esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation Model
\tag{3}\]  
\[\left\{ \begin{array}{l}M_{x}(\theta)=-\sum\limits_{i\in W_{\theta}}F_{n}^{i} \cdot r_{i}\cdot\cos\varphi^{i}(\theta)\\ M_{y}(\theta)=\sum\limits_{i\in W_{\theta}}F_{n}^{i}\cdot r_{i}\cdot\sin \varphi^{i}(\theta)\\ M_{z}(\theta)=\sum\limits_{i\in W_{\theta}}F_{i}^{i}\cdot r_{i}\end{array}  \right. \tag{4}\]  
Figure 4: Schematic diagram of calculation model: (**a**) Front view; (**b**) Side view. where \(F_{x}\), \(F_{y}\), \(F_{z}\) are the total forces of the cutter head on the pile in the \(x\), \(y\), \(z\) direction, respectively; \(M_{x}\), \(M_{y}\), \(M_{z}\) are the total moments of cutter head on the pile in \(x\), \(y\), \(z\) direction, respectively; \(F_{n}^{i}\) is the penetration force of tool No.\(i\); \(F_{t}^{i}\) is the cutting force of tool No.\(i\). In the above model, the force exerted by tools on reinforced concrete is decomposed into penetration force \(F_{n}^{i}\), cutting force \(F_{t}^{i}\) and side force \(F_{s}^{i}\). The side force is usually very small and is not considered in the model. In addition, due to the short and uncertain contact time between the tools and the steel bar, the contact force between the tool and steel bar is ignored in order to simplify the model. Moreover, the additional loads of the cutter head caused by the cutting pile and the force applied on the pile by cutter head have the same magnitude and opposite direction according to [PERSON]'s third law. Therefore, \(F_{z}\) and \(M_{z}\) can be considered the additional thrust and additional torque of the shield machine, which are crucial to the setting of shield operation parameters. \(F_{x}\), \(F_{y}\), \(M_{x}\), \(M_{y}\) are unbalanced forces and unbalanced moments of cutter head which can have an impact on the control of shield attitude. It should also be noted that the force and moment calculated by the above model are the additional force caused by the interaction between the pile and the concrete, which is part of all the loads on the cutter head, except for the loads from other surrounding strata. In addition, the thrust and torque are two of the most important shield advancing parameters that influence the performance of excavation in the actual construction. Compared with these two parameters, the unbalanced force and unbalanced moment are generally small. The shield machine body is strongly restrained by the surrounding strata in the radial direction of the cutter head. Hence, unbalanced forces and moments of shield machine caused by pile cutting have relatively little effect on shield tunneling performance. The following section of this paper mainly focuses on the analysis and discussion of the influence of the interaction between the cutter head and the pile foundation on the thrust and torque.
## 2 Thrust Vector Description and Mathematical Model
### Heliocentric Dynamics of a Sun-Facing Diffractive Sailcraft
In the previous equation, the single control term is the binary parameter \(\tau\), whose variation with \(\widetilde{t}\) is chosen in order to obtain an assigned phasing angle \(\Delta\phi\
eq 0\) with the minimum flight time \(\Delta\widetilde{t}=\widetilde{t}_{f}-\widetilde{t}_{0}\equiv\widetilde{t}_{f}\), where \(\widetilde{t}_{f}\) is the final time. In particular, the phasing angle \(\Delta\phi\) indicates the variation of the angular position along the elliptical reference orbit of the sailcraft, at the end of the maneuver, with respect to a virtual point whose initial position coincides with that of the sailcraft and which continues to travel along the reference (Keplerian) orbit in the time interval \(\Delta\widetilde{t}\). More precisely, a value \(\Delta\phi>0\) (or \(\Delta\phi<0\)) refers to a phasing ahead (or behind) the virtual point. The latter may represent, for example, a celestial body such as a planet, or another spacecraft with respect to which the diffractive sailcraft changes angular position along the same orbit. In particular, the polar angle \(\theta_{p_{f}}\) of the virtual point at the end of the phasing maneuver (i.e., at time \(\widetilde{t}=\widetilde{t}_{f}\)) can be obtained by solving a classical Kepler problem [51] or by integrating the equations of motion (6) enforcing the condition \(\widetilde{a}_{c}=0\) to obtain a Keplerian heliocentric motion. Bearing in mind that, (1) at the end of the phasing maneuver, the osculating orbit of the diffractive sailcraft coincides (again) with the reference elliptical orbit, and (2) the polar angle \(\theta\) is measured from the (fixed) apse line of the reference orbit, the dimensionless boundary conditions at time \(\widetilde{t}=\widetilde{t}_{f}\) are  
