esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Experimental Results
#### 4.2.3 Comparison of the Entirely Human-Driven Vehicle Experiment  
To verify the superiority of leading AVs, the entirely HV experiment was considered over different real traffic volumes. Figure 10 shows the results of the entirely HV experiment compared to those of the proposed experiment. As seen in Figure 10a, the fully autonomous condition increased the average speed 1.49 times compared to the entirely HV experiment. In particular, the entirely HV experiment obtained the lowest value for average speed of 4.4 m/s with a traffic volume of 1000 veh/h. As seen in Figure 10b, the fully autonomous condition increased the average reward 55.70 times compared to the entirely HV experiment. In particular, the entirely HV experiment obtained the lowest value for average reward of 401.26 with a traffic volume of 1000 veh/h. Therefore, the leading AVs outperformed the entirely HV experiment regarding mobility and training policy. Moreover, the average speed and average reward decrease when the traffic volumes increase.  
Figure 10: A comparison of the entirely human-driven vehicle experiment with different traffic volumes: (**a**) Average speeds; (**b**) Average rewards.  
Figure 9: Emissions with different market penetration rates and real traffic volumes: (**a**) NOx; (**b**) HC.
## 4 Discussion
### Impact of Rebalancing Frequency on Performance  
The choice between continuous rebalancing (every 1 period) and periodic rebalancing (every 10 periods) introduces clear trade-offs in terms of performance and risk-reward dynamics. Continuous rebalancing allows agents to capture short-term market movements more effectively, potentially leading to higher returns. However, this approach also incurs increased transaction costs and slippage due to frequent adjustments. For instance, _DDPG-MLP-16_ with continuous rebalancing achieved an annualized return of 17.5% and a Sharpe ratio of 0.70. However, it also experienced a downside risk of 21.04%, reflecting the heightened costs and volatility associated with more frequent market exposure. In comparison, the same agent using a 10-period rebalancing strategy delivered a slightly lower return of 17.4%, but with a marginally lower Sharpe ratio of 0.67 and reduced downside risk of 22.47%. This suggests that less frequent trading helped mitigate exposure to transaction costs and volatility.  
Periodic rebalancing, by contrast, allowed agents to avoid some inefficiencies associated with daily slippage, leading to more favorable risk-adjusted performance. For example, _PPO-CNN-16_ with a 10-period rebalancing strategy achieved an annualized return of 18.5% and a Sharpe ratio of 0.72. In contrast, the same agent with continuous rebalancing delivered a slightly lower Sharpe ratio of 0.68, despite similar volatility levels and a marginally lower downside risk. These results highlight that while continuous rebalancing may offer more responsiveness to market changes, the higher transaction costs and slippage associated with frequent adjustments tend to erode its long-term advantage over periodic rebalancing.
# Double Dead-Time Signal Injection Strategy for Stator Resistance Estimation of Induction Machines
## 1 Introduction
Overmodulation and discontinuous PWM methods should also be considered using this method. The implementation of this method could be possible with the proper hardware and the required tuning effort. Unfortunately, this implementation becomes even more complicated in applications such as railway traction units, where all this precision hardware is not usually installed. Another DC injection strategy can be found in [10], where the authors propose some simplifications in order to compensate the non-idealities of the inverter. Unfortunately, these simplifications are not valid for medium-/large-sized machines. In addition to the DC injection strategy proposed in [23], a second-order harmonic current is injected to compensate the non-linearities. The harmonic component phase varies according to the load. The main references to estimate the temperature from resistance are summarized in Table 1.  
