esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
All data were expressed as mean and standard deviation (SD) for continuous variables or numbers (percentages) for categorical variables. Detection accuracy (Ac), Sn, and Sp for all participants were computed according to the severity of OSAHS on the basis of ACP thresholds. Following the PSG diagnosis, an AHI over the severity threshold was defined as a true positive, and an AHI under the threshold was defined as a true negative. Ac was represented by the ratio of the total number of correct classifications over the total number of subjects. Sn was represented by the ratio of the number of positive measurements over the number of counts of true positives, and Sp was represented by the ratio of the number of negative measurements over the number of true negatives. Statistical significance was set at \(p<0.05\). Statistical analysis was performed using SPSS version 18.0 (SPSS Inc., Chicago, IL, USA).
# Wind Field Characteristics of Complex Terrain Based on Experimental and Numerical Investigation
## 5 Wind Tunnel Test Results
### Turbulence Intensity  
Turbulence intensity can quantify the severity of velocity fluctuations. As shown in Figure 14, the turbulence intensity gradually decreases with the increase in height. Compared with the turbulence intensity 0.12 at 10 m height recommended by the specification, the turbulence intensity at most measuring points is large. The turbulence intensity of P2 is the smallest of all measuring points at 40\({}^{\circ}\), while the turbulence intensity of P5 on the hillside is larger. This is related to the windward slope and leeward slope of the two points. The turbulence intensity of P1 and P3 in a flat area is also different due to the different disturbance degree of the incoming flow. In addition, the turbulence intensity of the measurement points presents the stratification phenomenon. Below 200 m, the turbulence intensity is the largest at P4 in the valley; at 200 m or more, the turbulence intensity at P1, where the inflow is last to reach, is the largest.  
It can be seen from Figure 14 that the turbulence intensity profiles of each measuring point are relatively close. The turbulence intensity of P2 is high due to the existence of a small mound in front of P2. P5 is located on the leeward slope, so the turbulence intensity near the ground is relatively large. The turbulence intensity of P3 is small because the incoming flow has developed over a certain distance. At the same time, the turbulence intensity of P1 in the flat area is slightly higher. In addition, the turbulence intensity in the valley is at a medium level.  
Figure 14: Profiles of turbulence intensity at measurement locations. (**a**) 40\({}^{\circ}\) (**b**) 90\({}^{\circ}\) (**c**) 270\({}^{\circ}\).  
In Figure 14c, the turbulence intensity of P3 in the flat area is larger, which is due to the large fluctuation in wind speed here due to the bulge in front of P3. The turbulence intensity of P1 is smaller than that of P3. It can be observed that the turbulence intensity at the lowest point of P2 reaches 0.4. This may be because there is also a height dividing point for the air separation at the top of the mountain in front of P2. At this height, the air flow is chaotic and the turbulence intensity increases. Above this height, the turbulence intensity of P2 decreases because the air flow separation has ended. The turbulence intensity of P5 is the lowest because it is close to the top of the mountain. Although the air flow is climbing, it has stabilized. Similarly, the turbulence intensity in the valley is at a medium level.  
It can be seen from the above test results that the turbulence intensity is controlled by the incoming wind direction and local terrain. If the inflow develops stably, the turbulence intensity is small, and if it is in a disturbed state, the turbulence intensity is large.
# Enhancement of Wheat Flour and Dough Properties by Non-Thermal Plasma Treatment of Wheat Flour
## 2 Materials and Methods
### Experimental Setup  
The experimental setup for a batch treatment of flour samples using NTP, as shown in Figure 1, consists of three parts: (1) a plasma generator (HVG 80-3000, company Diener, Germany) [48]; (2) a cylindrical, rotational reactor, for the treatment of flour samples with DBD plasma; and (3) a control and data acquisition system (DAQ) system.  
