esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No** & **Compound** & **RI** & **AI** & \multicolumn{3}{c}{**Content (\%)**} \\ \cline{4-6}  & & & **NBEO** & **SBEO** & **TEO** \\ \hline
1 & \(\alpha\)-Thujene & 943 & 930 & - & 0.1 & 0.3 \\
2 & \(\alpha\)-pinene & 947 & 939 & Tr & 0.5 & 0.1 \\
3 & _trans_-Pinene & 968 & 975 & 0.5 & - & - \\
4 & Sabinene & 977 & 976 & 0.7 & - & - \\
5 & \(\beta\)-pinene & 981 & 980 & - & 1.1 & 18.2 \\
6 & Myrcene & 993 & 979 & - & - & 0.1 \\
7 & dehydro-1,8-Cineol & 995 & 991 & - & Tr & tr \\
8 & \(\alpha\)-Phellandrene & 1007 & 1002 & - & - & tr \\
9 & \(\alpha\)-Terpinene & 1018 & 1017 & - & 0.7 & 0.5 \\
10 & _p-Cymene_ & 1026 & 1024 & 0.2 & Tr & 1.7 \\
11 & _Limonene_ & 1030 & 1029 & 0.1 & 0.1 & 0.2 \\
12 & 1,8-Cineol & 1031 & 1031 & Tr & - & - \\
13 & \(\gamma\)-Terpinene & 1061 & 1059 & - & 0.3 & 0.4 \\
14 & _Cis-Sabinene hydrate_ & 1070 & 1070 & - & 0.2 & 0.9 \\
15 & _m-Cresol_ & 1080 & 1076 & 0.8 & - & 0.5 \\
16 & Terpinolene & 1093 & 1088 & - & 0.1 & 0.3 \\
17 & _Linalool_ & 1104 & 1096 & 0.1 & 0.5 & 0.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The chemical profiles of essential oils extracted from _Pimpinella lutea_ (Desf.) subspecies from various locations of Tunisia.
# On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control +
## 5 Numerical Results
### Results in Internal Eavesdropping Scenario  
In an internal eavesdropping scenario, eavesdroppers are assumed to be internal nodes such as compromised base stations. Thus, we assume that the eavesdroppers operate in the same way as the base station does. Except for the case of _OS-MRC_, eavesdroppers are assumed to use receive random beamforming at receivers. In case of _OS-MRC_, we assume eavesdroppers adopt MRC at the receivers.  
Figure 3 shows the average achievable secrecy sum-rate for varying the number of users, where system parameters are set as \(M=2\), \(K=2\), and \(\rho=10\) dB. Both _IHP_ and _TOS_ outperform conventional schemes. Comparing _IHP_ with _TOS_, the performance gain for using power control is very marginal. This indicates that there are enough users satisfing (13) without using power control. In addition, _MaxSNR_ and _MinGI_ show degraded secrecy performance compared to the proposed schemes since they do not fully utilize channel information for user scheduling. Even though _OS-MRC_ adopts threshold-based opportunistic scheduling, there is a significant performance gap between the proposed schemes and _OS-MRC_ since it is hard for eavesdroppers to obtain desired users' information when the eavesdroppers use the receive random beamforming technique.  
Figure 4 shows the average achievable secrecy sum-rate for varying SNR, where system parameters are set as \(M=2\), \(N=100\), and \(K=2\). Similar to the result of Figure 3, the proposed schemes outperform conventional schemes. Interestingly, _MinGI_ shows good secrecy performance in a high SNR regime whereas the secrecy sum-rate of _MaxSNR_ is saturated as SNR increases. It indicates that inter-beam interference at desired receiver is dominant factor to determine secrecy sum-rate when the eavesdroppers use random beamforming at the receiver. In addition, when eavesdroppers are assumed to adopt MRC at receivers (_OS-MRC_), information leakage to the eavesdroppers is also a main factor in restricting secrecy rate of the _OS-MRC_.  
**Remark 4**.: _In an internal eavesdropping scenario, the secrecy sum-rate depends on the operations of eavesdroppers. When eavesdroppers use random beamforming at receivers, the amount of information leakage is small since it is hard for eavesdroppers to obtain desired users' information. Thus, a main factor that affects the secrecy sum-rate is inter-beam interference at desired receiver. However, when eavesdroppers are assumed to adopt MRC at receivers (results of OS-MRC), there is no inter-beam interference (\(S=1\)) and information leakage is a key factor to restrict secrecy rate._  
Figure 4: Average achievable secrecy sum-rate for varying SNR when \(M=2\), \(N=100\), and \(K=2\)[21].  
