esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Pulse-OTFS Signal
### Derivation of ACF and PSD  
#### 2.2.1 Derivation of ACF  
The ACF of OTFS can be written as:  
\[\begin{array}{l}R(t+\tau,t)=E\Big{(}\overline{s(t)}s(t+\tau)\Big{)}\\ =\frac{1}{M}E\bigg{(}\sum\limits_{l=-\infty}^{\infty}\sum\limits_{k=0}^{M-1} \overline{\mathbf{X}_{\mathbf{t}}[k,I]\Pi(t-lT)e^{j2\pi k\Delta f(t-lT)}}\sum \limits_{n=-\infty}^{\infty}\sum\limits_{m=0}^{M-1}\mathbf{X}_{\mathbf{t}}[m, n]\Pi(t+\tau-nT)e^{j2\pi m\Delta f(t+\tau-nT)}\bigg{)}\\ =\frac{1}{M}\sum\limits_{l=-\infty}^{\infty}\sum\limits_{r=-\infty}^{\infty} R_{\mathbf{X}_{\mathbf{t}}}(i,r)\sum\limits_{l=-\infty}^{\infty}\sum\limits_{k=0}^{M-1} \overline{\Pi(t-lT)e^{j2\pi k\Delta f(t-lT)}}\cdot\Pi(t+\tau-(l+r)T)e^{j2\pi( k+i)\Delta f(t+\tau-(l+r)T)}\end{array} \tag{9}\]  
Since ISFFT does not change the power and orthogonality of information symbols [25], then the power of information symbols is:  
\[E\big{[}\overline{\mathbf{X}_{\mathbf{t}}}\mathbf{X}_{\mathbf{t}}\big{]}=E \big{[}\overline{\mathbf{X}_{\mathbf{d}}}\mathbf{X}_{\mathbf{d}}\big{]}= \sigma_{X}^{2} \tag{10}\]  
where \(\overline{\mathbf{X}}\) is the conjugation of \(\mathbf{X}\). \(\sigma_{X}^{2}=1\) when the information symbols are PRN code chips. The autocorrelation of the information symbol is  
\[R_{\mathbf{X}_{\mathbf{t}}}(i,r)=\left\{\begin{array}{l}1\;i=0\;\&\;r=0\\ 0\;\;\text{otherwise}\end{array}\right. \tag{11}\]  
We can observe from (9) that the ACF \(R(t+\tau,t)\) is a function with a period of \(T\) for variable \(t\), and \(s(t)\) conforms to the definition of generalized cyclostationary processes. Thus, the variable \(t\) in \(R(t+\tau,t)\) can be eliminated, and the ACF \(R(\tau)\) can be written as:  
\[\begin{array}{l}R(\tau)=\frac{1}{T}\int\limits_{0}^{T}R(t+\tau,t)\text{d} t\\ =\frac{1}{MT}\int\limits_{i=-\infty}^{\infty}\sum\limits_{r=-\infty}^{\infty} R_{\mathbf{X}_{\mathbf{t}}}(i,r)\sum\limits_{l=-\infty}^{\infty}\sum\limits_{k=0}^{M-1} \overline{\Pi(t-lT)e^{j2\pi k\Delta f(t-lT)}}\cdot\Pi(t+\tau-(l+r)T)e^{j2\pi( k+i)\Delta f(t+\tau-(l+r)T)}\text{d}t\\ =\frac{1}{MT}\sum\limits_{l=-\infty}^{\infty}\sum\limits_{r=-\infty}^{\infty} R_{\mathbf{X}_{\mathbf{t}}}(i,r)\sum\limits_{l=-\infty}^{\infty}\int\limits_{-l}^{ \infty}\overline{(1-l)T}\sum\limits_{k=0}^{M-1}\overline{\Pi(t)e^{j2\pi k \Delta f}}\cdot\Pi(t+\tau-rT)e^{j2\pi(k+i)\Delta f(t+\tau-rT)}\text{d}t\\ =\frac{1}{MT}\sum\limits_{i=-\infty}^{\infty}\sum\limits_{r=-\infty}^{\infty} R_{\mathbf{X}_{\mathbf{t}}}(i,r)\sum\limits_{k=0}^{M-1}e^{j2\pi k\Delta f(\tau-rT)} \cdot\int\limits_{-\infty}^{\infty}\overline{\Pi(t)}\Pi(t+\tau-rT)e^{j2\pi i \Delta f(t+\tau-rT)}\text{d}t\end{array} \tag{12}\]  
Substituting (11) into (12) yields:  
\[\begin{array}{l}R(\tau)=\frac{1}{M}\sum\limits_{k=0}^{M-1}e^{j2\pi k\Delta f \tau}\frac{1}{T}\int\limits_{-\infty}^{\infty}\overline{\Pi(t)}\Pi(t+\tau) \text{d}t\\ =\frac{1}{M}\sum\limits_{k=0}^{M-1}e^{j2\pi k\Delta f\tau}\bigg{(}1-\frac{| \tau|}{T}\bigg{)}\end{array} \tag{13}\]The above equation is both the theoretical expression for the ACF of OTFS and Pulse-OTFS signals since the Pulse-OTFS signal is non-zero for at least one complete symbol duration.