DBD plasma type was produced under atmospheric conditions using a high-voltage plasma generator (power: 120 W, frequency: 80 kHz, voltage: 7 kV). Air was used as plasma gas. The reactor has a capacity between 50 and 500 g of flour. It is designed as a rotational cylindrical vessel, made of stainless steel, and contains two concentric electrodes (Figure 2): (1) an outer (ground) electrode, which is in the form of a hollow, rotating cylinder (\(D\) = 150 mm, \(L\) = 300 mm, wall thickness = 2 mm) made of stainless steel and mounted axially on a rotating shaft. A dielectric barrier (thickness = 2 mm), made of Plexiglas (Polymethyl-methacrylate, PMMA), was attached to the inner side of the outer electrode. To ensure a uniform mixing during the NTP treatment, baffles (made of plastic, height = 30 mm and thickness = 3 mm) were placed on the inner wall of the outer cylinder. The outer electrode is connected to a motor, in order to provide its rotation; and (2) an inner (high-voltage) electrode (\(D\) = 50 mm, made of stainless steel), which is stationary and is positioned coaxially to the outer electrode. In order to reduce the electric power needed to induce DBD, a row of thin pins (made of aluminum, height = 43 mm and thickness = 5 mm) are attached to the high-voltage electrode, with a discharge gap of 2 mm between the pin tips and the dielectric layer (Figure 2). The reactor is closed with a cap from one side, and the other side is left open to the atmosphere.  
Figure 1: Experimental setup for Non-thermal plasma (NTP) treatment of wheat flour.  
At the beginning of each experiment, flour is added to the reactor and partially fills the annular gap between the electrodes. During the treatment, the reactor rotates at a constant speed of 10 RPM. Although the reactor is open to the environment, a low rotational speed was selected to avoid the formation of flour clouds that might lead to an explosive atmosphere. Since plasma-generated reactive species, including ozone, have very short half-lives, the described configuration enables a direct contact between these species and the target flour. After the treatment, flour samples were analyzed using a set of analytical techniques which will be described further in the text.
## 3 Flexible Demand Resources (FDRs) and Demands Response Program (DRPs)
### Price Elasticity of Demand and Load Modeling  
A change in price of a service will have an impact on the amount of quantity demanded. For instance, a change in the price of electricity (\(\partial E_{std}^{pr}(i)\)) in the \(i\)th period will result in a change of the load demand (\(\partial L_{D}(j)\)) in the \(j\)th period either by increasing or decreasing the load demand. Thus, a change in electricity price during the single period \(i\)th affects the load demand during all the periods (\(T\)). The price elasticity of demand (\(PE_{\phi(i,j)}\)) gives a measure of the responsiveness at which the end-user time-shift their energy consumption patterns with respect to change in electricity as shown below:  
\[PE_{\phi(i,j)}=\frac{E^{pr}(i)}{L_{D}(i)}.\frac{\partial L_{D}(i)}{\partial E ^{pr}(i)};\ \ \forall i,j\in T \tag{9}\]  
The price elasticity of demand entails self and cross-elasticity; the self-elasticity defines the sensitivity of load demand with respect to price within the same pricing interval (single period elasticity) and usually has a negative value implying some proportion of the load cannot be transferred from one period to another. On the other hand, cross-elasticity (\(PE_{\phi(i,j)}\)) defines the load demand sensitivity of the (\(i\)th) pricing period in response to the electricity price variation in the (\(j\)th) pricing period (multi-period elasticity) and usually has positive value implying some proportion of the load demand is shiftable to another period. The cross-elasticity of load demand is given by [50];  
Figure 3: Flowchart for the proposed integrated system planning framework.  
\[PE_{\phi(i,j)}=\frac{E_{std}^{pr}(i)}{L_{D}(j)}.\frac{\partial L_{D}(j)}{\partial E ^{pr}(i)};\;\;\forall i,j\in T \tag{10}\]
# Effects of Freeze-Thaw and Wet-Dry Cycles on Tension Stiffening Behavior of Reinforced RAC Elements
## 3 Results and Discussion
### Effects of freeze-thaw and wet-dry Aging on Bond Behavior of Reinforced RAC Elements  
As already mentioned, the experimental results of the tension stiffening elements were simulated using a finite difference method in order to estimate the steel-to-concrete bond-slip law strength (\(\tau_{\text{b}}\) in Table 5).  
