esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In addition, the power consumption of the hardware can be increased as well. In this paper, we propose an ultrasound blood-flow-monitoring circuit system using a single-element transducer, as shown in Figure 1. The circuit system consists of a single-element ultrasonic transducer, an analog interface circuit, and a FPGA. The analog interface circuit performs analog signal conditioning, such as generating a bipolar high-voltage pulse and filtering for the amplified echo signal. The proposed circuit system includes a new automatic range-gate-positioning scheme which can blindly track the position of an arbitrary vessel on a scanline without additional manual adjustment of the range-gate position. The proposed range-gate-positioning scheme is implemented in FPGA for real-time acquisition of Doppler sample volumes. Since the proposed range-gate-positioning scheme uses Doppler-amplitude deviations and does not depend on specific A-mode patterns, an arbitrary vessel on a scanline can be tracked. In addition, the required carrier frequency of the ultrasound can be lowered, because vessel detection can be accomplished even though an aliasing of the echo signal in the time domain occurs. The feature of a relatively lower carrier frequency can potentially result in efficient hardware design in terms of power and complexity. The proposed circuit system has been implemented with discrete commercial chips including FPGA. The implemented circuit system can achieve a real-time acquisition of Doppler sample volumes from a consistently positioned range gate, and the acquired sample volume can be utilized to reconstruct a spectrum. This paper is organized as follows. Section 2 describes the materials and methods for the image-free ultrasound blood-flow-monitoring circuit system. Section 3 presents discussions along with results of implementations and measurements. In Section 4, the conclusions of this work are described.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 3 Path-Tracking Controller
### MPC Controller  
The output of the control system in the predicted time horizon can be expressed in the following form:  
\[\left\{\begin{array}{l}\xi(k+1)=\Phi_{k}\xi(k)+\Theta_{k}\Delta U(k)\\ Y(k)=\mathbb{C}\xi(k)\end{array}\right. \tag{22}\]  
In the formula, \(N_{p}\) is the prediction domain, and \(N_{c}\) is the control domain.  
The definition of the objective function for the MPC controller can be stated as follows:  
\[J=\min\left(\sum_{k=0}^{N_{p}}\xi^{T}(k)Q\xi(k)+\sum_{k=0}^{N_{c}-1}\Delta u^{ T}(k)R\Delta u\right) \tag{23}\]  
\[S.T.\left\{\begin{array}{l}\xi_{0}=\xi(0)\\ \xi(k)\in\xi_{b}\\ \Delta u\in\Delta u_{b}\end{array}\right. \tag{24}\]  
\(Q\) represents the output weight matrix, \(Q\)\(=\) diag\([Q_{1},Q_{2}]\), \(Q_{1}=\) diag\([q_{1},q_{2},q_{3},q_{4}]\), \(Q_{2}=\) diag\([q_{5}]\). The greater the weight of \(Q_{1}\) is, the higher the accuracy of path-tracking control; the greater the weight of \(Q_{2}\) is, the smoother the change in the front wheel angle of the vehicle. The matrix \(R\) represents the control weight with \(R=\) diag\([r_{1}]\). A larger value of \(R\) results in a more significant restriction on the increment of the front wheel angle, thereby increasing the suppression of abrupt changes in this angle. \(\xi_{0}\) is the initial state of the vehicle, and \(\xi_{b}\) is the state quantity constraint matrix. \(\Delta u_{b}\) is the control quantity constraint matrix, which is determined by the maximum steering wheel rotation speed of the vehicle and the sampling frequency.  
The objective function is transformed into the standard quadratic programming form for solving.  
\[J=\left[\Phi_{k}\xi(k)+\Theta_{k}\Delta U(k)-Y_{ref}\right]^{T}+\widetilde{Q }\left[\Phi_{k}\xi(k)+\Theta_{k}\Delta U(k)-Y_{ref}\right]+\Delta U^{T} \widetilde{R}\Delta U \tag{25}\]Obtain the standard quadratic programming as follows:  
\[J=\frac{1}{2}U^{T}HU+G^{T}U+E^{T}\widetilde{Q}E \tag{26}\]  
\[\frac{1}{2}H=\Theta_{k}\widetilde{Q}\Theta_{k}+\widetilde{R} \tag{27}\]  
\[G^{T}=2\Big{(}E^{T}\widetilde{Q}\Theta_{k}-Y_{ref}\widetilde{Q}\Theta_{k}\Big{)} \tag{28}\]  
The optimal front wheel steering angle control sequence that has been solved takes the first control quantity of the optimal control sequence at time \(k\) to participate in subsequent calculations.  
\[\delta(k)=u(k-1)+\Delta u(k) \tag{29}\]  
where \(\delta(k)\) is the front wheel corner.
