ a requested bandwidth & \(RqB\) \\ a residual bandwidth & \(RsB\) \\ a transmission window size & \(TW\) \\ a difference of starting cycle times & \(dif(i)\) \\ finishing cycle times & \(FC_{i}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the EDBA algorithm. process). Otherwise, the requested bandwidth will be divided among the already allocated wavelengths. The next wavelength is added to the \(W\) parameter, and the number of utilized wavelengths is increased:  
\[W=W+1 \tag{4}\]  
If a next wavelength is necessary, then the remaining residual bandwidth \(RsB\) is uniformly allocated to wavelengths. Consequently, it is divided by \(W+1\). Over the iteration, the residual bandwidth \(RsB\) is decreasing according to the following formula:  
\[RsB=RsB-({\it dif}(i).K) \tag{5}\]  
Based on the residual bandwidth \(RsB\), a decision about allocating the next wavelength is performed according to the Equation (3). After finishing the iteration, the \(TW\) size on the allocated wavelength is calculated as follows:  
\[TW_{i}=\sum_{i=1}^{K}{\it dif}(i)+\frac{RsB}{K} \tag{6}\]  
After determining \(TW\) sizes on particular wavelengths, starting cycle times of wavelengths for a subsequent ONU unit with its reported bandwidth are calculated and separated by the \(GT\) data space to avoid collision at the scheduling of the next ONU unit. The formula is used simultaneously for the finishing cycle time \(FC\) of the previous ONU unit and for the starting cycle time of the next ONU unit:  
\[FC_{i}=SC_{i}+TW_{i}+GT=SC_{i+1} \tag{7}\]  
For increasing the bandwidth utilization of the dedicated wavelengths, the \(Rt\) decision criterion must be optimized for a diverse number of connected ONU units.
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## 4 Simulation Results
### Recognition Performance of Code Type  
Figure 4 indicates the recognition performance of different FEC codes at \(-10\) dB to 10 dB. It can be observed from the figure that BCH codes rise fastest, while LDPC codes and Turbo codes rise slowly from \(-10\) dB to \(-5\) dB. It is concluded that the more complex the coding is, the higher the SNR is needed to achieve the equivalent classification accuracy. When the SNR is greater than 0 dB, the accuracy of all coding types can reach at least 90%. With the further improvement of SNR, the accuracy of all coding types can reach 99%, all code words can almost be correctly recognized. Furthermore, with the increase of SNR, the recognition accuracy tends to be stable at 99%.  
So as to better show the difference of the accuracy of these coding types at different SNR, we present confusion matrices for several classifiers at specific SNR and compare the differences of recognition accuracy at low (\(-5\) dB), medium (0 dB) and high (10 dB). As indicated in the Figure 5, under the circumstance of 10 dB SNR, all code words can be recognized correctly and the classifier has good performance for various code types.  
Similarly, in the Figure 6, when the SNR is 0 dB, errors begin to appear in all kinds of code words, but the overall recognition rate is still very high, above 95%. We can find that the recognition rate of BCH codes is the highest, because BCH code is the most basic code type, its performance is also better. Turbo code is the most misjudgment, which can be understood as a problem caused by close datasets. The confusion matrix under the condition of very low SNR is shown in the Figure 7. It shows that recognition at \(-5\) dB SNR is challenging. However, it is still possible to recognize each type of codewords in general. The recognition performance of BCH codes is still the best, while the recognition result of Turbo codes is not ideal. Furthermore, we can also find that the convolutional codes are easier to recognize than LDPC codes and Turbo codes, this is because the context information for convolutional codes is much more remarkable than other two codes. It shows that under the condition of high noise, the recognition rate of code type of the improved RNN network still decreases obviously.  
Figure 4: Recognition performance of the improved RNN model for each code type.  
Figure 5: Confusion matrix for improved RNN trained at 10 dB SNR.  
Figure 6: Confusion matrix for improved RNN trained at 0 dB SNR.
