esa-sceva/satcom-chunk-collection huggingface dataset:
# A Full-Duplex 60 GHz Transceiver with Digital Self-Interference Cancellation
## 4 System Measurement
### HD Link Measurement  
The half-duplex 802.11 ad modulation is measured at a 1.5 m communication distance for all four channels and is shown in Figure 9. It is defined based on the measured maximum output power, with a 6 dB peak-to-average power ratio (PAPR) linear protection range for the modulated signals. In this HD measurement, the transmission powers for MCS9 and MCS12 are set at 3 dB and 6 dB, respectively, lower than the maximum output power to have good linearity of the modulated signal. As shown in Figure 9, the measured EVM is very close to \(-21\) dB for all channels, which is the basic EVM requirement for MCS12 of the IEEE 802.11 ad standard. In the constellation diagram, the measured average EVM across the four channels for 1000 symbols under MCS12 are \(-21.7\) dB, \(-21.5\) dB, \(-20.5\) dB, and \(-21.3\) dB, respectively, for the 16-QAM-modulated data symbols, and \(-22.4\) dB, \(-22.1\) dB, \(-21.4\) dB, and \(-22.4\) dB, respectively, for pilot symbols modulated using \(\pi/2\)-BPSK. The proposed transceiver fulfills the EVM requirement of MCS12 for channels 1, 2, and 4 and the 0.5 dB margin for channel 3. The QPSK-modulated signal is plotted in the same figure, which is better than the EVM specification requirements of \(-16\) dB defined in MCS9.1.  
MCS12.3 to 12.6 were added to the WiFi standard in 2016 to support a higher data rate by introducing 64-QAM support in a single carry modulation. Figure 10 shows the 64-QAM-modulated signal with a symbol rate of 1.5 GSym/s and a raw data rate of 9 Gbps. Figure 10a shows the raw data demodulation without a frame header, and Figure 10b is the constellation plot for an MCS12.6 frame package. The reported EVM value is 23.16 dB for the raw data. Other than the frame-based measurement for the 802.11 ad standard, the raw data in Figure 10a without a package header may degrade the measured EVM value because of poor synchronization and equalization. Figure 10b captured a frame package with a \(\pi/2\)-BPSK-modulated header and guide symbols. Figure 10b shows symbol gathering better, especially for high-power symbols located at the constellation diagram corner. However, the EVM result here is 21.88 dB, as the VSE software cannot separate the guide symbols from the data symbols correctly in the current release.  
Figure 9: The measured link EVM for the IEEE 802.11 ad standards.  
The power consumption values of the transmitter and the receiver are 194.1 mW and 231.4 mW, respectively. Here, the power consumption is measured at a 1.5 m communication distance using the MCS12 of the 802.11 ad standard. The communication distance can be further increased for a constant amplitude modulation, such as BPSK and QPSK, with the PA's saturated output. The maximum transmitter power consumption is 312.7 mW when using a 2.4 V power supply.
## 3 Validation
### Hybrid Ceramic Bearing RUL Prediction Results
The hyperparameters for each built network was done by employing a grid search and evaluating candidate hyperparameters on the MSE using a 10 fold cross validation on the training set. Cross validation is a widely used method to avoid overfitting when selecting hyperparameters [39]. The chosen hyperparameters were obtained by those that minimized the MSE during cross validation. For all of these networks, the activation function was set as the rectified linear unit (ReLU) function. It solves the vanishing gradient problem that other non-linear activation functions can cause [40]. The ReLU activation function for the input \(x\) of a neuron is defined as:  
Footnote 2: [[https://github.com/tensorflow/tensorflow/](https://github.com/tensorflow/tensorflow/)]([https://github.com/tensorflow/tensorflow/](https://github.com/tensorflow/tensorflow/))  
\[ReLU(x)=max\{0,x\} \tag{36}\]  
Since the particle filter is the most competitive RUL prediction method for bearings, for a comparison purpose, the RMSE and MAPE of the RUL predictions obtained by the particle filter-based approach are also provided in Table 3. In comparison with the results obtained using the particle filter-based approach, the RMSE and MAPE values of the integrated approach were slightly lower and the \(\alpha-\lambda\) metric values were higher. Note that since in [28], it was reported that using the same bearing data, the particle filter gave a better RUL prediction accuracy than a DBN based approach did, we consider the integrated method presented here as better than the DBN-based approach used on the same dataset. The comparison results showed the promising performance of combining the deep learning based approach with particle filter for hybrid ceramic bearing RUL prediction. Given that the integrated approach did not require explicit model equations like the particle filter-based approach and is scalable for big data applications, the RUL prediction performance achieved by the integrated approach has shown great potential for bearing RUL prediction with big data.
