esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Information-Centric Networking
### Why ICN for INC
ICN eliminates this dependency by naming content rather than endpoints, allowing content to be retrieved from any location where it is cached or hosted [52]. This feature is particularly advantageous in dynamic and mobile environments, such as those involving smartphones, autonomous vehicles, or IoT devices. When clients move, ICN seamlessly enables requests to be satisfied in an anycast fashion, from the nearest provider. This also minimizes the need for frequent updates to routing or forwarding tables, making ICN highly scalable even as the number of mobile devices grows exponentially. For INC, the native client mobility support ensures that computational tasks and their results remain accessible regardless of client location, enabling their uninterrupted access to in-network services. Managing mobility of content producers in ICN may present additional challenges, such as ensuring consistent routing updates. However, recent approaches, leveraging localized updates and stateful forwarding, have demonstrated that producer mobility can be effectively supported in a seamless fashion [53; 54].
# Grasshopper Algorithmic Modelling: Parametric Design for Product Platform Customisation
## 4 Case Studies
### Case Study 3: Desk Organiser Platform  
The third and final case study addressed the design of a highly customisable modular desk organiser. This strategy aimed to adapt the product to a wide range of specific requirements by modifying predefined parameters [79].  
Figure 4: Grasshopper algorithm of Case Study 2.  
The use of a modular platform ensured that each component could be modified, added, or replaced without affecting the integrity of the overall design. This approach is especially relevant in contexts where the diversity of user needs is high. It is also of high interest for its adaptation to various typologies of workspaces, whether shared or personal. It also enhanced the interconnectivity between various components of the system, improving efficiency and functionality.  
The following describes the process of creating the algorithm in GH (Figure 5) to design this platform based on a six-cell grid. First, the grid was created using the 'Square' command, which assigned binary values to the cells to indicate the placement of a lobe in the cells. Using the 'Dispatch' command, active cells can be selected and circles placed at their centres. They can then be related by tangent arcs with the command 'Tangent Arcs'. These relations allow a geometric pattern to be generated based on combinations of cells. The 'Loft' command was then used to generate surfaces between tangent arcs, and these were merged with the 'Merge' tool to create a single set of geometries. To generate a volume, the extrusion operation was applied, and a hollow inside the module was created by a smaller-scale solid using move and offset operations. Finally, a Boolean operation was performed to obtain the basic geometry, and the edges were rounded with the 'Fillet Edge'. The process also included the application of gradient perforations by separating surfaces and selecting the appropriate ones to apply the texture. The full script of the case study can be consulted (see Data Availability Statement).  
For the desk organiser, the analysis incorporated the full set of Group 1 design parameters (Table 1 in Section 2) relevant to customisation: dimensions, proportions, colour, shape, rounding, materials, and pattern. Group 2 platform parameters were also fully considered.
## 3 Results  
### Performance of Depolarization Fading with Self-Designed Probe  
The experimental system for measuring the performance of depolarization fading is shown in Figure 1. A tunable laser (HP 8168F) with a linewidth of 100 KHz was adjusted to 1550 nm. A fiber phase shifter was added in the reference arm to change the phase difference of the two interference arms with more than \(2\pi\). The driving frequency of the piezoelectric ceramic driver was set to 100 Hz. From Equation (9), it can be seen that \(\eta\) will equal \(\
u\) when \(I_{1}\) equals \(I_{2}\). Consequently, the degradation coefficient \(\eta\) can be measured through Equation (8). To make \(I_{1}\) equal to \(I_{2}\), three steps should be followed: (1) Remove the FRM and record the results of the PDs. (2) Place black tissue paper between the self-designed probe and the reflector to ensure that the outputs of the PDs are zero and then add the FRM. (3) Add an attenuator to the arm with the FRM and adjust it to make the results of the PDs equal to the record in Step 1. After the above three steps were implemented, three outputs of the PDs were acquired to measure the degradation coefficient \(\eta\) of three interference signals. The maximum and minimum of the interference intensity were acquired every 6 ms and the visibility was calculated according to Equation (8). First, the measured results were obtained under the condition with a Faraday rotator. All the operating steps were repeated under the condition without a Faraday rotator. The results recorded for 45 s are shown in Figure 5, and it can be seen that the visibility varies from approximately 0.1 to 0.4 without the Faraday rotator. However, the visibility is close to 1 and remains stable with the Faraday rotator. Meanwhile, the results show that the three interference signals have an almost equal visibility under the same conditions. This proves that the polarization fading is eliminated in the interference system with the self-designed probe.
