esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Architecture of Model-Based Predictive Control for Thermal Comfort in Building
### Multiple Linear Regression Based PMV Prediction Model  
Regression equations can be used for predicting environment factors such as temperature and relative humidity in single-zone buildings [45]. A linear regression model that contains more than one regressor variable is called an MLR model. The MLR model can be formulated as follows:  
\[Y=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_{2}+\cdots\beta_{k}X_{k}+\varepsilon. \tag{5}\]  
\(Y\) is the dependent variable or response, \(k\) is the number of independent or regressor variables, \(X_{k}\) is the independent or regressor variable, \(k\) ranges from 0 to \(k\), \(\beta_{k}\) is the regression coefficient, and \(\varepsilon\) is the intercept that includes the effects of un-modelled sources of variability that affect the dependent variable. In this paper, we apply the MLR for modeling the PMV index. Table 1 details the way of data collection including gathering period, time, and data interval. It also represents measured environmental parameters for building the PMV regression model as well as their measure ranges.  
Figure 2: Conceptual architecture of the proposed Model-based Predictive Control (MPC) system.  
It can be seen from the table that the metabolic rate was set at 1.2 (met that is equal to 70 W/m\({}^{2}\)) for a static activity and the clothing insulation at 0.5 (clo that is equal to 0.078 m\({}^{2}\cdot\)K/W) for average clothing in summer. Some of the air temperature and humidity data can be considered as outliers as they are out of the range according to ISO 7730; however, they only count for a very small percent (3.1%), hence we consider these data to be valid.  
The cost function \(e\) is given by:  
\[e=\sum_{i=1}^{m}(y_{i}-f(x_{i}))^{2}, \tag{6}\]  
where \(m\) is the number of training samples, \(y_{i}\) is the true value of each sample and \(f(x)\) is the predicted value. Each regression coefficient represents the mean change in response variable per unit change in the predictor variable while other predictors are held constant. For example, in the regression equation, if the air temperature variable increases by 1 and the other variables remain the same, PMV index increases by about 0.1 on the average. The coefficients of each variable are listed in the order of lower influence as shown in Table 2. Coefficient of Determination (R-square) that ranges from 0 to 1 is a widely used measure for a regression model. R-square is the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It can be seen from the table that the designed predictive model has a high R-square (0.8945). The simplified PMV regression model is generated by four parameters that are used as the independent variables: air temperature (\(T_{a}\)), mean radiant temperature (\(T_{mr}\)), air velocity (\(V_{ar}\)) and relative humidity (\(H_{r}\)), as shown in Equation (7):  
\[PMV_{regression}=0.1153T_{a}+0.1012T_{mr}+0.0035H_{r}+0.0015V_{ar}-4.4566. \tag{7}\]
# Improving the Performance of Vietnamese-Korean Neural Machine Translation with Contextual Embedding
## 3 Linguistic Annotation
### Vietnamese Parts of Speech  
For Vietnamese sentences, we rely on the PhoNLP model [33] to tag POS, as shown in Figure 6. In this model, the PhoBERT-based variant is applied in the encoding layer to generate the embedding of each token of the given input. If we assume that the given input is a sequence in the form \(x=(x_{1},x_{2},\dots,x_{n})\), where \(n\) is the length of the sequence, the embedding \((e_{i})\) of the word \((x_{i})\) is calculated by:  
\[e_{i}=PhoBERT_{base}(x_{1:n},i) \tag{5}\]  
The contextualized word-embedding \((e_{i})\) is fed into a feed-forward network; then, with a softmax predictor, the POS of each word in the given input is predicted as follows:  
\[p_{i}=softmax(FFN(e_{i})) \tag{6}\]  
This experimental exercise is based on the benchmark datasets of the VLSP 2013 POS tagging corpus ([[https://vlsp.org.vn/resources-vlsp2013](https://vlsp.org.vn/resources-vlsp2013)]([https://vlsp.org.vn/resources-vlsp2013](https://vlsp.org.vn/resources-vlsp2013)), (Accessed 20 October 2021)). The result of this method is superior to other research on POS for Vietnamese, as shown in the following table.  
