esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The effects of various B4C additions on the microstructural, mechanical, and tribological properties of Al-20%Mg2 Si composite were investigated. It can be concluded that:  
1. With the addition of 5 wt% B4C, the mean size of primary Mg2 Si in Al-20%Mg2 Si composite decreased from 47 to 33, and its morphology was altered from octahedral to polygonal shape. However, with further addition of B4C to 10 wt%, the morphology of primary Mg2 Si remains in polygonal shape with an increase in its size to 38.
2. The tensile result showed that the best level of B4C was 5 wt% in which the UTS and El.% of Al-20%Mg2 Si composite increased from 177 MPa and 4.6% to 217 MPa and 7% in the Al-20%Mg2 Si-5%B4C hybrid composite, respectively. The mechanisms of strengthening of the composites were load transfer effects, dislocation strengthening, and grain refinement. In addition, the Vickers hardness value of Al-20%Mg2 Si composite increased from 75 Hv to 100 Hv after the addition of 5 wt% B4C particles to the composite.
3. Examination of the fracture surface revealed that the fracture of the Al-20%Mg2 Si composite was brittle (including the cleavages). With the addition of 5 wt% B4C particles, the fracture surface owned more dimples, which represents the domination of the ductile fracture.
4. The results of the sliding wear test showed that the addition of 5 wt% B4C to the Al-20%Mg2 Si composite improved the wear resistance of the alloy by decreasing the wear rate by 46%. The enhancement in wear resistance was due to the self-lubrication function of B4C, uniform distribution of B4C particles and good bonding of B4C with the matrix as well as the small size of primary Mg2 Si particles, which in turn decreased the friction coefficient of the composite.
5. Examination on the worn surface revealed that the governed wear mechanisms of the Al-20%Mg2 Si composite were abrasion and delamination, which changed to mid abrasion in the Al-20%Mg2 Si-5%B4C hybrid composite.  
H.G., M.A.J., and A.B. methodology, formal analysis, investigation, writing--original draft; H.G., K.K., and S.S.R.K. conceptualization, methodology, software, formal analysis, investigation, writing--original draft; T.A.A.B., K.K., and M.P. validation, resources, supervision, writing--review & editing. All authors have read and agreed to the published version of the manuscript.  
This research is financially supported by Universiti Teknologi Malaysia (UTM) under the research grants 04E95 and 06G22. In addition, this research was supported by the Ministry of Education, Youth, and Sports of the Czech Republic and the European Union (European Structural and Investment Funds Operational Program Research, Development, and Education) in the framework of the project \"Modular platform for autonomous chassis of specialized electric vehicles for freight and equipment transportation\", reg. no. CZ.02.1.01/0.0/0.0/0.16_025/0007293.
# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
## 3 System Model
### Network Model
To handle this problem, we use a multipath routing algorithm as explained in Section 4. As noted earlier, the sensor nodes monitor dynamic events and, on detection, collect and forward the information to the cluster head. The event can be detected by a set of nodes, \(S_{event}=\{v_{1},\ v_{2},\ v_{3},\ \ldots,\ v_{m}\}\), where \(m<n\) and \(S_{event}\subseteq V\). The performance of a WMSN can be profoundly influenced by congestion [16; 20] since the sensor nodes have limited resources in terms of bandwidth, memory, and computational energy. Thus, a multipath routing approach can be utilized as a proper means to minimize the occurrence of network congestion by dividing the traffic into many independent paths [41]. Besides, sharing the network traffic load among several nodes can enhance the life span of the network by reducing the energy consumption of each node. It has also been proven that the multipath routing protocol improves the performance of the wireless sensor network in many aspects. Moreover, the data delivery ratio, network throughput, and end-to-end delay are vital performance parameters in designing a multipath routing protocol in WMSNs. In multipath routing, these parameters help to identify a suitable path to forward data to the sink node, since all the discovered paths between the source and sink node have a different quality of service (QoS) value. For time critical and delay sensitive data packets, the protocol needs to direct the traffic in relatively hassle-free routes according to the QoS demands of the application for which the network is deployed. Figure 1: Wireless multimedia sensor network model.
