esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
 & & & & & & & \\ \hline Area EO & 687.92 \(\pm\) 750.55 & 454.64 \(\pm\) 791.51 & **0.036** & 665.06 \(\pm\) 500.40 & 490.38 \(\pm\) 307.23 & 0.273 & **<0.001** \\ \hline Area EC & 493.21 \(\pm\) 373.11 & 285.82 \(\pm\) 451.64 & **0.012** & 485.03 \(\pm\) 444.32 & 541.79 \(\pm\) 449.65 & 0.237 & **0.011** \\ \hline Romberg Index & 1.52 \(\pm\) 1.09 & 1.99 \(\pm\) 1.34 & 0.327 & 2.48 \(\pm\) 10.02 & 1.90 \(\pm\) 2.83 & 0.068 & 0.060 \\ \hline COP EO & 121.03 \(\pm\) 45.24 & 98.60 \(\pm\) 51.11 & **0.023** & 148.14 \(\pm\) 70.57 & 149.14 \(\pm\) 77.55 & 0.465 & **0.004** \\ \hline COP EC & 130.86 \(\pm\) 50.40 & 89.73 \(\pm\) 56.79 & **0.012** & 130.53 \(\pm\) 65.54 & 148.79 \(\pm\) 69.04 & 0.273 & **0.031** \\ \hline Trunk movement EO & 0.10 \(\pm\) 0.03 & 0.08 \(\pm\) 0.02 & **0.012** & 0.10 \(\pm\) 0.03 & 0.10 \(\pm\) 0.04 & 0.086 & **<0.001** \\ \hline Trunk movement EC & 0.11 \(\pm\) 0.04 & 0.08 \(\pm\) 0.03 & **0.012** & 0.11 \(\pm\) 0.03 & 0.11 \(\pm\) 0.04 & 0.114 & **<0.001** \\ \hline Trunk, swing range AP EO & 11.53 \(\pm\) 6.21 & 8.73 \(\pm\) 6.58 & 0.263 & 13.40 \(\pm\) 7.43 & 11.95 \(\pm\) 6.32 & 0.456 & 0.374 \\ \hline Trunk, swing range AP EC & 11.93 \(\pm\) 8.16 & 9.38 \(\pm\) 9.03 & 0.093 & 13.88 \(\pm\) 8.59 & 14.76 \(\pm\) 8.58 & 0.447 & 0.118 \\ \hline Trunk, swing range ML EC & 11.96 \(\pm\) 6.66 & 9.82 \(\pm\) 6.37 & 0.208 & 12.93 \(\pm\) 5.67 & 11.77 \(\pm\) 5.42 & 0.144 & 0.980 \\ \hline Trunk, swing range ML EC & 12.06 \(\pm\) 6.90 & 9.19 \(\pm\) 7.61 & **0.012** & 11.43 \(\pm\) 6.95 & 11.25 \(\pm\) 6.35 & 0.455 & **0.046** \\ \hline Sitting, burden \% EO & 81.41 \(\pm\) 5.08 & 81.43 \(\pm\) 4.75 & 0.779 & 83.31 \(\pm\) 4.39 & 82.20 \(\pm\) 4.22 & 0.177 & 0.332 \\ \hline Sitting, burden \% EC & 82.13 \(\pm\) 5.82 & 83.71 \(\pm\) 5.55 & 0.347 & 84.29 \(\pm\) 5.38 & 84.05 \(\pm\) 4.81 & 0.068 & 0.085 \\ \hline Sitting, positioning R EO & 34.80 \(\pm\) 25.05 & 42.07 \(\pm\) 30.08 & 0.575 & 53.83 \(\pm\) 23.63 & 59.65 \(\pm\) 28.99 & 0.671 & 0.106 \\ \hline Sitting, positioning R EC & 45.91 \(\pm\) 31.65 & 57.64 \(\pm\) 33.44 & 0.173 & 59.26 \(\pm\) 23.14 & 58.38 \(\pm\) 21.11 & 0.539 & 0.106 \\ \hline Sitting, positioning L EO & 65.19 \(\pm\) 25.05 & 57.93 \(\pm\) 30.08 & 0.575 & 46.17 \(\pm\) 23.63 & 40.35 \(\pm\) 28.99 & 0.614 & 0.899 \\ \hline Sitting, positioning L EC & 54.09 \(\pm\) 31.65 & 42.36 \(\pm\) 33.44 & 0.178 & 40.74 \(\pm\) 23.14 & 41.61 \(\pm\) 21.11 & 0.593 & 0.297 \\ \hline Sitting, swing range COP & 15.40 \(\pm\) 7.49 & 10.85 \(\pm\) 9.34 & **0.036** & 17.34 \(\pm\) 8.95 &  
in sitting mean speed COP EC in ML (\(p\) = 0.036). Regarding LOS, statistically significant differences emerged for trunk maximum inclination right (\(p\) = 0.004), left (\(p\) = 0.013), and forward (\(p\) = 0.047) (Supplementary Figure S3). On the other hand, regarding static balance with the raised seat, only COP path EC (\(p\) = 0.031) and trunk movement EO (\(p\) = 0.047) and EC (\(p\) = 0.050) were found to be statistically significant when comparing between groups (Supplementary Figure S4). As for reactive balance, no statistically significant differences were found between the two groups.
