esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hardware and System
### Hardware Architecture
In order to highlight the added value of our sensor device in terms of energy consumption and the performance of the network, it is imperative to note that the used microcontroller and ZigBee transceiver are, in addition to the low power consumption, open to be configured in all network layers, contrary to the commercial sensor nodes described in Tables 1 and 2, where it is very difficult to access or configure the operations performed in the physical and MAC layers. However, the ZigBee specifications only define three topologies inside the MAC layer: star, mesh and cluster-tree. Since the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name of** & **Transmitter Power** & \begin{tabular}{c} **Average** \\ **Consumption** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Indoor Range** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Outdoor Range** \\ \end{tabular} & \begin{tabular}{c} **Receive** \\ **Sensitivity** \\ \end{tabular} \\ \hline \begin{tabular}{c} Texas \\ Instruments Mote \\ \end{tabular} & \(-24\) dBm\(-0\) dBm & \(24\)–\(30\) mA & \(108\) ft & \(173\) ft & \(-90\) dBm (min), \(-94\) dBm \\ \hline \begin{tabular}{c} Microchip \\ \end{tabular} & \(-24\) dBm\(-0\) dBm & \(19\)–\(27\) mA & \(110\) ft & \(183\) ft & \(-94\) dBm \\ \hline \begin{tabular}{c} TelosB \\ \end{tabular} & \(-28\) dBm\(-0\) dBm & \(27\)–\(36\) mA & \(82\) ft & \(102\) ft & \(-90\) dBm (min), \(-94\) dBm \\ \hline \begin{tabular}{c} Memsic \\ \end{tabular} & \(-26\) dBm\(-0\) dBm & \(25\)–\(37\) mA & \(89\) ft & \(103\) ft & \(-90\) dBm (min), \(-94\) dBm \\ \hline \begin{tabular}{c} Tiny Node \\ \end{tabular} & \(\max\) \(12\) dBm & \(43\)–\(62\) mA & \(121\) ft & \(210\) ft & \(-106\) dBm \\ \hline \begin{tabular}{c} Lotus \\ \end{tabular} & \(3\) dBm & \(34\)–\(52\) mA & \(51\) ft & \(82\) ft & \(-101\) dBm \\ \hline \
\begin{tabular}{c} Sun SPOT \\ \end{tabular} & \(-24\) dBm\(-0\) dBm & \(23\)–\(46\) mA & \(112\) ft & \(169\) ft & \(-90\) dBm (min) \\ \hline
\begin{tabular}{c} Our Designed Node \\ \end{tabular} & \(-24\) dBm\(-0\) dBm & \(17\)–\(25\) mA & \(117\) ft & \(153\) ft & \(-92\) dBm \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sensor Nodes Comparison 2: energy and power. Figure 2: Circuit design of the sensor node. goal of this work is to develop and test a new topology, it was necessary to design a new sensor node prototype that must be fully open to configuration. The development of this new device has also allowed performing some improvements related to the physical layer and transceiver registers, such as reducing the size of packets to minimize the computational delay and save energy. Thus, the hardware has permitted obtaining a new platform that represents a cross-layer solution, where users can define the specifications of the network based on the type and application of the network.
# Developing an I4.0 Cyber-Physical System to Enhance Efficiency and Competitiveness in Manufacturing
## 6 Discussion  
In the proposed design, both sensors are essential, as the optical-flow sensor can only tell the feed rate and estimate the amount of material being consumed. It cannot estimate the amount of material left on the roll. In contrast, the depth sensor can estimate the amount of material left on the roll, but without the feed rate, it cannot estimate when it would run out and issue the warning at an appropriate time.  
When comparing the two sensors, the depth sensor can consistently provide highly accurate readings. As shown in Figure 10, when measuring the distance between the depth sensor and a roll of material, even with the curved surface, the majority of the readings landed within \(\pm 1\) mm of the ground-truth value. In practice, such fluctuations can be alleviated by adopting a moving average filter.  
In contrast, it was observed that readings from the optical-flow sensor could be impacted significantly by the distance between the sensor and the material passing underneath it. In the system integration process, experiments were conducted with the sensor placed at 40 mm, 50 mm, and 60 mm from the material, respectively. In each test, the material underneath the sensor had a translational displacement of 10 cm. The test was repeated 10 times for each distance to obtain averaged values. The results are presented in Figure 11. By performing linear interpolation, it can be found that the optimum distance is 54.3 mm from the sensor. This adjustment process is essential for the system to compensate for variations of sensors in their manufacturing processes.  