# Channel Measurement and Modeling for 5G Urban Microcellular Scenarios
## 4 Evaluations
### Delay Spread Statistics
The CCDFs are also shown in Figure 5. It can be seen that the ray tracer tends to overestimate the DS. The curves of the SC-CC scenario are still fairly close to each other, and the maximum DS is around 100 ns for the measurement as well as for the simulation. However, the deviation is larger for the OS-CC scenario. More detailed comparisons revealed that the RT predicts paths with relatively large delays, which do not exist in the measurement results, or whose power is larger (see also exemplary comparison in Figure 3). It is important to note that the DS observed by an operational mm-wave system using directional antennas or antenna arrays with analog beamforming will be different from the reported values measured with omnidirectional antennas, since only a subset of propagation paths will be illuminated. However, the purpose of the presented evaluations is to quantitatively investigate the characteristics of the (antenna-decoupled) propagation channel on a fully comparable statistical basis. The results are valuable to parameterize and validate stochastic models and deterministic prediction tools, taking in turn into account directional information from the latter and from measurements e.g., with mechanical antenna steering. A directional channel model is then able to incorporate arbitrary antenna patters and multi-antenna configurations [14].
## 2 Methodology & Case Study  
### The Low-Cost & Portable Monitoring System  
The schematic figure (Figure 1) describes a portable and cost-effective multi-source wireless monitoring system for measuring several state variables, which is including the rainfall intensity, soil moisture content, matric suction and inclination of slope. The device of the system can be directly installed and stabilized in the slope, and the sensor units that can be inserted into the soil by using a small hammer. The sensor unit is small in size (16 \(\times\) 12 \(\times\) 8 cm) and easy to install. The weight of the sensor unit shown in Figure 2a is only 400 g, so that the proposed system can be used extensively in hard-reached places in a landslide-prone area. The system can measure multivariable parameters at certain points on a slope surface at the same time. The total cost for one monitoring site including a soil moisture sensor is only 1500 US dollars, far below the conventional GPS system that costs around 6000 US dollar for a single device. The system can be powered with batteries, and the real-time data can be transferred via a wireless network, providing all the data to the internet on time. The applicability and effectiveness of the multi-source wireless monitoring system have been tested in a natural slope under heavy artificial rainfall [17]. Considering the proposed system is cost-effective and easy to install, it can be widely used in large-scale landslide-prone areas in developing countries.  
Figure 1: Wireless sensor unit with tilt and water content sensors on a slope, rain gauge and tensiometer are also compatible with the sensor unit as an alternative.  
Figure 2: Pictures of instruments proposed for landslide real-time monitoring and early warning system. (**a**) The tilt sensors, (**b**) soil moisture sensors and (**c**) tensiometer integrated with (**d**) sensor unit and installed at the head of the slope in hollow in Yindongzi landslide and (**e**) a tipping bucket rain gauge was connected to the data logger.
# Effective Uptake of Cadmium and Chromium from Wastewater Using Carbon-Based _Capsicum annuum_
## 4 Results
### Physicochemical Analysis
#### 4.2.2 Effect of pH  
pH is the critical parameter for the detection and uptake of metal ions, and the acidity of the sorbent solution also plays a crucial role in the formation of metal chelates [46]. To establish the effect of pH at which the activated carbon can remove Cd(II) and Cr(VI) ions, a pH range between 1.0 and 9.0 was used. Figure 5 shows the influence of the pH value on the absorption of Cd(II) and Cr(VI) ions. The highest absorption was observed at pH 1 for Cr(VI) and then a decrease was observed as the pH increased from 3 to 9. This is because Cr(VI) is ionic in all pH ranges, specifically as \(\text{HC}\text{O}_{4}\)\({}^{-}\) and \(\text{Cr}_{2}\text{O}_{7}\)\({}^{2-}\) at a pH \(<\) 6 and \(\text{Cr}\text{O}_{4}\)\({}^{2-}\) at a pH \(>\) 6 [47]. The activated carbon and the Cr(VI) ions existed as opposite charges, which ultimately favored the interaction at an acidic pH [48]. At alkaline pH, Cr(VI) and the adsorbent tend to be negatively charged, leading to a repulsion interaction and thus lower sorption. A different trend was observed for the uptake of Cd(II), which showed that the highest capacity was reached at pH 5. These results indicate that the adsorption of Cd(II) ions was low in an acidic medium. With increasing pH, the adsorption of Cd(II) ions increased. At neutral pH and above, the uptake of Cd(II) ions decreased due to competition from the hydroxyl group [49]. Adsorbents are usually affected by the specific pH solution. In other studies, the adsorption efficiency increased with increasing pH because of the reduced electrostatic repulsions between the adsorbent functional surface and adsorbate. This favoured the adsorption of Cd(II). A similar observation was reported in [PERSON] et al. (2020) and [PERSON] et al. (2020) [50; 51].  
