esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Subjective Evaluation Using Recording Sources and Headphone Listening
### Procedure of the Subjective Experiment  
The subjective test was analyzed using headphone reproduction in a very quiet traditional Japanese room with 10 sheets of \"tatami\" mats (approximately 18.2 m\({}^{2}\)), where the A-weighted 10-s equivalent continuous sound pressure level (\(L_{\text{Aeq,10s}}\)) was 22.7 dB. The recorded sounds were reproduced from a personal computer to a SENNHEISER H25-1 headphone through an RME Fireface UC as a Digital to Analog (DA) converter. The reproduction level was manually adjusted to be almost equal to the level in the full-scale room model, and the volume control was fixed to reproduce the level difference of the absorption pattern. Before the experiment, the headphones were placed on the dummy head, and the sound sources of the five absorption patterns were reproduced and recorded. The A-weighted single-event sound exposure levels (\(L_{\text{EA}}\)) of absorption patterns A-E were 72.6, 67.4, 64.2, 60.0, and 56.3 dB, respectively. The frequency characteristics of the last step in each absorption pattern are shown in Figure 6b.  
Figure 6: Binaural recording: (**a**) The experimenter holds the dummy head microphone and walks from the front chamber to the evaluation space generating a footstep sound; (**b**) frequency characteristics of the last step for each absorber’s condition.  
The subjective experiment using headphones was organized with [PERSON]'s modified method of [PERSON]'s paired comparisons, with the 10 pairs, as in the former test for the full-scale room model. The subjective items were the same as those in the former test, as shown in Table 2. The participants, who different from those in the former test, were six people in their 20s. The experimenter instructed that the object for evaluation was not \"the room you can see\" but \"the sound you hear\". The participants rated the difference between a pair of sound sets for the eight items and were allowed to listen to a pair of sound sets again if they wanted.
## 4 Experimental Study
### Classifier Results  
Data preprocessing and predictive analysis modules were implemented in Python using the Sklearn library [39]. Data preprocessing was performed using SPSS version 23.0. Tables 5 and 6 present the comparative results based on the following feature-importance methods: baseline (with all 165 features), MC (with 24 selected features), XGB (with 66 features), and RF (with 22 features). This differentiation of feature sets enables a comparison of model performance across varying levels of feature complexity and relevance. Table 5 provides a comprehensive performance comparison between the baseline models and proposed method. The results revealed that the FS-based baseline models were the XGB, KNN, DT, NB, and RF. The findings indicate that our proposed method, which incorporates MAH-based outlier removal, demonstrates superior performance across all metrics compared to the baseline models. The accuracy, AUC, and F1-score of the performance results are presented in Table 5, with the highest scores highlighted in bold. The XGB model exhibited the highest accuracy of 96.78%, which improved to 97.98%, and the RF model exhibited the highest accuracy of 95.42%, which improved to 97.62 when \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Total Case** & **Cronbach’s Alpha** & **Features Number** \\ \hline Dataset without MAH * & 27,282 & 0.599 & 24 \\ Dataset with MAH ** & 23,611 & 0.641 & 24 \\ \hline \hline \end{tabular}  
* Experimental dataset based on Figure 2. ** Target dataset based on Figure 2.  
\end{table} 
Table 4: Comparison of Cronbach’s alpha for KNHANES 2020–2021. MAH-based outlier removal was applied to the baseline model. The XGB and RF performed well across multiple metrics, indicating that they may be strong candidates for a given task. NB appears to have a lower accuracy and F1-score than the other algorithms, suggesting that it may not be the best choice for this specific task. Figure 4 presents the multiclass ROC curves for each model and compares them with the average ROC curves obtained from the target dataset. Our analysis suggests that the XGB model demonstrates superior performance in predicting outcomes in the target dataset. The accuracy, AUC, and F1-score of the performance results are presented in Table 6, with the highest scores highlighted in bold based on the k = 10 cross-validation. The XGB model exhibited the highest accuracy of 97.02%, which improved to 98.04% when MAH-based outlier removal was applied to the baseline model. The XGB and RF appear to perform well across multiple metrics, indicating that they are strong candidates for a given task. NB appears to have a lower accuracy and F1-score than the other algorithms, suggesting that it may not be the best choice for this specific task.
