esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Power Two-Phase Clocking Adiabatic PUF Circuit
## 4 Simulation and Results
### Simulation Result
#### 4.2.1 Energy Dissipation  
A major reason for employing adiabatic switching techniques in PUF circuits is to reduce power consumption. In this study, energy is collected from the total instantaneous power along the duration of the challenge bit cycle as follows:  
\[E=\int_{0}^{T}(\sum(Vpc_{s}lpc_{s}))dt \tag{9}\]  
where \(T\) is the period of challenge bit (\(f_{\text{Cb}}=10\) MHz or inversely equal to 100 ns).  
Start-up energy and energy per cycle are considered in this study. For instance, Figure 7 shows that the start-up energy is the total power consumed in the time range of 0-0.1 us, and the energy-per-cycle is the total power consumed in the time range of 0.1-0.2 us, or average of cyclical energy of challenge-bit after start-up period. Instead of the start-up energy, we consider the start-up power in this study, which is derived from the energy-per-cycle/time period.  
We evaluated the energy consumption of the proposed adiabatic PUF circuit in comparison with the QUALPUF circuit at the PUF cell level (1-bit) and 4-bit cascaded PUF circuit, as graphically illustrated in Figures 8 and 9, respectively. Figure 8 shows the 1-bit PUF cell energy comparison of the proposed PUF and QUALPUF with respect to the body effect of the PMOS transistors along with the temperature variation. Figure 9 illustrates the energy comparison of both the 4-bit circuit topologies. The energy loss shown in Figure 9 is the average of the energy consumed by 100 chips emulated using Monte Carlo simulation with VTH and TOX variations of 10%. The energy data of the proposed PUF in Figures 8 and 9 were obtained from the proposed circuit with all PMOS bulks tied to each respective source pin. In addition, both VTH and TOX variations consume comparable amounts of energy with respect to the temperature change.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multirow{2}{*}{Power supply} & \multicolumn{1}{l}{\begin{tabular}{l} Vpc: swing from 0.9–1.8 V trapezoidal clock, \(f_{\text{Vpc}}=100\) MH \\ \end{tabular} } \\  & \begin{tabular}{l} \(\text{zVpc-: swing from 0–0.9 V trapezoidal power clock, \(f_{\text{Vpc}}=100\) \\ \end{tabular} } \\  & \begin{tabular}{l} \(\text{MHzCb voltage: 1.8 pulse signal, \(f_{\text{Cb}}=10\) MHz \\ \end{tabular} } \\ \hline \multirow{2}{*}{Transistor parameter and ratio} & \multicolumn{1}{l}{\
\begin{tabular}{l} Parameter: 0.18 μm ROHM standard CMOS process \\ \end{tabular} } \\  &
\begin{tabular}{l} Ratio: W/L = 0.6 μm/0.18 μm for all NMOS and PMOS Transistors \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation conditions.
## 4 A Routing Protocol for LLNs (RPL)  
Since none of the routing protocols mentioned in Section 3 meets the requirements of LLN, the IETF designed RPL [1] to provide routing services over IPv6 for LLN within the network and node constraints. This section reviews RPL and RPL routing solutions proposed recently. The solutions are then analysed against the reliable path requirements to identify knowledge gaps.
