esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Communication Model  
Assume that all vehicles are traveling at a constant speed \(S_{p}\). Each car has a computational task, defined as \(T_{i}=\Big{\{}d_{i},c_{i},f_{i}^{i},T_{i}^{\text{max}}\Big{\}}\), \(i\in N=\{1,2,3,\ldots,N\}\), Where, \(d_{i}\) represents the size of input data used for calculation, \(c_{i}\) represents the computing resources required to complete task \(T_{i}\), \(f_{i}^{i}\) represents the computing resources of vehicle \(i\), \(T_{i}^{\text{max}}\) represents the maximum delay constraint of task \(T_{i}\), the computing resource of MEC is \(f^{\text{m}cc}\).  
Offload tasks to Local, MEC, or Cloud Servers through collaborative computing unitstall. The communication model is shown below.  
(1) Uninstall to the MEC server: \(R_{i}^{1}\) represents the data rate of upload and download between the mobile vehicle and MEC, according to [PERSON]'s theory, given by the following equation.  
\[R_{i}^{1}=B_{1}\log_{2}\Bigg{(}1+\frac{Qw_{1}^{2}}{N_{0}}\Bigg{)} \tag{1}\]  
where, \(B_{1}\) represents the V2R (Vehicle to Road Side Unit) communication channel bandwidth, \(Q\) represents the transmitting power between vehicles on the channel and RSU, \(w_{1}\) represents the channel gain of moving vehicles on the channel to RSU, and \(N_{0}\) represents the white noise power.  
(2) Offloading to the cloud server: When the task is offloaded to the cloud server, the task is first transmitted to BS and then to the cloud server. \(R_{i}^{2}\) represents the data rate of upload and download between the moving vehicle and BS.  
\[R_{i}^{2}=B_{2}\log_{2}\Bigg{(}1+\frac{Q_{1}w_{2}^{2}}{N_{0}}\Bigg{)} \tag{2}\]  
Figure 1: Network model.  
where, \(B_{2}\) represents the channel bandwidth, \(Q_{1}\) represents the transmitted power between the vehicle on the channel and BS, and \(w_{2}\) represents the channel gain of moving the vehicle on the channel to BS.  
The vehicle in the scenario is traveling at a constant unidirectional speed, and the speed of the vehicle is represented by \(V\), the mobility of the vehicle causes the distance \(d_{l}\) between it and the center of the RSU coverage to vary with time, the law of change can be expressed by the following equation.  
\[d_{l}(t)=\sqrt{e^{2}+\left(\frac{s}{2}-Vt\right)} \tag{3}\]  
where, \(e\) represents the distance between the vehicle travel level and the RSU, and \(s\) represents the coverage of the RSU.  
Since tasks can be computed locally, on MEC servers, and on Cloud servers, it is important to segment the tasks, and determine which platforms to offload to. This paper defines \(S=\left\{s_{i}\middle|s_{i}\in\left\{s_{i^{c}}^{s},s_{i}^{msc},s_{i}^{cloud} \right\},i\in N\right\}\) as the offloading decision of the vehicle. \(s_{i}^{c}\), \(s_{i}^{msc}\), and \(s_{i}^{cloud}\) indicate that the task is uninstalled on the Local, MEC server, and Cloud Server, respectively.
