esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in High-Speed, High-Power Photodiodes: From Fundamentals to Applications
## 5 Discussion
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Device Type \({}^{*}\)** & **Coupling Type** & **Si Lens** & \(I_{\text{ph}}\) **(mA)** & **Output Power (dBm)** & **Ref.** \\ \hline Hybrid absorber UTC-PD & Bowtie antenna & Yes & 6 & \(-30\) (1 mW) @ 1 THz & [72] \\ \hline Hybrid absorber UTC-PD & Bowtie antenna & Yes & 6 & \(-35.2\) (\(0.3\) mW) @ 1.5 THz & [72] \\ \hline Hybrid absorber UTC-PD & Bowtie antenna & Yes & 6 & \(-40\) (0.1 mW) @ 2 THz & [72] \\ \hline Hybrid absorber UTC-PD & Bowtie antenna & Yes & 6 & \(-44\) (0.04 mW) @ 2.5 THz & [72] \\ \hline MUTC-PD & Patch antenna & No & 45 & 20 (100 mW) @ 60 GHz & [98] \\ \hline TWPD & Bowtie antenna & Yes & 6.3 & \(-33\) (0.5 mW) @ 40 GHz & [91] \\ \hline UTC-PD & Slot antenna & Yes & \(>\)20 & \(-5.23\) (0.3 mW) @ 120 GHz & [123, 124] \\ \hline UTC-PD on SiC & Tapered slot antenna & No & 14.5 & 1.59 (1.44 mW) @ 273 GHz, – 1 V & [134] \\ \hline UTC-PD on SiC & Tapered slot antenna & No & 18 & 4.04 (2.53 mW) @ 273 GHz, bias optimized & [134] \\ \hline WG-UTC-PD & Bowtie antenna & Yes & 13.5 & \(-12.22\) (60 mW) @ 250 GHz & [142] \\ \hline UTC-PD & Log-periodic antenna & Yes & 13 & \(-25.9\) (2.6 mW) @ 1.04 THz & [204] \\ \hline UTC-PD & Log-periodic antenna & Yes & 20 & \(-5.23\) (300 mW) @ 300 GHz & [205] \\ \hline PIN-PD array & Bowtie antenna & Yes & NA & \(>\)10 dB vs. an emitter @ 300 GHz & [206] \\ \hline UTC-PD & Log-periodic antenna & Yes & 15 & \(-11.2\) (75 mW) @ 414 GHz & [208] \\ \hline UTC-PD & Log-periodic antenna & Yes & 15 & \(-17.4\) (18 mW) @ 609 GHz & [208] \\ \hline UTC-PD & Log-periodic antenna & Yes & 15 & \(-20.8\) (8.3 mW) @ 804 GHz & [208] \\ \hline UTC-PD & Twin dipole antenna & Yes & 14 & \(-19.6\) (10.9 mW) @ 1.04 THz & [209] \\ \hline UTC-PD & Log-periodic antenna & Yes & 20 & \(-9.2\) (120 mW) @ 300 GHz & [224] \\ \hline Type-II UTC-PD & Dual-ridge horn antenna & No & NA & \(-15\) (31.6 mW) @ 240 GHz & [225] \\ \hline UTC-PD & TEM-horn antenna & Yes & 2.75 & \(-29.5\) (1.13 mW) @ 940 GHz & [226] \\ \hline UTC-PD & TEM-horn antenna & Yes & 2.5 & \(-33.4\) (0.46 mW) @ 1.04 THz & [226] \\ \hline UTC-PD & TEM-horn antenna & Yes & 2.88 & \(-35.1\) (0.31 mW) @ 1.365 THz & [226] \\ \hline UTC-PD & slot antenna & Yes & 10 & \(-15.53\) (28 mW) @ 700 GHz & [227] \\ \hline UTC-PD & slot antenna & Yes & 10 & \(-24.56\) (3.5 mW) @ 1.25 THz & [227] \\ \hline \hline \end{tabular}  
* \({}^{*}\)Waveguide– and traveling-wave-type PDs are denoted as \({}^{\prime}\)WGPD\({}^{\prime}\) and TWPD, while others are vertical-mesa PDs.  