# Design Strategies for Mobile Click-and-Load Waiting Scenarios
## 4 Results
### Loading Symbol Strategies for Hierarchical Page Transitions  
#### 4.2.1 Tab Page Scenario  
(1)Icons  
In the tab page scenario, repeated measures of ANOVA were conducted to examine participants' responses to different loading icon strategies. The results showed that in page-level lateral transitions, there was no significant difference in time perception across the icon conditions. Among them, the no icon condition yielded the highest average of perceived duration. Similarly, there was no significant difference in loading speed across the icon strategies. However, satisfaction showed a significant difference (F = 7.953, \(p\) < 0.01). In terms of mean values, the ranking was hedonic-style icon > regular icon > no icon. Emotional valence also showed a significant difference (F = 10.233, \(p\) < 0.001), with the same ranking: hedonic-style icon > regular icon > no icon. There was no significant difference in arousal level across the conditions, although the no icon condition recorded the lowest average arousal level. LSD post hoc tests were conducted for the dimensions with significant within-subject effects. The results are presented in Figure 11.  
1. Satisfaction: Participants reported significantly higher satisfaction for the hedonic-style icon conditions compared to both the regular icon and no icon conditions. This suggests that users tend to prefer hedonic-style icons, likely due to their visual appeal and entertainment value. No significant difference was found between the regular icons and no icon conditions.
2. Emotional Valence: The hedonic-style icon condition also led to significantly more positive emotional responses than the other two conditions, indicating that hedonic-style icons can evoke more favorable affective experiences. There was no significant difference between regular icons and no icon conditions.
3. Arousal Level: No significant difference was found across the icon strategies, suggesting that icon type does not notably affect users' arousal levels.
2. Text  
In the tab page scenario, participants' responses to different loading text strategies were also analyzed using repeated measures of ANOVA. The results revealed no significant difference in time perception among the text conditions, with personified text yielding the lowest average perceived duration. Similarly, loading speed did not differ significantly among the conditions, although the personified text condition achieved the highest average subjective speed rating. No significant differences were found for satisfaction, emotional  
Figure 11: Results of icon strategy for the tab page scenario (Experiment 2). Vertical lines: standard deviations (SDs). ***: \(p\) < 0.001, ***: \(p\) < 0.01.  
valence, or arousal level. However, personified text consistently scored the highest means for satisfaction and emotional valence, while the no-text condition showed the highest average for the arousal level. The results are shown in Figure 12.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Miniaturization  
Antenna miniaturzation is a key specification for IBBDs. In MedRadio, MICS and common ISM bands, the effective size of the in-body antenna at the desired resonance frequency becomes significantly larger. This can create difficulties during the implantation process of IBBDs in human tissue [2]. Therefore, the size of in-body antennas is considered very crucial, and miniaturization techniques, such as changing the physical properties of  
Figure 2: Frequency bands for in-body antennas.  
the structure, varying the material characteristics or introducing supplementary elements, are used to solve these kinds of challenges.
