esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Model
### Electric Vehicles  
The multi-microgrid system considered in this paper is a living-working combined system. Residents are all staff from office building areas. Thus, all the EVs considered in this paper are commuting cars. They park in the RMG to charge during the knock-off hours from 19:00 to 8:00 and park in the OBMG during working time from 9:00 to 18:00. Considering the random behaviors of the staff, the arrival and departing times of EVs were derived by Monte Carlo experiment and random variables were then added.  
Energy variation of EV batteries came from two aspects. One is the energy consumption of EVs for their driving on the commuting route, and the other is charging and discharging through EVBCDIs. State of capacity (SOC) was used to present the energy remaining in the mobile batteries. During the driving distance, the SOC of PEVs could be calculated as (1).  
\[SOC_{i,j}^{EV}(t)=SOC_{i,j}^{EV}(t-d_{i,j})-D_{i,j}^{EV}\cdot C_{d} \tag{1}\]  
where \(SOC_{i,j}^{EV}(t)\) is the SOC of the \(j\)th EV in the \(i\)th microgrid in the \(t\)th hour, and \(d_{i,j}\) is the driving time. \(C_{d}\) is the energy consumption rate of EV's driving, and \(D_{i,j}^{EV}\) is the driving distance. Since EV's driving distances are slightly different due to each owner's random behaviors, the distance was also generated from Monte Carlo experiment, and can be demonstrated as Equation (2) [19].  
\[D_{i,j}^{EV}=E(D_{i,j}^{EV})+k_{i,j}\cdot\sigma_{i,j}^{EV} \tag{2}\]  
where \(D_{i,j}^{EV}\) is the actual value of \(D_{i,j}^{EV};E(D_{i,j}^{EV})\) is the mathematical expectation of the driving distance; \(k_{i,j}\) is a random parameter and is normally distributed; \(\sigma_{i,j}^{EV}\) is the standard deviation of the driving distance. Notice that according to the statistical data of automobile's driving, the driving distance is also normally distributed.  
When parking in the OBMG or RMG, the SOCs of EVs were determined only by the charging and discharging behaviors. Considering the self-discharging of EV batteries, the SOC can be calculated according to Equation (3).  
\[\begin{cases}SOC_{i,j}^{EV}(t)=SOC_{i,j}^{EV}(t-1)\cdot(1-\delta_{SD})\\ -\rho_{i,j}^{EV}(t)\cdot\Delta t\cdot\eta_{C}&if\;P_{i,j}^{EV}(t)<0\\ SOC_{i,j}^{EV}(t)=SOC_{i,j}^{EV}(t-1)\cdot(1-\delta_{SD})\\ -\rho_{i,j}^{EV}(t)\cdot\Delta t/\eta_{D}&if\;P_{i,j}^{EV}(t)>0\end{cases} \tag{3}\]  
where \(P_{i,j}^{EV}(t)\) is the charging and discharging power of the \(j\)th EV in the \(i\)th microgrid in the \(t\)th hour, while \(P_{i,j}^{EV}(t)<0\) for charging and \(P_{i,j}^{EV}(t)>0\) for discharging. \(\delta_{SD}\) is the self-discharging rate. \(\eta_{C}\) and \(\eta_{D}\) are the charging and discharging efficiency rates, respectively.
# Similarity Model Test on Rainfall Scouring Mechanism of High-Speed Railway Subgrade Slope
## 2 Materials and Methods  
### The Materials of the Model Test  
The strongbox shown in Figure 1, used for the model test, has dimensions of \(1800\) mm \(\times\)\(800\) mm \(\times\)\(1400\) mm in length, width and height, respectively. Referring to the \"Code for Design of Railway Earth Structure\" (TB10001-2016) [6], grading curves of coarse-grained soil, used to study the impact of particles of less than 1 mm, are divided into two groups, S1 and S2, as shown in Figure 2. According to the grading characteristics of coarse-grained soil, model test fillers, which are screened and then grouped for dyeing treatment, are configured, as shown in Figure 3. The soil particle size groups 0.075-1 mm, 1-2.5 mm, 2.5-5 mm, and 5-10 mm were stained and labeled as R1, R2, R3, and R4 in black, green, red, and gray, respectively. The percentages of R1, R2, R3, and R4 consist with the grading curve in Figure 2.  