\end{table} 
Table 4: \(Cont.\)with waveguide-based Bragg grating devices in glass inscribed using femtosecond laser in the future.
# Enhancing the Reliability of Cellular Internet of Things through Agreement
## 3 The Proposed Protocol
In the first time of the _Data Collection Stage_, \(\mathrm{PU}_{i}\) transmits its initial request to other PUs. In this study, it is assumed that each receiver PU can correctly identify the sender of the request. When a normal PU receives a request sent from \(\mathrm{PU}_{i}\), it will store the received value (denoted as _dc_ (\(i\))) in the root of its _dc-tree_. In the second time, each PU transmits the root value of its _dc-tree_ to all other PUs. If \(\mathrm{PU}_{1}\) sends a request _dc_ (\(i\)) to \(\mathrm{PU}_{2}\), \(\mathrm{PU}_{2}\) stores the received request (denoted as _dc_ (\(i\)1)) in vertex \(i\)1 of its _dc-tree_. Similarly, if \(\mathrm{PU}_{2}\) sends a request _dc_ (\(i\)1) to \(\mathrm{PU}_{1}\), the received request will be named _dc_ (\(i\)12) and stored in vertex \(i\)12 of \(\mathrm{PU}_{1}\)'s _dc-tree_ in the third time. A request _dc_ (\(i\)12... \(n\)) stored in the vertex \(i\)12... \(n\) of the _dc-tree_ indicates that the request just received was sent through \(\mathrm{PU}_{i}\), \(\mathrm{PU}_{1}\)... \(\mathrm{PU}_{n}\); \(\mathrm{PU}_{n}\) is the latest PU that passed the request. When a  
Figure 4: The execution steps of CIoT Agreement Protocol (CIoTAP). request is transmitted through a PU multiple times, the name of the PU will be repeated accordingly. In order to avoid the repeated influence of faulty PUs in the _dc-tree_, vertices with duplicate PU names will be deleted. In short, the root of the _dc-tree_ is always named \(i\) to indicate that the storage request was sent from \(\text{PU}_{i}\) in the first time; the vertex of the _dc-tree_ is marked by a list of PU names. The PU name list contains the name of the PU through which the stored request is transmitted. An example of a _dc-tree_ is shown in Figure 5.  
The _Decision_ (\(i\)) function is used by all normal PUs to eliminate the impact caused by the faulty PU and obtain the decision value in the _Data Resolution Stage_ of CloTAP.
## 4 Numerical Results  
### Behavior of the Dry-Friction and Resistance Force Terms  
Figure 2 shows the evolution of the non-dimensional dry-friction terms (as written in the Equations (4) and (5)) with the variations of the non-dimensional velocities of the two masses \(m_{1}\) and \(m_{2}\) respectively. From the Figure 2a, it can be said that when the non-dimensional velocity (\(\dot{y}_{1}\)) has the negative values, the dry-friction value reaches to a negative constant value, i.e., \(-0.2413\). This is the value of the negative \(t_{1}\). When the non-dimensional velocity has the positive values, the non-dimensional dry-friction term reaches the \(0.2413\) value, equal to the value of \(t_{1}\). Those values are well agreed with the definition of a dry-friction in mechanical systems. In the case of the Figure 2b, the same agreement holds for the evolution of the non-dimensional dry-friction term with the non-dimensional velocity (\(\dot{y}_{2}\)) of the mass \(m_{2}\). The dry-friction term reaches a constant value of \(-0.288\) and \(0.288\) when the non-dimensional velocity, \(\dot{y}_{2}\), has the negative and positive values, respectively.  
Figure 3 depicts the evolution of the resistance force terms \(f_{\rm R1}(\dot{y}_{1})\) and \(f_{\rm R2}(\dot{y}_{2})\) of the two oscillators with the variation of the velocities \(\dot{y}_{1}\) and \(\dot{y}_{2}\), respectively. From the Equations (4) and (5), we can say that the resistance force terms consist of two functions, one is linear damping, and another is the dry-friction term. So, when the velocity values continuously increase in a negative direction, the resistance force terms have linearly increasing slopes from the constant values in the negative direction along the \(y\)-axis with the negative directional variation of the velocities. The constant value is due to the dry-friction term, and the negative slope is due to the linear damping term. A similar thing happens when the velocity state variables are continuously increased in positive directions. The Figure 3a,b confirm the behavior of the resistance force terms, \(f_{\rm R1}(\dot{y}_{1})\) and \(f_{\rm R2}(\dot{y}_{2})\) with the variations of the velocity state-variables \(\dot{y}_{1}\) and \(\dot{y}_{2}\) respectively.
