esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Quantitative Data Analysis Results
#### 4.1.2 Effects of the Voice Agent  
Descriptive statistics for the measures are shown in Table 6. Agent C scored the highest values on intimacy, trust, perceived safety, and perceived anthropomorphism. Although Agent A scored the lowest on intimacy and perceived anthropomorphism, it had the highest score on intention to use.  
Figure 2: Diagram of the experimental procedure.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Measures** & **Agent A** & **Agent B** & **Agent C** \\ \hline Intimacy & 0.830 & 0.942 & 0.921 \\ Trust & 0.837 & 0.787 & 0.881 \\ Intention to use & 0.834 & 0.915 & 0.915 \\ Perceived safety & 0.873 & 0.890 & 0.846 \\ Perceived anthropomorphism & 0.866 & 0.927 & 0.938 \\ \hline \end{tabular}
\end{table}
Table 5: Reliability test results.  
The results of the one-way repeated measures ANOVA revealed significant effects of the voice agent on intimacy (\(F(2,\,58)=33.997,\,p<0.001,\,{\eta_{\mathrm{p}}}^{2}=0.540\)), intention to use (\(F(2,\,58)=3.509,\,p<0.05,\,{\eta_{\mathrm{p}}}^{2}=0.108\)), and perceived anthropomorphism (\(F(2,\,58)=30.449,\,p<0.001,\,{\eta_{\mathrm{p}}}^{2}=0.512\)). Multiple comparison analyses were performed to identify significant differences between the agents (Figure 3). Intimacy levels were significantly different between each pair of agents, increasing progressively from Agent A to B and from B to C. Intention to use was significantly lower for Agent B than for Agents A and C. Perceived anthropomorphism also showed significant differences between each pair of agents, similar to the pattern observed for intimacy.
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 6 Privacy-Preserving Architectures for Scalable IoT-Based Healthcare Systems
### Centralized Privacy Management  
Centralized privacy-preserving architectures safeguard sensitive healthcare data through concentrated data processing and storage [43].  
1. Efficient Data Management: Making the handling of data more streamlined by having consistent privacy policies and access controls in place.
2. Enhanced Security Measures: Implementing robust security, including advanced encryption standards and access controls.
3. Streamlined Compliance: The centralized approach makes it easier to adhere to regulatory frameworks, simplifying the process of meeting compliance requirements with centralized data storage.
## 2 IoV Distributed Intelligence
### Network Slicing  
Network softwarization is an important emerging trend in 5G and B5G-based networking systems aimed at creating a flexible network architecture with a reduced time to market for new services. NS is one of the major enabling technologies of the network softwarization realm allowing one to support diverse sets of services. NS has been introduced in the context of 5G, allowing mobile operators to create and customize their networks to provide optimized solutions for different market scenarios with diverse requirements [5]. Among others, Automation, Isolation, Customization, Elasticity, Programmability, End-to-End, and Hierarchical abstraction are the main principles of NS technology. NS provides dynamic resource management by enabling efficient resource sharing by considering various key performance indicators (KPIs) for each slice. NFV and SDN are two of the main technologies that enable NS over a common networking infrastructure. With NFV, network functions can be decoupled from proprietary hardware and run as software instances over virtualized environments, allowing them to overcome the lack of flexibility of traditional hardware-based network functions. On the other hand, SDN can help to create a fully softwarized wireless network by logically separating the data and control plane.  
Recently, the vehicular community has shown great interest in NS technology for providing emerging services with complex structures to end users in a limited time [23]. Thus, the implementation of vehicular services over a distributed Vehicular Network (VN) through the deployment of several logical slices is gaining importance. Within this scenario, DL is a fundamental element required to support advanced IoV services. In the context of the DL ecosystem, a set of functions with different interdependencies is required to be executed. For example, in the case of centralized FL, functions such as data acquisition, data cleaning, hyperparameter settings, learning technique selection, data training, the transmission of learning data from devices to servers, data processing at the server, the averaging process performed by the server, and broadcasting of global model data from the server to devices are required to be implemented for completing one single learning iteration. Different forms of FL with advanced learning tools and technologies (i.e., FL with transfer learning) can require additional sets of functions. Several of these functions can be implemented as virtualized learning functions on network infrastructures. With the availability of distributed computing and communication infrastructures, such as the edge-cloud continuum and integrated T/NTN, along with virtualization technologies, these learning functions can be implemented at different locations based on their characteristics and requirements.
