esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 M2M Communication
### Overview on M2M Communication Technologies
To solve this problem, IETF CoRE (Constrained RESTful Environments) working group has specified Constrained Application Protocol (CoAP) standard with the goal of supporting REST-like applications in constrained environments. The second challenge is related to the sizes of IP packets and headers. To solve this problem, IETF has created the 6 lowpan (IPv6 Low Power wireless Area Networks), which describes an adaptation layer between IPv6 and a layer 2 protocol, such as (but not limited to) IEEE 802.15.4, to handle MTU sizes and compress IPv6 headers from 60 bytes to 7 bytes. There are also other challenges arising from heterogeneity of M2M devices and local M2M asset networks, and coding and integration of M2M application content.
# Digital Twin for Supply Chain Coordination in Modular Construction
## 5 Discussion
Presently, many construction players such as general contractors and sub-contractors may not be willing to participate in modular construction projects because of uncertain risks issues. Nobody wants to have liability for the uncertain risks such as schedule deviation and quality degradation in their supply chain. If such risks can be accurately predicted or detected in real-time, they can be clarified, allocated, and shared fairly or prevented altogether. This paper focused only on predicting schedule deviation risks in the logistics process. Further research should be conducted for predicting overall supply chain risk (e.g., quality issues and cost overrun) management. First, offsite module manufacturing processes and onsite module assembly processes should be modeled in detail in a digital twin. For example, we can allow the digital twin can monitor the module manufacturing progress, or perform onsite module quality inspections (e.g., alignment checking and surface quality inspection). Then, such detailed models should be linked to the logistics scenario to enable overall supply chain simulation. Second, various IoT sensor data should be obtained for richer risk management. For example, this paper only considered the module's location data for logistics simulation. To consider overall supply chain coordination in modular construction other important data (e.g., module production status, geometrical quality, surface quality, and equipment schedule) should be obtained with different sensors in the future study. Lastly, when a risk occurs as predicted, the burden or loss due to the risk must be shared fairly among related stakeholders. For example, if the delivery of a module is delayed because of an unexpected weather change, the onsite assembly schedule may also be delayed, resulting in a large cost overrun. By sharing the loss due to such risk among related stakeholders, the negative impact of the unexpected event will be absorbed. In a future study, we will investigate how risks in modular construction can be fairly shared among stakeholders with aid of the digital twin.
# A Review on Vibration Control Using Piezoelectric Shunt Circuits
## 2 Types, Applications, and Recent Advances of Piezoelectric Shunt Circuits
### Single-Mode Resistive (R), Resonant (RL), and (RLC) Circuits
The first optimization approach is the transfer function optimization method that works based on optimizing circuit parameters to eliminate or minimize peaks in the transfer function at resonance frequencies, hence reducing the system's response to vibrations [29; 30; 31]. The other method is the pole placement technique. This method focuses on tuning electrical characteristics so that the system poles are as far to the left as possible on the complex plane. Shifting these poles improves the system's stability and damping properties, resulting in betterattenuation [32; 33]. [PERSON] et al. [34] proposed an approach for selecting the circuit parameters based on a mathematical formulation of the real and imaginary parts of the complex eigenfrequencies by solving for the natural vibration problem. [PERSON] et al. [35] demonstrated a method for resistor and inductor tuning for shunt circuits using commercial finite element software ANSYS 18.0. The tuning method is based on using the finite element software to find the short circuit and open circuit natural frequencies of the given structure to find the optimal resistor and inductor values for vibration attenuation. In order to maximize the time-averaged electric power dissipation in the shunt resistor and optimize a shunt piezoelectric vibration absorber, [PERSON] [36] introduced an online extremum seeking tuning technique. The method sequentially adjusts the shunt resistance and inductance using a two-path gradient search algorithm. Those approaches stand out from others that use optimization algorithms for tuning circuit parameters, such as particle swarm optimization [37; 38], ant colony optimization [39], and other tuning techniques [40; 41]. The optimization in piezoelectric shunt circuits is not exclusive to electrical parameters. The dimensions and the position of the piezoelectric patch play a huge role in the performance of the system by affecting the EMCF and the capacitance of the piezoelectric patch [42]. They suggested adjusting the arrangement and configuration of shunt piezoelectric patches to enhance vibration damping by improving the modal electromechanical coupling factor (MEMCF). The appropriate patch thickness is dependent upon the Young's modulus ratio, and its placement must correspond with regions of maximal mode shape slope difference. Symmetric configurations featuring two patches demonstrated greater efficacy than asymmetric arrangements.