The physical and mechanical properties of the soils for the model test are summarized in Table 1[28]. According to similarity theory, the physical size of the subgrade slope is scaled to \(1/5\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Soil**} & **Saturated Permeability** & **Dry Bulk** & \multirow{2}{*}{**Compactness**} & **Cohesion** & **Internal Friction** \\  & **Coefficient** & **Density** & **(kN/m\({}^{\prime}\))** & & **Angle (’)** \\ \hline S1 & 1.318 & 24.5 & 0.95 & 28 & 37 \\ S2 & 1.620 & 21.6 & 0.95 & 19 & 37 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The physical and mechanical properties of soils for the model test.  
Figure 1: The model test system: (**a**) details of the test system, (**b**) rainfall equipment.  
Figure 3: Dyeing treatment: (**a**) Grouping, (**b**) Mixing.  
Figure 2: The grading curve of coarse-grained soil.  
When rainfall converges on the subgrade slope for scouring erosion, the time required for the rainwater to flow from the top to the slope foot is relatively short, usually minutes. Thus, mm/min is utilized as the dimension. The electronic flow meter controls the artificial rainfall change between light rain, moderate rain, and rainstorm, with intensities of 0.5 mm/min, 1.0 mm/min, and 1.5 mm/min, respectively [9]. The groups for the model test are summarized in Table 2.
# Measurement of Heat Flow Transmitted through a Stacked-Screen Regenerator of Thermoacoustic Engine
## 4 Results and Discussion
### Discussion
Figure 6 presents a comparison of the effective radii in Equation (15) and that obtained with the experimental acoustic fields of #30 and #80 mesh screens given in Figure 4. We have determined the experimental effective radius shown by symbols in the way that the difference of the calculated acoustic powers from the measured acoustic powers at two ends of the regenerator [16] is minimized. Because the experimental effective radius reflects all mesh screens stacked in the regenerator holder, the kinematic viscosity \(v\) in Equation (15) is evaluated at average temperature \((T_{\mathrm{R}}+T_{\mathrm{H}})/2\). In addition, the horizontal axis represents the spatial average velocity amplitude \(|V_{\mathrm{m}}|\) over the regenerator area. The experimental effective radii were obtained with error \(<8\%\) between the measured and calculated acoustic powers. It apparently decreases with velocity \(|V_{\mathrm{m}}|\). Therefore, the acoustic field at the sides of the regenerator is better estimated using effective radius \(r_{\mathrm{eff}}\) than \(r_{0}\), when the velocity amplitude is finite. Figure 7 presents comparison of the heat flow rates \(|Q|\) obtained by inserting \(r_{0}\) and \(r_{\mathrm{eff}}\) into Equations (3) and (4). Results show that \(|Q|\) obtained with \(r_{\mathrm{eff}}\) yields a faulty result because it goes away from that obtained with \(r_{0}\) with increasing \(|\langle V\rangle|\). This result indicates that \(r_{0}\) should be used instead of \(r_{\mathrm{eff}}\) for better estimation of heat flow. Other earlier studies [29; 31] have demonstrated that two effective radii are necessary to explain the viscous behavior and the thermal behavior in tortuous porous media because the viscous effects are governed by narrower regions of the channel, whereas the thermal effects are determined by wider regions [32; 33]. Our experimentally obtained results provide another example representing the need of the effective thermal radius, in addition to the effective viscous radius. Figure 5: Relation between \(\mathrm{Im}\left[g_{\mathrm{D}}\right]\) and \(r_{0}/\delta_{a}\). The curve of \(\mathrm{Im}\left[g_{\mathrm{D}}\right]\) was calculated from the thermoacoustic theory. The symbols represent the experimental values evaluated from Equation (14) with measured data and the thermal properties of the gas at the hot end of the regenerator (\(T_{\mathrm{H}}=543\) K).
# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value
(3) Communication challenges, such as efficient communication algorithms toward limited resources, centralized and decentralized data-fusion-based edge-AI system architecture, transmission clock synchronization, and transmission security should be solved for data transmission [46];
* _Cognitive computing and data intelligence._ After ensuring safe and effective storage of the data obtained from the edge network, the cognitive computing capability of the big-data system of the intelligent IoT is reflected in a combination of edge computing and cloud computing. Cognitive computing provides users with data intelligence, including automatic labeling of data, fine-grained quality assessment, multi-dimensional cross-domain semantic understanding, multi-model real-time scheduling, lightweight, efficient and secure parallel computing, data security, and privacy protection. This requires the system to provide large amounts of data, deploy machine-learning algorithms, realize reliable industry models, and so on. Traditional data-analysis technologies include sketching and streaming, dimensionality reduction, numerical linear algebra, and compressed sensing for sensor data. The emergence of AI technology has also prompted the development of more advanced data-fusion algorithms, including data association, state estimation, decision fusion, covariance consistency, and distributed data fusion [43; 47]. In summary, realizing cognitive computing and data intelligence needs to address the following challenges: (1) Data integration, storage, and synchronization. (2) Data security and privacy. (3) Data mining and information value enhancement. (4) Multidimensional data fusion and decision fusion [48];
* _Application and service intelligence._ After the system has a large amount of data and in-depth analysis results, the information needs to be fed back to the user. These applications include data visualization, personalized service and recommendations, information distribution and sharing, big-data control platforms, intelligent authority, and exploratory analysis. With the development of human-computer interaction technology, the multidimensional visualization of data seems to be particularly important in heterogeneous intelligent IoT applications, which is a service that is very friendly to users. Moreover, reactions, experience, feelings, opinions, suggestions, and other data generated by users in the service process can be used as a supplement to the system's perception data, and they are also the feedback of the system's computing intelligence. In this way, the three systems--infrastructure, AI, and visualization--are closely integrated, and a virtuous cycle is formed [49]. To realize intelligent applications and services, the following challenges need to be addressed: (1) heterogeneous smart IoT applications, (2) multi-dimensional visualization fusion for data display, and (3) human-enhanced fusion.
## 1 Introduction  
The need to preserve as large an area of tissue and viable skin as possible in the case of burns, especially in areas with complex anatomy, raises the question of whether an agent that would effectively ensure eschar debridement without a negative effect on adjacent viable structures is needed. Thus, several surgical and non-surgical methods have been used, such as hydrosurgery, laser therapy, therapy with worms or larvae, and, of course, enzymatic debridement. Since 1970, early surgical excision and grafting in the case of deep burns have been the standard of care (SOC) [1]. In 2012, a new agent used in enzymatic debridement was approved in Europe, with bromelain being its active substance. Previousstudies in animals and humans have shown its effectiveness in eschar debridement and viable tissue preservation [2; 3].  
Bromelain is an extract from pineapple (Ananascomosus) and is a mixture of proteolytic enzymes and non-enzymatic substances. Its anti-edematous, anti-inflammatory, anti-thrombotic, analgesic, and exfoliating effects have been scientifically proven. The mechanisms incriminated in its activity are the kallikrein-kinin pathway, the arachidonic acid pathway, and cell-mediated immunity [4; 5; 6]. The usefulness of perioperative bromelain therapy has been demonstrated in orthopedics, obstetrics, otorhinolaryngology, dentistry, ophthalmology, sports medicine, and, last but not least, the topical use of bromelain in wound and burn debridement [7; 8]. Thus, the enzymatic approach with bromelain is an alternative to surgical debridement [9; 10]. The topical application of bromelain avoids skin grafts or reduces the surfaces to be grafted, thus also avoiding the utilization of skin substitutes and preserving more dermis, thanks to the protection of viable tissues. It can be done at bedside and has proven to be very effective and rapid, displaying minimal blood loss compared to classical debridement methods [11]. In 2017, the first European consensus about how to use bromelain for the treatment of deep and partial-thickness skin burns was reached. Since then, the utilization of bromelain in some deep or partial-thickness burns has been included in SOC [1].  
This study aims to present our experience with the use of bromelain in a group of 15 patients with burns of different etiologies, with varying surface area and depth, and compare it with a control group of 15 patients.