# Spatial Downscaling of Soil Moisture Based on Fusion Methods in Complex Terrains
## 3 Methodology
The error estimate of position \(i\), \(\hat{\epsilon}_{\text{HRSM}}\left(i\right)\) or \(\hat{\epsilon}_{\text{HRSM}}\left(i\right)\), was the sum of the error weights of \(k\) neighboring stations \(F_{\text{Situ}}\). The weight \(W_{j}\) was obtained by the Kriging or inverse distance weight (IDW) spatial interpolation method [60], and its corresponding fusion field \(\hat{F}(i)\) was the sum or product of the error estimate and background field \(F_{\text{HRSM}}\). \[\left\{ \begin{array}{c}\hat{F}(i)=F_{\text{HRSM}}\left(i\right)+\hat{ \epsilon}_{\text{HRSM}}\left(i\right)\\ \hat{\epsilon}_{\text{HRSM}}\left(i\right)=\sum_{j=1}^{k}W_{j}\left(F_{\text{ Situ}}\left(j\right)-F_{\text{HRSM}}\left(j\right)\right)\end{array} \right. \tag{1}\]  
\[\left\{ \begin{array}{c}\hat{F}(i)=F_{\text{HRSM}}\left(i\right)\times\hat{ \epsilon}_{\text{HRSM}}\left(i\right)\\ \hat{\epsilon}_{\text{HRSM}}\left(i\right)=\sum_{j=1}^{k}W_{j}\left(F_{\text{ Situ}}\left(j\right)/F_{\text{HRSM}}\left(j\right)\right)\end{array} \right. \tag{2}\]  
In the evaluation step, the root-mean-square error (_RMSE_), \(MAE\), and Pearson correlation coefficient (CC) methods were used, as shown in Equations (3)-(5), where \(X_{i}\) refers to the evaluation data, and \(Y_{i}\) refers to the estimated data. \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(X_{i}-Y_{i})^{2}} \tag{3}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|X_{i}-Y_{i}|. \tag{4}\]  
\[CC=\frac{\sum_{i=1}^{n}(X_{i}-\bar{X})(Y_{i}-\bar{Y})}{\sqrt{\sum_{i=1}^{n}(X_{i }-\bar{X})^{2}}\sqrt{\sum_{i=1}^{n}(Y_{i}-\bar{Y})^{2}}} \tag{5}\]
## 1 Introduction
A recent experimental study on LECs in vitro demonstrates that acute or chronic inflammation has many different effects on AnchFs [37]. Therefore, the effect of obesity with accompanying metabolic anomalies and the effect of Ang II in vivo on the AnchFs is unknown. The abluminal side of the LECs within the initial capillary compartment is either naked or covered with a discontinuous BM. The BM forms a barrier for fluid and solute diffusion and cell trafficking. However, no studies have focused on the BM of the initial LyVs and therefore, it is not clear whether it changes in the pathological environment. Precollectors are endowed with bi-leaflet secondary valves preventing backflow of lymph into initials and zipper-like intendothelial junctions that prevent lymph leakage [18; 19]. In general, little is known about the structural changes of cardiac lymphatic initials in vivo under an inflammatory and abnormal metabolic state and hypertension. Only one study has previously examined the ultrastructure of lymphatic initials in the myocardium of hearts in Dahl S rats, demonstrating that cardiac fibrosis may have a strong effect on changes in the structure and function of cardiac lymphatics [38]. In contrast, the specificity of remodeling of the cardiac LyV system, including initials in obese individuals, has not been characterized. This is especially important since LyVs can actively adapt to changes within the tissue environment and its biomechanical properties, as has recently been revealed [39]. In addition, other studies demonstrate that cardiac LyV insufficiency leads to myocardial edema, chronic inflammation, and subsequently fibrosis, and it is usually associated with cardiac hypertrophy [40], which may finally result in cardiac diastolic dysfunction [41]. Therefore, our studies aimed to visualize the structural remodeling of the cardiac LyV system in obese db/db mice treated with Ang II. In reference to the literature data on the biological properties of lymphatic vessels in various environments, we hypothesized that Ang II in obese individuals may have an increased adverse effect on cardiac lymphatic initials. Accordingly, in the present study, we analyzed the cardiac LyV density and their structure in relation to myocardial tissue remodeling. Here, we used histopathological, fluorescence, confocal, ultrastructural, and morphometric methods to assess the structural differences in cardiac lymphatics and the myocardial tissue in obese Ang II-treated db/db mice, Ang II-treated control mice, db/db mice, and healthy control mice. We showed that Ang II treatment of db/db mice causes a decrease in the density of LyVs compared with that in Ang II-treated control mice, in addition to the pathological effect of Ang II on the myocardium in both groups. At the same time, there were specific changes in the structure of the LyV walls and their abnormal connection with the ECM. It appears that the environment of db/db mice itself has a limited effect on the myocardium and cardiac LyVs.
