esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calculation Method of Shear Carrying Capacity of UHPFRC Beam-Column Joint
### Verification of Proposed Calculation Formula for Shear Carrying Capacity  
In order to verify the proposed formula, the shear carrying capacities of 13 existing specimens (including nine UHPFRC beam-column joints in this paper and four interior steel fiber reinforced high-strength concrete beam-column joints described in the literature [30]) were calculated using Equation (14) and compared with the experimental values calculated using Equation (2). The comparison results of the 13 specimens are displayed in Table 4. All selected specimens were subjected to cyclic loading on the beam tip, and their failure modes were mostly joint shear failure. It can be seen from Table 4 that the calculated shear carrying capacities of both the exterior and interior reinforcement beam-column joints are in close agreement with the experimental values, with the exception of specimen EJ-1. The ratio of the mean calculated values \(V_{j}^{c}\) to experimental values \(V_{j}^{t}\) is 1.018, while the variance and coefficient of variation are 0.106 and 0.105, respectively. The experimental value of specimen EJ-1 is approximately 25% smaller than the calculated value, which can be explained by the fact that more serious flexural failure at the beam end was observed for specimen EJ-1 than for other specimens.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multirow{2}{*}{\(f_{\text{o}}\)**/MPa**} & \multirow{2}{*}{\(\begin{array}{c}\mathbf{b_{c}}\times\mathbf{h_{c}}\end{array}\)} & \multicolumn{2}{c}{\(\begin{array}{c}\mathbf{b_{c}}\times\mathbf{h_{c}}\end{array}\)} & \multicolumn{2}{c}{\(\begin{array}{c}\mathbf{b_{y}}\times\mathbf{h_{y}}\end{array}\)} & \multicolumn{2}{c}{\(\begin{array}{c}\mathbf{Volume}\\ \mathbf{Percentage}\end{array}\)} & \multicolumn{2}{c}{\(\begin{array}{c}\mathbf{Stirrup in}\\ \mathbf{Percentage}\end{array}\)} &
## 2 System Description and Fault Identification  
The object of this paper is a three-phase PWM converter widely used in electric drive systems. Its circuit topology is a typical three-phase full-bridge structure as shown in Figure 1, consisting of six IGBTs (\(T_{1}\)-\(T_{6}\)) and corresponding anti-parallel connected diodes (\(D_{1}\)-\(D_{6}\)). \(I_{u}\), \(I_{b}\), \(I_{c}\) are the output currents of the converter, which are controlled by the control system with 100 kHZ sampling rate, 20 kHZ control sampling rate. The IGBT is controlled by the corresponding drive signals.  
In this paper, the proposed open-fault diagnosis system is as shown in Figure 1, which is composed of data caching field programmable gate array (FPGA) and industrial personal computer IPC. FPGA resamples the high speed three-phase current signals at a sampling rate of 10 kHZ to caches one cycle of current signals. IPC executes the fault diagnosis algorithm according to the current signal of one cycle to obtain the fault diagnosis result. Once a fault signal is detected, IPC gives instructions via direct memory access (DMA) to shut down the control system. By this way, the fast IGBT open fault protection is realized. The main advantage of this system is that it reduces the computational burden by down sampling, so that one fault diagnosis system can protect multiple power sources simultaneously.  
However, in the actual operation of the fault diagnosis system, the three-phase current signal is affected by many disturbance factors, which may lead to misdiagnosis and power shutdown. Therefore, the reliability and anti-interference ability of fault diagnosis are very important. Usually, the IGBT open-circuit fault mainly includes the following two situations:  
1. When the chip \(Sn\) fails, the body diode \(Dn\) also fails at the same time.
2. When the chip \(Sn\) fails, the body diode \(Dn\) works normally.  
Case 1 usually causes over-voltage to trigger the hardware protection, so this type is not the research scope of open-circuit fault diagnosis in this study.  
Different IGBT open-circuit fault scenarios will generate different output current signals. In total, there are six single IGBT fault types and 15 dual IGBT fault types. Considering normal operating conditions, there are a total of 22 fault tags. All labels are listed in Table 1. Based on these fault labels, this paper proposed a data-driven fault diagnosis strategy. The input of the method is a periodic three-phase output current signal, and the output is a fault label representing the type of fault and the location of the fault.  