# Activity Prediction Based on Deep Learning Techniques
## 1 Introduction
Further, there are lots of classifiers to realize the prediction tasks using the collected data. Those classifiers include classic machine-learning-type classifiers as well as modern deep-learning-type classifiers. The classic machine learning types of classifiers include the naive Bayes classifiers [2; 11; 12], decision trees [13], hidden Markov models [3; 14; 15], support vector machines [16] and etc. Modern machine learning algorithms include well-known convolutional neural networks (CNN) [17], recurrent neural networks (RNN) [18; 19], long short-term memory (LSTM) [20; 21; 22; 23; 24; 25; 26; 27; 28; 29], etc. These classifiers are data-driven classifiers that require labeled data to train the classifiers themselves. As we described previously, we implement two classifiers for activity recognition using data collected from portable body-worn sensors to obtain personal environmental information. Most previous research primarily addressed activity recognition in either indoor or outdoor environments. However, our research attempted to predict activities in more heterogeneous environments, reflecting our various daily life in diverse environments. We assumed 13 different activities that are typical to Korean families and tried to predict the activities using well-known MLP and LSTM models. Simulation results showed that the LSTM model has higher accuracy compared to that of the MLP model. This paper is organized as follows. The first section provides in-depth details on the collection of raw data, including the place where the data were collected and the subject persons who volunteered for the collection task. The second section provides technical details on the deep learning models used. The third section describes simulation results comparing the performance of the two deep learning models. Finally, the fourth section provides conclusions and insights from the results.
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 7 Experimental Evaluation
### Accuracy of DBSCAN Clustering Algorithm  
Each client connected to the central AP is a normal client and does not initiate a Sybil attack. Clients perform 12 different types of periodic movements or rest at random locations for 10 s, respectively, when connecting the central AP, including walking fast and slowly in a small area, sitting down fast and slowly, jumping, running in place, boxing, fighting, turning quickly and slowly, crouch and moving slightly (simulating an attacker's behaviour). So far, we have collected a validation dataset through which we can verify the clustering characteristics and attack detection accuracy of the dynamic detection algorithm when there are multiple motions in the environment.  
Figure 12 shows the DBSCAN clustering algorithm's accuracy when there are different numbers of mobile clients. The algorithm can distinguish the different categories with an accuracy of over 82.7% when there are seven and fewer movements. However, when there are more than seven types of client movements, the accuracy of the clustering and the purity of the clusters are drastically reduced. At the same time, there are no more than seven kinds of movements in most cases of WLAN. The most obvious features of those movements are walking fast and slow, jumping, turning quickly and slowly, moving slightly and sitting down slowly. The purity of these clusters is higher than 0.81, which can be used as a basis for the dynamic attack detection of client actions. The limitation is that these clients cannot move too far away from the original position, and their motions need to be consistent. Therefore, the DBSCAN algorithm can effectively distinguish the usual movement in the indoor WLAN environment. At the same time, when the number of motions increases by more than seven, some nodes will gather, reducing the detection accuracy due to the similar amplitude characteristics of other clients.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 4 Evaluation Results of Proposed Method
### Robustness  
#### 4.2.1 Evaluation on Real Recordings  
To evaluate the proposed method, we first performed 5-fold cross-validation on the origin of A&D FECG database and three derived datasets with varying noise levels: SNR 0 dB, 5 dB, and 10 dB. These datasets are denoted as AD_Origin, AD_0, AD_5, and AD_10, respectively. With the post-processing threshold fixed to \(Thr_{1}\) = 0.3, the average performance metrics are summarized in Table 2. TP, FP, and FN, as well as PPV, SEN, and F1 scores for each test record are listed in Table 3.  