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
## 6 Analytical Investigation
### Cost Modeling
#### 6.1.1 Generic Link Service  
When a handover of NMP occurs on an existing NDN, the entire data packet should be re-transmitted, even if the consumer has already received part of the data. When data retransmission is required, the data packet should be segmented into N data segments by the generic link service. Therefore,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Comments** \\ \hline \(P_{Data}\) & Data packet for the interest packets \\ \hline \(P_{Int}\) & Interest packet to request data \\ \hline \(M_{Data}\) & Data segment in which a data is divided into N segments \\ \hline \(M_{Update}\) & Face update Message from the NMP \\ \hline \(M_{Status}\) & Status report message from the previous NAR \\ \hline \(N\) & Total number of segmented data messages \\ \hline \(K\) & The sequence number of the first missing segment \\ \hline \(H_{Hop}\) & Hop count from consumer to new NAR \\ \hline \(H_{{\it{D}}{\it{bN}}}\) & Hop count from previous AR to new NAR \\ \hline \(H_{Wireless}\) & Hop count from AP to producer \\ \hline \(\alpha\) & Weights of the wire link \\ \hline \(\beta\) & Weights of the wireless link \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of parameters required for analytical Investigation.  
the handover cost of the generic link service is the sum of the cost of transmitting interest from the consumer to the NMP and the cost of retransmitting the M data segments from the NMP to the consumer. The hop distance from consumer to pNAR and weight of wire and wireless link were reflected to the formula. Therefore, the handover cost of generic link service applying the NAR crossing rate of the NMP is expressed as  
\[C_{S}^{(Generic)}= \mu_{a}\{(P_{Int}+N*M_{Data}) \tag{1}\] \[\qquad*(H_{Hop}*\alpha+H_{Wireless}*\beta)\}\]
# Effect of the Anti-Yaw Damper on Carbody Vertical Vibration and Ride Comfort of Railway Vehicle
## 3 Equations of Motion
The theoretical curve of the power spectral density is representative ofthe average statistical properties of the European railway, as in the relation [47]  
\[S(\Omega)=\frac{A\Omega_{c}^{2}}{(\Omega^{2}+\Omega_{r}^{2})(\Omega^{2}+ \Omega_{c}^{2})}, \tag{24}\]  
where \(\Omega\) is the wavelength, \(\Omega_{c}=0.8246\) rad/m, \(\Omega_{r}=0.0206\) rad/m, and \(A\) is a coefficient depending on the track quality. For a high-level quality track, \(A=4.032\cdot 10^{-7}\) radm, whereas for a low-level quality, the coefficient \(A\) is \(1.080\cdot 10^{-6}\) radm. As a function of the angular frequency \(\omega=V\Omega\), where \(V\) is the vehicle velocity, the power spectral density of the track irregularities can be written in the below form  
\[G(\omega)=\frac{S(\omega/V)}{V}=\frac{A\Omega_{c}^{2}V^{3}}{[\omega^{2}+(V \Omega_{c})^{2}][\omega^{2}+(V\Omega_{r})^{2}]}. \tag{25}\]  
In terms of ride comfort, what interests is the dynamic response in three carbody reference points, located at the carbody center and against the two bogies. The response functions of the vertical acceleration at the carbody center \(\overline{H}_{C}(\omega)\) and in the points above the two bogies \(\overline{H}_{B_{1,2}}(\omega)\), respectively, are expressed in the below equations  
\[\overline{H}_{C}(\omega)=\omega^{2}\Big{[}\overline{H}_{zc}(\omega)+X_{c}(I_{c }/2)\overline{H}_{T_{c}}(\omega)\Big{]}, \tag{26}\]  
\[\overline{H}_{B_{1,2}}(\omega)=\omega^{2}\Big{[}\overline{H}_{z_{c}}(\omega) \pm a_{c}\overline{H}_{\theta_{c}}(\omega)+X_{c}(l_{1,2})\overline{H}_{T_{c}}( \omega)\Big{]}, \tag{27}\]  