## 3 Controller Design
### Linear/Nonlinear Extended State Observer Design  
Assuming \(x_{1}=x\), \(x_{2}=\dot{x}\), \(\cdots\), \(x_{n}=x^{(n-1)}\), and \(x_{n+1}=f(\cdot)\) represents the extended state variable of the system, then the general form of the continuous extended state observer (ESO) is correspondingly designed as  
\[\left\{\begin{array}{l}e=z_{1}-y\\ \dot{z}_{1}=z_{2}-\beta_{1}\cdot g_{1}(e)\\ \dot{z}_{2}=z_{3}-\beta_{2}\cdot g_{2}(e)\\ \vdots\\ \dot{z}_{n}=z_{n+1}-\beta_{n}\cdot g_{n}(e)+b\cdot u\\ \dot{z}_{n+1}=-\beta_{n+1}\cdot g_{n+1}(e)\end{array}\right. \tag{10}\]  
where \(z_{i}(i=1,2,\cdots,n+1)\) represents the estimates of the state variable \(x_{i}(i=1,2,\cdots,n)\), and total disturbance \(x_{n+1}=f(\cdot)\), \(g_{i}(e)(i=1,2,\cdots,n+1)\) represents the function of estimation error \(e\). Under specific conditions, ESO can accurately estimate the various states of the object and the total disturbance of the system. Then, we have  
\[\left\{\begin{array}{l}z_{1}\to x_{1}\\ z_{2}\to x_{2}\\ \vdots\\ z_{n}\to x_{n}\\ z_{n+1}\to f(\cdot)\end{array}\right. \tag{11}\]  
When \(g_{i}(e)(i=1,2,\cdots,n+1)\) is a select nonlinear function, ESO (10) is a nonlinear extended state observer (NESO). The specific nonlinear function can be express as  
\[g_{i}(e)=\text{fal}(e,\alpha_{i},\delta)=\left\{\begin{array}{l}|e|^{\alpha _{i}}\text{sign}(e),|e|>\delta\\ \frac{e}{\delta^{1-\alpha_{i}}},|e|\leq\delta\end{array}\right. \tag{12}\]  
where \(0<\alpha_{i}<1\), \(\delta>0\); it is worthy to point out that when \(\alpha_{i}=1\), then \(g_{i}(e)=e\), and the ESO (10) is a linear extended state observer (LESO), which can be rewritten as  
\[\left\{\begin{array}{l}e=z_{1}-y\\ \dot{z}_{1}=z_{2}-\beta^{\prime}_{1}\cdot e\\ \dot{z}_{2}=z_{3}-\beta^{\prime}_{2}\cdot e\\ \vdots\\ \dot{z}_{n}=z_{n+1}-\beta^{\prime}_{n}\cdot e+b\cdot u\\ \dot{z}_{n+1}=-\beta^{\prime}_{n+1}\cdot e\end{array}\right. \tag{13}\]  
Theoretical analysis and simulation research indicate that LESO parameter tuning is convenient, theoretical analysis is simple, and the disturbance tracking performance hardly changes with the disturbance amplitude. The parameter tuning of NESO is complex and theoretical analysis is difficult, but it has advantages such as high parameter efficiency, high tracking accuracy, and fast response speed. In order to fully utilize the advantages of LESO and NESO, this paper proposes a LESO/NESO switching control strategy based on the interval type-2 fuzzy logic system.
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 4 Validation and Discussion
### Analysis of Simulation Results
After 20 Mbps, the downward trend of them tends to be smooth. Conversely, the GIN protocol begins to decline slowly and speeds up later. When the transmission frequency of packet exceeds 40 Mbps, the packet loss ratio of the GIN, GEAR and GPSR protocols is almost the same. However, the value of flooding is still higher than other protocols. In one word, the GIN protocol outperforms other three protocols in the packet loss ratio. Figure 10: The results for GIN in packet loss ratio. (**a**) Relationships between the packet loss ratio and the packet length; (**b**) Relationships between the packet loss ratio and the transmission rate of packet. Figure 9: The results for GIN in delay. (**a**) Relationships between the delay and the number of nodes; (**b**) Relationships between the delay and the packet size. As to the throughput aspect, the number of sensor nodes and packet sizes are two significant factors. In Figure 11a, as the number of sensors continue to increase, the throughput of the four protocols gradually declines. For the GIN and GEAR protocols, they have similar trends in throughput, while the GPSR and flooding protocols exist the analogous downward trend. Before the number of nodes hitting 80, the values of the four protocols all fall quickly. However, as the number of nodes exceeding 80, the speeds of decline all becomes smoother. From an overall perspective, GIN protocol outperforms the other three. Particularly in the scenario of 200 nodes co-existing, the value of GIN protocol is still around 120 Mbps, which is about three times that of the flooding protocol and two times that of the GPSR protocol. From Figure 11b, we can arrive at the conclusion that the throughput is proportional to the packet size. The upward trends of the GIN and the GEAR protocols are basically similar. However, the value of the GIN protocol is higher than that of the other three protocols. The gap between the GPSR and flooding protocols gradually increase starting from 1024 KB, where the flooding protocol has the worst performance. The maximum of the flooding protocol is less than the minimum value of the GIN protocol. When the packet size reaches 5120 KB, the throughput of the GIN protocol becomes three times higher than that of the flooding protocol. Due to the IN mechanism, plenty of packets are securely delivered to the event area.
