esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Networks: A Pillar of Digital Transformation
## 4 Collaborative Networks

Table 1: _Cont._In turn, long-term strategic networks are characterized as strategic alliances created to act as source or breeding environments for \"goal-oriented networks\", i.e., aimed at providing proper conditions and support environment for dynamic creation of goal-oriented networks whenever a business opportunity arises. This class includes the _(a)_ professional virtual communities (PVC), which are networks composed of individual professionals that get together in a long term basis to be prepared to rapidly react in response to business opportunities through the dynamic creation of temporary VTs, and the (_b_) VO breeding environment (VBE) that represents \"an association of organizations and a number of supporting institutions committed to a long term cooperation agreement, complying with common operation principles and infrastructures, with the main goal of increasing their preparedness towards rapid configuration of temporary alliances for collaboration in potential VOs\" [35; 37]. Examples of such classes are industry clusters or business ecosystems. To enable and support this wide variety of CNs, a substantial portfolio of models, mechanisms, tools, and platforms/infrastructures, has been developed and refined in the last years [36; 38], as briefly illustrated in Figure 3.  
Figure 2: A partial taxonomy of collaborative networks (CNs). Figure 3 shows the main areas of CN developments which are theoretical foundation, VBE management, VO/VE creation, VO/VE management, and ICT infrastructure. Some of the main developments in each focus area include:  
* Theoretical foundation: the ARCON reference model and modeling framework [39], complex models such as trust building [40], value systems [41], readiness assessment [42], and affective computing with the modeling of collaborative networked emotions [43]. * VBE management: competency models and management [44], trust building management [45], VBE management systems [46]. * VO/VE creation: negotiation and contracting [47], VO creation services [48; 49]. * VO/VE management: performance management [50], self-organizing principles and collaborative business processes [51]. * ICT infrastructure: interoperability [52], service-oriented architectures [53], security infrastructure and blockchain [54]. Many of the above developments were oriented to manufacturing and other industrial domains, which facilitates their adoption in Industry 4.0 and digital transformation. Further examples can be found in Section 5.
## 2 Materials and Methods
### Hyperspectral Imaging System and Image Acquisition  
A VNIR hyperspectral imaging system was used to capture images of the soil samples [33; 34; 35; 36] (Figure 1a). The system consisted of the following components: a SNAPSCAN hyperspectral imaging camera (Imec, Belgium), operating in the spectral range of 470-900 nm, with a spectral resolution of 3 nm, producing a total 147 spectral bands; two current-controlled wide spectrum quartz halogen lights; a sample station for scanning; a dark chamber; and data acquisition software (Imec snapscan acquisition, Imec Corp, Leuven, Belgium). Soil samples were positioned on a moving stage and moved into the camera's field of view. Samples were shaken after capturing each image for homogenization, and the imaging was repeated until reproducible spectral signatures were obtained for consecutive images. The acquired imagery (R: 640 nm, G: 548 nm, B: 470 nm) is illustrated in Figure 1b.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 4 Simulation Results and Discussion
### Verification of Proposed Algorithm  
In the comparative simulation, the distribution of network nodes and working nodes corresponding to the four algorithms of LEACH, LEACH-DBCH, LEACH-RARE, and LEACH-MTC is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Symbol** & **Value** \\ \hline Network monitoring area & \(S_{M}\) & 300 m \(\times\) 300 m \\ Number of network nodes & m & 100 \\ Base station location & \((x_{BS},y_{BS})\) & \((150,150)\) \\ Initial energy of the node & \(E_{0}\) & 1 J \\ Transmitting circuit energy consumption & \(E_{TX}/E_{RX}\) & 50\(n\) J/bit \\ Signal amplification energy consumption in unit data free space & \(\varepsilon_{fs}\) & \(10p\) J/bit/m\({}^{2}\) \\ Signal amplification energy consumption under unit data multipath attenuation & \(\varepsilon_{mp}\) & \(0.0013p\) J/bit/m\({}^{2}\) \\ Data fusion energy consumption & E\({}_{\text{DA}}\) & 5 nJ/bit/packet \\ Control signal size & \(l_{1}\) & 100 bit \\ Signal amplification energy consumption in unit data free space & \(l_{2}\) & 4000 bit \\ Expected probability of cluster head & \(p_{ex}\) & 0.1 \\ Maximum number of running rounds & \(k_{max}\) & 1300 \\ Threshold weight & \(k\) & 0.8 \\ Threshold weight & \(\mu\) & 0.2 \\ Threshold weight & \(\alpha,\beta,\gamma\) & \(10,0.01,0.01\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
In Figure 4, \(\square\) stands for base station, \(\vartriangle\) stands for moving target, \(\diamondsuit\) stands for working node, \(\ast\) stands for sleep node, curve stands for target trajectory, the target velocity at the current moment is \(v\), and the direction is horizontal. In Figure 4a,b, the network nodes in the monitoring area corresponding to the LEACH and LEACH-DBCH algorithms are all in working state. As the two algorithms do not consider the factor of the moving target, all nodes in the network are working nodes. They all need to collect data, process data, and transmit data. However, in Figure 4c, the nodes closer to the target in the LEACH-RARE algorithm are in the working state, while the nodes farther from the target are in the dormant state, and there are 52 working nodes and 48 sleep nodes. In Figure 4d, the nodes inside the elliptical area are in working state, and their number is 14; the nodes outside the elliptical area are in the dormant state; and as the position of the moving target changes, the shape and size of the ellipse area also changes.
