esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Process of the Usability Experiment
#### 4.2.1 Pre-Procedure  
Before creating the 3D models, the factor of colors was excluded so that these four concepts were not affected by colors. Moreover, blue dots in the videos indicate the location of a participant's hand when using the stereo systems. To distinguish the LCD monitor from a stereo system model, the area of the message interface is in dark blue colors.  
The three-view drawings, specifications and the simulation of product mechanism for these four concepts are shown in Figures 4-7 and Table 4. The simulation of product mechanism includes the actions of taking out or placing the CD, adjusting volume, external source (MP3, iPhone) and so forth. The videos allow participants to understand how a concept operates and what the human-machine interaction looks like. Three-dimensional CAD software SolidWorks and the rendering and Three-dimensional rendering animation software KeyShot were used in this study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Necessary Function** & \multicolumn{4}{c}{**Sub-Function**} \\ \hline P1 & P11 & P12 & P13 & P14 \\ Way of placing the CD & Open-lid Type & Slot type & Suction type & Slide type \\ \hline P2 & P21 & P22 & P23 & P24 \\ CD-ROM location & Top & Bottom & Front & Middle \\ \hline P3 & P31 & P32 & P33 & P34 \\ Control button style & Square & Round & Triangular & Oxal \\ \hline P4 & P41 & P42 & P43 & P44 \\ LCD dimensions and size & Overall & 3/4 area & 1/2 area & 1/4 area \\ \hline P5 & P51 & P52 & P53 & P54 \\ Control button location & Top & Bottom & Front & Edge \\ \hline P6 & P61 & P62 & & \\ Mp3/iPhone device & Yes & No & & \\ \hline P7 & P71 & P72 & & \\ Speaker & Integrated & Separated & & \\ \hline P8 & P81 & P82 & & \\ Stacked design & Allowed & Not allowed & & \\ \hline P9 & P91 & P92 & & \\ Remote control & Yes & No & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Morphological chart method.  
Figure 4: Three-view drawing of Idea 1.  
Figure 5: Animation screenshots of mechanical simulation for Idea 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Main Dimensions (mm)** & **140 (Width)** & **230 (Height)** & **216 (Depth)** \\ \hline Speaker dimensions (mm) & 130 & 230 & 150 \\ \hline Product weight in package (kg) & & 4.75 & \\ \hline LCD dimensions (mm) & 70 & 22 & R = 2 \\ \hline Input interface & USB socket & Headset audio source input & iPhone \\ \hline Output interface & Speaker output socket & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Product specifications of Idea 1.  
In order to carry out the usability experiment, videos of the mechanical simulations for Idea 2, 3, 4 were also prepared. After these videos were ready, the next step was to design the questionnaire for the participants including potential customers, management and design team. The highlights of this questionnaire are as follows:  
1. Learnability and efficiency
2. Memorability and errors
3. Function
4. Emotion
5. Satisfaction
## NLOS Correction/Exclusion for GNSS Measurement Using RAIM and City Building Models  
**[PERSON] *, [PERSON] and [PERSON]**  
Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan; E-Mails: [EMAIL_ADDRESS] (Y.G.); [EMAIL_ADDRESS] (S.K.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +81-354-526-273 (ext. 56273); Fax: +81-354-526-274.  
Academic Editor: [PERSON]  
_Received: 19 May 2015 / Accepted: 9 July 2015 / Published: 17 July 2015_  
**Abstract:** Currently, global navigation satellite system (GNSS) receivers can provide accurate and reliable positioning service in open-field areas. However, their performance in the downtown areas of cities is still affected by the multipath and none-line-of-sight (NLOS) receptions. This paper proposes a new positioning method using 3D building models and the receiver autonomous integrity monitoring (RAIM) satellite selection method to achieve satisfactory positioning performance in urban area. The 3D building model uses a ray-tracing technique to simulate the line-of-sight (LOS) and NLOS signal travel distance, which is well-known as pseudorange, between the satellite and receiver. The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange. The measurement of the satellite will be excluded if the simulated and raw pseudoranges are inconsistent. Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal. According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.  
