esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Stability Indices with CP  
The results of stability indices and CP detected from human walking are shown in Figure 7. In Figure 7a, the trajectory of CP is similar to the COG and the ZMP in the \(xy\)-plane. The inversion point of the incline of CP is at nearly the same position as the stance moment of ZMP in a single foot. Furthermore, the phase of ZMP leads the phase of COG, unlike the order in the simulation. This is because the upper body in the human is straighter than the upper body of the designed robot. In Figure 7b,c, the blue and red areas refer to the \(\text{SS}_{\text{R}}\) (right single-stance) and the \(\text{SS}_{\text{L}}\) (left single-stance), respectively; additionally, the area between the \(\text{SS}_{\text{R}}\) state and the \(\text{SS}_{\text{L}}\) state indicates the DS (double stance). Figure 7b displays the \(x\) values of CP and COG altered up and down for \(x_{ZMP}\) with time; moreover, \(x_{CP}\) generally overlaps with \(x_{COG}\). In the middle of the step, \(x_{CP}\) occasionally possesses unsmooth points caused by the crossing speed of the leading limb when one side limb passes the other side limb. From the comparison, it can be seen that \(x_{ZMP}\) is always smaller than \(x_{COG}\) and \(x_{CP}\) in the DS state. Figure 7c shows the \(y\) values for left and right, along with the positive and the negative directions with time. The \(y_{COG}\) is drawn inside of \(y_{ZMP}\), as in the simulation results; however, the results for \(y_{CP}\) are different from the simulation. The shape of \(y_{CP}\) seems similar to the form of \(y_{COG}\) except for the peak point, where the direction of the slope of \(y_{CP}\) changes dramatically. In summary, the CP results resemble the COG values overall, with the exception of the moment of inversion in CP. Additional discussion regarding experiment results and the relationship between the COG, ZMP, and CP is provided in the Discussion section.  
Figure 6: Results of foot pressure (unit: N) with CP (unit: m) detected from walking. (**a**) Results of right-foot pressure with CP (blue line: \(y_{CP}\), blue shadow: area stood by right limb); (**b**) results of left-foot pressure with CP (red line: \(y_{CP}\), red shadow: area stood by left limb).
## 2 Materials and Methods
### Statistical Analysis  
The sample size was computed setting an alpha level of 5% and a power of 80%. Data were reported in terms of mean \(\pm\) standard deviation. Percentage change (\(\Delta\)) was computed as the difference in the value recorded at T2 and at T1, divided by the value at T1 and multiplied by 100. The Shapiro-Wilk test was used to verify the normality of data and showed that most of the parameters were not normally distributed, so nonparametric statistics were performed for inferential tests, such as the Wilcoxon rank test used for pairwise comparison of data within subjects between T1 and T2, and Spearman's coefficient (R) used to assess correlation between couples of variables. Data analysis was performed using Jamovi software version 1.6.23.
# SMYOLO: Lightweight Pedestrian Target Detection Algorithm in Low-Altitude Scenarios
## 3 Proposed Method
### Low-altitude Pedestrian Detection Network Architecture: SMYOLO
#### 3.1.2 Add Batch Normalization Layer  
The SMYOLO model is a fully convolutional network specifically composed of conventional convolution, depth separable convolution, and point convolution. At the same time, the output of each layer of the convolutional network is processed by batch normalization. That is, a batch normalization (BN) layer is added. The proposal of the BN layer solves the gradient disappearance, and gradient explosion in the backpropagation process accelerates the convergence and improves the generalization ability. As shown in the Formula (4), the BN layer uses small batches of normalized convolution features.  
\[f=\gamma\times\frac{x-\mu}{\sigma}+\beta \tag{4}\]  
Among them, \(\mu\) and \(\sigma\) represent the mean and standard deviation of the input features. \(\gamma\) and \(\beta\) represent the scale factor and deviation.