After the adjustment process, another test was conducted to compare the distance data measured by the optical-flow sensor against the ground truth. The corresponding result is shown in Figure 12. While an offset was observed, raw readings from the sensor \(d_{\text{raw}}\) have a linear relationship with the ground truth, which can be compensated by finding the slope, i.e., 1.1458, and the y-intercept of the line, i.e., \(-22.008\), such that the sensing reading after calibration, \(d_{\text{calibrated}}\), is expressed as  
\[d_{\text{calibrated}}=1.1458d_{\text{raw}}-22.008 \tag{7}\]  
Figure 9: Estimated values of remaining materials on the rolls and the corresponding ground truth values.  
Figure 11: Data collected in the calibration process of the optical-flow sensor.  
Figure 10: Data collected in the evaluation process of the depth sensor.
## 4 Numerical Examples
### Nonseparable Spectrum
#### 4.1.3 Simulation Efficiency  
The suggested method used three accelerations to simulate nonstationary spatially varying seismic motions, including Cholesky decomposition, time-frequency decoupling and the FFT. To quantitatively study the simulation efficiency of the suggested method, the traditional efficient approximation method [21] was used for the comparison, where the traditional method adopted the FFT with the help of POD. Without loss of generality, it was assumed that the autospectrum of each simulation point was different. Table 2 displays the consumed time of two methods for 50, 100, 150 and 200 simulation points, where the 'time ratio' was the consumed time of the traditional method divided by that of the proposed method. Clearly, the suggested method was more efficient than the traditional method, and the simulation efficiency could be increased by more than tenfold. Furthermore, when the simulation points increased, the efficiency gains became more apparent.
## 3 Results and Discussion
### Polyphenols and Carotenoids Content  
Barley grains are rich in secondary metabolites such as polyphenols, represented mainly by phenolic acids and flavonoids. These bioactive compounds, known for their health-promoting antioxidant properties, make barley a valuable resource for developing food products and animal feeds [42]. Ferulic and \(p\)-coumaric acid are the main phenolic acids in barley grains [43], known for their antioxidant properties [44]. Other phenolic acids, reported in the scientific literature to represent smaller amounts in the grains, include caffeic and sinapic acids, belonging together with ferulic and \(p\)-coumaric acids to the group of hydroxycinnamic acids, and also vanilic and protocatechic acids, belonging to the group of hydroxybenzoic acids [45]. The highest concentration of phenolic acids is found in bran and whole grains [46]. Flavonoids detected in barley grains include, among others, quercetin and kaempfer [47]. Secondary metabolite synthesis in plants is influenced by many factors such as the plant genotype, the environment, agronomic practices, and stress conditions [41] e.g., higher flavonoid content occurs in the purple grain varieties, which have the Ant2 gene [48]. Since these compounds are known to play a role in plant defense against stress [49], abiotic stress factors were previously shown to enhance phenolic acids and flavonoid content in cereal grains. Another abiotic stress factor, nitrogen deficiency, was also suggested to trigger the accumulation of phenolic compounds in grains [50]. Scientific literature suggests that nitrogen limitation may promote secondary metabolite synthesis by redirecting plant metabolism towards carbon-based assimilates [41,49]. On the other hand, some researchers indicate the opposite, that nitrogen fertilization can increase the total phenolic content, with this effect being dependent on the dose of nitrogen [51, 52, 53]. The content of these compounds is also affected by harvest time, plant maturity, part of the grain, and post-harvest storage conditions [54]. In our study in the case of phenolic acids, no significant differences were observed in the content of the sum of these compounds between barley grains grown at different nitrogen rates, nor was there any variation between varieties in this respect (Table 3). The impact of genetic background and N fertilization levels could have been overweighted by some other subtle abiotic and biotic factors not specifically controlled in the experiment [41]. However, as shown in Table 3, some significant differences were found regarding the content of individual identified phenolic acids, both depending on nitrogen dose and barley variety. This concerned phenolic acids occurring in smal
## 3 Results and Discussion  
### Truncation of the Catalytic Domain of PCV2 Rep  
We previously developed a method for site-specific conjugation of ssDNA to a protein of interest via the fused replication initiator protein (Rep), such as conjugation of Gene A* from bacteriophage phiX 174 and the catalytic domain of porcine circumvirus type 2 Rep comprising residues 1-116 (pRep) [5; 13]. Recently, Gordon's group reported a similar strategy for construction of DNA-protein conjugates using several Reps, including pRep, as HUH-tags [21]. pRep is much smaller than Gene A* and well-expressed in E. coli. The structure of pRep (Protein Data Bank (PDB) ID:2 HW0) exhibits flexible regions at the N- and C-terminals [18]. In our designed fusion protein, Rep was positioned between Venus and NanoLuc. For construction of BRET-based biosensors, flexible linkers are generally inserted between the sensory domain and the BRET molecule to allow for a certain degree of movement of the BRET molecule [14]. In the present study, to allow for movement of BRET molecules only when the target molecule binds to the DNA aptamer conjugated to the fusion protein, use of truncated variants of pRep was required. Truncated variants of pRep were constructed and their DNA binding abilities were evaluated.  