## 3 LC-Tank Oscillator  
### LC-Tank Schematic Design  
The second architecture designed is based on an LC-tank resonator. This architecture bases its oscillation frequency on the filtering effect of an LC-tank, leaving for active components only the role of setting the feedback gain [25] and compensate for the loss of the inductor.  
The design guideline to respect Barkhausen oscillation criterion must be  
\[g_{m}>1/R_{P} \tag{4}\]  
where \(g_{m}\) is the value of the transconductance of the n-MOSFETs devices inside the cross-coupled cell, and \(R_{P}\) is the parasitic resistance of the inductor [26]. Figure 5 shows the schematic of the LC-VCO designed to generate the target 6.25 GHz frequency. A polysilicon resistor is used to shift the output common-mode level at VDD/2, preventing the damaging or lifetime reduction of the low-voltage MOSFETs used for the cross-coupled pair.  
This resistor is connected to the center tap of the symmetrical inductor chosen for its lower layout area than that of two separate inductors. In order to achieve the best frequency performance of this technology, the cross-coupled pair is sized using minimum length MOSFETs and a MOSFET width  
Figure 4: Full custom layout of the RO-VCO based on the CML buffer designed with Cadence Virtuoso [24].  
Figure 5: LC-tank VCO circuit schematic and a couple of varactors connected at the outputs.  
of 3.6 \(\upmu\)m to guarantee a cell gain of at least 6 dB for start-up condition. The VCO bias current is controlled by the external current Io through the simple current mirror M3 and M4 with a current gain of 5, and the power consumption is less than 3 mW. The oscillation frequency of the LC-VCO is set by \(f_{0}=1/\left(2\pi\sqrt{L(C+Cvar)}\right)\)[26], where C is the equivalent capacitance due to the cross-coupled cell and the first stage of the output buffer, and Cvar is the capacitance of the accumulation-mode MOSFETs varactors connected at the controlled oscillator outputs. The Tuning Range (TR) is made with the control voltage Vctrl in the range from 0 V to VDD, and varactors assume, respectively, the value in the range from 629.6 fF to 197.6 fF, as shown in Figure 3b. A single varactor is composed of 120 fingers divided into 6 groups, and each finger is designed with 300 nm finger length and 1.2 \(\upmu\)m finger width.  
Figure 6 shows the simulated frequency response of the VCO for the two extreme values of the control voltage, and a minimum cell gain of about 10 dB for the minimum value of the control voltage, allowing to achieve a robust start-up condition for the oscillator.
## 1 Introduction  
In response to increasingly severe global pollution, environmental protection solutions are being sought worldwide. The United Nations announced the Sustainable Development Goals (SDGs) policy and set a goal of \"enduring access to affordable, reliable, sustainable, and modern energy for all\" by 2030 [1]. Air pollution is the most urgent problem to be addressed because it has immediate health effects. Policies to reduce greenhouse gas (GHG) emissions by reducing the use of fossil energy and to increase renewable energy production are actively being promoted at the global level [2; 3].  
In the building industry, building and building-construction sectors are responsible for almost one-third of the total global energy consumption and nearly 15% of direct CO\({}_{2}\) emissions. Energy demand from buildings and building construction continues to increase, driven by improved access to energy in developing countries, growing demand for air conditioning in tropical countries, greater ownership and use of energy-consuming appliances, and rapid growth in global building floor area [4]. As the building sector greatly impacts the global environment, reducing building energy use by converting to renewable energy can reduce global environmental pollution. From this perspective, an experimental architectural design to increase the energy independence of buildings was developed in this study through generation of renewable energy from natural sources.  