Table 1 shows that POS tagging using the BERT model provides the best results. BiLSTM-CNN-CRF, VnCoreNLP-POS, JPTDP-v2, and JointWPD use Bi-LSTM to predict output. The Bi-LSTM system learns words left-to-right and right-to-left separately; therefore, this system does not capture the meaning of words optimally. The BERT model, on the other hand, can learn the context from both directions simultaneously, thus enabling better learning of the word context.  
Figure 6: Illustration of POS tagging with BERT.
## 3 Experiment
### Ablation Study
#### 3.5.4 Loss Function  
In this experiment, we verified the commonly used CIoU loss in object detection. CIOU loss considers the bounding box regression's overlapping area, center point distance, and aspect ratio. However, the difference in aspect ratio varies compared to the real difference between the width and height and their confidence, so it may hinder the model from effectively optimizing the similarity. Focal loss prevents a large number of simple negative samples from overwhelming the detector during training and focuses the detector on a sparse set of difficult examples. Therefore, the performance was better than CIoU loss. Varifocal loss proposes an awareness of the IoU based on focal loss to sort dense targets. At the same time, the weight of positive and negative samples was different. It was more  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Window Size** & **AP50** & **AP75** & **mAP** & **AR500** & **mAP** & **AR500** & **Score** \\ \hline Original & 0.508 & 0.353 & 0.332 & 0.4012 & 0.34 & 0.4228 & 0.378 \\ \(6000\times 4000\) & 0.683 & 0.545 & 0.50 & 0.6075 & 0.45 & 0.6181 & 0.519 \\ \(3000\times 3000\) & 0.755 & 0.599 & 0.563 & 0.675 & 0.57 & 0.6802 & 0.620 \\ Single-class fusion & **0.799** & **0.630** & **0.584** & **0.685** & **0.59** & **0.6995** & **0.641** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cropping window size.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classes** & **Pedestrian** & **Vehicle** & **Total** \\ \cline{2-5}
**Method** & **AP** & **AP** & **AP50** & **AP** \\ \hline Region-NMS & 46 & - & - & - \\ SARNet & 49.1 & 60.2 & - & 51.9 \\ PVDet & 59.2 & **63.3** & **82.2** & 61.1 \\ Ours & **62.1** & 59.9 & 81.9 & **61.4** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with SOTA results.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Croped** & \multicolumn{2}{c}{**Pedestrian**} & \multicolumn{2}{c}{**Vehicle**} & \multicolumn{2}{c}{**Total**} \\ \cline{2-9}
**Method** & **AP50** & **AP75** & **mAP** & **AP50** & **AP75** & **mAP** & **AP50** & **AP75** & **mAP** \\ \hline Without & 0.810 & 0.644 & 0.596 & 0.785 & 0.616 & 0.580 & 0.799 & 0.630 & 0.584 \\ Midline & **0.833** & **0.682** & **0.621** & **0.809** & **0.644** & **0.599** & **0.819** & **0.673** & **0.614** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Midline method.  
in line with the characteristics of gigapixel-level images where the target is concentrated in a specific image area. Table 5 demonstrates the superior performance of varifocal loss for this dataset.
## 3 Results and Discussion
### Guiding Circular Strategies: Design, Certification, and Policy  
The framework applied in this study is intended to foster circular product management at the firm level. Some companies have already introduced certain practices to manage their circular practices and leverage the communication thereof.  
When a company decisively undertakes efforts to make its products and services more sustainable and circular, it can be exposed to the market failure of asymmetric information:  
Figure 8: Benchmarking of the BIORADAR circularity monitoring framework and other CE assessment tools.  
customers may not know or understand what the company has achieved and why it represents an advantage.  
There, certifications and labels appear as a solution to overcome the asymmetric information problem. Through certifications and labels, the companies can effectively convey to their customers and stakeholders that they have engaged in significant efforts for increasing circularity and reducing environmental impacts, and such efforts have been verified by a third party.  
At the time of writing, the only seal specifically dedicated to the circular economy is Cradle to Cradle, although no bio-based fertilizer has yet obtained this certification. Other seals focus on various aspects, such as Italy's CIC quality seal for compost [74; 75], the UK's biofertilizer certification scheme [76], the USA's USDA Certified Biobased Product label [77], and over 230 technical standards (ISO, EN, and UNI) mapped by BIORADAR [78]. However, none of these specifically emphasize the circularity of bio-based fertilizers.  
