esa-sceva/satcom-chunk-collection huggingface dataset:
# Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
## 5 Results
### Classification Performance  
Table 3 summarizes the evaluation results of different models. The confusion matrix of the proposed method is shown in Figure 5. The results indicate that the proposed method outperformed the other classifiers with an accuracy of 80.87%, precision of 84.02%, recall of 82.53%, F1 score of 82.57%, and AUC value of 87.10%. The algorithm in this study demonstrates an average improvement of 3% across all metrics when compared to the sub-optimal algorithm. And models such as XGB, GB, and DT also perform well, indicating that the tree structure is useful for dealing with high-dimensional features and imbalance problems. The results indicate that the model can fully learn the minority class features while also removing noisy samples that can affect classification, which addresses the issue of data imbalance in gout.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline
**Proposed** & **80.87** & **84.02** & **82.53** & **82.57** & **87.10** \\ [28] XGB & 79.72 & 82.80 & 77.78 & 80.53 & 84.41 \\ [15] SVM & 71.36 & 59.51 & 47.47 & 49.85 & 67.25 \\ [10] KNN & 65.17 & 55.21 & 44.53 & 46.67 & 64.62 \\ [17] GB & 78.46 & 83.43 & 76.98 & 79.78 & 83.71 \\ [16] DT & 71.36 & 66.06 & 67.01 & 66.53 & 77.47 \\ [31] MLP & 72.96 & 67.26 & 55.44 & 59.00 & 71.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the different models.  
Figure 5: The confusion matrix of the experiments. (**a**) Imbalanced dataset with features selected. (**b**) Balanced dataset with resampling algorithm. (**c**) Results with resampling and ensemble algorithm.  
In this part, the effectiveness of the proposed method in addressing data imbalances is discussed. This study evaluates the performance of various resampling methods, including SMOTE, BorderSMOTE, SMOTE+ENN, SMOTE+Tomek link, RandomOver, and the proposed ENaNSMOTE-Tomek link, using a single XGB model. The results are presented in Table 4. It is evident that the proposed method outperforms the other resampling methods in terms of classification performance. SMOTE, BorderSMOTE, and RandomOver achieve AUCs of 84%, but the proposed method generates higher-quality samples, resulting in an AUC of 85.98%, accuracy of 80.52%, precision of 82.62%, recall of 81.96%, and F1 score of 81.08%. The proposed method exhibits the best overall performance, which indicates the effectiveness of the resampling method proposed in this study for addressing the imbalance problem.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 6 Analyses and Discussion
Dynamic power consumption (\(P_{dyn}\)) is directly proportional to the square of the operating voltage (\(V\)) [125], as described by the equation \(P_{dyn}=\alpha CV^{2}f\), where \(C\) is the switching capacitance, \(f\) is the clock frequency, and \(\alpha\) is a constant representing the switching factor [126]. This squared relationship between \(P_{dyn}\) and \(V\) can be exploited to significantly lower power usage, provided that the reliability of operation remains intact, as lowering the voltage increases the switching time and reduces the maximum operating frequency. * Moreover, both the voltage and frequency can be dynamically adjusted based on performance requirements through _dynamic voltage and frequency scaling (DVFS)_. Thistechnique can also be explored in the context of physiological monitoring by using WMDs for power optimization while maintaining the necessary performance [126]. * Despite its benefits, _compressed learning_ was found to be underutilized in the reviewed articles, requiring further investigation by exploring the sparse nature of physiological signals. In conclusion, FPGAs offer significant power optimization potential due to their reconfigurability, customizability, and granular control. Additionally, the flash-based and hybrid FPGAs, which are designed for low-power applications, could be used for power-sensitive WMDs. Moreover, features like clock gating, power gating, and dynamic partial reconfiguration enable designers to create energy-efficient designs tailored to specific application needs. For instance, _compressive sensing_ can minimize sampling activity at the sensing stage, complemented by an _event-driven_ approach using _clock gating_ to activate sensors only when necessary. At the computing stage, a _compressed learning_ approach can be used for analytics, avoiding reconstruction overheads. The algorithm can be modularized and deployed in a _time-division multiplexed_ fashion by using a _partial reconfiguration_ to reduce idle power consumption. This comprehensive strategy reduces the unnecessary sensor on time, minimizes redundant data sampling, decreases transmission and computation loads, and reduces power leakage, significantly lowering energy consumption.