Figure 4: pH at the point zero charge by activated carbon.
# Influence of the Microwave Heating Time on the Self-Healing Properties of Asphal Mixtures
## 2 Materials and Methods
### Self-Healing Level of Asphalt Mixtures by Microwave Heating  
The evaluation of the self-healing level of asphalt mixtures with fibres using microwave heating has been carried out according to the following methodology [17]: first, each sample was submitted to a three-point bending test (see Section 2.4) at \(-20\)\({}^{\circ}\)C, until it breaks in two parts. After this, the broken samples were conditioned during 2 h at a temperature of 20 \({}^{\circ}\)C, until the surface moisture due to defrosting was completely dried. Then, the two parts of the sample were put together and heated using microwave radiation during four different heating times (10 s, 20 s, 30 s and 40 s). After heating, the samples were left to cool at ambient temperature to later be conditioned at a temperature of \(-20\)\({}^{\circ}\)C during 24 h and submitted again to the three-point bending test, thus completing a cracking-healing cycle. Hence, the healing level reached by each tested sample (\(HL\)) can be defined as the quotient between the maximum force resisted by the sample initially tested (\(F_{i}\)) and the maximum force resisted by the sample after the healing process (\(F_{r}\)).  
\[HL=\frac{F_{r}}{F_{i}} \tag{1}\]  
Finally, to quantify the efficiency of the healing methodology, 10 cracking-healing cycles were carried out for each asphalt sample.
## 5 Future Challenges and Scope
### Multi-Bit Structures  
To reduce 1-bit reconfigurable transmitarray phase quantization insertion loss, several designs were investigated for a 2-bit or 3-bit fixed beam (passive) transmitarray [117; 118; 119]. A multi-bit phase quantization transmitarray significantly improves execution for collimating characteristics over a 1-bit structure. The most important design principle for a transmitarray is insertion loss and phase quantization of the structure elements, in particular for beam steering (using reconfigurable electronic techniques). High phase quantization improves the directivity, aperture efficiency, and side lobe levels of the transmitarray. In an ideal case, a complete 360\({}^{\circ}\) phase shift and high phase quantization are required to achieve high directivity and low side lobe levels. Multi-bit phase quantization for electronically reconfigurable transmitarray still has challenges. A large number of solid-state devices, bias network complications, bandwidth, and power ingestion are compared to a 1-bit transmitarray. Using a 2-bit reconfigurable transmitarray can improve 30-40% aperture efficiency, as indicated in [120]. Only one paper on a 2-bit electronically reconfigurable MEMS system is available [103]. Briefly, this structure consists of wafer technology with five MEMS with bias lines, and six independent bias lines to control the complex biasing network. Due to its complexity, size, and other structures, no attempt for multi-phase quantization has been performed yet for RF-MEMS switches. In [103], it is suggested that structure significantly affects high switching resistance and insertion losses of around 4.2-9.4 dB. Moreover, novel 2-bit electronically reconfigurable transmitarray experimental results have been demonstrated in [27] for Ka-band. This unit cell is fabricated with dimensions \(\lambda\)/2 \(\times\)\(\lambda\)/2 \(\times\)\(\lambda\)/8 at 29.5 GHz with six metal layers and three substrate layers. Four switches using PIN diodes are integrated on unit cells to achieve a 2-bit phase resolution of a reconfigurable transmitarray. An increase in the number of diodes from 2 to 4 produces an effect on insertion losses; the maximum measured insertion losses reach up to 2.03 dB, corresponding to the minimum insertion loss to any stage 1.39 dB at 29.5 GHz. This is a remarkable effort compared to the MEMS-based unit cell. This work is further extended in [75] to convert observed unit cells into a 14 \(\times\) 14 reconfigurable array. The proposed unit cell array obtains 19.8 dB gain with 15.9% of aperture efficiency at 29.5 GHz for 2-bit resolution. A multi-bit electronically reconfigurable transmitarray has a significant gap for future research, particularly for a higher frequency. There is no reconfigurable phase quantization researchupdated with liquid crystal or microfluid at high frequency. Extending this technique to other configurations may be a practical way forward in the future for realizing high gain reconfigurable apertures.