**Keywords:** GNSS; GPS; NLOS; multipath; RAIM; 3D maps; building models; urban canyon; consistency check; particle filter
## 3 Results and Discussion
### Cavitation Characteristics of the Bileaflet Mechanical Heart Valve  
To investigate the cavitation characteristics of the bileaflet mechanical heart valve, the minimum pressure in the blood vessel is monitored. Figure 8a shows the minimum pressure with time varying in the blood vessel at the moment of valve closing. It can be found that the time length of the saturated vapor pressure in the tube is about 210 microseconds after the moment of valve closing.  
Figure 8: The variation of minimum pressure and volume of vapor phase: (**a**) The minimum pressure around the moment of valve closing; (**b**) The volume distribution of the vapor phase after valve closing.  
Figure 7: Distribution of downstream pressure after leaflet closing.  
Results are consistent with the experimental results of [PERSON] [42], which prove that numerical results are correct and effective. After valve closing, the volume distributions of the vapor phase at three different times are shown in Figure 8b. It can be found that cavitation mainly occurs on the upstream of the valve leaflet, away from the rotating axis and in contact with the valve housing. The area where cavitation occurs decreases gradually and moves to the gap between the leaflet and the valve housing over time.  
In order to find out the causes of the cavitation in the blood vessel after the valve is closed, the streamlines distribution near valve leaflets are analyzed. Figure 9 shows the streamlines distribution near valve leaflet at four different times after the valve closing. It can be found that in the initial stage of the valve closing, the main flow at downstream of leaflets is blocked but high-speed leakage flow can be found in the gap between leaflets and the valve housing. The maximum instantaneous velocity reaches 11 m/s, and most of the fluid flows directly are driven back to the inlet of the valve. Then, the flow passes through the gap and is driven back to the upstream surface of the leaflet by the larger pressure from the outlet, causing high-speed leakage flow in the blood vessel. Finally, the flow area and the maximum velocity of high-speed leakage flow decrease.  
Figure 10 shows the pressure distribution near leaflets at different times after the valve closing, where \(t\) is the time of valve closing. High-pressure can be found downstream of the leaflet when the valve is closed, and then the upstream pressure and the downstream pressure of leaflets return to balance rapidly. However, the high-speed leakage flow at the gap reacts slower than the pressure field and lasts longer time. Based on the above, it can be inferred that the water hammer effect and the high-speed leakage flow effect are the causes of cavitation phenomenon and high-speed leakage flow plays a dominant role.  
Figure 9: The streamline distributions after valve closing.
## 2 System-Level Design Optimization  
Unlike conventional design and optimization practices that treat components in isolation, SLDOM treats the system as a whole--the controller, power electronics, and machine--into one single optimization framework. This integrated approach accounts for interdependencies, feedback loops, and cross-coupling interactions that are typically neglected under traditional methodologies. Hence, SLDOM results in an improved and more precise optimization path, especially in large systems such as SRM drives. This method permits engineers to analyze performance metrics not only at the component level but in conjunction with system-level interactions. For instance, design decisions related to the motor can influence control strategies, thermal behavior, and system efficiency overall. System-level optimization is, therefore, required to obtain balanced trade-offs among competing objectives, like cost, performance, and reliability.
# A VHF Circularly Polarized Turnstile Antenna with Balun for Meteor Radar
## 4 Experimental Results and Improved Design
### Experiment 1  
To verify the antenna's performance experimentally, an association test with a meteor detection radar system is carried out. The experimental results are compared with the Australian meteor detection radar (ATRAD). The meteor detection radar deployed in Wangjiahe Town, Huangpi District, Wuhan City, Hubei province, China, is located approximately 55 km away from ours. It consists of one transmitting antenna (left-handed circularly polarized, LHCP) and five identical receiving antennas (proposed right-handed circularly polarized antenna) designed for meteor observation. By designing the receiving antenna array, the meteor radar can be used for meteor tracking, detection of meteor trails, summarizing quantities and distributing meteors. The measured results are shown in Figure 9.  
Figure 8: Comparison of VSWR measurement results and simulation results.  
