esa-sceva/satcom-chunk-collection huggingface dataset:
# Optically Pumped Magnetometer Measuring Fatigue-Induced Damage in Steel
## 2 Experimental Procedures
Reducing environmental fields in the magnetic shield below 10 nT using the coil system shown in Figure 1;
4. Mechanical loading of the specimen by exerting a tension and compression force on the specimen;
5. Measuring a change in the magnetic field \(B\) in the vicinity of the specimen using the readout of the OPM. Apart from the tuned geometry, specimen buckling was prevented by specimen alignment. The lateral specimen holder alignment was confirmed using dedicated alignment markers in the holders while the vertical alignment was approximated by focusing on the support area of both holders. Figure 2: Piezomagnetic signals of a low-cycle fatigue (LCF) experiment using a small specimen made of ferritic steel (length 1.5 mm). The experiment used a uniaxial strain amplitude of \(\pm\)1.5 percent and 108 cycles over approximately 430 s. The force \(F\) and magnetic field \(B\) are displayed together with the corresponding DIC images of the SUT on the top (color coded). The green squares in (**a**) mark the search areas for the DIC. The arrows on the images (**b**,**c**) mark the locations of the crack formation. The macroscopic crack initiation is evaluated visually with the features in (**c**). After that point, the force signal decreases rapidly. The SUT breaks at the very end of the time series.
## 3 System Model and Problem Formulation
### System Model
#### 3.1.5 Link Cost Model  
To satisfy the growing number of service requirements and improve service quality in SDN, we expect to improve the scalability of SDN with minimal network congestion cost. In this paper, the network congestion cost is expressed as the link bandwidth consumption of each control path during network operation. Its control path includes control paths for switch-controller communication and inter-controller synchronization. Due to the limited network resources, we expect to reduce the link consumption of each control path, to minimize the link occupancy rate of the control path as much as possible and meet the user's available bandwidth requirements. Each control link is included in the set of all control links, which is denoted as \(P_{ij}\). The switch-controller link occupancy rate is calculated by Equation (11). The inter-controller link occupancy rate is calculated by Equation (12).  
\[B_{prop}=\sum_{e_{ij}\in P_{ij}}\sum_{n_{i}\in N}\sum_{c_{j}\in C}\frac{z_{e_{ ij}}\rho_{ne}x_{ij}}{B_{e}}, \tag{11}\]Here, \(z_{e_{ij}}\) is a binary variable, indicating whether the link \(e_{ij}\) belongs to the set of control paths. \(p_{ne}\) is the link bandwidth consumption occupied by each switch-controller communication.  
\[B_{sync}=\sum_{e_{ij}\in P_{ij}}\sum_{c_{i},c_{j}\in C}\frac{z_{e_{ij}}p_{sync} \rho_{ce}}{B_{e}}, \tag{12}\]  
Here, \(z_{e_{ij}}\) is a binary variable, indicating whether the link \(e_{ij}\) belongs to the set of control paths. \(p_{sync}\) is the frequency of timing synchronization between controllers. \(\rho_{ce}\) is the link bandwidth consumption occupied by each inter-controller communication.  
Therefore, the link cost metric is defined as the utilization of all control path links of the whole network, which is calculated by the following formula:  
\[B=B_{prop}\times B_{sync}, \tag{13}\]
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 2 VLC Transmitter Light Source
### Theoretical Background of Laser for VLC Application
#### 2.2.3 Vertical Cavity Emitter Lasers (VSCELs)  
VCSELs, illustrated in Figure 7, are ideal for high-speed communication systems owing to their high optical beam quality and short cavity. However, the GaN-based violet-blue-green emitting VCSELs suffer from the large lattice-mismatch AlGaN/GaN distributed Bragg reflectors (DBRs). Research efforts are required to develop room-temperature continuous-wave operation high power GaN-based VCSELs. Experimental results have revealed the modulation bandwidth of violet-blue InGaN/GaN quantum-well VCSEL beyond GHz, confirming that GaN-based VCSELs have great potential for high-speed VLC transmitters [78].  