## 2 Materials and Methods
### Determination of Heavy Metals  
Just as the type of oil was carefully selected for this study, the most commonly analyzed metals worldwide were also chosen [28]. This selection was based on their relevance in previous studies, with the predominant metals being cadmium (Cd), lead (Pb), copper (Cu), and iron (Fe). Additionally, antimony (Sb) was considered due to its presence in the PET container as a catalyst used in the PET polymerization process. The determination of heavy metals Cd, Pb, Cu, and Fe was performed using inductively coupled plasma mass spectrometry (ICP-MS; Agilent Model 7900, Santa Clara, CA, USA). The calibration curve was prepared by diluting a multi-element standard solution with high-purity deionized water to obtain concentrations of 0.5 ppb, 1 ppb, 5 ppb, and 10 ppb for the calibration standards. The ISTD MIX 500 ppb multi-element standard containing internal standards45-Sc, 72-Ge, 103-Rh, and 193-Ir used for the determination of the various metals studied was prepared. Subsequently, the prepared solutions were introduced into the ICP-MS equipment. Once the solutions from the different types of oils were ready for analysis, the blanks, calibration standards, and samples were placed in the automatic sampler. On the other hand, the determination of Sb was performed using high-resolution inductively coupled plasma mass spectrometry (HR-ICP-MS; Element XR, Thermo Fisher Scientific, Bremen, Germany). For this, the samples were digested by weighing 1 g into a PTFE digestion tube using an analytical balance. Then, 4 mL of HNO\({}_{3}\) + 4 mL of H\({}_{2}\)O + 1 mL of H\({}_{2}\)O\({}_{2}\) was added, and digestion was carried out in a microwave digestion oven at pressure. Once digestion was completed, the samples were diluted to a final volume of 25 mL with deionized water quality 1 (18.2 M\(\Omega\)-cm). The digestion procedure followed the program detailed in Table 2.  
To ensure precision and robustness in the analysis, instrumental parameters and validation criteria were established. Tables 3 and 4 outline the validation criteria used to assess the accuracy and precision of the method, thereby ensuring the quality and consistency of the obtained results.
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 3 Comparison with Analytical Methods
### Rain Attenuation Models for Terrestrial Radio Links
#### 3.1.4 Brazilian Model  
In the model proposed by [PERSON] et al. in [11], the effective path length \(d_{eff}\) is calculated as:  
\[d_{eff}=\frac{1}{1+d/d_{0}}L, \tag{11}\]  
where:  
\[d_{0}=119R(P)^{-0.244}. \tag{12}\]  
The prediction of the rain attenuation exceeded for \(P\) percent of the time is achieved as:  
\[A(P)=\gamma d_{eff}=kR_{eff}^{\alpha}d_{eff}, \tag{13}\]where \(R_{\textit{eff}}\), i.e., the effective rain rate, is:  
\[R_{\textit{eff}}=1.763R(P)^{0.753+0.197/L}. \tag{14}\]
## 7 Nanomedicine to Enhance the Therapeutic Effectiveness of BC
### Nanoparticle-Mediated Drug Delivery
#### 7.1.4 Carbon-Based Nanoparticles  
Recently, a novel study on a dual-drug delivery system was carried out using potassium-containing graphene oxide as a fluorescent nanocarrier, where the cell viability assay showed that only 18% of the breast cancer cell lines survived. The authors suggested that potassium-containing graphene oxide could provide a viable nanocarrier for dual-drug delivery [110]. An alkylating chemotherapy medication for a variety of malignancies is carboplatin. Using chitosan-coated magnetite graphene oxide, carboplatin was loaded for breast cancer treatment. The results from the research verified the drug-loading capacity as opposed to other nanomaterials. The drug releasing/biodistribution within the BC cell lines were also examined as good nanocarriers for drug delivery, particularly for carboplatin delivery [111]. The encapsulation and carboplatin capacities were maximized by PEGylated multiwall carbon nanotubes (MWCNTs). The release of carboplatin from PEGylated MWCNTs, particularly at pH 6.8, indicated pH-dependent drug activity, making them a promising carrier for chemotherapy drugs facing high resistance, significant side effects, or limited oral bioavailability [112].