Figure 7: The fabricated antenna unit (**a**) and array (**b**).  
The azimuthal distribution and counts of meteors from 3 to 7 July 2022 were observed with the proposed receiving antenna array, as shown in Figure 9. It can be seen from Figure 9a and Table 3 that the maximum of meteors reaches to 9500-13,000 per day. Figure 9  
Figure 9: Comparison of count and azimuth distribution of meteors. Meteor radar with antennas of proposed (**a**,**c**) and ATRAD (**b**,**d**).  
compares the universal-time distributions of meteor counts measured by the meteor radar with antennas of ATRAD and with proposed antennas. We can see from Figure 9c that the azimuthal distribution of meteors measured by the meteor radar with our proposed antennas mismatched with that observed by the ATRAD radar. Unlike the ATRAD meteor azimuth distributions that only have one depression, the system with proposed antennas shows two depressions. The disagreement in meteor azimuth distribution is unusual under the condition that the radiation pattern of the proposed antenna is approximately standard along with the direction of horizontal to the ground. The phenomenon gives an indication that the radiation patterns of the fabricated antenna at the operation frequency are distorted.
# Plant Electrical Signal Classification Based on Waveform Similarity
## 2 Related Works  
### Plant Electrical Signal Recognition and Classification  
At present, main analysis methods for plant electrical signals are focused on time domain and frequency domain. Time domain features analysis includes waveform length, peak amplitude, etc. Frequency analysis involves Fourier transform [42], power spectrum [42], coherence analysis [36], independent component analysis (ICA) [43], etc. In addition, the researchers also attempted to build the computational models of plant electrical signal evoked by external stimuli [8; 44; 45; 46; 47].  
[PERSON] et al. acquired the _descoingsii x haworthioides_ (Pepe) AP in different light modes, and translated the frequency signals into voltages by inverse fast Fourier transform. Furthermore, it was implied that the changed frequency of action potential in different light modes can be used as a control signal for the bio-machine [45].  
[PERSON] et al. developed a control model for sensing and actuating mechanism of Venus Flytrap. The AP model describes the mechanical stimuli of _Dionaea muscipula_ (Venus Flytrap). Furthermore, they developed a biomimetic robot to demonstrate the feasibility of the control model [46].  
[PERSON] et al. tried to classify different stimuli by acquired electrical signals of tomatoes and cucumbers. The stimuli include 5 mL 0.05 M H\({}_{2}\)SO\({}_{4}\), O\({}_{3}\) 16 ppm for a minute, 5 mL 3 M NaCl, and 10 mL 3 M NaCl. Eleven statistical features were extracted from plant electrical signal by linear and nonlinear methods. Then the features were trained by five classifiers including [PERSON] linear discriminant analysis, quadratic discriminant analysis, naive Bayes classifier (diaglinear classifiers and diagquadratic classifiers), and Mahalanobis classifier. Using the five classifiers, then the average accuracy of classification was 70%, and the best individual accuracy was 73.67% [47]. The forward and inverse modelling approaches for prediction of light stimulus from electrophysiological response in plants were established by [PERSON] and his coworkers [48].  
To our knowledge, the recognition and classification works for plant electrical signal are still few. It is still a challenge to recognize and classify plant action potentials to fulfill the requirements of researchers as well as plant physiology research. Therefore, we proposed a new plant action potential recognition and classification methods.
