esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Time-Series Classification of Water and Non-Water  
To determine a universal adaptability threshold, nine grids for the three classifications (high \(>66\%\), medium 33-66%, low 0-33%) of the WeiFang site (15 June 2018) were used to calculate thresholds, using the method explained in Section 3.3. They were calculated according to Formulae (2) and (3). In this experiment, the 20 km grids of the WeiFang site were analyzed. This shows that the type of the histogram can be accurately judged when the value of coefficients \(\alpha,\beta\) are set to 3. Finally, the three thresholds were determined as follows:  
* First, if \(\sigma^{0}_{VV}>-16\) dB, mark as water.
* Second, if \(\sigma^{0}_{VH}>-22\) dB, mark as water.
* Third, if \(\sigma^{0}_{VVVH}<15\) dB, mark as water.  
To validate the universality of the threshold, water areas were extracted during the flood period at two sites. The overall results are shown in Figure 6. The detailed results during the flood at both sites were assessed via a time-series analysis. To simulate the water level changes at the WeiFang site, the six images during the flood events of the Fushan Reservoir were separately analyzed (21 July-1 September 2018) (Figure 7a). According to relevant reports, the Fushan Reservoir did not discharge floodwaters downstream during this period, and its area was significantly changed after the flood. At the Ji'An site, three images from 1 June 2019 were analyzed (Figure 7b). The image that was taken on 13 June 2019 revealed a widened river surface with some submerged areas. The three sub-panels reflected the situation before, during, and after the flood, respectively. Comparing the images of the two sites, the flooding lasted longer at the WeiFang site than at the Ji'An site, and the data downloaded from the former failed to monitor its recovery. At the Ji'An site, the incident was monitored from onset to recovery.  
Figure 5: Locations of 200 random points sampled at each of the two sites for visual inspection of the extraction results of the AT-EWC method (Ji’An (**a**) and WeiFang (**b**)).
# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 3 SFM Preliminaries
### Filter Out Blurred Frames  
Firstly, we take the underwater environment into consideration. Due to the instability of underwater equipment caused by water flow, a few frames in the video may be blurred, so we hope to obtain clearer image frames as much as possible for subsequent 3D reconstruction efforts. The first step is to filter out the blurred frames. In this paper, we measure the degree of ambiguity with the Laplace variance algorithm.  
The Laplacian operator is the second-order derivative of the image, which can detect rapid changes in the gray value of the image. It highlights the region in the image that contains fast gradient changes. Referring to Equation (1), \(I(x,y)\) is the grayscale image, \(L(x,y)\) is the Laplacian-based image, and Equation (2) is the discrete form of the Laplacian operator:  
\[L(x,y)=\frac{\partial^{2}I}{\partial x^{2}}+\frac{\partial^{2}I}{\partial y^{2}} \tag{1}\]  
\[L(x,y)=I(x+1,y)+I(x-1,y)+I(x,y+1)+I(x,y-1)-4I(x,y) \tag{2}\]  
This process can be calculated using a convolution kernel and can generate an image that describes abrupt changes in the grayscale. As early as 2000, an algorithm [26] based on Laplacian variance was proposed to measure sharper focus. The boundary in a clear image is clear, and the Laplacian variance is large; the boundary information of a blurred image is less clear, and the variance is small. Therefore, the Laplacian operator can be used for image blur detection, and in this paper we choose this method to measure the blur degree of underwater images.  
Figure 2: SFM preliminary flow chart.  
The algorithm steps are as follows. Firstly, we convolve a certain channel of the picture (generally use gray values) with a Laplacian mask and then calculate the variance (the square of the standard deviation) of the response as the ambiguity of the image. We set the average blur of all frames as the threshold for judging clarity, and then filter out the frames that are lower than the threshold.