Our method achieves high detection accuracy on real recordings from the origin A&D FECG database, with a PPV of 100.00%, SEN of 99.84%, and an F1-score of 99.92%. The sole exception was record r10, where five FP predictions occurred in regions lacking both fetal ECG activity and expert annotations, likely due to electrode disconnection When subjected to extreme 0 dB noise conditions, the U-Net maintains robust detection capability, surpassing 97% F1-score in three of five test records (r01: 97.89%, r07: 99.68%, and r08: 98.85%). The performance degradation observed in records r04 and r10 under noisy conditions due to their characteristically challenging waveforms: these recordings already contained inherent high-amplitude noise prior to the addition of artificial noise, and the subsequent 0 dB SNR amplification further obscured the true R-peaks beyond typical detection thresholds, as evidenced by the morphological analysis in Figure 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **PPV (\%)** & **SEN (\%)** & **F1-Score (\%)** \\ \hline AD\_0 & 88.53 & 86.31 & 87.38 \\ AD\_5 & 98.77 & 97.95 & 98.35 \\ AD\_10 & 99.84 & 99.62 & 99.73 \\ AD\_Origin & 100.00 & 99.84 & 99.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PPV, SEN, and F1-score on A&D FECG database with different SNR conditions. Five-fold cross-validation is applied for each dataset. Figure 4: Impact of post-processing threshold (\(Thr_{1}\)) on FECG detection performance: PPV, SEN, and F1-score optimization: (**a**) across SNR levels (0–10 dB) in the A&D FECG database and (**b**) across SNR levels (6–12 dB) in the FECGSYN database. The respective maximum values of the three metrics are highlighted for each case in red circles. The ability of our method to accurately predict the peak location and the role of the post-processing step on the method performance are demonstrated in Figure 6. The signal condition is significantly worse at 0 dB (left sub-figure) than 10 dB (right sub-figure), which leads to the generation of more weak pulse predictions that are corrupted by noise. The false weak pulses caused by noise are excluded from potential beats by applying \(Thr_{1}\), which helps to reduce the generation of FP predictions. However, it may also cause missed heartbeats (causing FNs), which are highlighted in blue boxes.
# Path Planning for Localization of Radiation Sources Based on Principal Component Analysis
## 2 Proposed Method
### Path Planning via PCA  
For the accurate localization of radiation sources, a mobile robot is required to closely approach radiation sources to acquire a sufficient number of incident events, which then moves along a path that surrounds the radiation sources. The proposed method generates the measurement paths via PCA. After the reconstruction of the radiation source distribution with the simple back-projection method, PCA of \(f(V)\) being projected onto a 2D plane is conducted. The next measurement point is determined along the direction of principal components. The measurement path is defined as a set of the next measurement points, and the path is generated by repeating the above calculation.  
PCA is a method for expressing variables distributed in space using specific indices. Specifically, it extracts the principal component axis that maximizes the variance of the data. The obtained directional component of this axis is called the first principal component. In this study, PCA is applied to 2D variables; thus, the axis orthogonal to the first principal component is uniquely determined. The directional component of this axis is called the second principal component.  
The principal component axis can be obtained from the eigenvector of the variance-covariance matrix of the data, while the variance of the data in the direction along the principal component axis can be obtained as the eigenvalue. The procedure for performing PCA based on the pixels projected on the \(xy\) plane is as presented follows:  
The PCA is performed with the current measurement point as the coordinate origin. Therefore, it is necessary to perform a coordinate transformation because the reconstructed distribution is represented in world coordinates. Let \(\mathbf{X}=\{\mathbf{x}_{i},\cdots,\mathbf{x}_{N}\}\) be the coordinates of the voxels that have been translated. The coordinate origin is the current measurement point, and let \(\mathbf{X}\) be the coordinates of the pixels on the \(xy\) plane. \(\mathbf{x}_{k}=\left[\mathbf{x}_{k}\quad y_{k}\right]^{T}\) is defined by the index \(k\), and the centroid \(\mathbf{\overline{x}}\) of these data can be represented as  
\[\mathbf{\overline{x}}=\frac{1}{N}\sum_{k=1}^{N}\mathbf{x}_{k}. \tag{5}\]  
Figure 6: Reconstruction of radiation source distribution: the measurement data at each point is projected into the 3D voxel space via the simple back-projection method. Because the mobile robot can move in 2D space, the voxel data are projected onto a 2D plane.