# Spectrum Awareness for Cognitive Radios Supported by Radio Environment Maps: Zonal Approach +
## 2 Related Works
The RMSE shows a dissimilarity in Received Signal Strength (RSS) between the estimated values and real values. However, a drawback of such an approach lies in the fact that it is impossible to identify the reason why the SU could not take advantage of the spectrum opportunities [31]:  
* When the RSS from PU was overestimated, the transmission from SU was needlessly restrained to avoid interferences,
* when the RSS from PU was underestimated, the transmission from SU was allowed and resulted in interferences. To overcome the drawback of RMSE in [31; 32], the Correct Detection Zone Ratio (CDZR) and False Alarm Zone Ratio (FAZR) were introduced. When on account of the inaccuracy of the estimation REM forbids the transmission, although it should be allowed, the area is named False Alarm Zone (FAZ). The area in which the spectrum is occupied but REM allows transmission because of the estimation imprecision is called a Missed Detection Zone (MDZ). In our research, we have used quality measures similar to those presented above, and their description is given in the chapter devoted to the analysis of the results.
# Impact of Cooking Processes on Volatile Flavor Compounds and Free Amino Acids in Fish Sauce
## 3 Results and Discussion
### Correlation Network Analysis of Key Volatile Compounds and FAAs of Fish Sauce Samples  
This study demonstrated that the intensity of key aroma compounds and the content of FAAs in fish sauce varied with both cooking time and method. The degradation of FAAs was closely linked to the formation and content of key flavor compounds. The correlation between key aroma compounds and FAAs was analyzed to gain deeper insights into flavor formation during fish sauce cooking. As shown in Figure 5, during cooking, the content of FAAs in fish sauce (except for gly, lys, and his) was positively correlated with several flavor compounds. Some of these correlations were significant, the most notable being the positive correlation with pyrazine content. This may be due to the \(\alpha\)-aminoketone produced in the amino acid-based Maillard reaction, which is the main precursor of pyrazine [31]. These findings are consistent with those reported by [PERSON] et al. [47], where key flavor compounds in grass carp were positively correlated with differential lipids under different freeze-thaw cycles and subsequent heat treatments. In stir-fried fish sauce, FAAs were primarily negatively correlated with key flavor compounds, although only a few correlations were statistically significant. This is consistent with the negative correlation between key flavor components and flavor precursors observed during the cooking of raw crayfish, as reported by [PERSON] et al. [40]. This suggests that cooking temperature, rather than cooking time, has a more significant effect on FAAs and key flavor compounds, as both proteolysis and the Maillard reaction are directly influenced by temperature [4].
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 5 Simulation Results
### Simulation Results of ML-KF Loop  
To demonstrate the effectiveness of the proposed adaptive KF, two simulations in two dynamic scenarios, pedestrian-level and vehicle-level, are made. The pedestrian-level velocity is expressed as 3 sin(0.6t) m/s with a velocity, acceleration and acceleration rate that can reach a of maximum 3 m/s, 1.8 m/s\({}^{2}\) and 1.08 m/s\({}^{3}\), respectively. The vehicle-level velocity is expressed as 30 sin(0.3t), with a velocity, acceleration and acceleration rate that can reach the maximum 30 m/s, 9 m/s\({}^{2}\) and 2.7 m/s\({}^{3}\), respectively. Considering the maximum acceleration (9 m/s\({}^{2}\)), the Doppler change over the integration time is only 0.047 Hz. Similarly, the phase change caused by the Doppler change over the integration time is only 0.0003 rad. Therefore, the assumption of constant Doppler frequency and phase over the integration time is still valid. \(C/N_{0}\) is 25 dB-Hz. In the ML-KF loop, \(q_{a}\) is set to 1.8 and 9 in these two dynamic scenarios, respectively, which are equal to the maximum acceleration. The other parameters are the same as those in Table 1. The proposed MLE carrier tracking loop with and without KF is abbreviated as ML-KF and ML loop, respectively.  