# Single Photon Counting Performance and Noise Analysis of CMOS SPAD-Based Image Sensors
## 3 Single Photon Counting Noise Modelling and Analysis
### Peak Separation and Width Method  
The Peak Separation and Width (PSW) method is proposed in this paper, and has been used in this paper to measure the SPAD-based image sensors in our recent work [15; 16]. The previous VPM measurement evaluates vertically in the PCH, whereas this PSW method operates in the voltage domain or horizontally in the PCH. By determining, the centroid of each single photon counting peak (whether by taking the peak position, or using a centroid weight algorithm, or similar), the peak separation data may provide a number of measurements:  
* The sensitivity or CVF per pixel (\"\(i\)\") is established by mean peak separation in a per-pixel PCH.
* The PRNU and the average CVF of the sensor are evaluated through a histogram of the compiled peak separation data from step 1 above, taking RMS and mean respectively.
* Vertical, horizontal and pixel to pixel FPN (VPFN, HFPN, PPFPN) are exhibited as horizontal offsets to the peaks, in the set of per pixel PCHs.  
The width of each peak is measured to deduce the noise characteristics of the sensor. The full width half maximum (FWHM) of each peak is captured (preferably using interpolative fitting between PCH bins to lessen errors from quantisation and non-linearity in calculations). Assuming the noise around each peak is normally distributed, the FWHM may be converted to standard deviation using the conventional expression:  
\[\sigma=\frac{FWHM}{2\sqrt{2\text{ln}2}}\rightarrow\sigma\approx\frac{FWHM}{2.3548} \tag{6}\]  
The interested reader may create a more complete noise model by expanding Equations (4) and (6) to take into account other read noise sources (reset, flicker, etc.). Ideally the peak width remains constant across the full signal range, and RN is determined by the mean of the peak width data. However, if a signal dependent noise source is present then the peak widths will increase (and peak heights decrease) for increasing signal. There is no lower limit to the PSW method. However, the upper limit is set by the height of the valley between two peaks: by definition this valley must be lower than half of the two adjacent peak heights which evaluates at \(<\)0.3 e\({}^{-}\) RN approximately.  
Figure 1: Photon counting histogram (PCH) generated by the read noise model with CVF equivalent of 10 mV/e, mean \(\lambda\) = 5 e\({}^{-}\) exposure and 0.1 e\({}^{-}\) equivalent RN.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
###### Abstract  
Sign languages are characterized by complex and subtle hand movements, which are challenging for computer vision systems to accurately recognize. This study suggests an innovative deep learning pipeline specifically designed for reliable gesture recognition of Kazakh Sign Language. This approach combines key point detection using the YOLO model, optical flow estimation, and a bidirectional long short-term memory (BiLSTM) network. At the initial stage, a dataset is generated using MediaPipe, which is then used to train the YOLO model in order to accurately identify key hand points. After training, the YOLO model extracts key points and bounding boxes from video recordings of gestures, creating consistent representations of movements. To improve the recognition of dynamic gestures, the optical flow is calculated in an area covering 10% of the area around key points, which allows the dynamics of movements to be captured and provides additional time characteristics. The BiLSTM network is trained on multimodal input that combines data on keypoints, bounding boxes, and optical flow, resulting in improved gesture classification accuracy. The experimental results demonstrate that the proposed approach is superior to traditional methods based solely on key points, especially in recognizing fast and complex gestures. The proposed structure promotes the development of sign language recognition technologies, especially for poorly studied languages such as Kazakh, paving the way to more inclusive and effective communication tools.  
sign language recognition; BiLSTM; optical flow; YOLO; gesture analysis Article
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 4 Case Study
### The Verification of the Autonomous Navigation Decision-Making
#### 4.2.1 Single-Ship Encounter Scenario 1  
Two target ships (TS\({}_{1}\) and TS\({}_{2}\)) are designed with identical initial states. TS\({}_{1}\) maintains a constant course and speed, while TS\({}_{2}\) initiates a starboard turn (75\({}^{\circ}\) steering angle) after 100 s. The ships' states are summarized in Table 3. The autonomous navigation decision-making process and results for this scenario are illustrated in Figure 16.  
Based on the initial motion analysis, TS\({}_{1}\) is about to sail out of the tributary and into the mainstream. If TS\({}_{1}\) exits the tributary and turns to starboard to follow the upstream channel (same direction as the OS), the OS is a give-way ship. Similarly, if TS\({}_{1}\) turns to portside to enter the downstream channel (opposite direction to the OS), the OS remains the give-way ship, necessitating proactive collision avoidance measures. TS\({}_{2}\) follows the same logic as TS\({}_{1}\).  