where \(\overline{H}_{z_{c}}(\omega)\), \(\overline{H}_{\theta_{c}}(\omega)\), \(\overline{H}_{T_{c}}(\omega)\) are the frequency response functions corresponding to the bounce, pitch and vertical bending of the carbody. Starting from the response functions of the vertical acceleration of the carbody and the power spectral density of the track irregularities, the power spectral density of the carbody vertical acceleration in three carbody reference points can be calculated as  
\[G_{C}(\omega)=\omega^{4}G(\omega)\Big{[}\overline{H}_{zc}(\omega)+X_{c}(I_{c}/ 2)\overline{H}_{T_{c}}(\omega)\Big{]}^{2}, \tag{28}\]  
\[G_{B_{1,2}}(\omega)=\omega^{4}G(\omega)\Big{[}\overline{H}_{zc}(\omega)\pm a _{c}\overline{H}_{\theta_{c}}(\omega)+X_{c}(l_{1,2})\overline{H}_{T_{c}}( \omega)\Big{]}^{2}, \tag{29}\]  
To determine the ride comfort index, it is required to calculate the root mean square of the carbody vertical acceleration. This is calculated based on the dynamic response of the vehicle carbody, expressed as the power spectral density of acceleration,  
\[a_{C}=\sqrt{\frac{1}{\pi}\int G_{C}(\omega)\mathrm{d}\omega}\,\ a_{B_{1,2}}= \sqrt{\frac{1}{\pi}\int G_{B_{1,2}}(\omega)\mathrm{d}\omega} \tag{30}\]
## 4 Performance Evaluation and Discussion  
Since the proposed audio marker system was composed of two parts, its performance was evaluated by (1) an LFU-based object detection in terms of the true positive rate (TPR) and false positive rate (FPR) and (2) the communication quality in terms of the successful transmission rate (STR). In addition, the performance of the proposed system was evaluated under different noise conditions to examine the robustness of the transmission against ambient noise. Finally, the proposed system was compared with other positioning systems in view of AR applications.
## 2 Materials and Methods
### Atmospheric Conditions and Other Variables  
We compiled weather conditions from nearby National Oceanic and Atmospheric Administration (NOAA) weather stations on the ESVA (Climate data online; [[https://www.ncdc.noaa.gov/cdo-web/](https://www.ncdc.noaa.gov/cdo-web/)]([https://www.ncdc.noaa.gov/cdo-web/](https://www.ncdc.noaa.gov/cdo-web/)); accessed on 9 November 2019; Figure 1). We used the nearest available weather station to each site to approximate hourly weather conditions. We extracted hourly data on wind speed (m/s), wind gust speed (m/s), temperature (deg C), visibility (0-16.2 km), pressure (mmHg), precipitation duration (hours), precipitation (cm), and absolute humidity (mg/cm\({}^{3}\)). We filtered these data to reflect dates and hours in which the detector stations were active, i.e., reducing to a nightly basis between sunset and sunrise on active detector nights. We summarized each weather variable to reflect nightly conditions taking the nightly mean of wind speed, temperature, visibility, pressure, and relative humidity, the maximum wind gust speed, nightly cumulative sum of precipitation, and nightly cumulative number of hours precipitating. We also created a change in pressure variable calculated as the mean at the current night minus the mean of the previous night. Lastly, because bats may be more likely to be present at individual sites during different times of the year if they contain viable day-roosting habitat, we created a binary roosting habitat variable as has viable roost availability (forests) or, none or limited roost availability for each detector station. We also noted additional potentially relevant variables including ordinal date (day of year), site name, and year. We did not include the potentially relevant variable of detector height because we were limited by the number of unique heights (\(n=5\)).