Figure 1: Fault diagnosis system of three-phase PWM converter.
Previous studies discourage the possible applications of currently available solar cells in smartphones and IoT devices, but consider PV energy charging with a supplemented operating system (OS) in low-power mode that can enhance the user's experience, behavior, and utility [49; 50]. The challenging task of converting PV energy to usable electrical power depends on the efficiency of the PV cell, charging technique, available light energy, variations in indoor and outdoor light energy levels, size, the location of PV, and its energy management system. To be attractive for large-scale users, an energy harvesting solution should therefore be of small size, user-friendly, and without an additional battery or large capacitor. In this paper, we use a commercialized PV cell, small enough to be installed on a smartphone or wearable device surface, and analyze their output power under indoor light conditions. Many people use a smartphone cover; an accessory to protect it from external damage. The paper is organized in the following ways. Section 2 discusses the material and algorithm of integration methodology. Section 3 discusses a proposed design scheme, and simulation results are presented in Section 4. A detailed discussion is also presented in Section 5. Section 6 concludes this paper with the final remarks.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 4 Biological Signals for Wearable-Based HR
### Pulse wave arrival time (PAT)  
A combination of ECG recording with a PPG waveform from the finger allows for the estimation of pulse arrival time (PAT) or the time interval between the R wave and the PPG waveform [6] as illustrated in Figure 7c.
## 5 ESEDG Comparison with Existing Routing Protocol
### Performing Assessment
#### 5.3.3 Analysis of Network Throughput and Lifetime with Number of Dead Nodes and Energy Consumption  
Network lifetime and its throughput are the fundamental characteristics used to evaluate the performance of the schemes under study. Network throughput for compared techniques is shown in Figure 8. Network lifetime due to an elliptical path would be recorded as maximum which is 80 % compared to other schemes that have values below the proposed scheme. ESEDG outperformed other baseline schemes as shown in Figure 9. The simulation recorded network throughput analysis as both are directly proportional to each other. In addition, we also evaluated the secondary parameters that ensure the node's  
Figure 6: Analysis of path loss with baseline schemes.  
Figure 7: Analysis of transmission loss with baseline schemes.  
quantity before and after network deployment. Reanalysis performed with a number of dead nodes validates the working of ESEDG with its pros and cons. Figure 10 represents the detailed analysis of the number of dead nodes against all baseline schemes, which shows that the ESEDG has the lowest dead nodes ratio. In Figure 11, the overall energy consumption of the network under different routing protocols is shown. Initially, the energy consumption of ESEDG is higher compared to other protocols due to the routing path association of MN and GN. Once paths are created, their energy consumption level decreases compared to other techniques.  
Figure 8: Analysis of network throughput loss with baseline schemes.  
Figure 9: Analysis of network lifetime loss with baseline schemes.
# Fate of Soil Carbon Transported by Erosional Processes
## 3 Soil Erosion as a Source or Sink of Carbon  
There are numerous interacting processes affecting the fate of SOC and SIC transported by erosional processes. The net effect as a source or sink depends on the balance of emission vs. sequestration, as outlined in Figure 1. Factors which increase the source from erosion-induced transport include disruption/breakdown of aggregates, a selective removal of the low-density/lighter fractions, fast-transport landscape vs. slow-transport landscape, and severe degradation of the soil at the eroded site so that its agronomic productivity is curtailed (Figure 1). There could be deposition in short-distance anaerobic lakes that would be a source of carbon, and in long-distance aerobic environment with reaggregation at depositional sites leading to sequestration of C. Some conditions which may lead erosion-induced transport of soil C as a sink may include a fast transport landscape with short transit time to the deposition site, deep burial of C, rapid reaggregation of buried C so that it is protected against microbial processes, and slight or moderate degradation of soil at the eroded site so that its agronomic productivity is not jeopardized (Figure 1).  
Figure 1: Pedological processes which impact emission of greenhouse gases from erosion-induced transport of soil carbon. Available literature highlights the importance of soil, climate and land attributes which aggravate the breakdown of aggregates and severely degrade the quality and productivity of eroded soil which has a long recovery period. Some steep land coastal coregions with a short transit time can be a sink of eroded soil carbon. Globally, however, accelerated erosion (hydric, aeolian, tillage) is a source of greenhouse gases especially those with high global warming potential (i.e., CH\({}_{4}\), N\({}_{2}\)O).  