## 2 Materials and Methods
### Cell Culture  
Before bioprinting, the L929 fibroblast cell line was used for cytotoxicity experiments according to ISO 10993-5 specification. The cells were thawed from a liquid nitrogen container, collected by centrifugation (1500 rpm for 5 min), and cultured in medium contained Dulbecco's modified Eagle's medium with high glucose, L-glutamine (DMEM-HG; Gibco, Taipei, Taiwan) and 10% fetal bovine serum (FBS; Gibco, Taipei, Taiwan) at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). Thereafter, the cells were maintained in 35 mm \(\times\) 10 mm culture dish for seven days and the medium was replaced every three days. Once the cells reached 80% confluence, they were seeded in 96-well plate with different concentrations of 3 BE-0 (0%, control), 3 BE-1 (25%), 3 BE-2(30%), and 3 BE-3 (35%); 100 \(\mu\)L/well). Prior to that, cells were rinsed with phosphate-buffered saline (PBS; Sigma, Taipei, Taiwan) and detached with 0.05% trypsin-0.02% ethylenediaminetetraacetic acid (EDTA; Sigma, Taipei, Taiwan), the equal volume of medium was added to stop the trypsin-EDTA reaction. The neutralized solution was homogenously mixed and counted with hemocytometer, then cells were resuspended in the fresh medium (10 mL DMEM-HG). The cell suspensions were inoculated in each well with the same number of viable cells (1 \(\times\) 10\({}^{3}\) cells/well). After that, the plate was pre-incubated in a humidified incubator (37 \({}^{\circ}\)C; 5% CO\({}_{2}\)) for 24 h.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hydrogels** & **Viscosity** & **Needle Head** & **Write Height** & **Mean Line Width** & **Extrudate** & **Lift Nozzle** \\ \hline  & **(Pa*)** & **(mm)** & **(mm)** & **(mm)** & **(step/min)** & **(mm/min)** \\ \hline  & & 0.13 & 0.1 & 0.156 \(\pm\) 0.005 & 45 & 300 \\
3 BE-1 & 30 \(\pm\) 0.21 & 0.33 & 0.3 & 0.329 \(\pm\) 0.002 & 100 & 300 \\  & & 0.9 & 0.5 & 0.866 \(\pm\) 0.005 & 400 & 300 \\  & & 0.13 & 0.1 & 0.145 \(\pm\) 0.008 & 45 & 300 \\
3 BE-2 & 1500 \(\pm\) 1.83 & 0.33 & 0.3 & 0.332 \(\pm\) 0.001 & 100 & 300 \\  & & 0.9 & 0.5 & 0.913 \(\pm\) 0.001 & 400 & 300 \\  & & 0.13 & 0.1 & 0.154 \(\pm\) 0.003 & 45 & 300 \\
3 BE-3 & 266,001,67 & 0.33 & 0.3 & 0.336 \(\pm\) 0.003 & 100 & 300 \\  & \(\pm\) 10,905 & 0.9 & 0.5 & 0.931 \(\pm\) 0.006 & 400 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Printing parameters of 3 BE using self-fabricated 3D bioprinting system.  
A short-term culturing experiment was utilized to evaluate its acute cytotoxicity response according to ISO 10993-5 specification.
## 4 Simulation Results  
In order to validate the theoretical analysis in Section 3 by means of simulation, it is necessary to study the transitions among operation modes superposed to the steady-state behavior of the UFCS. For that reason, we will use the data of one-day steady-state simulation given by the software developed in [14] as a starting point. This implies the selection of time intervals to visualize different operating points and transitions of the HESS and to obtain information about the transferred power between elements in the main bus, the SOC of the battery bank, and the occupancy of the ChPs. This information will be used in a PSIM simulation of the dynamic transitions among the operating modes, also taking into account the different time scales between static and dynamic simulations, which require time steps of 1 s and 0.1 us, respectively.  
Figure 7 shows a steady-state simulation of the UFCS for one day of operation, providing service to 180 EVs. Figure 7a shows the power balance in the main bus. The blue profile shows the consumption of the ChPs, while the red one depicts the power delivered by the grid, and the green one illustrates the power processed by the HESS. Consequently, Figure 7b shows the sequence of operating modes of the UFCS and allows for the selection of the transition points. Finally, Figure 7c shows the SOC evolution of the battery pack.  
\begin{table}
\begin{tabular}{c c} \hline
**Nomenclature** & **Description** \\ \hline \(C_{\text{\tiny{CGS}}}(s)\) & \(\frac{1}{s}\) \\ \hline \(C_{\text{\tiny{CGS}}}(s)\) & \(\frac{12.5}{s}\) \\ \hline \(C_{\text{\tiny{ccD}}}(s)\) & \(\frac{1}{s}\) \\ \hline \(C_{\text{\tiny{ccD}}}(s)\) & \(\frac{4.8}{s}\) \\ \hline \(C_{\text{\tiny{dcs}}}(s)\) & \(\frac{115.74}{s}\) \\ \hline \(C_{\text{\tiny{dcs}}}(s)\) & \(\frac{937.5(s+12.5)}{s}\) \\ \hline \(C_{\text{\tiny{dcb}}}(s)\) & \(\frac{1}{s}\) \\ \hline \end{tabular}
\end{table}
Table 2: Outer controllers’ values in Figures 5 and 6.  
