esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Evaluation Experiment and Analysis
### Evaluation Experiment of Calibration Method with Multiple Geometric Constraints and Result Analysis
The calibration accuracy obtained by reference [19] was 0.0451 mm, which was lower than the calibration accuracy of this paper (the measurement accuracy of the gauge block was 0.024 mm when using four parallel planes in the paper). The measurement accuracy of the three methods for gauge blocks was compared. Method A was the basic structured light calibration algorithm, which directly substituted all calibration light stripe center points into the light plane equation, and the light plane equation parameters were obtained by least square fitting. Method B performed the measurement with structured crosshair light, and the measurement data were from the literature [20]. Method C was the light plane calibration method proposed in this paper, and the number of parallel planes was four. The measurement results of the three methods are shown in Table 6.  
Figure 10: Gauge block measurement errors corresponding to different numbers of parallel constraints. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Standard Gauge** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\
**Block Thickness** & & & & & & & & & & \\ \hline
3 & 0.043 & 0.031 & 0.029 & 0.028 & 0.023 & 0.024 & 0.027 & 0.025 & 0.031 & 0.029 \\
4 & 0.040 & 0.035 & 0.034 & 0.030 & 0.027 & 0.029 & 0.029 & 0.029 & 0.027 & 0.029 \\
5 & 0.034 & 0.032 & 0.026 & 0.025 & 0.025 & 0.029 & 0.027 & 0.030 & 0.029 & 0.024 \\
6 & 0.042 & 0.038 & 0.025 & 0.029 & 0.021 & 0.023 & 0.030 & 0.033 & 0.030 & 0.029 \\
8 & 0.039 & 0.033 & 0.029 & 0.021 & 0.023 & 0.021 & 0.029 & 0.025 & 0.027 & 0.025 \\
10 & 0.046 & 0.039 & 0.027 & 0.029 & 0.027 & 0.030 & 0.032 & 0.030 & 0.028 & 0.028 \\ Mean error & 0.041 & 0.035 & 0.028 & 0.027 & 0.024 & 0.026 & 0.029 & 0.029 & 0.027 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Measurement error of gauge block (mm). As shown in Table 6, the calibration accuracy of the line structure proposed in this paper was close to that of Method B. However, this method required the use of a pair of perpendicular light planes to optimize the line-structured light, and the perpendicular relationship between the two light planes needed to be ensured by the manufacturing accuracy of the laser, which increased the hardware cost of line-structured light measurement. Secondly, this method required projecting a pair of perpendicular line-structured lights onto the object being measured at the same time. However, for some measurement environments, the auxiliary calibrated light plane may affect the measurement process. For example, when the surface of the measured object was a highly reflective mirror-like object, noise caused by stray light could appear in the acquired image, which affected the measurement process.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Fuzzy Logic
#### 3.1.4 ICT2 TSK  
ICT2 TSK is an improved clustering algorithm which uses a type-2 Takagi-Sugeno-Kang (TSK) as FL system [34]. ICT2 TSK is used to elect the CH and to choose the one which can deal with the rule uncertainties better than a type-1 TSK FL system. It balances the network load by introducing a fixed competition radius for each CH so that energy is improved efficiency. When the network starts to work, all nodes use the LEACH protocol to send information about their location and their residual energy. ICT2 TSK is centralized and uses the BS to select CHs. The FL system ICT2 TSK is used to calculate the probability for each node to become a CH according to its residual energy, distance to the BS, and number of neighbor nodes.
## 4 Analysis and Results
### Performance Results
#### 4.2.1 Computation Cost  
The proposed authentication scheme is computationally lightweight and designed for the Industrial IoT environment. The scheme provides high security, utilizing only simple hash and XOR computations, and thus consumes less storage compared with other schemes that are based on symmetric and asymmetric encryption. The scheme uses two operations, namely, the XOR operation and the one-way hash function. To identify the computation cost, we indicate the times that a hash function is conducted using the symbol T\({}_{\text{h}}\), and XOR operations are indicated by the symbol T\({}_{\text{xor}}\).  