## 3 Control Design and Stability Analysis
### Controller Design
\begin{array}{l}(z_{1}-C_ {b})^{2}\;\;\mbox{for}\;\;z_{1}\geq C_{b}\\ 0\;\;\mbox{for}\;\;z_{1}\in(-C_{b},C_{b})\\ (-1)(z_{1}+C_{b})^{2}\;\;\mbox{for}\;\;z_{1}\leq-C_{b}\end{array} \right. \tag{11}\]Incorporating the expression for \(\dot{z}_{1}\) (8) into Equation (10) and arranging yields  
\[\dot{V}_{z1} = -k_{1b}|f_{z1}|-k_{1}f_{z1}^{2}+f_{z1}a_{1}x_{2} \tag{12}\] \[+f_{z1}(k_{1}f_{z1}-\dot{y}_{d}-\dot{\psi}_{1}-a_{2}x_{1}-r_{x_{1} }+k_{1b}sign(f_{z1}))\]  
where \(-k_{1b}|f_{z1}|\) was incorporated in order to provide robustness and \(|f_{z1}|\) was expressed as \(|f_{z1}|=f_{z1}sign(f_{z1})\), which would imply the presence of the signal \(sign(f_{z1})\) in the definition of \(z_{2}\) and would hamper the determination of \(\dot{z}_{2}\). Thus, notice that from the definition of \(f_{z1}\) (11) it follows that  
\[|f_{z1}|=f_{z1}sat_{z1}\] (13) where \[sat_{z1}=\left\{ \begin{array}{l}\frac{z_{1}}{C_{b}}\left(2- \frac{|z_{1}|}{C_{b}}\right)\text{ for }\ z_{1}\in(-C_{b},C_{b})\\ sgn(z_{1})\text{ otherwise}\end{array} \right. \tag{14}\] \[\frac{dsat_{z1}}{dz_{1}}=\left\{ \begin{array}{l}\frac{2}{C_{b} }\left(1-\frac{|z_{1}|}{C_{b}}\right)\text{ for }\ z_{1}\in(-C_{b},C_{b})\\ 0\text{ otherwise}\end{array} \right. \tag{15}\]  
Using (13) in (12) instead of \(|f_{z1}|=f_{z1}sign(f_{z1})\), yields  
\[\dot{V}_{z1} = -k_{1b}|f_{z1}|-k_{1}f_{z1}^{2}+f_{z1}a_{1}x_{2} \tag{16}\] \[+f_{z1}(k_{1}f_{z1}-\dot{y}_{d}-\dot{\psi}_{1}-a_{2}x_{1}-r_{x_{1 }}+k_{1b}sat_{z1})\]  
To obtain the required right hand side of \(dV_{z1}/dt\), the effect of the term \(f_{z1}a_{1}x_{2}+f_{z1}(k_{1}f_{z1}-\dot{y}_{d}-\dot{\psi}_{1}-a_{2}x_{1}-r_{x _{1}}+k_{1b}sat_{z1})\) should be tackled by adequate definition of the new state \(z_{2}\). However, \(a_{2}\) is unknown and \(r_{x1}\) is unknown, and its upper bound comprises and unknown constant \(\bar{\mu}_{1}\). Therefore, the term \(f_{z1}(-a_{2}x_{1}-r_{x1})\) should be expressed in terms of updated parameters and parameter estimation error. Recall from Assumption 3 that \(r_{x1}\leq\bar{\mu}_{1}\bar{r}_{x1}\) or \(r_{x1}=\bar{\mu}_{1}\bar{r}_{x1}\), therefore  
\[f_{z1}(-r_{x1})\leq\bar{\mu}_{1}\bar{r}_{x1}|f_{z1}| \tag{17}\]  
The term \(f_{z1}(-a_{2}x_{1}-r_{x1})\) can be parameterized accounting for (17) and (13):  
\[f_{z1}(-a_{2}x_{1}-r_{x1})\leq\varphi_{1}^{\top}\theta_{1}f_{z1} \tag{18}\] \[\varphi_{1}=[-x_{1},\ sat_{z1}r_{x1}]^{\top},\ \theta_{1}=[a_{2},\ \bar{\mu}_{1}]^{\top} \tag{19}\]  
As the parameter vector \(\theta_{1}\) is unknown, we express it in terms of an updated parameter vector and a parameter updating error as follows. Let \(\bar{\theta}_{1}=\bar{\theta}_{1}-\theta_{1}\), where \(\bar{\theta}_{1}\), \(\bar{\theta}_{1}\) are the parameter estimation error and the updated parameter, being \(\bar{\theta}_{1}\) provided by an updating mechanism defined later. Thus, \(\theta_{1}\) can be expressed as \(\theta_{1}=\bar{\theta}_{1}-\bar{\theta}_{1}\). Substituting into Equation (18) yields \(f_{z1}(-a_{2}x_{1}-r_{x1})\leq\varphi_{1}^{\top}\bar{\theta}_{1}f_{z1}-\varphi _{1}^{\top}\bar{\theta}_{1}f_{z1}\).