## Appendix B  
**Proof of Lemma 1.**  
\[D_{21}=\underbrace{\left(1-\Pr\left(\chi_{D2}^{PDMA}\geq\varepsilon_{1} \right)\right)}_{\kappa_{1}}\underbrace{\left(1-\Pr\left(\gamma_{SD1\gets 2,k}^{PDMA}\geq\varepsilon_{1}\right)\right)}_{\kappa_{2}}.\] (A10)  
Here, \(\kappa_{1}\) can be calculated as  
\[\begin{split}&\kappa_{1}=1-\Pr\left(\chi_{D2}^{PDMA}\geq \varepsilon_{1}\right)\\ &=1-\Pr\left(|h_{2}|^{2}\geq\frac{\varepsilon_{1}\varpi|f_{2}|^{2 }+\varepsilon_{1}}{\rho}\right)\\ &=1-\frac{1}{\lambda_{f_{2}}}\int_{0}^{\infty}\exp\left(-\frac{ \varepsilon_{1}\varpi y+\varepsilon_{1}}{\rho\lambda_{h_{2}}}-\frac{y}{ \lambda_{f_{2}}}\right)dy\\ &=1-\frac{\rho\lambda_{h_{2}}}{\rho\lambda_{h_{2}}+\varepsilon_{1 }\varpi\rho\lambda_{f_{2}}}\exp\left(-\frac{\varepsilon_{1}}{\rho\lambda_{h_ {2}}}\right).\end{split}\] (A11)  
Similarly, \(\kappa_{2}\) can be calculated as  
\[\begin{split}&\kappa_{2}=1-\Pr\left(\gamma_{SD1\gets 2,k}^{PDMA}\geq\varepsilon_{1}\right)\\ &=1-\Pr\left(\left|g_{2,k}\right|^{2}\geq\frac{\varepsilon_{1} \varpi\rho|f_{2}|^{2}+\varepsilon_{1}}{a_{1}\rho-\varepsilon_{1}a_{2}\rho} \right)\\ &=1-\frac{1}{\lambda_{f_{2}}}\int_{0}^{\infty}\sum\limits_{k=1}^{ K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\exp\left(-\frac{(\varepsilon_{1}\varpi\rho y+ \varepsilon_{1})k}{(a_{1}\rho-\varepsilon_{1}a_{2}\rho)\lambda_{2}}-\frac{y}{ \lambda_{f_{2}}}\right)dy\\ &\\ &=1-\sum\limits_{k=1}^{K}\left(\begin{array}{c}K\\ k\end{array}\right)(-1)^{k-1}\vartheta_{2}.\end{split}\] (A12)  
This completes the proof of Lemma 1.
