esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Transient Model of a Doubly Fed Induction Generator (DFIG)
### Mechanical Part  
A schematic of the extracted model of the DFIG mechanical part is shown in Figure 2.  
Figure 1: The electrical part model of doubly fed induction generator (DFIG).  
The model includes a series combination of turbine controller and induction generator controller. Pcmd is applied to the individual wind turbine controller from WF control unit. WF control unit receives its set point from AGC unit in the power system.  
Considering the available wind power, wind turbine rotor speed and the proposed control strategy in the decision-making unit, Pcmd and a set point for wind turbine blade pitch angle are determined. Pcmd is applied to the electrical part and the set point for blade pitch angle is sent to the pitch angle controller. All of the described procedure is intended to trace the determined output power by WF control unit. The mechanical torque in the wind turbine shaft is calculated applying Equation (14).  
\[\tau_{\rm m}\omega_{\rm m}=\frac{1}{2}C_{\rm P}(\beta,\lambda)\rho{\rm AV}_{ \rm w}^{3}, \tag{14}\]  
\(C_{\rm P}\) is the power coefficient of the wind turbine, \(\rho\) is the air density, A (m\({}^{2}\)) is the cross section of the WTG aerodynamic rotor, \(V_{\rm w}\) is the wind velocity (m/s), and \(\omega_{\rm m}\) is the wind turbine rotor speed. In \(C_{\rm P}\), \(\beta\) is the pitch angle of the turbine and \(\lambda\) is the tip speed ratio. Tip speed ratio can be calculated applying Equation (15).  
\[\lambda\ =\frac{R\omega_{\rm m}}{V_{\rm w}}, \tag{15}\]  
where R (m) is the blade radius. \(C_{\rm P}\) is calculated applying Equation (16) [21].  
\[C_{\rm P}(\beta,\lambda)\ =\frac{1}{2}(\frac{RC_{\rm f}}{\lambda}-0.022\beta-2) \mathrm{e}^{-0.255\frac{RC_{\rm f}}{\lambda}}, \tag{16}\]  
\(C_{\rm f}\) is the blade design constant. The electromagnetic torque (\(\tau_{\rm em}\)) produced by electrical part, according to the applied Pcmd, will then react to the mechanical torque (\(\tau_{\rm m}\)) by the swing equation mentioned in Equation (17).  
\[\tau_{\rm m}-\tau_{\rm em}\ =\ \mathrm{J}\frac{d\omega_{\rm r}}{dt}, \tag{17}\]
## 4 Agent Training with Markov Decision Process (MDP)  
In the AWR-PF proposed in Section 3, the time-varying epoch observation characteristics (SNR, Doppler frequency, azimuth angle, etc.) and the temporal dependencies of epoch-weighting decisions collectively constitute a complex sequential decision-making  
Figure 5: The diagram of the proposed Agent-Weighted RLS Positioning Framework.  
problem. The MDP model provides a mathematical foundation for the agent to comprehend the AWR-PF and formulate decisions by modeling the state evolution mechanisms, action influence pathways, and long-term reward quantification mechanisms. The model's definitions of state space, action space, state transitions, and reward function directly determine the upper bound of the agent's decision-making performance.  
To efficiently solve for optimal policies in such high-dimensional continuous state spaces, we employ DDQN--a DRL algorithm that decouples action selection from value estimation to mitigate Q-value overestimation. This approach establishes a novel paradigm for addressing the dynamic epoch-weighting optimization challenges in LEO SOP positioning. The DDQN-based training framework enables the agent to learn a robust weighting policy while maintaining generalization performance in complex positioning scenarios.
# Settlement Prediction of Footings Using \(V_{s}\)
## 3 Centrifuge Study
### Results and Discussion
#### 3.2.2 Soil Parameters Used in Predictions  
In order to compare the load-settlement curves from centrifuge tests with predictions, two different \(I_{z}\) values from the elasticity theory and Schmertmann's triangles [6] were utilized simultaneously. In case of the elastic solution, it is necessary to determine the Poisson ratio. As discussed in the previous section, it is evident that the Poisson ratio is less than typically imagined and its range under the working load condition is less than 0.2 [18; 19]. A Poisson ratio of 0.2 was, therefore, selected as an equivalent value, and the corresponding shapes with Schmertmann's triangles are shown in Figure 5. As the peak value of Schmertmann's triangle increases with increasing surcharge load (\(q\)), the shapes shown in Figure 5 are fixed when the surcharge load is 300 kPa for comparison.  
Figure 4: Shear wave velocity increments after 1 st loading.  