# Performance Development of Fluidic Oscillator Nozzle for Cleaning Autonomous-Driving Sensors
## 3 Research Results and Considerations
### Analysis for Radius of Outlet and Radius Outlet Curvature Models  
The analysis of the main variables revealed that the models with the most increased sweeping angles were the R4 and R6 models of the radius of outlet and the RC9, RC10, and RC11 models of the radius of outlet curvature. Therefore, additional analysis was conducted by combining the two design variable models. The analysis was based on the pressure condition of 5.0 bar, which had the best frequency performance in the base model, and the sweeping angle and frequency of the analysis results for each model are shown in Table 6. Results show that the sweeping angle increased in the model with two design variables compared to the model with one. This further improved performance by increasing the amount of inflow into the feedback channel through the two variables, radius of outlet and radius of outlet curvature. The sweeping angle and frequency for each variable are shown in Table 6. The velocity distribution used to measure the sweeping angle for each model is shown in Figure 13. Figure 14 shows that the sweeping angle of the model with two design variables was larger than that of the model with one design variable. The highest sweeping angle was 75\({}^{\circ}\) for R8+RC10, and the frequency was almost constant. Compared to the base model, the sweeping angle of R8+RC10 showed excellent performance improvement with an increase rate of 39%. It is believed that the radius variable in this outlet curvature has an excellent effect on sweeping angle change.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **R4+** & **R4+** & **R4+** & **R6+** & **R6+** & **R6+** & **R8+** & **R8+** & **R8+** \\  & **RC9** & **RC10** & **RC11** & **RC9** & **RC10** & **RC11** & **RC9** & **RC10** & **RC11** \\ \hline Sweeping Angle (\({}^{\circ}\)) & 68 & 73 & 72.5 & 67.5 & 72.5 & 73.5 & 70.5 & 75 & 73.5 \\ \hline Frequency (Hz) & 333 & 323 & 323 & 323 & 313 & 323 & 323 & 313 & 323 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis results for radius of outlet and radius outlet curvature parameter.  
Figure 12: Sweeping angle and frequency (radius of outlet curvature).
## 4 Principles of Technical Methods  
By comparing major methods in geophysical-seismic waveform inversion, Self-Organizing Maps (SOM), traditional deterministic impedance inversion, Kriging, and single-attribute thresholding, this study summarizes their core principles, characteristics, and applicable domains (Table 1) [80]. The analysis highlights the inherent limitations of conventional approaches. Deterministic inversion and Kriging rely heavily on physical or statistical assumptions, while single-attribute thresholding suffers from over-simplification. These limitations make these techniques inadequate for addressing complex geological challenges [81, 82]. In contrast, SOM leverages unsupervised learning to achieve multi-attribute integration and nonlinear modeling, rendering it particularly effective for automated seismic facies classification and reservoir prediction [83, 84]. Integrating SOM with seismic waveform inversion demonstrates potential in resolving the delineation of thin-bedded beach-bar sand body boundaries in the study area, offering a hybrid approach that combines data-driven pattern recognition with physics-based constraints [85, 86].  
Figure 6: Petrophysical analysis of the Lower E3d2 Member in HHK Depression. (Testing wells: B7-2-a, C4-2-a, C4-3-a, C4-3-bD, C4-1S-a).
# Energy-Efficient Region Shift Scheme to Support Mobile Sink Group in Wireless Sensor Networks
## 2 Related Work
Usually, it assumes that the movement plan of a mobile sink group is not notified in advance and thus the data band is wrapped around a mobile sink group by using virtual infrastructure such as a grid. Then, the sink group receives the data after a while passing by the band. If the movement plan is known or could be easily predicted, it could reduce the size of the band. The approach effectively works in case the sink group has continuous movement but, in case of intermittent movement of the group, special treatments should be considered. Pipe-based geocasting [11] reduces the data dissemination overhead by storing data in the form of a pipe line along with a diameter in a group region. After formation of the pipe line, member sinks query the pipe and retrieve data from the pipe. Since the approach constructs only pipe line virtual infrastructure on the diameter, it requires less communication cost compared to the band-based approach. In addition, although the sink group has intermittent movement pattern, it does not require extra operation for data dissemination to the sink group in temporary stop. Meanwhile, it causes unnecessary communication cost since member sinks require periodic query for data retrieval. ERMG (Energy-efficient and Reliable Mobile Group communication protocol) [12] is a grid-based mobility support scheme. ERMG handles both region discovery and data dissemination phases. Instead of flooding for control and data packets, messages are delivered via cell heads on a predefined grid structure. However, it still suffers communication overhead on large expected areas for the region discovery. The studies mentioned above have focused on the data dissemination phase under the assumption that region discovery works well. However, studies for the region discovery are at the primitive stage, and it still suffers from periodic and large area communications. We thus suggest an efficient region discovery scheme eliminating the large communication overhead.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 3 Methodology
### Semantic Segmentation Module  
Conventional SLAM mapping module can obtain a complete 3D map of the surrounding environment. However, it cannot obtain semantic information from the map, which makes it difficult to perform high-level semantic tasks. At the same time, loopback detection and global map optimization will be affected when there are dynamic objects in the local map. Therefore, we add a separate semantic segmentation thread to semantically segment the keyframes. In addition, a method is proposed to optimize semantic segmentation based on depth images. Finally, the dynamic region detection of the tracking module is combined to eliminate the dynamic objects.