\[\widetilde{r}(\widetilde{t}_{f})=\frac{1}{1+e_{0}\,\cos\
u_{f}}\,\ \theta( \widetilde{t}_{f})=\theta_{p_{f}}+\Delta\phi\,\ \widetilde{v}_{r}(\widetilde{t}_{f})=e_{0}\,\sin\
u_{f}\,\ \widetilde{v}_{t}( \widetilde{t}_{f})=1+\ e_{0}\,\cos\
u_{f} \tag{7}\]  
where \(\
u_{f}=\text{mod}\left[\theta(\widetilde{t}_{f}),2\pi\right]\) is the true anomaly of the Sun-facing diffractive sailcraft along the elliptical reference orbit at the end of the phasing maneuver. The value of \(\
u_{f}\in[0,\,2\pi]\,\text{rad}\) is calculated through the modulo operation. The (dimensionless) temporal variation of the binary control term \(\tau\) is obtained through the optimization approach briefly described in the next subsection.
# Seismic Design of Steel Moment-Resisting Frames with Damping Systems in Accordance with KBC 2016
## 4 Design Example
### Three-Story Steel Moment-Resisting Frames with Metallic Yielding Dampers (3F-OMRF-MD)  
#### 4.2.1 Initial Design of Seismic-Force-Resisting System  
An example building with displacement-dependent damping devices is designed based on the KBC 2016, which is composed of three stories, five spans in X direction, three spans in Y direction. Figure 9a,b shows a three-dimensional view and plan view of the building. All the X-directional internal frames are identical and only one frame is used in this design example and represented in Figure 9c. The dead and live loads of the structure applied to building floors are 5.0 kN/m\({}^{2}\) and 3.5 kN/m\({}^{2}\), respectively, and identical for all stories. The building is assumed to be located in seismic zones I and belong to seismic risk category 'Special'. Site class \(S_{D}\) is assumed for the building. The seismic-force-resisting system of the building is designed as an ordinary moment-resisting frame of which design factors are listed in Table 1. Allowable story drift of 1.0 % for seismic risk category 'Special' is adopted.  
Figure 8: Load-deformation relationship for panel zone [22].  
Figure 9: Three-story steel moment resisting frame with metallic yielding dampers: (**a**) Isometric view, (**b**) plan, (**c**) elevation of internal frame.  
The initial design of the moment-resisting frame was performed for the minimum base shear with \(\eta=0.75\). The properties of columns and beams of the designed frame are listed in Table 3. The same section is used for each member in all stories. SM490 material was applied to all the members. The DCR of the initial design result is 0.95. Thus, the target base shear reduction factor required for damping systems is moderated to be 0.79, considering the DCR.  
From linear dynamic analysis using the response spectrum method, the maximum story drift without damping devices was 1.32%, which occurs at the first story and is higher than the allowable story drift of 1.0%. Therefore, it is necessary to reduce the story drift as well as base shear using damping devices. TADAS damping devices are installed at the center span using brace members listed in Table 3. Thus, the center frame and the damping devices shown in Figure 9c comprises a damping system.
# Quantification and Analysis of Group Sentiment in Electromagnetic Radiation Public Opinion Events
## 3 EMR Public Sentiment Analysis System Design
### Public Sentiment Analysis of EMR
#### 3.2.2 The Group Emotions Quantification Model Based on Cluster Propagation Chains  
This model for quantifying collective emotions goes beyond merely calculating the sentiment values of single-level comments. It incorporates multi-level emotion propagation chains and user behaviors to provide a more accurate measurement. When a Weibo post about EMR is published, users react by reposting, commenting, and liking the content. These actions, which directly engage with the post, are considered first-level feedback. Additionally, users can respond to first-level interactions through further reposts, comments, and likes, which are considered second-level feedback. The model integrates these multi-level interactions to form emotion propagation chains, simulating the spread of emotions in EMR discussions more realistically. Using this approach, the model identifies the original Weibo poster and first-level respondents as nodes, with the first-level feedback forming directed edges, thereby creating a macro cluster. Similarly, first-level and second-level respondents are treated as nodes, with second-level feedback forming directed edges, creating a micro cluster. By calculating the collective emotions within these two clusters and combining the results, the model provides a comprehensive group emotion value for the EMR event.  
