esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Dataset  
The transcription experiments were conducted on the MIDI aligned piano sounds (MAPS) dataset [38]. The MAPS dataset provides piano recordings, the related aligned MIDI files, and annotated text files. The overall size of MAPS is about 60 h of audio, and it is the largest database for piano transcription. There are nine categories of recordings corresponding to different piano types and recording conditions. Seven categories of audio are produced by software piano synthesizers, while two categories of recordings are obtained from a real Yamaha Disklavier upright piano. The dataset consists of isolated notes, chords, and 30 pieces of music in each category. For music pieces, the number of concurrent notes ranges from one to nine. Each music piece lasts more than 30 s, and all 270 pieces contain 18 h of audio signal.  
We aim at the transcription of the Disklavier piano, which is in category \"ENSTDkCl\" of the MAPS dataset. For the real piano, the recording room was a studio with dimensions equal to about \(4\times 5\) m. The distance between the piano and the microphones was about 50 cm. MIDI files were created beforehand and were sent to the MIDI input of the Disklavier. Then, the audio was recorded using two omnidirectional microphones.  
To build a universal model independent of the real individual, we trained the CNNs using 210 music pieces of synthesized pianos in the MAPS dataset. The training set contains 460,988 notes and the overall size is about 14 h. The proposed system was evaluated on the music pieces of the Disklavier piano. In the testing set, there are 30 music pieces, and only the first 30 s of each piece was used for transcription. The testing set contains 7345 notes in total. The setting is realistic because the training set and testing set are disjoint on piano types. During the note verification, the attack templates were obtained from the isolated notes produced by the same piano.
# A Low BER DB-PAM4 Adaptive Equalizer for Large Channel Loss in Wireline Receivers
## 2 The Principle and Realization of the Adaptive Equalizer  
### Adaptive-Equalizer Overall Architecture  
The DB-PAM4 signal is a special seven-level modulation signal. Due to the limitation of the output voltage, de-emphasis is often used to equalize the transmitter, resulting in a reduction in the total voltage swing. When the equalizer is not used at the transmitter, the transmitter becomes simpler, avoiding the additional complexity introduced by FFE at the transmitter and relaxing the linearity specification of the transmitter. Moreover, amplifying the signal at the receiving end is more conducive to power optimization in later designs.  
Figure 1 shows the overall-architecture block diagram of the adaptive equalizer. In order to reduce power consumption and meet the requirements of high-loss channels, this paper adopts a new DB-PAM4 generation method and joint adaptive algorithm. The equalizer mainly works at the receiving end. The transmitter uses a PRBS generator to generate a 64-bit digital signal and then modulates the data into a Pre-PAM4 signal at low speed to avoid erroneous transfer when generating a DB-PAM4 signal. The receiver data uses a T-coil to enhance input matching. A subsequent common-mode conversion stage boosts the input common-mode voltage from 550 mv to the CML level with some peaking. This is followed by a two-stage CTLE to eliminate long-tail ISI, reducing the number of taps in the FFE while increasing the SNR at the ADC input. After the VGA adjusts the signal amplitude, the ADC samples the analog signal into 64 parallel 7-bit digital signals. The DSP is composed of a 16-tap FFE and a one-tap DFE, and the operating frequency is 875 M to deal with the residual ISI that is not equalized by CTLE.
## 2 LEO Satellite Signal and PFM Interference Signal Model  
In an interference environment, the signal model at the input of the LEO satellite downlink receiver can be expressed as:  
\[x(t)=\sum_{i}^{N}s_{i}(t)+\sum_{m}^{M}j_{m}(t)+n(t) \tag{1}\]  
where \(s_{i}(t)\) represents the received signal from the i-th LEO satellite (\(i\) = 1, 2, 3,.., N); N is the number of visible LEO satellites during the reception period; \(j_{m}(t)\) represents the \(m\)-th component of the interference signal affecting the receiver (\(m\) = 1, 2, 3,.., M); M is the number of interferences received; and \(n(t)\) represents the zero-mean stationary additive white Gaussian noise (AWGN).  