Figure 5: Strain influence factor (\(I_{z}\)) profiles used in predictions.  
Given that the nonlinear deformation characteristic of soils is determined by certain parameters in Equation (11), the selection of appropriate soil parameters is important. For the stress exponent, \(n\), 0.5 was used since this value is commonly assumed for clean quartz and silica sands [15; 20; 26]. For the determination of model parameters, \(f\) of 0.96 and \(g\) of 0.09 were determined through regression analysis using the results of the triaxial test (TX) with the confining pressure of 100 kPa and 200 kPa as shown in Figure 6. More weight was taken into account for the results from the small-strain region. The ultimate bearing capacity, \(\sigma_{max}\), depends on the \(L/B\) ratio and can be obtained using the Meyerhof method [21] based on the internal friction angle. All soil parameters used in predictions are tabulated in Table 2. In order to determine the initial stiffness of model ground, the \(V_{S}\) profile measured before each loading was used in each settlement prediction for representing the reasonable stress state of model ground. Finally, a thickness of each sublayer (\(\Delta z_{i}\)) of 0.1 m was applied in Equation (11).
## 3 Proposed Supervision System and Method for Process Modeling
### Definition of SCVAD Processes
#### 3.2.2 Modeling of SCVAD Processes  
The processes are defined within the event-oriented modular structure presented in Figure 3 to address patient needs based on SCVAD specifications. For each process, two modules are specified: one represents the evolution of undesirable states following the occurrence of an adverse event (digitization of the adverse process), while the other focuses on actions recommended by the medical team for mitigating the adverse process. Three steps are planned for specifying modules that meet the requirements of an adverse process: Step 1: Identification of risks associated with the occurrence of an adverse event; Step 2: Development of the adverse process model; and Step 3: Development of intervention protocols.  
In Step 1, for identifying risks associated with the occurrence of an AE, one of the techniques that can be utilized is hazard and operability studies (HAZOP), which is used to identify operational problems in a facility or processes [48]. It involves thoroughly investigating each process to find potential deviations based on design requirements and identifying causes and consequences. By identifying the causes and consequences associated with each deviation, actions are specified to eliminate or control the operational hazard in the facility or processes [48]. The HAZOP methodology was developed to meet the needs of projects in the chemical process industry; however, ISO 14971 [49] provides specifications for regulating health-related products and suggests HAZOP, among other techniques, for risk identification involving the use of medical devices. Therefore, the recommendations can be applied to the risk identification study, considering VADs and AEs according to the needs required for this work.  
Step 2 refers to modeling the adverse process based on the information obtained in the previous step, which combines specialized knowledge with clinical care protocols and management throughout the life cycle of a patient with an implanted VAD. Models are developed in PFS and then transcribed into PNs to constitute an adverse process diagnostic module specific to each AE and its respective adverse process.  
Step 3 involves the development of models that implement intervention protocols based on the risk identification step, where actions recommended by the medical team to interrupt the adverse process are specified. Models are also developed in PFS and then transcribed into PNs to constitute an adverse process mitigation module, which is also specific to each AE and its respective adverse process.  
The proposed method can be applied to any AE, with the three steps presented being repeated for each one. In this way, the application of the method proposes models that reflect combined actions, based on specialized knowledge and clinical care protocols, which can implement evidence-based best practices.
## 3 Cluster Routing Protocol  
We make the following improvements to cluster routing to address the deficiencies of traditional clustering algorithms and the signal time-varying fading characteristics of the application environment:  
1. To maintain the optimal number of CHs, we consider that the number of nodes actually participating in data transmission in a network changes continuously because some nodes run out of energy; the original fixed number of CHs \(K\) also changes accordingly (mainly depending on the number of active nodes and the rate of generating CHs); \(K=(k_{1},k_{2},k_{3},\cdots,k_{r})\) and \(k_{r}=\zeta_{0}N_{active}\).
2. To keep the number of CHs in each round under the optimum, the use of a random number of each node compared with the threshold used for CH selection is improved and corrected from the original fixed variation range of [0,1] to a self-adapting variable sliding window \(W_{r}\).
3. The original threshold \(T(n)\) is improved in two ways: the total number \(N\) of deployment nodes in the original threshold is replaced by the number of active nodes \(N_{active}\) to better guarantee that the number of CHs formed in each round follows optimization, and a node residual energy ratio factor and channel efficiency ratio factor are introduced.
4. When member nodes select their own CH to join, the final communication loss of data transmission to the BS should be considered instead of simply the distance and energy factors because in a channel fading environment, the energy consumed by nodes at the same distance while successfully transmitting equivalent data may be different; this is mainly affected by channel fading.  