## 3 Results and Discussion
### Phytochemical Analysis  
#### 3.4.1 Total Phenolic Content in WFSP and WFP  
Figure 4 represents the results of total phenolic content (TPC) of both varieties (WFSP and WFP) of potatoes. Results showed that WFP and WFSP presented the TPC of about 2.91 \(\pm\) 0.42 and 9.27 \(\pm\) 0.88 mg GAE/g DW, respectively. It is observed that WFSP showed a higher TPC compared to WFP. Our results are in agreement with the findings of [PERSON] et al. [34], who reported that BARI SP7 variety of Bangladesh cultivated orange-fleshed sweet potato (OFSP) presented the highest total polyphenol content (TPC) and total carotenoids content (TCC) of 136.05 \(\pm\) 3.53 mg GAE/100 g FW and mg/100 g FW, respectively. Similarly, [PERSON] et al. [28] verified that the UMUSPO1 and UMUSPO2 varieties of Nigerian orange-fleshed sweet potato showed the highest total phenolic content of 7.69 \(\pm\) 0.08 and 7.33 \(\pm\) 0.00 mg GAE/g, respectively. [PERSON] et al. [38] demonstrated that wild species of potato (_S. michoacanum_), potato hybrid (DG 97-952), and cultivated potato (Ari) showed the maximum TPC content of 28.58 \(\pm\) 3.66, 40.50 \(\pm\) 4.02 and 20.27 \(\pm\) 4.37 \(\upmu\)g/mL, respectively. [PERSON] and co-workers [39] stated that the K. Surya cultivar of potato peel showed the maximum TPC of approximately 0.27 mg GAE/g FW after 30 days of incubation at room temperature.
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Frequency Domain  
It is difficult to maintain the actual position between neighbor communicating nodes when considering the underwater acoustic channel's long latency. Simplified frequency domain protocol, which includes FDMA, TDMA and CDMA, is initially utilized in UWASNs. Simple and dependable FDMA divides the existing frequency band into many sub-bands and assign the sub-band directly into communicating node. However, a significant drawback is the availability of low bandwidth ratio [46; 77]. In contrast to TDMA, CDMA protocols differentiate between users utilizing pseudo noise, which despite having a high channel usage and a straightforward technique, is still exposed to the \"near-far effect\" which is inherent to CDMA. The fundamental concept of FDMA is to select the appropriate frequency band for transmission in accordance with various transmission distances, which enhances the availability ratio of the bandwidth [78; 79]. MAC protocols for the UWSNs  
Figure 6: Flow diagram of data aggregation method [75]  
Figure 7: Classification of energy-efficient MAC protocols for UWSNs.  
based on frequency domain, will be discussed in this section. Here we evaluate the MAC protocols frequency domain existing variations that are, multi-access technologies. Such as Frequency-Division-Multiple-Access (FDMA) Time-division multiple access (TDMA) and Code division multiple access (CDMA). MAC protocols for the UWSNs based on frequency domain, will be discussed in this section. Here we evaluate the MAC protocols frequency domain existing variations that are, multi-access technologies. Such as Frequency division multiple access (FDMA) Time division multiple access (TDMA) and Code division multiple-access (CDMA).