Some critical factors which impact the gaseous processes during the transit phase and at the depositional site are briefly described below.
# Enhanced Phase Retrieval Method Based on Random Phase Modulation
## 2 Methods
The proposed method combines the serial and parallel reconstruction framework to simultaneously guarantee noise immunity and convergence speed, which can be divided into two steps: (a) using the serial reconstruction algorithm to obtain an acceptable phase estimation; (b) using the estimation as an initialization to carry out the iterations of the parallel algorithm. We use a few iterations from the serial algorithm to replace a large number of iterations from the parallel algorithm in the earlier stage of the reconstruction process. Subsequently, the robustness and the ability of noise suppression are enhanced by the parallel framework. Accordingly, the proposed serial-parallel cascaded reconstruction method can achieve both quality reconstruction results and rapid convergence. The iteration number of the proposed method is the sum of serial and parallel processes iterations. We use \(n_{1}\) and \(n_{2}\) to denote the iteration numbers of the serial and parallel processes, respectively, as \(n_{1}+n_{2}\ =\ \hat{N}\), and \(\hat{N}\) is the eventual iteration number of the serial-parallel cascaded method.
## 4 Experiment and Result
Figure 9 shows the effect of map resolution on topological map connectivity and occupancy. Finally, topological maps built in the office environment, pillar environment, and open space are saved, as shown in Figure 10. Experiments show that the topological map representation proposed in this paper can save the occupied information and topological information well. Figure 8: The influence of three parameters on the construction of the topological map. (**a**) Impact of changes in the obstacle ratio on the region. (**b**) Impact of changes in the point addition ratio on the region. (**c**) Impact of changes in the points pass ratio on the region. Figure 9: The impact of map resolution on topological map construction. What's more, the system proposed in this paper was integrated into the turtlebot2 equipped with the hokuyo laser sensor and NVidia TK1, and the topological map construction was successfully completed in the author's laboratory, refer to the Supplementary Materials. This experiment proves the practicality of the pipeline proposed in this paper on mobile platforms such as service robots. The configuration and map of the turtlebot2 experimental system is shown in Figure 11.  
Not only is the topological map construction method not limited by the environment geometry, but the topological map preservation method can also simultaneously save the occupied information and topological information to achieve the map conditions required for accurate navigation. In order to better illustrate that, Table 1 summarizes some of the information about the topological map creation in various environments. As can be seen from the table, the topological map can cover the entire environment, and the more complex the environment, the more topological vertices the map construction method will use to describe it. Although the topological map storage requirements are larger than the metric maps, more information will be saved to provide the same precision navigation environment.
## 5 Conclusions  
Within the seismic wavelength range, the waveforms of the depth-domain seismic wavelets change when there is a structure change or fluid in the formation. And the waveforms of the depth-domain seismic wavelets are also affected by the attenuation media. In this case, using the constant wavelet to characterize the depth-domain seismic wavelets will cause a large error in the synthesized seismogram. This work proposes a wavelet extraction method of attenuation media based on the generalized seismic wavelet to address this issue. Tests of weak and strong attenuation models demonstrate that the proposed method can correctly depict the depth-domain attenuated wavelets. Therefore, our method can be effectively applied to the depth-domain well-to-seismic calibration. Moreover, the AI inversion result of a 3D field dataset shows high accuracy and fine structure because the depth-domain generalized seismic wavelets are more suitable for describing the change in seismic facies than the constant wavelet. Hence, the proposed method can correctly depict the depth-domain attenuation wavelets. Therefore, the proposed method can correctly depict the depth-domain attenuation wavelets. Therefore, our method can be effectively applied to the depth-domain well-to-seismic calibration. Moreover, the AI inversion result of a 3D field dataset shows high accuracy and fine structure because the depth-domain generalized seismic wavelets are more suitable for describing the change in seismic facies than the constant wavelet. Hence, the proposed method can correctly depict the depth-domain attenuation wavelets.  