When considering the received signal from a single LEO satellite, the signal from the i-th satellite can be expressed as:  
\[s_{i}(t)=AD(t)\cos(\omega_{0}t+\Phi(t)) \tag{2}\]  
where \(A\) is the signal amplitude, \(D(t)\) is the data code level transmitted by the satellite, \(\omega_{0}\) is the signal transmission frequency, and \(\Phi(t)\) is the transmission phase.  
\(j_{m}(t)\) is the PFM interference, whose instantaneous frequency \(f(t)\) changes periodically with time, expressed as:  
\[f(t)=f_{0m}+\Delta f_{m}\cdot\sin(\frac{2\pi t}{T_{m}}) \tag{3}\]  
where \(f_{0m}\) is the carrier frequency of the PFM interference signal, \(\Delta f_{m}\) is the modulation amplitude of its frequency, \(T_{m}\) is the modulation period (MP), \(\sin(\frac{2\pi t}{t_{m}})\) is the periodic modulation function, and the instantaneous frequency of the interference oscillates periodically within the range of \([f_{0m}-\Delta f_{m},f_{0m}+\Delta f_{m})\).  
Then, the phase function \(\Phi(t)\) can be expressed by Equation (3) as:  
\[\Phi(t) =2\pi\int\limits_{0}^{t}f(\tau)d\tau \tag{4}\] \[=2\pi\int\limits_{0}^{t}(f_{0m}+\Delta f_{m}\cdot\sin(\frac{2\pi t }{T_{m}}))d\tau\] \[=2\pi f_{0m}t+\frac{\Delta f_{m}T_{m}}{2}\cdot\cos(\frac{2\pi t}{ T_{m}})\]  
So, the PFM interference signal \(j_{m}(t)\) can be expressed as:  
\[j_{m}(t)=A_{m}\exp\biggl{[}2\pi f_{0m}t+\frac{\Delta f_{m}T_{m}}{2}\cdot\cos( \frac{2\pi t}{T_{m}})+\theta_{m}\biggr{]} \tag{5}\]  
where \(A_{m}\) is the carrier amplitude of the PFM interference signal, and \(\theta_{m}\) represents the initial carrier phase of the PFM interference, which is a random variable uniformly distributed within the range of \([-\pi,+\pi)\). \(2\pi f_{0m}t\) is the linear phase term of the interference, which determines the central frequency of the signal; and \(\frac{\Delta f_{m}T_{m}}{2}\cdot\cos(\frac{2\pi t}{T_{m}})\) is the nonlinear phase term, representing the periodic variation in the frequency with time, with a period of \(T_{m}\).
###### Abstract
This is accomplished by introducing two machine learning approaches, namely, _topic modeling with sentiment analysis_ and _topic modeling with clustering_. * To the best of our knowledge, this is the first research study to provide a framework for analyzing the Arabic user reviews of a business sector super app in the context of Saudi Arabia utilizing topic modeling, sentiment analysis, and clustering approaches. * We create an Arabic dataset collected from the Apple and Google Play Stores for some of the most well-known super applications found in the KSA. The objectives of this paper are (1) to extract the most important topics that affect super applications in the field of business and (2) to measure the extent of users' satisfaction and dissatisfaction with each extracted topic to explore the areas that require improvement. The remaining sections of this work are structured as follows: Section 2 explores the relevant research; Section 3 discusses the methodology utilized to attain the proposed objectives; Section 4 highlights the experimental design and findings; Section 5 tests the proposed framework using the testing dataset; Section 6 presents the results and discussion; and finally, Section 7 offers this work's conclusions and future research direction.
## 6 Discussion Section
### Safety and Security Challenges  
As we have mentioned above, the main research aim is to design of safe roadside environment (i.e., to ensure driver safety, pedestrian safety, and neighboring vehicle safety). Safety and security properties to be taken into account in our work addressing sensitive data can be summarized as follows.  