Since TS\({}_{1}\) approaches from the OS's starboard side, the OS reduces speed to avoid collision, achieving a minimum distance of 123 m from TS\({}_{1}\) at 221 s--outside the OS's ship domain. After passing and clearing TS\({}_{1}\), the OS resumes the recommended route, stabilizes its course, and increases its propeller RPM to restore its original speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ship List** & **Position** & **Velocity (kn)** & **Course (\({}^{\circ}\))** & **Turning Rate (\({}^{\circ}\)/s)** & **Turning Angle (\({}^{\circ}\))** \\ \hline OS & \(119^{\circ}28.812^{\prime}\) E, \(32^{\circ}16.308^{\prime}\) N & 12.5 & 275 & / & / \\ TS\({}_{1}\) & \(119^{\circ}27.906^{\prime}\) E, \(32^{\circ}16.704^{\prime}\) N & 7 & 178 & 0 & 0 \\ TS\({}_{2}\) & \(119^{\circ}27.906^{\prime}\) E, \(32^{\circ}16.704^{\prime}\) N & 7 & 178 & 1 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The initial information of scenario 1.  
Figure 15: Simulation validation of route tracking model.  
For TS\({}_{2}\), which executes a starboard steering, the OS initially decelerates to avoid conflict. After 100 s, TS\({}_{2}\) turns to starboard to exit the tributary and merge into the upstream channel. Given TS\({}_{2}\)'s steering maneuver and trajectory, the conflict resolves itself if TS\({}_{2}\) maintains its current turn rate. Consequently, the OS resumes its rated speed early and continues along the recommended route.
###### Abstract  
The carrier-to-noise ratio (_C/N\({}_{0}\)_) is an important indicator of the signal quality of global navigation satellite system receivers. In a vector receiver, estimating _C/N\({}_{0}\)_ using a signal amplitude Kalman filter is a typical method. However, the classical Kalman filter (CKF) has a significant estimation delay if the signal power levels change suddenly. In a weak signal environment, it is difficult to estimate the measurement noise for CKF correctly. This article proposes the use of the adaptive strong tracking Kalman filter (ASTKF) to estimate _C/N\({}_{0}\)_. The estimator was evaluated via simulation experiments and a static field test. The results demonstrate that the ASTKF _C/N\({}_{0}\)_ estimator can track abrupt variations in _C/N\({}_{0}\)_ and the method can estimate the weak signal _C/N\({}_{0}\)_ correctly. When _C/N\({}_{0}\)_ jumps, the ASTKF estimation method shows a significant advantage over the adaptive Kalman filter (AKF) method in terms of the time delay. Compared with the popular _C/N\({}_{0}\)_ algorithms, the narrow-to-wideband power ratio (NWPR) method, and the variance summing method (VSM), the ASTKF _C/N\({}_{0}\)_ estimator can adopt a shorter averaging time, which reduces the hysteresis of the estimation results.  
global navigation satellite system (GNSS); vector tracking loops; carrier-to-noise ratio; strong tracking Kalman filter +
Footnote †: journal: Sensors
# Detection and Localization of Failures in Hybrid Fiber-Coaxial Network Using Big Data Platform
## 4 Big Data Platform  
Telecom service providers can collect enormous amounts of data from their networks. Information extracted from the collected data can be used for various purposes. For example, data can be used to obtain information for business planning, efficient network expansions, better offers to their customers, performance monitoring, and performance optimization. Obviously, there are numerous possibilities for how to use collected data. However, traditional methods for processing and storing collected data are not powerful enough. For this reason, big data technologies are used to manage collection, processing and storing of these enormous amounts of data.  
In this section, we present the big data platform that FDLBD utilizes for failure detection and localization purposes. Figure 2 shows the overall big data platform architecture that comprises the data collection layer and big data cluster. The data collection layer is responsible for data collection, while the big data cluster is responsible for data processing and storage. Information obtained from collected data is consumed by data consumers (call centers, dashboards, alarm systems, etc.).  
The data collection layer collects data from CPEs and CMTSs in HFC network. SNMP is used for data collection. Given the large number of CPEs, the collection period from CPEs is set to 1 h. On the other hand, the number of CMTSs is significantly lower than the number of CPEs. Additionally, CMTSs should be constantly online and highly responsive. Furthermore, CMTSs contain a lot of useful data, where some data refer to end-users themselves. For all these reasons, the collection period for CMTSs is set to range from one to five minutes, depending on the importance of the particular data metric that is collected. In case of troubleshooting, corresponding troubleshooting mechanisms can decrease the collection period to the order of seconds for the corresponding (inspected) device.  
Figure 2: Big data platform.  
