esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Sharp-Switching Band-Modulation Devices
## 7 Conclusions  
The band-modulation mechanism was discovered thanks to the advent of high-quality ultrathin silicon films in FD-SOI technology. Without an ultrathin body, the necessary voltage-controlled blocking barriers could not be maintained across the whole film thickness. Given an insufficiently tall barrier at one of the interfaces, the regular forward current of the PIN diode would undermine the positive feedback mechanism and the excellent sharp-switching capability.  
We do not know of any other device that can challenge the \(Z^{2}\)-FET for steep switching (1 mV/decade) at low voltage (<1 V). Ferroelectric or tunneling FETs are unlikely to ever achieve this level of performance. The simplicity of the integration scheme, fully transferable from FD-SOI to FinFETs and nanowire transistors, is a strong argument for further development. Another important merit of band-modulation devices is their reconfigura  
Figure 26: Experimental current–voltage characteristics of an electrostatic Esaki diode fabricated on undoped FD-SOI, showing the role of the gate voltage; with the same device parameters as in Figure 4 (adapted from [69]).  
bility. For example, the \(Z^{2}\)-RAM stands as the best option for embedded capacitorless 1T-DRAM memory. On the other hand, the \(Z^{2}\)-FETs can protect their own circuits from electrostatic discharges.  
The gate length can be scaled down, as in any advanced FD-SOI transistor. However, the presence of the ungated region is an inevitable obstacle to reaching less than twice the minimum length of a MOSFET.  
While \(Z^{2}\)-FETs can perform most of the key electronic functions (logic switching, memory, sensing, and ESD protection), they are still diodes in a world dominated by MOS transistors. Whether diodes have a chance to compete against, or work alongside, MOSFETs is a matter for future studies focusing on their reliability, variability, and downscaling.  
All authors have equally contributed. All authors have read and agreed to the published version of the manuscript.  
The European authors are grateful for support from the EU project _REMINDER_ (_H2020-687931_). [PERSON] acknowledges the support of the U.S. National Science Foundation (award QII-TACS-1936221_).  
The authors declare no conflict of interest.
# Economic Analysis of a Zero-Water Solar Power Plant for Energy Security
## 3 Results and Discussion
### Economic Analysis of the Overall CSP Plant
#### 3.2.3 Studying the Effect of the Cost of Water
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pipe Number** & **Pipe Unit Cost** & **Total Pipe Costs** & **Initial** & **NPV** \\
**(-)** & **(EUR/m)** & **(MEUR)** & **(MEUR)** & **(MEUR)** \\ \hline \hline
10,965 & 30.76 & 102.71 & 148.29 & \(-\)139.59 \\  & 25.00 & 83.76 & 129.34 & \(-\)120.64 \\
10,965 & 20.00 & 67.31 & 112.89 & \(-\)104.20 \\  & 15.00 & 50.86 & 96.44 & \(-\)87.75 \\  & 10.00 & 34.42 & 80.00 & \(-\)71.30 \\  & 30.76 & 74.93 & 108.18 & \(-\)99.15 \\  & 25.00 & 61.11 & 94.36 & \(-\)85.33 \\
8000 & 20.00 & 49.11 & 82.36 & \(-\)73.33 \\  & 15.00 & 37.11 & 70.36 & \(-\)61.33 \\  & 10.00 & 25.11 & 58.36 & \(-\)49.33 \\  & 30.76 & 46.83 & 67.61 & \(-\)58.24 \\  & 25.00 & 38.19 & 58.97 & \(-\)49.60 \\
5000 & 20.00 & 30.69 & 51.47 & \(-\)42.10 \\  & 15.00 & 23.19 & 43.97 & \(-\)34.60 \\  & 10.00 & 15.69 & 36.47 & \(-\)27.10 \\  & 30.76 & 28.10 & 40.56 & \(-\)30.96 \\  & 25.00 & 22.92 & 35.38 & \(-\)25.78 \\
3000 & 20.00 & 18.42 & 30.88 & \(-\)21.28 \\  & 15.00 & 13.92 & 26.38 & \(-\)16.78 \\  & 10.00 & 9.42 & 21.88 & \(-\)12.28 \\  & 30.76 & 9.37 & 13.51 & \(-\)3.69 \\  & 25.00 & 7.64 & 11.78 & \(-\)1.96 \\
1000 & 20.00 & 6.14 & 10.28 & \(-\)0.46 \\  & 15.00 & 4.64 & 8.78 & 1.04 \\  & 10.00 & 3.14 & 7.28 & 2.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Influence of the pipe cost on the initial investment and the NPV of the Earth-CAT system.