A recent addition to the circular economy standardization landscape is ISO 59020 [60], along with a series of forthcoming documents being developed by the ISO's technical committee ISO/TC 323 \"Circular Economy\". The BIORADAR product circularity monitoring framework, together with this manuscript, offers significant insights and validation of circular bioeconomy indicators applicable to the fertilizer sector. This work lays the ground for further development and the integration of such metrics into standardized norms and certifications.  
The same holds true from the perspective of eco-design. As discussed above for each of the four indicators, all enable companies to identify opportunities to advance their circularity levels. Consequently, the monitoring framework can serve as a compass for making strategic and eco-design decisions regarding manufacturing processes, suppliers, and waste management, ultimately leading to higher circularity.  
Similarly, policymaking processes can leverage the results of the monitoring framework to identify patterns and opportunities when it is widely applied. Such actions may include subsidy schemes, target setting, and incorporation into sustainable finance criteria, such as the EU taxonomy [79].
## 5 Results and Analysis
### Uncertainty Analysis (UA) of Models
#### 5.3.2 Wavelet-Based Models UA  
The visualization of interrelationship of the variables between the wavelet-based models for both catchments is shown in Figure 22 (Fal at Tregony) and Figure 23 (Teifi at Glanteifi). Figures 22a-c and 23a-c show the interrelationship of LB, UV and mean, SE and ME and WCB, respectively, for the models.  
Figure 21: Visual representation of uncertainty analysis (UA) for lag-based models (Teifi at Glanteifi), (**a**) LB, UB and Mean; (**b**) SE and ME; (**c**) width of confidence (WCB).  
Figure 20: Visual representation of UA for lag-based models (Fal at Tregony), (**a**) lower bound (LB), upper bound (UB) and mean; (**b**) standard error (SE) and margin of error (ME); (**c**) WCB.  
For catchment Fal at Tregony, LB, UV and mean (Figure 22a) and SE and ME (Figure 22b) are lower for WEO-ELM compared to other models. It can be observed that WCB (Figure 22c) also lower in WEO-ELM. WDNN is comparable with WEO-ELM in terms of SE and ME (Figure 22b) and WCB Figure 22c for Fal at Tregony. From UA, it is confirmed that WEO-ELM has better prediction accuracy in the prediction runoff variable along with WDNN for Fal at Tregony.  
For the catchment of Teifi at Glanteifi, LB, UV and mean (Figure 23a) and SE and ME (Figure 23b) are lower for WDNN compared to other models. It can be observed that WCB (Figure 23c) is also lower in WDNN. The prediction accuracy of WEO-ELM follows WDNN and is better than the other six models for Teifi at Glanteifi. From UA, it is confirmed that WDNN has better prediction accuracy in prediction runoff variable for Teifi at Glanteifi.
## 5 Conclusions  
An ultrasonic array system consisting of one transmitter and five receivers for object localization and tracking is proposed in this paper. The system uses the VAD algorithm to detect the target's echo signal and locate the target. The envelope is then extracted by the demodulation algorithm, and the TOF estimation is performed by the NRLM algorithm with the GN model or the DE model. Furthermore, the detected object can be located by the NRLM algorithm or the EKF algorithm with outlier rejection. In this study, these proposed methods are compared and experimentally evaluated in different scenarios for TOF estimation and tracking on target position. The NRLM algorithm that uses nonlinear curve-fitting provides a more reasonable TOF than other methods that use linear interpolation. Although the GN model is more suitable for the rising edge than the DE model, the TOF estimation of the GN model is more sensitive to the variation of the rising edge. The EKF method uses previous states to decrease the influence of  
Figure 14: Case 3-3: Target moves from (20, 36) to (20, 48), the velocity in the \(y\)-direction is about \(1\) cm/s. (Blue dots: the estimated coordinates of moving target; red line: the reference target trajectory.)  