Truncated variants of pRep, namely pRep\({}_{1\text{-}109}\) lacking the last 7 amino acids in the sequence, pRep\({}_{14\text{-}116}\) lacking the first 13 amino acids in the sequence, and tpRep (pRep\({}_{14\text{-}109}\)) lacking the flexible regions at both the N- and C-terminals of pRep, were expressed in E. coli and subsequently purified. Truncated pReps were purified from the soluble fraction, similar to pRep. Proteins were mixed with ssDNA containing a Rep recognition sequence for evaluation of the DNA binding abilities of the truncated pReps. After incubation, samples were analyzed by SDS-PAGE (Figure 2). In addition to the bands appearing at the expected sizes of the protein, all samples also showed other bands athigher molecular weights than pReps without conjugation. Those bands appearing at higher molecular weights were attributed to pReps conjugated with ssDNA. Upon cleavage of specific sequence of ssDNA, PCV2 Rep is conjugate to DNA covalently. In addition of this reaction, PCV2 Rep also catalyze the reaction in the opposite direction [18]. PCV2 Rep has an ability to catalyze joining two ssDNA fragments, a free 3'-OH and the 5'-phosphate covalently linked to Rep, for regeneration of PCV2 Rep recognition sequence. Therefore, there is a possibility to exist unreacted protein. However, this joining activity is not efficient compared to cleavage activity followed by conjugation of DNA with Rep. In this experiment, the ratio of protein and ssDNA is 1:1. By increasing ssDNA concentration, the conjugation efficiencies should increase. These results demonstrate that tpRep retains the ability to covalently bind to ssDNA even after truncation of the flexible regions located at N- and C-terminals of pRep. Therefore, tpRep was used for construction of a BRET-based biosensor with a DNA-protein conjugate.
# An Efficient GPS Algorithm for Maximizing Electric Vehicle Range
## 5 Discussion  
As discussed, numerous factors identified in the results can impact the range of motion for electric vehicles and may deplete the battery capacity. Some of these factors are overlooked by conventional geographical positioning systems (GPSs), which prioritize routes based solely on time or distance, disregarding energy considerations. Each case presented in the Section 4 features a graph depicting the state of charge (SOC%).  
In Case 1, research findings and resultant data demonstrate that variables such as aerodynamics, drag coefficient, frontal area, and vehicle velocity can exert a significant influence on total energy consumption. Disparities exceeding 60% between two vehicles were observed.  
Moving to Case 2, the outcomes revealed that wind speed and direction could conserve approximately 49% of the battery's capacity during a short journey spanning approximately 50 km. Considering a longer distance, the risk of depleting the entire battery capacity escalates, emphasizing the crucial role of wind speed; therefore, at this situation wind speed can rescue the situation if it was in the same direction of motion.  
Regarding the third case, findings indicate that road topology can substantially impact range prediction and energy consumption. Opting for a road with fewer inclines can result in savings of about 46% compared to the aforementioned scenario. Additionally, in windy locations, energy consumption may experience exponential growth.  
Lastly, the final case involved the validation of the MATLAB Simulink model, ensuring its alignment with real-life experiences with minimal tolerance. Knowing that the first two cases were at 0 degrees of road inclination, while in the other two cases, inclination and road topologies were taken into consideration in the calculation.  