Considering the authentication scheme shown in Figure 8, the client (publisher or subscriber) performs 11 hash functions and 6 XOR operations, which result in a total computation cost of 11 T\({}_{\text{h}}\) + 6 T\({}_{\text{xor}}\). Usually, the computation cost of an XOR operation is negligible and can be discarded, so it is assumed that T\({}_{\text{xor}}\approx 0\). On the other hand, the broker performs seven T\({}_{\text{h}}\) and five T\({}_{\text{xor}}\), which results in a total computation cost of 7 T\({}_{\text{h}}\) + 5 T\({}_{\text{xor}}\). Therefore, the total computation costs that should be taken into account for both the client and broker after ignoring the cost of T\({}_{\text{xor}}\) are T\({}_{\text{h}}\approx 11\) and T\({}_{\text{h}}\approx 7\), respectively. However, in our proposed Mu-TiMB architecture, since fog brokers (FB) (including the local broker (LB) and the aggregation broker (AB)) can act as traditional MQTT brokers and as clients, both LB and AB bear the total computation cost of the client and broker combined, which yields 11 T\({}_{\text{h}}\) + 7 T\({}_{\text{h}}\approx 18\) T\({}_{\text{h}}\). The computation cost is illustrated in Table 4.
# Actuating Performance Analysis of a New Smart Aggregate Using Piezoceramic Stack
## 3 Comparison and Discussion
### Experimental Validation  
As shown in Figure 15, to verify the proposed one-dimensional axial model, two SAPSs with 4 PZT discs and 6 PZT discs were assembled. In the experimental setup, as shown in Figure 16, the impedance of the two SAPSs was measured and recorded by a Wayne Kerr 6500B high-precision impedance meter (Wayne Kerr Electronic Instrument Co., Shenzhen, China) and a laptop.  
Figure 16: Experimental setup.  
Figure 14: The influence of the elastic modulus \(C_{33\epsilon}\) of epoxy on the displacement at \(z=h_{11}\) with the frequency \(f=20\) kHz.  
Figure 15: The fabricated SAPSs with different numbers of PZT discs.  
The measured impedance signatures of the SAPSs with 4 PZT discs and 6 PZT discs are plotted in Figure 17, and the measured and experimental resonance and anti-resonance frequencies are listed in Table 3. The results in Table 3 indicate that the relative error between the theoretical and experimental frequencies is less than 3.15%, which demonstrates that the resonance and anti-resonance frequencies obtained by the proposed model are consistent with the measured ones, and the proposed one-dimensional axial model can be employed to investigate SAPS's characteristics.
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 5 Performance Evaluation
### Ablation Experiments
#### 5.4.2 Multimodal Fusion Module Analysis  
Comparing the performance of III and IV in Table 2, it can be verified that the fusion features obtained using the EFM module enhance the tracker's performance. To further validate the advanced nature of the fusion strategy used by the EFM module, the following experimental performance comparisons were performed: (1) baseline, which fused  
Figure 11: Qualitative comparison of RMFNet with other trackers.  
the two-modality features at the third convolutional layer output through element-wise addition; (2) baseline+EFM-AFF, which replaced the attention mechanism in AFF [26] with ECA attention and used the feature fusion strategy in AFF for feature fusion; and (3) baseline+EFM, which utilized the proposed EFM multimodal fusion module for feature fusion. Table 3 shows the results of the comparison. We found that baseline+EFM outperformed baseline, demonstrating that our multimodal fusion module effectively integrated different modality features and significantly improved the tracker's performance. Moreover, the performance of baseline+EFM-AFF was inferior to that of baseline, which indicated that our approach was more suitable for multimodal feature fusion than the fusion strategy employed in AFF.
# Optimization of a Truss Structure Used to Design of the Manipulator Arm from a Set of Components
## 3 Application
### Design of the Vector of Components C  
The vector of components **C** (Equation (3)) was selected from the basic set of components (Appendix B) under the conditions set out in the previous text. The number of components is at first reduced by the criteria defined in Section 3.2:  
* Components 1-5 can be made only as L profiles because the corresponding thickness of the F profile must be 4 mm. Components 1-5 did not meet criteria 1 (F profile thickness 4 mm is bigger than the limit 3 mm) or 3 (profile L but without profile F).
* Component 20 can be made only as an L profile (3 mm) because the corresponding thickness of the F profile must be 6 mm. The component 20 did not meet the criteria 1 (F profile thickness 6 mm is bigger than the limit 3 mm) or 3 (profile L but without profile F).
* Component 21 can be made only as an F profile (3 mm) because the corresponding thickness of the F profile must be 1.5 mm, and manufacturing technology (3D print) allows a minimum thickness of 3 mm. Component 21 did not meet the criteria 3 (profile F but without profile L).  
Figure 8: Simulation of the effect of the notch, see Appendix B, Table A2, Label 16,F.  