## 3 Health Benefits
### Antioxidant Activity
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Type**} & **Botanic** & **Experiment Design** & **Results** & **Ref.** \\ \cline{2-4}  & **Part** & & & \\ \hline \multicolumn{4}{l}{Antioxidant activity} \\ \hline \multirow{2}{*}{In vitro} & \multirow{2}{*}{Head} & Utilization of a hydroalcoholic extract of artichoke & \multirow{2}{*}{Inhibition of LDL-C oxidation} & \multirow{2}{*}{[87]} \\  & (0.18–1.44 \(\mu\)g/mL; 1–4 h) on Caco-2 human intestinal cells & & \\ \hline \multirow{2}{*}{In vitro} & \multirow{2}{*}{Stem} & Application of extracts from artichoke stems (1 and 2 mg/mL) in & \(\downarrow\) ROS & \\  & & LPS-stimulated human THP-1 macrophages for 1 h & \(\downarrow\) IL-6, CCL\({}_{2}\) & \\ \hline \multirow{2}{*}{In vitro} & \multirow{2}{*}{Leaf} & Artichoke leaf extract (25, 50 and 100 \(\mu\)g/mL; 24 h) on & \(\downarrow\) Oxidized LDL & \\  & & endothelial cells and monocytes obtained from umbilical cords & \(\downarrow\) ROS & \\ \hline \multirow{2}{*}{In vivo} & \multirow{2}{*}{Leaf} & Alloxan-induced diabetic rats treated with artichoke leaf extracts & \(\uparrow\) CAT, SOD and GSH in liver & \\  & & (two doses of 200–400 mg/kg bw) daily for 4 weeks & \(\downarrow\) adney & \\ \hline \multirow{2}{*}{In vivo} & \multirow{2}{*}{Leaf} & Cd-toxicity-induced rats treated with artichoke leaf extract & \(\uparrow\) CAT, GSH and GPx in liver & \\  & & (300 mg/kg bw) daily for 4 weeks & \(\downarrow\) Liver MDA and liver and & \\  & & kidney SOD & \\ \hline \multirow{2}{*}{In vivo} & \multirow{2}{*}{Head} & ALD-induced mice treated with an ethanolic extract of artichoke & \(\uparrow\) SOD and GSH levels & \\  & & (0.4, 0.8, and 1.6 g/kg bw) for 10 days & \(\downarrow\) MDA & \\ \hline \multirow{2}{*}{In vivo} & \multirow{2}{*}{Leaf} & Urolithiasis-induced rats treated with artichoke leaf extract (125, 250 and 500 mg/kg bw) for 28 days & \(\downarrow\) MDA and GSH in kidney & \\ \hline \multicolumn{4}{l}{Hepatoprotective action} \\ \hline \multirow{2}{*}{In vitro} & \multirow{2}{*}{Head} & Application of artichoke water extract (100 \(\mu\)M; 24 h) in & \(\downarrow\) Expression of PEPCK & \\  & & palmitate-induced insulin resistance HepG2 hepatocytes & \\ \hline \multirow{2}{*}{In vitro} & \multirow{2}{*}{Head} & Utilization of artichoke extract (400–1200 \(\mu\)M; 24 h) on rat & \(\uparrow\) GSH \\  & & hepatocytes and human HepG2 cells exposed to H\({}_{2}\)O\({}_{2}\) & \(\downarrow\) MDA & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Health benefits of different artichoke parts.
## 3 Materials and Methods
### Model Design  
#### 3.4.1 Backbone  
The basic backbone model uses the EfficientNet model, which achieves State-of-The-Art in five dataset segments, including Flowers and CIFAR-100. EfficientNet achieved both top-1 and top-5 accuracy in ImageNet while reducing the number of parameters and attaining high accuracy, unlike the existing CNN model, which had a significant number of parameters. To improve model performance, compound scaling is essential, and the optimal values were found by organically adjusting the Width, Depth, and Resolution scaling.  
As shown in Table 7, the optimized value exhibits the best performance in terms of computation and accuracy, and the compound scaling combination formula is based on Equation (3) below. In this equation, \(\alpha\), \(\beta\), and \(\gamma\) are constants and are found using grid search, and \(\phi\) is a factor that can be controlled by the user and takes an appropriate value according to the available resources.  
\[\begin{split}& Depth:d=\alpha^{\varnothing}\\ & Width:w=\beta^{\varnothing}\\ & Resolution:r=\gamma^{\varnothing}\\ & s\cdot t\cdot\alpha\cdot\beta^{2}\cdot\gamma^{2}\approx 2\\ &\alpha\geq 1,\ \beta\geq 1,\gamma\geq 1\end{split} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Non-Label Smoothing** & **Label Smoothing** \\ \hline Negative & 0 & 0.02 \\ Positive & 1 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The result of label smoothing on open-access tympanic membrane dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean** & **Standard Deviation** \\ \hline SCH pediatric hearing GT & 18.0557 & 12.6491 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The _Mean_ and _Standard Deviation_ on SCH pediatric hearing dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Mean** & **Standard Deviation** \\ \hline SCH pediatric hearing GT & 18.0557 & 12.6491 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The _Mean_ and _Standard Deviation_ on SCH tympanic membrane disease subset dataset.  