Figure 3: Average achievable secrecy sum-rate for varying the number of users when \(M=2\), \(K=2\), and \(\rho=10\) dB [21].
# Analysis of Inductive Displacement Sensors with Large Range and Nanoscale Resolution
## 4 Q Value and Excitation Frequency Analysis  
### Basic Concept of \(Q\) Value  
The quality factor of an inductive sensor is defined as \(Q\), which is given by,  
\[Q=\frac{2\pi fL^{*}}{r} \tag{18}\]  
where \(f\) is the excitation frequency, \(L^{*}=L+L_{S}\) is the total inductance of inductive sensors, and \(r\) is the resistance of the sensor coil. \(Q\) value reflects the loss of the inductive sensor, and it is an important factor affecting the performance of inductive sensors.  
For differential inductive sensors, increasing the \(Q\) value can reduce the error in principle, as mentioned in Section 3.4. In addition, increasing the excitation frequency can help improve the measurement bandwidth. Hence, we expect both the \(Q\) value and excitation frequency of the inductive sensor to be as large as possible. However, the \(Q\) value is influenced by the excitation frequency, which, in turn, is constrained by the \(Q\) value. They have a tremendous relationship with core loss, which will be discussed below.
# Time Shared Optical Network (TSON): A Programmable Network Edge Solution for Multi-Access Support
## 2 TSON, the Past
### Evolution Stage 1: TSON Baseline
The parameters of TSON nodes are programmable by an SDN controller, including quality of transmission (QoT), programmable overhead, programmable time-slice size, programmable time-slice numbers in a frame, and time-slice allocation. The TSON is client friendly (e.g., operator, service provider). When required to setup a network service, after evaluating its requirements such as bit-rate, connectivity type, QoS, and QoT by the network controller, the TSON nodes choose the corresponding QoT overhead size, time-slice size, time-slice number, and time-slice allocation to fulfil such requests to compose a dedicated network function slice of the node. TSON provides a hierarchy of three levels of resource granularity:  
* Connections--referring to a sub-wavelength light path establishment between any two end points in the TSON domain. * Frames--wherein each connection lasts for a number of frames to improve statistical multiplexing of data units. * Time-slices--wherein each frame is divided into time-slices as the smallest units of network resource. The minimum granularity achievable by the TSON network is related to the frame length and the number of time-slices inside a frame. The TSON baseline implementation has been participated in several use cases [6; 7; 8]. Figure 4: The time shared optical network (TSON) edge node baseline data plane architecture. Rx: receiver, Tx: transmitter, MAC: medium access control, FIFO: first in first out, WL: wavelength, MUX: multiplexer, DEMUX: demultiplexer.
## 6 Performance Comparisons
### Efficacy Comparisons  
This subsection demonstrates the efficiency comparisons of [PERSON] et al. (2018), [PERSON] et al. (2018), and the proposed TCVK scheme. [PERSON] et al. (2018) applies symmetric encryption and decryption operations, and [PERSON] et al. (2018) adopts lightweight operations. Our article applies two-stage lightweight operations. According to the experimental data, the proposed scheme includes three main communication parties--participant/user, RCRS/server, and checkups station/gateway node (GWN). Table 2 shows an efficacy comparison table of authentication and key agreement phase, where \(T_{H}\) means the operating time of hash operation, \(T_{X}\) means the operating time of XOR operation, \(T_{C}\) means the operating time of string concatenation operation, and \(T_{S}\) means the operating time of symmetric encryption and decryption. Although the operating time of the concatenation operation is light, the parameter length will greatly affect the operating time of the hash function.  