Figure 7: _Cont._To illustrate the islanded operation of the station, two disconnections of the SST have been produced at different intervals: the first one lasting 1 h at 8:00 a.m. and the second one lasting 45 min at 4:00 p.m. Points A and B in Figure 7 have been selected for validation, and they correspond to the operation and transition of the HESS during grid-connected and islanded operations, respectively.  
For both simulations, maximum power levels have been established, namely, 600 kW for the SST, 1.5 MW for the discharging process in the BESS, and 350 kW for each ChP. Furthermore, in steady-state simulation, the HESS is modeled as a battery, while the supercapacitor contribution is neglected.
## 3 Proposed Signal Demodulation Method
In addition, the block diagram of its structure is given in Figure 5. It can be seen from the transfer function expression and the structure diagram that the cut-off frequency of the filter changes adaptively with the given frequency:  
\[D(s)=\frac{x^{\prime}(s)}{x(s)}=\frac{k\omega^{\prime}s}{s^{2}+k\omega^{ \prime}s+{\omega^{\prime}}^{2}} \tag{17}\]  
where \(x(s)\) and \(x^{\prime}(s)\) are the input and output signals, respectively, \(\omega^{\prime}\) is the center frequency of this filter, and k is the damping factor. According to the Routh criterion, it can be seen from the transfer function that the stable condition of the system is \(k\omega>0\). Therefore, as long as \(k\) is greater than 0, the system is stable. Rewriting Equation (17) into the form with the quality factor \(Q\) is  
\[D^{\prime}(s)=A.\frac{\frac{\omega^{\prime}}{Q}s}{s^{2}+\frac{\omega^{\prime} }{Q}s+{\omega^{\prime}}^{2}} \tag{18}\]  
where \(A\) is the gain coefficient, \(Q=1/k\). From Equation (18), it can be seen that only parameter k needs to be adjusted to achieve the performance adjustment of the SFF. It has obvious advantages compared with the traditional BPF. Figure 6 shows the amplitude-frequency and phase-frequency  
Figure 5: Block diagram of SFF. characteristic curves when the center frequency is 10 Hz, and k is taken as 0.1, 0.5 and 1, respectively. It can be seen from the figure that there are obvious attenuations at all frequencies except 10 Hz. The more the frequency is away from the center frequency, the better the filtering performance of the filter. This just meets the frequency selection requirements of the rotor position estimation system. The estimated rotor speed \(\hat{\omega}_{e}\) can be used to adaptively adjust the center frequency of the SFF. The calculating formula is:  
\[i_{\text{a}\beta}=i_{\text{a}\beta\prime}+i_{\text{a}\beta\prime}+i_{\text{a }\beta\prime} \tag{13}\]0  
The relationship between \(\hat{\omega}_{e}\) and the estimated mechanical speed \(\hat{n}\) can be further expressed as follows:  
\[i_{\text{a}\beta}=i_{\text{a}\beta\prime}+i_{\text{a}\beta\prime}+i_{\text{a }\beta\prime} \tag{13}\]1  
From Equations (19) and (20), it can be seen that the center frequency of the SFF is influenced by the number of pole pairs, and the range of \(\hat{\omega}_{e}\) will be larger for multi-pole motors. From the phase-frequency characteristic curve, the phase margin is greater than 90 degrees. This also shows that the system is stable.
## 5 Security Analysis
### Heuristic Analysis
#### 5.2.5 User Impersonation Attacks  
To launch user impersonation attacks based on the exposure of keys, \(\mathcal{A}\) requires the precise value of \(V_{i}\). In the proposed protocol, legitimate users may derive it utilizing the parameter set \(\{ID_{i},PW_{i},B_{i},a_{i}\}\) at their disposal; And the aggregation server might employ the acknowledged parameters \(r_{i}\) and \(x\) for an identical computation. However, \(\mathcal{A}\) is bound to encounter formidable computational barriers in accessing these key parameters, rendering the emulation of legitimate user \(U_{i}^{\prime}s\) identity towards \(S_{j}\) unfeasible. Consequently, the proposed protocol showcases robust resistance against such impersonation endeavors.
## 3 Proposed Circuit Implementation  
In the standard CMOS process, devices are isolated by reverse-biased PN junctions. As the temperature increases, the current through the reverse-biased PN junction grows exponentially, leading to a significant increase in leakage current. In contrast, in the SOI process, devices are isolated by silicon dioxide insulating dielectrics, which substantially reduce leakage current and help prevent latch-up. Therefore, the circuit in this chapter is designed using the SOI process.  
