esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Future Research Challenges
### High-Spectrum Access  
The modeling, as well as the measure of high-spectrum channels, play a vital role in guiding toward the complete knowledge of how this spectrum differs from the currently used spectrum [268]. Limited coverage is another big issue for the mmWave spectrum; therefore, detailed stochastic geometric coverage analysis studies with the realistic channel and antenna radiation models are required [269; 270]. Moreover, the use of passive reflectors of different shapes and sizes can help enhance the received power, thus improving signal coverage in the NLOS region [271]. Besides, NLOS is assumed to be more important for a lower 6 GHz band rather than in mmWave communication links. On the contrary, high propagation losses and high absorption in mmWave makes the LOS inevitable [272]. However, only a few research studies have focused on designing the channel models for the NLOS scenario to deliver sufficient results [273; 274]. Although existing channel models provide some insights into the propagation characteristics of mmWave in cellular environments, further research is needed to capture the shades of the propagation and fade in the mmWave scenario [275]. The utilization of clustering in narrow-beam antenna [276] and accurate estimation of departure and arrival angles, as well as the time-of-arrival for each observed radio propagation path, can be used to enhance the overall network performance [277]. Various new frequency spectra, such as 60 and 73 GHz bands, can be studied for various propagation environments and compared with the existing frequency band below 6 GHz. Different multifrequency propagation path loss models (in particular, ABG, which is CIF) can be investigated for the evaluation of future high-frequency mmWave networks [278]. Furthermore, some new self-organizing techniques based on ML are required to provide clustering and efficient spectrum allocation for the mmWave system [279]. Moreover, the beam selection for the uplink scenario requires an efficient ML mechanism to deliver a high directional beamforming effect [280]. The AI-based framework can also be used to optimize high-spectrum mmWave compressed sensing for high-speed 5G/6G image transmission [281].
# Mechanics Model of Floor Heave: Case Study on Thin Coal Seam with Soft Roof and Floor
## 5 Discussion
### The Influence of Elastic Modulus of Immediate Floor on Floor Heave  
Four different types of floor elastic modulus are built with values of 0.25, 0.5, 0.75, and 1, respectively, using the 400 m buried depth as an example. In Figure 14, the displacement curve of the floor of the gob-side entry holding with paste filling is depicted under the assumption that the floor elastic modulus is the same. The relationship between maximum of gob-side entry retaining floor deformation and the immediate floor elastic modulus is shown in Figure 15.  
Figure 14: Deformation curve of gob-side entry retaining floor under different immediate floor elastic modulus.  
Figure 13: Relationship between maximum of gob-side entry retaining floor deformation and roadway width.  
Elastic modulus is a metric used to gauge how difficult it is to deform elastic material. Figure 12 illustrates how the deformation of floor have reduces as the elastic modulus of the roadway floor rises. The maximum deformation falls nonlinearly as the roadway's elastic modulus rises. When the roadway floor's elastic modulus is 0.25, 0.5, 0.75, and 1, the greatest highway floor displacement occurs at 1.14 m, 0.63 m, 0.45 m, and 0.36 m. Therefore, by hardening the roof of the roadway, thereby increasing the strength and deformation resistance of the roadway floor, the roadway floor heave can be reduced. The formula of deformation of the roadway floor and the immediate floor elastic modulus is \(y=-1.0084x+1.2774\).
# Real-Time Monitoring of Passenger's Psychological Stress
## 2 Results
### Learning Phase
#### 2.1.2 Cross-Validation  
This personalized stress model was designed by linear regression on features extracted from 60-s time windows--for further details, see Section 4. To account for its performances on the learning dataset, we performed a leave-one-event-out cross validation. This consists in including all stressful events, except one, in the training set for linear regression, and then using the remaining one for validation. The same procedure was applied to the non-stress condition, which was divided in three successive partitions (to equate the number of stressful events). Since our goal was to assess model's ability to generalize its predictions to the same subject--but in a different context, this approach was preferred to the classical k-fold cross-validation.  
For each cross-validation trial, the root mean square error (RMSE) between the predicted values and the test set was computed to assess the model performance in regression. To account for the sample size influence, the upper bound of the 95% confidence interval in \(RMSE\) was computed this way:  
\[U(RMSE,N)=\sqrt{\frac{N\cdot RMSE^{2}}{\chi_{(0.025,N)}}} \tag{1}\]  
Here \(N\) is the total sample size, and \(\chi_{(0.025,N)}\) is the lower bound of the 95% confidence interval in the inverse cumulative Chi-Squared distribution with \(N\) degrees of freedom.  