## 2 MECS Structure Description  
WPPs and PVs (or HSs) are significantly complementary. The output of WPPs is low in the daytime, whereas the output of PVs is high. The output of WPPs is high at night-time (or in rainy weather), whereas the output of PVs is low. Similarly, the output of WPPs is lowest in the summer and highest in the winter and spring, and the output of HSs is highest in summer and lowest in winter [21]. Therefore, the complementary power generation (CPG) of WPPs and PVs ensures a continuous power supply, whereas the CPG of WPPs and HSs could exploit more wind power and improve the HS peaking capacity. Moreover, TPPs could handle the output fluctuations of WPPs and PVs. The bundling operation of WPPs, PVs, and TPPs could smooth out the output curve. As the short-term operation characteristics of WPPs, PVs, HSs, and TPPs exhibit great differences and a strong complementarity, a sensible joint operation scheduling could result in significant comprehensive benefits.  
In this study, a MECS is proposed by integrating WPPs, PVs, TPPs, HSs, and ESDs. For analyzing the complementary of different power sources, the paper assumes all the power sources are independent. Then, the MECS is controlled by an independent operation center (IOC) based on the prediction of weather conditions, which is priority-scheduled to meet terminal customer load demand. The surplus output of the MECS can be transferred to other regions to earn more economic revenue. Moreover, when the available output of MECS cannot meet the load demand, the IOC can purchase power from a generation company (GC). Figure 1 shows the basic structure of the MECS operation.  
In the MECS, the WPP and the PV are uncontrolled power sources, whereas the HS, the TPP, and the ESD are controlled power sources. The ESD charges when the load demand is lower than the available power output; otherwise, the ESD discharges (when the load demand is higher than the actual power output). In this study, the maximum benefits, the minimum abandoned energy, and the minimum output fluctuations of the MECS operation are taken as the objective functions for obtaining comprehensive scheduling optimization.
# Two-Phase Deep Learning-Based EDoS Detection System
## 3 Background Knowledge
### Basics of Long Short-Term Memory  
LSTM, which is a variant form of an RNN [23], is known to be a solution to overcoming the major disadvantage of an RNN, i.e., an inability to process long sequences of data or information. Thus, an LSTM is commonly used for anomaly detection problems such as network intrusion detection, because this problem requires a long sequence of network traffic information. An LSTM takes the form of a repeating cell chain. The cell contains four types of interactive neural networks that interact in a special way to enable the network to remember historical information. The LSTM protects and controls the state of the cells  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Bandwidth Depletion Attacks** & **Resources Depletion Attacks** \\ \hline ICMP Flooding attack & TCP SYN Flooding attack \\ UDP Flooding attack & PUSH+ACK attack \\ Database API request attack & Low and Slow rate attack \\ Fraggle attack & TCP-HTP Flooding attack \\ Smurf attack & \\ Yo-Yo attack & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Common types of EDoS attack.  
through the input gate, output gate, and forget gate. Figure 2 depicts the architecture of the LSTM. The figure below shows an LSTM cell.  
The internal calculation formula of the LSTM cell is defined as follows:  
\[f_{t}=\sigma(W_{f}\times[h_{t-1},x_{t}]+b_{f}) \tag{4}\]  
\[i_{t}=\sigma(W_{i}\times[h_{t-1},x_{t}]+b_{i}) \tag{5}\]  
\[\tilde{C}_{t}=\tanh(W_{C}\times[h_{t-1},x_{t}]+b_{C}) \tag{6}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\tilde{C}_{t} \tag{7}\]  
\[o_{t}=\sigma(W_{o}\times[h_{t-1},x_{t}]+b_{o}) \tag{8}\]  
\[h_{t}=o_{t}*\tanh(C_{t}) \tag{9}\]  
where \(i\), \(o\), \(f\) indicate the input gate, output gate, and forget gate, respectively. Here, W is a weight matrix, and b is the bias. In addition, \(\tilde{C}\) and \(C\) are the candidate state and new state, \(h\) is the output, \(x\) is the input, \(i\) is the input time, and \(\sigma\) denotes a sigmoid function. However, the disadvantage of an LSTM is that it requires long sequential input data. Because the LSTM model does not know where the most important part of the sequential input data is located, it requires a long sequence to extract the historical information and make a prediction or classify this input data. In this study, we combine an ANN and an LSTM to classify each flow in a traffic network. The ANN model acts as a period detector to detect when an attack occurs, from which the LSTM model, which plays the role of a flow detector, can know where the important part of the sequential information is located to reduce the sequential input data.
## 2 Materials and Methods
### Data Collection
#### 2.2.2 Meteorological Data  
Daily meteorological data including minimum temperature (Tmin), maximum temperature (Tmax), mean relative humidity (RH) and rainfall were collected from a weather logger at Site A from 2006 to 2016 and three meteorological stations belonging to the Thai Meteorological Department from 2010 to 2013. All daily data were calculated as average monthly minimum and maximum temperature, average monthly relative humidity, and accumulated monthly rainfall.
