esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Algorithm Description
### Dynamic Addressing
performances in packet loss rate and delay. Therefore, this algorithm selects different update paths based on different service types. Service 1 has equally important requirements for packet loss rate and delay, Service 2 has higher requirements for packet loss rate, and Service 3 has higher requirements for delay. When a certain segment of the link needs to be replaced, the path is updated using Schemes 1 to 6, and the path state weights of the three services corresponding to the six schemes are calculated. The scheme with the lowest weight value is selected as the new path for transmitting this type of service. Before calculating the weight, it is necessary to normalize the data. The specific weight calculation formula for the train running to a certain point is computed as  
\[{{\it loserate}^{\prime}(i)}=\frac{{{\it loserate}(i)-\min({{\it loserate}})}}{{ \max({{\it loserate}})-{{\it loserate}(i)}}}, \tag{7}\]  
\[{{\it T\_delay}^{\prime}(i)}=\frac{{{\it T\_delay}(i)-\min({{\it T\_delay}})}}{{ \max({{\it T\_delay}})-{{\it T\_delay}(i)}}}, \tag{8}\]  
\[{{\it weight\_1}(i)}=0.5*{{\it loserate}^{\prime}(i)}+0.5*{{\it T\_delay}^{ \prime}(i)}, \tag{9}\]  
\[{{\it weight\_2}(i)}=0.7*{{\it loserate}^{\prime}(i)}+0.3*{{\it T\_delay}^{ \prime}(i)}, \tag{10}\]  
\[{{\it weight\_3}(i)}=0.3*{{\it loserate}^{\prime}(i)}+0.7*{{\it T\_delay}^{ \prime}(i)}, \tag{11}\]  
where the range of \(i\) is 1 to 6, which represents the six schemes for updating the path. _Loserate_(\(i\)) represents the packet loss rate value of the new path updated by the six schemes. _Loserate_(\(i\)) represents the normalization of the packet loss rate. _T_delay_(\(i\)) represents the delay value of the new path updated by the six schemes, and _T_delay_(\(i\)) represents the normalization of delay. Moreover, _weight_1, _weight_2,_ and _weight_3 represent the path state weight values of six new paths corresponding to the three service types. Finally, we select a new path with the lowest weight as the update path for this service type. The following will give an example of the specific dynamic addressing section, as shown in Figure 3, the cross symbols indicate that the link needs to be replaced, and the letters A-M represent the nodes. Suppose a statically configured path A \(\rightarrow\) B \(\rightarrow\) C \(\rightarrow\) D \(\rightarrow\) E \(\rightarrow\) F \(\rightarrow\) G. When the cluster head dynamically monitors the packet loss rate and finds that the packet loss rate of the three links A \(\rightarrow\) B, B \(\rightarrow\) C, and D \(\rightarrow\) E is bigger than the threshold, the link needs to be replaced. Initiate the routing discovery algorithm and select a new path according to the six schemes in Table 2.  
Figure 3: Dynamic addressing process. If Service 1 is being transmitted, first calculate the packet loss rate and delay of the path updated by the above six schemes, then calculate the weight of each path through Formulas (7)-(9). Similarly, for Service 2 and Service 3, calculate the weight through Formulas (10) and (11) and ultimately select the path with the lowest weight value as the update path.
# MobiDabet: Mobile Telemonitoring System for Patients with Type 2 Diabetes Mellitus (T2 DM)
## 3 Results: Telemonitoring System
### Mobile-Web-Application Focused on the Patient
#### 3.2.4 Nutrition Module  
In this module, the patient can consult the eating pattern, assigned menus, and feedbacks, and answer the food intake frequency questionnaire. These services are described below:  
* Pattern: This service shows the patient a scheme for the distribution of rations by food groups assigned by the nutritionist. These eating patterns are previously registered by the nutritionist and are changing according to the indicated calories (see Figure 40). This scheme provides each food group's list with the different foods and the suggested portions for consuming (see Figure 41).
* Menus: This service shows the patient, based on the calories established by the nutritionist, different food menu options so that the diet can be varied (see Figure 42).
* Food intake frequency questionnaire: In this sub-module, the patient answers the food intake frequency questionnaire to inform on food intake preferences (see Figure 43).