The technology to produce renewable energy from natural energy is called energy harvesting. It uses kinetic energy, such as micro vibration, and natural energy sources, such as solar, geothermal, and wind energies, to generate renewable energy. Recently, the scope of energy harvesting research is expanding, such as the development of storage devices for renewable energy generated with energy harvesting and material research to increase energy generation efficiency [5; 6; 7].  
This study proposes an energy harvesting architectural design that uses solar energy sources, and the renewable energy generated through architectural design is supplied to the building to increase its energy independence. Eco-friendly architectural designs based on an energy harvesting principle can produce optimal energy with application to a building facade and help create a new urban landscape by contributing to building aesthetics.
## 3 Methodology
### Lfm-Spe  
To assess the efficacy of the proposed SPE method, it is integrated with conventional LFM and BiasSVD models, resulting in the LFM-SPE and BiasSVD-SPE models, respectively. Subsequently, this section presents the procedure for combining the LFM-SPE model.  
Utilizing the propensity score to mitigate bias in recommendation systems primarily involves employing inverse propensity weighting. This approach entails assigning weights to the rating data based on the reciprocal of the propensity score, thereby enabling unbiased estimation and mitigating the influence of selection bias within the dataset.  
The propensity score is incorporated into the loss function of LFM using the inverse propensity weighting approach, as demonstrated by the following equation:  
\[L_{SPE}=\sum_{\begin{subarray}{c}i\in I\\ \mathbf{u}\in\mathbf{U}\end{subarray}}\frac{(\mathbf{r}_{\mathbf{u}i}-\hat{\mathbf{r}}_{\mathbf{u}i}) ^{2}}{\mathbf{P}_{(\mathbf{u},i)}}+\lambda\Big{(}\|\mathbf{p}_{\mathbf{u}}\|^{2}+\|\mathbf{q}_{ \mathbf{i}}\|^{2}\Big{)} \tag{10}\]  
The loss function is solved using the stochastic gradient descent algorithm [23], which involves the following steps:  
Step1. Deriving the parameters \(\mathbf{p}_{\mathbf{u}}\) and \(\mathbf{q}_{\mathbf{i}}\) to determine the direction with the steepest gradient descent.  
Step2. Iteratively optimizing the parameters through calculations while continuously updating the direction of the steepest decline for each parameter until convergence.  
Step3. Obtaining the user implicit semantic preference vector \(\mathbf{p}_{\mathbf{u}}\) and the product implicit semantic attribute vector \(\mathbf{q}_{\mathbf{i}}\), which effectively eliminates selection bias.  
Step4. Combining the preference vectors of each user and the attribute vectors of each item to form the user latent semantic preference matrix \(\mathbf{P}\) and the item latent semantic attribute matrix \(\mathbf{Q}\).  
By following this iterative process, the LFM-SPE model can effectively learn the latent factors that capture the underlying patterns and preferences in the data, resulting in accurate recommendations. The overall algorithm flow is shown in Algorithm 1.  
The approach of incorporating the SPE method into the BiasSVD matrix factorization model is similar to the methodology employed in the LFM-SPE model, where the propensity score is weighted reciprocally in the model's loss function. Hence, the training process of the BiasSVD-SPE model follows a similar framework and is not explicitly discussed in this section.
## 5 SDN-based VANETs Security Challenges  
Security remains a significant test, as the spread of falsehood from unapproved elements can prompt genuine mishaps. The key purposes of carefulness from a security angle are discussed below [55]. To begin, the SDN controller remains the focal basic leadership point, and ought to be firmly ensured. A barrier top to bottom methodology is prescribed, likewise to verify regular SDN frameworks [12]. Second, firmly coupled SDN layers encourage the spread of dangers between layers. APIs between layers should in this manner be solidified and institutionalized. Third, vehicle versatility and receptiveness in the lower information plane enhance SDN dangers to the control and application layers [56]. Hence, the two layers of the information plane ought to be verified, with the requirement for ongoing confirmation. A portion of these difficulties may likewise affect both connected and autonomous vehicle (CAV) security and safety. As CAVs are a firmly coordinated network of networks, disappointments on a sub framework may proliferate to other sub-frameworks and make recuperation troublesome. Digital hazards on the vehicle basic leadership rationale remain basic; vulnerabilities of artificial intelligence (AI) designs (e.g., to create injection attacks) in the \"cerebrum\" of the self-ruling vehicle, can cause disastrous results as far as wellbeing if information trustworthiness is undermined [56; 57].  