###### Abstract
Simulation results showed that the newly proposed MPTC had a faster torque response, fewer computations, less torque pulsation, and good dynamic and steady-state performance. The method did not require offline optimization like other MPTC algorithms. It simplified unnecessary calculations in prediction and increased the processing performance of the algorithm. The proposed method enabled decoupled and rapid control of motor torque and stator magnetic flux, which made it applicable to various PMSMs regardless of their specific parameters. The rest of the work is arranged as follows. Section 2 presents the model of the PMSM. Section 3 proposes a multi-objective sorting algorithm based on the traditional MPC. Section 4 builds a simulation model and compares the proposed algorithm with traditional MPC algorithms. The experimental results are presented in Section 5. The conclusions are given in Section 6.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 2 Background
### Ethereum Accounts  
The basic element of Ethereum is _accounts_, also called _account state_, and each account has four fields: _nounce_, _balance_, _storage_, and _code_. The nonce is a transaction counter, which means for every new transaction sent by this account, the nonce will be increased by one and attached in the transaction data structure; the balance is the amount of Ether (the currency used in the Ethereum platform) the account owns; the storage is memory space for code and its execution; the code is where the smart-contract code is stored [20].  
There are two kinds of accounts: _external accounts_ and _contract accounts_. The major difference between the two is whether the _code_ field is empty or not. The _external accounts_ are controlled by public-private key pairs (owned by human account holders) while the _contract accounts_ are controlled by their code [20; 21]. Both accounts are hashed and stored in a data structure called a modified Merkle Patricia tree [21], which has its root hashed and stored in every block. The _external accounts_ could initiate an action that alters the state of the EVM, which is called a _transaction_. Transactions are broadcast to the whole network. A miner will later pick and execute transactions and propagate the resulting state change to the rest of the network. The execution of transactions is not free because the execution of transactions and the accompanying state change that must be universally accepted requires computing resources that consume a lot of energy. The _gas_ is created for transaction execution and smart-contract interactions. Gas is units that the initiator needs to pay for the transaction executions; the gas limit is the maximum amount of gas that the initiator is willing to pay. The initiator also sets the _gas price_, which shows the amount of Ether that the initiator is willing to pay for each unit of gas. Usually, the higher the price a user is willing to pay, the higher chance the user's transactions are chosen to be executed by miners. A smart contract is a program that resides in a _contract account_ and controls the behaviour of the contract account.
## 2 Distance Measurement Module Development
### Vehicle Detection Module Data Flow  
The distance measurement module collects information on the driving vehicle by connecting to two LiDAR sensors that detect two measuring points in the lane. The collected raw information is encapsulated and transmitted to the DIAM. In addition, it should be possible to control and set the operation of the LiDAR sensor for the DIAM command signal. Therefore, the VDM control specifications for the distance measurement module are as follows:  
* use of three or more communication ports;
* 32 kbps or higher communication speed specification;
* circular queue that can store 100 data per communication port;
* communication port 2000 bytes per second interrupt processing capacity;
* sensor information parsing function;
* framing function for the DIAM transmission;
* sensor control function and status check function.  
Figure 4 shows the vehicle detection flow chart of the VDM. The light emitting diode (LED)/watchdog/LiDAR sensor periodically checks the hardware status of the VDM and communicates data processing to the UART. The data processing receives raw data and the vehicle detection status from periodic and beaglebone black communication (BB comm). Then, it transmits the data to the gate keeper in the form of a frame. In addition, the measured data from the LiDAR sensor is converted into distance information and stored as a queue. The BB comm controls each lane measurement LiDAR sensor to analyze information received from the DIGM using UART. The gate keeper sequentially transmits information to the DIAM from the distance information queue.  
Figure 4: Vehicle detection module (VDM) data flow diagram.
###### Abstract  
Corn was frozen at harvest time in high-latitude areas, when corn kernel is wetter and more easily broken. When frozen corn was threshed and separated by the longitudinal axial threshing cylinder of a combine harvester, it caused a significantly high kernel damage rate and loss rate. The process parameters of threshing cylinder were optimized using RSM (response surface method) and NSGA-II (Non-Dominated Sorted Genetic Algorithm-II). The drum speed (Ds), feed rate (Fr) and concave clearance (Cc) were determined as the optimized process parameters. The loss rate (Lr) and damage rate (Dr) were indicators of operational performance. The RSM was used to establish a mathematical model between process parameters and indicators. With an elite strategy, NSGA-II was used for multi-objective optimization to obtain the optimal operational performance of the threshing cylinder. Overall, when the drum speed was selected as 384.1 rpm, the feed rate as 8.6 kg/s and the concave clearance as 40.5 mm, according to the requirement of corn harvest, the best operational performance of the longitudinal axial threshing cylinder on frozen corn was obtained. The Lr was 1.98% and the Dr was 3.49%. This result indicated that the applicability of the optimal process parameters and the optimization method of combining NSGA-II and RSM was effective for determining the optimal process parameters. This will provide an optimization method for synchronously reducing the loss rate and damage rate of grain harvesters.  