## 5 Results and Discussion
### \(\mathcal{F}_{inter}\) Cross-Lingual Experiments  
Motivated by the results in the previous section and to further avoid another round of excessive experiments, we limited the \(\mathcal{F}_{inter}\) cross-lingual protocol by using only the AIRM measure. On the other hand, we choose to keep and test both the SVM and DSC-BFS classifiers, as well as the \(\Psi_{\mathbf{X},\mathbf{Y}}\), \(\Psi_{\mathbf{X},\mathbf{Y}}^{\dagger}\) RDVs and the \(\omega^{-100\%RF}\), \(\omega^{-0\%RF}\) setups accompanied by the _LFW1_ or _LFW2_ protocols. Figure 12 presents the average \(EER_{SF}^{user}\) for the _LFW1_ protocol in which we learn the verifiers with one dataset \(D_{i}\) and test it over the entire writers of a blind testing set \(D_{j/i\mu}\). Focusing on the content of Figure 12, we can draw a number of comments regarding EER efficiency with respect to the following design parameters of the local _LFW1_ protocol: (a) SVM vs. DSC-BFS, (b) local pole \(\Psi_{\mathbf{X},\mathbf{Y}}\) vs. common pole \(\Psi_{\mathbf{X},\mathbf{Y}}^{\dagger}\) RDVs, and (c) \(\omega^{-100\%RF}\) vs. \(\omega^{-0\%RF}\) setup. * With respect to the employment of the SVM against the DSC-BFS, the results indicate that the two forms of classifiers demonstrate comparable performance levels, exhibiting marginal disparities in their operational capabilities. * With respect to the local \(\Psi_{\mathbf{X},\mathbf{Y}}\) or common pole \(\Psi_{\mathbf{X},\mathbf{Y}}^{\dagger}\) RDVs, it is again evident that clearly the common pole \(\Psi_{\mathbf{X},\mathbf{Y}}^{\dagger}\) RDV provides the best average \(EER_{SF}^{user}\) in the majority of the cases. * With respect to the \(\omega^{-100\%RF}\) vs. \(\omega^{-0\%RF}\) negative class formation, an enhancement in the verification performance of the learned verifiers is observed for the \(\omega^{-100\%RF}\) when compared to the \(\omega^{-0\%RF}\). Thus, contrary to the elevated verification leverage of the \(\omega^{-0\%RF}\) on the \(F_{intra}\) cases, the development of the classifiers under the \(\omega^{-100\%RF}\) assumption on the \(F_{inter}\) cases yields more robust models. Again, for the case of the \(\mathcal{F}_{inter}\) and the _LFW2/TFW2_ protocol, in which a larger 770-dimensional vector is utilized, Tables 7 and 8 present the corresponding average \(EER_{SF}^{user}\) error rates. Again, in order to compare the derived results between the serial _LFW1_ and parallel _LFW2_ fusion protocols, we complement their content by reporting the optimal _LFW1/TFW1_ average \(EER_{SF}^{user}\) extracted out of Figure 12 for the common pole \(\Psi_{\mathbf{X},\mathbf{Y}}^{\dagger}\)
# A Coordination Technique for Improving Scalability of Byzantine Fault-Tolerant Consensus
## 3 A Coordination Technique for Scalable BFT Consensus
### Step 2. Collecting Transactions from Transaction Pool  
Once the coordinator gets the prime node election notification from the prime node, it collects all transactions from each node's transaction pool as the second step. This collection step is presented in Algorithm 2. The input parameters of this step are \(sig_{prime}\) and \(pk_{prime}\) from the prime node. Then, the coordinator checks elected prime node's validity by generating a \(random\) number with the delivered _seed_ and \(Node_{i}\) (see Lines 3-5). If the generated \(random\) is equals to \(Node_{i}\) received from the prime node, the consensus coordinator requests all nodes to send all transactions accumulated in the transaction pool of each node between the previous time \(time_{p}\) to the current time \(time_{c}\). When \(random\) is different from \(Node_{i}\), the coordinator terminates all coordination steps of this round and waits for the next idle state.  