Figure 8a,b shows the frequency tracking errors of these two loops in pedestrian and vehicle level scenarios, respectively. It can be seen that the errors of the ML-KF loop are smaller than that of the ML loop in two dynamic scenarios. The tracking accuracy is improved significantly by KF. For the ML-KF loop, the bit error rate is 0.001 and 0.002, respectively in pedestrian- and vehicle-level scenarios. For the ML loop, the bit error rate is 0.013 and 0.006 respectively in pedestrian and vehicle level scenarios. This means the bit error rate is reduced by filtering too. Therefore, the proposed KF can improve the tracking accuracy and reduce the bit error rate.  
Figure 8: Tracking results of the ML (maximum likelihood) and ML-KF loops in different dynamic circumstances. (**a**) Frequency tracking results of pedestrian-level velocity; (**b**) Frequency tracking results of vehicle-level velocity.
# An 8 kb RRAM-Based Nonvolatile SRAM with Pre-Decoding and Fast Storage/Restoration Time
## 3 nvSRAM Peripheral Circuit Design
### Write Drive Circuit  
The write drive circuit is shown in Figure 8. The input buffer generates two reverse signals QN and QT through two input NANDs, a three-state gate and inverter. The outputs of QN and QT are controlled by the three-state gate. When the input DIN and AE are high, output is low. Meanwhile, the control signal CKP is low and CKN is high, so the three-state gate is on. Then, QT is high, and QN is low.  
Before the write operation starts, DL and DLB are pulled to high through the pre-charge transistor. When the signal WE is high, the system enters the write operation mode. Then, MN12 and MN11 are on, and QT and QN are high and low, respectively. Therefore, a path is formed between DLB, MN12, MN14 and VS. DLB will be pulled down and output to BLB through the transmission gate. MN13's off keeps BL high. When WL is on, BLB forms a discharge path, which flips the voltage of the memory cell QB. At the same time, the storage node Q will also reverse with the flip of QB, so that the node data of the memory cell change from Q = 0, QB = 1 to Q = 1, QB = 0, and the operation of writing 1 is completed. When QT is low and QN is high, DL forms a path to ground so that DL is low. DL and DLB are sent to BL and BLB through the transmission gate. At this time, BL is low, and BLB is high. The memory cell changes from Q = 1 and QB = 0 to Q = 0 and QB = 1 after internal discharge, and the operation of writing 0 is completed.  
Figure 8: Write driver circuit.  
Figure 7: Read drive circuit.
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 3 Methodology
### Hybrid Loss Function for Knowledge Distillation and Class Separation  
In this paper, the knowledge distillation loss function \(L_{KD}\), the classified cross-entropy loss function \(L_{CE}\), and the class separation loss function \(L_{SP}\) are combined to enable the model to achieve better plasticity-stability balance and forget less about the previously learned knowledge. The knowledge distillation loss is applied to the classification layer of the old target, and the cross-entropy loss function is applied to the classification layer of all targets. The class separation loss is used to separate the old and new target samples better. The hybrid loss function \(L\) is denoted as:  
\[L =\lambda L_{KD}(x)+(1-\lambda)L_{CE}(x)+L_{SP}(x) \tag{1}\] \[\lambda =\sqrt{\frac{n}{n+m}} \tag{2}\]where \(\lambda\) is a dynamically changing weight coefficient defined by (2), \(n\) and \(m\) denote the number of old and new classes at each phase, respectively. As the incremental learning phases increase (\(m\) is fixed and \(n\) increases), the value of \(\lambda\) gradually becomes larger, and the model increasingly tends to retain the existing knowledge.