This paper proposes a novel injection strategy for stator resistance estimation for thermal protection purpose. A controlled DC signal is injected in the machine through the inverter, adding an offset in the control drive. This strategy uses a double dead-time injection in order to compensate the non-linear voltage drop in the inverter due to the dead-time effect. Other detected non-idealities of the inverter injection are compensated for easily with a look-up table. The proposed solution solves the limitations of other methods, as it can be applied to the whole region of speeds, and it compensates the dead-time effect. The injection strategy, the modeling math and the validation experiments are presented in this publication. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Description** & **References** \\ \hline Machine based models & Comparison between thermal model based and resistance-based methods. & [13] \\  & Rotor resistance estimated from motor model and measurements of voltages, currents, and speed. & [14] \\  & High frequency signal injection and recursive least- square algorithm. & [11] \\ AC signal injection & Low frequency signal injection. Voltage and current measurements needed. & [16] \\  & Two signals with proper frequencies injected to estimate rotor and stator resistances. & [17] \\  & High frequency signal injection. Low sensitivity to the operation point. & [18] \\  & A correction of measured DC current is proposed to compensate dead-time effect. & [9] \\  & Periodical injection of small DC current offset. Nonlinearities are compensated for. & [10] \\ DC Signal injection & Review of different methods. & [15] \\  & Additional hardware required for injection. & [19; 20] \\  & Limited to scalar control. & [21] \\  & The introduction of an offset in the controllers is proposed. Tested in several operation points. & [22] \\  & In addition to DC signal injection, a second order harmonic is injected to compensate non-linearities. & [23] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methods to estimate the temperature from resistance.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Wavelet Transform  
The Gabor transform is not adaptive, because the sliding window function is fixed once selected. On the contrary, the wavelet transform has a time-frequency window that can be adjusted; that is, the width of the window changes with the frequency. Thus, it overcomes the limitation of the Gabor transform. Wavelet transform was first put forward in 1974. The method was first used for defect detection Kaya [44] in 1995, because of the poor performance in diagonal detection using traditional edge-detection methods. Soon after, there were a lot of extensions of the method based on wavelet transform, such as the snake projection wavelet algorithm Li [68], undecimated wavelet transform algorithm [17], wavelet transform to obtain the approximate sub-image method Zhang [48], three-layer Haar wavelet feature set method Ghorai [26], discrete wavelet transform combined with adaptive local binarization method Yun [49], wavelet filtering in combination with center-surrounding difference method Xu [69] and, recently, anisotropic diffusion filter based on wavelet transform method [31]. The characteristics of wavelet transform are presented incisively and vividly in a large number of publications.
# Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control
## 2 Preliminaries
### Fuzzy Logic Systems (FLS)  
Fuzzy logic has the advantage which allows complex processes to be expressed in general terms, without the usage of complex models [22]. The main advantage of fuzzy logic systems is the use of fuzzy if/then rules. These rules embed the knowledge of the expert and can express relations among fuzzy variables using linguistic terms [22]. The generic form of a fuzzy rule is:  
\[Ru:\textit{ if }(\textbf{x}_{1}\text{ is }D_{1})\text{ and/or }(\textbf{x}_{2}\text{ is }D_{2})\text{ }\dots\text{ and/or }(\textbf{x}_{m}\text{ is }D_{m})\textit{ then }(c\text{ is }E),\]  
where \(D_{j},\ j=1,\dots,m\) is a fuzzy set of \(j\)th input, \(\textbf{x}=(\textbf{x}_{1},\textbf{x}_{2},\dots,\textbf{x}_{m})\) is the crisp input vector, \(c\) is the output variable and \(E\) is a fuzzy set defined by the expert. The operators \"and/or\" combine the conditions of the variable inputs that should be satisfied arising the firing strength \(w_{i}(\textbf{x})\) of the rule. Two types of FLS are used conventionally, the FLS type Mamdani [23] and the FLS type Sugeno [24]. The main difference between these two types is the way the crisp output is generated; the Mamadani uses a defuzzifier while the Sugeno uses the weighted average [3]. Due to Sugeno's computational efficiency (lack of defuzzifier), in this paper we are using the zero-order type Sugeno, where the consequents of the rules are constant numbers. The global output of the FLS can be calculated by the Wang-Mendel model [3].  
\[a(\textbf{x})=\frac{\sum_{i=1}^{N}w_{i}(\textbf{x})\ a_{i}}{\sum_{i=1}^{N}w_{i} (\textbf{x})} \tag{8}\]  
where \(a_{i}\) is the consequent of rule \(i\).