## 2 Data and Methods
### Methods
#### 2.3.1 Quantifying Urban Expansion Dynamics  
This research adopts the quantitative method proposed by [PERSON] et al. to measure urban areas [62], utilizing changes in the area and the urban land growth rate to quantitatively analyze the expansion dynamics of urban areas from 2001 to 2021. The calculation formula for changes in urban areas is as Equation (1):  
\[Urb_{Ak}=A_{t_{1}}-A_{t_{2}},\ t_{2}>t_{1} \tag{1}\]  
where \(Urb_{Ak}\) represents the change in urban land from year \(t_{1}\) to year \(t_{2}\), and \(A_{t_{1}}\) and \(A_{t_{2}}\) denote the urban land areas in years \(t_{1}\) and \(t_{2}\), respectively. We employed this method to conduct a quantitative analysis of urban expansion and its area in the Lanxi urban cluster between the years 2001 and 2021, precisely determining the specific area of urban expansion over these two decades. Given the differences in the initial sizes of various cities, the sheer magnitude of the expansion area does not directly reflect the rate of expansion. Therefore, we used Equation (2) to calculate the expansion rate for each city:  
\[R_{(t_{1},\ t_{2})}=\frac{A_{t_{2}}}{A_{t_{1}}},\ t_{2}>t_{1} \tag{2}\]  
where \(R_{(t_{1},t_{2})}\) represents the ratio of urban land between year \(t_{2}\) and year \(t_{1}\), indicating the urban expansion rate.  
This formula calculates the multiple of urban expansion by comparing the sizes of the city's area before and after the expansion, intuitively reflecting the changes in the city's scale. Subsequently, we used Equation (3) to further calculate the average annual expansion rate for each city:  
\[YR_{(t_{1},\ t_{2})}=\left(\left(\frac{A_{t_{2}}}{A_{t_{1}}}\right)^{\frac{1} {t_{2}-t_{1}}}-1\right)\times 100\%,\ t_{2}>t_{1} \tag{3}\]  
The Urban Expansion Intensity Index (UEI) is calculated by dividing the expanded area by the product of the initial area and the time interval [63]. This method provides a measure of expansion intensity relative to the initial area and time used to assess the intensity of urban area growth over a specific period. The calculation method is Equation (4):  
\[UEI=\frac{(A_{t_{2}}-A_{t_{1}})}{(A_{t}\times\Delta t)},\ t_{2}>t_{1} \tag{4}\]  
where \(A_{t_{1}}\) and \(A_{t_{2}}\) represent the built-up area between any two consecutive years, \(A_{t}\) represents the total built-up area of the study area, and \(\Delta t\) represents the time interval between \(t_{1}\) and \(t_{2}\).
## 4 Results
### Performance Evaluation
#### 4.1.2 Feature Selection and Dimensionality Reduction
Better accuracy results are observed, in general, when a feature selection method is used, in contrast to the whole feature set model (Table 4). The accuracy performance increased 4% for the \(C1\) corpus for EN-EL and 3% for EN-IT. It seems that the application of these methods is more efficient for the SMT for the informal \(C1\) corpus and NMT for the formal (well-structured) \(C2\) corpus. More specifically, there is an increase up to 4% for the SMT class for \(C1\) and 2% for \(C2\), while, for the NMT class, there is 2% for \(C1\) and \(C2\). In addition, the feature selection methods work better for \(C1\) (an increase up to 3.5% in average for both language pairs) rather than the \(C2\) (an increase up to 2.5% in average for both language pairs). We conclude that feature selection methods help more the noisy corpus. This is in accordance with the accuracy results of the previous model. Concerning the features, it is verified that, for the proposed model, the more effective features are those containing ratios, features identifying the presence of noise in a segment (for example the occurrence of repeated characters) and features used linguistic information from the \(SSE\). They all seem to be useful for prediction. Also, the new string-based features added in this paper are presumed to enclose valuable information for the model as they capture the similarity between the MT outputs and the reference translation. The new string-based features were selected almost from every method. Regarding the \(FS\) method, it seems that better accuracy results were produced with \(CHI\) square and \(IG\). Additionally, it is observed that the feature reduction space method (PCA) does not help the accuracy performance regardless of the corpora structure-type, since in all experiments the performance was less than or equal to the classifier performance using the whole feature set.