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 2 MPC Implementation
### Linear MPC  
Most of the real-world processes are nonlinear by nature. However, for a limited working range of values, they frequently resemble linear behaviour. Linear MPC techniques include corrections of independent variables on the basis of the feedback received regarding the mismatch between the predicted and actual process outputs. The influence of changes in a large number of independent variables can be integrated in order to forecast the response of the dependent variables in MPCs that are purely based on linear models. The control problem is thus reduced to a series of quick and dependable linear matrix algebra calculations. There are several approaches that can be used when linear models are insufficiently precise to describe real-world nonlinearities. For instance, some process variables can be corrected after generating the linear MPC model to account for the nonlinear system behaviors. On the other hand, nonlinear MPC models can be utilised to effectively control such processes directly. Such nonlinear MPC models could be based on energy and mass balance fundamentals, or they could be empirical relations based on the information/data available (e.g., artificial neural networks). Moreover, a nonlinear model can also be linearized to yield a linear MPC or a Kalman filter. Table 2 displays the basic differences between the linear and nonlinear MPC configurations.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Linear MPC** & **Nonlinear MPC** \\ \hline Uses linear model \(x=Ax+Bu\) & Nonlinear model—\(x=f(x;u)\) \\ Quadratic cost function \(F=xTQx+uTRu\) & Cost function can be nonquadratic \(F=(x;u)\) \\ Linear constraints \(Hx+Gu<0\) & Nonlinear constraints \(h(x;u)<0\) \\ Quadratic program & Nonlinear program \\ \hline \hline \end{tabular}
\end{table}
Table 2: Linear and Non Linear MPCs.  
According to [PERSON] et al. [30], following a dual-mode MPC approach can drastically reduce online calculations while maintaining performance comparable to the isolated implementation of the same algorithm. By utilising information exchange among dual controllers, the authors proposed an information-sharing-based parallel solution approach to effectively solve complex optimization problems.
## 2 Basic Theory  
### Continuous Wavelet Transform (CWT)  
CWT is a time-frequency analysis method, using wavelets to decipher the signal in the time-frequency domain. It can quantify the time change in non-stationary signal frequency; the scale and translation parameters can adjust the size of the sliding window without losing time and frequency resolution. The fundamental theory of continuous wavelet transform (CWT) can refer to the relative investigations [24; 25; 26]. For signal x(t), CWT is defined as follows [24]:  
\[\mathrm{w_{t}}(\alpha,\tau)=\frac{1}{\sqrt{\alpha}}\int_{-\infty}^{\infty} \mathrm{f}(\mathrm{t})\cdot\varphi(\frac{\mathrm{t}-\tau}{\alpha})\mathrm{dt} \tag{1}\]  
\(\frac{1}{\sqrt{\alpha}}\varphi(\frac{\mathrm{t}-\tau}{\alpha})\) is the wavelet's basic function, known as the mother wavelet. The mother wavelet's choice has a big effect on the time-frequency analysis. \(\tau\) is the shift factor that has to do with time. \(\alpha\) is the scale factor that has to do with frequency. The time and frequency resolution ratio can be adjusted, and the formula for proportional conversion frequency is:  
\[\mathrm{F=}\frac{\mathrm{F_{c}\times f_{g}}}{\alpha} \tag{2}\]  
The wavelet transform coefficient reflects the correlation between the function and the wavelet on the selected scale, and the one-dimensional signal is converted into the wavelet coefficient through the CWT to project the two-dimensional time-frequency image.
## 3 Results and Discussion
### Flow Field
#### 3.1.3 Effect of \(L_{d}\) on the Flow Field  
Three cases with \(L_{d}\) values of 12, 16, and 20 m were studied. As Figure 8 shows, the effect of \(L_{d}\) on cross-sectional average wind speed is most obvious in the vortex zone. The synthesis in Figure 7 shows that the growth of \(L_{d}\) does not significantly increase the length of the vortex zone. However, \(L_{d}\) affects \(u_{\rm return}\) in the return zone, even though the total length of the model is not very long. At the same \(u_{0}\), the steady \(u_{\rm return}\) measured by the model tends to decrease as \(L_{d}\) grows.