## 4 Detector Prototype and Readout Electronics
### Scintillator Array and Detector Prototype  
As discussed above, an EJ200 scintillator array with a pixel size of 1 mm \(\times\) 1 mm \(\times\) 20 mm was cut and optically isolated to form the fast neutron-sensitive scintillator. The scintillator array is shown in Figure 8. The effective area of the EJ200 array was about 5 \(\times\) 5 cm\({}^{2}\). A mixture of titanium oxide and glue with a thickness of less than 0.2 mm was applied between adjacent pixels as photo isolation. The 16 \(\times\) 16 SiPM array and the capacitive multiplexed networks were packaged on the same PCB board, as shown in Figure 9a part 1. Figure 9a part 2 shows the preamplifier of the four position signals (P, Q, R, and S). Figure 9a part 3 shows the data acquisition module. A gigabit network port was used in the data output module to connect the detector to a personal computer. The acquired data were accessed online via a computer or transferred to a personal computer for offline processing. Figure 9b shows the SiPM array after the assembly of all components.  
A K9 glass with a thickness of 3 mm as the optical guide was placed between the scintillator array and the SiPM array to enhance the diffusion of scintillation light and transmit it to the SiPM array. Silicon oil was used as the coupling medium between the EJ200 array, the optical guide, and the SiPM array.  
The K9 glass exhibited high transparency and high radiation resistance. The 3 mm thickness ensured a suitable balance while spreading scintillating light from EJ200 over more SiPM pixels and not excessively decreasing the light intensity at each signaled SiPM pixel. Moreover, K9 was a favorable material for the fabrication of optical guides owing to its low cross-section of neutron scattering.  
Figure 8: EJ200 scintillator array.
## 2 Data and Methods
### GOCI Data Collection and Processing  
In this study, GOCI Level-1B data were used to assess the application of the developed refractive index (\(n_{\rm p}\)) model. As a geostationary ocean color remote sensing satellite, GOCI receives images eight  
\begin{table}
\begin{tabular}{c c c} \hline
**Cruise Date** & **Measured Parameters** & **Sample Numbers** \\ \hline
7 November 2014–23 November 2014 & \(R_{\rm rs}(\lambda)\) (sr\({}^{-1}\)) & 27 \\  & TSM (mg\(\cdot\)L\({}^{-1}\)) & 108 \\ \hline
17 August 2015–5 September 2015 & \(R_{\rm rs}(\lambda)\) (sr\({}^{-1}\)) & 37 \\  & TSM (mg\(\cdot\)L\({}^{-1}\)) & 101 \\ \hline
29 June 2016–14 July 2016 & \(R_{\rm rs}(\lambda)\) (sr\({}^{-1}\)) & 58 \\  & TSM (mg\(\cdot\)L\({}^{-1}\)) & 123 \\ \hline
29 December 2016–13 January 2017 & \(R_{\rm rs}(\lambda)\) (sr\({}^{-1}\)) & 24 \\  & TSM (mg\(\cdot\)L\({}^{-1}\)) & 103 \\  & \(b_{\rm bp}\) (m\({}^{-1}\)) & 102 \\  & \(b_{\rm p}\) (m\({}^{-1}\)) & 102 \\  & \(c_{\rm p}\) (m\({}^{-1}\)) & 103 \\ \hline \end{tabular}
\end{table}
Table 1: Description of in situ observed datasets collected during four cruise surveys in the study area. TSM—total suspended matter.  
times a day from 12:15 a.m. to 7:45 a.m. Greenwich Mean Time (GMT) (8:15 to 15:15 local time) with 1-h temporal resolution. The GOCI satellite data have a spatial coverage of about 2500 km \(\times\) 2500 km covering the northwest Pacific Ocean, a spatial resolution of 500 m, and eight spectral bands with the central wavelengths of 412, 443, 490, 555, 660, 680, 745, and 865 nm. In this study, a total of 2825 GOCI satellite images during the one-year period from December 2014 to November 2015 were downloaded from the Korea Ocean Satellite Center (KOSC). By utilizing the GOCI Data Processing System (GDPS, version 1.3), the image data focusing on the BS and YS regions were firstly extracted and then processed by the default atmospheric correction method of [PERSON] & Gordon (1994) [PERSON] & [PERSON] (1994) to output \(R_{\rm rs}(\lambda)\) data. These \(R_{\rm rs}(\lambda)\) data were further quality controlled by using the various flags such as stray light and cloud coverage to avoid interference from invalid satellite data signals.