A stress rate threshold of 4/10 reported by the participant has been set for stress detection, to assess the model performance in classification. For each cross-validation trial, the classification rate \(C_{r}\) was computed as the proportion of model outputs correctly labelled with this threshold criterion. For such a value, the margin of error at 95% for a sample size \(N\) is defined:  
\[m(C_{r},N)=1.96\cdot\sqrt{\frac{C_{r}\cdot(1-C_{r})}{N}} \tag{2}\]
## 2 Methodology
### Interpolation Model
#### 2.1.3 Usm  
The coefficients and value of atmospheric delay are two ways to express the trend of atmospheric delay. According to the characteristics of atmospheric delay, the USM constructs the atmospheric delay by using the longitude and latitude of the network center and IPP. Each satellite model can be expressed as follows [19]:  
\[\overline{A}_{i}^{p}=a_{0}^{p}+a_{1}^{p}\cdot\left(\varphi_{p}-\varphi_{0} \right)+a_{2}^{p}\cdot\left(\lambda_{p}-\lambda_{0}\right) \tag{3}\]  
where \(\overline{A}_{i}^{p}\) represents the atmospheric delay of the station \(i\) and the satellite \(p\). The SD ionospheric delay interpolation can be expressed as follows:  
\[\Delta\overline{I}_{i}^{p,q}=(a_{0}^{p}+a_{1}^{p}\cdot\left(\varphi_{p}- \varphi_{0}\right)+a_{2}^{p}\cdot\left(\lambda_{p}-\lambda_{0}\right))-(a_{0}^ {q}+a_{1}^{q}\cdot\left(\varphi_{q}-\varphi_{0}\right)+a_{2}^{q}\cdot\left( \lambda_{q}-\lambda_{0}\right)) \tag{4}\]  
In this equation, \(q\) represents the reference satellite; \(p\) represents other satellites; \(\varphi_{p}\) and \(\lambda_{p}\) represent the latitude and longitude of the IPP of satellite \(p\), respectively; \(\varphi_{q}\) and \(\lambda_{q}\) represent the latitude and longitude of the IPP of satellite \(q\), respectively; \(\varphi_{0}\) and \(\lambda_{0}\) represent the latitude and longitude of the center of the network, respectively; \(a_{0}\), \(a_{1}\), andare model coefficients; and \(\Delta\overline{I}_{i}^{p,q}\) represents the SD ionospheric delay. This method requires at least four stations and four satellites to solve for the model coefficients.
# Event Knowledge Graph: A Review Based on Scientometric Analysis
## 2 Background Knowledge
### Event Extraction
The machine-learning-based method involves a complex process, including feature engineering and natural language processing, which may result in the accumulation and propagation of errors that negatively affect the extraction results. Compared with the pattern-matching-based method and the machine-learning-based method, the deep-learning-based method directly transfers data to the constructed network to extract events, which does not require manual feature engineering or domain expert knowledge. The strong portability and high flexibility have prompted more and more researchers to explore event extraction techniques based on deep learning in recent years. [PERSON] et al. [16] described event extraction as a two-stage multi-classification task, including trigger word extraction and event entity extraction, where the Bidirectional Encoder Representations from Transformers (BERT) model, Long Short-Term Memory (LSTM) model, and Bidirectional Long Short-Term Memory (Bi-LSTM) model were adopted [17; 18; 19; 20; 21]. In order to leverage syntactic representations for connecting words with their informative contexts, the Graph Convolutional Networks (GCN) [22], Edge-enhanced GCN [23], Graph Transformer Networks [24], and Graph Edge-conditional Attention Networks with Hierarchical Knowledge Graphs [25] were introduced to detect events in sentences and they performed well on some test datasets (e.g., ACE2005). Instead of splitting the event extraction task into the above-mentioned two stages that may result in propagation errors, [PERSON] et al. [26] proposed a joint model based on the Recurrent Neural Networks (RNN) to perform trigger word detection and event entity extraction. The joint models had been applied for detecting events in the legal field [27] and financial field [28]. Despite learning the patterns embedded in the sentences for event extraction, [PERSON] et al. [29] proposed a multi-grained model based on Bi-LSTM to dynamically fuse paragraph-level representations, where the dependency relationship between these two types of representations at different granularities was captured using the Conditional Random Fields (CRF) model. [PERSON] and [PERSON] [30] further improved the performance of capturing dependency relationships by introducing the Deep Value Network (DVN). Integrating sentence-level information with paragraph-level information to extract events has become one of the research hotspots in recent years, but research on the paragraph level is still immature, leaving a lot of room for exploration.