In this article, we apply only lightweight operations XOR and hash functions. In our pre-checkups phase (TCVK-PC), our protocol requires a computation cost of \(25T_{X}+12T_{H}\)., and in the subsequent verification phase (TCVK-FV), our protocol requires a computation cost of \(13T_{X}+5T_{H}\). Statistics show that the hash function operation time of our proposed scheme totals \(17T_{H}\)., which is better than that of [PERSON] [25] and [PERSON] [26]. Comparing the length of the computation time, the longest of the three is the encryption and decryption operation time, and the shortest is the bitwise XOR operation time. The length of the string also affects the operation time of the hash function. Therefore, in contrast, our method obviously has better performance even if it involves two stages of computation time.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Supervised Learning
#### 4.1.2 Fault/Anomaly Management  
In [48], the authors propose an extended SVM, which is called support Tucker machine, to detect any fault/outlier detection in IoT systems. The model improved the accuracy and efficiency of anomaly detection and was able to retain the structure of the big sensor data.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Paper** & **ML Approach** & **Application Problem** & **Description** \\ \hline
[37] & Random Forest, Knn & Path loss & Prediction and optimization of PL in mm-wave systems \\ \hline
[38] & Novel semi-supervised method & Power allocation, joint user association & Prediction of optimal function for network’s parameters and power allocation \\ \hline
[39] & ANN & Dynamic Bandwidth Allocation (DBA) & Allocation of bandwitddh and improvement of latency performance \\ \hline \end{tabular}
\end{table}
Table 5: Supervised ML models in B5G/6G optimization problems.
# Fatigue Crack Propagation Analysis of Rail Surface Under Mixed Initial Crack Patterns
## 2 Materials and Methods
### Ultrasonic B-Image Data  
The rail flaw detection car, mounted on the track, is a specialized train used for detecting rail damage (as shown in Figure 3). This train employs ultrasonic methods for rail damage detection, capable of identifying fatigue cracks in the rail head and rail waist areas. The detection system, illustrated in Figure 4, uses ultrasonic technology to collect data via a probe wheel [32]. The real-time A-scan waveforms are displayed, and the data are converted into B-scan images. These B-scan images can be reviewed to assess the internal damage to the rail, helping to determine the extent and angle of fatigue crack propagation.  
Figure 2: Schematic of the 3D wheel track finite element model; (**a**) model of track; (**b**) Refinement of the grid in the contact patch; (**c**) schematic of the cracked interface insertion. (See red circle for crack location).  
To investigate the propagation characteristics of rail surface cracks, semi-circular cracks were introduced at the wheel-rail contact point on the rail head, with an axle weight of 25 T. The crack angle was set to 0\({}^{\circ}\) and the radius was 1 mm. The shape change of the fatigue cracks during expansion is shown in Figure 5a. Under cyclic loading, fatigue cracks originating from the initial semi-circular 'coin-type' cracks gradually evolve into elliptical cracks. This progression occurs because, during crack propagation, the crack tip moves closer to the rail surface, where the equivalent stress intensity factor is higher, resulting in an accelerated crack growth rate.  
Figure 4: Ultrasonic flaw detection system; (**a**) ultrasonic flaw detection equipment; (**b**) B-image data.  
Figure 5: _Cont._  
Figure 3: Rail flaw detection vehicle.  
The crack extension angle in the simulation shown in Figure 5b is approximately 29\({}^{\circ}\), which closely matches the observed crack extension angle of 30\({}^{\circ}\) on the actual track (Figure 5c), confirming the accuracy of the finite element simulation.
## 5 Results and Analysis
### Test Results
#### 5.2.2 Comparison of the Effect on Adding Different Attention Modules  
The six models listed in Figure 7 were evaluated using the test dataset in order to investigate the performance differences resulting from the embedding of the CBAM modules into different locations of the YOLOv4 network. All these models used the K-means++ clustering algorithm in obtaining the Anchor box. The results are shown in Figure 10. As shown in Figure 10, YOLOv4-B1, YOLOv4-B2, and YOLOv4-N1 expressed worse results compared to YOLOv4 (K-means++); the YOLOv4-B1 model almost failed; and for the YOLOv4-B2 model, the detection accuracy of transverse cracks decreased. In the YOLOv4-N1 model, there were transverse cracks, longitudinal cracks, crack sealing, and Alligator cracks, and YOLOv4-N2, YOLOv4-H1, and YOLOv4-H2 were otherwise better than YOLOv4 (K-means++), but the detection of crack sealing showed a weakness after adding the CBAM module.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MethodsTypes** & **P (\%)** & **R (\%)** & **F1-Score (\%)** & **mAP (\%)** & **FPS (f/s)** \\ \hline YOLOv4 (K-means) & **96.47** & 37.92 & 54 & 79.99 & **15.43** \\ YOLOv4 (K-means++) & 95.81 & **50.48** & **65.5** & **80.92** & 15.38 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of K-means++ and K-means algorithms.  
Figure 9: Detection effect of YOLOv4 (K-means) and YOLOv4 (K-means++).  