Although SOI process devices exhibit smaller leakage currents and a better suppression of latch-up, the electron mobility and threshold voltage of MOS devices still vary with temperature. This results in a reduction in parameters such as drain current and transconductance as temperature increases. To ensure circuit stability, the same input-stage structure and bias current are maintained across each amplifier stage. This approach ensures that, when the temperature changes, the input-stage transconductance of each stage undergoes nearly identical variations, thereby preserving overall frequency stability.  
Additionally, the input stage operates in constant current mode. In the weak inversion region, where the current is low, the change in transconductance is primarily influenced by leakage current, leading to significant temperature-induced variation. In the strong inversion region, the change in transconductance is mainly driven by electron mobility, but the variation is considerably smaller than in the weak inversion region. To counteract the drop in input stage transconductance at high temperatures, a Proportional-To-Absolute-Temperature (PTAT) current source is employed, which mitigates the temperature effects and helps maintain overall frequency stability.
## 5 Results and Analyses
### LSTM-Based Benchmark Model Results  
The LSTM neural network model was built and trained on the PyTorch 1.3 platform to predict the temperature in the fresh-keeping area. The prediction results and training results of the LSTM-based temperature prediction model are shown in Figures 8 and 9, respectively.  
Figure 7: Temperature change at each monitoring point.  
The training results show that the LSTM model was able to accurately track the changes between the upper and lower limits of the temperature and predict the future temperature; on the other hand, the predicted values were in agreement with the actual values.
## 2 Materials and Methods
### 1d-Cnn
#### 2.4.1 1D-CNN Formatting of Mathematical Components  
Regression analysis using 1D-CNN is primarily a model that processes time-series or sequential numerical data to predict continuous values. The mathematical structure of 1D-CNN involves performing convolution operations on the input data to extract features, which are then used for prediction. Given input data \(x\in R^{n}\), the convolution operation of 1D-CNN uses a filter \(w\in R^{k}\) is to extract local features, which can be expressed as the following equation:  
\[h_{1}[i]=f\left(\sum_{j=1}^{k}x[i+j-1]*w[j]+b\right) \tag{3}\]  
Here, \(h_{1}[i]\) is the \(i\)-th value of the feature map extracted from the convolutional layer, and \(f()\)is a non-linear activation function such as ReLU. In this process, the filter moves along the input data to learn local patterns and generates a feature map. Next, a pooling operation is applied to reduce the dimensionality of the feature map and prevent overfitting. Either average pooling or max pooling can be applied, and the pooling operation is expressed as follows:  
\[h_{2}[i]=\max(h_{1}[i],\ h_{1}[i+1],\ \ldots,\ h_{1}[i+p-1]) \tag{4}\]  
where \(p\) is the size of the pooling window.  
These processes are repeated across multiple convolutional and pooling layers to extract high-dimensional features from the input data. Finally, a fully connected layer performs regression analysis based on the extracted features, and the output layer produces the final predicted value \(y\).  
\[y=Wh_{n}+b \tag{5}\]  
Here, \(W\) is the weight matrix, \(b\) is the bias, and \(h_{n}\) is the output of the final convolutional layer. This entire process mathematically represents the 1D-CNN model, which learns essential patterns from the input data to perform regression analysis and predict continuous values.
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 2 Related Work
[28] formulated resource allocation for a 5G D2D network in an IIoT environment for minimizing interference and increasing the data rate. To optimize a D2D 5G network, a channel assignment and power optimization problem was proposed and simulation results were presented. [PERSON] et al. [29] investigated 5G networks for industrial factory automation to guarantee ultra reliable low-latency communication (URLLC) wireless communication and presented coordinated multipoint communication to support a time-sensitive network including time synchronization and quality of service (QoS) for 5G networks. [PERSON] et al. [30] focused on how to coordinate multiple energy-harvesting mobile devices with limited battery capacity to execute computation tasks in D2D networks. Also, the proposed D2D network aimed to minimize the long-term system cost, which is defined as the weighted sum of the average latency of task processing, the number of dropped tasks, and the battery energy penalty. Many real-world networks for IIoT applications have diverse connectivity characteristics that make routing protocols difficult. In the literature about mobility in IIoT environments, although routing protocols have been introduced with various scenarios, there is no mechanism to support a lot of mobile nodes and their link quality for a reliable and real-time-supported industrial environment. In our work, we have developed a MDP model for supporting mobility in the industrial environment. Our proposed model manages reliable connectivity with maximum data rate communication to maximize the data rate between IIoT devices. Furthermore, we present an algorithm to derive an optimal solution for the MDP model.