* Recommendations: This sub-module shows recommendations that the nutritionist sends to the patient; the information includes the date and the details of the recommendations. The patient can also search for the recommendations that the nutritionist sent in some range of dates (see Figure 44).
# High-Speed Vehicles in Low-Pressure Tunnels--Influence of Choked Flows
## 7 Summary and Conclusions  
The influence of a wide range of parameters on aerodynamic conditions in an evacuated tunnel conveying extra-high-speed vehicles has been studied. To avoid unnecessary complexity, the tunnel used in most of the study has been a single tube that is closed at both ends. Likewise, most of the comparisons have been based on the travel of a single vehicle. This simple combination has been sufficient to demonstrate the dominant role that can be played by the choking of flow at the rear of the annulus alongside a vehicle. For any particular ambient pressure in the tunnel, this is a strong determinant of the mass flow rate along the tunnel. The vehicle gradually compresses the air ahead of it to much larger proportional pressure changes than those in conventional railway tunnels. For instance, with a vehicle:tunnel blockage ratio of 0.5 travelling at 200 m/s, the maximum pressure ahead of the vehicle is more than double the ambient pressure.  
Because the process is rapid, the increased pressures are accompanied by strong increases in temperature. For the same blockage ratio and vehicle speed, sustained temperatures just ahead of the vehicle can be around 60 \({}^{\circ}\)C and peak temperatures at the end of a closed tunnel can reach around 100 \({}^{\circ}\)C. However, these decay strongly because of heat transfers with the tunnel walls, albeit much more slowly than the initial increases caused by pressure waves.  
It has been shown that the vehicle:tunnel area blockage ratio has a much stronger influence on pressures and temperatures than factors such as skin friction at the vehicle and tunnel surfaces or stagnation pressure losses at the vehicle nose.  
The power required to overcome aerodynamic drag for a long vehicle is much smaller than for multiple shorter vehicles of the same overall length. Nevertheless, if multiple short vehicles are chosen (for operational reasons, say), there will be an advantage in operating them in discrete convoys--to maximise the periods of travel when they provide mutual assistance. For similar reasons, if adjacent tubes exist--e.g., for opposite directions of travel--it is beneficial to link them aerodynamically. This increases the number of vehicles providing mutual assistance and thereby reduces both the maximum power demand and the overall energy consumption. Furthermore, it greatly reduces the maximum pressure ahead of vehicles stopping at the end of a tunnel.  
A brief mention has been made of various aerodynamic phenomena that have not been assessed quantifiably in this paper. In particular, attention has been drawn to the potential for the development of short regions of extremely large temperatures in the event of a need to re-pressurise an evacuated system rapidly.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
# Torque Vectoring Control Strategies Comparison for Hybrid Vehicles with Two Rear Electric Motors
## 3 Control Systems and Performance Evaluation
### Linear-Quadratic Regulator (LQR)  
The linear quadratic regulator strategy is part of the optimal control theory, in which the control law is defined to minimize a cost function. This function is typically defined through a quadratic cost function as in (8):  
\[J=\int_{t_{0}}^{t_{1}}\Big{(}x^{T}Qx+u^{T}Ru+2x^{T}Nu\Big{)}dt \tag{8}\]  
where \(x\) is the state vector; \(u\) is the control vector; and \(Q\), \(R\), and \(N\) are the weight matrixes related to the system states, to the control inputs, and to the cross-contribution of \(x\) and \(u\), respectively. The control law is defined by (9):  
\[u=-Kx \tag{9}\]  
where \(K\) is the matrix that minimizes (8), defined by solving the continuous time Riccati differential equation.  
The control law is like the pole-placement strategy, but instead of choosing the ideal poles of the system (not always a straightforward task), the challenge is to define relevant matrixes \(Q\), \(R\), and \(N\) to balance the state error and control effort. For the studied system, the used values of \(Q\) and \(R\) are as in (10), while no cross-contribution was included (\(N\) is null):  
\[R=\begin{bmatrix}1\times 10^{5}&0\\ 0&1\end{bmatrix},\ Q=1\times 10^{6}\begin{bmatrix}1&0\\ 0&1000\end{bmatrix} \tag{10}\]  
As the system state-space matrixes \(A\) and \(B\) are dependent on the vehicle's speed, as defined in (1), so will be the results of \(K\) after the cost function minimization. Therefore, for the practical implementation of the LQR, the \(K\) matrix was calculated beforehand with a dedicated MATLAB script for a wide range of discrete velocities (from 1 to 100 m/s) and, during the simulations, the coefficients of the control were linearly interpolated from the closest neighbors of the actual vehicle's velocity for each time step. This approach allowed for good precision of the control system without the requirement of excessive computational power.  