Therefore, components 1-5, 20 and 21 did not meet the criteria defined in Section 3.2, so components 6-19 were used.  
In Section 2.2, the sorting conditions in Equation (9) for components in vector **C** were defined. They were defined for a truss structure as:  
1. A fictive weight of the \(j\)-th rod \(m_{j}^{f}\) (\(A_{j}\), \(\rho_{j}^{f}\));
2. An extension of the rod \(\Delta I_{j}\) (\(A_{j}\), \(E_{j}^{f}\));
3. An ordering force of the \(j\)-th rod \(P_{j}^{Y}\) (\(A_{j}\), \(\sigma_{j}^{f,Y}\)).  
First, the sorting was done in a table (see Appendix C), and this shows that the sorting of \(m^{f}\) and \(\Delta I\) is almost the same. Therefore, \(m^{f}\) and \(P^{Y}\) were used to select the optimization sets. The selection of the optimization sets was done using a graph, where the horizontal axis contains the values of \(m^{f}\), the vertical axis the values of \(P^{Y}\) and the points in the graph represent the individual components (6-19), as shown in Figure 9.  
The lines denoted as \(\textbf{C}_{A}\) and \(\textbf{C}_{B}\) show the label of components that are correctly sorted, and for nearby points, one is chosen (points 10, 16 and 11, 17). Vectors called \(\textbf{C}_{A}\) and \(\textbf{C}_{B}\) meet the prescribed conditions and can be used for optimization. For the next solution, '**C**=\(\textbf{C}_{A}\)' was selected, as shown in Table 4, because \(\textbf{C}_{A}\) includes components that, for the same weight (\(m^{f}\)), transfer greater force \(P^{Y}\) than \(\textbf{C}_{B}\).
# Diamonds for Life: Developments in Sensors for Biomolecules
## 5 Diamond-Based Electric Biosensors
### Biosensors for Immunoglobulins  
Detection of antigen-antibody recognition using a biosensor built from a diamond thin film was first reported in 2007, resorting to electric impedance measurements of the immunoglobulin G/anti-immunoglobulin G antigen-antibody system [29].  
A biosensor for immunoglobulin E (IgE) was subsequently reported by [PERSON] et al. [55]. Instead of using the native anti-IgE antibody, an alternative approach was employed, which resorted to a smaller, synthetic biomolecule called an aptamer for the receptor layer (bridged via undecenoic acid). Aptamers are oligonucleotide or peptide chains designed to recognise DNA or proteins. The aptamer was a synthetic chain of oligonucleotides enriched with a terminal sequence of twenty thymidine bases for maximum flexibility in binding to the IgE protein. The biosensor offered a low detection limit of 0.03 \(\upmu\)g/mL (at S/N = 3) and a linear dynamic range between 0.03 and 42.8 \(\upmu\)g/mL. Reusability was tested for six cycles, with relative standard deviations (after the sixth cycle) rounding to 1%. The practical utility of the biosensor was tested in human serum samples, with results showing a linear dose-response for concentrations of IgE up to 5.8 \(\upmu\)g/mL and an excellent correlation coefficient of 0.99 between quantification data provided by the biosensor and the reference method, enzyme-linked immunosorbent assay (ELISA).
# Design and Application of Daylight-Based Lighting Controller on LED Luminaire
###### Abstract  
This paper proposes a control circuit for Light Emitting Diode (LED) luminaire based on the amount of daylight. This system uses both natural daylight and artificial light from a luminaire to reach the desired illuminance on a working plane. The control circuit operates by receiving an illuminance value from a light sensor and comparing it with a set value in the microcontroller before limiting the light intensity of LED by sending a control Pulse Width Modulation (PWM) signal to the LED driver. In the experimental setup, the controller and a physical model of a working space were built in order to field test the LED daylight control circuit. Electrical characteristics such as voltage, current, and power were measured with a power meter. Illumination characteristics in terms of illuminance on a working plane were determined using a lux meter. The results from actual field tests revealed that this LED control circuit was able to reduce the power consumption of the lighting system while keeping the illuminance on the working plane at the standard value. Both simulation results and the actual experiment of the control circuit proved that it could be used to improve the energy efficiency of both newly installed and retrofitted lighting systems.  
daylight; illumination control; light emitting diode +
Footnote †: journal: Applied Sciences
# Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors
## 3 Bit-Cell External Driver Design
### WL Driver Structure and Performance
This dynamic switching behavior, controlled by the combination of the W_R, CLK, and Y signals, highlights the versatility of the WL driver in managing both read/write operations and in-memory computations.