## 3 Research Methodology
### Preparation Dataset
#### 3.1.3 Data Labeling  
The next step is data labeling; it implies assigning a label to each tweet in the datasets based on its class. The tweets from datasets _D0_, _D1_ and _D2_ are bound to the three classes \"normal\", \"potentially depressed\" and \"potentially anxious\", respectively. Therefore, we have labeled tweets from dataset _D0_ with value '0', tweets from dataset _D1_ with value '1' and finally tweets from dataset _D2_ with value '2'. This data labelling aims to build classification models that only classify tweets as potentially positive towards depressive and anxiety mental disorders or not; thus, the analysis is done at the tweet level. If so, the behaviors of concerned users on social media platforms will be analyzed through other systems which further process user data in order to make decisions (user level analysis).  
In general, data collected from social media should always be taken with a certain degree of confidence. This is why we collected a large volume of data relating to users self-reporting their cases, in order to increase the degree of confidence in the statements contained in the tweets. Moreover, according to the above-stated objectives, our models may allow a certain tolerance regarding the confidence of tweets toward mental disorders because they do not make decisions about users but only classify tweets for further processing. In addition, large volumes of data are generally more suitable for deep learning approaches in order to obtain good results.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Datasets** & **Tweets before** & **Tweets after** & **Percentage of Data after** \\  & **Preprocessing** & **Preprocessing** & **Preprocessing (\%)** \\ \hline _D0_ (Normal) & 2,892,049 & 1,017,101 & 32.00 \\ _D1_ (Depressed) & 2,295,038 & 1,037,050 & 32.63 \\ _D2_ (Anxious) & 1,996,568 & 1,124,419 & 35.37 \\ Total Dataset & 7,183,655 & 3,178,570 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of tweets before and after preprocessing sub-steps.
## 3 Proposed System Model
### Integration of DQN-Base Approach
#### 3.3.1 State Definition S  
The state \(S\) represents the current network condition, including:  
* **Energy Status:**\(E_{Rx},E_{Tx}\) (such as the residual energy of receiver and transmitter nodes).
* **Traffic Load:** Number of pending packets or arrival rate \(\lambda\).
* **Node Position:** Distance between the receiver (Rx) and transmitter (Tx), denoted as \(d_{Rx},T_{x}\).
* **Packet Type:** Critical (\(D_{crit}\)) or routine (\(D_{routine}\)).
* **Channel Conditions:** Signal-to-noise ratio (SNR) or interference level.  
\[S=\{E_{\text{Rx}},E_{\text{Tx}},\lambda,d_{\text{Rx},\text{Tx}},D_{\text{type}}, \text{SNR}\}. \tag{14}\]  
By evaluating these parameters, the receiver node determines the most efficient communication strategy.
# Motor Vector Control Based on Speed-Torque-Current Map
## 4 Verification of the Proposed Vector Control Strategy
### Simulation Analyses
#### 4.2.2 Torque Coupling Mode Performance Simulation
If the target torque of MG1 is too small, then MG2 will work near its peak torque, and vice versa. These situations will lead to an increase in system losses and lower efficiency. The transformation law of demand power with MG1 target torque is shown in Figure 19, when the demand torque is constant. Table 4 shows that when the MG1 target torque is 148 Nm, the demand power of the electric power system reaches a maximum of 27.140 kW. When the target torque is 88 Nm in the static model, the average demand power of the system is 21.822 kW. When the target torque is 68 Nm (obtained from the dynamic model), the demand power of the system has a minimum of 21.549 kW, which is 1.27% lower than the average power consumption obtained by the static model, and 20.6% lower than the maximum demand power of the electric drive system. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MG1 Target Torque/Nm** & **MG1 Power/kW** & **MG2 Power/kW** & **Demand Power/kW** \\ \hline \(-2\) & 0.248 & 25.856 & 26.104 \\ \(68\) & 13.476 & 8.073 & 21.549 \\ \(88\) & 17.814 & 4.007 & 21.822 \\ \(148\) & 32.297 & \(-5.157\) & 27.140 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Demand power under different MG1 target torques. Figure 19: Demand power under different MG1 target torques.