Differently from the previous control systems PID and SMC, the LQR is a full-state control, meaning that the control gains and strategy use as input not a single error signal, but rather assume the availability of all of the relevant internal states of the plant. In the case of the DYC problem using the 2 DoF bicycle model, this means knowing not only the yaw rate \(\gamma_{a}\) as before, but in addition being able to observe the side-slip angle \(\beta\). The side-slip angle is a variable notoriously hard to directly measure, so its availability for an actual control system would depend on the estimation of the variable based on other output signals. This estimation is not the focus of this paper, so it will be assumed that \(\beta\) is available with sufficiently high precision, as it is demonstrated to be achievable by some works present in the literature [28; 29].
Sensors_**2011**, _11_, 3527-3544; doi:10.3390/s110403527  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**On Prolonging Network Lifetime through Load-Similar Node Deployment in Wireless Sensor Networks**  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 3**  
1 School of Computer Science and Engineering, University of Electronic Science and Technology of Chengdu, Chengdu 610054, China; E-Mails: [EMAIL_ADDRESS] (Q.-Q.L.); [EMAIL_ADDRESS] (M.L.)  
2 Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV 89154, USA; E-Mail: [EMAIL_ADDRESS]  
3 Department of Computer Science and Engineering, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 30 January 2011; in revised form: 17 March 2011 / Accepted: 21 March 2011 / Published: 24 March 2011_  
**Abstract:** This paper is focused on the study of the energy hole problem in the Progressive Multi-hop Rotational Clustered (PMRC)-structure, a highly scalable wireless sensor network (WSN) architecture. Based on an analysis on the traffic load distribution in PMRC-based WSNs, we propose a novel load-similar node distribution strategy combined with the Minimum Overlapping Layers (MOL) scheme to address the energy hole problem in PMRC-based WSNs. In this strategy, sensor nodes are deployed in the network area according to the load distribution. That is, more nodes shall be deployed in the range where the average load is higher, and then the loads among different areas in the sensor network tend to be balanced. Simulation results demonstrate that the load-similar node distribution strategy prolongs network lifetime and reduces the average packet latency in comparison with existing nonuniform node distribution and uniform node distribution strategies. Note that, besides the PMRC structure, the analysis model and the proposed load-similar node distribution strategy are also applicable to other multi-hop WSN structures.  
**Keywords:** energy hole; load-similar node distribution; energy balance; network lifetime
## 3 Multi-Criteria Analysis (MCA)
### MCA Results Evaluation  
The MCA matrices and final scoring table obtained from the study are indicated in Figure 2 and Table 2. The MCA consisted of eight different approaches used in load forecasting from the existing literature that were critically analysed. By considering different perspectives and analysing different performance criteria, the framework yielded an accurate shortlist of the most effective ML techniques in estimating energy consumption in a residential building.  
Figure 2: MCA matrices and scoring table.  
As the results, it was demonstrated that ML techniques based on neural networks such as ANN (MCA score = 189), ANFIS (MCA score = 187), and WNN (MCA score = 181) exhibited better performance in estimating energy consumption than those based on decision trees such as XGBoost (MCA score = 185) and regression analysis such as Gaussian process regression (MCA score = 144) and ARIMA (MCA score = 140). ANN produced the most accurate predicting results as it is well-known for its ability to handle noise and perform non-linear analysis of data-set like the investigated load profiles [21]. Furthermore, ANN also tends to ignore excess input data that are of minimal significance and concentrate on the more critical input [21]. On the other hand, despite performing better than MLR, XGBoost or the decision tree method generally does not outperform neural networks for non-linear data and is susceptible to noisy data [19]. The technique is more suitable for predicting categorical outcomes, and unless visible trends and sequential patterns are general, decision trees are less appropriate for application to time series data [19]. Regarding the MLR, despite being the most comfortable and most intuitive approach of prediction, it is the least appropriate for predicting energy usage due to its weakness in working with data with no apparent pattern.  