## 5 Discussion
Our framework adds specificity to educational contexts by incorporating tailored measures of efficiency (response time), engagement (UES-SF scores), usability (CUQ scores), and effectiveness (error rates). For example, while the BUS-15 addresses general usability, our framework emphasizes response accuracy as a critical component of timely and educationally relevant support. Our approach improves the user engagement aspect of the BUS-15 by concentrating on maintaining user interaction and ensuring clear elements for fostering engagement in educational settings. By catering to these requirements, our system provides an advanced, context-aware benchmark tool that facilitates thorough and precise assessments tailored for educational chatbot usage. Combining the CUQ, UES-SF, and effectiveness into a single composite score streamlines a holistic user experience in educational chatbots. A unified metric that identifies the interaction modes that excel across dimensions can be developed using weighting. Weighting can prioritize certain aspects, e.g., the CUQ for usability-centered goals or the UES-SF for engagement focus, enhancing flexibility based on research objectives. This integrated metric enables comprehensive and precise evaluation, facilitating clear comparisons between interaction modes and question complexities. Such a composite score is particularly useful for identifying modes that offer balanced strengths and ensuring that the chatbot experience remains effective, engaging, and efficient. To this end, we propose a new composite UX metric that combines several metrics and weighs each metric's contribution based on PCA. The newly proposed metric streamlines the comparison of educational chatbots by integrating multiple dimensions of user experience into a single score. This metric combines the CUQ (usability), the UES-SF (engagement), effectiveness, and efficiency with weights derived from their contributions to the explained variance in user satisfaction, as identified through PCA. The weights, calculated as approximately 0.696 for effectiveness, 0.267 for the CUQ, 0.073 for the UES-SF, and \(-\)0.036 for efficiency, reflect the relative importance of each metric. This distribution emphasizes that task accuracy and usability significantly impact satisfaction more than engagement and response speed in educational chatbots, aligning with the findings that educational contexts prioritize accuracy and usability over speed alone. This composite metric maintains flexibility in comparing interaction modes and question complexities, allowing researchers to adjust their focus based on objectives. For example, if user-centered design is prioritized, the CUQ's weight can be increased; if engagement is more relevant, the UES-SF can be emphasized. Therefore, combining these dimensions into a single composite UX metric enhances adaptability and applicability across various research needs when evaluating chatbots in educational settings. Such a holistic metric aligns with existing research that underscores the value of multi-dimensional assessments in evaluating user experience for educational technologies [7; 78].
# RoPT: Route-Planning Model with Transformer
## 4 Methodology
### Next Node Prediction Module  
This module predicts the next node from latent representations of the destination node \(N_{Des}\) and latent representations of the current route \(M_{S}\). As shown in Formula (18), latent representations of the destination \(M_{Des}\in\mathbb{R}^{l\times h}\) are looked up from the node representations output with _GCN_.  
\[M_{Des}=LookUp(M_{S},N_{Des}) \tag{18}\]  
Then, latent representations of neighboring nodes of current node \(M_{\text{Nei}}\in\mathbb{R}^{dne\times h}\) are also obtained with the above methods. Here, the matrix of transition probability is computed with similarity calculation:  
\[Ne_{i}=getNe(M_{A},v_{i}) \tag{19}\]  
\[M_{Ne_{i}}=LookUp(M_{V},Ne_{i}) \tag{20}\]  
With Formula (19), latent presentations of neighboring nodes \(N_{\text{ei}}\in\mathbb{R}^{l\times\text{one}}\) of current node are found, where \(M_{A}\) represents the network adjacency matrix, \(v_{i}\) represents the current node, \(getNe\) is a function to search neighboring nodes of \(v_{i}\), and IDs of neighboring nodes are concatenated into a vector \(N_{\text{ei}}\in\mathbb{R}^{l\times\text{one}}\). When the number of neighbors is less than the hyper-parameter \(d_{ne}\), it should be padded to \(d_{ne}\). Then, the representations of the neighbors of current node \(M_{\text{Nei}}\) are obtained with Formula (20).  