# Design of Capacitive Paddy Moisture Sensor Based on Electrical Impedance Spectroscopy Analysis
## 4 The Paddy Moisture Sensor
### Evaluation of Paddy Moisture Sensor  
A moisture detection experiment was carried out to evaluate the designed paddy moisture sensor. Paddy variety Nanjing 9108 (_Oryza sativa_ L.) was obtained from Jiangsu Runguo High Efficiency Ecological Agriculture Base in Yaoqiao Town, Zhenjiang New Area, Zhenjiang City, Jiangsu Province. The initial moisture contents of the three samples were 16.45%, 19.20%, 23.29% (w.b.). After drying operations or re-watering operations, twenty test samples were obtained with moisture contents randomly distributed between 13% and 25%. The paddy samples were sealed in polyethylene bags to avoid the moisture exchange between the samples and the surrounding environment and preheated in drying chamber with temperature range from 40-80 \({}^{\circ}\)C. A designed paddy moisture sensor was used to detect the moisture of paddy samples. The coaxial cylindrical capacitor of the paddy moisture sensor was also preheated before every detection. The detection results are shown in Figure 8. It can be seen from Figure 8 that the designed paddy moisture sensor can realize the detection of the paddy moisture content, but the detection error is relatively high, especially in the high moisture content.  
Figure 7: Hardware and software of the paddy moisture sensor.  
The standard deviation of the detection residual was 1.23%, which is close to the standard deviation in the fitting test. Therefore, the paddy moisture detection model needs to be optimized in the future to improve paddy moisture detection accuracy.
## 1 Introduction
### Main Contributions  
Details of this paper's major contributions are as follows:  
* We deploy mPCs under coexisting environments of public safety networks (PS-LTE and LTE-R) to assess the proposed scenario's performance.
* We propose a QoS_mPCD scheme for load balancing and interference reduction that can associate users wisely during a cell range extension (CRE) offset to an mPC according to their connection priority levels, as mentioned in Table 1.
* Since high-priority users can be offloaded to the mPC, we thus employ an eICIC scheme to further decrease interference with the mPC's offloaded users.
* Unlike fixed small cells, the dynamic ratio of the almost blank sub-frame (ABS) muting ratio is considered in this paper [13].
* Sine LTE-R is assumed to be the train control signal, zero error tolerance is needed for reliable communications. Therefore, we provide the best resources to the LTE-R user during the resource allocation process.
* We evaluate important performance indexes (throughput, interference, and outage).  
The breakdown of the remaining sections is as follows. Section 2 contains the system methodology, in which we explain the priority gates model, the network layout, and the channel model. Section 3 describes the procedure for the proposed QoS_mPCD scheme in detail. The conducting of system-level simulations is described in Section 4. Finally, the conclusion is Section 5.
## 4 Conclusions  
In this research, a full Ka-band amplifier with a flat-gain response was fabricated using a 65 nm CMOS technology. To compensate for the gain roll-off in the upper end of the bandwidth, an inductive neutralization technique was used in the first stage. Neutralization inductance was carefully determined by considering gain and stability. To achieve a wideband flat gain, three CS stages designed at different center frequencies of 26, 34, and 42 GHz were cascaded. Using the frequency staggering technique, we found that the whole amplifier exhibited a flat-gain response of 13 \(\pm\) 0.2 dB over the full Ka-band frequencies.  