Collected data are sent to the big data cluster. More precisely, data are sent to the OpenTSDB (Open Time Series Database) for data aggregations, and the aggregation results are used immediately by the data consumers. At the same time, a copy of the same data is stored to the HDFS (Hadoop Distributed File System) for later batch processing that is conducted using the spark. Batch processing results are stored in the HDFS and can be used by the OpenTSDB for data aggregation purposes and furthermore, for data consumption by data consumers.
## 4 Evaluation Models
### Glass Box ML Models
The EBM's training procedure of decision trees (DTs) with \(k\) input explanatory variables is visualized in Figure 6.  
In addition, EBMs take the combined impacts of two or more independent variables known as the interaction effect (GA2 Ms). To compute the interactive effects, first, the additive model, which merely incorporates one-dimensional elements, is fitted. In the next phase, the one-dimensional functions are taken into account as frozen model terms, and models are trained to account for the remaining pairwise interactions. Thereby, an algorithm called FAST [35] is used to rank the pairs based on their relationship with residuals. The EBM can be expressed by the following:  
\[g(E[y])=\beta_{0}+\sum\,f_{j}^{DT}\,(x_{j})+\sum\,f_{ij}^{DT}\,(x_{i},\,x_{j} )+\varepsilon \tag{11}\]  
The direct impact of an input feature on the target feature is known as the main effect, while the combined impact of two or more features is known as the interaction effect. EBMs are highly interpretable because the contribution of each independent variable or combination of independent variables to a final prediction can be visualized and understood by plotting \(f_{j}\) and \(f_{ij}\), respectively. The analysis of the data in our study based on EBM is performed by using the toolkit called InterpretML from Microsoft [33]. The model's hyper-parameters are not optimized by us and are set to default values. In addition, based on decaying characteristics of the signal strength when it propagates from the base station  
Figure 6: The architecture of the EBM with \(k\) input explanatory variables and N iterations. towards different angles, we applied polar coordinate transformation to transform the geographical data into a polar coordinate system before training the model. Thereby, image axis-independent variables (longitude and latitude) are converted into polar coordination consisting of distance and the transmitting signal direction, i.e., Tx-Rx Angle. The Tx-Rx angle is computed by us through translating the longitude and latitude of the points to X-Y geographic points and setting the (X, Y) coordinate values of the transmitter location equal to the reference point (0, 0).
## 5 Numerical Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Parameters** & **Dataset I** & **Dataset II** & **Dataset III** \\ \hline \multirow{4}{*}{LSTM} & Learning rate & 0.020 & 0.029 & 0.016 \\  & Beta\_1 & 0.725 & 0.883 & 0.900 \\  & Beta 2 & 0.812 & 0.913 & 0.997 \\  & Decay & 0.023 & 0.012 & 0.004 \\ \hline \multirow{2}{*}{BPNN} & Learning rate & 0.900 & 0.200 & 0.100 \\  & Momentum & 0.500 & 0.800 & 0.800 \\ \hline \multirow{2}{*}{GRNN} & Spread & 0.100 & 0.200 & 0.900 \\ \hline \multirow{2}{*}{LSSVR} & \(\gamma\) & 480 & 500 & 350 \\  & \(\sigma\) & 1 & 50 & 100 \\ \hline \multirow{4}{*}{RF} & Number of trees & 1000 & 1100 & 1050 \\  & Minimum number of samples to split & 2 & 4 & 4 \\  & Minimum number of samples at a & 1 & 2 & 3 \\  & leaf node & Maximum depth & 50 & 54 & 60 \\ \hline \multirow{2}{*}{GPR} & Kernel bounds & \([-11,11]\); \([-16,16]\); \([-18,18]\); \([-11,11]\) & \([-11,11]\) \\  & & \([-11,11]\) & \([-11,11]\) & \([-11,11]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of forecasting models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Measurements**} & \multicolumn{5}{c}{**Models**} \\ \cline{3-8}  & & **LSTM** & **BPNN** & **GRNN** & **LSSVR** & **RF** & **GPR** \\ \hline \multirow{2}{*}{Dataset I} & MAPE (\%) & 16.618 & 17.391 & 20.254 & 19.449 & 21.747 & 22.447 \\  & RMSE & 13.221 & 14.698 & 17.364 & 16.494 & 18.753 & 22.043 \\ \hline \multirow{2}{*}{Dataset II} & MAPE (\%) & 16.883 & 17.898 & 20.721 & 19.062 & 20.7  
Figure 8: Absolute errors with customers’ rating scores. Figure 7: Absolute errors with sentiment analysis of review text. Figure 9: Absolute errors with sentiment analysis of review text and customers’ rating scores.