Additionally, more criteria can be included for analysis to cover all the aspects of each ML technique. As a next step of the MCA, we model the performance of ANN, XGBoost, and MLR to validate the literature review on the performance of neural networks, decision tree, and regression analysis using python. The shallow or a simple ANN model is also used as a benchmark for testing the proposed prediction model's performance. Furthermore, a new hyperparameter-tuned deep neural network model will be developed and evaluated based on the prediction of energy consumption load profiles.
# Downregulation of Squalene Synthase Broadly Impacts Isoprenoid Biosynthesis in Guayule
## 4 Materials and Methods
### Quantification of Isoprenoid Intermediates by LC-MS  
LC-MS was used to analyze metabolite profiles according to a previous method whose LC [72] and MS [73] conditions have been, respectively, previously described.
# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
## 3 Sleep Stage Detection
### Bio-Signal Detection  
Figure 1 shows the subject's breathing data collected using the intermediate-frequency signal of the FMCW radar. Figure 1a shows the change in the intermediate-frequency signal over time, where the regular changes in amplitude are caused by the body's micromovements resulting from the subject's respiratory activity. The larger the amplitude changes of the breathing signal, which can be observed in the color bar on the right, the greater the strength of the received intermediate-frequency signal. Here, a change in the radar signal is defined as respiration. The subject's respiratory signal was specified using a phase component corresponding to a certain distance (distance between the radar and the detection target) for the entire signal detection range, by using the FMCW radar to minimize the signal interference due to clutter in the surrounding environment. The respiratory signal was extracted using the distance determined based on the coherence between the magnitude and phase of the intermediate-frequency signal [32]. As shown in Figure 1b, the waveform changed over time, and the peak points of the radar-detected breathing signals used in this study were consistent with the abdominal pressure-sensor-measured signals used as the reference ground truth. Thus, the potential application of the respiratory information extracted using the FMCW radar was verified.
# Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA
## 3 5G Overview  
There is a massive increase in the market for wireless services demanding faster, higher-capacity networks owing to numerous devices that require to be connected to the Internet simultaneously, for instance: mobile phones, computers, tablets, smartwatches, etc. The IoT applications earn significant attention that is employed with restriction to the high-speed broadband connection. For instance, E-Health and E-Transportation fuel a need for massive connectivity and URLLC. These devices constantly require higher reliability communications within low latency constraints. The 5G NR consider bringing provision for diverse applications with an extensive range of communication requirements. These requirements are linked with three generic service classes, known as eMBB, mMTC, and URLLC. The 3 GPP has considered these service classes as the primary leading drivers for designing 5G. The major steps in the evolution of telecommunication technologies are summarized in Table 1.
## 3 Empirical Results
### Forecasting Results
#### 3.2.2 Gaussian Process Regression Results  
The analysis that follows is based on GPR results which were done in two parts, one without interactions and the other with interactions. GPR analysis was done based on the four kernels: Matern, rational quadratic, dot product and radial basis function.  
VEN Data without Interactions  
Figure 13 shows a static plot of the predicted solar irradiance values against the observed values of VEN data without interaction. The predicted values are in blue which are following closely the observed values in orange. The appropriate kernel was chosen  
Figure 11: Lasso regression plot for VEN data.  
using the MEB technique and the radial quadratic kernel had the minimum enclosed ball, hence it was used in the forecasting process.  
Figure 14 shows a real-time plot for VEN data, the observed data are in red and predicted in blue which were observed over 10 h. On real-time analysis, forecasting was done for 10 h using 150 observations because the period was found to be equal to 15 from the periodogram for 1 h.  
VEN Data with Interactions  
Forecasting was also done applying interactions, again the radial quadratic kernel produced the minimum radius, hence it was used to come up with the forecasts. Figure 15 shows a static plot for VEN data with interactions, the observed data are in red and predicted in blue. On real-time analysis, forecasting was done for 10 h using 150 observations and is shown in Figure 16.  
Figure 12: Lasso regression plot for SUN data.  