## 3 Satellite IP Route Lookup Using Cuckoo Bloom Hybrid Filter
### Satellite IP Route Lookup Using CBHF  
As mentioned earlier, the proposed satellite IP lookup scheme is based on the PR-Trie architecture. Now, let us briefly review the PR-Trie algorithm, Algorithm A4 (see Appendix A), which describes the core idea of PR-Trie.  
Like the TBM algorithm, PR-Trie also uses a multibit-trie-based data structure called Bitmap. However, the most significant feature of PR-Trie is that IP lookups can be realized by calculating both Prefix Trie and Route Trie, where Prefix Trie (P-Trie) and Route Trie (R-Trie) denote the data structures of routing tables and input IP addresses, respectively. Therefore, the perfect lookup complexity of \(O(1)\) can be theoretically achieved.  
In the original PR-Trie paper [31], we recommend the parallel version of PR-Trie that can achieve the fastest lookup and update speeds. Unfortunately, it may be impractical to employ a lot of computing and memory resources in satellite IP route lookup. Now, let us consider the serial version of PR-Trie architecture. Generally, this shared memory-based lookup mostly adopts priority search, in which a secondary priority phase will be accessed if there is no match in the high-priority phase. Hence, unnecessary search phases lead to an increase in lookup time, lowering lookup algorithm performance.  
To ensure only one off-chip memory access, or reduce unnecessary search phases, for a single IP lookup, the CBHF structure is introduced. Each potential subtrie corresponding to a given input address is probed using the CBHF at the pre-lookup phase, and then a level-priority-based PR-Trie lookup is performed according to probe results. Note that since not all subtries have a real root (i.e., a root node with prefix information), the mere existence of a subtrie does not ensure that there is at least a match. Therefore, we maintain an on-chip imaginary root table that stores the LPM information for every imaginary root (i.e., a root node without prefix information).  
Algorithm A5 (see Appendix A) shows the pseudocode of the satellite IP route lookup procedure using the CBHF. For an input IP address, if there is a matching prefix in the subtrie (i.e., the OHT bitmap \(\
eq\) 0), look up the off-chip NHI table; if there is no match in the subtrie (i.e., the OHT bitmap \(=\) 0), the imaginary root table will be accessed.
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 4 Analysis of Test Results
### Calculation of Internal Force and Safety Factor  
According to the strain monitoring curve of the steel bars, it can be seen that the sections of the secondary lining structure were always under eccentric compression during the stress process. Based on the idea of component positive section bearing capacity calculation in \"Principles of Structural Design\" [26], the calculation Equation of the secondary lining section internal force was derived, and the bending moment and axial force of secondary lining were obtained through Equations (5) and (6). On this basis, the axial force and bending moment obtained through calculation according to the code could be further obtained through the Equation that obtains the safety factor of each measuring point on the section. The calculation idea is as detailed below:
## 3 Results and Discussions
### Growing Environment Measurement by the WSN
#### 3.1.5 Vigor Measurement of Mandarin Orange Leaves Using Fluorescent X-ray Method  
It is necessary to determine the suitable beam exposure time for the fluorescence X-ray analysis of a leaf. Firstly, after changing the irradiation time to 30, 60, 90, 120 and 150 s we carried out FXR analysis of the leaf, using two kinds of beams, beam 1 and beam 2. As a result, the characteristic peak heights for the typical elements were almost the same in all the data. Therefore, the irradiation time for beam 1 was selected to be 30 s, and that for beam 2, as 60 s and these irradiation times were used for all the experiments. The measured values of the fluorescence X-rays were normalized using the peak intensity of RhL\(\alpha\) (approximately 2.71 keV) generated by irradiation [8]. The measurements were then made at three areas of each leaf shown in the Figure 5b in order to find the element distribution inside the leaf. The distribution of elements inside each leaf was examined for 10 leaves taken from three trees of weak vigor (weak leaf) and for another 10 leaves taken from three trees of strong vigor (strong leaf) by fluorescence X-ray analysis the same as the fluorometric analysis.  