The clustering protocol is mainly divided into a clustering creation stage and a stable transmission stage on the time axis, as shown in Figure 3.  
\(T_{CF}\) is the cluster construction period, which consists of initialization, CH selection and cluster set formation, whereas \(T_{IC}\) refers to the inter-cluster communication period. The CH of each cluster creates TDMA scheduling based on the number of nodes that participate. Member nodes in a cluster transmit data to the corresponding CH based on the TDMA time slot created by the CH, and during \(T_{TS}\), the CH transmits the collected data to the BS or an aggregation node. By referring to the round-robin communication adopted in LEACH, the round's communication time is \(T_{r}=T_{CF}+T_{IC}+T_{TS}\), where \(T_{CF}\) is regarded as the creation stage and \(T_{IC}+T_{TS}\) is regarded as the stable data transmission stage.
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 4 Fast Time Simulation Results
### LECMTLU Sector
#### 4.2.2 Environment and Airspace User Cost-Efficiency  
Figure 9 shows the results obtained for the LECMTLU sector. Positive results were obtained, although they are not as pronounced as those achieved in the LECMZMU sector. The most significant benefits are derived from resolving lateral _LOS_ through horizontal maneuvers (vector).  
These values should be placed in the context of the sector's characteristics. In LECMTLU, the average crossing distance is 115 NM and the average flight time in the sector is 15.5 min (obtained from NEST data [46]). Therefore, if the lateral _LOS_ with horizontal resolution maneuvers are analyzed, the following is obtained:  
* Distance: reduction, on average, of 2.14 NM per _LOS_ saved, representing 1.86% of the total distance travelled in the sector.
* Delay: reduction, on average, of 0.13 min per _LOS_ saved, representing 0.84% of the total flight time in the sector.
* Fuel and _CO\({}_{2}\)_: average reduction of 10.76 of kg fuel and 33.88 kg of _CO\({}_{2}\)_ emissions.  
Benefits are also obtained for longitudinal _LOS_, although fewer than in lateral _LOS_. Horizontal resolution maneuvers predominate for both types of _LOS_, longitudinal and lateral. Finally, in general terms, favorable average results are obtained for the analyzed indicators, with all of them being positive.
# Dynamics Investigation on Axial-Groove Gas Bearing-Rotor System with Rod-Fastened Structure
## 5 Conclusions  
Different from the cylindrical bearing and continuous integral rotor structure, in this report, a rod-fastened rotor and two- and three-axial-groove gas bearings are applied in the bearing-rotor system. The continuous gas film forces of the three-axial-groove gas bearing are obtained from the time-based dependency compressible Reynolds equation in the gas bearing lubrication nonlinear system, which is solved by the differential transformation method. The Newmark-\(\beta\) method is used and modified to compensate the calculation error by considering the disturbance compensation. The modified Newmark-\(\beta\) method conforms to the dynamic motion equation of the rod-fastened rotor-gas bearing system, while satisfying the motion constraint condition of the Newmark-\(\beta\) method.  
A dynamic mathematical method of a rod-fastened rotor system with two- and three-axial-groove gas bearings support is established. The dynamic characteristics of the rod-fastened rotor-gas bearing system under different rotating speeds are analyzed by the orbits, Poincare maps, and spectrograms. The orbits and Poincare maps of the rod-fastened rotor and continuous integral rotor, three-axial-grooved and two-axial-grooved gas bearings are obtained and compared. In this report, the comparison shows that the rod-fastened rotor has better stability than the continuous integral rotor at \(\omega=1500\) rad/s, and that the rotor system with the two-axial-grooved gas bearing has better stability. The bifurcation characteristics of the rod-fastened rotor-gas bearing system are investigated; the bifurcation behavior shows that there are different periodic movements, including periodic and quasiperiodic motion. The effects of the position angle of the pad on the orbits and Poincare map of the journal center of rod-fastened system are studied. The vibration amplitude can be decreased when the position angle of the pad is \(\alpha_{0}=62.5^{\circ}\) versus \(\alpha_{0}=10^{\circ}\).  
Conceptualization, S.L. and Y.L.; methodology, S.L. and Y.L.; software, S.L.; validation, S.L. and Y.Z.; formal analysis, Y.Z.; investigation, S.L.; resources, D.H.; data curation, S.L.; writing--original draft preparation, S.L. and X.Z.; writing--review and editing, S.L.; visualization, S.L.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.Z. and Y.L. All authors have read and agreed to the published version of the manuscript. This work was funded by the National Natural Science Foundation of China, grant numbers 52075438, 51505375; the Key Research and Development Program of Shaanxi Province of China, grant number 2020 GY-106; the Open Project of State Key Laboratory for Manufacturing Systems Engineering, grant number sklms2020010.  