```
1:Inputs:\(sig_{prime}\), \(pk_{prime}\)
2:Initialize:\(Txs\leftarrow\{\}\)
3:\(Node_{i}\), \(seed=\text{Signature}(sig_{prime})_{pk_{prime}}\)
4:\(random=Random(seed)\) % \(N(Node)\)
5:ifrandom equals to \(Node_{i}\)then
6:fori=0, \(i\leq N(Node)\), i++do
7:\(Txs_{i}\leftarrow\) request\(Node_{i}\) to send \(Tx_{(time_{p}\sim time_{c})}\) of \(TxPool_{i}\)
8:endfor
9:else
10: Terminate
11:endif
```  
**Algorithm 2** [_Coordinator_] Collecting Transactions from TxPool
###### Abstract
Other paths for the induction of the formation of the extractant phase include the reduction in temperature, the introduction of small amounts of hydrophobic solvents, the addition of buffer solutions for the acidification of the sample solution and the introduction of aprotic solvents [21; 23]. Due to its inherent benefits, SI-HLLME has established itself as a useful bioanalytical tool. Typical applications of SI-HLLME include the extraction of azole antimicrobial drugs [19], amphetamines [26], fluoroquinolones [27], antiepileptic drugs [28], amantadine [29], etc. It is noteworthy that HLLME is also in good agreement with some principles of green sample preparation (GSP) that promotes the minimization of waste generation and energy demand, the achievement of high sample throughput and the miniaturization and simplification of the analytical procedure [30]. In this work, an SI-HLLME sample preparation scheme is proposed for the simultaneous extraction of piroximam and meloxicam from human urine followed by their determination by HPLC-PDA. Initially, a screening 2-level fractional factorial experimental design was used to evaluate which parameters significantly affect the extraction procedure. These parameters were finally optimized by means of a Box-Behnken experimental design. The herein developed SI-HLLME method was validated following the total error concept approach, aiming to take into consideration the random and systematic errors and the accuracy profiles of the target analytes that were constructed.
## 3 Spectrum Auction Structures  
The most commonly used auction formats exhibit strengths and weaknesses, which stem from their underlying structure. In this section, we provide a concise overview of the primary auction formats proposed, along with the key issues encountered in auction design.  
The definition of the auction process must be clearly established before it can be discussed. According to [13], auctions can be defined by their primary characteristic: the promotion of Walrasian equilibrium, which refers to the balance between supply and demand. The authors further illustrate that the auction process is particularly effective when applied to the buying and selling of items for which there is no well-defined market, such as privatizations, spectrum licenses, and works of art.  
During each auction, players are presumed to be selfish and greedy, meaning they are uninterested in the outcomes of bids made by other participants. Each player prefers to win rather than lose. In addition, players are assumed to be straightforward bidders.  
Each participant strives to maximize their utility function, which can be expressed through a variety of inputs, including revenue, costs, accessibility, the pairing of auctioned items, market fragmentation, and any other factors that the player (including the auctioneer) may consider valuable.  
However, without loss of generality, the efficiency of each auction structure can be assessed by analyzing the prices paid or the bids submitted by each participant, considering all benefits and costs consolidated into a single variable that represents the agent's payoff (\(\pi_{i}\)). This method has the added advantage of simplifying analysis by reducing it to a single observable variable.  
Throughout this section, we review the English auction, Dutch auction and its variants, beauty contest, Vickrey auction, Simultaneous Multiple-Round Auction, and Combinatorial Clock Auction models.  
As most structures exhibit similar weaknesses, they are categorized following the presentation of several auctions to enhance readability. The explanations assume either SMRA or CCA structures. As the previous models develop, the applicable solutions are also relevant for earlier structures.
## 3 Results and Discussion
### Model Validation and ANOVA Results
At mid SOC levels (SOC 50%), the battery begins to experience more significant voltage drops as the available charge decreases. The effects of both pulse time gap and discharge pulse time become more pronounced in this range, especially at low temperatures, where voltage drops due to increased resistance can be severe if the pulse duration is too long or the time gap between pulses is too short. Similarly, at high temperatures, extended discharge times at mid SOC can cause thermal runaway, impacting RMSE. At low SOC levels (SOC 30%), the battery voltage is already near its cutoff threshold, and any additional stress due to inappropriate pulse durations or insufficient recovery time between pulses can cause sharp voltage drops, increasing the RMSE. At low temperatures, the battery becomes more susceptible to issues with voltage predictions as a result of its elevated internal resistance. Similarly, at high temperatures, the increased electrochemical activity makes the battery more sensitive to pulse time changes. Figure 5:0 Regression model results for different SOC values at the ambient temperature of \(40\)\({}^{\circ}\)C, (a) SOC 90, (b) SOC 50, (c) SOC 30.