# Cosmeceutical Potentials of _Equisetum debile_ Roxb. ex Vaucher Extracts
## 3 Results
### Skin-Whitening Activities of E. de debile Extracts via the Inhibition of Tyrosine Enzyme  
Figure 7 and Table 3 demonstrate that the tyrosinase inhibitory capacities of _E. debile_ extracts were influenced by the extraction methods and solvents. The regression equation derived from the kojic acid calibration curve was y = 8.8622x + 1.9838, R\({}^{2}\) = 0.998 and used to compute KAE values. The highest anti-tyrosine activity was from EF, which was higher than the lowest activity from HF by 114-fold KAE values. HF and DF exhibited similar  
Figure 5: Total phenolic contents (TPC) of _E. debile_ extracts. \({}^{1-\
u}\) Successive partition of crude extract (CE) (10.45 \(\pm\) 1.20 g) into hexane fraction (HF), dichloromethane fraction (DF), ethyl acetate fraction (EF), methanol fraction (MF), and methanol insoluble residue (MR). All data are presented as mean \(\pm\) SD of triplicates. Total phenolic contents were expressed as GAE (gallic acid equivalents). ND indicates not detected. Letters (a, b, and c) indicate significant differences of means between the groups based on [PERSON]’s HSD one-way ANOVA (_p_-value \(<\) 0.05).  
Figure 6: Relative scatter plot of total phenolic content and inhibition activities of crude extract (CE), hexane fraction (HF), dichloromethane fraction (DF), ethyl acetate fraction (EF), and methanol fraction (MF); (**a**) anti-tyrosine activity (regression _p_-value = 0.0236); (**b**) anti-5\(\alpha\)-reductase activity (regression _p_-value = 0.7437); (**c**) anti MMP-1 activity (regression _p_-value = 0.2424); and (**d**) anti MMP-2 activity (regression _p_-value = 0.1753).  
potency of inhibition to CE; however, these three extracts exerted lower activities than that of MF. Anti-tyrosinase activities were ranked by KAE and IC\({}_{50}\) values as the following: EF, MF, DF, CE, and HF. This suggests that the partitioning extraction by ethyl acetate was able to isolate strong tyrosinase inhibitors from _E. debile_ crude extract.
## 1 Introduction
Additionally, the evaluation of IDS systems usually relies just on performance comparison, where the authors share few or no details regarding the methodology followed [13]. Moreover, there are no standard adopted methodologies for IDS evaluation, with standardized steps, especially the ones related to data preparation and homogenization. In this sense, recent relevant works surveying ML-based NIDS systems [2; 11] highlight the importance of Feature Extraction/Engineering and Feature Selection processes for accurate, reliable and enhanced NIDS solutions which are not always found or mentioned in the literature. In the end, fair NIDS evaluation and comparison needs of standard and consolidated ways comprising mandatory steps accepted by all the research community. Otherwise, the goodness of the proposed solutions could be called into question. Throughout the present work, we firstly introduce a comprehensive study of recent and relevant work in NIDS concluding the absence of common and standardized ways to evaluate IDSs in general, and NDISs in particular, for fair performance comparisons. In this sense, a simple framework comprising all the necessary steps for accurate and reliable NIDSs evaluation is proposed: from the feature engineering process to the performance metrics. All together, not only provides a structured guide to deal with network attack classification problems but also increases the usability of every ML-based NIDS to be deployed in network environments different from the one they were trained. Finally, the proposed framework is tested by comparing classical supervised approaches against semi-supervised and unsupervised techniques by using the recently built dataset UGR'16 [14]. The rest of the paper is organized as follows. Section 2 studies recent works and methods involved in developing ML-based NIDS. Section 3 introduces the recently built UGR'16 dataset as an alternative to the most used and widely accepted datasets. After that, Section 4 describes the proposed methodology and stages for ML-based NIDS evaluation and comparison. The experimentation and results for network attack classification are discussed in Section 5. Finally, remarked conclusions and future work are introduced in Section 6.