###### Abstract  
The monitoring of respiratory rate is a relevant factor in medical applications and day-to-day activities. Contact sensors have been used mostly as a direct solution and they have shown their effectiveness, but with some disadvantages for example in vulnerable skins such as burns patients. For this reason, contactless monitoring systems are gaining increasing attention for respiratory detection. In this paper, we present a new non-contact strategy to estimate respiratory rate based on Eulerian motion video magnification technique using Hermite transform and a system based on a Convolutional Neural Network (CNN). The system tracks chest movements of the subject using two strategies: using a manually selected ROI and without the selection of a ROI in the image frame. The system is based on the classifications of the frames as an inhalation or exhalation using CNN. Our proposal has been tested on 10 healthy subjects in different positions. To compare performance of methods to detect respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for the automatic strategy is \(3.28\pm 3.33\%\) with and agreement with respect of the reference of \(\approx\)98%.  
Keywords:respiratory rate estimation; non-contact monitoring; motion video magnification; hermite transform +
Footnote †: journal: Applied
## 4 Conclusions  
Electroabsorption has been measured for the first time for metallic nanoparticles in transparent dielectric media. In particular, gold nanoparticles in glass and subnanometer-size metallic domains in iodine-doped nonconjugated conductive polymer have been studied. The measurements have been made using ac field at 4 kHz. Electroabsorption has a quadratic dependence on electric field. The imaginary part of Kerr coefficient (K) has been determined for different sizes of metallic nanoparticles. The magnitude of K\({}^{\prime}\) has been found to increase rapidly (as \(\sim\)d\({}^{-3}\)) for smaller diameters (d) of nanoparticles. The magnitudes of K\({}^{\prime}\) are: \(\sim\)\(4\times 10^{-15}\) m/V\({}^{2}\) for 15 nm gold particles and \(\sim\)\(3.2\times 10^{-11}\) m/V\({}^{2}\) for subnanometer metallic domains in nonconjugated conductive polymer respectively at 633 nm. This is similar to the results obtained in the quadratic electro-optic measurements (real part of Kerr coefficient) of these systems reported recently [6]. Nonconjugated conductive polymers have metallic domains of subnanometer dimensions (quantum dots) leading to the largest known Kerr coefficients (real and imaginary parts). The results reported here imply various applications of these systems in ultrafast switching/modulation, and other applications in laser-optics and optoelectronics.  
**Author Contributions:** Conceptualization, M.T.; data curation, J.V.C.; formal analysis, M.T.; investigation, J.V.C.; methodology, M.T.; validation, M.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Integrating BIM and GIS for an Existing Infrastructure
## 3 Interoperability Issues
### BIM-GIS Incompatibilities  
BIM-GIS integration has some incompatibilities and limitations. Firstly, they are two technologies that utilise different approaches, languages, and common data environments (CDEs). Most GIS applications, formats, and tools are 2D based, but BIM-GIS integration is associated with 3D modelling of infrastructure. Therefore, much GIS data and analyses do not have a counterpart in BIM.  
Standardization in GIS has aimed at the transmission of information by means of service models, facilitating use through open standards and online formats. However, in BIM, there is no equivalent concept; this approach is in opposition to the concept of collaboration in BIM. BIM collaboration within cloud environments is restricted to project members, enhanced by defining CDEs. This collaboration is defined as access to information through the so-called Exchange of Information Requirements (EIR) or the uses established for the BIM model in the BIM Execution Plan (BEP). In the GIS domain, free access to information is provided through the services derived from the model, whereas in the BIM domain, access to the model is restricted.  
On the other hand, the information in GIS is stored in databases, so it is possible to access the information using query languages, without the need for the geometric model. This is not possible in the BIM domain without intermediate programming.