The group emotion value of a cluster is calculated by integrating cluster density (\(A\)), trustworthiness (\(T\)), and average emotional tendency (\(E\)). For each cluster node, its in-degree \(\mathbf{In}d\) is determined by the total number of comments and likes it receives. The formula for quantifying cluster density (A) is \(\mathbf{D}=\frac{\mathbf{In}d}{Max(\mathbf{In}d)}\).  
If a post about EMR does not attract any comments or likes, indicating minimal user engagement and low cluster density, then even strong negative emotions expressed in the post will not propagate through the emotional transmission chain, thus preventing any significant adverse impact. Cluster trustworthiness (\(T\)) is measured using three indicators: cohesion \(T_{1}\), authority \(T_{2}\), and influence \(T_{3}\). When mainstream media, which are authoritative and have a large and influential follower base, report on EMR events, their emotions can influence a significant number of netizens, forming large clusters and guiding the overall group's emotional trend. The average emotional tendency (\(E\)) of a cluster is derived from the emotional value of comment texts using natural language processing. Ultimately, the group emotion value of a cluster is calculated as \(A*T*E\).
# Preparation of Magnetic Polymers for the Elimination of 3-Isobutyl-2-methoxypyrazine from Wine
## 2 Results and Discussion
### Adsorption Isotherms
Nonetheless, the \(m\) value of the Freundlich isotherm ranging from 0 to 1 indicates surface heterogeneity, where approaching zero means greater heterogeneity. The \(m\) value of 0.5436 for MMIP suggests that some heterogeneity was present; however, a more homogeneous surface could be assumed when the \(m\) value ranges between 0.5 and 0.9 [40]. The \(m\) value of 0.8822 suggested a more  
Figure 4: Adsorption isotherms of 3-isobutyl-2-methoxyyrazine (IBMP) (mean \(\pm\) SD, standard deviation, \(n=3\)) for (**A**) conventional and MW putative imprinted polymers and (**B**) thermally synthesised MMIP, MMIP, and unwashed MMIP. \(Q\): equilibrium adsorption amount; _Ci_: initial IBMP concentration. homogeneous surface for MNIP within the tested range, which is in line with the [PERSON] assumption. Similar observations were also found in previous studies where the surface of imprinted polymers was more heterogeneous than that of the non-imprinted polymers [39]. In addition, the heterogeneity may also be caused by the addition of templates. \(K\) value (Dubinin-Radushkevich) relates to the free energy \(E\) (kJ/mol) of adsorption per molecule of adsorbate when it is transferred to the surface of the solid from infinity in the solution. \(K<1\) represents a rough surface with many cavities, and chemisorption can be assumed when the value of \(E\) is over 40 kJ/mol [40]. Thus, chemisorption could be expected for the polymers based on values of 1000 and 316 kJ/mol for MMIP and MNIP, respectively. As shown in the isotherm graph (Figure 4B), specific binding was not observed in the adsorption analysis using model wine. This may be a result of the polymers being synthesised in a non-polar environment (toluene) rather than a wine matrix. A polar environment such as model wine (water, ethanol, tartaric acid) would destabilise the prearranged polymer complex [42] and MIPs should yield a better adsorption performance (relative to NIPs) in the same solvent as they were made [43; 44].
# Optimization of a Centrifugal Compressor Using the Design of Experiment Technique
###### Abstract  
Centrifugal compressor performance is affected by many parameters, optimization of which can lead to superior designs. Recognizing the most important parameters affecting performance helps to reduce the optimization process cost. Of the compressor components, the impeller plays the most important role in compressor performance, hence the design parameters affecting this component were considered. A turbocharger centrifugal compressor with vaneless diffuser was studied and the parameters investigated included meridional geometry, rotor blade angle distribution and start location of the main blades and splitters. The diffuser shape was captured as part of the meridional geometry. Applying a novel approach to the problem, full factorial analysis was used to investigate the most effective parameters. The Response Surface Method was then implemented to construct the surrogate models and to recognize the best points over a design space created as based on the Box-Behnken methodology. The results highlighted the factors that affected impeller performance the most. Using the Design of Experiment technique, the model which optimized both efficiency and pressure ratio simultaneously delivered a design with 3% and 11% improvement in each respectively in comparison to the initial impeller at the design point. Importantly, this was not at the expense of sacrificing range, of critical concern in compressor design.  