However, a laser diode could be a possibility for a Li-Fi light source; Nobel Laureate in Physics [PERSON] anticipated that laser illumination would eventually replace LED lighting, but the current lighting technology is led by the rather established and low-cost LED. The development of high-brightness, high-efficiency, high-speed modulation micro-LED modules and laser diodes, among other features, may accelerate Li-Fi technology's commercialization. We summarize the recent research achievements in LED and laser-based VLC in Table 2.  
Figure 5: An integrated surface mount device (SMD) package for laser lighting applications. Figure reused from [73] with permission from SPIE.  
Figure 6: Schematic of a SLD [77]. © 2016 Optical Society of America.
# Optimization of Coverage and Capacity Using Smart Antennae
## 4 Evaluation
### Field Sizes  
The test area was adjusted to different dimensions, including 100 m by 100 m, 200 m by 200 m, 400 m by 400 m, 600 m by 600 m, 800 m by 800 m, and 1000 m by 1000 m. The number of user equipment (UE) items was fixed at 1000, and the number of base stations (BSs) remained at 10. The experimental parameters were set as shown in Table 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline _Area_ & **1000 m \(\times\) 1000 m** \\ \hline \(n\) & 2\(\sim\)12 \\ \(j\) & 5 \\ \(P_{max}\) & 19 dBm \\ \(P_{min}\) & \(-\)30 dBm \\ \(B\) & 20 Mbps \\ \(K\) & 1000 \\ \(\gamma\) & 2 \\ \hline \end{tabular}
\end{table}
Table 5: Parameter settings for different numbers of BSs case.  
Figure 7: Experimental results (1000 m \(\times\) 1000 m, no. of BSs = 10).  
The results of the experiments are presented in Figure 9, indicating that the coverage rate decreased as the test area increased. This is because, with a larger test area and a fixed number of base stations, the number of base stations available to serve each UE remained the same. As a result, the probability of UEs being served by a base station decreased, leading to a reduction in the overall coverage rate as the test area expanded. The simulation results demonstrated that, in larger testing areas, such as 1000 square meters, compared with conventional omnidirectional antennas, this method could enhance the field coverage rate by 42.8%.  
\begin{table}
\begin{tabular}{c c} \hline \hline _Area_ & **100 m \(\times\) 100 m–1000 m \(\times\) 1000 m** \\ \hline \(n\) & 10 \\ \(j\) & 5 \\ \(P_{max}\) & 19 dBm \\ \(P_{min}\) & \(-\)30 dBm \\ \(B\) & 20 Mbps \\ \(K\) & 1000 \\ \(\gamma\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameter settings for field sizes case.  
Figure 8: Experimental results (1000 m \(\times\) 1000 m, no. of UEs = 1000).  
Figure 9: Experimental results (no. of BSs = 10; no. of UE = 1000).
###### Abstract  
We report the transformation of a conventional composite material into a multifunctional structure able to provide information about its structural integrity. A purposely positioned grid of carbon fabric strips located within a glass fibre laminate in alternating 0/90 configuration combined with a ternary nanomodified epoxy matrix imparted structural health monitoring (SHM) topographic capabilities to the composite using the impedance spectroscopy (IS) technique. The matrix was reinforced with homogenously dispersed multi-walled carbon nanotubes (MWCNTs) and carbon black (CB). A sinusoidal electric field was applied locally over a frequency range from 1 Hz to 100 kHz between the junction points of the grid of carbon fabric strips. The proposed design enabled topographic damage assessment after a high-velocity impact via the local monitoring of the impedance. The data obtained from the IS measurements were depicted by magnitude and phase delay Bode plots and Nyquist plots. The impedance values were used to create a 2D and a multi-layer (3D) contour topographical image of the damaged area, which revealed crucial information about the structural integrity of the composite.  
composites; structural health monitoring; impedance spectroscopy; damage mapping; carbon nanotubes +
Footnote †: : 10.11
## 3 Proposed Framework
### Feature Selection
#### 3.4.2 Embedded Method  
The author of [31] represented a geometrical approach for analyzing high-dimensional data. An embedded method performs feature selection during the model creation process itself. A regularization method is one of the most used methods for penalizing a feature based on a coefficient threshold [32]. If the objective function connects with the absolute values concerning the model parameters, LASSO (L1) regularization allows a penalty term by shrinking some coefficients to zero [33]. L1 provides the regression coefficients to be overcome to zero, collecting aggregated important features concurrently. Equation (4) illustrates how to determine the outcome. A regularization parameter lambda provides the model input. A higher lambda reduces overfitting. We have used C = 0.3, penalty = '11', solver = 'liblinear', and max_iter = 300 as the optimal hyperparameters of GB-RFE that help in selecting the most important features.  