As shown in Table 2, it can be found that among the six models, YOLOv4B1 was the least effective and directly led to network failure. The reason may be that the weights of useless features were enhanced before the feature data deepened the features through the residual network, which led to the decrease in target weights and poor detection effect. In addition, the accuracy of YOLOv4B2 and YOLOv4N1 also showed different degrees of degradation compared with the original network without the attention module added (YOLOv4 (K-means++)). This phenomenon may have been caused by the presence of too much irrelevant data prior to concatenating the feature data, and the attention mechanism amplified the weight of these irrelevant data.  
Figure 10: The detection effect of six models with different attention modules.  
In addition, in terms of mAP, YOLOv4N2 improved by 1.59%, YOLOv4H1 by 0.24%, and YOLOv4H2 by 1.53% compared to YOLOv4 (K-means++). All three models added the attention module after the Concat feature data layer, indicating that the attention module can indeed improve the accuracy of the YOLOv4 algorithm, but the embedding position should be after the Concat layer.
# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 4 Results and Discussion
### Comparison Between Our Findings with Other Studies
Word vectors, which successfully capture the context in electronic medical records, represent the semantic content of words in the text, which is learned unsupervised. Character sequences are sent into the BERT model, which learns their state properties. The CRF layer uses this information to optimize sequence transition constraints. The BERT-IDCNN-CRF model obtained an average accuracy of 94.5%, recall of 93.8%, and F1-score of 94.1%, respectively. Our research demonstrates that CRF performs better than Bi-LSTM-CRF in the majority of the 23 assessed categories. This is probably because of the size of the dataset and the significant chance of overfitting in deep learning models on smaller datasets. Our findings show that while Bi-LSTM-CRF shows potential for larger and more diverse datasets, CRF offers a solid baseline with robust generalization. The dual-model strategy used in our work demonstrates the significance of choosing a model based on the properties of the data and the necessity of giving similar importance to deep learning and simpler architectures to attain the best results across a variety of datasets. By doing this, we offer a thorough analysis that clarifies how our methodology successfully manages the trade-offs between model complexity and performance, resulting in a more sophisticated knowledge of PHI detection challenges. Table 11 compares the methods for de-identification used in other recent research. \begin{table}
\begin{tabular}{l l l l l} \hline
**Reference** & **Methods** & **Dataset** & **Results** & **Limitations** \\ \hline
[34] & Created a method for automatically & Rennes University Hospital’s EHRs & F1-score of 96.96\% & Lack of annotated data for medical \\  & de-identifying clinical documents. & & de-identification. & \\ Three different models: Bi-LSTM, & 206,926 sentences used for & Bi-LSTM achieved accuracy of 90\%, & \\ Bi-LSTM with attention, and BERT & & classification. & BERT accuracy of 93\% & High computational cost \\  & & & & \\ \hline \end{tabular}
\end{table} 
Table 11: Comparison of methods for medical text de-identification and entity recognition.
# Dynamic and Stability Analysis of Multibolt Plane Joints under Normal Forces
###### Abstract  
In this paper, a stiffness model of contact surfaces based on a modified three-dimensional fractal contact model is built, which is in accordance with the experiment results. Additionally, the static, dynamic, and stable behaviors of the bolt joint between the spindle box and the machine bed are analyzed. The mathematical relationship between fractal parameters of the surface topography and the stiffness of the system was established to accurately study its static behaviors. Asymmetric curves are observed from the load-deflection results and the nonlinear stiffness characteristic is also presented. It is shown that both the stress and the stiffness increase with the increase of the displacement near the static equilibrium position. Meanwhile, a simplified model without the consideration of roughness is compared with joint interfaces composed from milling, scraping, and grinding surfaces. Numerical calculation was employed to investigate effects of design parameters on the system under harmonic excitation when the processing method, excitation force, bolt pre-tightening force, topography parameters, and other structural parameters, i.e., nominal contact area, joint thickness and bolt number, are eventually regarded as the control parameters. The aim of the article is to analysis the influence of these parameters, including surface morphology, on nonlinear characteristics of the bolt interface with fractal contact surfaces andto provide some references to improve the characteristics.  