## 6 Simulation Results
### Network Lifetime Performance  
To complete the evaluation of the network lifetime performance, in this subsection we suppose that the time of the first dead node corresponds to the network lifetime. Figure 17 represents the relationship between the network lifetime and the number of CS measurements among non-CS, plain CS, hybrid CS, \(Dijkstra\), and our proposed algorithm, while Figure 18 demonstrates the relationship between the network lifetime and the number of sensor nodes based on the different CS schemes and our algorithm. In Figure 17, we see that the network lifetime of the three data-gathering schemes non-CS, plain CS, and hybrid CS is short, and the number of CS measurements varies from 30 to 220. However, the network lifetime of \(Dijkstra\) and our algorithm is longer than in the above three schemes. The main reason is that the two methods also utilize a sparse measurement matrix. However, the advantage of our proposed algorithm is that the projection sensor nodes select the optimal routing to the sink node, aiming to lessen the transport load of the nodes nearest the sink node. Furthermore, the improved ant colony algorithm considers the residual energy of the sensor nodes, guaranteeing the load balance of the whole network, as is observed in Figure 17. We plot Figure 18 comparing the network lifetime of  
Figure 16: Energy consumption comparison of different data-gathering schemes vs. the number of sensor nodes.  
Figure 15: Energy consumption comparison of different data-gathering schemes with a change in the number of CS measurements.  
the different schemes with a change in the number of sensor nodes. It is obvious that our proposed algorithm is better than the other techniques. In Figure 18, we see that the red bar denotes the network lifetime of our algorithm, which is greater than that found with the other algorithms. When the number of sensor nodes is 800, in terms of the network lifetime, non-CS is about 200, plain CS is about 500, hybrid CS is about 620, _Dijkstra_ is about 850 and our proposed algorithm is about 2600, respectively. Even though the number of sensor nodes is small, -100-200, the network lifetime of the other schemes is far shorter than that of our proposed algorithm.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 4 Discussion
### Limitations and Future Perspectives  
This research has some limitations. First, climate factors were limited to temperature and precipitation. As shown in [PERSON] et al. (2018) and [PERSON] et al. (2018), climate factors are complex and fickle, making the separation of natural and anthropogenic factors difficult. Second, the scale of WUE was 1 km, while the vegetation cover at this scale may be different, causing a scale effect and making the result complex. Therefore, to better evaluate ecosystem WUE and its influencing factors, more accurate climate factors and more spatially accurate data are necessary.
# Energy Efficient Multicast Communication in Cognitive Radio Wireless Mesh Network
## 6 Conclusions  
An energy efficient channel assignment and relay node selection problem for multicasting in CR-WMNs for multimedia transmission has been formulated. To achieve this objective, the number of transmissions should be reduced. For this purpose, two methods were chosen to reduce (1) the number of forwarding/relaying nodes and (2) the number of channels used for transmission. As a result, the energy consumption of a particular multimedia session is reduced. In order to select the forwarding nodes and channels for multimedia transmission, we proposed a heuristic algorithm in which, first and foremost, a bipartite graph for each forwarding node is generated. This bipartite graph consists of two sets. One set includes all the nodes to which the data must be forwarded and the node that will forward. The second set consists of all the channels available with these nodes. A graph is extracted from this in such a way that the product of the number of channels and nodes is maximized. The same method is repeated for all nodes and the node with the largest value is chosen as the forwarding node. The hyperlink is generated based on the method mentioned above. This method is repeated for all remaining nodes that must forward the data and are uncovered by this hyperlink as well as for all other levels. This process starts at the last level and goes back to the first level. As a result, a tree is formed that allows the operation of a multicast session with the minimum number of relaying nodes and channels that eventually reduces the number of transmissions and the energy consumed. The result has been compared to the flooding approach and we observed a significant drop in energy consumption and the number of channels used while varying the number of destination nodes. In the future, this work can be extended to multiple sessions running simultaneously. Furthermore, this work is limited to data packet energy consumption, which can be broadened by incorporating control information energy consumption, including spectrum sensing results communicated among neighboring nodes prior to selecting relay nodes and forwarding channels.  