## 2 Theoretical Foundation and Hypotheses Development
### Technological Factors  
Technology (network) readiness is a country's ability to capitalize on opportunities provided by information and communications technology [30]. A country's networked readiness is determined by whether it has the resources needed for technological innovations to reach their full potential and whether these developments have an impact on the economy and society [31]. The speed at which a nation embraces new technology depends on its citizens' levels of readiness, knowledge, and political leadership [32]. Consequently, we hypothesize that network readiness affects a country's central bank's digital currency acceptance because it assesses individuals, organizations, and macro-level readiness for the availability of facilities, knowledge, and the drives required for technology adoption.  
**H1.**_A country's technological readiness is positively related to its adoption of CBDCs._
## Appendix A Modeling of the Wind/PV Hybrid System
### Modeling of a Wind Turbine  
A wind turbine converts the kinetic energy of the wind into mechanical energy, which is then transformed into electrical energy through an electric generator. The power generated by a wind turbine depends on the interaction between its rotor and the wind. The power available in the wind is described by the following:  
\[P_{wind}=\frac{1}{2}\rho AV_{w}^{3}, \tag{2}\]  
and the extractable power can be written as  
\[P_{Rtot}=\frac{1}{2}C_{p}\rho AV_{w}^{3}, \tag{3}\]  
where:  
\(\rho\): The density of the air (kg/m\({}^{3}\));  
\(A\): The area swept by the blades of the turbine (m\({}^{2}\));  
\(V_{w}\): Wind speed (m/s);  
\(C_{p}\): The power coefficient of the rotor, which is defined as the ratio between the extractable power and the available power.  
[PERSON] [35] proved that \(C_{p}=0.5926\) under ideal conditions. The height of the wind turbine can improve the performance of the wind turbine by increasing the wind speed according to the following expression:  
\[V=V_{0}(\frac{h}{h_{0}})^{\alpha}, \tag{10}\]  
where:  
\(h\) is the mast height (m);  
\(h_{0}\) is the reference height (10 m);  
\(V\) is the wind speed (m/s) at the height h of the mast;  
\(V_{0}\) is the wind speed (m/s) at the reference height \(h_{0}\);  
\(\alpha\) is the power law exponent (taken as \(\frac{1}{2}\)).
# Speeding up Statistical Tolerance Analysis to Real Time
## 6 Results and Discussion
### Statistical Tolerance Synthesis
#### 6.2.2 Sample Size Required for a Reliable Estimate  
Figure 9a displays the manufacturing costs calculated by the statistical tolerance synthesis (with CuPy Float 32 F-Order) vs. the sample size, from Section 6.2.1. Figure 9b displays, on the basis of a sample size of \(1.0\times 10^{7}\), the corresponding standard deviation \(\sigma_{p}\), in order to determine if a sample size of 10,000 is sufficient for the tolerance optimization problem.  
