esa-sceva/satcom-chunk-collection huggingface dataset:
# Tradeoffs When Building and Running Cohort and Patient-Level Markov Simulation Models
## 3 Results
### Variability, Sample Size, Computation Time  
Table 1 presents the variability in results and simulation runtimes for different sample sizes in the PL model. More detailed results for the NMB can be found in Appendix C.  
Figure 3 shows the average computation times required for the PL model and the changes in the coefficient of variation (CV) as sample size increases. Computation times for the CH model were almost instantaneous (less than 1 s). In contrast, the PL model took around 10 s to run 1000 simulated patients and just over 1 min (69 s) for 10,000 patients. Excluding additional programming procedures, such as workbook protection and progress bar display, reduced computation time by about 4 s, irrespective of the sample size. Table 1 also shows that while increasing sample size reduces uncertainty (CV), the process becomes only marginally more time-consuming.  
Figure 2: Heat map of patient distribution across health states, highlighting deviations between the cohort (CH) and patient-level (PL) models. Rows represent cycles and columns represent health states numbered from 1 to 8 and 1′ to 8′. The percentages below the tables indicate the range of differences (as described in the ‘Patient pathways’ section) used to determine the color scheme: (**A**) shows a range of 0.00–1.00%, while (**B**) shows 0.00–0.12%. Lighter cells denote minor deviations, whereas darker cells represent larger discrepancies between patient pathways in the CH and PL models.  
The computation times of the DSA and PSA differed significantly between the CH and PL models. The DSA for the CH model took over 6 min (379 s), compared to around 90 min (5360 s) for the PL model with 1000 patients. The PSA for the CH model (10,000 iterations) required fewer than 4 min (226 s), while for the PL model, it took about 2 h (7372 s) for 1000 patients with 1000 iterations, and 20 h (1194 min) for 10,000 patients with 1000 iterations.
## 2 Materials and Methods
### Data Source  
#### 2.2.1 Precipitation Data  
This study employed daily-scale precipitation data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and GLDAS (Global Land Data Assimilation System). Community Land Model version 3.5 (CLM3.5), developed by the National Center for Atmospheric Research (NCAR), was utilized to simulate the exchanges of energy, moisture, and carbon cycles between the land surface and the atmosphere. Released in 2007, this model incorporates recent observational data and theoretical advancements from the fields of ecology and hydrology. It features a modular design that includes components for vegetation dynamics, soil stratification, and snowpack processes. The driving data for the CLM3.5 and the assimilation system require meteorological variables such as the near-surface air temperature, barometric pressure, specific humidity, wind speed, downward shortwave surface radiation, downward longwave surface radiation, and precipitation rate. Additionally, the spatial resolution of the CLM3.5 data was enhanced using the Delta downscaling method. This method involves interpolating low-resolution simulated data to a high-resolution grid and then calculating the relationship between the simulated and observed data at the raster scale for the historical period's multi-year average.  
This study employed CLM3.5 precipitation data for the period from January 2008 to December 2023, with a spatial resolution of 2 km \(\times\) 2 km, obtained via the Delta downscaling method. Additionally, ERA5 daily precipitation data were selected for the  
Figure 1: An overview map of the study area. (**a**) Location of the Northeast region in China; (**b**) spatial distribution of the river network and major terrain in the study area; (**c**) land use types in Northeast China.  
period from May to September 2008-2014, with a spatial resolution of 0.1\({}^{\circ}\). To accurately analyze the precipitation remote sensing products, data from 90 meteorological sites in the study area were selected to verify the accuracy with CLM3.5 and ERA5 precipitation data, respectively.
## 4 System Design and Goals
### Message Sequence
#### 4.2.8 DB_ROUTES State  
The final state in the proposed research is the DB_ROUTES. Nodes across the network use the advertised links extracted from TC messages to populate the following tables or sets:  
* Advertising Remote Router Set;  
Figure 8: HI message process for sender.  
Figure 9: HI message process for receiver. Receivers who did not hear initial HELLO message must set EMERGENCY_HELLO to 0.  
* Router Topology Set;
* Routable Address Topology Set;
* Attached Network Set.  