As a result, both the weak and strong leaves have a slight difference in the element contents among the three areas. Figure 22 shows the FXR spectroscopic data of three areas for the strong leaf. Although there is a slight distribution inside the leaf, the average value of the three areas was used as a representative value of each leaf in the later discussion. Table 4 shows the average element values of 10 weak leaves and that of 10 strong values. The kind of the detected element was quite different between these two groups. Twelve kinds of elements were detected in the weak leaf while 24 kinds were found in the strong leaf.  
Figure 21: Correlation between ANTH and NBI: (**a**) Leaves of strong vigor; (**b**) Leaves of weak vigor.  
Figure 22: Normalised fluorescence X-ray spectroscopic data of strong leaf.  
The meaning of the amount of each detected element for the tree vigor was then examined using \"The Shizuoka Prefecture Soil Fertilizer Handbook\" as a reference (Table 5).  
Table 5 shows the information on the appropriate kinds and contents of the elements inside the mandarin orange leaf. A comparison was made between the actual measurement results in Table 4 and a desirable amount of the element listed in Table 5. As a result, we had a diagnostic outcome that the content of Ca, K, Fe, Zn, and Cu are suitable, P is a somewhat small amount, and Ni and Mo are in excess for the strong leaf, while Ca and K are suitable, Fe is in a somewhat small amount, P is lacking, and Mn and Ni are in excess for the weak leaf (Table 4). In this diagnosis, however, the elements of N and B cannot be detected by the hXRF among the essential elements shown in Table 5, and the proper quantity of Mg for the plant, 0.3 to 0.6%, was not evaluated because the hXRF techniquer can only detect weight percent concentrations of Mg of no less than 1% in a sample.
# 5G/B5G mmWave Cellular Networks with MEC Prefetching Based on User Context Information +
## 3 Prefetching Algorithm
### Objective Function Including User Context Parameters  
Since a common backhaul line is used for prefetching all UE traffic associated with a specific small-cell BS, we need an algorithm to decide the scheduling order on the backhaul line. In other words, we consider which \(n\)-th traffic of the \(u\)-th user should be selected to allocate backhaul resource \(C_{\text{B}}\) at time instant \(t\). Figure 7 illustrates the procedure of traffic generation when focusing on a small-cell BS. We utilize the weighted proportional fairness (WPF) scheduling method [37]. The objective function \(O_{u,n}(t)\) for the \(n\)-th traffic of the \(u\)-th user at time instant \(t\) can be defined as:  
\[O_{u,n}(t)=\frac{L_{u,n}}{B_{u}(t)}w_{u,n}^{\alpha}(t), \tag{1}\]  
where \(L_{u,n}\) is the total traffic generated (in the future) for the \(u\)-th user with \(n\)-th traffic. The larger \(L_{u,n}\) enlarges the value of the objective function \(O_{u,n}(t)\). This indicates that large traffic needs to be prefetched at a higher priority to take full advantage of high-speed mmWave access. \(B_{u}(t)\) is the accumulated backhaul resource allocated to the \(u\)-th user up  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \multirow{2}{*}{Traffic quantity} & Gamma distribution shape parameter \(k=0.2892\) scale \\  & parameter \(\theta=2.012\times 10^{5}\) \\ \hline Traffic bias & 4 kbits \\ \hline Occurrence interval & Exponential distribution of avg. 8 s \\ \hline Timeout & 60 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters for the traffic model.  