## 4 Mathematical Analysis
### Energy-Consumption Analysis  
Here, we call the nodes which can directly communicate with sink nodes as the first-hop nodes. The nodes that can directly communicate with the first-hop node are named the second-hop nodes, and so on. We assume the average number of data packets generated by a node is \(N_{d}\), and the number of data packets generated by nodes in the \(i\)th-hop, \(N_{ip}\), is as in Equation (16). \(V_{i}\) indicates the volume of the candidate area from the \(i\)th hop to the next-hop.  
\[N_{ip}=n\times V_{i}N_{d} \tag{16}\]  
When using broadcast routing, such as VBF and DBR, the number of nodes involved in forwarding in the \(i\)th hop is \(N_{i}=nV_{i}p\). Each candidate next-hop forwards a data packet with probability \(p\), and the energy consumption of delivering a data packet from the \(i\)th hop to a sink node is \(E_{ic}\). In the same way, we can get Equation (17).  
\[i-1:\ N_{i-1}=\sum_{i=1}^{i=N_{i}}nV_{i-1}p\]  
\[i-2:\ N_{i-2}=\sum_{i=1}^{i=N_{i-1}}nV_{i-2}p\]  
\[1:\ N_{1}=\sum_{i=1}^{i=N_{2}}nV_{1}p\]  
\[N_{it}=N_{1}+N_{2}+\cdots N_{i}\]  
The number of nodes forwarding the data packets from the \(i\)th hop to a sink node is:  
\[E_{ic}=N_{it}E_{F} \tag{17}\]  
Equation (17) denotes the total energy consumption of a data packet from the \(i\)th hop to the sink node when the priority is not set.  
Even if the candidate area is limited, we can see that the number of forwarding nodes is large in a dense network, which wastes limited energy. So, in RRSS we employ unicast routing, and set a  
Figure 9: Volume of the candidate area.  
priority for each candidate next-hop. In each hop, only the node with the highest priority forwards the data packet.  
After setting the priority, the energy consumption of a data packet from the \(i\)th hop to a sink node is as in Equation (18):  
\[E_{ic}^{\prime}=iE_{F} \tag{18}\]
## Abbreviations  
The following notations are used in this manuscript:  
**Acronyms**  
PV Photovoltaic  
MLR Multivariate linear regression  
ANN Artificial neural network  
BP Back-propagation  
ELM Extreme learning machine  
VIFs Variation inflation factors  
HU The Hashemite University  
**Notations**  
\(D\) Dust accumulation in terms of exposure days (days)  
\(T\) Average daily ambient temperature (\({}^{\circ}\)C)  
\(\eta\) Daily system conversion efficiency (%)  
\(n\) Number of available data points of the PV system  
\(i\) Index of number of data point, \(i=1,\ldots,n\)  
\(a_{0}\) Regression model intercept  
\(a_{1},a_{2}\) Regression coefficients  
\(D_{i}\) The \(i\)-th exposure day, \(i=1,\ldots,n\)  
\(T_{i}\) The \(i\)-th average daily ambient temperature, \(i=1,\ldots,n\)  
\(\eta_{i}\) The \(i\)-th true daily system conversion efficiency, \(i=1,\ldots,n\)  
\(\dot{\eta}_{i}\) The \(i\)-th predicted daily system conversion efficiency, \(i=1,\ldots,n\)  
\(\epsilon_{i}\) The \(i\)-th error of the daily system conversion efficiency prediction, \(i=1,\ldots,n\)  
\(H_{i}\) Number of input layer's neurons  
\(H_{h}\) Number of hidden layer's neurons  
\(H_{0}\) Number of output layer's neurons  
\(\stackrel{{ x}}{{x}}_{i}\) Input vector of the prediction models  
\(\stackrel{{ x}}{{\theta}}_{i}\) The hidden-output weight vector of the \(h\)-th neuron, \(h=1,\ldots,H_{h}\)  
\(\stackrel{{ w}}{{w}}_{i}\) The input-hidden weight vector of the \(h\)-th neuron, \(h=1,\ldots,H_{h}\)  
\(b_{i}\) The bias of the \(h\)-th neuron, \(h=1,\ldots,H_{h}\)  
\(G()\) ELM/ANN neuron activation function  
h Index of number of ELM/ANN hidden neurons  
\(R^{2}\) Coefficient of determination performance metric  
\(R^{2}_{adjusted}\) Adjusted coefficient of determination performance metric  
\(MSE\) Mean square error performance metric  
\(Accuracy\) Accuracy performance metric  
\(RMSE\) Root mean square Error