## 5 RT POD Processing and Analysis
### Availability of PPP-B2b in the RT POD of the TJU-01 Satellite
Secondly, more GPS satellites than BDS-3 satellites are observed by the TJU-01 satellite at each epoch. Figures 11 and 12 reveal that except over the southeastern Pacific, southwestern Atlantic, and South America, the TJU-01 satellite receives PPP-B2b signals from above most parts of the world. The main receiving area of the PPP-B2b signal for the TJU-01 satellite is over the Chinese mainland. Figure 11: Sub-satellite point trajectory of the TJU-01 satellite with available numbers of GPS PPP-B2b (**top**) and BDS PPP-B2b (**bottom**) corrections. The detailed statistical availability of the GPS PPP-B2b for the TJU-01 satellite is given in Table 8. The valid number of GPS observations of the TJU-01 satellite is 2711 epochs. The number of satellites observed is 20,237, and the number of PPP-B2b corrections received is 5253. The numbers of epochs receiving different numbers of corrections are given in Table 8. The epochs receiving PPP-B2b account for 70.5% of all epochs. The epochs receiving more than three PPP-B2b corrections account for nearly 35% of all epochs. The number of PPP-B2b corrections received by the TJU-01 satellite exceeds 50% of the number of satellites observed at the same epoch account in nearly 19% of all epochs. Relative to the GPS broadcast ephemeris, the GPS PPP-B2b at these observed epochs should improve the accuracy of the RT POD of the TJU-01 satellite. The detailed statistical availability of the BDS-3 PPP-B2b for the TJU-01 satellite is given in Table 9. The valid number of observations of the TJU-01 satellite is 2667 epochs. The number of satellites observed is 20,295, and the number of PPP-B2b corrections received is 6669. The epochs receiving PPP-B2b account for 87.3% of all epochs. The epochs receiving PPP-B2b corrections account for nearly 50% of all epochs. The number of PPP-B2b corrections received by the TJU-01 satellite exceeds 50% of the number of epoch satellites in nearly 30% of all epochs. Relative to the BDS-3 broadcast ephemeris, the BDS-3 PPP-B2b at these observed epochs improves the accuracy of the RT POD of the TJU-01 satellite. Figures 11 and 12 and Tables 8 and 9 reveal that compared with the case for the BDS-3 satellites, the GPS satellites receive PPP-B2b data over a smaller geographical range. There are two main reasons. (1) The constellation configurations are different. The MEO satellites of BDS-3 are higher than the GPS satellites, which allows the TJU-01 satellite to monitor the BDS-3 MEO satellite flying over the Asia-Pacific region for longer. (2) Owing to the smaller number of signal channels, the GPS observation data integrity is lower than that of  
Figure 12: Distribution of the proportion of PPP-B2b signal visibility from the TJU-01 satellite. the BDS-3 observation data, resulting in fewer available GPS PPP-B2b corrections and a smaller geographical range of reception for the GPS PPP-B2b. These differences may result in the different accuracy of the RT POD of the TJU-01 satellite.
## 2 Materials and Methods
### Electrochemical Measurements  
The electrochemical performances were evaluated throughout cyclic voltammetry (CV), galvanostatic charge/discharge (GCD), electrochemical impedance spectroscopy (EIS), and long-term cycling on an electrochemical workstation (IM6, Zahner, Kronach, Germany) at room temperature. The configuration, potential window, and electrolyte were set as a three-electrode system, 0-0.5 V and 2 M KOH, respectively. The Ni\({}_{1\text{-}}\)xCo\({}_{\text{x}}\)(OH)\({}_{2}\)/NF served as a working electrode directly while Pt net and Ag/AgCl (3.5 M KCl) were employed as a counter electrode and reference electrode. The measured ranges of scan rate for CV, current density for GCD, and frequency for EIS were 2-50 mV/s, 1-20 A/g and 100 mHz-100 kHz, respectively. The long-term cycling was executed at a constant current density of 20 A/g for 2000 cycles.  
The specific capacitance, energy density, and power density based on GCD measurement are defined as Equations (1)-(3), respectively [43]:  
\[C= \frac{It}{mV}, \tag{1}\]  
\[E= \frac{CV^{2}}{7.2}, \tag{2}\]  
\[P= \frac{3.6E}{t}. \tag{3}\]  
where, \(C\) (F/g) is the specific capacitance, \(I\) (A) is the discharge current, \(\Delta t\) (s) is the discharge time, \(m\) (g) is the mass of active material, \(\Delta V\) (V) is the voltage window, \(E\) (Wh/kg) is the energy density, and \(P\) (kW/kg) is the power density.