Figure 13: GPR with interactions.  
Forecasting was also done for SUN data with no interactions included. The appropriate kernel was chosen using the MEB technique and the radial quadratic kernel had  
Figure 16: GPR VEN with interaction relative influence plot.  
Figure 14: GPR no interaction relative influence plot.  
Figure 15: GPR VEN with interactions.  
the minimum enclosed ball, hence it was used for forecasting. Results of the forecasts are shown in Figure 17, which is a static plot of SUN data, the predicted solar irradiance in blue follows closely the observed values in orange.  
The plot on Figure 18 shows a real-time plot for SUN data, the observed data are in red and predicted in blue which were observed over 10 h and they follow each other very closely.  
SPR analysis was also done on SUN data with interactions included, the results are shown in Figure 19, a static plot, the predicted solar irradiance in blue follows closely the observed values in orange. The appropriate kernel was chosen using the MEB technique and the radial quadratic kernel had the minimum enclosed ball, hence it was used for forecasting. The plot in Figure 20 shows a real-time plot for SUN data, the observed data  
Figure 17: GPR with no interactions.  
Figure 18: GPR with no interaction real-time plot.  
are in red and predicted in blue which were observed over 10 h and they follow each other very closely.
## 2 Hf Isotope Mapping's Application to Mineral Prospection  
In recent years, some researchers have made more attempts to use zircon Hf isotope mapping to explore the resolution of the three-dimensional tectonic framework and mineralization pattern, mainly focusing on the regions of the Yilgarn Craton of Western Australia, the Lhasa terrane in China, the Eastern Qinling Orogen, and the North China Craton [16; 20; 22; 23; 24; 27; 35].  
[PERSON] et al. [20] performed regional zircon Hf isotopic mapping of the Yilgarn Craton of Western Australia and found that the distribution of magmatic-related deposits is closely related to the formation of material from earth's deep mantle and the new crust. [PERSON] et al. [16] conducted zircon Hf-isotopic mapping of regional magmatic rocks in the Lhasa terrane, revealing that the upper lithosphere material structure and composition of the Lhasa terrane are consistent with the deposit distribution of the Gangdese metallogenic  
Figure 1: Geological map showing the tectonic framework of the Sanjiang Tethyan Orogen and its adjacent areas. Adapted with permission from Ref. [17]. Copyright 2016 ELSEVIER.  
belt. The porphyryv Cu(-Mo-Au) deposits are all distributed in the positive zircon \(\varepsilon\)Hf(t) area, which is associated with the juvenile crust formation in the South and North Lhasa sub-terrane. Granite-related Pb-Zn deposits are typically localized along the margin of the old crustal block bounded by lithospheric faults. The oldest crustal region developed along the margin of the old crustal block is bounded by lithospheric faults, which is in the negative zone of zircon \(\varepsilon\)Hf (t). The Hf isotope mappings show that the juvenile crust has a primary control on the formation of porphyry Cu(-Mo-Au) deposits. The distribution of Pb-Zn-Mo deposits in the central sub-terrane is constrained by the inhomogeneity of the composition of ancient crustal blocks and remelting or reworking. [PERSON] et al. [22] revealed that the formation of porphyry and porphyry-skarn Mo(-W) deposits are closely related to the remelting of the ancient crust through zircon Hf isotope mapping of the east Qinling orogen. [PERSON] et al. [23] studied the zircon Hf isotope mapping of the North China Craton and found that the formation of BIF-type iron deposits is associated with the remelting modification of ancient crust, and the formation of orogenic gold deposits is related to the formation of juvenile crust [36]. [PERSON] et al. [24] demonstrated that isotope mapping can constrain the distribution pattern of large-scale gold deposits by conducting zircon Hf isotope mappings of magmatic rocks in the southeast North China Craton. Jiao Dong-type gold deposits and porphyry-skarn Mo(-W-Cu) deposits are typically localized at regions with negative \(\varepsilon\)Hf(t) values, showing an ancient crustal base composition. Porphry-skarn Cu(-Au-Mo) deposits in the west of Shandong province cluster variable \(\varepsilon\)Hf (t) values, showing an ancient crustal and juvenile crustal composition, indicating the contribution of mantle-derived material to mineralization.