Next, transition probability based on current route is calculated \(M_{prob}^{T}\in\mathbb{R}^{l\times\text{dne}}\).  
\[M_{prob}^{T}=\frac{exp\big{(}M_{Ne_{i}}\cdot M_{T}\big{)}}{\sum_{i}exp\big{(}M_{Ne _{i}}\cdot M_{T}\big{)}} \tag{21}\]  
Similarly, transition probability based on destination \(M_{Prob}^{Des}\in\mathbb{R}^{l\times\text{dne}}\) can also be calculated.  
\[M_{prob}^{Des}=\frac{exp\big{(}M_{Ne_{i}}\cdot M_{Des}\big{)}}{\sum_{i}exp \big{(}M_{Ne_{i}}\cdot M_{Des}\big{)}} \tag{22}\]  
Then, the calculation of complete transition probability \(\text{M}_{prob}\in\mathbb{R}^{l\times\text{dne}}\) is shown in Formula (23), where \(\lambda\) is a hyper-parameter to balance importance of the two transition probabilities listed above. The index of the position with the highest probability is obtained by the argmax function.  
\[M_{\text{prob}}=(1-\lambda)M_{prob}^{Des}+\lambda M_{prob}^{T} \tag{23}\]  
Finally, a Cross-Entropy Loss function is used to train the whole model RoPT, which is shown in Formula (24).  
\[Loss=-\frac{1}{n}\sum_{i}\sum_{c=1}^{dne}q(y_{ic})log(p_{ic}) \tag{24}\]  
Here, \(y_{ic}\) denotes the index of node in the real route. And \(q\) denotes a signum function, whose output is 1 when, in sample \(i\), the index is equal to the real index; otherwise, it is 0. The transition probability of each node in sample \(i\) is denoted as \(p_{ic}\).
###### Abstract  
We analyzed the possibility of detecting and predicting ventricular fibrillation (VF), a medical emergency that may put people's lives at risk, as the medical prognosis depends on the time in which medical personnel intervene. Therefore, besides immediate detection of VF, the possibility of predicting VF 40 or even 50 min in advance was analyzed. For testing the proposed algorithm, we used ECG signals taken from the MIT-BIH database, namely, Malignant Ventricular Ectopy Database, Sudden Cardiac Death Holter Database and Normal Sinus Rhythm Database. The presented method is based on features extracted from the ECG signals in the time domain, frequency domain and complexity measures. For VF detection, the possibility of identifying the VF episode in the first 3 s after its occurrence was tested. For this, the first 3 s immediately after the appearance of VF were cut out and the features were computed on these sections. For VF prediction, 3 min of the ECG signal clipped 40 or 50 min before VF onset was used. Then, on these pieces of ECG signal, the specific features were calculated for 1 s segments. For the normal signal situation, 3 min was randomly selected from the database with normal ECGs. For the classification or detection stage, both an MLP-type neural network and the classifiers from the Machine Learning toolbox of the MATLAB(r) environment were used. The results obtained for both detection and classification are over 94% in both cases. The novelty of our results compared to those previously obtained is the time interval with which the possibility of prediction was analyzed, namely, 50 min in advance of the VF installation date. This means that the patient will be informed that it is possible to suffer a VF and has time to take the necessary measures to overcome a possible medical emergency.  
v 2024 14 3050 1
## 2 Primary Theory  
### Fuzzy Entropy  
Fuzzy Entropy (FE) is defined to measure the complexity and irregularity of the time series; the computation process of FE is as follows [18; 19]:  
1. Assume that a time series is denoted as \(X=\{x(i):1\leq i\leq N\}\), where \(N\) is the length of time series. Then, the mean \(x_{0}(i)\) of \(m\) consecutive \(x(i)\) values can be calculated as follows: \[x_{0}(i)=\frac{1}{m}\sum_{j=0}^{m-1}x(i+j),\] (1) where parameter \(m\) is called the embedding dimension and is a positive integer. Then \(m\) dimensional vector \(X_{i}^{m}(i=1,2,\cdots,N-m)\) is reconstructed as: \[X_{i}^{m}=\{x(i),x(i+1),\cdots,x(i+m-1)\}-x_{0}(i).\] (2)Suppose that \(d_{ij}^{m}\) (\(i,j=1\sim N-m;j\
eq i\)) is denoted as the maximum distance between \(X_{i}^{m}\) and \(X_{j}^{m}\). Then, \(d_{ij}^{m}\) can be calculated according to Equation (3): \[d_{ij}^{m}=max\big{(}\big{|}x(i+k)-x_{0}(i)-(x(j+k)-x_{0}(j))\big{|}\big{)},\] (3) where \(k=1,2,\cdots,m-1\).