EfficientNet has a group of models such as B0, B1, B2, B3, B4, B5, B6, B7, B8, and L2 (added after the launch of EfficientNet for B8 and L2), and each model has its own Compound Scaling value. As the number of models increases, the amount of computation doubles and the intensity of regulations to prevent overfitting also increases.
# A High Capacity Watermarking Technique for the Printed Document
## 2 Related Theory  
This section introduces some theories in watermarking algorithm. They are as follows: print-scan invariant, Fourier boundary descriptor, quadratic quantization function and the QR code.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Num** & **Author-year** & **Features** & **Capacity** & **Visual** & **Robustness** & **Drawbacks** \\ \hline
1 & [15]-2012 & Stroke & High & Medium & Medium & Only for characters with strokes \\
2 & [16]-2007 & Grayscale & High & Low & High & Affecting visual effects \\
3 & [17]-2006 & Grayscale & High & Low & High & Affecting visual effects \\
4 & [18]-2017 & Grayscale & High & Low & High & Affecting visual effects \\
5 & [19]-2004 & Boundary & Medium & High & Low & Watermark is not blind, not \\  & & & & & & resistant to print scanning \\
6 & [20]-2004 & Pixel & Medium & Medium & Medium & Pixel flipping strategy is \\  & & & & & & complex, affecting visual effects \\
7 & [21]-2008 & Pixel & Medium & Medium & Medium & Pixel flipping strategy is \\
8 & [22]-2019 & Pixel & High & Medium & Medium & complex, affecting visual effects \\
9 & [23]-2008 & Shading & Medium & Low & Low & poor robustness, affecting \\
10 & [24]-2018 & Shading & High & Low & Low & visual effects \\  & & & & & & Not blind, affecting visual \\  & & & & & & effects, poor robustness \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of text watermarking algorithms based on image processing.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 4 Experiments
### Experimental Setup
#### 4.1.2 Implementation Details  
The basis for our implementation is the Bag of Tricks (BOT) approach [66] implemented in the framework FastReID [69]. BOT uses a slightly modified ResNet-50 [70] and several data augmentation techniques to establish a strong baseline for person re-identification. The modifications include the removal of reduction of resolution at one place in the ResNet and the addition of batch normalization between the extracted feature vector and the classification layer during training. The data augmentation techniques include scaling of the input images to a resolution of \(256\times 128\) pixels, Random Erasing [71] with probability \(p=0.5\), random horizontal flipping with probability \(p=0.5\), and padding by 10 pixels followed by random cropping of size \(256\times 128\). As in the UAL approach [25], which serves as the basis for our architecture, we employ the modified ResNet model as the backbone of our architecture and also include the batch normalization before the classification layer. As a result of this choice of backbone, the dimensionality of the feature vectors, as well as the uncertainty vectors, is \(d=2048\).  
The training pipeline follows the one of the UAL approach [25]. We train our model for 120 epochs by utilizing the Adam optimizer [72] with a basic learning rate of \(\eta=0.00035\). The learning rate scheduling includes a warm-up phase for 2000 iterations, which are approximately 10 epochs, with a linear increase from \(0.1\eta\) to \(\eta\). Furthermore, the learning rate is reduced by a factor of 0.1 at epochs 40 and 90. The training batches are constructed byfirst selecting eight individuals at random and then four random images for each individual. Therefore, the batch size is 64. Our loss consists of the classification and data uncertainty loss \(\mathcal{L}_{\text{C\&DU}}\) and the Triplet Hard Loss \(\mathcal{L}_{\text{TriH}}\) with a margin of 0.3. As regularization, we use weight decay with a strength of \(\lambda=0.0005\) and label smoothing with a smoothing factor \(\alpha=0.1\).  