In the non-degraded samples, it was observed that the higher the amount of attached mortar to the aggregate, the lower the value of the bond strength. For example, for REF(FT150), the natural concrete achieved a \(\tau_{\text{b}}\) value equal to 17.4 MPa, while the recycled  
Figure 5: Main properties of crack analysis in the freeze-thaw and wet-dry durability studies: (**a,b**) Initial crack spacing (at 500 \(\mu c\)); (**c,d**) Final crack width (at 2500 \(\mu c\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mixtures**} & \multirow{2}{*}{**ID**} & \(\boldsymbol{\tau_{\text{b}}}\) & \multirow{2}{*}{**ID**} & \(\boldsymbol{\tau_{\text{b}}}\) \\  & & **(MPa)** & & **(MPa)** \\ \hline \multirow{4}{*}{C35-NAT} & REF(FT150) & 17.4 & REF(WD25) & 17.4 \\  & FT150 & 16.5 & WD25 & 16.7 \\  & REF(FT300) & 17.9 & REF(WD50) & 17.9 \\  & FT300 & 16.0 & WD50 & 16.5 \\ \hline \multirow{4}{*}{C35-L-C1} & REF(FT150) & 16.1 & REF(WD25) & 16.1 \\  & FT150 & 15.2 & WD25 & 15.3 \\ \cline{1-1}  & REF(FT300) & 16.6 & REF(WD50) & 16.6 \\ \cline{1-1}  & FT300 & 14.1 & WD50 & 14.7 \\ \hline \multirow{4}{*}{C35-D-C1} & REF(FT150) & 16.7 & REF(WD25) & 16.7 \\ \cline{1-1}  & FT150 & 15.7 & WD25 & 16.0 \\ \cline{1-1} \cline{2-5}  & REF(FT300) & 17.1 & REF(WD50) & 17.1 \\ \cline{1-1}  & FT300 & 14.7 & WD50 & 15.2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of freeze-thaw and wet-dry aging on bond strength of reinforced RAC.  
mixtures registered lower values equal to 16.7 and 16.1 MPa (C35-D-C1 and C35-L-C1, respectively). As expected, the results showed that this property is directly influenced by the hydration time. For C35-NAT mixture, the \(\tau_{b}\) increased from 17.4 to 17.9 MPa and similar trend was observed for mixtures with recycled aggregates.  
Regarding the degradation processes, up to 150 freeze-thaw cycles, the natural and recycled mixtures appear to behave in the same way, reaching similar decrease values from 5.2% to 6.2%. However, by increasing the level of aging up to 300 cycles, the rate of drop in bond increases for recycled concrete, while the natural mixture maintains the same rate. Consequently, at the end of 300 cycles, C35-NAT was represented by a smaller decrease of 10.9%, while the concrete produced with RCA from demolition waste reached 14.0% and the concrete produced with RCA from laboratory waste reached the highest impact of 14.8%.  
Therefore, the presence of RCAs in concrete promotes a greater impact on the steel-concrete bond performance of structural elements after degradation processes than for concrete produced with only natural aggregate.
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 5 Results
### Revolutionizing Administrative Efficiency  
The proposed system has improved administrative functions by incorporating workflow automation, flexible decision-making, and smooth inter-departmental collaboration. By automating standard administrative duties, bureaucratic delays have significantly diminished, guaranteeing that permit approvals, budget evaluations, and compliance verifications achieve a 95% success rate. The TreasuryAgent has enhanced the allocation of financial resources, improving budget efficiency by 25% and guaranteeing effective distribution of funds across government operations. Additionally, the MayorAgent and SecretariatAgent have attained a 96% success rate in enforcing policies and ensuring regulatory compliance, further enhancing the accuracy of decision-making.  
Utilizing the JADE along with DRL, the system enables organized and smart task execution, permitting agents to react dynamically to real-time information. JADE's FIPA-ACL messaging protocol improves interoperability, facilitating seamless cooperation among various municipal departments, such as the Treasury, the Secretariat, and the Event Management department. This guarantees that agents can work together fluidly to reach shared goals, like prioritizing urgent tasks and reallocating resources in real time. The system's flexibility allows for easy scalability, expanding to support wider governmental functions without needing significant redesigns. Moreover, regular procedures like permit approvals and resource distribution have been optimized, minimizing manual involvement and enabling public officials to concentrate on strategic governance. This has resulted in a 40% decrease in human workload, facilitating more efficient and initiative-led decision-making.  
The system's adaptability and resilience have been enhanced by DRL's ability to modify policies according to feedback, allowing it to better respond to abrupt shifts in regulations or resource availability. These innovations collectively transform governmental functions, optimizing workflows, boosting decision-making, and increasing the overall efficiency and flexibility of public administration.
# Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks
## 4 Discussion
### Comparison with a Conventional Schemes  
In this subsection, we compare our protocol with a conventional protocol without network coding based on entanglement swapping (or quantum repeater [25; 26; 28; 32]), where maximally entangled states are distilled between a source node and a target node; then, asymmetric optimal UQCs are sent by teleportation. For simplicity, we concentrate on the multicast butterfly network, which is given as an undirected underlying graph of the directed graph on the left-hand side of Figure 1. Suppose each channel on the quantum network can transmit a \(q\)-dimensional quantum system in a single session, where \(q\) is a prime power, and the target nodes share 2 ebit of entanglement. Then, since the rate of the linear solvable network code given on the left-hand side of Figure 1 is 2, our protocol can multicast an asymmetric optimal UQC of a \(q^{2}\)-dimensional unknown input state from source node \(s\) to target nodes \(t_{0}\) and \(t_{1}\).  