Figure 19: Comparison between inverted AI and well-log AI of wells (**a**) D, (**c**) H, and (**e**) N. (**b**,**d**,**f**) The enlarged view of the cyan box area of (**a**,**c**,**f**), respectively. The black, blue, and red lines represent the well-log AI, SPE result, and result of the proposed method, respectively. The results of SPE method indicated by the green arrows exhibit more dislocation than those of the proposed method.  
Figure 20: (**a**) MREs and (**b**) PCCs between the inverted and well-log AI at the well locations, where the letters are the name of the well.  
method can be utilized effectively for depth-domain AI inversion, and it has promising potential in reservoir characterization, fluid prediction, and attribute extraction in the depth domain. In the future, this method could be extended to applications on depth-domain prestack data for inverting subsurface elastic properties.  
Conceptualization, C.S. and R.C.; methodology, R.C. and Z.Y.; validation, C.S., R.C. and Z.Y. 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 numbers 41874153, 42004113, and 42174140.  
Not applicable.  
The data presented in this research are confidential and cannot be released.  
The authors declare no conflicts of interest.
## 1 Introduction
### Layers of Integrated System  
For the terrestrial part, 3 GPP Rel. 15 was completed and frozen in the third quarter of 2019, which is confirmed by ITU as the 5 th generation (5G) international mobile broadband standard [1]. Advanced research of 5G evolution and 6G has thus been conducted. For the airborne part, HAPS has received much attention [2, 3]. TR 38.811 Rel. 15 of 3 GPP is a detailed standard on the HAPS air interface technology. The HAPS has advantages of being of relatively lower altitude, smaller transmission delay, and higher networking flexibility. The same as with 5G systems, HAPS adopts the OFDMA multiple address access mechanism. It can lower the manufacturing cost and can take full advantage of the scale effect of the mobile telecommunication industry effectively. The HAPS-IMT integrated system studied in this paper is convenient for 5G users to access.
ITU launched the research on future-oriented service requirements more than 10 years ago. Three important traffic types for different service requirements have been defined, which are ultra-high reliability and ultra-low latency service (URLLC), enhanced mobile broadband service (eMBB) and mass machine communication services (mMTC). Given the characteristics of HAPS-IMT integrated systems, it is difficult to guarantee the QoS requirement of URLLC service. For this reason, this paper mainly focuses on the eMBB and mMTC services. eMBB is a kind of differentiated service (DiffServ), also called soft QoS services, such as AR/VR and streaming media. eMBB requires a certain QoS guarantee to make the transmission smoothly. There is no difference when the data rate is beyond the higher threshold. In the meantime, the data rate cannot be below the lower threshold to avoid a poor user experience. mMTC should be considered as a best-effort service, which does not require a QoS guarantee.
## 4 Results and Discussion
### Performance Analysis
#### 4.1.3 Delay  
In the current IoT context, packets arrive every 0.1 s, resulting in a high network traffic rate. Some packets get dropped at the base station (BS). To compute the delay, use Equation (11).  
\[T=BT_{j}+ET_{j}+CT_{j}+KT_{j}+GT_{j} \tag{11}\]  
where '\(BT_{j}\)' represents the propagation delay of the packet '\(j\)' and the time needed for the signal to travel from the base station to send the packet. '\(ET_{j}\)' denoted as access delay, '\(CT_{j}\)' denoted as switching delay, the time needed to send a packet to SDN. '\(KT_{j}\)' is the queuing delay, the time spent waiting in the switch to transmit. '\(GT_{j}\)' is the controller delay, the time for a controller to respond to a request and provide a response. The dynamic adaptability of BSHNN ensures minimal delays in complex network conditions, consistently outperforming ML-IDP as shown in Figure 6a. Initially, both ML-IDP and BSHNN demonstrate minimal delays of 0.001 s, indicating rapid processing with negligible impact on the network. As the number of switches increases to 7, ML-IDP experiences a delay escalation to 0.12 s, whereas BSHNN maintains a lower delay at 0.09 s. This consistent efficiency in processing, especially in scenarios with a higher number of switches, underscores the superior performance of BSHNN.
# Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects +
## 2 Dynamic Shortest Path
### DSP Problem Classes  
There are three classes of DSP problems that represent the dynamic characteristics of the problem [10]:  
1. _Decremental dynamic shortest path:_ a decremental DSP problem operates only with the deletion of vertices or edges.