The spread of the results of the statistical tolerance syntheses in Figure 9a,b show that for the given tolerance problem, a sample size of at least 50,000 samples should be used to satisfy the boundary condition (\(\sigma_{p}<0.1\) mm) with sufficient confidence. Smaller sample sizes tend to result in manufacturing costs, and thus tolerances, that do not comply with the required boundary conditions of the statistical tolerance synthesis. On this basis, computation using the GPU is preferable due to its lower runtime for the differential evolution. Figure 10 illustrates the reduction of manufacturing costs during optimization. The optimization is carried out using the parameter settings shown in Table 5 and a sample size of 100,000 with the CuPy single precision Float F-Order i  
Figure 8: Performing Statistical Tolerance Synthesis on CPU (NumPy) vs. GPU (CuPy): Average runtime of the differential evolution step with error bars. ** workers = \(-1\): Distributes the populations among all available CPU cores for parallel computation, * workers = 1: A single Python process is spawned.  
Figure 9: Statistical tolerance synthesis using GPU (CuPy Float 32 F-Order): (**a**) Manufacturing costs vs. sample size for 10 runs, and (**b**) standard deviation \(\sigma_{p}\) (calculated with \(1.0\times 10^{7}\) sample size) vs. sample size.  
optimal tolerance specification is obtained after 55 steps of the evolution (approximately 13.5 s).
## 5 Experimental Outcomes, Comparative Analysis, and Discussion
### Experiment 2: Marine Trajectory Forecasting  
The second type of experiment involved trajectory prediction of the vessel. Multivariate forecasting was performed with ('LAT', 'LON', 'SOG', 'COG', 'Heading') input features. The target features represented the location of the vessel as ('LAT, 'LAN'). A validation set was not employed, and the data was grouped into training and testing sets in a 50/50 ratio. The LSTM-BPSO obtained the best performance with the best metrics, as Table 9 indicates. The second best in the same category was the original PSO. The LSTM-ChoA obtained the lowest value for the worst category, while the LSTM-GA had the best results for mean and median values. The LSTM-BA model had the smallest standard deviation and variance. The MSE and RMSE were the best with the LSTM-BPSO, and the LSTM-PSO had the best results for the R\({}^{2}\), MAE, and EDE, as displayed in Table 10.  
The results of the trajectory predictions for all metaheuristics are visualized using box plots for the objective function and \(R^{2}\), as well as kernel density estimation (kde) and diversity swarm plots for the same two predictions in Figure 7. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & \(\mathbf{R}^{2}\) & **MAE** & **MSE** & **RMSE** & **IoA** & **EDE** \\ \hline LSTM-BPSO & **0.997921** & 0.007313 & **0.000098** & **0.009985** & 0.999769 & 0.012385 \\ LSTM-PSO & 0.997912 & **0.006937** & 0.000099 & 0.009987 & **0.999774** & **0.011048** \\ LSTM-GA & 0.997311 & 0.008302 & 0.000128 & 0.011299 & 0.999708 & 0.013484 \\ LSTM-ABC & 0.9979361 & 0.008045 & 0.000126 & 0.011216 & 0.999711 & 0.012139 \\ LSTM-BA & 0.997554 & 0.007366 & 0.000117 & 0.010794 & 0.999735 & 0.011591 \\ LSTM-WOA & 0.997002 & 0.009676 & 0.000142 & 0.011930 & 0.999672 & 0.015390 \\ LSTM-HHO & 0.997090 & 0.009045 & 0.000139 & 0.011795 & 0.999679 & 0.013913 \\ LSTM-ChoA & 0.997654 & 0.008605 & 0.000111 & 0.010547 & 0.999744 & 0.013513 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Detailed metrics for the best performing models—trajectory forecasting experiment. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Learning Rate** & **Dropout** & **Epochs** & **Layers** & **Neurons Layer 1** & **Neurons Layer 2** \\ \hline LSTM-BPSO & 0.000656 & 0.005000 & 50 & 1 & 27 & / \\ LSTM-PSO & 0.000125 & 0.020000 & 40 & 1 & 31 & / \\ LSTM-GA & 0.006729 & 0.060654 & 39 & 1 & 22 & / \\ LSTM-ABC & 0.004661 & 0.105701 & 44 & 2 & 16 & 24 \\ LSTM-BA & 0.000086 & 0.005000 & 40 & 1 & 32 & / \\ LSTM-WOA & 0.003918 & 0.154873 & 34 & 1 & 27 & / \\ LSTM-HHO & 0.001845 & 0.093174 & 41 & 1 & 26 & / \\ LSTM-ChoA & 0.002802 & 0.144773 & 46 & 1 & 20 & / \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Best obtained model hyper-parameters—trajectory forecasting experiment.