# Hierarchical Phoneme Classification for Improved Speech Recognition
## 4 Experimental Results
### Performance of the Hierarchical Classification  
We implemented a hierarchical phoneme classification model that combines all the phoneme group models with the optimal window sizes for the corresponding phoneme groups and evaluated the performance. We used the Adam optimizer, which adaptively adjusts the learning rate of the backpropagation learning algorithm for the artificial neural networks [53]. The initial learning rate of the Adam optimizer is set to be \(10^{-4}\), and gradually lowered as the learning proceeds. Batch size 512 is used during training of each phoneme group model. Dropout [54] with a probability of 0.2 is added across the BLSTM layers. Table 8 represents the performance of the overall model. Compared with the baseline, models with different window sizes for phoneme groups had an improved performance. In addition, the overall model using the phoneme group model combining MFCC and autocorrelation showed better performance.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Baseline**} & \multicolumn{6}{c}{**Analysis Window Sizes**} \\ \cline{3-8}  & & **5 ms** & **10 ms** & **15 ms** & **20 ms** & **25 ms** & **30 ms** \\ \hline
**Vowels** & 0.700 & - & - & - & - & - & - \\
**Mixed** & 0.919 & - & - & - & - & - & - \\
**Nasals** & 0.568 & 0.565 & 0.553 & 0.564 & **0.579** & 0.557 & 0.568 \\
**Fricatives** & 0.724 & 0.710 & **0.739** & 0.738 & 0.737 & 0.735 & 0.736 \\
**Affcates** & 0.632 & 0.632 & 0.642 & **0.653** & 0.590 & 0.653 & 0.642 \\
**Stops** & 0.628 & 0.657 & **0.682** & 0.651 & 0.647 & 0.626 & 0.620 \\ \hline
**Group** & & & & & & & \\
**avg** & 0.695 & 0.697 & **0.706** & 0.704 & 0.695 & 0.698 & 0.698 \\
**Integrated** & 0.730 & 0.732 & **0.738** & 0.735 & 0.735 & 0.731 & 0.731 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of overall model using MFCC with varying window sizes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multirow{2}{*}{**Group Specific**} & \multicolumn{3}{c}{**MFCC/Autocorrelation Combinations**} \\ \cline{3-5}
**Phoneme** & & **Group Specific** & **Input Concat** & **Concat BLSTM** & **Concat BLSTM + FCN** & **Concat BLSTM + FCN** \\ \hline
**Nasals** & 0.742 & **0.757** & 0.742 & **0.757** \\
**Fricatives** & 0.844 & **0.847** & 0.834 & 0.827 \\
**Affricates** & 0.768 & 0.737 & **0.779** & 0.747 \\
**Stops** & 0.796 & 0.781 & **0.806** & 0.778 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The results that different window sizes in MFCC and MFCC with autocorrelation and a three feature ensemble model.
## 7 Experiments and Results
### Experiment 1: Comparison of Different Vessel Enhancement Filters  
First, the five vascular enhancement filters were compared: Hessian, Frangi, Meijering, RORPO, and Sato. The five different neural networks were trained to compare the filters in terms of their effect on vessel segmentation. In this experiment, the 3D U-Net architecture was used. The processing workflow from the raw input to the segmented output is shown in Figure 10. Table 4 provides the comparison of different filters concerning segmentation quality measures. The table is divided into two sections. The first one provides results using original annotations, and the second one uses improved annotations.
## 3 Materials and Methods
### System Pipeline
#### 3.2.4 Evaluation of the Segmentation Model  
Since the dataset has an uneven class distribution regarding the different wound types, utilizing appropriate metrics was crucial to tackle class imbalance. To overcome this limitation, intersection over union (IoU) (Equation (1)) and Dice coefficient (Equation (2)); were chosen as the evaluation metrics for the wound segmentation problem, giving preference to the mean IoU (average IoU of all classes), which considers the segmentation performance and pixel-wise classification for each class.  
\[IoU=\frac{TP}{FP+TP+FN} \tag{1}\]  
Figure 6: Example of a final output image from the segmentation model.  
\[Dice=\frac{2 TP}{2 TP+FN+FP} \tag{2}\]  
Lastly, the final segmentation model's precision was calculated with the Microsoft COCO challenge's primary metric [31], which evaluates the mean average precision at specific IoU thresholds, ranging from 0.5 to 0.95 with 0.05 increments. For calculating the precision, only the mean class-wise IoU without the background class was taken into account because it is important to evaluate if the system performs the wounds' segmentation and the classification of the wound types well.  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
The precision score (Equation (3)) is calculated from the true positives (TP) and false positives (FP). In this case, a TP was observed when the overlap area between the ground truth and the predicted mask gives an IoU greater or equal to the threshold. If a segmented region does not meet the mentioned criterion with the ground truth, it is considered a false positive (FP). Lastly, a false negative (FN) indicates that the ground-truth mask has no overlapping with the predicted mask.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 4 Experiments and Results
### Fusion Results  
In this part, nine other fusion methods are recommended to compare the fusion results with ours, including curvelet transform (CVT) [71], dual-tree complex wavelet transform (DTCWT) [72], Laplacian pyramid (LP) [73], nonsubsampled contourlet transform (NSCT) [74], two-scale image fusion based on visual saliency (TSIFVS) [75], guided filtering based fusion (GFF) [76], and convolutional neural network based fusion (CNN) [24], dense block based fusion [55] which includes Dense-add and Dense-L1 according to the different fusion strategies, and a GAN based method (FusionGAN) [26]. We used the codes provided by the authors or a well-known toolbox to generate the fused images from source image pairs except for the last method which cannot get the ideal outputs directly. The first six methods are conventional fusion methods and the last three methods are the DL-based methods proposed recently.