## 2 Materials and Methods
### Free Acidity, Peroxide Value (PV), and Specific Extinction Coefficients
Standard solutions were obtained by dissolving in HPLC grade methanol, chemical standards of pure phenols: hydroxytyrosol, tyrosol, vanillin, vanilllic acid, p-coumaric acid, luteolin, apigenin, oleupropein and pinoresinol, purchased from Sigma-Aldrich--Merck KgaA (Darmstadt, Germany), Fluka (Buchs, Switzerland), Acros Organics (Geel, Belgium), Alfa Aesar (Haverhill, MA, USA), Cayman Chemical Co. (Ann Arbor, MI, USA), Extrasynthese (Genay, France), and Honeywell International Inc. (Morris Plains, NJ, USA). Quantification was semi-quantitative using the ratio between the response factors of tyrosol (Fluka, Buchs, Switzerland) vs. syringic acid at 280 nm, based on which the final results for total and single phenols were expressed in mg kg\({}^{-1}\) as tyrosol, using syringic acid as internal standard. Phenolic compounds were grouped as simple phenols (tyrosol, hydroxyityrosol, hydroxytyrosyl acetate, tyrosol acetate, vanillin), phenolic acids (vanillic acid, caffe acid, ortho- and para-coumaric acid, cinnamic acid), flavonoids (luteolin, apigenin, methyl-luteolin), and secooriridoids (oluropein, dialdehyde form of oleupropein aglycone, oxidized dialdehyde form of de-carboxymethyl oleupropein aglycone, 3,4-DHPEA-EDA (3,4-dihydroxy phenyl ethyl-alcohol linked to the dialdehyde form of de-acetoxy elenolic acid) or oleacein, the oxidized dialdehyde form of de-carboxymethyl ligstrozide aglycone, p-HPEA-EDA (p-hydroxy phenyl ethyl-alcohol linked to the dialdehyde form of de-acetoxy elenolic acid) or oleocanthal, oxidized aldehyde and the hydroxylic form of oleupropein aglycone, aldehyde and the hydroxylic form of oleupropein aglycone, the dialdehyde form of ligstrozide aglycone, the oxidized aldehyde and hydroxylic form of ligstrozide aglycone, and the aldehyde and hydroxylic form of ligstrozide aglycone). Pinoresinol and 1-acetoxy-pinoresinol were not chromatography separated, therefore they were reported as lignans.
# Intrinsic Interference Use for FBMC-IOTA Systems
## 4 Evaluation of IIU
### Performance with Perfect Channel Estimation and Synchronization  
Figure 1 shows BER comparisons between conventional FBMC-IOTA and our proposed scheme. Please note that in the simulations, perfect channel estimation and synchronization are assumed. For uncoded systems (without channel coding), the intrinsic interference use technique demonstrates about 1 dB gain over conventional FBMC-IOTA. Moreover, the performance comparison is also provided when a (1024, 512) quasi-cyclic LDPC code (LDPC1 coded in the figure) and a (4096, 2048) irregular repeat accumulate code (LDPC2 coded in the figure) are adopted to the systems. It can be seen that when different LDPC codes are used, the intrinsic interference use still achieves noticeable performance improvement.