Conceptualization, B.K. and S.J.; methodology, B.K. and S.J.; investigation, B.K. and S.J.; data curation, B.K. and S.J.; formal analysis, B.K. and S.J.; writing--original draft preparation, B.K.; writing--review and editing, S.J.; supervision, S.J.; project administration, S.J.; funding acquisition, S.J. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2021-0-00260, Research on LEO Inter-Satellite Links).  
Not applicable.  
The authors would like to thank the IC Design Education Center (IDEC), Korea, for the chip fabrication and EDA tool support.  
The authors declare no conflict of interest.
# Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound
## 3 Results  
### Platelet Aggregation  
The results of a blood test with a platelet aggregometer (Figure 2) conducted with epinephrine can also show the suppression of other receptors. The results showed that in the absence of ultrasound, the maximum platelet aggregation recorded was 104% (Figure 2a). Conversely, when sonicated with ultrasound, 100% platelet aggregation was not observed (Figure 2b). Instead, a gradual decrease in platelet aggregation was observed as follows: 95% at an ultrasound intensity of 100-150 mW/cm\({}^{2}\), 92% at an ultrasound intensity of 100-150 mW/cm\({}^{2}\) with a duration of 90 s, 86% at an ultrasound intensity of 50-70 mW/cm\({}^{2}\), and 80% at an ultrasound intensity of 50-70 mW/cm\({}^{2}\).  
The test results shown in Figure 2 indicate that the process of blood platelet aggregation initiates faster in sonicated samples compared to the sample treated only with epinephrine (adrenaline). For the sonicated samples, 10% platelet aggregation was achieved within 1 min and 40% aggregation was achieved within 2 min after induction of platelet aggregation with epinephrine. For the blood samples treated only with epinephrine (adrenaline), 5% platelet aggregation was achieved within 1 min and 10% aggregation was achieved within 2 min. In vitro [3], it was found that ultrasound produces more stable platelet aggregates than a natural platelet aggregation stimulant. Ultrasound-induced platelet aggregation holds promise as a potential solution to platelet receptor (P2Y\({}_{12}\) receptor) issues, such as defective responses to soluble agonists [27], and as an effective bleeding control.
## 4 Results and Analysis
### Trend Prediction of Dual-System Coupling Coordination Degree
#### 4.3.3 GM (1, 1) Spatial Evolution Trend Prediction  
Figure 5 display the spatial distribution of the linkage coordination between innovation in technology and the natural environment development from 2021 to 2025.  
(1) The overall spatial pattern distribution continues to exhibit the convex spatial pattern characteristics commonly referred to as \"high in the middle and low at both ends\".  
(2) Specifically, the coupling coordination degree in Liaoning, Guangxi, and Hainan is relatively low. Because Liaoning, Guangxi, and Hainan are still dominated by resource-based industries and have a high proportion of labor-intensive industries, the secondary industry is not suitable for the ecological environment, scientific and technological innovation is slowly improving, and the degree of coordination cannot be improved quickly. Other coastal provinces have shown varying degrees of growth trends, and Hebei and Fujian have developed from a moderate coupling and coordination stage to a good coupling and coordination state. The industrial structure adjustment carried out in Hebei and Fujian has achieved remarkable results, and the construction of an ecological civilization has been vigorously strengthened through policy guidance and industrial upgrading.  
(3) Overall, the economic strength of coastal areas is relatively muscular, and most special economic zones are located in coastal areas with rapid urbanization. As the country vigorously promotes the construction of an ecological civilization, strengthens investment  
Figure 4: Trend chart of predicted value of coupling coordination degree in various provinces in coastal areas from 2021 to 2025.  
in environmental governance, and improves environmental standards, technological innovation and ecological environment development promote and influence each other. While strengthening the construction of ecological civilization, coastal areas realize green scientific and technological innovation, thereby achieving a harmonious situation in which the coordinated development of the two systems is jointly promoted, thereby enhancing the interaction between scientific and technological innovation and ecological environment development, causing the coupling coordination degree of each province to show a \"retreat\". The development trend shows \"weakness, trending toward excellence, and excellence creation\". In the future, coastal regions will urgently need to pay attention to the socio-economic benefits and ecological and environmental issues brought about by the development of technological innovation, as well as deal with the coordinated development relationship between technological innovation and ecological and environmental development.