Figure 15: (**Left**): The target moves from (20, 36) to (20, 48), the velocity in the \(y\)-direction is about \(1\) cm/s; (**Right**): The target moves in a circle with center at (16, 42), both the start point and the end point are at (16, 48). (Blue dots: the estimated coordinates of moving target; red line: the reference target trajectory.)the current interference of measurement. Moreover, outlier rejection provides a more stable and smoother result for the localization and tracking of moving targets. Therefore, the experiment results verify that the NRLM algorithm with the DE model has the best performance for TOF estimation, and the EKF algorithm outperforms the other methods in object localization.  
Conceptualization, [PERSON]-[PERSON] and [PERSON]-[PERSON]; methodology, [PERSON]-W.J. and [PERSON]-[PERSON]; software, C.-W.J.; validation, C.-W.J.; formal analysis, [PERSON]-W.J. and [PERSON]-[PERSON]; investigation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; resources, [PERSON]-W.J. and [PERSON]-[PERSON]; data curation, C.-W.J.; writing--original draft preparation, C.-W.J.; writing--review and editing, [PERSON]-W.J.; visualization, [PERSON]-W.J.; supervision, [PERSON]-W.J. and [PERSON]-[PERSON]; project administration, [PERSON]-W.J. and [PERSON]-[PERSON]; funding acquisition, [PERSON]-W.J. and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan (Grant Number: MOST 108-2221-E-009-122-MY2).  
Not applicable.  
The data presented in this study are included within the article.  
The authors declare no conflict of interest.
# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
## 3 Results
### Performance Evaluation for Airway Invasion Detection  
The performance of airway invasion detection was evaluated in the following two ways: (1) airway invasion classification for individual image frames using the Xception module-based CNN and (2) airway invasion judgment for video clips containing swallowing motions using context information. The classification performance for individual image frames was evaluated on 76,797 frames, corresponding to 20% of all videos not used for network training, and the video-level accuracy was also evaluated on 82 video files not used for training. On average, two to three swallowing motions were recorded in one video file, and we evaluated a total of 179 swallowing motion videos by manually separating the parts of the videos corresponding to different swallowing motions.  
Figure 8: ROC curve for the detection of the cervical spinal column.  
Figure 9: Results of cervical spinal column localization: (**A**) cervical spinal column (red area) detected by U-Net, and (**B**) ROI image.  
The accuracy, precision, recall, F1-score, and NPV were used as the evaluation metrics for both individual image frames and videos.  
Table 3 and Figure 10 show the classification results and ROC curve, respectively, obtained for individual image frames using the CNN based on Xception. In Table 3, the recall refers to the ratio of the number of images classified as positive by the proposed detector to the number of images manually labeled as positive by the two physiatrists (where a positive label indicates that airway invasion was found). As shown in Table 3, the proposed detector achieves 74.2% recall, 97.2% accuracy, and 59.1% precision (Figure 10).  
Table 4 shows the final airway invasion detection results obtained for complete videos by utilizing the context information (by means of Equation (1)) provided by the classification results for individual image frames. As seen from Table 4, the decision process for airway invasion during swallowing shows 91.2% recall, 93.2% accuracy, and 88.1% precision.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 5 Details of Proposed Protocol
### Periodic Credential Generation  
Periodically (on a daily basis), the vehicles request a road reporting credential (\(RReq\)) which permits the reporting of the abnormal scenes captured by the vehicle's camera. To acquire a RReq from \(DMV_{k}\), \(v_{i}\) performs the following:  
* \(v_{i}\) composes a credential request message \(RReq=\{alias_{i},K,ts\}\) where \(ts\) is the time stamp and \(K\) is a secret key to be used later.
* \(v_{i}\) sends \(C_{1}=Enc_{PK_{DMV_{k}}}\{RReq,pk_{v_{i}},\delta_{i}\}\) to the \(DMV_{k}\), where \(\delta\) is the signature for the \(RReq\) set as \(\delta_{i}=\) CertS.Sign(\(RReq\), \(sk_{v_{i}}\)).