## 4 Discussion
### Uncertainties of SIF and GPP Relationship under Stressful Conditions
This suggests that when environmental factors have different impacts on vegetation SIF and photosynthesis, the magnitudes of the decrease in SIF and GPP may differ, and SIF may not effectively capture the influence of environmental factors on GPP. In conclusion, the response of SIF to different stress factors is not uniform, and the mechanisms are complex due to the coexistence of photochemical quenching (PQ) and NPQ. Therefore, the relationship between SIF and photosynthesis under different stress conditions is varied. Hence, when studying the relationship between SIF and photosynthesis, it is essential to consider whether vegetation is under stress and the type of stress. Generally, at smaller spatial (leaf or canopy) and shorter-time (instantaneous) scales, the decoupling phenomenon between SIF and vegetation photosynthesis is more apparent. As spatial and temporal scales increase, the different responses of SIF and photosynthesis to environmental factors may be masked or weakened. Therefore, in stress conditions, careful consideration is necessary when using SIF to estimate GPP at different spatial and temporal scales and for different plant types.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 3 Scenarios for 5G Network and Its Requirements
### Massive Machine-Type Communications (MMTC)  
The scenario is related to the wireless sensor network, M2M communication, and IoT communications. The main objective that is related to this scenario is energy efficiency and connection density. For example, in [62], it is stated that the connections density is equal to \(10^{6}\) devices/m\({}^{2}\) and  
Figure 5: Requirements and technologies trend of 5G based smart health-care.  
energy efficiency should be 100 times improved than 4G networks. In [27], it is specified that the devices in the network are required 10-year battery life.
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 9 Biofilm Formation of Gram-Positive Organisms in Diabetic Foot Ulcer
### Ica Independent Biofilm Formation in MRSA  
Alternatively, biofilms can be synthesized without the use of the _ica_ operon. This type of biofilm formation is the typical mechanism for MRSA. However, the SarA gene remains essential, as it activates the _agr_ gene set in MRSA [70]. Deletion of the _ica_ genes does not affect the ability of _S. aureus_ to form biofilms, as first demonstrated by the University of Arkansas for Medical Sciences [71]. MRSA possesses a gene encoding penicillin-binding proteins, specifically the _mecA_ gene [72]. The main operon responsible for biofilm formation in MRSA is the _agrBDCA_ operon, which is transcribed by RNA III [73].  
Interestingly, both MRSA and MRSA strains can switch from PIA-dependent biofilm formation in environments with high glucose concentrations [74; 75]. The LPXTG proteins, such as Sortase A [76], along with _fribA_ and _fribB_, are necessary for inducing biofilm formation in glucose-rich environments [2].  
The peptide precursor autoinducer protein, AgrD, is cleaved by AgrB, a membrane-bound peptidase. This cleavage produces an eight-residue peptide chain, with a thiolactone ring formed by the last five residues. This peptide then phosphorylates AgrC, a membrane-bound histidine kinase, which undergoes autophosphorylation in the presence of the autoinducer molecule. This autophosphorylation relays a signal to AgrA, which binds to the promoters P2 and P3, initiating the upregulation of _agrBDCA_ and subsequently increasing RNA III production, which produces the delta toxin [73].  
A key component of MRSA biofilm generation is the release of extracellular DNA upon cellular lysis by CidA hydrolases, which occurs independently of PIA [77]. MRSA also produces the autolysin Atl [78], which aids in biofilm formation and promotes intercellular aggregation with a specific phenotype associated with _fribA_ and _fribB_[79].  
Therefore, it can be inferred that even if a diabetic foot ulcer patient reduces their sugar intake, a high salt concentration in the blood may prompt MRSA to switch from _ica_-independent to _ica_-dependent biofilm formation. Conversely, high sugar concentrations and low salt concentrations may lead to the opposite effect [80]. Consequently, a comprehensive approach, including proper diet and treatment, is crucial for the complete recovery of the patient. Non-_ica_-mediated biofilm formation is demonstrated in Figure 4.