to time \(t\). \(\alpha\) is defined as the PF coefficient. \(w_{u,n}(t)\) is the timing weight taking into account the traffic generation time for the \(n\)-th traffic of the \(u\)-th user at time instant \(t\), which can be expressed as:  
\[W_{u,n}(t)=\frac{T_{\text{P}}}{t_{u,n}-t}, \tag{2}\]  
where \(t_{u,n}\) is the time instant when the \(u\)-th user will demand the \(n\)-th traffic in future. In short, at the time \(t\), values of the objective function, \(O_{u,n}(t)\), and the timing weight, \(W_{u,n}(t)\), are enlarged as the traffic generation time becomes closer. Finally, one \(u\)-th user and the corresponding \(n\)-th traffic is selected at time \(t\), maximizing \(O_{u,n}(t)\) among all users and the total traffic within the target prefetching duration \(T_{p}\).  
\[\{u(t),n(t)\}=\underset{u,n}{\text{arg max}}O_{u,n}(t). \tag{3}\]  
Backhaul resource \(C_{\text{B}}\) is allocated to the \(n(t)\)-th traffic of the \(u(t)\)-th user and the corresponding data is stored in the Edge Cloud storage.  
Figure 5: The pre-association process.  
Figure 6: Illustration of pre-association.
## Appendix D  
Proof of Theorem 3.: First, we assume that a Pareto improvement exists for the matching \(\hat{\mathcal{X}}\). The improvement for TR \(i\) is defined as \(RC_{j,k}\); thus, we can obtain \(RC_{j,k}\succ_{i}\hat{\mathcal{X}}(i)\). If there exists an RC unit \(RC_{j,k}\) that has not been matched with any TR under the matching \(\hat{\mathcal{X}}\), i.e., \(\hat{\mathcal{X}}(RC_{j,k})=\emptyset\). It is obvious that \(i\succ_{RC_{j,k}}\hat{\mathcal{X}}(RC_{j,k})\). That is, TR \(i\) and \(RC_{j,k}\) prefer to be matched with each other and form a blocking pair. This contradicts Theorem 1 that \(\hat{\mathcal{X}}\) is stable. On the contrary, if there exists a \(RC_{j,k}\) that has already been matched with TR \(i^{{}^{\prime}}\), then RS icannot be matched with \(RC_{j,k}\) due to the one-to-one matching rule. Since TRs \(i\) and \(i^{{}^{\prime}}\) propose the same RC unit, the algorithm would turn to the process of rising prices to solve the conflict. During the price raising process, the matching cost of the RC unit is increased by \(s\) at each step, and the payoff of \(i\) to be matched with \(RC_{j,k}\) would reduce correspondingly. As a result, \(RC_{j,k}\succ_{i}\hat{\mathcal{X}}(i)\) can no longer held.  
Based on the above analysis, we prove that there exists no Pareto improvement, and the matching \(\hat{\mathcal{X}}\) is weak Pareto optimal for D2D transmitters.