All the listed and studied data discussed above can be easily incorporated in a commercial mobile application that includes predefined data about all EV models, with a large database about each vehicle from the frontal area to the weight and drag coefficient.  
Figure 18: (**a**) Battery SOC equals 100% at the start of the journey by ID3. (**b**) Battery SOC decreased to 92% at the end of the journey. (**c**) Some real data from the vehicle.  
All the data needed from the user in this mobile application pertains to the used vehicle model and the number of passengers inside the vehicle, as this will affect the total weight which simultaneously affects the energy consumption. Finally, the starting point and the destination reached need to be defined. The output will be the best or more economical and energy efficient route. Also, the approximate calculation of data for energy consumed for the specified destination considers the speed limits of the routes.
## 3 Stamping Simulation and Property Mapping
### Simulation Outputs  
The simulation outputs provided essential data for downstream FEM modeling, including the following:  
* The thickness distribution map reveals localized thinning and thickening, as shown in Figure 3a, ranging from \(-23.4\%\) to \(+43.3\%\), relative to the initial sheet thickness of 1.2 mm. This means that in the most deformed regions, the local thickness is reduced to approximately 0.92 mm (1.2 mm \(\times\) 0.77), while in thickened areas, it reaches a maximum of approximately 1.72 mm (1.2 mm \(\times\) 1.43). The red areas (thinning above 37.3%) are concentrated around sharp bends and edges, indicating significant stretching and potential failure risks. In contrast, blue areas (thinning below 6.9%) represent flatter regions with minimal deformation. The major strain distribution confirms these findings, as shown in Figure 3b, with strain values from \(-6.87\times 10^{-2}\) to \(8.77\times 10^{-1}\), showing high-strain zones at upper edges and corners subjected to tensile forces. Together, these maps identify critical areas prone to thinning and stress, essential for evaluating structural integrity.  
Figure 2: Stamping simulation, (**a**) mesh part and (**b**) stamping direction.  
* Figure 4 displays the effective stress distribution across the stamped seat, highlighting regions of varying stress intensity under simulated loading conditions. The stress values range from a minimum of 14.5 MPa (blue regions) to a maximum of 567 MPa (red regions), as indicated by the color gradient in the scale. Regions experiencing the highest stress concentrations, represented by red and yellow zones, are primarily located around sharp bends, edges, and connection points. These areas are subjected to significant tensile and compressive forces during the stamping process, making them critical zones for potential material failure or yielding. On the other hand, blue and green regions, which exhibit lower stress levels, correspond to relatively flat areas or sections with less deformation, where the material retains its original integrity more effectively.  
Figure 4: Effective stress resulting from the stamping simulation.  
Figure 3: Stamping simulation, (**a**) thickness distribution and (**b**) strain values.
## 2 Network Model
### Multi-Scale Integration  
In convolutional neural networks, feature maps obtained from different scales of convolutional layers contain varying feature information of the target. Deep convolution generates low-resolution feature maps that lose a significant amount of location features but contain rich image semantic features, whereas shallow convolution generates high-resolution feature maps that retain location features but lack sufficient semantic information. Therefore, fusing the deep and shallow feature maps is advantageous for detecting targets at various scales. By combining the feature pyramid network (FPN) to transfer solid semantic information top-down and the path aggregation network (PAN) to transfer location information bottom-up, the location information of the target can be retained while obtaining deep semantic information from the feature map, thus enhancing the multi-scale target detection capability [21]. The module structure is depicted in Figure 3.