## 2 The Continuum Model for EIT  
The primary vector fields in electrodynamics are the electric field \(\mathbf{E}\) and the magnetic field \(\mathbf{H}\). These fields, when applied to a material, produce electric displacement \(\mathbf{D}=e\mathbf{E}\) and magnetic flux \(\mathbf{B}=\mu\mathbf{H}\)[27], where \(e\) and \(\mu\) are the electric permittivity and the magnetic permeability, respectively. For a static electric field, \(\mathbf{E}\) can be written as \(-\
abla\phi\)[27], where \(\phi\) is then the electric potential. Here, we will work assuming that the electric field is static. The current density at a point is given by \(\mathbf{J}=\sigma\mathbf{E}\)[27]. \(\sigma\) is the conductivity of the material at that point. In general, \(\sigma\) is a tensor of rank two that can be represented using a \(3\times 3\) matrix. However, we will assume that \(\sigma=\sigma(x,y)\,I\), where \(I\) is the identity matrix. The equation of interest in both the EIT forward problem and the inverse problem is found by taking the divergence on both sides of [PERSON]'s equation corresponding to [PERSON]'s law [28]  
\[\
abla\times\mathbf{H}=\frac{\partial\mathbf{D}}{\partial t}+\mathbf{J}\]  
which gives in the static electric field case  
\[\
abla\cdot(\sigma\
abla\phi)=0 \tag{1}\]  
on a region of space \(\Omega\). The forward problem is to find \(\phi\) inside \(\Omega\), given \(\sigma\) and the Neumann boundary condition  
\[\sigma(\mathbf{x})\
abla\phi(\mathbf{x})\cdot\mathbf{n}=\sigma(\mathbf{x}) \frac{\partial\phi((x)}{\partial\mathbf{n}}=j(\mathbf{x})\ \ \ \text{for}\ \mathbf{x}\in\partial\Omega\]  
\[\text{subject to}\ \ \ \ \ \int_{\partial\Omega}j(\mathbf{x})ds=0 \tag{2}\]  
where \(\mathbf{n}\) is the outward normal unit vector at \(\partial\Omega\), and hence, \(j\) is the surface normal component of the current density at the boundary. The Dirichlet boundary value problem has a unique solution [29], at least within the weak sense, assuming that \(\sigma\leq m<0\) and \(\sigma\in L^{\infty}(\overline{\Omega})\). The Neumann boundary value problem has a unique solution [29] up to an additive constant, which is fixed by the choice of ground:  
\[\int_{\partial\Omega}V(\mathbf{x})ds=0 \tag{3}\]  
Equations (1) and (2) are usually called the EIT forward continuum models [5]. Solving the forward problem from the Neumann boundary conditions gives us the NtD map since, we can always compute the Dirichlet data from \(\phi(x)\).  
For the EIT forward problem, we use only Neumann boundary conditions, i.e., we solve the problem given Equation (2). So, we are solving for the NtD map. However, the continuum model does not accurately represent the EIT measurement setup. For a much more realistic simulation, we use the complete electrode model.
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 3 Receiver for Optical Wireless Communication
### OWC Receiver with an Array of Surface Grating Couplers  
#### 3.5.1 Description  
In previous sections, the proposal to decouple light collection and light detection was discussed and we have also demonstrated that SGC can be used to collect light from free space efficiently. This section proposes an improvement for light collection in PICs, as already published in [35]. Here, we present an array of six surface grating couplers arranged in a \"windmill\" shape for an increased field of view compared to an individual surface grating coupler, as shown in Figure 13. The PIC is fabricated on the IMOS platform, which was introduced in the previous section. The grating couplers are standard focusing grating couplers used for fibre-to-chip coupling. The active area has a length of 16 \(\upmu\)m and a width that decreases from 18 \(\upmu\)m at its end to 10 \(\upmu\)m at the input to the taper. The output waveguides of the SGCs are combined with Multi-Mode Interferometers (MMI). The simulation results are shown in Figure 14. For certain combinations of wavelength and incidence angle, the coupling efficiency is the highest.  
Figure 12: (**a**) Measured relation between the wavelength, angle and coupling loss of the grating coupler. (**b**) Measured spectrals of four WDM channels at 25 Gbps. (**c**) BER curves of four WDM channels vs. received power compared to back-to-back connection.