## 2 System Description
### Satellite Air Interface  
There are several types of satellite systems that can be adopted for this work, these include Low Earth Orbit (LEO), Medium Earth Orbit (MEO) and Geostationary Equatorial Orbit (GEO) satellites. Each of these satellite types comes with its own challenges. The GEO satellite can maintain constant transmission delay across multiple sites and a single GEO satellite is able to provide coverage for dense spotbeams [28]. However, GEO satellites comes with issues of long propagation delay, new technologies like edge caching can be used to mitigates this. While LEO satellite provides a lower propagation delay, it is not able to achieve a constant delay due to its constant movement and its smaller coverage area. However, the LEO satellites can be preferred due to its low propagation delay, the GEO satellites are preferred for multimedia communications due to high bandwidth and constant delay. The GEO satellites have also been used to implement 5G satellite networks [28]. The scenario considered in this paper is used to investigate the co-existence in terms of spectrum management for both broadband and IoRT use cases. It is on this premise, that a transparent GEO Satellite has been adopted for this work.  
As shown in Figure 1, the satellite gNodeB located on the earth connects to UEs via the transparent GEO satellite. This setup supports simultaneous transmissions from the satellite gNodeB via the transparent GEO satellite to different UEs [29]. This transmission setup is envisaged to have feedback from the UE to the satellite gNodeB for not only link adaptation purposes but also to determine the transmission rate [30]. Hence, the UE is assumed to send Channel Quality Indicator (CQI) report via the transparent GEO satellite to the gNodeB located on the earth station at certain intervals. The reported CQI at the satellite gNodeB can then be used for transmission and scheduling purposes.  
At the MAC layer of the satellite gNodeB, the scheduling scheme module schedules UEs and IoRT devices. The scheduled UEs and IoRT devices are then mapped to the available resource blocks at every Transmission Time Interval (TTI). The available resource blocks are a function of the available spectrum and the available spectrum depends on how the spectrum is being managed or shared. The size of spectrum used, and the number of antennas deployed determines the number of available resource blocks at every TTI. The allocation of available resources is implemented at every TTI.
## 3 Optimization Strategy
### Transformation into Displacement Constraints  
With \(F_{local}\) applied to the structure of current design, the static analysis, formulation can be expressed as  
\[F_{local}=K(X)u_{local} \tag{10}\]  
where \(u_{local}\) is the vector of nodal displacements. Substitution of Equation (9) into Equation (8) gives the expression  
\[F_{local}=\frac{\left(\omega_{local}^{0}\right)^{2}}{\left(\omega_{local} \right)^{2}}K(X)\varphi_{local}^{0} \tag{11}\]  
Substitution of Equation (10) into Equation (11) gives the expression  
\[K(X)u_{local}=\frac{\left(\omega_{local}^{0}\right)^{2}}{\left(\omega_{local} \right)^{2}}K(X)\varphi_{local}^{0} \tag{12}\]  
According to Assumption 3, that matrix \(K(X)\) has inverse matrix, the two ends of Equation (14) are multiplied by \(K^{-1}(X)\). Then, we obtain the relationship between the nodal displacements and the local frequency  
\[u_{local}=\frac{\left(\omega_{local}^{0}\right)^{2}}{\left(\omega_{local} \right)^{2}}\varphi_{local}^{0} \tag{13}\]  
Here, the relationship can be used for the transformation. According to the frequency constraints in Equation (1), that is  
\[\omega_{local}^{0}\leq\frac{1}{p}\omega_{local} \tag{14}\]  
Substitution of Equation (13) into Equation (14) gives the expression of nodal displacement constraint  
\[u_{local}\leq\frac{1}{p^{2}}\varphi_{local}^{0} \tag{15}\]  
At this point, the frequency constraints of the original problem can be transformed into the displacement constraints of the approximate problem.  
\[g^{*}(X)=u_{local}-\frac{1}{p^{2}}\varphi_{local}^{0}\leq 0 \tag{16}\]  
It can be evidenced from the final \(g^{*}(X)\) that the displacements of each node of the structure are required to be constrained, which brings a lot of work on the establishment of the approximate optimization model as well as the complexity of problem-solving. However, the nodal displacements of the structure mean the same as the modal shape of the structure. That is to say, for local vibration mode, the relatively obvious nodal displacements are almost limited in local vibration area. Thus, as a reasonable simplification approach, the nodal displacements in local vibration area are only constrained in establishing the approximate optimization model.