# Falls among Older Adults: Screening, Identification, Rehabilitation, and Management
## 6 Rehabilitation and Management
### Physical Exercise
Several studies show that using a multicomponent program (e.g., strength, aerobic, and balance exercises) is effective and has fewer adverse effects, compared with other types of exercise [61; 62; 63; 64; 65; 66]. Indeed, evidence suggests that multimodal physical interventions, especially under supervision, appear to reduce the risk of falls and improve balance [67]. Resistance-based and multimodal exercises enhance physical performance in general [62]. In addition, resistance-based exercises improve several other conditions that may indirectly contribute to falls or complicate a fall, such as sarcopenia, osteoporosis, musculoskeletal health, and promotes maintenance of functional abilities and physical performance and a stable mood [68; 69; 70; 71]. In contrast, other studies suggest that moderate- to high-intensity balance training plans substantially reduce falls compared with controls, even in patients with cognitive impairment or Parkinson's disease [59; 72; 73]. In addition, it is unclear whether acting with exercises that improve muscle strength and balance also have a protective effect concerning fall-related injuries, hospitalizations, or mortality [74; 75; 76; 73]. Nutritional modifications, such as the supplementation of protein or vitamin D supplements, are synergistic with exercise, improving outcomes in terms of muscle strength, risk of sarcopenia, and frailty [77; 78; 79; 80]. In addition, some studies seem to indicate long-term maintenance of the results obtained after the end of the active period of exercise [81; 82]. Starting a rehabilitation program with physical exercise reduces the risk of falls in both institutionalized and noninstitutionalized patients [81; 83]. Meditative movements are among the modalities investigated for a possible role in fall prevention. Tai Chi Chu'an (Tai Chi) and Yoga are two of the most famous and worldwide accessible forms of such physical activities. Tai Chi is a Chinese practice that blends the ancient philosophy of Yin-Yang with a combination of deep breathing techniques and slow, gentle movements, improving co-ordination, proprioception, and flexibility [84; 85; 86; 87]. Tai Chi also appears to be a protective activity against falls. However, it is unclear whether practicing this activity changes the time to first fall [63]. Yoga is a well-known type of intervention that appears to improve balance in those with balance deficits. Yoga appears to improve motor function, balance, and walking [84]. This type of activity is light-hearted and, therefore, may be better tolerated and accepted than conventional techniques. In addition, this type of activity is frequently associated with meditative spiritual activities that can be a vehicle of motivation for the patient. This modality seems to reduce fall number, although some studies indicate that the benefit is not maintained beyond six months after the end of the activity [88; 89]. It is still unclear if Yoga can effectively reduce short- or long-term falling risk.
## 2 Mathematical Model
### Entropy Generation Analysis
#### 2.1.2 Fluid Flow  
The outlet pressures (\(P_{\rm fo}\) and \(P_{\rm eo}\)) refer to the analysis of the pressure drops experienced by the two streams. The Reynolds number \(Re\) is defined in Equation (14).  
\[Re=\frac{\rho_{\rm a}v_{\rm m}D_{\rm h}}{\mu} \tag{14}\]  
The parameter \(v_{\rm m}\) is the area-weighted mean velocity in the inlet (m/s). \(\mu\) is dynamic viscosity (Pa s). The parameter \(D_{\rm h}\) is the hydraulic diameter of the channel and is defined in Equation (15) [1].  
\[D_{\rm h}=\frac{4V_{\rm cyc}}{A_{\rm cyc}} \tag{15}\]  
The parameters \(V_{\rm cyc}\) and \(A_{\rm cyc}\) are the volume and surface area of the channel, respectively.  
\[A_{\rm cyc}=4\cdot(2H\cdot\tan\frac{\theta}{2})\cdot\frac{H}{\cos\frac{\theta} {2}} \tag{16}\]  
\[V_{\rm cyc}=2\cdot(H^{2}\cdot\frac{\theta}{2})\cdot(2H\cdot\tan\frac{\theta}{ 2}) \tag{17}\]  
The correlation friction factor \(f\) is used by the correlations of Zhang [33], presented as follows:  
\[f=6.536 Re^{-0.421} \tag{18}\]  
Pressure drop of channels is associated with the air density, area-weighted mean velocity, channel length \(L\) and hydraulic diameter, thus the outlet pressures (\(P_{\rm fo}\) and \(P_{\rm eo}\)) of the heat exchanger are expressed as follows:\[P_{\text{fo}} =P_{\text{fi}}-\frac{\frac{1}{2}f_{\text{f}}\rho_{\text{a}}v_{\text{m} }^{2}L_{\text{f}}}{D_{\text{h}}} \tag{19}\] \[P_{\text{eo}} =P_{\text{ei}}-\frac{\frac{1}{2}f_{\text{e}}\rho_{\text{a}}v_{\text {m}}^{2}L_{\text{e}}}{D_{\text{h}}} \tag{20}\]  
In addition, the comprehensive thermal hydraulic performance index \(JF\) is defined in Equation (21) [34].  
\[JF=\frac{j}{f^{1/3}} \tag{21}\]