# Numerical Quantification of Controllability in the Null Space for Redundant Manipulators
## 3 Simulation and Experiment
### Step and Sinusoidal Response
(**c**) shows the type 2 singular state caused by the joint constraints. (**d**) shows that the dot product of every vector is nearly zero after the step input. This implies that the presence of subset of joint bases that forms the bases in the null space is minimal. Figure 4: Simulation results with respect to the step and sinusoidal input are shown. The configuration and movement of the secondary task are described in the left side of the figure. (**a–d**) are the results when the secondary task is controlled along the z-axis. (**a**,**b**) show the response of the robot and the corresponding null space quality when the step input is applied. (**c**,**d**) show the response of the robot and the corresponding null space quality when the sinusoidal input is applied. When the desired task is in the z direction, the value of \(V_{z}\) plays a key role. In this situation, \(V_{z}\) is enough to control the robot in the z direction, but the force input projected to \(V\) improves the performance. With the step input, the proposed method, which projects the input force into \(V\), decreases the rise time by 51.79% and the settling time by 54.82%. Furthermore, the RMS error has been decreased by 33.94% when the sinusoidal input is applied. (**e–h**) are the results when the secondary task is controlled along the y-axis. In this case, controlling to y direction is difficult because the \(V_{y}\), controllability to y direction, is relatively low. In such a situation, the proposed method is more effective. As a result, the rise time, the settling time, and the RMS error decreased by 73.48%, 72.35%, and 38.97%, respectively.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 7 Application Fields of Electrospun Chitosan Nanofibers and Chitosan Derivatives
### Biomedicine
#### 7.4.1 Wound Dressings
After 4 h, microorganisms in the open control sample had grown to an uncountable number of colonies. Drug-free chitosan/PVA nanofibrous films showed antimicrobial effects when compared to pure PVA films, indicating that they could limit the growth of microorganisms, which is attributable to chitosan's antibacterial characteristics. When comparing drug-free and drug-loaded formulations, it was observed that with higher drug quantities in the film formulation, the drug release resulted in a stronger and faster reduction in viable bacterial count. For _Pseudomonas aeruginosa_ (_P. aeruginosa_), nanofibers containing 40%/mafenide acetate, the most efficient antibiotic for scarring, resulted in a significant decrease in the bacterial count after 4 h and the eradication of live bacteria after 8 h. As a consequence, the medium's transmittance was comparable to or near that of the negative control tube. Overall, the findings showed that in spite of the porous and non-compact structure of the films, both drug-loaded and drug-free nanofibrous chitosan/PVA membranes have core power to protect wounds from secondary bacterial infections, thanks to their sufficiently small pore size, which only allows water vapor and air to pass through the membrane, while airborne microorganisms are trapped. Moreover, chitosan's NH\({}^{3+}\) groups may easily bind to negatively charged bacteria, preventing them from penetrating. Films containing mafenide acetate had a stronger antibacterial impact on the microorganisms tested, indicating that they could be useful as a protective, healing, and antimicrobial wound dressing in the future [202].
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
## 4 Experimental Validation and Discussion
### Experiment Setup  
The experimental data are generated by the Mechanical Fault Simulator (MFS) test stand, which is a product of the Spectra Quest company. The MFS test stand, shown in Figure 2, consists of an IM, a variable frequency drive (VFD), a rotor system, a load unit, three current sensors, and a tachometer.  
The IM used in the experiment is MARATHON 56T34F5301 squirrel-cage motor. The detailed parameters of the IM are listed in Table 1. Four rotor bars are broken by the milling process. The root mean squared amplitude of the AC power source is 220 V, and the frequency is 50 Hz. The power current is divided into three-phase currents by the VFD to drive the IM, and the root mean squared amplitude of VFD output voltage is also 220 V. The motor is loaded by the load unit, which consists of a  
Figure 1: Flow chart of the Sum-of-Squares (SS) method.  
Figure 2: Mechanical fault simulator and its faulty motor.  
magnetic brake. The magnetic brake can be set to six load levels of 0%, 20%, 40%, 60%, 80%, and 100% of full load. The full load of the magnetic brake, used in the MFS, is 1.13 Nm.  