\[\sum_{i=1}^{n}\left(y_{i}-\sum_{j=1}^{p}x_{ij}\beta_{j}\right)^{2}+\sum_{j=1}^ {p}|\beta_{j}| \tag{4}\]  
In Ridge (L2) regularization, the function is penalized for adding the sum of the squares from the model parameters. The author of [3] described that L2 regularization is a method that accounts for nearly all the features and attempts to withdraw many coefficient measures towards zero in Equation (5).  
\[\sum_{i=1}^{n}\left(y_{i}-\sum_{j=1}^{p}x_{ij}\beta_{j}\right)^{2}+\sum_{j=1}^ {p}\beta_{j}{}^{2} \tag{5}\]  
when a lambda is too large, it adds too much weight and causes underfitting. Meanwhile, overfitting is avoided effectively with the L2-regularization technique. The optimal hyperparameter of this classifier for the data used in the present study are C = 0.5, penalty = '12', and max_iter = 300.
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
### The Key Hardware Components for Physical Testbed
#### 3.1.3 Raspberry Pi  
The widely used Raspberry Pi (RPi) is a small single-board computer that is adaptable, affordable, fully customizable, and programmable with contemporary high-definition multimedia capabilities, as shown in Figure 9, and is equipped with Internet connectivity. It can be seamlessly integrated within the domain of WSN applications for research and prototyping purposes. The RAM memory, CPU (central processing unit), power supply connector, USB ports (where a Wi-Fi USB dongle or adaptor can be attached), ethernet ports, GPU (graphics processing unit), and lower-level peripherals of general purpose input-output (GPIO) pins that can be used for I2C, UART, SPI-based serial communication buses or interfaces, among other components, are all found on a RPi board. It has a slot for an SD card, allowing it to be utilized for datalogging or large-scale data storage (in cases of Internet outage). Operating systems like Raspbian, NOOBS, and others can be used to run RPi. The board can be used as an IoT gateway-capable node to collect, store, and process the data received from sensor nodes in a local database within RPi called SQLite or to communicate the received data from the sensor nodes to a remote database such as MySQL depending on the application. Herein, the stored real data in a remote database can be retrieved and fed into the virtual platform, i.e., the Contiki configuration unit, within the cloud using the appropriate database interface. Contiki can use the real-time data stored in the database from the most recent test of the physical network to compile and configure a given virtual Cooja note (i.e., the simulation). This can be placed in the simulation window within the simulation platform for virtual testing purposes, thus virtualizing an established physical network. This approach can offer better virtual network testing wherein real data from a physical network is used. The RPi board can also be used as a mobile node by being connected to a portable battery HAT that can be placed on top of the RPI to provide a 5v regulated power supply to the RPi, allowing the board's mobility to meet a specific requirement, mainly for VN scenarios.
# Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review
## 3 Green Radio-Frequency Identification
### Passive RFID Systems
Thus, choice of calculation path loss model is a vital decision. In [103], the author discusses and compares more accurate path loss predicting models extensively, such as [PERSON]'s model, CCIR model, Hata model, etc., for different environments. However, these models have application-specific device operating frequencies. For more of clarity, we discuss, in the following, the relationship among distance, operating frequency, and path loss based on the most rudimentary mathematical model to calculate path loss, Free Space Path Loss (FSPL) model that can be written as follows:  
\[P_{FSPL}=\left(\frac{4\pi fd}{c}\right)^{2} \tag{3}\]  
where \(f\) and \(c\) denote the operating frequency (in Hz) and speed of light (in metres/second), respectively. Also, \(\frac{f}{c}\) is called the signal wavelength (\(\lambda\)). The term \(d\) refers to the distance between the RFID reader and RFID tag antennas. \(\lambda\) and \(d\) are in the same unit of length (in metres). If \(d>>\lambda\), it is assumed that both antennas are in the far field of each other. Equation (3) shows that the operating frequency is directly proportional to the square root of the path loss considering all other variables constant. In addition, the distance between the antennas is also directly proportional to the square root of the path loss considering all other variables constant including the operating frequency, which means that the received signal power at RFID tag rapidly decreases with increasing transmit-receive distance, which also results in an increase in path losses. The wake-up signals deployed in duty cycle leads to energy inefficiency as lots of energy are utilized. Low energy powered radios can be deployed to wake up a sensor node only when need arises, such as sending or receiving packets. Meanwhile, power-consuming transceivers are deployed for information transmission. [PERSON] et al. [104] suggests a network consisting of non-active RFID wake-up transceiver called WISP-Motes and RFID readers. A non-active RFID wake-up transceiver utilizes the energy derived from the reader transmitter to initialize an interruption that awakens the node. In reality, it is impossible to equip all the sensor nodes with RFID readers because of its power-hungry capability. The aforementioned issue and the limited operational range have restricted passive RFID from being utilized as only as a single-hop scenario. Software simulations have indicated that WISP-Motes can conserve a greater quantity of energy at the cost of more hardware and magnified latency in information delivery. The authors showcase their advantages in the scenario of lightly delay-tolerant system mobile elements accompanied with RFID readers. Figure 3: Passive RFID system.