# A Designed Calibration Approach for the Measurement -While-Drilling Instrument
## 2 Materials and Methods
### Basic Calibration Algorithm
#### 2.2.2 Plane Fitting Method (PF)
During the solution process, six parameters, \(A_{kx}\),\(B_{kx}\),\(C_{kx}\), and \(A_{kz}\),\(B_{kz}\),\(C_{kz}\), are introduced, and the equations can be rewritten as follow. \[\left\{ \begin{array}{c}A_{kx}=\cos\beta_{k}\cos\alpha_{k}\\ B_{kx}=\sin\gamma_{k}\sin\beta_{k}\cos\alpha_{k}-\cos\gamma_{k}\sin\alpha_{k} \\ C_{kx}=\cos\gamma_{k}\sin\beta_{k}\cos\alpha_{k}+\sin\gamma_{k}\sin\alpha_{k} \end{array} \right. \tag{14}\]  
\[\left\{ \begin{array}{c}A_{kz}=-\sin\beta_{k}\\ B_{kz}=\sin\gamma_{k}\cos\beta_{k}\\ C_{kz}=\cos\gamma_{k}\cos\beta_{k}\end{array} \right. \tag{15}\]  
Then the misalignment angles can be calculated with the tangent formula, which is shown in the following equations. \[\tan\gamma_{k}=\frac{B_{kz}}{C_{kz}} \tag{16}\]  
\[\left\{ \begin{array}{c}\tan\alpha_{k}=\frac{C_{kz}\sin\gamma_{k}-B_{kz}\cos \gamma_{k}}{\sqrt{\left(B_{kx}\sin\gamma_{k}+C_{kx}\cos\gamma_{k}\right)^{2}+A_ {kx}^{2}}}\\ \\ \tan\beta_{k}=\frac{B_{kx}\sin\gamma_{k}+C_{kx}\cos\gamma_{k}}{A_{kx}}\end{array} \right. \tag{17}\]  
Note that the dataset \(\mathbf{D}_{k-z}\) and Equation (15) are only used for solving \(\gamma_{k}\), and the calculation of both \(\alpha_{k}\) and \(\beta_{k}\) requires the value of \(\gamma_{k}\). Thus, this feature may be utilized to reduce calibration steps. In addition, if the axis around which the carrier platform is rotated, such as the \(x_{b}\)-axis and the \(z_{b}\)-axis, parallelize to its measured vector, all of the measuring points will shrink into a point. In a word, the PF method could completely determine the misalignment between any mirror coordinate frame of a sensor triplet and its \(car\) frame, and the calibration equation is shown as follows. \[\mathbf{u}_{k}^{b}=R_{kso}^{b}L_{ks}^{so}(\mathbf{v}_{k}^{s}-b_{k}) \tag{18}\]
## 4 Optimization of Microseismic Monitoring Network Design Based on Particle Swarm Optimization
### Evaluation Indicators of Network Design  
When designing a network of microseismic monitoring stations in an actual mine, the standard error map of the seismic event parameters H\([t_{0},x_{0},y_{0},z_{0}]\) corresponding to the station layout scheme obtained by the above method is expressed by [PERSON] and [PERSON] [31]. The source error is shown in Equation (10):  
\[\begin{gathered}\sigma_{xy}=\left[\left(C_{x}\right)_{22}\left(C _{x}\right)_{33}-\left[\left(C_{x}\right)_{23}\right]^{2}\right]^{\frac{1}{4} }\\ \sigma_{z}=\left[\left(C_{x}\right)_{44}\right]^{1/2}\\ \sigma_{xyz}=\left[\left(\sigma_{xy}\right)^{2}+\left(\sigma_{z} \right)^{2}\right]^{1/2}\end{gathered} \tag{10}\]  
In the equation, \(\left(C_{x}\right)_{ij}\) is the matrix \(C_{x}\) of the element \((i,j)\). \(\sigma_{xy}\) is the epicenter error. \(\sigma_{z}\) is the focal depth error, and \(\sigma_{xyz}\) is the focal spatial error.  
Whether a microseismic monitoring system with a certain monitoring station network configuration can detect earthquake events with magnitude \(M_{L}\) at H\({}_{i}\) constitutes the concept of monitoring system's sensitivity [32]. The expected standard deviation graph drawn by Equation (10) is a function of the event magnitude; that is, the equation represents the source location standard error whose magnitude is \(M_{L}\) and whose source coordinates are H\({}_{i}\). In the proposed monitoring area, the event magnitude \(M_{L}\) can be related to its measurable distance \(r\). The distance \(r\) from the point H\({}_{i}\) to the monitoring station can be calculated, and then the distance can be converted into an earthquake magnitude, and a sensitivity contour map can be drawn. All stations within this distance \(r\) are used to calculate the expected error of the source [14]. The ideal microseismic station network layout must have good sensitivity and positioning error. Therefore, we combine the sensitivity contour map and the positioning error map to evaluate the network array.