For further implementation details, we refer to the source code that is publicly available Source code available at [[https://www.tu-ilmenau.de/neurob/data-sets-code/uncertainties-for-embeddings](https://www.tu-ilmenau.de/neurob/data-sets-code/uncertainties-for-embeddings)]([https://www.tu-ilmenau.de/neurob/data-sets-code/uncertainties-for-embeddings](https://www.tu-ilmenau.de/neurob/data-sets-code/uncertainties-for-embeddings)) (accessed on 22 August 2024).
# Optical Beamforming Networks for Millimeter-Wave Wireless Communications
## 5 Scalability and Application Potentials
### Scalable Techniques for OBFNs
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**PerformanceMaterial** & **Si** & **Si\({}_{3}\)N\({}_{4}\)** & **Si\({}_{3}\)N\({}_{4}\)** & **Si\({}_{3}\)N\({}_{4}\)** & **Si\({}_{3}\)N\({}_{4}\)** & **Si\({}_{3}\)N\({}_{4}\)** & **InP** \\ \hline Propagation loss (dB/cm) & \(\sim\)1.0 & \(<\)0.1 & \(\sim\)2.0 & \(\sim\)0.8 & \(\sim\)0.8 & \(\sim\)2.0 \\ \([\)120\(]\) & [121] & [122] & [123] & [124] & [125] \\ \hline \end{tabular}
\end{table} 
Table 2: Typical propagation losses of Si, Si\({}_{3}\)N\({}_{4}\) and InP waveguides. Figure 22: A Si-Si3N4 monolithic integration platform provided by Advanced Micro Foundry (AMF) [120]. Figure licensed under a Creative Commons Attribution 4.0 License. PDs are significant devices as well. The linearity of PDs can be evaluated via a consideration of the maximum output power that approaches the 1 dB compression point [130; 135]. One alternative to using high-speed and high-power PDs is to use uni-traveling carrier (UTC) PDs. These PDs have superiorities of low bias voltage, high operation speed and high output power compared to common PDs, since electrons are the majority carriers as shown in Figure 23b [136; 137; 138]. Currently, UTC-PDs are mainly fabricated on Si/Germanium, InP and InGaAs material platforms [139; 140; 141]. The maximum output power of Si/Germanium UTC-PDs has reached \(\sim\)0 dBm at 20 GHz [140; 141; 142]. Compared to those with a Si/Germanium platform, InP/InGaAs-based UTC-PDs have a wider bandwidth and higher output power, which has resulted in a maximum output power larger than 20 dBm at a low-frequency band of mmWave spectrum such as at 28 GHz, 40 GHz and 48 GHz [143; 144; 145; 146]. Therefore, heterogeneous integrations of InP-based UTC-PDs on Si and Si\({}_{3}\)N\({}_{4}\) can be employed [147; 148; 149], to obtain high-speed and high-power PDs which are comparable to their InP-based counterparts. In summary, it is promising to integrate high-linearity modulators and PDs on Si/ Si\({}_{3}\)N\({}_{4}\) platforms, achieving compact and high-linearity OBFNs.
## 3 Finite Element (FE) Modeling of Side Joints with URSP Connectors
### Validation of Modeling with URSP Connectors  
In order to validate the proposed finite element modeling approach for a URSP connector, an FEM-based verification test on a simply supported beam with URSP connectors along the entire span subjected to negative bending moment is performed in this section [45], since there has been no experiment on the structural performance of the composite frames with URSP connectors till now. The loading scheme, cross-section, URSP connector, and material information of the tested beam are depicted in Figure 8a-c. The numerical results are compared with the test results, as shown in Figure 8d. It can be seen that the predicted bending moment-deflection curve at the midspan is consistent with the experimental results presented in reference [45], which verifies the accuracy of the FE modeling methods in this study.  
Figure 6: Shear-slip model of the URSP connector.  
Figure 7: Shear-slip model of the traditional headed stud.  