1. Confidentiality, which is the property through which data is disclosed only as intended by the data owner;
2. Integrity, which is the property guaranteeing that critical assets are not altered in disagreement with the owner's wishes;
3. Availability, which is the property according to which critical assets will be accessible when needed for authorized use;
4. Accountability, which is the property according to which actions affecting critical assets can be traced to the actor or automated component responsible for the action.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Performance Metrics**} & \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**Proposed and Existing Approaches**} \\ \cline{3-5}  & & **DRN** & **CNN** & **MODAL-IoCV** \\ \hline Accuracy (\%) & \#of vehicles & \(71.7\pm 4\) & \(78.6\pm 3\) & \(90.5\pm 2\) \\  & \#of edges & \(72.6\pm 3\) & \(78.5\pm 2\) & \(90.1\pm 1\) \\ Latency (Sec) & \#of vehicles & \(0.39\pm 0.4\) & \(0.29\pm 0.3\) & \(0.16\pm 0.2\) \\  & \#of edges & \(80.5\pm 3\) & \(66.1\pm 2\) & \(44.6\pm 1\) \\ Motion prediciton error (\%) & \#of vehicles & \(22.8\pm 4\) & \(18.5\pm 3\) & \(11\pm 1\) \\  & \#of edges & \(25.6\pm 3\) & \(15.5\pm 2\) & \(9.5\pm 1\) \\ False alarm rate & \#of vehicles & \(0.62\pm 0.4\) & \(0.50\pm 0.3\) & \(0.37\pm 0.1\) \\  & \#of edges & \(0.82\pm 0.4\) & \(0.65\pm 0.3\) & \(0.37\pm 0.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance analysis.  
All above security properties can be achieved when continuous monitoring of vehicle motion and lane change deviation and other multimodal features processing is important for accurate prediction driver behavior analysis. We set our aim in this study to reduce the overhead of the whole network by introducing a multiaccess edge environment. An end-to-end architecture for driver behavior analysis and solving roadside risks via personalized recommendation of assistance (and route planning) is therefore proposed.
# Research Trends and Future Direction for Utilization of Woody Biomass in Japan
## 6 Economic Evaluation of Woody Biomass Utilization
### Feasible Conditions for Woody Biomass Power Utilization  
The cost structure of a wood biomass power generation plant and the relevant factors of feasible conditions for wood biomass power generation are shown in Tables 6 and 7, respectively. Feedback, depreciation, and maintenance costs account for more than 90% of the total cost of wood biomass power generation. Additionally, nearly half of the total cost comprises feedstock costs, and the effective reduction of fuel costs is considered the key to improving the economic efficiency of wood biomass power generation. However, the economics of woody biomass power generation are influenced by many factors. Studies often keep certain variables constant and analyze the impact of changes in other variables on the viability of the project through scenario analysis when assessing the economics of woody biomass power generation. There are three main criteria for judging the feasible conditions for wood biomass power generation: the internal rate of return (IRR), payback period, and balance of income and expenditure. In general, eight percent of the estimated value published by the Japan Agency for Natural Resources and Energy was used for the IRR. In many cases, the payback period is based on the 15-year legal life of a power generation facility or the 20-year FIT project period [57; 58]. The evaluation of the balance between income and expenditure is relatively simple, and a business is judged viable if it does not generate  
Although many factors influence the feasible conditions of wood biomass power generation, many studies have indicated that wood biomass power generation has the characteristics of economies of scale. Paper 4 in Table 7 showed that, when using woody chips with a moisture content of 50% W.B. and a unit cost of 9000 JPY/t, small-scale power generation below 1600 kW is unprofitable [58]. Additionally, Paper 1 in Table 7, by calculating the break-even point for wood biomass power generation, showed that small-scale power generation requires the purchase of feedstock at lower prices to ensure economic viability [59]. In addition to economies of scale, combined heat and power (CHP) positively affect the economics of wood biomass power generation. Papers 3 and 4 in Table 7 examined the conditions under which CHP is feasible and showed that CHP greatly improves utilization efficiency and is more economical than only power generation at the same scale. However, as mentioned previously, large-scale heat utilization is difficult to fully utilize because of the large amount of heat produced. Paper 4 in Table 7 found that, if the produced heat cannot be completely sold, the effect of improved economic efficiency is not considerable. Moreover, in the case of Paper 3 in Table 7, half of the heat produced is disposed of [58,60].