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 4 Model Setup and Validation
### Model Validation  
#### 4.2.1 Wind Speed Validation  
The simulated wind speeds were compared with the measured data from verification points No. 1, No. 2, Taiwan Strait, and Gulei buoys. The location of each verification point is shown in Figure 4a.  
Figure 5 shows a comparison between the simulated and measured wind speeds at each verification point. The figure reveals that although there are some errors in the  
Figure 4: Verification points map. (**a**) For wind speed validation, (**b**) For tide level validation.  
Figure 3: Tide level simulation for Typhoon Mekkhala. (**a**) 2020-08-10 T00:00, (**b**) 2020-08-10T06:00, (**c**) 2020-08-10 T12:00, (**d**) 2020-08-10 T18:00.  
simulation results at each moment, the overall trend remains consistent. This consistency indicates the high accuracy of the numerical model and highlights its potential for further study and analysis of the results.
## 3 Proposed Foundation Framework for Assessment of Website Design for Posting Privacy Policies  
[PERSON] et al. [21] performed a study on the use of WCAG checkpoints and the design of higher education and financial institution websites for the visually impaired to locate a privacy policy. The study used compliance to six selected WCAG checkpoints for website assessment. Predictability issues included the number of URLs to click, the name of the entry title of the URL location, and the size of the title font. Readability issues included content spanning several tabs and the need to visit multiple URLs to be able to review the notice fully. Using these learnings, this study expands to the healthcare industry and proposes a set of questions to develop a reusable assessment framework. The following questions were developed to assess different-sized US hospitals' website designs for posting a privacy policy:  
1. Is the initial entry to the privacy notice located on the first page of the hospital website?
2. Where on the website page is the initial entry to the privacy notice located?
3. What is/are the terms used for the title for the initial entry to the privacy notice?
4. How many URL clicks does it take to get to the privacy notice?
5. Is any attention-eliciting website design elements used, as cited in research [16; 17], such as drag, dropdowns, swipe, animation, or checkboxes?
6. Are there website design differences amongst hospitals of varying sizes?
# Mdpj _sciences_
## 3 Review of the DMRN Protocol
### Assumptions
#### 3.2.2 Assumptions on Cryptography Primitives  
To ensure consistency with 3 GPP standards, we followed the same notation used in [1]. In the DMRN Protocol, we used seven symmetric key algorithms labeled by \(f_{1}\), \(f_{2}\), \(f_{3}\), \(f_{4}\), \(f_{5}\), \(f_{1}^{*}\), and \(f_{5}^{*}\). The functions \(f_{1}\), \(f_{1}^{*}\), and \(f_{2}\) are responsible for message authentication, while \(f_{3}\), \(f_{4}\), \(f_{5}\), and \(f_{5}^{*}\) are employed for key derivation purposes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline AK & Anonymity Key \\ AKA & Authentication and Key Agreement \\ AUSF & Authentication Server Function \\ AUTN & AUhentication TokeN \\ CK & Cipher Key \\ HN & Home Network \\ HMAC & Hash-Based Message Authentication Code \\ HRES & Hash RESponse \\ HXRES & Hash eXpected RESponse \\ \(ID_{SN}\) & Identifier of Serving Network \\ IK & Integrity Key \\ \(K\) & A permanent key shared between UE and HN \\ \(K_{AUSF}\) & A master session key \\ \(K_{SEAF}\) & An anchor key \\ KDF & Key Derivation Function \\ MAC & Message Authentication Code \\ ME & Mobile Equipment \\ PFS & Perfect Forward Secrecy \\ \((pk_{HN},s_{HN})\) & HN’s KEM public–private key pair \\ PQC & Post-quantum cryptography \\ RES & RESponse \\ \(R_{SN}\) & 256-bit Random Number \\ SEAF & SEcurity Anchor Function \\ SN & Serving Network \\ SUCI & SUDiscriber Concealed Identifier \\ SUPI & SUDiscrip Permanent Identifier \\ UE & User Equipment \\ USIM & Universal Subscriber Identity Module \\ XRES & eXpected RESponse \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations.