Figure 8: Verification of the proposed model by reference [45]. (**a**) Loading scheme of the beam test (unit: mm); (**b**) cross-section of the beam specimen (unit: mm); (**c**) URSP connectors in the test (unit: mm); (**d**) comparison of test and simulation results.
## 4 Measurements
### Lower UWB Channel 3.95 GHz  
In this section the results of the lower UWB channel are shown. The investigated centre frequency is 3.95 GHz with 500 MHz bandwidth. The S\({}_{21}\) measurements of a walking subject as a function of the  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Minimum path loss [dB]** & **Maximum path loss [dB]** & **K-factor [dB]** \\ \hline Meas 1 & 53 & 67 & 9.16 \\ Meas 2 & 52 & 65 & 9.97 \\ Meas 3 & 50 & 66 & 10.5 \\ Meas 4 & 48 & 68 & 7.5 \\ Meas 5 & 48 & 63 & 9.7 \\ \hline \end{tabular}
\end{table}
Table 3: K-factor estimation for the different measurements for the 2.45 GHz channel.  
Figure 9: **(a)** measured S\({}_{21}\) for a subject walking; **(b)** CDF of the path loss for the five cases.  
time, is shown in Figure 10a. The S\({}_{21}\) is almost constant for the stationary subject at the starting point and in front of the wall. When the subject is walking the S\({}_{21}\) oscillates with a greater range of values, with peaks of maximum S\({}_{21}\) equal to \(-51.89\) dB and minima of \(-57.23\) dB. The CDF of the five measurements is shown in Figure 10b.  
The CDF distributions for the measurements are very similar. Table 4 summarizes the minimum and maximum values and the K-factor for the corresponding Rician distribution. The K-factor for the 3.95 GHz frequency range is always greater than 16.2, corresponding to a more directive link between the footwear and the wrist compared to the 2.45 GHz case. This is also due to a greater antenna realized gain compared to the 2.45 GHz case. The increased bandwidth decreases the fading, reducing the maximum path loss variation to 4 dB. The greater K-factor compared to the narrowband channel can also be explained by the smaller antenna beamwidth, reducing reflected signals from the surrounding environment.
## 4 Implementation
### Bridge
#### 4.3.2 Data Processing  
The Bridge processes all the messages that arrive over the serial connection. If the Bridge recognizes an incoming message with the appropriate start character, it begins processing the message; if the message does not have an end character, it indicates that the message is a segment, and it must continue reading the serial port until receiving the whole message.  
After all segments have been received, the Bridge examines the length field to guarantee that the message was received completely and that no fragments were lost. It is not necessary to verify the integrity of the data at this point since this verification is performed at another level of the proposed opportunistic stack; specifically, libp2p is responsible for this verification once the data are decoded. The full message is then decoded from hexadecimal to UTF-8 and delivered to the function that handles libp2p communications.
## 5 System Performance
#### Uncertainty  
The current system was developed under conditions of data scarcity and discontinuity. Uncertainty surrounding the validity of rating curves for each streamflow dataset presented a particular challenge. The uncertainty is produced because of limited opportunity to measure river flows during high flow conditions, and ongoing changes in channel morphology during such conditions without associated changes to the rating curve information. For example, it can be seen in Figure 7 that the flood peak at the Lelata site on 18 th December 2020 was not recorded because the river over-topped the monitored channel at just below 2 m and resulted in a flat hydrograph. Limited data for the development of river rating curves mean that assumptions made during construction of the unit hydrographs will impact the performance of the model in predicting the magnitude of peak flows.  
A second source of uncertainty lies within the area-weighted rainfall estimates used by the model for each catchment, which despite being informed by new rain-gauges within the catchment will be susceptible to errors for spatially heterogeneous storm events. The impact of rainfall estimation uncertainty on model performance at this site will be reported in a subsequent publication.  
To improve model performance, the model was calibrated using the rainfall loss factor which was dynamically set to reflect different antecedent soil moisture at the start of each rainfall event. It should be noted, however, that the model was less accurate at predicting lower peak flows.