Not applicable.  
Figure 14: When \(\omega=2000\) rad/s, comparison of the rod–fastened rotors with 3-axial grooved and 2-axial grooved gas bearings, (**a**) comparison of the orbits at the left bearing station; (**b**) comparison of the orbits at the left disk station.
## 4 Case Studies
### Case Study 1--Lettuce  
In this case study in particular, a single water activity scenario (\(a_{\text{w}}\) = 0.996) was considered, as shown in Table 1, converted by Equation (5) into an equilibrium relative humidity scenario, \(\varphi\) = 99.6%. Regarding the bacteria considered for this vegetable, the growth of _Aeromonas hydrophila_, _Bacillus cereus_, _Listeria monocytogenes_ and _Salmonella_ was estimated. The analysis of this horticultural product starts at \(T\) = 1 \({}^{\circ}\)C, temperature in which _Listeria Monocytogenes_ starts to replicate, and ends at \(T\) = 10 \({}^{\circ}\)C, temperature at which, for every pH value, food safety threshold is reached in \(t\)\(\leq\) 24 h. In other words: at \(T\) = 10 \({}^{\circ}\)C, regardless the pH value in analysis, the remaining marketing period will always be less than 24 h. At the end of this period, the food safety threshold is reached through contamination by, in this case, _Listeria monocytogenes_. For this reason, any higher temperature values are disregarded by the DSS, given that the obtained commercialization period will always be inferior to 24 h, and, therefore, irrelevant for pricing purposes. In  
Figure 4: DSS user interface exemplified on onion: (**a**) Entering data regarding storage conditions and pricing; (**b**) Visualization of the obtained results.  
matters of pH, values between \(5.8\leq\mathrm{pH}\leq 6.0\) were considered, according to Table 2. The sampling of the results obtained in the study of lettuce, concerning its remaining shelf life, is shown in Table 6. The presented results immediately allow the observance of the influence of temperature on shelf life of this vegetable. Among the studied combinations, in the most unfavourable scenario for bacterial growth, minimum temperature and pH values (\(T=1\)\({}^{\circ}\)C; pH = 5.8) and, as such, more favourable for the conservation of foodstuffs, a shelf life of \(t=155.2\) h after the horticultural enters storage is predicted at the threshold of contamination by _Listeria monocytogenes_. This is the only bacterium capable of replicating under such conditions. On the other hand, when the selected temperature and pH values are at their maximum for the range under study (\(T=10\)\({}^{\circ}\)C; pH = 6.0) and, therefore providing the most favourable scenario for bacterial replication, shelf life is dictated when contamination by _Aeromonas hydrophilic_ is reached, after \(t=20.5\) h of storage. Under these storage conditions, and as the most favourable scenario for bacterial growth, the proliferation of every bacteria under study is registered. In the stated sample, there were no observed cases of contamination by _Bacillus cereus_ or _Salmonella_. In this particular case study, it was not possible to determine the effect of relative humidity on the lifetime of the horticultural product, due to the lack of a comparison term, since only a water activity value found in the literature was considered.