Keywords:rough surface; bolt joint; nonlinear contact stiffness; dynamic behavior +
Footnote †: journal: Applied Sciences
# Comparing Human Activity Recognition Models Based on Complexity and Resource Usage
## 3 Materials and Methods
### Evaluation Criteria
#### 3.1.2 Model Complexity  
As HAR is often applied in the mobile or wearable sector, model complexity becomes an important factor to consider when deciding which approach to go with. Hardwarelimitations like memory usage or power consumption can be critical to the applicability of the approach. However, it is also important to consider the effects of the model complexity on the usability, because if the model works perfectly but a user needs to wait a long time to actually get the result, it will not be beneficial for the application. This is why we use memory consumption, prediction time and number of trainable parameters as metrics to capture the complexity of the models. The memory usage and prediction time act as indicators for the strain on the hardware, while prediction time also is an important factor for the usability. The number of trainable parameters is used as a comparable measure of the size and complexity of a model that can be related to the two other hardware and usability relevant measures.  
To record the memory usage and prediction time, the models are deployed on an iPhone XR using the Core ML framework for the machine learning models and Tensorflow. Lite for the deep learning models. This shows the two metrics in a real world usage scenario on a mobile device that is more representative than gathering the data on the development machine. Using a debug session within Xcode, the memory consumption for each of the models can be determined. To get the mean prediction time, a set of samples from the test data are fed into the Core ML and Tensorflow Lite models and then averaged. The samples are not recorded from the sensors of the device because for these two metrics, reproducing the exact processing pipeline and getting the result of the prediction in a full real world scenario is not of interest. However, to evaluate the performance of these models on real world data would definitely be interesting for future work (see Section 6), even though it is not included in this work.  
The number of trainable parameters is a metric that is typically used for deep neural nets, where each weight that can be learned during model training represents one such trainable parameter. For deep neural nets, the higher the number of parameters, the more complex the data it can represent, but also the more likely it is to overfit the training data if it is not that complex. As it is quite difficult to compare the complexity of models of different types to each other and there is no perfect solution for this problem available, we try to apply the metric of trainable parameters to all of our evaluated models. For the deep learning models, this number can be obtained through the respective deep learning libraries used to train them. For the machine learning models, we use the following formulas to approximate the value:  
\[Parameters_{KNN}=num\_samples \tag{1}\]  
\[Parameters_{RF}=\sum_{n=0}^{num\_estimors-1}(node\_count_{n}) \tag{2}\]  
\[Parameters_{SVM}=num\_support\_vectors\cdot num\_features \tag{3}\]
## 2 Traditional MFAC  
### Dynamic Linearization  
This study adopts the FFDL method for data linearization, which incorporates the influence of inputs and outputs within a fixed length [32].  
Consider the following SISO system  
\[I(k+1)=f(I(k),\ \cdots,\ I(k-n_{I}),u(k),\ \cdots,\ u(k-n_{u})) \tag{1}\]  
where \(u(k)\) and \(I(k)\) represent the input and output, respectively, and \(n_{u}\) and \(n_{I}\) are the orders.  
\(\mathbf{H}(k)\) is defined as follows  
\[\mathbf{H}(k) =\left[\Delta I(k),\cdots,\Delta I(k-L_{I}+1),\Delta u(k),\cdots, \Delta u(k-L_{u}+1)\right]^{\mathrm{T}} \tag{2}\]  
where \(L_{I}\) and \(L_{u}\) are the lengths of the output and input linearization, respectively.  
**Assumption 1** ([33; 34]).: _The partial derivatives of \(f(\cdot)\) for all system variables exist._  
**Assumption 2** ([33; 34]).: _For any time \(k_{1}\
eq k_{2}\geq 0\) and \(\mathbf{H}(k_{1})\
eq\mathbf{H}(k_{2})\), one has_  
\[\left|I(k_{1}+1)-I(k_{2}+1)\right|\leq p\|\mathbf{H}(k_{1})-\mathbf{H}(k_{2})\| \tag{3}\]  
_where \(p\) is a constant._  
**Theorem 1**.: _For System (1) that meets Assumptions 1 and 2, there must be a vector called a pseudo-gradient (PG) so that the system can be transformed into the following FFDL data model [32]._  
\[I(k+1)=I(k)+\mathbf{\Lambda}^{\mathrm{T}}(k)\Delta\mathbf{H}(k) \tag{4}\]  
_where \(\mathbf{\Lambda}(k)=[\Lambda_{1}(k),\cdots,\Lambda_{L_{I}+L_{u}}(k)]^{\mathrm{ T}}\) is bounded._  
Proof.: See [35].