DB_ROUTES has the longest TTL amongst the proposed message sequence. The proposed TTL for DB_ROUTES is 10 s per MPR group.  
The new HI message packet format conforms to the generalized MANET packet format as specified in [47]. The general format of a HI message is shown in Figure 10.
## 4 Results
### Regression Analysis
#### 4.1.2 Development of the Regression Model  
A second-degree polynomial regression model was selected to capture potential nonlinear relationships among the variables. The independent variables incorporated in the model were as follows:  
* Froude Number (\(Fr\)): A dimensionless parameter defined as \(Fr=\frac{v}{\sqrt{g\times h}}\), where \(v\) is the towing speed and \(h\) is the clean clearance height.
* Squat Ratio (\(Sq\)): Represents the ratio of initial clearance height to final distance to the seabed (\(Sq=\frac{\zeta}{\zeta}\)).
* Time (\(t\)): Duration of towing (s), squared to capture nonlinear effects.
* Floating Object Type (\(Obj\_T\)): Categorical variable representing bow shape (0 for blunt bow, 1 for naked bow).
* Interaction Terms:
* \(Fr\times Obj\_T\): Interaction between Froude_Number and \(Obj\_T\),
* \(Sq\times Obj\_T\): Interaction between squat ratio and \(Obj\_T\),
* \(\text{t}^{2}\times\text{Obj\_T}\): Interaction between squared time and \(\text{Obj\_T}\).  
The regression equation for the Box-Cox-transformed suspended sediment concentration is expressed as follows:  
\[\begin{array}{c}BoxCox(S)=7.80+5.11\times Fr+1.1\times Sq-0.79\times\text{t }^{2}-1.06\times Obj\_T-\\ 0.72\times\text{Fr}\times Obj\_T+0.12\times Sq\times Obj\_T-0.08\times\text{ Obj\_T}\end{array} \tag{15}\]  
where  
\(S=\text{suspended sediment concentration (kg/m${}^{3}$)}\),  
\(t=\text{time (s)}\),  
\(Sq=\text{squat ratio}\),  
\(Obj\_T=\text{floating object type (0 for blunt bow, 1 for naked bow)}\),  
\(Fr=\text{Froude number}\).  
The summary statistics of the regression model, including coefficients, standard errors, \(t\)-values, and \(p\)-values, are presented in Table 3 for each term.
# Effect of Radial Height of Helical Static Blade on the Cavitation Performance of Inducer
## 3 Simulation Method
### Governing Equations  
Cavitation flow is commonly considered to be an incompressible viscous fluid, so in this paper, the Reynolds-averaged Navier-Stokes equations considering the continuity equation are employed as follows:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u_{i})}{\partial x_{i}}=0 \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & \(k\) & **Radial Height \(h_{r}\)/mm** \\ \hline Scheme 1 & 0.05 & 4 \\ Scheme 2 & 0.075 & 6 \\ Scheme 3 & 0.1 & 8 \\ Scheme 4 & 0.125 & 10 \\ Scheme 5 & 0.15 & 12 \\ Scheme 6 & 0.175 & 14 \\ Scheme 7 & 0.2 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the radial height of helical static blade in different schemes.  
Figure 2: Computed area and calculated grids.  
\[\frac{\partial(\rho u_{i})}{\partial t}+\frac{\partial\big{(}\rho u_{i}u_{j}\big{)} }{\partial x_{j}}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x _{i}}\Bigg{[}\mu\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{ j}}{\partial x_{i}}-\frac{2}{3}\delta_{ij}\frac{\partial u_{k}}{\partial x_{k}} \right)\Bigg{]}+\frac{\partial\big{(}-\rho\overline{u_{i}^{\prime}u_{j}^{ \prime}}\big{)}}{\partial x_{j}} \tag{2}\]  
where \(\rho\) is the density of the mixed fluid, \(u_{i}\), \(u_{j}\), and \(u_{k}\) are the velocity components, \(p\) is the pressure, \(\mu\) is the turbulent viscosity, and \(-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\) is the Reynolds stress.