# Nozzles for Spraying Coal-Water Fuels
###### Abstract
After the discharge from the nozzle, the CWF is mixed with the spraying agent (SA) or already represents a mixture of fuel and SA (1), depending on the type of the nozzle. In some cases, almost a single-phase jet of fuel is possible near the nozzle. Large fragments of the slurry are destroyed after the CWF leaves the nozzle, usually due to the environmental resistance forces, centrifugal forces, or as a result of chaotic rotation (2). Small and rather large drops of CWF (3) are formed as a result of these processes. The CWF droplets warm up under the thermal influence of the environment--combustible recirculation gases and radiation from the structural elements of the combustion chamber or reactor. This is accompanied by evaporation of moisture (4). After that, the already drained drops undergo additional destruction during movement (5). Then, the combustion of CWF particles (6), including those that have been baked, begins. At the same time, coagulation of large fuel droplets (7) and their sintering (8) partially occurs. After the complete combustion of small  
Figure 1: The process of CWF spraying. droplets in the air, ash is formed (9). At the same time, large sintered agglomerates of CWF in the composition with unburned coal fall into ash. Thus, it turns out that not all the coal in the fuel burns during CWF combustion. It should also be taken into account that CWF has an erosive effect on the nozzle channels of the injectors due to the presence of fine coal [14]. As a result, premature wear of the nozzle may occur. As a rule, the area of the nozzle channel is exposed to this [15]. Its destruction occurs, accompanied by an increase in the diameter of the outlet nozzle [16]. The latter factor has a negative impact on the characteristics of the CWF spraying. The fact that the fuel channels of the injectors are clogged with large particles of coal or agglomerates of CWF formed during the preparation of fuel is also important. There are not many results from studies of the characteristics of CWF spraying and designs of coal-water fuel nozzles in the modern literature. As a rule, known variants of CWF spraying devices were developed and created for specific tasks--coal-water fuel combustion or gasification. The principle of operation of injectors may depend on the design features of combustion chambers or reactors. In general, the known designs of coal-water fuel nozzles can be divided into two types. The first one is with an internal mixing of CWF and SA, for example [17]. The second one is with an external mixing of CWF and SA, for example [7]. At the same time, the main requirements for spraying devices of coal-water fuels remain the formation of a fine jet and the reliable uninterrupted operation of the nozzle. A number of issues related to the types of injectors, their designs and principles of operation remain unresolved. Therefore, it is relevant to study the existing CWF spraying devices and develop criteria and requirements corresponding to the efficient and troubles-free operation of coal-water fuel nozzles.
## 2 Related Work  
This work focuses on key information extraction and associated entity detection in MER document image processing. Most of the early key information extraction methods used rule-based schemes, which usually required a lot of manual operations, were time-consuming, delivered average performance, and had poor generalization ability. However, with the development of deep learning, several high-performance visual information extraction methods have demonstrated broad applicability and superiority in applications. In addition, the task of associated entity detection for MER document images essentially employs techniques related to object detection. Previous object detection methods mainly relied on manual feature extraction, which was not very effective in recognition and was computationally slow. In order to overcome these limitations, researchers have started to explore deep learning-based object detection methods.
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Load Branch Correlation Matrix Method