# Interspectral Error Tracking and Compensation of DSDT in Satellite Internet of Things
## 3 Adaptive Error Tracking and Equalization
### Sub-Spectrum Error Synchronization Tracking and Adaptive Compensation
\(\mathbf{R}_{e}\) is the covariance matrix of vector \(\mathbf{e}\), \(\mathbf{e}=[A_{1}^{\prime}e^{(2\pi\Delta f_{1}iT+\Delta\phi_{1})},\ldots,A_{ N}^{\prime}e^{(2\pi\Delta f_{N}iT+\Delta\phi_{N})}]\). \(\sigma_{n}^{2}\) is the noise power, and \(\lambda\) is the forgetting factor. As known from [19], its optimal forgetting factor is  
\[\lambda_{opt}\approx 1-\frac{1}{\sigma_{n}}(\frac{tr\mathbf{R}_{y}}{tr\mathbf{ R}_{e}})^{1/2} \tag{14}\]  
Therefore, substituting Equation (14) into Equation (13), our minimum of mean square error is  
\[y_{l,i}=s^{\prime}_{ri}(lT_{s}).\]0  
\[y_{l,i}=s^{\prime}_{ri}(lT_{s}).\]1 Algorithm iteration flow chart. In fact, \(D_{min}\) reflects the difference between the actual symbol decision and the ideal constellation in terms of the Euclidean distance.
# On Ad Hoc Communication in Industrial Environments
###### Abstract  
Wireless communication is becoming vital in the industrial environment. New communication technologies, including ad hoc communication, are researched for this application. A thorough understanding regarding the connection characteristics of industrial networks could benefit this trend. In this work it was possible to record the time-variant network topology of such a network utilizing a novel method. Using this method and the generated recordings, novel insights into the behavior of industrial ad hoc networks are presented. The recorded time-variant topology, the tools and method of acquisition, and tools for processing and examination are published. This enables researchers and engineers to check their communication technologies in terms of applicability to the industrial use case and record more network topologies in a wide variety of wireless networking scenarios.  
mobile ad hoc networks; industrial application; nodal encounter pattern; communication characteristics +
Footnote †: journal: Applied  
_Article_  
On Ad Hoc Communication in Industrial Environments
## 6 Simulation Experimental for Multimodal Vehicle Logistics  
### Experimental Parameters  
Taking the end-to-end vehicle logistics of Company A in east China as an example, it is assumed that the vehicle logistics center located in the City of Shenyang provides distribution services for four stores in various cities in east China by combining roads, railways, and waterways. Twenty-seven city nodes with transit functions are selected as research objects in this paper. The distance of the roads, railways, and waterways between cities, as well as the real-time congestion index of the road network around the transport center, are collected by Amap (v15.00.0.2031).  