## 6 By-Products and Intermediates' Determination  
Identification and quantification of by-products and intermediates formed during degradation of pollutants are of crucial importance for the following reasons:  
(_i_) They can help to build more comprehensive detailed kinetic models, also taking into account their relative effect on the overall degradation process. Some of the formed intermediates, in fact, may act as scavengers, significantly change absorption of the fluid, change the adsorption state of solid catalysts' surfaces, etc.;  
(_ii_) They can show worse eco-toxicological responses, both acute and chronic, than their parent compounds;(_iii_) Treatment unit sizing--the required time for complete removal of toxic species might be much longer than the time to fully remove the target parent compound.  
[PERSON] et al. [60] have highlighted that as a result of the high surface-to-volume ratio typical of microdevices, intermediates are present at higher concentrations in these systems. This makes microreactors particularly suitable for the identification crucial species that can be formed at very low concentrations at lab macro scale or at pilot or full scale. Another main advantage is that this type of continuous flow microdevice can be easily coupled with automated analytical tools, i.e., GC, LC, MS, etc., for the determination of intermediates in a fully automated fashion [27; 28; 29]. Despite this, the number of studies making use of microreactors for intermediates' identification in AOPs for wastewater treatment is still relatively low.  
Using a TiO\({}_{2}\) catalyst, the intermediates in the degradation of six dyes [60], Diuron [50], and phenol [42] were identified and quantified by coupling microreactors with LC-MS/MS analysis. The results were used in some cases to propose detailed mechanisms and develop full kinetic models.  
[PERSON] et al. [68] studied the degradation of several target pollutants using ZnO nanowires in a microreactor and used GC analysis to elucidate the chemical nature of the formed by-products.  
Finally, in the case of homogeneous processes, intermediates generated by UV-C/H\({}_{2}\)O\({}_{2}\) were identified for benzoylecgonine and benzisothiazolinone [52; 69] using LC-MS/MS analysis and samples generated during kinetic investigation with a microcapillary photoreceptor. Samples easily generated at different reaction times by using the above-mentioned experimental set-up were also used to investigate eco-toxicological effects on target aquatic organisms [11; 51; 53], demonstrating that toxic degradation products can persist in treated water even when the target compounds are fully degraded, and longer residence times are needed to reduce eco-toxicological responses.
## 3 First Generation: Virtual Inertia Topologies
### Drop-Based Approaches  
The approaches described so far try to mimic or approximate the behavior of SGs to improve inertial response of inverter-dominated power systems. Different from these techniques, the frequency-droop based controllers have been developed for autonomous operation of isolated microgrid systems [62; 63]. Based on the assumption that the impedance of the grid is inductive, the frequency droop is implemented as:  
\[\omega_{g}=\omega^{*}-m_{p}(P_{out}-P_{in}) \tag{14}\]  
where, \(\omega^{*}\) is the reference frequency, \(\omega_{g}\) is the local grid frequency, \(P_{in}\) is the reference set active power, \(P_{out}\) is the measured active power output from the DG unit, and \(m_{p}\) is the active power droop. Similarly, the voltage-droop is implemented as:  
\[v_{g}=v^{*}-m_{q}(Q_{out}-Q_{in}) \tag{15}\]  
Figure 9: Virtual synchronous generator (VSG) topology.  
where, \(v^{*}\) is the reference voltage, \(v_{g}\) is the local grid voltage, \(Q_{in}\) is the reference set reactive power, \(Q_{out}\) is the measured reactive power output from the DG unit, and \(m_{q}\) is the reactive power droop.  
The schematic of a frequency-droop controller based on Equation (14) is shown in Figure 10. Often a low pass filter with a time constant \(T_{f}\) is used when measuring the output power to filter out high frequency components from the inverter [14]. In the literature [59; 64; 65], it has already been shown that the use of this filter makes the droop-based control approximate the behavior of virtual inertia systems. The proof was first presented by [PERSON] et al. [59] and is repeated here for convenience.  