#### 2.4.2 Load Branch Matrix Role
Take the sum of load power of branch 7 as an example; the load on its subordinate branch L2 has only one power supply path, while L3 and L20 have alternate power supply paths (Equation (19)):  
\[p_{7}=w_{2}\times 120+(2-w_{3})\times w_{3}\times 200+(w_{20}-1)\times w_{20}/2 \times 30, \tag{19}\]  
where \(w_{1}w_{2}w_{3}\) denotes the power-up state of L2 L3 L20, respectively, substituting 0, 1, and 2, respectively, to verify that it is completely in line with the actual situation, thus deducing the power value of the remaining branch loads:  
Branch 35 load power gross value calculation formula (Equation (20)):  
\[p_{35}=w_{7}\times 80+(2-w_{8})\times w_{3}\times 325+(w_{20}-1)\times w_{20} \times 160/2, \tag{20}\]  
The formula for calculating the total power value of the branch 63 load (Equation (21)):  
\[p_{63}=w_{12}\times 205+(2-w_{13})\times w_{13}\times 110+(w_{10}-1)\times w_{ 10}\times 44/2, \tag{21}\]  
Branch 41 load power gross value calculation equation (Equation (22)):  
\[p_{41}=w_{9}\times 185+(2-w_{10})\times w_{10}\times 44+(w_{13}-1)\times w_{ 13}\times 110/2, \tag{22}\]  
Branch 16 load power gross value calculation equation (Equation (23)):  
\[p_{16}=w_{4}\times 150+(2-w_{5})\times w_{5}\times 160+(w_{6}-1)\times w_{ 6}\times 100/2, \tag{23}\]  
Branch 69 load power gross value calculation formula (Equation (24)):  
\[p_{69}=w_{14}\times 72+(2-w_{15})\times w_{15}\times 87+(w_{17}-1)\times w_{ 17}\times 205/2, \tag{24}\]  
The formula for calculating the total value of the load 91 power of a branch circuit (Equation (25)):  
\[p_{91}=w_{17}\times 205+(2-w_{18})\times w_{18}\times 200+(w_{15}-1)\times w_{ 15}\times 87/2, \tag{25}\]  
Branch 35 load power gross value calculation formula (Equation (26)):  
\[p_{92}=w_{19}\times 165+(2-w_{20})\times w_{20}\times 30+(w_{3}-1)\times w_{ 3}\times 200/2, \tag{26}\]  
3. Generator efficiency equalization  
In this paper, one of the objective functions in the second section is to consider the balanced efficiency of generators through the load branch correlation matrix, which can also be calculated for each generator output power and load, and through the control of the state of the switch, regulating the generator's output power, to achieve a balanced distribution of loads, so as to achieve the  
The efficiency calculation formula is as (Equation (31)):  
\[\eta_{n}=PG_{n}/PG_{N}, \tag{31}\]  
while \(PG_{N}\) is the rated power of the four generators. Research on Network Reconfiguration of Ship Power System with Swarm Exchange Particle Swarm Optimization Algorithm
## 6 Conclusions  
We have investigated TRNG and PUF implementation using magnetic memory and manipulation of write time and number of writes. We have analyzed the practical implications of designing TRNG/PUF using commercial MRAM and addressed these issues to achieve high quality.  
**Author Contributions:** Conceptualization, M.N.I.K. and S.G.; methodology, M.N.I.K.; software, A.A.-S.; validation, M.N.I.K., C.Y.C., S.H.L. and A.A.-S.; formal analysis, M.N.I.K.; investigation, M.N.I.K.; resources, S.G.; data curation, C.Y.C. and S.H.L.; writing--M.N.I.K., A.A.-S. and S.G.; original draft preparation, M.N.I.K. and A.A.-S.; writing--review and editing, M.N.I.K., A.A.-S. and S.G.; visualization, M.N.I.K.; supervision, M.N.I.K. and S.G.; project administration, M.N.I.K. and S.G.; funding acquisition, S.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by Semiconductor Research Corporation (2847.001 and 3011.001) and National Science Foundation (CNS-1722557, CCF-1718474, DGE-1723687, OIA-2040667 and DGE-1821766).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**TRNG** & **Correlation** & **Entropy** & **Area (MTJ)** & **Bandwidth (Gbit/s)** & **Energy/bit (pJ)** & **Experimental** \\ \hline
[20] & 0.003 & - & 0.014 & 0.0005 & 14.97 & Yes \\ \hline
[21] & - & - & 0.0085 [1] & 0.0833 [1] & 0.3386 [1] & Yes \\ \hline This Work & 0.05 & 0.95 & 0.769 [2] & 0.12 & 0.41 [2] & Yes \\ \hline \hline \end{tabular}  
* Considering 3 ns of read time; 2 includes all peripheral circuitry and considers only 4 bits of each row. Thus, area is 2\(\times\) of wPUF.  
\end{table}
Table 4: Performance comparison of different MRAM/STTRAM TRNG with proposed TRNG.