The basic concern in a vehicular systems administration condition is security. Throughout the years, various security arrangements have been conceived for VANETs that principally depend on customary cryptographic plans using open key frameworks and endorsements [55]. By and by, cryptographic based arrangements are not attainable for vehicular systems since the vehicles are profoundly unique in environment and are disseminated all through the system, the accessibility of a systems administration framework cannot be ensured consistently, and customary cryptography-based arrangements are likewise helpless against insider attacks. Subsequently, trust has been as of late presented as an option for guaranteeing security in vehicular systems [55].  
Figure 10: SDN security taxonomy.
# Research on a Ship Deflection Anti-Collision Method Based on a Water-Jet Interference Flow Field
## 2 Generalized Model Experiments
### Renormalization Group (RNG) k-\(\epsilon\) Turbulence Model  
As different turbulence models use different assumptions and derivations, they have their own scope of application. In order to obtain more accurate simulation results, the standard k-\(\epsilon\) turbulence model does not reflect this flow well because of the high anisotropy of the jet. [PERSON] and [PERSON] [35; 36] introduced the Renormalization Group approach to turbulence studies and developed a new turbulence model, the RNG k-\(\epsilon\) turbulence model, which, compared to the standard k-\(\epsilon\) turbulence model, corrects the turbulent dynamic viscosity coefficients, takes into account the effect of eddies on turbulence as well as rotational and rotational flows in the mean flow, which helps the model to make better predictions of the effects of transient flows and streamline bending, and improves the accuracy of eddies [37; 38] and the accuracy of high-speed flow calculations. The flow control equations and the RNG k-\(\epsilon\) turbulence model are as follows:  
Continuity equation  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u_{i})}{\partial x_{i}}=0\]  
where: \(t\) is the time in s; \(u\) is the velocity of the water in m/s; \(\rho\) is the density of the water in kg/m\({}^{3}\).  
Momentum equation  
\[\frac{\partial(\rho u_{i})}{\partial t}+\frac{\partial(\rho u_{i}u_{j})}{ \partial x_{j}}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x _{j}}\left[(\mu+\mu_{i})\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}\right)\right]\]  
where: \(p\) is the pressure in Pa, \(\mu\) is the molecular dynamics viscosity coefficient, \(u_{i}\) is turbulent viscosity coefficient, \(\mu_{i}=\rho C_{k}k^{2}/\epsilon\), \(C_{\mu}=0.085\).  
The turbulence model RNG k-\(\epsilon\) equations are shown below:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho u_{i}k)}{\partial x_ {i}}=\frac{\partial}{\partial x_{i}}\left[\left(\mu+\frac{\mu_{t}}{\sigma_{k }}\right)\frac{\partial k}{\partial x_{i}}\right]+G-\rho\epsilon\]  
where: \(k\) is turbulent kinetic energy, \(\sigma_{k}=0.7179\)  
\[\frac{\partial(\rho\epsilon)}{\partial t}+\frac{\partial(\rho u_{i}\epsilon) }{\partial x_{i}}=\frac{\partial}{\partial x_{i}}\left[\left(\mu+\frac{\mu_{ t}}{\sigma_{\epsilon}}\right)\frac{\partial\epsilon}{\partial x_{i}} \right]+C_{1}\frac{\epsilon}{k}G-C_{2}\rho\frac{\epsilon^{2}}{k}\]  
where: \(\epsilon\) is the turbulent dissipation rate, \(\sigma_{\epsilon}=0.7179\), \(G=\mu_{t}\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{ \partial x_{i}}\right)\frac{\partial u_{i}}{\partial x_{j}}\)  
\[C_{1}=1.42-\frac{\widetilde{\eta}(1-\widetilde{\eta}/\widetilde{\eta}_{0})}{ 1+\beta\widetilde{\eta}^{3}},\ S_{ij}=\frac{1}{2}\left(\frac{\partial u_{i}}{ \partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\right),\ S=\sqrt{2S_{ij} S_{ij}},\ \widetilde{\eta}=Sk/\epsilon\]  
\[\widetilde{\eta}_{0}=4.38,\ \beta=0.0154,\ C_{2}=1.68\]
## 4 Results and Discussions
### Comparison to the Literature  
Finally, attention is here turned to the comparison of the numerical results to the literature. The performance characteristics for the surface-enhancing elements organized in the shape of an arc in a duct with an aspect ratio of 11 were experimentally investigated by [PERSON] and [PERSON] [18], and it was shown that the size and geometry of the elements have great effect on the heat transfer. The effectiveness of flat and dimpled SAHs in turbulent flow conditions was found by [PERSON] et al. [19].  