Proof.: Based on the schematic of Figure 10:  
\[P_{out}=(1+T_{f}s)\left\{\frac{1}{m_{p}}(\omega_{g}-\omega^{*})+P_{in}\right\} \tag{16}\]  
Rearranging,  
\[P_{in}-P_{out}=\frac{1}{m_{p}}(\omega^{*}-\omega_{g})+T_{f}.\frac{1}{m_{p}}s. \omega^{*} \tag{17}\]  
This equation is of the similar form of the virtual synchronous generator described in Equation (12). The exact approximation is as follows:  
\[K_{I}=T_{f}.\frac{1}{m_{p}} \tag{18}\]  
\[K_{D}=\frac{1}{m_{p}} \tag{19}\]  
Hence, the filters used for power measurements in these controllers constitute a delay which is mathematically equivalent to virtual inertia, while the droop gain is equivalent to damping. However, the traditional droop-based systems described by Equations (14) and (15) are known to have slow transient response. Moreover, the inductive grid assumption may not always be valid. Methods to improve the droop controllers, such as using virtual output impedance [16] or improving dynamic behavior of the droop scheme [14], have been proposed. In [10; 66], a technique to emulate virtual inertia by a modified droop approach was also presented.
## 3 Numerical Analysis of the Thermal Performance
### Energy Balancing  
The heat-energy balance equations of the insert system can be written as follows [14; 15]:  
\[\left\{\begin{array}{l}c_{t}\rho_{t}V_{t}\frac{dT_{Insert}}{dt}=qA_{c}-q_{r}- q_{f1}-q_{f2}-q_{tl}-q_{th}\\ c_{l}\rho_{l}V_{l}\frac{dT_{l}}{dt}=q_{tl}\end{array}\right. \tag{1}\]  
where all the physical meanings of the variables in Equation (1) are listed in Table 1.  
Figure 1: Schematic illustration of cutting tool assembly: (**a**) cutting tool; (**b**) the modified tool-holder with V-shaped channel.  
Figure 2: Schematic of the indexable insert: (**a**) surfaces definition and (**b**) coordinate systems definition.  
According to [PERSON]'s law of heat transfer, Equation (1) can be rewritten as below:  
\[\left\{\begin{array}{rl}c_{t}\rho_{t}V_{t}\frac{dT_{insert}}{dt}&=qA_{c}-\iint_{ A_{r}-A_{c}}h(T_{r}(x,y,t)-T_{air})dA\\ &-\iint_{A_{f1}}h(T_{f1}(x_{1},y_{1},t)-T_{air})dA\\ &-\iint_{A_{f2}}h(T_{f2}(x_{2},y_{2},t)-T_{air})dA\\ &-q_{tl}-q_{th}\\ c_{l}\rho_{l}V_{l}\frac{dT_{a}}{dt}=q_{tl}\end{array}\right. \tag{2}\]  
where \(h\) denotes the heat transfer coefficient of the area exposed to the air, and the quantities \(T_{r}(x,y,t)\), \(T_{f1}(x_{1},y_{1},t)\) and \(T_{f2}(x_{2},y_{2},t)\) denote temperature of the rake face, the main flank face and the minor flank face, respectively.  
As can be seen in the above, an essential problem of applying Equation (2) lies in that there are many unknown quantities (\(T_{insert}\), \(T_{r}\), \(T_{f1}\), \(T_{f2}\), \(q_{tl}\), \(q_{th}\)) that are difficult to be determined. Values of these unknown quantities will be explained and specified explicitly in detail in the following sub-sections.
## 4 Experiments and Results
### Baselines
#### 4.2.4 Correlation between the Size of the Recommendation List and the Results  
As shown in Figures 6 and 7, it is clear from the results that our proposed model outperforms the other models in the paper recommendation list. While the size of the recommendation list increases, the NDCG-value and HR-value of our model increases. It can be observed that among the chain table lengths of 10, 20 and 50, the chain table length of 50 gives the best