When we do not use the network coding scheme, a conventional scheme may be given as follows: First, maximally entangled states are distilled between source node \(s\) and target nodes \(t_{0}\) and \(t_{1}\). Then, quantum teleportation is implemented to send an asymmetric optimal UQC from source node \(s\) to target nodes \(t_{0}\) and \(t_{1}\). Since there are only two channels connected to the source node, there is no way to share more than \(2\log_{2}q\) ebit between the source node and the other nodes of the networks [25; 26; 28].  
Hence, the best strategy in the first step is that \(\log_{2}q\) ebit is shared between the source node share and target node \(t_{0}\), and other \(\log_{2}q\) ebit is also shared between the source node share and target node \(t_{1}\). Thus, the source node can only teleport an asymmetric optimal UQC of a \(q\)-dimensional unknown input state to the target nodes. Therefore, the rate of the conventional scheme is just a half of our scheme. We note that 2 ebit of entanglement shared between the target nodes is not used in this conventional scheme.
# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
###### Abstract  
6G vehicle-to-everything (V2X) communication will be combined with vehicle automatic driving technology and play an important role in automatic driving. However, in 6G V2X systems, vehicle users have the characteristics of high-speed movement. Therefore, how to provide stable and reliable wireless link quality and improve channel gain has become a problem that must be solved. To solve this problem, a new multi-user scheduling algorithm based on block diagonalization (BD) precoding for 6G ultra-massive multiple-input multiple-output (MIMO) systems is proposed in this paper. The algorithm takes advantage of the sensitive nature of BD precoding to channel correlation, uses the Pearson coefficient after matrix vectorization to measure the channel correlation between users, defines the scheduling factor to measure the channel quality according to the user noise enhancement factor, and jointly considers the influence of the correlation between user channels and channel quality, ensuring the selection of high-quality channels while minimizing channel correlation. Simulation results show that compared with the multi-user scheduling algorithm based on subspace correlation, condition number, and geometric angle, the proposed algorithm can obtain higher user channel gain, effectively reduce the system bit error rate, and can be applied to 6G V2X communication.  
6G V2X precoding; matrix correlation; noise enhancement factor; multi-user scheduling 2021216742[[https://doi.org/10.3390/s21206742](https://doi.org/10.3390/s21206742)]([https://doi.org/10.3390/s21206742](https://doi.org/10.3390/s21206742))  
1
## 3 Problem Formulation
### Problem Setup  
The TWR system presented in [5] enables localization, synchronization, and communications between a network of multi-antenna users. For simplicity, we limit the scope of this discussion to the interactions between just two users labeled \(A\) and \(B\), as depicted in Figure 1. In this example, user \(A\) is a stationary ground station and node \(B\) is a UAV moving with some velocity \(\vec{v}\) and acceleration \(\vec{a}\).  
Each transmission takes some time \(\tau\) to propagate between these users. We define the relative velocity and acceleration of one node with respect to the other in the direction of the line joining them as radial velocity (\(\hat{\tau}\)) and acceleration (\(\hat{\tau}\)). Users \(A\) and \(B\) are driven by independent clocks, which at any given time read \(t_{A}\) and \(t_{B}\). The relative time offset \(T\) is the time difference between the two clocks, \(T=t_{A}-t_{B}\). By convention, a positive \(T\) denotes that clock \(B\) displays an earlier time than clock \(A\). The relative frequency offset and drift between these two clocks are denoted \(\hat{T}\) and \(\hat{T}\), respectively. These interactions and variable definitions are summarized in Figure 2.