centrifugal compressor; Design of Experiment (DoE); full factorial analysis; Box-Behnken design; response surface method
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## 4 Feature Description
### Components and Connections
#### 4.2.2 Radar  
The radar system detects objects in the vehicle's path, measuring their speed and distance to assess collision threats. The connections for this component are as follows:  
* **Radar_to_Sensorfusion_CAN**: radar data are sent to the sensor fusion unit via the CAN for integration with other sensor data.
* **ESC_to_Radar_CAN**: the communication from the ESC to the radar is performed, possibly to align braking actions with detected threats.
* **SAS_to_Radar_CAN**: the SAS sends data to the radar to factor in the vehicle's current steering angle when assessing potential collision paths.
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### CoAP Management Interface (CoMI)  
The CoAP Management Interface (CoMI) [79] is a specialized protocol for managing IoT devices and networks with constrained resources. It uses CoAP (Constrained Application Protocol) as a transport layer to perform operations on device resources defined in YANG or converted from SMIv2 to YANG. CoMI focuses on lightweight device management and supports actions such as configuring, monitoring, and updating devices.  
In CoMI, resource management can be represented using a mapping between YANG-modeled resources \(\mathcal{R}_{y}\) and their CoAP representations:  
\[\mathcal{R}_{y}=\{\,\text{res}_{1},\text{res}_{2},\dots,\text{res}_{m}\},\quad \text{res}_{i}\ \mapsto\ \text{URI}_{\text{CoAP}} \tag{19}\]  
where each resource is identified by a URI. Operations such as GET, POST, PUT, and DELETE are performed over CoAP to manage resources.  
CoMI is widely used in _smart home systems_, _environmental monitoring_, and _wearable device configurations_, where lightweight communication is vital. However, despite its efficiency, CoMI faces scalability challenges in complex healthcare IoT networks, where diverse medical devices require high levels of security, fault tolerance, and real-time interoperability [80].
## 2 Materials and Methods
### Evaluation  
#### 2.3.1 Pseudonymization Performance  
Evaluation was based on a previously published study on the same text corpus but using an earlier version of the _Masketeer_ algorithm [25]. To validate _Masketeer_'s performance, 200 clinical notes were randomly selected from the complete corpus after applying _Masketeer_ to the whole corpus first. The resulting number of pseudonymizations per clinical note varied significantly due to the notes' contexts and lengths. Therefore, to ensure that the evaluation sample was representative of the overall corpus, the selection of the evaluation subsample was stratified based on the number of pseudonymizations per note.  
Each individual pseudonymization in all sampled notes was manually assigned its respective result for true positives (TPs) and false positives (FPs) according to the rules shown in Table 5. True negatives (TNs) were assigned for the note if no pseudonymization occurred correctly, and false negatives (FNs) were assigned to any missed PHI references.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Entity Type** & **Prefix** & **Recognition Rule** \\ \hline Healthcare professional & HCP & In HCP name dictionary \\ \hline Patient & PAT & In patient name dictionary \\ \hline Person & PER & In general name dictionary \\ \hline Medical site & SIT & In medical site dictionary \\ \hline Website URL & WEB & \\ Email address & MAI & \\ Physical address & ADD & Regular expression hit \\ Phone number & PHO & \\ ZIP code & ZIP & \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of entity types, pseudonym prefixes, and recognition rules applied during entity recognition.  
The achieved performance was compared to that of the older version of _Masketeer_[25] and out-of-the-box de-identification based on NER by the third-party library spaCy (ExplosionAI GmbH, Berlin, Germany).
# Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints
## 5 Simulation
### Simulation Parameters  
In order to objectively compare the performance of the constrained PL-MMSE with other algorithms, we adopt the same sensor moving trajectory as in [24], as shown in Figure 2. The sensor trajectory is divided into five constant velocity segments, where the end position of each segment trajectory is set as \([60\ 0]^{T}\) m, \([0\ 7.5]^{T}\) m, \([60\ 15]^{T}\) m, \([0\ 22.5]^{T}\) m, \([60\ 30]^{T}\) m and \([0\ 77.5]^{T}\) m. Starting from the initial position \(r_{0}=[60\ 0]^{T}\) m, the sensor takes the direct measurement value at every sampling interval \(T=0.1\) s. The bearing noise and process noise are a Gaussian mixture noise with zero mean. The estimated initial state \(\hat{x}_{1|1}\) is sampled from the true initial state \(x_{1}\) with a Gaussian mixture distribution, which has the initial covariance \(P_{1|1}\).  
Figure 2: Sensor trajectory with five constant velocity segments and the initial position marked by a star.
# Reliability Analysis of Military Vehicles Based on Censored Failures Data
## 2 Methodology
### Exponential Model  
The exponential model is a single-parameter distribution, described by the failure intensity \(\lambda\), assuming \(\lambda=\lambda(t)=\text{const}\). This model is applicable when describing the reliability of technical objects with a constant intensity of failure, which occurs as a result of random causes and not as a result of changes in technical conditions during the operation process. The reliability function for the exponential model takes the form:  
\[R(t)=e^{-\lambda t}. \tag{9}\]  
The failure probability density function for the exponential model takes the form:  
\[f(t)=\lambda e^{-\lambda t}. \tag{10}\]
## 3 Results and Discussion
### Mapping of the Wind Energy Potential
Conversely, during the warm season, notable variations emerge, particularly in the eastern sectors of the country, where there is a markedly higher wind potential in A1 and A2. The maximum monthly averages of the wind speed in the A1 and A2 regions (12.5-14.5 m/s) in July-August (Figure 8) are similar to the measured wind speeds in Zabol, Sistan during the summer season [106; 117]. The western border regions (A6 and A8), influenced by the northwesterly Shamal winds, also experience increased wind potential compared to the colder season. Furthermore, the outputs from the WRF model can be used to assess the economic feasibility of constructing wind turbine farms in appropriate areas in Iran. This evaluation involves considering the energy production capacity of wind turbines based on the simulated wind data, as well as estimating the associated costs and potential revenue from the generated electricity. Factors such as the capital investment required for constructing and maintaining wind turbines, transmission infrastructure, land availability, and government policies and incentives also need to be considered. By combining the meteorological data from the WRF model with atmospheric pollution, financial and economic analyses [119; 120; 121], decision-makers can make judgments about the viability and profitability of wind energy projects in specific regions of Iran. This information can guide the selection of suitable locations for wind turbine farms, optimize the design and layout of the turbines, and support the development of policies to promote renewable energy sources. It is worth noting that while the WRF model provides valuable insights, it is essential to validate its results with ground-based measurements and con  
Figure 8: The monthly mean wind speeds averaged over a 17-year period (2004–2020) based on WRF simulations for the designated regions (A1 to A10). sider other factors such as environmental impact assessments, social acceptance, and grid integration capabilities when making decisions regarding wind energy projects.
## 4 Experimental Results and Analysis
### Experiment Case III  
This test case introduces the complexity of epistatic interactions and therefore the challenge of finding the coupled parameters in a much enlarged search space. In this case, the epistatic interactions are introduced by a model with 4 chromosomes each 70 cM long and with 8 markers located at a distance of 10 cM. Like previous test cases QTLs are positioned exactly at marker loci; we modeled 3 QTLs with effect 0.7: marker 6 on chromosome 1, markers 1 and 5 on chromosome 2. We also considered an epistatic interaction between 2 QTLs with effect 0 independently, but in combination they will have effect 0.8: marker 2 on chromosome 1 and marker 3 on chromosome 3. There is no QTL on chromosome 4.  
The Pareto front for this test case is depicted in Figure 7. In this case, we see the same effect as before where the increase in quality of fit diminishes as over fitting occurs (beyond complexity 5). Detailed marker identification results are illustrated in Figure 8 showing the percentage of markers found. Since the epistatic interaction has the highest effect on the trait it is detected more than the other loci at low complexities, signalled by the two loci in the epistatic interaction being identified together with a high percentage. These results demonstrate the effectiveness of the method in identifying biologically significant coupled gene interactions.  
A comparison of the method with Bayesian Interval Mapping of Windows QTL Cartographer was not performed due to inability of the WQTLC tool to detect epistatic loci interactions.