## 4 Experimental Results  
Comprehensive experiments were conducted to evaluate the proposed ADN and AND_LS scheduling approaches. All algorithms were coded using Python (3.9.16) and implemented on a computing server with an Intel Core i9-13900K @ 3.00 GHz, 32 MB RAM, 48 GB GPU, and Windows 11.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 3 System Model
### Queues Dynamic Model  
We define \(I_{n}(t)\) as the data queue occupancy of sensor \(n\) carried by patient and \(I(t)=\{I_{1}(t),I_{2}(t),I_{3}(t),\cdots,I_{N}(t)\}\) represents a vector with the length of data queues of all sensors in time slot t. The data queue is given by:  
\[I_{n}(t+1)=max[I_{n}(t)-z_{n}(t)r_{n}(t),0]+c_{n}(t). \tag{5}\]  
We define \(J_{n}(t)\) as the energy queue occupancy of sensor \(n\) carried by patient and \(J(t)=\{J_{1}(t),J_{2}(t),J_{3}(t),\cdots,J_{N}(t)\}\) represents a vector with the length of energy queues of all sensors in time slot t. The energy queue is given by:  
\[J_{n}(t+1)=J_{n}(t)-a_{n}(t)+h_{n}(t), \tag{6}\]  
where \(h_{n}(t)\) is energy harvesting rate. The harvested energy comes from movement and other stable energy sources like body heat. In addition, the energy harvesting rate is bounded by the energy supply rate \(e_{n}(t)\), which determines the amount of harvestable energy of sensor.  
\(\Omega\) is a limited battery capacity. The total energy stored in battery is limited by the battery capacity \(\Omega\). Thus, in time slot \(t\), the sum of the energy left in energy queue \(J_{n}(t)\) and harvested energy \(h_{n}(t)\) must be less than the battery capacity. Then, the following inequality should be satisfied in each time slot:  
\[J_{n}(t)+h_{n}(t)\leq\Omega,\forall n\in N. \tag{7}\]
# Flow Analysis of a Novel, Three-Way Cartridge Flow Control Valve
## 2 Case Study: Working Principle of a Cartridge Flow Control Valve  
In the design of flow regulators, there is a series or parallel connection of a throttling valve and a differential valve. These two elements connected in series form a two-way flow regulator (Figure 1a). In the case when the differential valve is attached to the throttling valve in parallel (Figure 1b), a three-way regulator is obtained. In practice, the designation for flow regulators is simplified, as shown in Figure 1c. The flow regulator can be placed in various places in the hydraulic system. However, most often it is the supply line where the valve controls the flow rate, and thus, the speed of movement of the receiver, such as an actuator or hydraulic motor.  
The geometric model of the studied flow regulator is shown in Figure 2a and the cross-section in Figure 2b. Working fluid flows from supply port P to the output port A. The relief port B is used to discharge excess fluid. A pressure difference on both sides of the spool (4), moving inside the sleeve (3) is created by the throttling nozzle (5), depending on the flow velocity.  
Figure 1: Drawing symbol of a flow regulator; (**a**) valves connected in series, (**b**) valves parallel connected, (**c**) simplified; P, A, B—connection ports.  
The spool position is determined by adjusting the spring (6) tension and the pressure difference on both sides. In this case, the port B opening area is crucial. The cross-sectional area of the port B gap also depends on the position of the spool (4); however, with the spool displacement, it enlarges much faster than the area of port A decreases. By imposing the appropriate initial spring tension value and fixing the proper throttling nozzle diameter, the desired flow rate can be obtained, assuming the sufficient delivery of the supply unit.  
Figure 3 shows a 3D model of the working fluid flowing through the valve, built by performing Boolean operations on a geometric model.  
The incoming fluid from the P port is divided into two streams: main A and relief B. The A stream narrows in the area of the throttle nozzle and then bends at an angle of 90 degrees to flow out through the radial holes in the spool and the sleeve to the A port. The flow area of the radial holes decreases when the spool moves along the sleeve. This fluid jet contraction, together with the main throttling nozzle, forms a cascade flow throttling system.
# Dual Threshold Self-Corrected Minimum Sum Algorithm for 5G LDPC Decoders
###### Abstract
In this paper, a dual threshold self-corrected minimum sum (DT-SCMS) algorithm based on the SCMS algorithm is proposed for the design requirements of LDPC codes in 5G communication. In the process of iterative decoding, the new algorithm determines the reliability of the messages by setting thresholds and erasing the unreliable messages, which improves the decoding performance and efficiency. Simulation results show that the DT-SCMS algorithm is superior to the SCMS algorithm in terms of error characteristics and convergence characteristics. When the code rate is 2/3, the DT-SCMS algorithm can reduce the number of iterations by up to 20.46% compared with the SCMS algorithm, and the average proportion of reduction is 18.68%. The remainder of this paper is organized as follows. Section 2 introduces the basic decoding algorithm of LDPC codes. Section 3 proposes a new decoding algorithm for LDPC codes. Section 4 exhibits the performance analyses and comparisons. An architecture of the LDPC decoder is designed in Section 5. Finally, Section 6 concludes the paper.
# Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones
## 1 Introduction
The study on the end-user mobile app \"VoiceScreen\" also demonstrated an accurate and robust method for measuring voice quality and the potential to be used in clinical settings as a sensitive assessment of voice alterations throughout the results of phonsurgical treatment [15]. Despite the greater use and availability of technology expertise and equipment, the current study has revealed a lack of awareness of physiological, speech/language and culturally influenced aspects. The primary obstacles to this investigation are the following:  
1. Standardization and disclosure of acoustic analysis methods;
2. Recognition of the link between sensory and experimental acoustic outcomes;
3. The obligation to account for organic speech-related covariables, such as distinctions in conversing voice sound pressure level (SPL) and fundamental frequency f0;
4. The requirement for a significant larger dataset to comprehend regular variance between and within voice-disordered individual people. The results of the studies mentioned above enabled us to presume the feasibility of voice recordings captured with different smartphones in an ordinary clinical setting for the estimation of AVQI. Consequently, the current research was designed to answer the following questions regarding the possibility of a smartphone-based \"VoiceScreen\" app for AVQI estimation:  
1. Are the different estimated average AVQI values for smartphones consistent and comparable? 2. Is the diagnostic accuracy of the estimated AVQIs for different smartphones relevant to differentiate normal and pathological voices?We hypothesize that the use of different smartphones for voice recordings and the estimation of AVQI in an ordinary clinical environment will be feasible for the quantitative assessment of voice. Therefore, the present study aimed to develop the universal platform-based application suitable for different smartphones for the estimation of AVQI and evaluate its reliability in the measurements of AVQI and the normal/pathological differentiation of voices.
## 2 Methods
### Design and Fabrication of the Combiner
#### 2.3.3 High-Power Capability Validation  
The high-power test bench, as depicted in Figure 7, features the orange-highlighted combiner investigated in this study. The instrumentation comprises an E4438 RF signal generator, N9020 spectrum analyzer (Keysight Technologies, Santa Rosa, CA, USA), and SHX10K10 high-power attenuator load (SHHX, Shanghai, China). and the orange part is the combiner studied in this paper. As the core of the 3.0T MRI RFPA, combined the RF input (RFin) via a driver amplifier (PAd) and 1:4 divider to four PAs (PA1-PA4), achieving large power (large than 69.2 dBm) output (RFout). To verify the power handling capacity of the combiner, temperature changes and output power variations were tested under different voltage standing wave ratio (VSWR) conditions at full power output. Under 128 MHz pulsed operation (5% duty cycle) with water cooling (inlet at 30 \({}^{\circ}\)C), the PA1-PA4 outputs at 63.6 dBm yielded a 69.2 dBm combiner output when the input power was 0 dBm.  
Fiber-optic temperature probes were used to measure thermal profiles at three resistors and the output microstrip (four positions, Figure 8), with data collected at 0, 10, 20, 30, and 60 min. The temperature variations across the four test points are shown in Figure 9, while power variations (from 0 to 60 min) are summarized in Table 7, and at VSWR = 1:1.2, P\({}_{\text{max}}\) measured 69.2 dBm, and P\({}_{\text{min}}\) measured 69.11 dBm (\(\Delta\)P = 0.09 dB). At VSWR = 1:2, P\({}_{\text{max}}\) measured 68.69 dBm, and P\({}_{\text{min}}\) measured 68.60 dBm (\(\Delta\)P = 0.09 dB), demonstrating minimal power variation.  
Figure 8: Temperature test point for high-power output.  
Figure 7: Application of the combiner in MRI RFPA and test bench.  
It can be observed that, for VSWR values of 1:1.2 and 1:2, the temperature rise at the test points remained below 1 \({}^{\circ}\)C over 60 min, and the power fluctuation did not exceed 0.1 dB from Table 7. These results demonstrate that the developed high-power combiner exhibits excellent thermal stability and power integrity under demanding operational conditions.