## 1 Introduction
Indefinite increment in network depth, however, incurs a tiresome training process, requires high storage and computing capacities, and includes the difficulties of network architectural design that lead to performance degradation. Tiresome pre-training processes, such as input data labeling and the need for high performance computers, are common setbacks for deep network implementation. A companion computer on-board a UAV has to have enough memory to store huge amounts of activations and weights of the deep network and needs to conduct resource intensive image processing in real-time for safe navigation. Challenges related to large network training were presented and explained by [PERSON] [29]. [PERSON] et al. [30] also discussed the challenges of training CNNs. Many researchers suggested remedies to the challenges. [PERSON] et al. [31] suggested a deep neural network training procedure that leverages the performance of the network. [PERSON] et al. [32] proposed a learning framework, named residual learning, that resolves the difficulty of training large networks and performance degradation. [PERSON] et al. [33] proposed a stability training approach that enhances network tolerance to small perturbations in input image data. In addition to training difficulty, the work of selecting and designing a particular deep CNN architecture is not simple. This is because the optimization algorithms have to be gauged from the point of view of specific deep learning problems. There is, however, a most appropriate optimization for a particular problem which is the result of the \"no free lunch theorem\" of mathematical optimization reported by [PERSON] et al. [34]. [PERSON] et al. [35] took the aforementioned network parameters and parameters such as number of filters per layer and filter size in order to perform parameter optimization and obtain a network architecture with the best performance. [PERSON] et al. [36] introduced DenseNets,where each layer is connected to every other layer. They reported that this network reduces some of the problems that come with increasing the depth of CNN architecture. The remainder of this work is organized into sections. Section 2 describes the objective of this research and the methodologies followed. Problem specific input data augmentation techniques are listed and augmentation strategies are explained in Section 3. The designed network architecture and its training procedure are presented in Section 4. Results and discussions are presented in Section 5 and finally the conclusion is drawn in Section 6.
## 5 Experimental Results and Discussion
Thus, it is possible to incorporate driving functions, including localization, trajectory planning, vehicle control, maneuver-intention prediction, and driving strategy. Based on the testing results, the following parameters were considered:  
* Throughput;
* End-to-end delay;
* Energy consumption.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Duration** & **Consumption** & **Off-Peak Hours Winter Price** & \multicolumn{3}{c}{**Full Hours Winter Price**} & \multicolumn{3}{c}{**Peak Hours Winter Price**} \\  & **(s)** & **(kW/Protocol)** & & **(EUR/Protocol)** & & **(EUR/Protocol)** & & **(EUR/Protocol)** & & **(EUR/Protocol)** \\ \hline Spinal cord & 1029 & 8.24 & 0.35 & 0.45 & 0.17 & 0.54 & 0.70 & 6.89 & 0.65 & 0.70 & 6.89 \\ Neurovascular & 956 & 9.8 & 0.42 & 0.53 & 0.20 & 0.64 & 0.83 & 8.18 & 0.77 & 0.83 & 8.18 \\ Multiple sclerosis & 1046 & 10.7 & 0.45 & 0.58 & 0.22 & 0.70 & 0.91 & 8.91 & 0.84 & 0.91 & 8.91 \\ Neurovascular and Supra trunks & 1298 & 11.98 & 0.51 & 0.65 & 0.24 & 0.79 & 1.02 & 10.01 & 0.95 & 1.02 & 10.01 \\ Brain tumor & 1300 & 13.92 & 0.59 & 0.75 & 0.28 & 0.92 & 1.19 & 11.63 & 1.10 & 1.19 & 11.63 \\ Epilepsy & 1596 & 14.68 & 0.62 & 0.79 & 0.30 & 0.97 & 1.25 & 12.27 & 1.16 & 1.25 & 12.27 \\ Neurodegenerative & 1567 & 15.04 & 0.64 & 0.81 & 0.31 & 0.99 & 1.28 & 12.57 & 1.19 & 1.28 & 12.57 \\