# Post-Quantum Migration of the Tor Application
## 2 Literature Review
### Tor Design and Architecture
#### 2.1.1 Circuits and Handshake
On the other hand, their hybrid design heavily relies on the client role (661 \(\upmu\)s for client init and 74%), which could increase latency if the client's device has limited resources. However, their hybrid design implementation reduces the number of computational steps and amount of data exchanged. The evaluation is limited to NTRUEncrypt only; comparing it with other quantum-robust schemes would shed light on the efficiency of the overall hybrid implementation. They highlight a major obstacle: the size of the circuit-extension handshake \"CREATE\" cells is limited to 505 bytes (in NTOR), while ntruees443 ep1 requires 693 bytes [11]. The work resulted in two distinct specification proposals: one aiming to widen the cell size [5] #249, and the other to enable the hybridisation of the ntor protocol and a KEM. The first one has been superseded by proposal #340, introducing the sub-protocol \"RelayCell\", which focuses on cell packing and fragmentation. The second, Ref. [5] #263, was made obsolete by proposal #269; created by the authors of the papers and Tor's developers, it takes the incorporation of different post-quantum KEMs further and emphasises compatibility with Tor's handshake. Currently, the Tor code includes four different circuit-extension handshakes [10]:  
* \"CreateFast\" deprecated (unauthenticated, non-forward-secure) handshake, which was previously used for the first hop of each circuit. * The ntor handshake [12]. * The onion service not handshake variant allows each party to encrypt data (without forward secrecy) after the first message. Clients have used it since version 3 of the OS protocol to encrypt data in the introduction and rendezvous cells [5] #224. * The ntor v3 also permits each party to encrypt data at the cost of FS, enables the client to send an authenticated encrypted message within its onion skin and allows the relay to send an encrypted and authenticated reply as part of its response [5] #332. In 2019, [PERSON] et al. [14] proposed a 0-RTT handshake relying on puncturable KEMs to achieve a lower latency design than notr; yet, the paper shows that it can result in higher computational overhead on certain low-power devices. The authors claim to achieve \"immediate\" FS, which appears to meet Tor's need for perfect FS, as the immediate variant mandates the deletion of ephemeral keys instantaneously. This approach could facilitate key management and allow the integration of post-quantum KEMs within the Tor handshake.
## 2 Materials and Methods  
### LBFtomato Leaf Image Datasets  
The original experimental data utilized the publicly available Plant Village dataset, which comprises ten categories of tomato leaves, including nine categories of tomato diseases and one category of healthy tomato leaves. The categories are early blight, late blight, powdery mildew, leaf mold, Septoria leaf spot, bacterial spot, spider mites, yellow leaf curl virus, brown spot, and healthy leaves, totaling 18,835 images. The dataset was divided into a training set and a test set in a 7:3 ratio. To increase the number of samples in each category to approximately 1000 data augmentation techniques such as flipping, translation, and brightness adjustment were first applied to the training dataset in Plant Village. To ensure data balance, the dataset was then cleaned by removing some interfering samples, resulting in a Plant Village training dataset of 13,062 images. Finally, a new dataset named LBFtomato was created by adding real tomato leaf images taken from the Changsha tomato planting base and real-world tomato leaf images obtained from Kaggle and GitHub. LBFtomato comprises 10 categories of tomato leaves, including nine categories of tomato diseases and one category of healthy tomato leaves, and has real-world tomato leaf data, which can better verify the effectiveness of the model. A number of images in the Plant Village dataset and the LBFtomato dataset are shown in Tables 1 and 2, respectively. The initial tomato leaf images were resized to 256 \(\times\) 256, as depicted in Figure 1[37].  
The data augmentation was performed using the ImageDataGenerator function in Keras, with specific parameter settings: 40 degrees of rotation; 0.2 of horizontal and vertical translation; 0.2 of perspective transformation; 0.2 scaling; horizontal flip; and padding and random brightness in the nearest mode.
# Relative Positioning in Remote Areas Using a GNSS Dual Frequency Smartphone
## 3 Satellite Visibility and Observed Signals  
In this section, we present, for the tests carried out, the observation conditions regarding satellite visibility and signal availability, as well as the signal quality.
# Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition
## 5 Experiments  
The proposed method is evaluated on two large-scale datasets, including the NTU RGB+D dataset [6] (the largest skeleton based action recognition dataset so far), and the UOW large scale combined (LSC) dataset [34]. Adam optimization [35] is used with the initial learning rate \(2\times 10^{-3}\). The learning rate for each layer under the inputs of different lengths is adjusted based on the learning rate adjustment factor as shown in Section 4.2. The overall learning rate is decayed by 10 once the evaluation accuracy does not increase (with patience 100). Dropout is applied after each layer with the same dropping mask shared over time. The detailed dropping probability for each dataset is further described in the following Subsections. The lengths are sampled with a normal distribution \(normal(m,\sigma)\). For the NTU RGB+D dataset [6], \(m\) is set to 20, which is the most widely used in the existing works. For simplicity, the sequence length used in each batch is set to be the same, and the lengths of different batches are different. The IndRNN network, consisting of 6 layers and 512 neurons, is used as in [10]. The other training setups are similar as in [10]. Following the convention, the testing results are reported in the following experiments. Since this paper focuses on the investigation of the RNN-based skeleton action recognition, the graph convolution based ones [25; 26] are not discussed nor compared.
# Magnetic Properties of Nanocomposites
## 4 Ceramic Nanocomposites
### Ceramic Nanocomposites Containing Fe
In spite of such an incomplete transformation, the magnetic properties of the considered Fe NCs are compatible with usage in practical applications; these materials have been successfully exploited in water remediation [62] and DNA separation [63], as shown in Figure 9.  
The sorption isotherm of simazine on (Fe,H)A800C--0 min and (Fe,H)A600C--90 min samples is reported in Figure 9 (left), with other competing materials (zeolite H-Y and micro- and meso-porous silica SiO2(II)400) being included for comparison. The simazine concentration in the practice of water remediation from agrochemicals is typically in the range 9-10 umol/L to below a law limit of 0.25 umol/L [74; 75]; in such a concentration range, the adsorption isotherm of magnetic adsorbents (Fe,H)A800C--0 min and (Fe,H)A600C--90 min lies far above those of both silica sample SiO2(II)400 and zeolite H-Y, thus certifying their higher efficiency in the simazine adsorption process. Standard DNA adsorption isotherms using samples FeA800C--0 min and FeA750C--2 h are illustrated in Figure 9 (right). Both samples exhibit a sufficient magnetic response, allowing an easy separation of the solid adsorbent from the supernatant liquid by exposition to an external magnet. Considering the porosity and adsorption behavior of the final products, sample FeA800C--0 min,  
Figure 8: Room temperature hysteresis loops of four Fe NCs obtained from an A-type zeolite under different thermal treatments and exchange processes. although characterized by a weaker magnetic signal, exhibits a far higher surface area than sample FeA750C--2 h, which explains the better performance in DNA adsorption in the former sample. From this viewpoint, the thermal treatment used to obtain sample FeA800C--0 min generates a more suitable NC than the treatment used to obtain sample FeA750C--2 h.
## 6 Discussion
### Research Limitations and Future Research Prospects  
In addition to those problems listed in the previous sections, there are many research issues beckoning for further investigation. In this paper, we ignore the UAV energy factor in the objective function and assume that the UAV is fixed. The UAV can be a tethered UAV, which can adopt a ground power source transmitted via tethered cables to achieve a long period of hovering. Compared with the balloon-based platform or the fixed camera on a pole scheme, UAVs can be flexibly deployed in three-dimensional (3-D) space according to the user's special requirements and has better anti-damage capability.  
As a future work, we plan to study the UAV energy consumption and combine it with our proposed algorithm for practical application. In addition, cooperative secure transmission is also a factor that cannot be neglected in the MEC-enabled UAV-assisted VANET architecture, which can be an interesting aspect for further investigation.
## 2 Computational Model and Method
### Numerical Setup  
ANSYS CFX software was adopted for calculations and flow analysis. The continuity and momentum equations are expressed as follows:  
\[\frac{\partial(\rho u_{i})}{\partial x_{i}}=0, \tag{1}\]  
\[\rho\frac{Du_{i}}{Dt}=\rho F_{i}-\frac{\partial p}{\partial x_{i}}+\frac{ \partial}{\partial x_{j}}\left[\mu\left(\frac{\partial u_{i}}{\partial x_{j}}+ \frac{\partial u_{j}}{\partial x_{i}}\right)\right]+\frac{\partial}{\partial x _{i}}(\lambda\
abla\cdot v), \tag{2}\]  
where \(F_{i}\) is the unit mass force; \(P\) and \(\rho\) are the pressure and density, respectively; \(t\) is time; \(\mu\) is the dynamic viscosity; \(u_{i}\) and \(u_{j}\) are the components of absolute velocity, \(v\); and \(\lambda\) is the volume expansion coefficient.  