## 3 Results
### Operator
#### 3.4.3 Automation and Human-Robot Collaboration  
Human-Robot Collaboration (HRC) expects to obtain the best of both human and automation worlds. [PERSON] et al. developed a methodology for transitioning from manual to HRC assembly: (1) operation decomposition, (2) resource evaluation, (3) resource allocation, (4) collaborative assembly operation. [207]. However, HRC brings quality and reliability problems associated with robots and human operators separately, on top of their interactions, which needs to be addressed when establishing Quality Control [208]. Additionally, collaborative work with a robot has been found to cause stress in operators [209]. Moreover, operator safety remains a key concern for HRC systems. A safety strategy for HRC should consider the following key design areas: Human-Robot Collaboration spaces, robot safety systems, computer vision monitoring of safety conditions, and an operation control system that coordinates human-robot interaction [210]. Regarding the vision monitoring of safety conditions, [PERSON] et al. used depth sensors so that robots avoid collisions with operators [211]. Another way of ensuring human operator safety in HRC would be the \"safety bubble\" concept, which is based on live data sharing between reconfigurable assembly systems [38].  
**In conclusion**, Human-Robot Collaboration aims to obtain systems that are both flexible and highly productive. However, quality and safety concerns are yet to be solved.
## 2 Modeling and Problem Statement
### Composite Load Model  
The composite load model is used to accurately reflect the load characteristics of the practical system. The total real and reactive power are determined as:  
\[P=P_{s}+P_{m} \tag{8}\]  
\[Q=Q_{s}+Q_{m} \tag{9}\]  
where \(P_{s}\) and \(Q_{s}\) are the active and reactive power of static load, and \(P_{m}\) and \(Q_{m}\) are the active and reactive power of dynamic load portion. The active and reactive power of the static load model part can be expressed as:  
\[P_{s}=P_{Z}\Bigg{[}a_{p}\bigg{(}\frac{V}{V_{0}}\bigg{)}^{2}+b_{p}\bigg{(}\frac {V}{V_{0}}\bigg{)}^{1}+c_{p}\Bigg{]} \tag{10}\]  
\[Q_{s}=Q_{Z}\Bigg{[}a_{q}\bigg{(}\frac{V}{V_{0}}\bigg{)}^{2}+b_{q}\bigg{(}\frac {V}{V_{0}}\bigg{)}^{1}+c_{q}\Bigg{]} \tag{11}\]  
\[a_{p}+b_{p}+c_{p}=1-K_{pm}\big{(}K_{pm}=P_{motor}/P_{m0}\big{)} \tag{12}\]  
\[a_{q}+b_{q}+c_{q}=1-K_{qm}\big{(}K_{qm}=Q_{motor}/Q_{m0}\big{)} \tag{13}\]  
where \(P_{Z}\) and \(Q_{Z}\) are the real and reactive power of the load, respectively; \(P_{motor}\) and \(Q_{motor}\) are the initial real and reactive power of the IM; \(P_{m0}\) and \(Q_{m0}\) are the initial real and reactive power of the IM prior to the disturbance; \(K_{pm}\) and \(K_{qm}\) are the proportion of the IM in the total active and reactive load.
## 5 Case Study
### Validation of the SGCC Practical Criterion  
Next, the practical criterion of the SGCC was verified. As mentioned in Section 4, the system can be judged to be unstable if the bus voltage drops below 0.8 pu and remains low for more than 10 s. To verify this conclusion, a short-circuit fault was applied to the Lucheng bus, the Lucheng bus voltage dropped to 0.7 pu, and the fault lasted for 10 s, as Figure 9a shows. The slip curves of the induction motors are shown in Figure 9b.  
Figure 8: Simulation results to verify the WSCC practical criterion: **(a)** Lucheng bus voltage curve (condition 1), **(b)** motor slip curves (condition 1); **(c)** Lucheng bus voltage curve (condition 2); **(d)** motor slip curves (condition 2); **(e)** Lucheng bus voltage curve (condition 3); **(f)** motor slip curves (condition 3).  
As can be seen from Figure 9b, the motor connected with the Pupeng bus stalled. In fact, the Chuxiong power grid has the possibility of instability only when the Lucheng bus voltage is lower than 0.73 pu. To be exact, the Chuxiong power grid is unstable if the bus voltage drops below 0.73 pu and remains low for more than 10 s. However, a certain stability margin is usually reserved in practical engineering. Therefore, the conclusion that the system can be judged as unstable if the voltage drops below 0.8 pu and remains low for more than 10 s is considered reasonable.  
On the contrary, if the voltage remains lower than 0.8 pu for less than 10 s, the system cannot be judged to be stable. As Figure 9c shows, the Lucheng bus voltage dropped to 0.6 pu and remained low for 1.5 s. Under this disturbance, the slip curves of the induction motors are shown in Figure 9d. According to Figure 9c,d, the system may be unstable even if the voltage sag duration is less than 10 s.