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 1 Introduction  
Near-real-time (NRT) inundation extent mapping during flood events using remote sensing data is vital to support rescue and damage recovery decisions and to facilitate rapid assessment of property loss and damage. It also provides a two-dimensional reference for validating and calibrating real-time numerical modeling, including flood risk analysis at a regional to global scale [1; 2; 3]; flood event prediction at a small scale [4; 5; 6; 7]; and the tradeoff evaluation between the accuracy and complexity of hydrodynamic simulation [8; 9; 10; 11]. It is, moreover, a validation source for geomorphological analysis of flood vulnerability [12; 13; 14; 15; 16]. Retrieved inundation extent can also be converted to inundation depth [17; 18; 19; 20].  
Both optical sensors and passive and active microwave sensors can be used for inundation mapping, offering different levels of capacity, accuracy, and solution difficulty. By means of water indices, optical remote sensing data can be used to extract water surfaces straightforwardly and reliably. As long-term, fine-resolution satellite data (such as Landsat data) are accumulated and new satellites(such as Sentinel-2) are started to serve, optical remote sensing methods are contributing significantly to the delineation of global and regional water surfaces [21; 22; 23; 24]. The validity of optical observations is limited, however, by clouds and unclear weather conditions, which are common during flood events.  
Microwave remote sensing techniques, on the other hand, have good penetration through the atmosphere and therefore can provide more efficient measurement. Pixel water fraction estimated from passive microwave measurement--the brightness temperature--features low spatial (\(\sim\)25 km, \(\frac{1}{4}\) degree) but fine temporal resolution (daily) [25; 26]. The result is, therefore, usually downscaled up to 90 meters by topography [27] and/or optical sensor-derived water probability [28]. Such downscaling mechanisms may be valid for fluvial inundation at global and regional scale but can be problematic for small pluvial flooding at local scale.  
By detecting the scattering mechanism, synthetic aperture radar (SAR) can be used to map inundation under almost any weather condition at fine to very fine spatial resolution (from 30 m to \(<\)1 m). As an active sensor, SAR works noturnally as well. The retrieval algorithms using SAR data are more difficult to design than the sensors discussed above, and purely automated algorithms that require zero human interference are so far rare. With the growing availability of remote sensing data and the development of retrieval techniques, the automation and reliability of SAR data are expected to emerge soon.  
This study focuses on evaluating existing inundation mapping algorithms, using SAR data developed from 1980 to the present. Given that the data, study areas, and validation methods vary among studies, we primarily evaluate algorithms, along with some recently emergent approaches, according to four criteria: automation, robustness, applicability, and accuracy.
## 4 Conclusions  
This paper focuses on the response relationship between initial runoff volume control and runoff pollution control in sponge city construction, proposes a dimensionless constant Dc to evaluate the effect of runoff pollution control, and quantitatively evaluates the relationship between volume control and runoff pollution control through the Dc curve. The following are the key findings:  
The dimensionless constant Dc could be used to quantify the control impacts of various facilities on stormwater runoff pollution, as well as to directly represent the relationship between sponge facilities and runoff pollution control. It may be used to optimize the selection of sponge facilities at the planning and design stage by comparing the control impacts of source and terminal facilities on runoff pollution. When comparing the performance of the same facility in different scenarios, it can also offer a foundation for the operation and maintenance of sponge facilities at the operation management stage.  
Demonstrate that rainfall and runoff have a nonlinear relationship. The removal of contaminants transported in runoff is the most important aspect of runoff pollution control. The Dc value of source facilities is generally higher than that of terminal control facilities, demonstrating that source facilities reduce stormwater runoff pollution more effectively than terminal control facilities.  
Figure 8: Comparison of runoff pollution control effects between source facilities and terminal facilities under different underlying surface types.  
In order to accomplish initial runoff volume management and runoff pollution control, the fraction of sponge facilities with infiltration functions should be raised as much as possible. The facility with infiltration function has a greater capacity to control runoff pollution than the facility with regulating and storage function under the parameters of this article. The combination can achieve a higher Dc value when the percentage of facilities with a higher Dc value of a single facility is higher, suggesting a stronger runoff pollution control effect.  