# PIV Measurements of Turbulent Airflow in a Channel with Trapezoidal Ribs on One Wall
## 3 Results and Discussion  
### Development of Turbulent Flow  
After measuring the velocity at the channel inlet, experiments were conducted to determine the distance from the inlet, at which turbulent flow was fully developed. Turbulent flows are considered fully developed if the velocity profile does not change along the flow axis. The first stage of experimental studies included velocity measurements at x/H distance ranging from 18.6 to 49.6 from the inlet. A total of 332 double pictures were recorded, which were further analyzed using the adaptive correlation method. The instantaneous vector velocity fields obtained in this manner were averaged over time. The velocity was normalized by the average velocity over the rib U\({}_{\rm r}\) = 29 m/s. Figure 4 presents the U/U\({}_{\rm r}\) velocity distribution at the x/H = 18.6, x/H = 20.6, x/H = 27.6, x/H = 41.6 and x/H = 49.6 distances from the first rib.  
At y/H height ranging from 0.6 to 0.9, the profile of the streamwise velocity component obtained at distances x/H = 18.6 and x/H = 20.6 is almost flat. The profile becomes more convex with increasing distance from the inlet. The profiles at distances of x/H = 27.6 and 41.6 matched each other. This meant that turbulent flow was fully developed for x/H between 27.6 and 41.6. The profile measured at x/H = 49.6 differed from the remaining profiles, as it was close to the outlet of the measuring channel. The profiles of normalized turbulence kinetic energy (TKE) were consistent at x/H 27.6 and 41.6.
# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 5 Results
### Accomplished Result
The prime novelty of this outcome is associated with the symmetric network dynamics of the proposed consensus methods that balance both resource demands and security demands, unlike methods reported in the literature with unfair resource distribution during the consensus mechanism. * The presented scheme also offers a 27% reduction in cost along with a 38% faster responsiveness, proving this score as a high accomplishment to date in contrast to existing methods of blockchain operations.
###### Abstract  
With the tremendous increase of heterogeneous Internet of Things (IoT) devices and the different service requirements of these IoT applications, machine-type communication (MTC) has attracted considerable attention from both industry and academia. Owing to the prominent advantages of supporting pervasive connectivity and wide area coverage, the cellular network is advocated as the potential wireless solution to realize IoT deployment for MTC, and this creative network paradigm is called the cellular IoT (C-IoT). In this paper, we propose the three-layer structured C-IoT architecture for MTC and review the challenges for deploying green C-IoT. Then, effective strategies for realizing green C-IoT are presented, including the energy efficient and energy harvesting schemes. We put forward several strategies to make the C-IoT run in an energy-saving manner, such as efficient random access and barring mechanisms, self-adapting machine learning predictions, scheduling optimization, resource allocation, fog computing, and group-oriented transmission. As for the energy harvesting schemes, the ambient and dedicated energy harvesting strategies are investigated. Afterwards, we give a detailed case study, which shows the effectiveness of reducing power consumption for the proposed layered C-IoT architecture. Additionally, for real-time and non-real-time applications, the power consumption of different on-off states for MTC devices is discussed.  
Internet of Things (IoT); energy efficient; energy harvesting; machine-type communication; cellular IoT +
Footnote †: journal:  
_Renjoy_
###### Abstract  
Time-varying nonlinear external disturbances, as well as uncertainties in model and hydrodynamic parameters, make remotely operated vehicles (ROVs) trajectory tracking control complex and difficult. To solve this problem, this paper proposes a fast sliding mode controller with a fixed-time disturbance observer (FSMC-FDO), which consists of a sliding mode controller based on a fast reaching law and a novel fixed-time disturbance observer. The FSMC can solve the contradiction between system response time and chatter amplitude in sliding mode control. The FDO can compensate for time-varying external disturbances. The Lyapunov theory is used to prove the stability of the entire control scheme. Simulation results show that FSMC-FDO exhibits a good trajectory tracking performance with a better robustness than the conventional sliding mode control (CSMC) on the basis of exponential reaching law (ERL), while significantly reducing chatter.  
ROVs; trajectory tracking; sliding mode control; fixed-time disturbance observer; external disturbances +
Footnote †: journal: Applied  
0000-0002-414-4072]X.X.M.M.