In the case of flat and dimpled SAHs, it was determined that the greatest difference in temperature is \(10\,^{\circ}\mathrm{C}\) for the air mass flowrate of 0.05 kg/s. [PERSON] et al. [20] have experimentally and numerically studied the thermal performance of a solar air collector with a corrugated absorber. Numerical simulations have shown that the air temperature can be improved effectively by using the surface-enhancing elements. Maximal temperature differences between the inlet air and outlet air of 14.24 \({}^{\circ}\mathrm{C}\), 23.27 \({}^{\circ}\mathrm{C}\), and 26.11 \({}^{\circ}\mathrm{C}\) were observed, respectively, for three absorber cases that were tested.
# A Deadbeat Current and Flux Vector Control for IPMSM Drive with High Dynamic Performance
## 5 Evaluation Results  
### Assessment of Proposed MO-FOB Scheme  
To evaluate the effectiveness of the proposed flux linkage observer, the simulation results of conventional CL-FOB and proposed MO-FOB are generated using MATLAB-Simulink with the IPMSM given in Table 1. There are two scenarios regarding parameter mismatch by considering magnetic saturation and temperature variation to evaluate the robustness of the two flux observer methods discussed above. In Scenario A, the stator resistance, i.e., \(R_{\rm s}^{\rm A}\) is increased to 150% of the original \(R_{\rm s}^{\rm 0}\) (i.e., \(R_{\rm s}^{\rm A}=1.5R_{\rm s}^{\rm 0}\)), and simultaneously the \(d\)- and \(q\)-axis inductances are both reduced to 75% of the original ones (i.e., \(L_{\rm d}^{\rm A}=0.75L_{\rm d}^{\rm 0}\) and \(L_{\rm d}^{\rm A}=0.75L_{\rm d}^{\rm 0}\)). In Scenario B, the \(q\)-axis inductance is assumed to be unchanged (i.e., \(L_{\rm q}^{\rm B}=L_{\rm q}^{\rm 0}\)) while the resistance and \(d\)-axis inductance vary as in Scenario A (i.e., \(R_{\rm s}^{\rm B}=1.5R_{\rm s}^{\rm 0}\) and \(L_{\rm d}^{\rm B}=0.75L_{\rm d}^{\rm 0}\)). This aims to assess the sensitivity level of the closed-loop hybrid observers to resistance and \(d\)-axis inductance variations. The error percentages of the flux linkages estimated by the two methods based on the actual flux linkage are summarized in Table 2.  
As can be seen in Table 2, the error percentages of the estimation by the proposed MO-FOB are much lower than those of the conventional CL-FOB under various operating conditions. Moreover, the influence of parameter variations (especially changes in resistance  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Operation Condition**} & \multicolumn{2}{c}{**Conventional Closed-Loop**} & \multicolumn{2}{c}{**Modified**} \\  & \multicolumn{2}{c}{**Hybrid Flux Observer**} & \multicolumn{2}{c}{**Hybrid Flux Observer**} \\  & \multicolumn{2}{c}{**(CL-FOB)**} & \multicolumn{2}{c}{**(MO-FOB)**} \\ \cline{3-5}  & **Load (Nm)** & **Scenario A** & **Scenario B** & **Scenario A** & **Scenario B** \\ \hline
100 & 1.25 & 4.39\% & 0.95\% & 2.09\% & \(-\)0.41\% \\
700 & 1.25 & 7.48\% & 2.32\% & 2.75\% & 1.03\% \\
2300 & 2.5 & 8.37\% & 3.15\% & 3.28\% & 2.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error percentages of the estimated flux linkages (simulation result).  
Figure 5: (**a**) IPMSM drive test platform and (**b**) hardware-in-the loop toolkit for testing PMSM control.  
and \(d\)-axis inductance) on the application of the proposed hybrid flux observer can be considered insignificant, even when operating under rated conditions.