# Emerging Technologies for Injury Identification in Sports Settings: A Systematic Review
## 3 Results
### Biomarker-Based Solutions  
Biomarkers offer consistent diagnostic accuracy for SRC and PPCS by detecting neurological changes and stress. Six studies [11; 14; 16; 19; 21; 23; 25] investigated NfL, GFAP, Cerebrospinal Fluid (CSF), and salivary markers in boxing, rugby, and diving. NfL levels rose significantly post-SRC (\(p\) < 0.0001), correlating with impact severity and distinguishing rapid versus prolonged symptoms [21; 25]. GFAP peaked within one hour post-injury (\(p\) = 0.017), normalising by 10 days, unlike non-mTBI controls (\(p\) > 0.999) [21]. CSF markers, including IL-2 and TNF\(\alpha\), were elevated in athletes with PPCS (\(p\) < 0.05), indicating an ongoing inflammatory process [11; 14]. Salivary microRNAs (e.g., miR-27b-3p) showed up-regulation post-SRC (\(p\) = 0.016) [23]. NfL and GFAP provided superior sensitivity compared to salivary markers, but all required laboratory analysis, severely limiting their sideline use. Conducted post-injury by clinicians, these biomarkers excel in controlled diagnostics and could complement sensors' real-time potential following an integration of the two methodologies.
## 2 Method
### Optimizers
#### 2.2.2 LBFGS Optimizer  
Broyden-Fletcher-Goldfarb-Shanno is a quasi-Newton-based optimization algorithm commonly used for training neural networks. The loss landscape of a PINN is highly complex due to competing loss terms, making BFGS an effective choice for training PINNs.  
BFGS [49] is a gradient method that iteratively computes the Hessian matrix of the loss function, and this process requires \(O(n^{2})\) gradient evaluations, where n represents the number of parameters. The BFGS curvature matrix can be updated without the need for matrix inversion, and this reduces the computational cost significantly. However, since the Hessian matrix is the foundation of the BFGS algorithm, memory usage increases as the square of the number of parameters. This results in rapid memory usage growth, making it impractical to use this approach for neural networks with a large number of parameters. The implementation of BFGS algorithm is referred to Algorithm 2.  
The BFGS algorithm may use large amounts of memory, but L-BFGS [50] solves this issue by storing a few vectors that represent an estimate of the full Hessian matrix. Compared to BFGS, L-BFGS is more computationally efficient, uses less memory, and can handle problems with larger numbers of parameters. Due to its lower memory requirements, the L-BFGS algorithm has become the favourite among second-order optimization techniques.  
```
1:Input: Initial guesses \(x_{0}\) and \(B_{0}\), tolerance tol
2:Set \(k=0\)
3:repeat
4: Obtain descent direction \(d_{k}=-B_{k}^{-1}\
abla f(x_{k})\)
5: Set \(\alpha_{k}=1\)
6: Calculate the step \(s_{k}=\alpha_{k}d_{k}\)
7: Update the design \(x_{k+1}=x_{k}+s_{k}\)
8:if\(|x_{k+1}-x_{k}|<\) tol or \(|\
abla f(x_{k+1})|<\) tol then
9:break
10:endif
11: Obtain the variation in the gradient \(y_{k}=\
abla f(x_{k+1})-\
abla f(x_{k})\)
12: Update the Hessian approximation \(B_{k+1}=B_{k}\)
13: Increase the iterator \(k=k+1\)
14:until convergence
```  
**Algorithm 2** BFGS Method
# High-Expansion Foam: A Risk Control Option to Increase Passenger Ship Safety during Flooding
## 3 Solutions for Damage Stability Protection Using Foam: AREST System
### Deployable Watertight Barriers: AREST A2  
During the flooding process of a complex environment, the internal geometry of a ship and the progressive flooding paths can be divided in major progressive floodwater paths. Minor paths represent small corridors and spaces connected within each other in a specific zone, thus pathways that allow local flooding of the ship. Main paths are mainly large corridors connecting two contiguous zones of the ship, thus arteries that may rapidly spread floodwater along the ship (see Figure 5a). In order to most effectively deal with progressive flooding in ships, it is best to focus on blocking the major arteries.  