**Optimized protocols** & & & & & & & & & & \\ Knees & 304 & 2.29 & 0.10 & 0.12 & 0.05 & 0.15 & 0.20 & 1.91 & 0.18 & 0.20 & 1.91 \\ Lumbar spine & 436 & 3.17 & 0.13 & 0.17 & 0.06 & 0.21 & 0.27 & 2.65 & 0.25 & 0.27 & 2.65 \\ Pituitary gland & 518 & 4.25 & 0.18 & 0.23 & 0.09 & 0.28 & 0.36 & 3.55 & 0.34 & 0.36 & 3.55 \\ Spinal cord & 580 & 4.71 & 0.20 & 0.25 & 0.10 & 0.31 & 0.40 & 3.94 & 0.37 & 0.40 & 3.94 \\ Neurovascular & 621 & 5.77 & 0.25 & 0.31 & 0.12 & 0.38 & 0.49 & 4.82 & 0.46 & 0.49 & 4.82 \\ Multiple sclerosis & 769 & 7.38 & 0.31 & 0.40 & 0.15 & 0.49 & 0.63 & 6.17 & 0.58 & 0.63 & 6.17 \\ Neurovascular and supra trunks & 963 & 7.96 & 0.34 & 0.43 & 0.16 & 0.52 & 0.68 & 6.65 & 0.63 & 0.68 & 6.65 \\ Brain tumor & 1021 & 10.31 & 0.44 & 0.56 & 0.21 & 0.68 & 0.88 & 8.62 & 0.82 & 0.88 & 8.62 \\ Epilepsy & 1200 & 10.55 & 0.45 & 0.57 & 0.21 & 0.69 & 0.90 & 8.82 & 0.83 & 0.90 & 8.82 \\ Neurodegenerative & 1150 & 10.61 & 0.45 & 0.57 & 0.22 & 0.70 & 0.90 & 8.87 & 0.84 & 0.90 & 8.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._In 2023, the daily cost of running standard protocols was EUR 276.6, compared to EUR 247.4 for optimized protocols, resulting in a cost difference of EUR 29.18.
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 8 Experimental Verification
### Experimental Results of the Proposed Mixed-Mode Universal Biquad Filter in Figure 2  
The active and passive component values of Figure 2 were designated as \(g_{mki}=0.8\) mA/V (for \(I_{Bi}=42\)\(\mu\)A) and \(C_{1}=C_{2}=2\) nF, corresponding to the theoretical values of \(f_{p}=63.66\) kHz and \(Q=1\). Figures 19-21 show the measured time-domain responses and their corresponding frequency spectrums for the LP, BP, and HP filters in VM. The signal input voltage (\(v_{in}\)) for time-response measurements was set to 50 mV (peak) at a frequency of 63.66 kHz. The recorded \(f_{p}\) in the measured LP and BP voltage response of Figures 19 and 20 was 63.80 kHz, which is a \(-0.22\%\) deviation from the ideal value. In the HP voltage response of Figure 21, the measured \(f_{p}\) is 63.10 kHz, reflecting a deviation of +0.88% from the predicted value. Figure 22 represents the experimental frequency responses for the LP, BP, and HP voltage responses of Figure 2. The results indeed show that the measured BP and HP responses at low frequencies reach only about \(-10\) dB in their stopbands, which is unusually low and not characteristic of ideal filter behavior. This indicates a significant parasitic effect: a fraction of the LP response term contaminates the BP and HP numerators. Such contamination mainly could occur due to parasitic capacitances within the integrated circuit layout and incomplete pole-zero cancellation in the signal path caused by mismatches. This contamination leads to a partial addition of the low-frequency gain from the LP function into the BP and HP outputs, which should ideally have attenuated these components significantly more deeply--on the order of \(-40\) dB or lower. As also noted in [31], the gain-bandwidth product (GBP) of the IC LM13600 used in experiments is around 2 MHz. As a result, the discrepancies in gain for the measured high-frequency HP and BP responses arise mainly from GBP effects of the IC LM13600 OTAs. Nevertheless, the use of high-speed IC devices extends the useful frequency of the proposed circuit.  
Figure 20: Experimentally measured responses of the BP filter in VM. (**a**) Time-domain responses; (**b**) frequency spectrum.  
Figure 21: Experimentally measured responses of the HP filter in VM. **(a)** Time-domain responses; **(b)** frequency spectrum.  
Figure 22: Experimental frequency responses for the LP, BP, and HP voltage responses in Figure 2.