# Optimal Base Station Density of Dense Network:
## 2 System Model
### Channel Access Mode of the MaBS Tier  
In HetNets, since MaBSs and SmBSs operate at licensed and unlicensed bands, respectively, their channel-sharing modes are also different. For MaBS, we assume the resources (e.g., Long-Term Evolution (LTE) resource blocks) are allocated proportionally among the users. Therefore, a user's rate is  
\[\mathcal{R}_{u_{i}}=\frac{W_{\mathrm{M}}}{N_{\mathrm{M}}}\log\left(1+\mathrm{ SINR}_{u_{i}}\right). \tag{1}\]  
\(W_{\mathrm{M}}\) is the bandwidth of the MaBS tier and \(N_{\mathrm{M}}\) is the number of users that are served by a MaBS. Doing summation, we get the throughput of a MaBS as  
\[S_{\mathrm{M}}=\frac{1}{N_{\mathrm{M}}}\sum_{u_{i}=1}^{N_{\mathrm{M}}}W_{ \mathrm{M}}\log\left(1+\mathrm{SIN}_{u_{i}}\right). \tag{2}\]  
It demonstrates that the throughput of a MaBS depends on the SINR distribution of MaBS users. Since both MaBSs and users are distributed in PPP, with the method of Theorem 1 in [23], it can be proved that SINR distribution of the users in MaBS network only depends on the path loss exponent and deployment density of MaBSs. For the practical network, the path loss exponent and density of MaBS are normally determined and fixed so the throughput of the MaBS network does not depend on network densification. On the other hand, since SmBS density is much larger than MaBS density, the areal throughput of SmBS and MaBS are not in the same order of magnitude. The areal throughput of SmBS is far more than MaBS's and the areal throughput of MaBS is not notable in overall network performance. Thus, to improve the network throughput, we should focus on improvement of the SmBS network. Actually, in a general case where the base stations of both tiers can be deployed freely, the densities of MaBS and SmBS interact. The variety of MaBS density can directly influence the optimal SmBS density. Since the rate of MaBS is related to the MaBS density, we should calculate the rate of MaBS to derive the total throughput after MaBS density is determined according to the optimal SmBS density. However, when we focus on this paper, some specific assumptions are set in the system model. First, according to the practical network, the MaBSs have been already deployed and the density is fixed. We can only add new SmBSs into the network and can only control SmBS density. Since MaBS density is fixed and due to Theorem 1 of [23], the SINR distribution of MaBS is steady and the average rate of MaBS is also relative steady. It does not impact the rate of MaBS when we optimize the SmBS  
Figure 1: The spatial locations and coverage of macro base stations (MaBSs) and small base stations (SmBSs).  
density. Second, MaBSs and SmBSs operate at different spectrums. It means that these two tiers do not have mutual interference. Thus, the newly added SmBSs do not degrade the MaBS's' SINR. With these specific conditions, it is different from the general case. It allows us just to optimize deployment of SmBS in the HetNets.
# Pose-Based Gait Analysis for Diagnosis of Parkinson's Disease
## 2 Methodology
### Feature Extraction  
The early visible symptoms of PD include changes in posture, tremors, reduced arm swing, arm swing asymmetry, and reduction in step length and gait speed. Hence, we extract eight features from the body keypoint coordinates obtained as described in  
Figure 1: Overall methodology, showing the processes from video capture, human pose estimation, feature extraction, and PD prediction.  
Figure 3: A 50-frame subsequence illustrating the raw and smoothened nose motion coordinates for a healthy and a Parkinsonian gait sequence. Nose X and Nose Y refer to the coordinates of the nose in the 2D plane.  
Figure 2: Examples of subjects in the dataset with healthy gait and PD in outdoor and indoor environments.  
Section 2.2. If we let \(u=\{x_{t}^{j},y_{t}^{j}\}\) and \(v=\{x_{t}^{j},y_{t}^{j}\}\) be the coordinates of two adjacent keypoints \(i\) and \(j\) in frame \(t\), the Euclidean distance \(D_{E}\) between \(i\) and \(j\) can be found as:  
\[D_{E}(i,j)=[(x_{t}^{i}-x_{t}^{j})^{2}+(y_{t}^{i}-y_{t}^{j})^{2}]^{(1/2)} \tag{1}\]  
Suppose \(j\) is a central keypoint, such as the elbow joint, with \(i\) and \(k\) as its adjacent nodes; we can form vectors from \(\overrightarrow{j!}\) and \(\overrightarrow{j!}\) in order to obtain the range of motion at \(j\) as:  
\[\theta(j)=cos^{-1}\bigg{(}\frac{j!\cdot jk}{|j!||jk|}\bigg{)} \tag{2}\]  
We obtain a measure of the steadiness of a body keypoint \(j\) as the standard deviation of the moving differences of \(j\):  
\[\mathcal{S}(j)=std[\{p_{t+1}^{j}-p_{t}^{j}|t=1,...,F\}],\ \mathcal{S}(j)\in \mathbb{R}^{F\times 2} \tag{3}\]  
where \(p_{i}^{j}\in\mathbb{R}^{F}\) is a vector representing the 2D positional coordinates of keypoint \(j\) in frame \(t\), and \(F\) is the total number of frames. Hence, as shown in Table 1, subject posture, arm steadiness, arm swing, arm swing symmetry, maximum step height, mean step length, cadence (steps per minute), and gait speed (cycles per minute) are obtained from the keypoint coordinates obtained via pose estimation. As illustrated in Figure 4, the number of steps \(n_{steps}\) taken by the subject in the video is estimated by counting the peaks of the step length. To compute the peaks, we take the step length values per frame as a time series. We then find all local maxima by a comparison of neighbouring values.