# Stress Dispersion Design in Continuum Compliant Structure toward Multi-DOF Endoluminal Forceps
## 3 Evaluation
### Prototype Experiment  
This experiment aimed to validate the effectiveness of the proposed design method by performing motion generation using a prototype. Similar to the FEA results, the experiments confirmed that the smaller the radius of curvature, the more the stress can be dispersed at the base end. Therefore, the radii of curvature of the improved design and the initial design were compared as evaluation indexes. A 3D printer (Objet30, Stratasys, Eden Prairie, MN, USA) was used to ensure rapid prototyping. The material of the 3D printer was the acrylic resin Verowhite. This material will be replaced by biocompatible materials such as PAI in the future. The experiments considered motion generation by pulling a prototype fixed to a jig using an electric measuring stand (EMX-1000N, IMADA, Toyohashi, Japan) and a force gauge (ZTA-5N, IMADA, Toyohashi, Japan) with a stainless wire with an outer diameter of 0.3 mm. The prototype was printed together with the jig to provide stable fixation for the measurements. This approach prevented the fixed end from shifting or tilting. The hinge length was set to 0.4 mm, and other parameters were the same as the design used in FEA. The outline of this experiment is shown in Figure 8. The force gauge was varied up and down at a constant speed of 50 mm/min, and tension was applied to the tip of the prototype with a wire. Due to the difficulties in measuring the radius of curvature, we compared the displacement of the tips. The displacement was measured using a motion analyzer (VW-9000, KEYENCE, Osaka, Japan) from a video obtained with a camera. The prototype could bend within a range of 180 degrees. Figure 9 shows the displacement of motion generation using the prototype for each hinge length. The results show that the improved design for each hinge length had a smaller displacement than the initial design. Therefore, the prototype experiments confirmed that the stress was significantly reduced. The results with the fabricated prototype also show the effectiveness of the proposed design method for successful motion generation.  
Finally, a feasibility test was conducted to confirm that the mucous membrane could be lifted by the improved forceps. As shown in Figure 10, a pre-cut simulated intestinal four-layered sheet (Kyoto Kagaku, Kyoto, Japan) was successfully lifted by the 3D-printed prototype based on the improved design. Therefore, the result confirmed the feasibility of the improved design.  
Figure 8: Experimental image of the motion generation using the prototype: (**a**) experimental setup, (**b**) initial design, and (**c**) improved design.  
Figure 9: Displacement of the motion generation using the prototype for each hinge length.
## 2 Design of CRPDC
### Circuit Design of CRPDC  
To drive the coil current accurately and rapidly, a CRPDC including a MOSFET driving circuit, a capacitive reservoir circuit, and a current sampling circuit, is developed, as shown in Figure 2. By inputting the PWM voltage signal into the CRPDC, the proportional electromagnet is driven by the resulting coil current. The composition and working principle of the proposed CRPDC are introduced as follows. The MOSFET driving circuit consists of one optocoupler (U1), one P-MOSFET (Q1), and three resistances (R1, R2, and R3). The resistance (R1) is employed to limit the current of the LED side of the optocoupler (U1). While the high-level voltage of the input PWM signal is fed into the LED side of the optocoupler (U1), the detector side of the optocoupler (U1) and the D-S ports of the P-MOSFET (Q1) are in the turn-on state (the voltage difference between the G port and the S port of the P-MOSFET is smaller than \(-4\) V). While the low-level voltage of the input PWM signal is fed into the LED side of the optocoupler (U1), the detector side of the optocoupler (U1) and the D-S ports of the P-MOSFET (Q1) are in the turn-off state (the voltage difference between the G port and the S port of the P-MOSFET is larger than \(-2\) V).  
Figure 1: The proportional relief valve.  
Figure 2: Circuit design of CRPDC.  
The capacitive reservoir circuit is designed by connecting an energy-storage capacitance to the proportional valve coil in a parallel manner. While the D-S ports of the P-MOSFET (Q1) are in the turn-on state (the control voltage of the G port is below the VCC), the proportional valve coil is directly excited by the power supply (Vcc). Conversely, the proportional valve coil is actuated by the energy-storage capacitance if the P-MOSFET (Q1) is in the turn-off state (the control voltage of the G port is the same as the VCC). The resistance (R5) is applied for current sampling. To limit the charging power of energy-storage capacitance (C1), a power-limiting resistance (R6) is designed.  
The current sampling circuit is designed to sense the resulting coil current of the digital driving circuit, which consists of one differential amplifier [28] and one voltage follower [29]. The four resistances (R7, R8, R9, and R12) and one operational amplifier (U2) constitute the differential amplifier. The voltage follower is composed of two resistances (R10, R11) and one operational amplifier (U3), which provides a bias voltage for the differential amplifier to improve the zero-point acquisition accuracy. Moreover, a first-order low-pass filter composed of one resistance (R13) and one capacitance (C2) is designed to reduce the signal noise of the current sampling circuit [30]. The transformed and amplificated coil current can be sensed by measuring the resulting sampling voltage (Ui).