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 3 Implementation and Results
### Data Analysis and Result
The LoS ratio has the lowest contribution due to its sparsity; only 348 data samples had non-zero LoS ratio values. Finally, partial dependence between the features and the RSSI is examined to identify how these features impact LoRa's propagation and validate the feature engineering process. The partial dependence graph between the features and the RSSI is depicted in Figure 5.  
Figure 4: Impact and importance of environmental parameters to LoRa’s propagation estimation and modeling. Figure 5: Partial dependence of RSSI on model features. This graphical representation is useful for understanding how changes in a specific feature impact the RSSI in a quantitative manner. Graphs are generated from the tuned GR model as it achieves the best RMSE and quantifies the average dependence between the single feature and the RSSI. Therefore, the vertical axis shows the relative range of RSSI change, and the horizontal axis marks the deciles and shows the dynamic range of each feature. For instance, SF and the RSSI seem to have a linear but inverse dependence; SF 12 has a 1.5 dBm lower RSSI compared to SF 7. Suggesting that SF 12 can extend the reception of packets with 1.5 dBm lower RSSI. There is a direct dependence between the road width and the RSSI, where, on average, RSSI has increased by 1.5 dBm when transitioning from narrower to wider roads. The impact of the clutter is highly non-linear, and although initially attenuating the RSSI rapidly, it becomes ineffective after 20 buildings. When AoA (with respect to the road axis) is within 0 to 10 degrees, the RSSI is about 1.5 to 3 dBm higher, suggesting that the road could be acting as a waveguide.
## 4 Performance Evaluation  
### Estimation of 3D Cluster Size and Spectrum Reuse Factor  
Assume that \(\alpha_{\text{floor}}(d_{\text{CCI,inter}})=55\) dB, \(I_{\max,\text{intra}}=8\), and \(I_{\max,\text{inter}}=2\) for the worst case analysis. Since in Equation (3), \(\Big{(}10^{-5.5}\ \Big{)}<<(2/I_{\text{optimal,inter}})\) irrespective of the value of \(I_{\text{optimal,inter}}\), we can write \(\left(10^{-5.5}<<(2/l_{\text{optimal,intra}})\right)\cong\left(10^{-5.5}\right)\). Then, from Equation (3), this results in \(d_{\text{CCL,inter}}{}^{*}\geq d_{0}=1\) m, meaning it is sufficient to reuse the same spectrum. Hence, due to the high floor penetration loss, mmWave spectra can be reused on each floor. Figure 2 shows the required minimum distance in the intra-floor level \(d_{\text{CCL,intra}}{}^{*}\) with the variation of \(l_{\text{optimal,intra}}\). It can be found that for mmWave signals, \(d_{\text{CCL,intra}}{}^{*}\) decreases exponentially with an increase in \(I_{\text{optimal,intra}}\). Hence, it is preferable to set \(I_{\text{optimal,intra}}\) as high as possible, which can be compensated by \(I_{\text{optimal,inter}}\) due to the high floor penetration loss, such that the aggregate interference constraint in the intra-floor and inter-floor areas, \(\alpha_{\text{CCI}}^{\text{nor,total}}\leq I_{\text{optimal,total}}=\left(I _{\text{optimal,intra}}+I_{\text{optimal,inter}}\right)\) in Equation (1), can be satisfied. Since \(I_{\text{optimal,total}}\) can be considered independent of \(I_{\text{optimal,inter}}\), the value of \(I_{\text{optimal,total}}\) can be defined only by \(I_{\text{optimal,intra}}\).  