* Upon receiving the message \(C_{1}\), \(DMV_{k}\) first decrypts \(C_{1}\) using its private key, then verifies the signature as CertS.Verify(\(RReq\), \(alias_{i}\), \(pk_{v_{i}}\), \(\delta\)). If it holds, \(DMV_{k}\) generates reporting credential (\(R\)-_cred_) as follows:  
1. Generate \(R\)-_cred_\(=\langle alias_{i},exp,KN,\omega_{v_{i}}\rangle\) where \(exp\) is the expiration date, \(\omega_{v_{i}}\) the set of attributes and \(KN\) the keyword for the credential. Note that \(KN\) is not specific for each credential but is the same based on the access structure.
2. \(DMV_{k}\) sends \(C_{1}=Enc_{K}(R\)-_cred_) to \(v_{i}\). Then, \(v_{i}\) can recover \(R\)-_cred_ by decrypting the \(C_{1}\) under the shared secret key \(K\).
3. \(DMV_{k}\) sends periodically a list \(List_{KN}\) of all the keywords enclosed in the credentials to \(RSCs\).
# Analysis of Inductive Displacement Sensors with Large Range and Nanoscale Resolution
## 3 Principle Analysis
### Sensitivity Analysis of Differential Inductive Sensors Considering Leakage Inductance  
The resolution of the sensor is determined by the circuit noise and sensitivity. Therefore, correct prediction and design sensitivity are the key to ensure the measurement performance of the sensor. In a large range, the differential inductance sensor is nonlinear, and the sensitivity changes with displacement. The sensitivity near the zero position (\(\Delta\delta=0\)) is the smallest, so only the sensitivity near the zero position needs to be designed.  
Near the zero position, the sensitivity of differential inductance relative to displacement is defined as \(K_{L}\), and the sensitivity of bridge output voltage (the measuring circuit shown in Figure 11 is used) relative to the measured displacement is defined as \(K_{U}\). Based on Equation (5), \(K_{L}\) can be given by,  
\[K_{L}=\lim_{\Delta\delta\to 0}\left(\frac{\Delta L}{\Delta\delta}\right)=\lim_{ \Delta\delta\to 0}\left(\frac{2L_{0}\delta_{0}\Delta\delta}{\delta_{0}^{2}- \Delta\delta^{2}}/\Delta\delta\right)=\frac{2L_{0}}{\delta_{0}} \tag{16}\]  
Convert the AC bridge voltage (\(\tilde{U}_{\text{out}}\) and \(\tilde{U}_{\text{in}}\)) in equation (15) into RMS value (\(U_{\text{out}}\) and \(U_{\text{in}}\)) for calculation, \(K_{U}\) can be given by,  
\[\begin{array}{ll}K_{U}&=\lim_{\Delta\delta\to 0}\left(\frac{U_{\text{out}}}{ \Delta\delta}\right)=\lim_{\Delta\delta\to 0}\left(\frac{U_{\text{in}}}{2}\frac{ \Delta L}{L_{1}+L_{2}}/\Delta\delta\right)=\lim_{\Delta\delta\to 0}\left(\frac{U_{ \text{in}}\Delta L}{4\left(L_{0}+L_{\text{S}}\right)}/\Delta\delta\right)\\ &=\frac{U_{\text{in}}}{4\left(L_{0}+L_{\text{S}}\right)}\lim_{\Delta\delta \to 0}\frac{\Delta L}{\Delta\delta}=\frac{U_{\text{in}}}{2\delta_{0}\left(1+L_{ \text{S}}/L_{0}\right)}\end{array} \tag{17}\]  
Equation (17) shows that the ratio of leakage inductance to zero working inductance (\(L_{\text{S}}/L_{0}\)) is an important factor affecting the sensitivity (\(K_{U}\)) of differential inductance sensors. The smaller \(L_{\text{S}}/L_{0}\), the higher \(K_{U}\). Therefore, the leakage inductance modeling in Section 3.2 plays an important role here, which is an important part of the differential inductive sensor design. According to the leakage inductance model (Equation (11)), the relationship between \(L_{\text{S}}/L_{0}\) and the size of the sensor core is obtained, as shown in Figure 12. It shows that \(L_{\text{S}}/L_{0}\) decreases with the increase of sensor core size.  
The deviation between \(K_{UL\_Theory}\) and \(K_{UL\_Experiment}\) may be due to the small \(Q\) value of the prototype sensor, coil winding process, and the installation error of sensor probes.