Two experiments have been conducted to verify the effectiveness of the SS method. The first experiment is used to demonstrate the effectiveness of the SS method under different load conditions. In this experiment, the output frequency of the VFD is set to 45 Hz and the motor load levels are set to 100%, 60%, and 20%, respectively. The other experiment is used to validate the effectiveness of the SS method under different motor rotation speeds. In this experiment, the output frequency of the VFD is set to 30.5 Hz, 40 Hz, and 48.5 Hz, respectively, and the motor load level is set to 100%. Three hall sensors and a tachometer are used to measure three-phase currents and rotation speed. The current signals and tachometer signals are collected by a PC-based Date Acquisition (DAQ) system during the experiments. The sampling rate is 2048 Hz, and 15 s data are collected during each test.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### OFDM Generation
#### 5.8.1 Phase Rotation  
In the case of \(N_{sc}^{RU}=1\), the additional phase rotation \(\phi_{kJ}\) of the transmitted symbols is applied, fulfilling the following requirements:  
\[\phi_{kJ}=\rho(\bar{I}\bmod 2)+\varphi_{k}(\bar{I}) \tag{11}\]  
\[\rho=\begin{cases}\frac{\pi}{2}&for\quad BPSK\\ \frac{\pi}{4}&for\quad QPSK\end{cases} \tag{12}\]  
\[\varphi_{k}(\bar{I})=\begin{cases}0&\bar{I}=0\\ \varphi_{k}(\bar{I}-1)+2\pi\Delta f(k+1/2)(N+N_{CPJ})T_{s}&\bar{I}>0\end{cases} \tag{13}\]  
\[\bar{I}=0,1,...,M_{rep}^{NPUSH}N_{RU}N_{slots}^{UL}N_{spurb}^{UL}-1 \tag{14}\]  
\[\bar{I}=\bar{I}\bmod N_{spurb}^{UL} \tag{15}\]where \(T_{s}\) denotes the basic time unit 1/30,720,000 and \(k\) and \(l\) represent the subcarrier index and the SC-FDMA symbol consecutively.
# Mechanical Analysis of Ground Sleeve
## 3 Test Results
### Load-Displacement Curve  
Figure 9(a),b demonstrated the load-displacement responses of the grouted sleeve lapping connectors. For the specimens of the 100 series that failed by the mode of bar-grout slip, the displacement varied about linearly with the load increment initially and then failed suddenly when the load exceeded the tensile capacity of the specimen. Since the tensile strength of the specimens was smaller than the yield strength of the overlapped bars, the curve exhibited no plastic regime. Therefore, the specimens failed in a brittle manner.  
Although the specimens 150-1, 150-2, 150-3, and 200-1 also failed by the mode of the bar-grout slip, the tensile strength of these specimens was larger than the yield strength of the bar. Thus, the bars were in the post-yielding state when the specimens failed, and their load-displacement curves exhibited yielding and hardening stages, which was different from the behavior of the specimens of the 100 series. Therefore, these specimens failed in a ductile manner.  
Specimens of the 150-4, 200-2, 200-3, 250 series, and 300 series failed by bar tensile fracture and exhibited a ductile response. The load-displacement curves of these specimens that failed by the mode of bar fracture was basically the same as that of the bare bar. The curves exhibited an approximately elastic response at the initial stage. The stiffness gradually degraded as the rotation of the sleeve and the development of internal micro-cracks [14]. However, the magnitude of this change was insignificant. As the bar yielded, a large displacement was produced for a small load increment. The curve began to descend after the load exceeded the tensile capacity of the specimens.  
The load-displacement curves of the grouted splice connectors are shown in Figure 9(c). Since the ultimate tensile strength was smaller than the bar yield strength, all of the specimens failed in a brittle manner. For the specimens S-200-1, S-200-2, and S-200-3, the tensile load first increased to an initial  
Figure 9: Load-displacement curves.  
peak and then decreased to a certain value. Subsequently, the tensile load increased to a second peak as the displacement increased. Lastly, the tensile load decreased suddenly, and the loading process ended. The curve can be explained by the following destruction mechanism: immediately after the load reached the first peak, the grout was completely pulled off at the middle section and split into two pieces. Subsequently, the load was carried by the bond between the separated grout and the sleeve until the grout was pulled out of the sleeve. The second peak corresponded to the maximum bond strength between the separated grout and the sleeve.