Assume that \(I_{\text{optimal,intra}}=0.25\), such that by using Equation (2) and Figure 2, the corresponding value of the minimum distance between co-channel interferers in the intra-floor level can be found as \(d_{\text{CCL,intra}}{}^{*}=34.4\) m, which corresponds to a separation of at least 3 apartments between intra-floor co-channel interferers. Hence, using Equations (6) and (7), \(\kappa_{\text{intra}}=3\), such that \(\Theta_{\text{intra}}=\kappa_{\text{intra}}{}^{2}=3^{2}=9\). Since the mmWave spectrum can be reused on each floor (i.e., for \(d_{\text{CCL,inter}}{}^{*}\geq 1\) m), using Equation (8), \(\Theta_{\text{inter}}=1\). Now, using Equation (9), we can find the size of a 3D cluster \(\Theta_{\text{3D}}{}^{*}=(\Theta_{\text{intra}}\times\Theta_{\text{inter}})=(9 \times 1)=9\). Similarly, using Figure 2, the optimal size of a 3D cluster of small cells corresponding to any intra-floor interference value can be estimated. Now, consider that each multistory building consists of 10 floors, each containing 18 apartments, such that the total number of apartments per building is \(S_{\text{F,total}}=180\). Now, using Equation (10), the total number times the same mmWave spectrum that can be reused for small cells per building is given by \(\varepsilon_{\text{RF}}=(S_{\text{F,total}}/\Theta_{\text{3D}}{}^{*})=(180/9)=20\).
## 7 Conclusions  
Biometric authentication using the acoustic characteristics of the ear canal has not yet been studied for bilateral authentication. In this study, we propose a bilateral ear acoustic authentication method that uses signals from both ears. We constructed a system to acquire the acoustic characteristics of the ear canals of both ears of 52 subjects simultaneously. Subsequently, we measured the signals of both ears. The effectiveness of the system was verified by comparing the signals of the left and right ears and calculating authentication accuracy. As a result, bilateral ear acoustic authentication decreased the EER and increased the AUC by 0.92% and 0.0016, respectively, compared to single ear acoustic authentication, which solely relies on the signal of the left ear. Therefore, it can be concluded that the accuracy of bilateral ear acoustic authentication is higher than that of single ear acoustic authentication. This proves the effectiveness of our proposed method. Bilateral ear acoustic authentication (Joint method) achieved the highest accuracy, with an EER of 0.39% and an AUC of 0.9995. Observation error was caused by each attachment and detachment. The difference in the EER caused by observation error was 0.36%. This is owing to the challenges associated with attachment and detachment. An issue to be solved in future is that noise presence due to the improper placement or positioning divergence might be intensified by normalizing a signal. If the external noise is too large, the measurement should be restarted to minimize the possibility of noise intensification due to the normalization. In addition, whether the characteristics of ear acoustic authentication change with age and the effects of ear wax have not been investigated, thus further research is needed.  
Figure 12: Comparison of EER and AUC for Sessions 1 and 2. (**a**) EER. (**b**) AUC.  
**Author Contributions:** Conceptualization, M.Y. and S.Y.; methodology, M.Y.; software, M.Y. and S.Y.; validation, M.Y., S.Y. and I.N.; formal analysis, M.Y., S.Y. and I.N.; investigation, M.Y. and S.Y.; resources, S.Y. and I.N.; data curation, M.Y.; writing--original draft preparation, M.Y.; writing--review and editing, S.Y. and I.N.; visualization, M.Y.; supervision, S.Y.; project administration, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by JSPS KAKENHI Grant Numbers JP19H04112 and JP19K22851.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study, due to National Institute of Technology, Nagaoka College judged that the examination was unnecessary.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Face Swapping Consistency Transfer with Neural Identity Carrier
## 5 Discussion
### Advanced Framework  
As shown in Figure 2a, most previous face-swapping methods can be regarded as the facial attributes disentanglement and re-combination between identity and target. It is noteworthy that the face reconstruction models in such methods do not play a fixed role in training and inference. They use attributes from a natural portrait image for training while using edited attributes for inference. Apparently, switching the latent codes between different subjects must have a bad effect on the final result. As shown in Figure 2b,c, unlike  
Figure 5: Ablation study on 3D loss. Under the constraint of 3D loss, the generated result can obtain more detail information and make results more realistic.  
Figure 6: The testing accuracy comparison results on CelebDF-v2 of detection models trained on different datasets. The training data generated by our method provides better temporal coherence and quality which is challenging for detection and is able to help promote the ability of detection models.  
previous methods, our framework only takes \(X_{t}\) as input in both the training and inference stage. The identity information of \(X_{id}\) is already learned by NICe and keeps constant in the inference. Thus the final output results \(X_{o}\) can significantly retain more attributes of \(X_{t}\), such as gaze direction.  