## 3 Design Equations of Key Circuit Components in the Presented LED Driver
### Design of Resonant Inductor \(L_{r}\) and Resonant Capacitor \(C_{r}\)  
In reference to Figure 2b, the resonant frequency \(f_{r}\) is given by:  
\[f_{r}=\frac{1}{2\pi\sqrt{L_{r}\cdot C_{r}}}. \tag{5}\]  
The switching frequency \(f_{s}\) is designed to be larger than the resonant frequency \(f_{r}\) so that the resonant tank resembles an inductive network in order to obtain ZVS on for the two power switches [21]. The relationship between the switching frequency \(f_{s}\) and the resonant frequency \(f_{r}\) is selected as:  
\[f_{s}=4f_{r}. \tag{6}\]  
The quality factor \(Q_{r}\) is defined as:  
\[Q_{r}=\frac{\sqrt{L_{r}}}{R_{a}\sqrt{C_{r}}}, \tag{7}\]  
where \(R_{a}\) is the equivalent output resistor referring to the left side of the full-bridge rectifier, and could be expressed by the following equation:  
\[R_{a}=\frac{8V_{o}}{\pi^{2}I_{o}}. \tag{8}\]  
Combining Equation (2) with Equations (3)-(5), the design equations of resonant capacitor \(C_{r}\) and inductor \(L_{r}\) are given by:  
\[C_{r}=\frac{2}{R_{a}Q_{L}f_{s}}, \tag{9}\]  
and  
\[L_{r}=\frac{4}{\pi^{2}{f_{s}}^{2}C_{r}}. \tag{10}\]  
According to Equation (8), with a \(V_{o}\) of 235 V and an \(I_{o}\) of 700 mA, the equivalent resistor \(R_{a}\) is given by:  
\[R_{a}=\frac{8\cdot 235}{\pi^{2}\cdot 700m}=272.1\Omega.\]  
In reference to Equation (9), with an \(R_{a}\) of 272.1\(\Omega\), the relationship between the resonant capacitor \(C_{r}\) and the switching frequency under different levels of quality factor \(Q_{r}\) is shown in Figure 7. With a \(Q_{r}\) of 0.15 and a switching frequency \(f_{s}\) of 50 kHz, the capacitor \(C_{r}\) is selected to be 1.22 uF according to Figure 7.  
In reference to Equation (10), with a \(C_{r}\) of 1.22 uF, the resonant inductor \(L_{r}\) is given by:  
\[L_{r}=\frac{4}{\pi^{2}\cdot(50k)^{2}\cdot 1.22\mu}=133\mu H.\]
## 4 Results and Discussion  
### Rainfall Erosivity Factor (R-Factor)  
The average annual rainfall data were used to compute the rainfall erosivity (R factor). The latter varied from 79.36 to > 80.81 MJ mm ha\({}^{-1}\) h\({}^{-1}\) year\({}^{-1}\). The prevailing pattern of the average annual rainfall in the study area displays the highest values in the northern portions and exhibits a southwards gradient towards the Gulf. Therefore, a high R factor is anticipated in the northern parts of the study area as R increases in such parts, while it decreases in the south (Figure 3). Specifically, the R factor of the study area has five classes: 79.36 to 79.73; 79.73 to 80.09; 80.09 to 80.45; 80.45 to 80.81; and >80.81 MJ mm ha\({}^{-1}\) h\({}^{-1}\) year\({}^{-1}\) representing 23.5, 20.3, 11.8, 23.0, and 21.4% of the basin area, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**LULC** & **C Factor Range** & **Mean** \\ \hline Shrubland & 0.01–0.02 & 0.015 \\ Urban & 0.05–0.1 & 0.075 \\ Crops & 0.3–1 & 0.65 \\ Barren & 0.4–0.6 & 0.5 \\ Water/Wetland & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The C factor for different land use land cover (LULC) classes.