## 2 Current-Distortion Problem in Totem-Pole Bridgeless PFC Converter
### Current Distortion Caused by Limited Current Loop Bandwidth  
The response of the inner current loop directly determines the key performance of the PFC converter, including PF and THD. The inner current loop compares sampled average inductor current with a given sinusoidal reference. With the obtained current error, a PI compensator is adopted to calculate the duty ratio of the power switch. Then, the driving signal is generated according to the duty ratio. With the above principles, the inner current loop diagram is shown in Figure 3. where \(H_{i}\)(s) is the transfer function of current sampling, \(G_{ii}\)(s) is the transfer function of current loop compensator, \(G_{m}\)(s) is the PWM modulation transfer function, \(G_{ii}\)(s) is the transfer function from duty ratio to inductor current, and  
Figure 1: Synchronous rectification totem-pole bridgeless PFC converter based on SiC MOSFET.  
Figure 2: Control block diagram of traditional average current mode control.  
\(k=2P_{o}/v_{in,~{}m}\). Based on Figure 3, input admittance of the PFC converter is calculated as follows, to analyze the causes of the phase difference between input current and voltage.  
Based on Figure 3, input admittance of the PFC converter \(Y\)(s) is given by  
\[Y(s)=\frac{i_{L}(s)}{v_{in}(s)}=Y_{1}(s)+Y_{2}(s)=\frac{G_{ip}(s)}{1+T_{i}(s)}+ \frac{kG_{ci}(s)G_{m}(s)G_{id}(s)}{1+T_{i}(s)}. \tag{3}\]  
Note that since \(G_{m}\)(s) = 1/\(v_{in,~{}G_{ip}}\)(s) = 1/\(sL\), and \(G_{id}\)(s) = \(v_{o}\)/\(sL\), Equation (3) is simplified as  
\[Y(s)=Y_{1}(s)+Y_{2}(s)=\frac{v_{m}}{sLv_{m}+G_{ci}(s)v_{o}}+\frac{kG_{ci}(s)v_{ o}}{sLv_{m}+G_{ci}(s)v_{o}}. \tag{4}\]  
Substituting the experimental parameters into (4), where \(G_{ci}\)(s) = 0.06 + 240/\(s\), \(L\) = 350 uH, \(v_{m}\) = 1, and \(v_{o}\) = 400 V, Bode plots of the input admittance under different output power are shown in Figure 4. At 50 Hz, the phases of the input admittance at \(P_{o}\) = 600 W, \(P_{o}\) = 800 W, \(P_{o}\) = 1200 W, and \(P_{o}\) = 1600 W are 14.4\({}^{\circ}\), 10.9\({}^{\circ}\), 7.3\({}^{\circ}\), and 5.6\({}^{\circ}\), respectively. It indicates that the input admittance includes a susceptance component, resulting in capacitive current flow, particularly under light load conditions. Therefore, the input current leads the input voltage and degrades the power factor.  
Figure 4: Input admittance characteristics of the PFC converter under different output power (\(P_{o}\)). (**a**) \(P_{o}\) = 600 W. (**b**) \(P_{o}\) = 800 W. (**c**) \(P_{o}\) = 1200 W. (**d**) \(P_{o}\) = 1600 W.  
Figure 3: Current control loop diagram.
## 5 Performance Evaluation
### Simulation Model
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Cell size & 1000 m \(\times\) 1000 m \\ Number of MTC devices & 1000 \\ TTI & 1 ms \\ \(\mu\) & 0 \\ RB size & 12 sub-carriers, 0.5 ms \\ Scheduling frame & 10 ms \\ Sub-carrier spacing & 15 kHz \\ eNodeB Tx power & 46 dBm \\ D2D node Tx power & 23 dBm \\ Noise spectral density & \(-174\) dBm\(/\)Hz \\ Distance attenuation & 128.1 + 37.6*log\((d)\), \(d\) (km) \\ Shadowing standard deviation & 10 dB (cell mode); 12 dB (D2D mode) \\ BLER target & 1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters. The following performance metrics have been considered:  
* **Network Energy Efficiency (NEE)**, measured as the ratio between the downloaded file size and the energy consumption required for its reception/transmission;
* **Mean Throughput**, measured as the mean data rate achieved by the multicast members;
* **Delivery Time**, as the time needed for completely downloading the whole file;
* **Aggregate Data Rate (ADR)**, computed as the sum of the data rates achieved by the multicast MTC devices.