The shear stress transport k-\(\omega\) two-equation turbulence model was adopted in the present study. This model can precisely predict flow separation phenomena. The equations are as follows:  
\[\frac{\partial(\rho k)}{\partial t}+u_{j}\frac{\partial(\rho k)}{\partial x_{j }}=\frac{\partial}{\partial x_{j}}\left[(\mu+\frac{\mu_{t}}{\sigma_{k3}})\frac {\partial k}{\partial x_{j}}\right]+P_{k}-\beta^{*}\rho k\omega, \tag{3}\]  
\[\frac{\partial}{\partial t}(\rho\omega)+u_{j}\frac{\partial}{\partial x_{j}} (\rho\omega)=\frac{\partial}{\partial x_{j}}((\mu+\frac{\mu_{t}}{\sigma_{ \omega 3}})\frac{\partial\omega}{\partial x_{j}})+\frac{2\rho\sigma_{\omega 2}(1-F_{1})}{ \omega}\frac{\partial k}{\partial x_{i}}\frac{\partial\omega}{\partial x_{i}} +\alpha_{3}\frac{\omega}{k}P_{k}-\beta_{3}\rho\omega^{2}, \tag{4}\]  
where \(F_{1}\) and \(F_{2}\) represent the blending function; \(k\) and \(\omega\) represent the turbulence kinetic energy and turbulence eddy frequency, respectively; \(P_{k}\) is the excessive generation of turbulence energy; \(\mu_{t}\) is the eddy viscosity coefficient; and \(\alpha_{3}\), \(\beta^{*}\), \(\beta_{3}\), \(\sigma_{k3}\), \(\sigma_{\omega 2}\), and \(\sigma_{\omega 3}\) are the constants.  
In the present research, a total pressure of 101,325 Pa and mass discharge were adopted at the computational domain inlet and outlet. A no-slip wall boundary condition was imposed on all solid walls. For the interface between the rotating part and the fixed part, we adopted a frozen rotor. Cavitation was not taken into consideration.
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### Netconf/RESTCONF in IoT for Healthcare  
NETCONF [74] and RESTCONF protocols offer effective mechanisms for configuring and monitoring healthcare IoT networks. Utilizing structured data formats such as XML and JSON, they automate network updates, thus ensuring interoperability among diverse healthcare devices.  
Configuration updates across devices in the network can be represented by a set:  
\[C=\{X_{1},X_{2},\ldots,X_{n}\},\quad\text{where $X_{i}$ is the configuration parameter for device $A_{i}$}. \tag{3}\]  
The manager performs updates to a device by employing a remote procedure call (RPC) as follows:  
\[C^{\prime}_{i}=f(M,X_{i},\text{Update}), \tag{4}\]  
Figure 2: MQTT protocol.  
where \(f(\cdot)\) is a function describing the update process, \(M\) denotes the managing entity, and \(X_{i}\) specifies the current configuration parameters of device \(A_{i}\). Typically, \(f(\cdot)\) is implemented as an integral operation defined over time or update steps, represented as  
\[C_{i}^{\prime}=\int_{t_{0}}^{t_{f}}g(M,X_{i}(t),\text{Update}(t))\,dt, \tag{5}\]  
where \(g(\cdot)\) describes the instantaneous rate or mechanism of configuration change, and \(t_{0}\) and \(t_{f}\) are the initial and final limits indicating the duration of the update process.  
The NETCONF and RESTCONF protocols streamline remote configurations, enhancing the seamless integration and interoperability of IoT diagnostic devices in healthcare settings. Additionally, they facilitate secure firmware updates, thus bolstering both security and operational performance. These protocols also provide flexibility in dynamically adjusting configurations during emergencies, ensuring continuous connectivity and efficient operation within healthcare facilities. However, these protocols are relatively complex and may not be ideal for low-power healthcare IoT devices, as they require higher computational resources and introduce parsing overhead due to their reliance on structured data formats like XML and JSON [75].