The impact of various underlying surfaces on runoff pollution control should be fully considered when constructing rainwater facilities. Under the same catchment zone, the green space accounts for a large proportion, the Dc value of the source facilities and terminal facilities decreases, and the control rate of runoff pollutants decreases. Simulation calculations should be carried out according to the types of different underlying surfaces and different source facilities in order to obtain the best runoff pollution control effect.
# A Web of Things-Based Emerging Sensor Network Architecture for Smart Control Systems
## 5 Performance Evaluation
### Energy Consumption Analysis of the Household Appliances  
In this section, four different home users are interacting with various household appliances. They are programmed to randomly switch off and on a household device randomly. In order to be more realistic, the smart home user does not switch off most of the household appliances. Therefore, the user can check its status on the Web and can send a switch off request to the particular household appliance. A drop-down box with several records such as appliance status, energy consumption per day and month, and the number of times a device is switched off and on during a day is provided with each appliance. Those records help a user to tune the energy consumption of an appliance to optimal conditions. The proposed architecture is tested against a pure WSN. In the pure WSN scenario, the household appliances are directly controlled by the sensors. Whenever a user is not available in a scenario, the sensors automatically switch off devices. However, such systems do not perform actions based on the user context. Therefore, a pure WSN requires proper tuning of sensors for each new appliance. Such practices are only possible for controlling lights and home temperature. However, these WSN services cannot be integrated into real time scenario where the smart homes highly depend on user activities. In other words, the traditional WSN services cannot guarantee efficient services in the case ESN. The energy consumption of four different appliances, i.e., a television, fan, air-conditioner and refrigerator is shown in Figure 5(a)-d, respectively. In the case of proposed scheme the household appliances are controlled from the Web following a user context. However, in the case of pure WSN, the appliances are switched off and on considering the appliances' operating time. The results show significant improvement in the context of energy consumption in the case of the proposed scheme. At average 15% to 20% energy is saved with the proposed system.  
Figure 6: Energy consumption of various household appliances.  
A home user normally requires approximately 110 to 120 liters of water per day. Therefore, controlling the consumption of water optimizes the water usage per day. However, the traditional WSN architecture does not care about the user context and, therefore, a high amount of water is consumed due to the inappropriate usage. Similarly, the gas consumption is measured only for the burner in the kitchen. The rest of the gas consumption used for heating, etc. is not tested in this scenario. Initially, the home user used the kitchen for more time. However, with the passage of time the usage gets reduced. Thus, the energy consumption is significantly reduced following the proposed semi-automated model.
## 4 Results and Discussions
### The Influence of the Reservoir Region Condition on Nozzle Startup
When the initial pressure is greater than the critical pressure, with an increase in the initial pressure, the center startup duration and the instability duration increase proportionately with the pressure ratio to the power of 0.5, but there is still a difference. When the residence pressure is low, the growth is slightly faster, as shown in Figure 14.  
The low-velocity zone makes the complete startup of the nozzle lag behind the central startup, and the lag time depends on the size of the low-velocity zone. Generally speaking, the higher the initial pressure, the larger the low-velocity zone. The analysis of more calculation results shows that the complete startup duration and the instability duration of the nozzle starting process are approximately proportional to the pressure ratio of 0.5 power, and there is no critical pressure. Moreso, the higher the initial pressure, the longer the startup duration. Only at a very low initial pressure, the startup duration changes gently. When the reservoir region pressure increases, the velocity of the shock wave in the nozzle is accelerated, which is conducive to the rapid starting of the nozzle; however, the low-velocity zone has a greater impact on the starting, so the complete startup and the instability duration only decreases slightly. The influence of the total temperature on the complete starting is not significant in the calculation range. For the complete starting of the nozzle, the size of the low-velocity zone is the biggest influence factor, as shown in Figure 15a,b. Figure 14: The center startup time and instability time of the nozzle variation with the dimensionless pressure (\(T_{5}=6000\) K) (The dimensionless pressure is equal to the ratio of the initial pressure to the pressure at the outlet of the nozzle when the flow field is stable).
## 3 Controller Design and Genetic Algorithm Optimization Approach  
Based on the DAB mathematical model presented in the previous section, this section provides a brief introduction to PI control based on the pole placement method, SMC and MPC. Additionally, it outlines the optimization approach of using GA for controlling parameter optimization.