Figure 7 gives examples of attributes preservation, here we use DeepFaceLab [15] for comparison. Although DeepFaceLab can produce high-quality swapped results with plenty of post-processing operations, it still suffers from detail inconsistency, such as gaze direction and motion blur. But our framework perfectly inherits the gaze direction and motion blur from target \(X_{t}\).
## 3 Performance Analysis  
### Outage Probability for IRS-Assisted Bidirectional FD-MTC System  
The instantaneous SINR of the IRS-assisted bidirectional FD-MTC system, after compensation of the channel reciprocity, gains \(h_{i}^{(n)},n=1,2\) at the source nodes \(S_{1}\) and \(S_{2}\), is determined by using the Equations (12) and (13). It is given by,  
\[X_{S_{n}}=\frac{P_{5}A^{2}}{N_{0}+\sigma_{w}^{2}},n=1,2 \tag{16}\]  
Figure 3: PDF of continuous and discrete phase shifter IRS.  
The outage occurs at the source nodes \(S_{1}\) and \(S_{2}\) when it fails to decode the data successfully in the receiving mode and it is defined as [35],  
\[P_{Sn}^{\;\;outage}=P(X_{S_{n}}\leq X_{TH}),n=1,2 \tag{17}\]  
where \(X_{TH}=2^{R_{d}}-1,R_{d}\) is the required data rate at both source nodes. \(P_{Sn}^{\;outage}\) is rewritten as,  
\[P_{Sn}^{\;\;outage}=P\Bigg{(}A\leq\sqrt{\frac{X_{TH}}{\rho_{S_{n}}}}\Bigg{)},n= 1,2 \tag{18}\]  
where \(\rho_{S_{n}}=\frac{P_{S}}{N_{0}+\sigma_{w}^{2}}\), by substituting the PDF of \(A\) and solving using [36], the outage probability at the source nodes is determined as,  
\[P_{Sn}^{\;\;outage}=\frac{\gamma\Bigg{(}\frac{N\pi^{2}}{64-4\pi^{2}},\frac{\pi} {16-\pi^{2}}\sqrt{\frac{X_{TH}}{(\frac{H_{S}}{N_{0}+\sigma_{w}^{2}})}}\Bigg{)} }{\Gamma(\frac{N\pi^{2}}{64-4\pi^{2}})},n=1,2 \tag{19}\]
## 3 Wearable Sensors in Healthcare
### Electromechanical Biosensors
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Application** & **Analy Parameter** & **Materials** & **Mode of Transduction** & **Challenges** & **Wearable Platform** & **Reference** \\ \hline Motion detection & Surface deformation & Aligned nanowires & Electrochemical-mechanical & Proof-of-concept sensor needs integration into wearable device & Mountain sensor & [99] \\ \hline Sweet monitoring & Sodium & Ion-based SSE-fiber & Electrochemical-mechanical & The lack of on-body trials needs optimization for integration to textiles & Textile-based & [60] \\ \hline Breathing, motion detection and pulse rate & Strain and conductivity & Commercial spandex and carbon ink pigment-coated polyamides & Electromechanical & Cleaning, multistep fabrication process and textile/coated ink shelf like & Textile-based & [60] \\ \hline Tactile communication & Vibro-tactile feedback & Vibro-tactile feedback & Vibro-polymer impregnated & Electrochemical & Lacks longitudinal study to predict the interface success & Finger-hand based & [67] \\ \hline Motion and pulse detection & Pressure sensations & Ni-coated core-sheath & Product-of-concept design needs & Pro-of-concept design needs optimization and authentication for textile integration & Textile-based & [68] \\ \hline Motion detection & Tactile sensations & 3D-printed nanocomposites & Electrochemical & Needs miniaturization to fabricate the skin-compatible, compressible device & Skin-mounted & [69] \\ \hline Patient Monitoring & Sensors used for pulse rate, respiration rate and temperature sensor & - & Sensor operation: measure body temperature, pulse rate and monitor the breath rate of a person & - & - & [70] \\ \hline Stress Detection & - & Sensors used for ECG monitoring sensors and three-axis accelerometer & & Sensor operation: collects BC Signals using three level and measures the tilt angles between the user and the object & - & [71] \\ \hline Position Alerting & - & Sensors are used as an accelerometer, pulse and ultrasonic sensor & - & Sensor operation: measures the heat rate and finds the tilt angle between the object and the user & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the application of wearable sensors in healthcare.