To this end, deployable barriers are a suitable solution that may contain flooding without modifying the internal layout of the vessel. AREST deployable barriers comprise two lightweight shutters made of GRP or steel laths (normally A-class fire-rated), distanced 30 cm apart. Shutters can be deployed also in case of fire casualties or drills. In case of flooding, the cavity between shutters is filled with expanding foam delivered from a compressed foam canister. The shutters are mechanically operated to be deployed vertically (see Figure 5b) or horizontally (see Figure 5c) to suit local structural details and arrangements. The barrier can also span distances up to 30 m, with intermediate supports and can be deployed within minutes to curtail and control previously identified critical floodwater pathways.  
In this respect, from the results of time domain flooding simulations, an effective damage control plan could be set up and suitably executed with the aid, for example, of a suitable decision support system. This allows isolating the damage area following any foreseeable critical flooding event for which progressive flooding is responsible for loss.  
Figure 5: AREST A2 system; (**a**) main (red) and minor (blue) flooding path on a cruise ship deck zone. (**b**) Vertically deployable shutters. (**c**) Horizontally deployable shutters.  
Such an approach implies the execution of evacuation analyses for selected critical cases to identify that deployable barriers do not interfere with the evacuation process. This offers a distinct advantage over existing damage control plans, which rely entirely on fixed design measures, having limited flexibility and effectiveness with probable critical loss scenarios.
## 4 Conclusions  
In this work, a methodology to calculate the optimum homogeneous phantom size for plane wave electromagnetic (EM) attenuation characterization in the human torso is defined. It is applicable for far-field link budgets calculations in Active Medical Implants (AMI) at Industrial Scientific Medical (ISM) and MedRadio frequency bands.  
Increasing the size of the defined numerical phantom until field convergence is achieved poses a convenient strategy to truncate the dimensions that represent the whole body.  
Figure 10: Normalized measurements vs. simulation (using theoretical and manufactured muscle dielectric properties). (a) f = 2.45 GHz and (b) f = 5.8 GHz.  
By applying a 2 dB convergence criterion, a phantom size of 355 mm \(\times\) 160 mm \(\times\) 255 mm for 402 MHz and 868 MHz frequency bands and 100 mm\({}^{3}\) and 50 mm\({}^{3}\) cubic phantoms for 2.45 GHz and 5.8 GHz frequency bands, respectively, are recommended for subcutaneous applications (0-10 mm insertion).  
Likewise, for deep implanted applications (10-50 mm insertion), a size of 355 mm \(\times\) 260 mm \(\times\) 255 mm for 402 MHz and 868 MHz and a cube of 200 mm\({}^{3}\) and 150 mm\({}^{3}\) for 2.45 GHz and 5.8 GHz frequency bands, respectively, are recommended.  
Very significant reductions of the higher frequencies are obtained with respect to a full body model: between 49.8% and 77.7% for the lower frequency cases and between 87.7% and 99.8% for the higher frequency cases. Thus, solid foundations to reduce the computational and manufacturing resources to implement representative EM phantoms are provided.  
The measured field results by two implanted broadband probe antennas into simplified water and sugar liquid phantoms showed virtually the same values when normalized by the maximum field at the phantom/air interface as soon as the phantom size is above the specified limits.  
These measured results were compared with the model predictions and certified that they are independent from the implanted antenna as long as the latter was not electrically large.  
Conceptualization, I.O.-I., X.C. and D.V.; methodology, I.O.-I., Y.G. and D.V.; software, I.O.-I. and A.R.; validation, I.O.-I., A.R. and D.V.; writing--original draft preparation, I.O.-I. and D.V.; writing--review and editing, I.O.-I. and D.V.; supervision, D.V., Y.G. and X.C.; project administration, D.V., Y.G. and X.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Education Department of the Basque Country Government and by the Spanish Ministry of Science Innovation and Universities through the project Sistema de COMunicacion RFid para juals de acuicultura (FICASES-COM), ref TEC2017-89403-C2-1-R.  
Not applicable.  
The authors would like to thank [PERSON], the lab manager of the Antenna Measurement laboratory in Queen Mary University of London for his support in the anechoic chamber measurements. The authors appreciate the University (TECNUN) collaboration agreement with CST.  