3. Suppose that \(\mu(d_{ij}^{m},n,r)\) is denoted as a fuzzy function: \[\mu(d_{ij}^{m},n,r)=\exp(-\frac{(d_{ij}^{m})^{n}}{r}),\] (4) where \(\exp(\cdot)\) denotes the exponential function, parameter \(n\) is the boundary gradient, and \(r\) is the boundary width. Then the similarity degree \(D_{ij}^{m}\) between \(X_{i}^{m}\) and \(X_{j}^{m}\) is given as: \[D_{ij}^{m}(n,r)=\mu\big{(}d_{ij}^{m},n,r\big{)}.\] (5)
4. \(\Phi^{m}(n,r)\) is obtained from Equation (6): \[\Phi^{m}(n,r)=\frac{1}{N-m}\sum_{i=1}^{N-m}\big{(}\frac{1}{N-m-1}\sum_{j=1,j \
eq i}^{N-m}(D_{ij}^{m})\big{)}.\] (6)
5. Repeat Steps (1)-(4) for obtaining \(m+1\) dimensional vector \(X_{i}^{m+1}\), and \(\Phi^{m+1}(n,r)\) can be described as \[\Phi^{m+1}(n,r)=\frac{1}{N-m}\sum_{i=1}^{N-m}\big{(}\frac{1}{N-m-1}\sum_{j=1,j \
eq i}^{N-m}(D_{ij}^{m+1})\big{)}.\] (7)
6. The FE of time series \(\{x(i):1\leq i\leq N\}\) can be calculated as follows: \[FE(X,m,n,r)=\lim_{N\to\infty}(\ln\Phi^{m}(n,r)-\ln\Phi^{m+1}(n,r)),\] (8) where \(\ln(\cdot)\) denotes the natural logarithm function. If \(N\) is finite, \(FE(X,m,n,r)\) can be expressed as \[FE(X,m,n,r,N)=\ln\Phi^{m}(n,r)-\ln\Phi^{m+1}(n,r).\] (9)
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 5 Extensions of Merging-Squeeze-Excitation Feature Fusion
### MSE Feature Fusion with Skip Connections  
Skip connections were used in ResNet models [43] to solve the vanishing-gradient issue. Here, we will apply this technique to the MSE feature fusion such that feature maps entering the classification will be at least as good as the feature maps obtained from the earlier step. We consider two possible positions to add skip connections.  
* The MSE feature fusion with local skip connections is shown in Figure 2a, where we add a skip connection to each branch of \(\mathbf{A}^{(n)}\). As a result, we have \[\mathbf{Q}^{(n)}=\mathbf{A}^{(n)}+\mathbf{P}^{(n)}.\] (15) The feature maps \(\mathbf{Y}^{lsc}\) that will enter the classifier are obtained from \[\mathbf{Y}^{lsc}=\mathbb{F}_{Merge}\big{(}\mathbf{Q}^{(1)},\mathbf{Q}^{(2)}, \ldots,\mathbf{Q}^{(N)}\big{)}.\] (16)
* The MSE feature fusion with a global skip connection is shown in Figure 2b. We create a skip connection on the MSE feature fusion such that the pre-merged feature maps \(\mathbf{B}\) from the pre-merging step will be added to the channel-weighted and merged feature maps \(\mathbf{Y}^{mse}\). Thereafter, we have \(\mathbf{Y}^{gsc}\) entering to the classifier as follows: \[\mathbf{Y}^{gsc}=\mathbf{Y}^{mse}+\mathbf{B},\] (17) where \(\mathbf{Y}^{mse}\) is defined in (14). The prediction will be based on both \(\mathbf{Y}^{mse}\) and \(\mathbf{B}\).