###### Abstract  
Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor's energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-powered sensors. In this paper, we analyze the performance improvement and evaluation of EH sensors in various situations. A network model is developed to allow us to examine different scenarios. We borrow a clustering concept, as a proven method to improve energy efficiency in conventional sensor network and brought it to EH sensor networks to study its effect on the performance of the network in different scenarios. Moreover, a dynamic and distributed transmission power management for sensors is proposed and evaluated in both networks, with and without clustering, to study the effect of power balancing on the network end-to-end performance. The simulation results indicate that, by using clustering and transmission power adjustment, the power consumption can be distributed in the network more efficiently, which result in improving the network performance in terms of a packet delivery ratio by 20%, 10% higher network lifetime by having more alive nodes and also achieving lower delay by reducing the hop-count.  
energy harvesting; wireless sensor network; environmental monitoring; clustering; energy efficiency; network lifetime; transmission power +
Footnote †: journal:  
_Article_
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 6 Prediction Model  
Through being established the relationship between image features and subjective evaluation values of image in this paper, we propose a model of objective image quality evaluation model based on ROI-BRISQUE. We utilize measure factor of distortion image (\(V_{\text{all}}\)) which obtained from feature vectors effective fusion between region of interest feature vector (\(V_{\text{roi}}\)) and region of non-interest feature vector (\(V_{\text{non-roi}}\)) as the input of the SVR model. Moreover, we use the subjective quality evaluation value of the corresponding image as the output target to establish image quality evaluation model based on visual perception. The calculation formula is given by Formula (15).  
\[V_{all}=\lambda V_{roi}+(1-\lambda)V_{non-roi} \tag{15}\]  
The parameter \(\lambda\) is the weight of the image region of interest. Through training on the LIVE image database, the experimental results show that the model has a good learning accuracy and predictive ability.
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 5 Discussion
It aims to solve the power supply issue of the innumerable IoT sensors, thereby contributing to the need to reduce their power supply or battery. It is not necessary to restrict energy harvesting to RF signals because there are numerous other sources, such as kinetic, chemical, friction, heat radiation, solar, etc. Consequently, the future of energy harvesting may involve combining these forms of energy to increase energy efficiency. Regarding our future work, we intend to improve the PCE and the output voltage of the rectifier circuit. Moreover, we plan to make the rectifier operate at more frequencies. For example, we would like to make a rectifier that works at additional frequency bands such as 2.4 GHz and 6 GHz. In addition to that, we will try to improve the voltage and power efficiency of the total circuit.
## 3 Results and Discussion
### Numerical Simulations
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Unsupported Case**} & \multicolumn{2}{c}{**Supported Case**} \\ \cline{2-5}  & **Shale** & **Sandstone** & **Shale** & **Sandstone** \\ \hline Vertical displacements (mm) & 20.92 & 3.29 & 12.97 & 2.51 \\ Horizontal displacements (mm) & 18.32 & 2.72 & 11.62 & 1.54 \\ Thickness of EDZ (m) & 2.9 & 1.1 & 1.75 & 0.65 \\ \hline Axial load rock bolts (kN) & & & 188.9 & 18.5 \\ \hline \multirow{3}{*}{Shotcrete} & Axial force (kN) & & 883.64 & 56.29 \\ \cline{1-1}  & Bending moment & & 8.08 & 0.16 \\ \cline{1-1}  & (KNm) & & 22.35 & 0.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Numerical simulation results in the central tunnel. Figure 7: The central tunnel in shale formation: (**a**) rock bolts axial force [N]; (**b**) state of plasticity. Table 8 shows the results for the junction zone between the central and transversal tunnels. The maximum value of displacement reached 18.20 mm at the roof of the tunnels in shale rock mass. By installation of support, the thickness of the EDZ and deformations are reduced. In shale formation, the vertical and horizontal displacements decreased down to 13.84 mm at the roof and 11.51 mm in the walls, when the support system is applied. The axial force, bending moment and shear force in reinforced shotcrete layer have also been analyzed in the junction zone, reaching 1570 kN, 9.31 kNm, and 27.13 kN, respectively, in shale rock mass. The shear force indicated in Figure 8b shows peaks due to the head of the rock bolts. Figure 9 shows the rock bolts force in the central tunnel and the transversal tunnels depending on the distance from the rock bolt head. Rock bolts located at the walls in shale and sandstone formations have been selected. The maximum axial load is reached in shale rock mass, at the central tunnel. It has been observed that in all scenarios the axial load reached is less than 245 kN (load capacity designed). \begin{table}
\begin{tabular}{l l c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Unsupported Case**} & \multicolumn{2}{c}{**Supported Case**} \\ \cline{2-5}  & **Shale** & **Sandstone** & **Shale** & **Sandstone** \\ \hline Vertical displacements (mm) & 18.20 & 2.96 & 13.84 & 1.76 \\ Horizontal displacements (mm) & 16.03 & 2.17 & 11.51 & 1.82 \\ Thickness of EDZ (m) & 2.9 & 1.2 & 1.8 & 0.7 \\ \hline Axial load rock bolts (kN) & & & 160.4 & 29.6 \\ \hline \multirow{3}{*}{Shotcrete} & Axial force (kN) & & 1,570.0 & 168.72 \\  & Bending moment & & 9.31 & 0.21 \\ \cline{1-1}  & (KNm) & & 27.13 & 0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Numerical simulation results in the junction zone. Figure 8: Junction zone in shale formation: (**a**) horizontal displacements; (**b**) shear force in shotcrete.