Assume that 50 complete vehicles of two brands that cannot be mixed together are transported from Shenyang to Jiaxing. In road transportation, the empty-load and full-load fuel consumption of the lorry are 0.07783 L/km and 0.34051 L/km, respectively. For railway and waterway transportation, fuel consumption is proportional to the number of transport vehicles and the distance, where the fuel consumption coefficient \(\mu^{k}\) is 0.0046 L/(vehicle\(\cdot\)km) and 0.0058 L/(vehicle\(\cdot\)km), respectively. The fuel carbon emission coefficient \(\delta\), which represents the amount of carbon emissions produced by a unit of fuel, is 3.7932 kg/L, the unit fuel price G\({}^{k}\) is 0.64 USD/L, and the carbon tax price D is 0.014 USD/kg. Assume that the transport starts at 8:00 am and the time window is open at the same time. The unloading time window is set every 3 h from 8:00 am and is open for 3 h each. Train or ship departures are scheduled every 2 h from 8:00 am, with each waiting period lasting 1 h. Taking into account the decrease in departures at night, the departures are scheduled every 3 h from 20:00 pm to 8:00 am. The threshold delivery time Lt is 18 h, the time penalty coefficient \(\lambda\) is 0.01, and the waiting penalty in buffer area p\({}_{w}\) is 0.07 USD/(vehicle\(\cdot\)h). The parameters for different transport modes are shown in Table 4.  
The transformation of the transport modes at the transit nodes will inevitably lead to increased transit costs, transfer times, and carbon emissions, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{**Speed**} & \multirow{2}{*}{**Damage Rate**} & \multicolumn{2}{c}{**Cost**} \\ \cline{3-4}  & & & **0–500 km** & **Over 500 km** \\ \hline Road & 80 km/h & 4.0 \(\times\) 10\({}^{-7}\) & 0.141 USD/km & 0.127 USD/km \\ Railway & 100 km/h & 2.0 \(\times\) 10\({}^{-7}\) & 0.113 USD/km & 0.099 USD/km \\ Waterway & 30 km/h & 3.0 \(\times\) 10\({}^{-7}\) & 0.085 USD/km & 0.071 USD/km \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transportation parameters for different modes.
### Innovative and Sustainable Extraction Methods
#### 2.2.2 Pressurized Liquid Extraction (PLE)  
Pressurized liquid extraction (PLE) is another technique that is considered more modern; however, it has been consolidated mainly as a high-throughput and green extraction technique. It is considered sustainable and adequate for the extraction of bioactive compounds from biological origins and for the characterization and determination of a wide variety of compounds of interest in food and other matrix samples [54]. Several authors consider the PLE as a pressurized and accelerated solvent extraction [55].  
This methodology employs organic liquid solvents at high temperature (50 to 200 \({}^{\circ}\)C) and pressure (10 to 15 MPa) to implement a faster extraction of target compounds. The temperature and dielectric constant of the solvent have an inversely proportional relation; with the increase in the temperature, the dielectric constant decreases. Thus, the polarity of the solvent becomes lower [55]. Therefore, it is possible to tune the extraction temperature to the polarity of a solvent in order to recover the compounds of interest. Due to the high pressure, the matrix cells are saturated faster, which compels the solvent into the solid sample. This way, this technique is able to reduce the extraction time and the amount of solvents necessary, at the same time increasing the yields when compared with traditional solvent extraction [56]. Even if this method has some positive features, it also has clear drawbacks. It is not suitable for the extraction of thermolabile compounds, as high temperatures are necessary and they can cause degradation effects on the molecular structure of some desired compounds [57].  
Some researchers use water and generally recognized safe solvents (e.g., ethanol) to obtain food-grade extracts using the PLE method. In a recent work, the PLE conditions for the recovery of bioactive compounds from fruit byproducts, mainly pomegranate (_Punica granatum_) peels, were studied in order to achieve the best conditions, using a solution of water and ethanol [58].  
The use of extraction techniques to recover high-value substances from food industry waste is not a recent proposition, mainly because natural matrices are rich sources of important groups of compounds such as carotenoids, tocopherols, and polyphenols. Crop plants like red chicory have a considerable amount of waste (roots and steams) that are not consumed by either humans or animals. The mentioned characteristics led to the study performed by [PERSON] et al., where the phenolic profile was characterized after the extraction by PLE [59]. In other works, the use of PLE had a considerable positive effect when compared to the traditional methods such as maceration and Soxhlet. [PERSON] et al. studied the best conditions to achieve an enhancement in the antioxidant activities of the extracts from blackberry fruits [60].