# E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
## 3 E-Mqtt
### Packet Format  
In the existing MQTT, the first byte of the fixed header allots bits 7 to 4 for denoting the packet type. This allocation encompasses a total of 14 packets, ranging numerically from 0001 to 1110. The structure of a fixed header for a conventional MQTT PUBLISH packet is illustrated in Figure 6. Within the first byte, bit 3 is used for indicating DUP flags, whereas bits 2-1 are deployed to signify QoS levels. These QoS levels span from 0 to 2, while the unused value 3 has been repurposed to accommodate the proposed QoS level 3. That is, E-MQTT does not change the format of the PUBLISH packet but assigns the value 3 in the QoS level field to distinguish from the existing QoS levels 0, 1, and 2 of MQTT. This QoS level 3 necessitates the specification of the minimum response packet count (_waited_packet_num_) and, optionally, the inclusion of subscriber IDs (_subscriber_id(s)_) within the variable header of the PUBLISH packet.  
Figure 7 represents the fixed headers of PUBREC, PUBREL, and PUBCOMP, which are relevant packets in the publishing process with QoS 2 within the conventional MQTT. The initial byte of the fixed header reveals that bits 7-4 serve to signify the packet type, while bits 3-0 remain unutilized. The PUBREL and PUBCOMP packets are used by both MQTT and E-MQTT in the same way.  
Given MQTT's allocation of four bits for denoting packet types, any addition of two or more packets for E-MQTT would necessitate an expansion of the packet type bit count. To circumvent this concern, the solution lies in QoS level 3 adopting the identical packet types as those within QoS level 2. Instead, we leverage the unused bits within the packets to incorporate a QoS field, effectively distinguishing packets meant for QoS level 3 from those of QoS level 2. Figure 8 shows the modified fixed header of the PUBREC packet, reflecting E-MQTT integration. The new QoS field is allocated to bits 2-1, mirroring the QoS bits within the existing PUBLISH packet. Under the QoS level 2, the communication protocol aligns with QoS 2 standard. However, in cases where the QoS value is 3, the communication process adheres to the new QoS 3.  
The PUBLISH packet of E-MQTT is created when a publisher sends a message ((1) in Figure 9). The PUBLISH packet is received by a broker ((1) in Figure 10) that delivers it to subscribers ((1) in Figure 11). The PUBREC packet of E-MQTT is created and sent by a subscriber ((2) in Figure 9) after it receives a PUBLISH packet. The PUBREC packet is received by a broker ((2) in Figure 10) that forwards it to a publisher ((2) in Figure 11).  
In summary, we add QoS level 3 in PUBLISH and PUBREC packets by using the existing QoS field or the reserved field to distinguish E-MQTT packets from the other existing MQTT packets. In the PUBLISH packet, we also add the field for the minimum number of response packets and an optional list of subscriber IDs in the variable header.
## 3 Results and Discussion
### System Reliability  
To confirm the reliability of the overall platform in this study, the motion sensor was installed on the center of mass of the multi-axis motion platform and the passenger seat. The installation position is shown in Figure 30. The test method was to input trajectory of roll and pitch motion. After that, the platform motion angle was captured at each time point in the overall trajectory. Finally, the average value with the simulated motion trajectory output by the trajectory kinematics was compared to confirm that the platform moves repeatedly reliability.  
The trajectory error of roll motion was 0.04-0.91\({}^{\circ}\), the average error was 0.29\({}^{\circ}\), and the measurement standard deviation was 0.17. The comparison of roll motion is shown in Figure 31a. In the pitch motion, the trajectory error was 0.18-0.72\({}^{\circ}\), the average error was 0.4\({}^{\circ}\), and the measurement standard deviation was 0.17. The comparison of pitch motion is shown in Figure 31b. To sum up, the platform's motion angle return reliability was 97.43%.  
Figure 31: Comparison of the trajectory error (**a**) roll (**b**) pitch.  
Figure 30: The installation location of motion sensor.