2. _Incremental dynamic shortest path:_ unlike the decremental DSP problem, the incremental DSP operates only with the insertion of edges or vertices.
3. _Fully dynamic shortest paths:_ fully DSP problems allow edges and vertices' insertions and deletions.  
Incremental and decremental dynamic shortest paths are sometimes referred to as being partially dynamic [11]. The three DSP problem classes are still subject to the basic SP problem structure that is applied to both dynamic and static problems. The following items organize the three basic structural points of an SP problem [12]:  
1. _Directed or undirected graphs:_ Directed graphs have specific directions in their edges. A directed edge from vertex \(s\) to vertex \(d\) can only be traversed from \(s\) to \(d\), but not from \(d\) to \(s\). In contrast, undirected graphs are those whose edges allow traversing from one vertex to another in any direction.
2. _Weighted or unweighted graphs:_ Another factor that defines the structure of an SP problem is the cost to cross the edges of the graph. Weighted graphs are those with explicit costs \(c(e)\) at every edge \(e\in E\). Unweighted graphs have no costs at their edges, so a path's cost is defined by the number of hops (i.e., edges traversed) between the source vertex \(s\) and the destination vertex \(d\). Unweighted graphs can be seen as a specific case of weighted graphs where all the edge costs are equal, positive, and greater than zero.
3. _SP Problem Solution:_ The desired solution is also linked to the problem structure and can be split into two main categories, Single-Source Shortest Path (SSSP) and All-Pair Shortest Path (APSP). The SSSP aims to find the optimal route from a single-source vertex to all other vertices on the graph. On the other hand, APSP aims to find the optimal route for every pair of vertices on the graph.
# Post-Quantum Authentication in the MQTT Protocol
## 1 Introduction
### Public-Key Cryptography  
Public-key encryption and digital signatures are widely used, especially to provide assurance on the Internet [14]. Public-key cryptography can be used to provide confidentiality, integrity, authentication and nonrepudiation for individuals and organizations. PKI uses public-key cryptosystems to provide the basis of security for email, communication and transactions on the Internet [15].
# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 3 Methodology
### Forward Feature Fusion
This design aims to explore the interconnections among geometric features \(f_{geo}\), shape features \(f_{shape}\), and semantic features \(f_{sem}\), thereby enhancing feature expression and model performance. In the fully cross-attention mechanism, we compute attention scores for each pair of feature types. For example, considering the pair of geometric and shape features, the formula is given by \(\frac{f_{geo}f_{shape}^{T}}{\sqrt{||f_{shape}||_{2}^{2}}}\). Here, \(f_{geo}f_{shape}^{T}\) measures the similarity between the feature vectors, and \(\sqrt{||f_{shape}||_{2}^{2}}\) serves as a scaling factor to prevent gradient vanishing during softmax computation. Next, we apply the softmax function to normalize the scores, resulting in a distribution of attention weights. For instance, \(softmax(\frac{f_{geo}f_{shape}^{T}}{\sqrt{||f_{shape}||_{2}^{2}}})\) yields the attention weights of geometric features relative to shape features. Finally, we multiply these weights with the third feature type, such as \(softmax(\frac{f_{geo}f_{shape}^{T}}{\sqrt{||f_{shape}||_{2}^{2}}})f_{sem}\), to obtain the cross-attention output \(T_{sem}\), which allows semantic features to incorporate information from both geometric and shape features. Similarly, we can compute \(T_{gov}\) and \(T_{shape}\), completing the comprehensive cross-fusion of all three feature types. \[T_{sem}=softmax(\frac{f_{gov}f_{shape}^{T}}{\sqrt{||f_{shape}||_{2}^{2}}})f_{sem} \tag{1}\]  
\[T_{gov}=softmax(\frac{f_{sem}f_{shape}^{T}}{\sqrt{||f_{shape}||_{2}^{2}}})f_{geo} \tag{2}\]  
\[T_{shape}=softmax(\frac{f_{gov}f_{sem}^{T}}{\sqrt{||f_{sem}||_{2}^{2}}})f_{shape} \tag{3}\]