## 2 Materials and Methods
### Characterization  
Transmittance spectra were measured using a Photon RT (EssentOptics Europe UAB, Vilnius, Lithuania) spectrophotometer, the surface roughness was evaluated using 20 \(\times\) 20 \(\upmu\)m scans by an atomic force microscope (AFM) Dimension Edge (Bruker, Billerica, MA, USA), the bright-field cross-sectional analysis of the samples' subsurface layer, prepared by focused ion-beam milling (dual beam system Helios Nanolab 650, FEI Company, Hillsboro, USA) was performed with a transmission electron microscope (Tecnai G2 F20 X-TWIN, FEI Company, Hillsboro, USA). Since the LIDT testing area was limited, the R(1)-on-1 testing protocol was chosen. Generally, the R-on-1 test is done using multiple shots at constant fluence for the one test site, then moving to the other test site. Fluence is constantly increased by selected increment until damage is reached, or maximum available fluence is reached. The crystals used in this investigation were thin (1.15 mm), and there was a considerable risk that the damage might have grown up to the tested surface from the back side. Therefore, single-shot testing with incremented fluence R(1)-on-1 was selected. Each tested crystal surface was divided into 10 test sites, arranged in a hexagonal matrix with an 800 \(\upmu\)m distance between them. Each site was irradiated with a single pulse, starting with a low fluence of 10 J/cm\({}^{2}\). The fluence was increased by an average of 15% increment for every next testing step. The tested site after each single shot was inspected using a Nomarski microscope in order to determine possible damage. LIDT measurements were performed at AOI = 0 deg with a Q-switched, seeded Nd:YAG NL940-100 OPO L (Ekspla, Vilnius, Lithuania) laser having a near Gaussian spatial beam profile with a 55 \(\pm\) 0.6 \(\upmu\)m diameter (1/e\({}^{2}\)) and a 3.5 \(\pm\) 0.3 ns pulse duration. The laser-induced damage was defined as any permanent laser-radiation-induced change in the surface of the specimen, which could be observed by the Nomarski microscope. The LIDT was defined as the highest quantity of laser fluence for which the extrapolated probability of damage was zero. LIDT measurements and the following damage's curve fittings were performed at Lidaris Ltd.
# Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks
## 5 Experiments
### Parameter Analysis  
In this section, we delve into the influence of the \(\delta\) ratio, which signifies the proportion of unobserved links within the testing dataset. Our investigation encompasses a range of \(\delta\) values, and we comprehensively analyze the ensuing outcomes. Figure 2 graphically  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Facebook**} & \multicolumn{3}{c}{**DBLP**} & \multicolumn{3}{c}{**MovieLens**} \\  & **Accuracy** & **AUC** & **Precision** & **Accuracy** & **AUC** & **Precision** & **Accuracy** & **AUC** & **Precision** \\ \hline
**SVM** & 0.6316 & 0.7599 & 0.5801 & 0.6705 & 0.6572 & 0.6667 & 0.7021 & 0.6857 & 0.6934 \\
**SLiCE** & 0.7956 & 0.7144 & 0.7195 & 0.7807 & 0.7758 & 0.7766 & 0.8021 & 0.7936 & 0.8014 \\
**PME** & 0.7679 & 0.8405 & 0.7432 & 0.7956 & 0.7475 & 0.7609 & 0.7335 & 0.7498 & 0.7956 \\
**HHNE** & 0.7790 & 0.7713 & 0.7833 & 0.7367 & 0.7469 & 0.8066 & 0.8495 & 0.8752 & 0.8424 \\
**HAN** & 0.7402 & 0.7656 & 0.7493 & 0.7402 & 0.7656 & 0.7493 & 0.8312 & 0.8855 & 0.8282 \\
**SEAL** & 0.7546 & 0.8261 & 0.7346 & 0.8499 & 0.8029 & 0.8667 & 0.8533 & 0.8431 & 0.8189 \\
**TALP** & 0.8255 & 0.8118 & 0.8073 & 0.8546 & 0.8261 & 0.8746 & 0.8618 & 0.8676 & 0.8423 \\
**EPEM** & **0.8878** & **0.9036** & **0.8245** & **0.9083** & **0.8663** & **0.9052** & **0.8957** & **0.9074** & **0.8737** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of different methods on three datasets.  
presents EPEM's performance across Accuracy, AUC, and Precision metrics for the newly introduced type. This evaluation spans three datasets, each featuring varying ratios of labeled links. As illustrated in Figure 2, EPEM achieves its optimal performance at a \(\delta\) value of 0.2. Notably, our analysis reveals that, although diverse \(\delta\) values impact EPEM's performance, the model's efficacy remains largely stable even when the training set includes fewer unobserved links. This resilience can be attributed to EPEM's competence in assimilating distinctive features of various link types for unobserved link prediction. Overall, these insights underscore EPEM's adeptness at effectively forecasting unobserved links, even within scenarios characterized by limited training data samples.