## 5 Discussion
### Future Research Directions  
To address the identified limitations, future research should prioritize the following areas to enhance the robustness and generalizability of the proposed framework:  
First, expanding the diversity of data sources, including social media, product review platforms, and industry forums, is essential to improve data representativeness. A broader scope of data collection will facilitate a more comprehensive understanding of battery electric vehicle (BEV) users, encompassing various demographic groups, geographic regions, and user backgrounds.  
Second, enhancing the sentiment analysis model by employing larger, domain-specific datasets and leveraging advanced techniques, such as transfer learning, is crucial for improving adaptability. These refinements would enable the model to accurately capture subtle emotional nuances across diverse regions and contexts, thereby increasing its cross-regional applicability and ensuring more reliable sentiment detection.  
Lastly, integrating event extraction techniques is recommended to reveal causal relationships and temporal dynamics among emotional factors identified through co-occurrence network analysis (CONA). The incorporation of causal inference and time-series analysis will provide deeper insights into the evolution of these emotional factors and their impacts on user experience, thereby contributing to a more nuanced understanding of the user journey.  
These directions aim to enhance the generalizability and applicability of the findings, thereby laying a stronger foundation for practical improvements in user experience within the BEV industry and extending the utility of the framework to other sectors.
## 5 Conclusions  
Aiming at solving problems of the serious spatiotemporal fluctuations of the signal caused by the time-varying channel structure of the shallow sea, this study took identifying the spatiotemporal variation range of the SNR of underwater acoustic communication as the research goal, which was explored in the sound field from two aspects: the signal field and the noise field. The investigation of the temporal and spatial distribution of the signal field considered signal interference effects caused by surface reflection and scattering and theoretically deduced the variation of signal intensity fluctuations with horizontal distance, signal frequency, bandwidth, and deployment depth, which was further verified through both simulations and the Yellow Sea trial. The investigation of the noise field mainly considered the spatiotemporal distribution of high-frequency wind-induced noise and ship noise. Through processing and analysis of the experimental data, it was found that the time fluctuation of noise basically conformed to the Gaussian distribution, the spatial distribution consistency was high, and the near-sea surface noise was slightly higher than the bottom noise. Combining the analysis of the spatiotemporal distribution characteristics of the signal field and the noise field, it was found that when the signal frequency was high, the bandwidth was large; furthermore, when the horizontal distance between the transceivers was small and the depth of the receiver was deep, the spatial-temporal variation range of the SNR of the UAC system was relatively small. These characteristics were verified using sea trial data, and the derived law will be used to guide the parameter configuration and network protocol optimization of the UAC systems.  
The correlation radius of the acoustic signal decreased with increasing frequency. The vertical correlation radius was smaller than the horizontal correlation radius. Therefore, in shallow sea acoustic communication, vertical arrays should be used for receiving to enhance the SNR and improve system reliability.  
[PERSON] conceived of the study, designed the study, and wrote the manuscript. [PERSON] and [PERSON] provided guidance on the ideas and mathematical treatment. [PERSON] and [PERSON] compiled the experimental data. [PERSON] and [PERSON] assisted in the editing of manuscripts. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 11874061), the Youth Innovation Promotion Association CAS (No. 2021023), and Shandong Province \"Double-Hundred Talent Plan (WST2020002)\".  
Not applicable.  
The authors declare no conflict of interest.  
Figure 22: Bit error rate variation simulation results.