[PERSON] developed the main idea of the paper and defined the problem with the assistance of [PERSON], [PERSON] and [PERSON] developed the algorithm and simulated while [PERSON] assisted with the simulation and comparative analysis. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 7: Average energy consumption versus the number of destination nodes.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 3 Methods
### Performance Indicators (POCD and POFD)  
The accuracy of the dust areas identified by satellite needs to be validated by ground-based data [15]. The AERONET site AOD measurements and the Angstrom exponent have been commonly used to verify the accuracy of dust detection algorithms [58]. There are, however, few AERONET sites located in northwestern China, providing valid observations. PM10 concentration data are an alternative for validation. This is because (1) the PM10 concentration is sensitive to the change in large particles in the near ground atmosphere, especially dust particles, and (2) the high temporal frequency of PM10 concentration observations facilitates the validation of the high temporal resolution geostationary satellite identification results. According to the Ecology and Environment Department's Technical Provisions on Dust Weather Classification, a PM10 concentration greater than 600 ug\(\cdot\)m\({}^{-3}\) per hour is considered as dust weather [59]. Thus, any National Environmental Monitoring Station measurement with a PM10 concentration greater than 600 ug\(\cdot\)m\({}^{-3}\) was classified as a dusty observation.  
Figure 2: Frequency distribution of the BTD (**a**), NDDI (**b**), and IDDI (**c**) values for dust events.  
The probability of the correct detection (POCD) and the probability of false detection (POFD) were selected to indicate the accuracy of the dust detection results [33; 38]:  
\[\text{POCD (\%)}=(\text{YY})/(\text{YY}+\text{YN}) \tag{3}\]  
\[\text{POFD (\%)}=(\text{NY})/(\text{YY}+\text{NY}) \tag{4}\]  
where YY represents the number of matched points where the PM10 concentration is greater than 600 \(\text{\mu g\text{-}m}^{-3}\) and the AGRI dust detection result indicates 'dust', YN represents the number of matched points where the PM10 concentration is greater than 600 \(\text{\mu g\text{-}m}^{-3}\), while the AGRI detection result indicates 'no dust'. NY represents the number of matched points where PM10 is below 600 \(\text{\mu g\text{-}m}^{-3}\), while the AGRI detection result indicates 'dust'.
# Novel Beam Scan Method of Fabry-Perot Cavity (FPC) Antennas
## 2 Materials and Methods
### Operation Principle of the Proposed Antenna
Since both the reflection and transmission coefficients of the PRS are highly dependent on the incident angle and polarization [20], it is very important to consider the incident conditions when determining the coefficients, which will be further elucidated in Section 2.3. In the previous paragraphs, we described how the phase-uncontrollable rays passing directly through the PRS are considered for building a target wavefront; the phase of the most influential rays (blue-dotted line in Figure 2b) is selected as the reference. However, another phase of two rays (red dashed lines in Figure 2b) is still out of control, which may adversely affect beam-scanning. Thus, their transmission through the PRS should be suppressed, which is achievable by increasing the reflection magnitude of the PRS cells. On the other hand, at the same time, high reflection magnitudes of the PRS make impedance matching difficult for an FPC antenna. Therefore, the reflection magnitudes of the PRS cells, where the phase-uncontrollable rays pass through, should be determined based on a trade-off relationship between the beam-scan ability and the good impedance matching property. In this work, when the corresponding reflection magnitude is 0.86, the proposed antenna provides a well-scanned beam in the target direction with acceptable impedance matching performance.
# Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks
## 1 Introduction
Using the max-flow and min-cut theorem of multicast network coding [37; 38], for sufficiently large \(q\), this sufficient condition for the existence of a classical network code on \(G^{\prime}\) can be replaced by the condition that the minimum-cut between the source node \(s\) and a target node \(t_{i}\) is no less than \(r\) for all \(i\). An outline of our protocol is as follows:  
* We create two (three) asymmetric optimal UQCs of an input state with an ancilla system at a source node. * We measure the ancilla system and send the measurement outcomes to the target nodes. * We compress the whole \(d^{2}\) (\(d^{3}\))-dimensional system into a \(d\)-dimensional system. * We transmit the resulting state to two (three) target nodes using [PERSON] et al. 's multicast quantum network coding [44]. As a result, a GHZ-type state is shared among target nodes. * We reconstruct the asymmetric optimal UQCs of the input state from the GHZ-type state using LOCC with a small amount of entanglement among the target nodes and the measurement outcomes sent from the source node. Using the above protocol, we can multicast asymmetric optimal clones from one source node to two (three) target nodes (Figure 2). Here, we note that although the above outline of our protocol is almost the same as the protocol of [PERSON] et al. of symmetric optimal UQCs, the detail of each part is completely different. To see the efficiency of our protocol, it is convenient to consider the multicast butterfly network given on the left-hand side of Figure 1. When each edge corresponds to a \(q\)-dimensional noiseless quantum channel and the target nodes \(t_{0}\) and \(t_{1}\) share 2 ebit, our protocol distributes asymmetric optimal QCMs of \(q^{2}\)-dimensional input states. On the other hand, we can easily see that a conventional protocol without network coding based on entanglement swapping (or quantum repeater) only distributes asymmetric optimal QCMs of \(q\)-dimensional input states at most. Hence, the rate of our protocol is double that of the conventional protocol. We further show that the above protocol can be used for efficient preparation of quantum asymmetric telecloning [99,100] over a quantum network. The rest of the paper is organized as follows: We explain the asymmetric optimal universal quantum cloning and the quantum multicast network coding protocol of [PERSON] et al. in Section 2. We present all the results in Section 3, where a protocol for multicasting \(1\to 2\) asymmetric optimal clones is given in Section 3.1. We also present a protocol for multicasting \(1\to 3\) asymmetric optimal clones in Section 3.2. Finally, we give a discussion and summary in Section 4.
## 4 Renewable Carbon Sources for the Biosynthesis of 3-HP
### Microbial Production of 3-HP from Glucose or Xylose  
Most commercially available glucose is produced by hydrolyzing corn starch, which makes it a cheap and abundant resource. Most microorganisms can utilize glucose for the growth and production of various fermentation products, and the conversion pathway from glucose to 3-HP is redox neutral [11]. Therefore, the production of 3-HP from glucose as a common carbon source has been widely studied. In recent years, to avoid competition with humans for food, researchers have developed microbial cell factories that use lignocellulosic biomass (LCB) derivatives. The fermentable sugars formed by the hydrolysis of LCB are mainly glucose and xylose [67], so it is necessary to construct microbial cell factories for the synthesis of 3-HP that use xylose as the substrate. In contrast to glucose, many microorganisms are unable to use xylose naturally, which requires the introduction of heterologous xylose metabolic pathways. Moreover, recombinant strains tend not to grow well on xylose, which makes it more attractive to produce 3-HP using both glucose and xylose as mixed substrates.
# Applications of Magnesium and Its Alloys: A Review
## 3 Magnesium and Its Alloys as an Engineering Material
### Engineering Applications
#### 3.2.1 Aerospace Applications
For example, recent progress in applied magnesium research focused on addressing its ignition and flammability issues. These efforts cumulated to the discovery of less-flammable magnesium alloys, which opened the possibilities for them to be incorporated in passenger cabins. Accordingly, the Society of Automotive Engineers revised its standards, specifically AS8049C, in 2015 to allow magnesium alloys for aircraft seat construction if it complies with the Federal Aviation Administration requirements [24; 51]. Another area with significant progress addresses magnesium's meager mechanical strength for engineering applications. Progress in this area is driven by the potential benefits of using magnesium in weight-sensitive applications, such as aircraft [51]. As a result, it led to the introduction of high-performance magnesium alloys like the Elektron 21, which has already received an Aerospace Material Specification (AMS 4429), and Elektron 675, which is reported to exhibit twice the strength of aluminum while only bearing half the weight of titanium [53].