# Model Experimental Study on De-Icing Method of Bridge Pylon Beam Based on Electric Heating
## 4 Results and Analysis of De-Icing Tests
### Passive De-Icing Tests
#### 4.2.2 Energy Requirement  
In the scale model test, the area of the heating system covered half of the surface area of the beam, and the corresponding heating power was 576 W/m\({}^{2}\). If this paving method was applied to the beam of Wuhan Yangtze River Second Bridge, the corresponding paving area of a single beam would be 190.8 m\({}^{2}\) (two surfaces), and the corresponding heating power and total circuit current would be 109.9 kW and 499.5 A (the Chinese industrial standard voltage is 220 V). The distance of each beam on the bridge is long, and an independent power distribution system can be set up for a single beam. Therefore, the current required  
Figure 17: Variation of the de-icing time period with the wind velocity.  
for the heating system to be applied to a single bridge is about 499.5 A, which is relatively easy to meet with industrial electricity.
## 3 Case Study
### Prediction Results  
#### 3.4.1 Performance Comparison of Eight Algorithms  
Taking the parameter VDST as an example, the prediction results of eight different AI algorithms are shown in Figure 8 and Table 4. Different algorithms had different prediction accuracy for parameter VDST. The KNN algorithm in ML methods had the worst prediction accuracy, with a RMSE of 9.45 and an EVS of 0.19. In addition, the predicted values by the other three ML methods (SVR, RF, AdaBoost) was close to the measured VDST value. The predicted absolute error for the other three algorithms was basically less than 5 mm, the EVS was higher than 0.95, and the RMSE was lower than 2.0.  
As can be seen from Figure 8, the prediction results of LSTM and GRU algorithms in DL methods were the best. They captured the measured observations of the target parameters better. The evaluation indexes RMSE and EVS were around 1.1 and 0.99, respectively, and the absolute error was within 2.5 mm. Instead, the BPNN algorithm (RMSE = 2.9, EVS = 0.95) and CNN algorithm (RMSE = 3.65, EVS = 0.88) in DL methods had poor prediction effect. The prediction performance of the eight AI algorithms for VDST and the other methods were similar. The results of the two methods were similar.  
in descending order was as follows: LSTM > GRU > AdaBoost > RF > SVR > BPNN > CNN > KNN.  
Then, we comprehensively compared the HDSH, HDST, VDSH, and VDST of these AI algorithms, and the results are listed in Table 4. The prediction indexes for HDSH, HDST, VDSH, and VDST by different algorithms are as follows: EVS were 0.88-0.98, 0.92-0.99, 0.84-0.99, 0.88-0.99, and RMSE were 1.36-3.45, 1.25-3.33, 1.15-5.7, 1.09-3.65, respectively. The KNN algorithm was not included in the statistics due to it having the worst prediction effect. The remaining seven algorithms had better prediction results for different SMA parameters, with EVS above 0.8 and RMSE below 6. LSTM and GRU were the two best prediction performance algorithms, with EVS greater than 0.98 and the RMSE less than 1.5, followed by AdaBoost with an average RMSE of 1.392, and then followed by RF with an average RMSE of 1.413. The forecast effects of SVR and BPNN models were also within a reasonable range. By contrast, CNN was more sensitive to the choice of the SMA parameters, with the lowest EVS of 0.84 for VDSH parameter and the highest EVS of 0.92 for HDST parameter.  
In summary, these results indicated that most AI algorithms showed satisfactory performance in predicting the SMA parameters. Considering the EVS and RMSE indicators, the two best algorithms are LSTM and GRU.
# Output Force Density Saturation in COMSOL Simulations of Biomimetic Artificial Muscles
## 2 Materials and Methods
For each swept value e, the total output force of the device was calculated as the surface integral of the Von Mises Stress along the unconstrained outer top surface of the device, opposite to the boundary-constrained bottom surface. The total deformed surface area was calculated as the surface integral of the area of the unconstrained outer top surface of the device. Dividing the total output force by the total deformed top surface area produced the average output force density. For each value of N, the average force density was plotted as a function of the swept tendon thickness e. The result was a response curve for that N. These response curves were organized in a single plot vs. tendon thickness e (Figure 3). The maximum force density of each response curve determined the best performance for that N and the corresponding optimal value of the tendon thickness e. These maximal force densities were then plotted as a function of N, producing the force density saturation curve (Figure 4). Figure 3: For each N from 1 to 13, a series of simulations swept the tendon width from 100 to 200 \(\upmu\)m, in 10 \(\upmu\)m steps, to determine the maximal force density. Figure 2: Heat map of stress for COMSOL-simulated actuated 13 \(\times\) 13 \(\times\) 10 device. The black line grid shows the non-deformed dimensions of the array. When actuated, the muscle contracts longitudinally and expands laterally. The overall procedure is outlined in a flowchart in Figure 5.