The authors declare no conflict of interest.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 3 IEEE 802.15.7 Overview  
This standard defines a PHY and MAC layer for short range optical wireless communications using visible light, employing free space as propagation medium. Three network topologies are defined: star, peer-to-peer and broadcast. These topologies are shown in Figure 5, and their main characteristics are summarized as follows:  
* **Star**: communication is made between several devices and one central controller, called the coordinator, which is also used as an illumination infrastructure. The rest of devices are typically mobile terminals powered by batteries.
* **Peer-to-peer**: allows two devices to communicate, provided they are within reach of each other. One of the devices acts as the communication coordinator
* **Broadcast**: one coordinator sends data to one or several devices in a uni-directional way.
# Research on the Principle of a New Flexible Screw Conveyor and Its Power Consumption
## 1 Introduction  
Screw conveyors are widely used in modern production and in the logistics of transporting granular materials [1]. When traditional rigid screw conveyors convey sticky bulk materials, the phenomenon of material gathering at the inner wall happens. This will reduce the transmission efficiency of the screw conveyor.  
A flexible tube screw conveyor is proposed in this study to expand the self-loading range. The chief shaft and outer tube are made of rubber, and the screw blade is made of continuous-rigidity plastic. The conveyor to swing widely in a broaden range for the flexibility [2]. A method is proposed to reduce the wear and tear of the screw conveyor. In addition, the power consumption variation of the conveyor transmission affected by the structure parameters of the screw is analyzed [3]. The filling factor of the screw conveyor is a key point for the transmission efficiency. It can be determined by using a ribbon-type screw at different screw speeds [4]. A feeding device based on equal diameter and dual-spiral is designed for animals feeding. The structure parameters and rotary speed are optimized with the simulation software EDEM. Then, the theoretical results are verified by an experimental test [5].  
In this paper, a flexible, discrete spiral blades screw conveyor is proposed. The power consumption parameters when using a flexible helical blade structure were studied by theoretical analysis and simulation experiments.
## 5 Results
### Virtual Sensor Fault-Correction Effect  
The impact of detecting faults in the SCWT sensor was evaluated using a virtual sensor and by taking appropriate actions for correcting the sensor, such as sensor calibration or replacement. The evaluation focused on changes in indoor thermal comfort and energy consumption.
## 3 Results and Discussion
### Questionnaire Results
These findings reinforced the idea that occupants prefer interaction with the building and to have some personal autonomy to reach  
Figure 4: SBS Symptoms prevalence (%). their desired comfort level [53]. Improved personal control of the IEQ by applying more available controls to the occupants could further satisfy their thermal comfort and IAQ needs, and also reduce the energy consumption by centralized HVAC systems, especially in open space offices [54; 55]. These results are also enforce the findings in a recent study in which [PERSON] et al. revealed the link between occupant and their behaviors of sharing energy and environment control systems and interactions with their colleagues [56]. Figure 5: Frequency of controling (**a**) temperature, (**b**) ventilation, (**c**) shade, (**d**) light and (**e**) noise. The questionnaire also gathered information about the occupants' self-reported productivity ranging from \(-30\%\) up to \(+30\%\). Figure 6 shows that tself-reported productivity at workplace presents an increasing trend when the perception of comfort and control of the IEQ parameters is higher. More specifically, while occupants reported greater levels of overall comfort satisfaction, from \(3.5\) to \(5\), a remarkable increase in their productivity was noticed, i.e., from \(-30\%\) to \(+10\%\). The same pattern is also revealed for the controlling parameters, especially that of temperature, while on the other hand noise control seems to be the least affecting parameter on productivity. The significance of perceived control in productivity was highlighted in another study conducted at \(37\) European offices, where a small increase in the low control area resulted in a clear increase in productivity [34]. Also, a UK study discovered that the comfort, health, and productivity of workers were all related to their perceived personal thermal control [57]. The participants, after completing the evaluation of the current IEQ perception in their offices, were asked to reevaluate their perception during the working hours before the lock-down period. The Wilcoxon statistical test was used to compare the pre- and post-lockdown period satisfaction scores. Figure 7 presents the IEQ parameters that appeared to be statistically significant. The perception of overall comfort, temperature, light and noise was significantly higher during the time of the study. On the contrary, regarding privacy, the occupants' perception was evaluated as significantly lower after returning to offices.