f r ozen corn; threshing; multi-objective optimization; process parameters; RSM; NSGA-II 1
# V2X Communication over Cellular Networks: Capabilities and Challenges
## 4 Delay Analysis for V2X Communications
### 5G-NR Delay
The delay analysis below considers both control and user plane functions.
# A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data
## 3 Results and Discussion
### Performance Evaluation of Machine Learning Models for Cell Annotation of PBMC Datasets
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**PSMC Dataset**} & \multicolumn{2}{c}{**MS Dataset**} & \multicolumn{2}{c}{**Zhengh68K**} & \multicolumn{2}{c}{**Pancreatic Dataset**} \\ \cline{2-9}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline Decision Tree & 0.8712 & 0.8730 & 0.7038 & 0.5339 & 0.6958 & 0.6946 & 0.9834 & 0.9846 \\ \hline Random Forest & 0.8788 & 0.8686 & 0.7576 & 0.7373 & 0.7317 & 0.7177 & 0.9810 & 0.9803 \\ \hline Gradient Boosting & 0.9299 & 0.9289 & 0.8512 & 0.8419 & 0.7790 & 0.7729 & 0.9941 & 0.9969 \\ \hline KNN & 0.5152 & 0.4020 & 0.4458 & 0.4397 & 0.5959 & 0.5764 & 0.9921 & 0.9958 \\ \hline Naive Bayes & 0.5019 & 0.5117 & 0.5245 & 0.5339 & 0.2613 & 0.2441 & 0.8578 & 0.8456 \\ \hline SVM & 0.9583 & 0.9573 & 0.8823 & 0.8774 & 0.8476 & 0.8472 & 0.9953 & 0.9976 \\ \hline Logistic Regression & 0.9527 & 0.9515 & 0.8801 & 0.8744 & 0.8440 & 0.8415 & 0.9921 & 0.9934 \\ \hline Transformer & 0.9337 & 0.9299 & 0.8660 & 0.8610 & 0.8354 & 0.8330 & 0.9943 & 0.9958 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy and F1-score of models for various dataset. Figure 2: Cell annotation UMAP plots by various models for PBMC dataset. Figure 3: Confusion matrices for cell annotation of models for PBMC dataset.
## 6 Comparison
### Reliability  
Reliability is another factor where centralised and decentralised systems differ fundamentally in their approach. Centralised identity systems ensure reliability through well-defined failover mechanisms and high-performance servers. LDAP, for instance, achieves redundancy by employing multi-master replication, allowing backup servers to take over within seconds during outages, with a downtime of less than 1% annually in most enterprise environments [2]. However, such setups are vulnerable to single points of failure, with catastrophic failures reported in high-profile attacks, such as the Microsoft Azure AD outage in March 2021 that lasted several hours [62]. Decentralised systems like Hyperledger Indy offer enhanced reliability through fault-tolerant consensus mechanisms like RBFT, which can maintain network functionality even with up to 33% node failures [18]. Additionally, distributed networks reduce the risk of complete outages but may experience temporary synchronisation delays when nodes are added or updated.  
On the other hand, decentralised systems are built on distributed networks which makes them fault tolerant. Systems like Sovrin or Hyperledger Indy continue to work even if individual nodes fail, so the risk of the complete system going down is prevented. Also, RBFT used in some of these models increases reliability by ensuring consensus even in the presence of faulty or malicious nodes. Decentralised models are complex and may potentially have synchronisation delays but prove to be much more resilient.
# Compact Planar Super-Wideband Monopole Antenna with Four Notched Bands
###### Abstract  
A compact-sized planar super-wideband (SWB) monopole antenna with four notched bands is presented in this paper. The antenna consists of a rectangular ground plane and a circular radiator that is fed by a tapered microstrip feed line. The overall size of the antenna is 18 mm \(\times\) 12 mm \(\times\) 0.5 mm, and its impedance bandwidth (S\({}_{11}\)\(\leq\) 5-10 dB) ranges from 2.5 GHz to 40 GHz (bandwidth ratio of 16:1). Four notched bands are obtained using two inverted U-shaped slots, a split-ring resonator (SRR), and a meandered slot. The notched frequency bands can be adjustable by changing the parameters of parasitic slot elements, and the realized notched bands in this paper are Wi-MAX band (3.5 GHz), WLAN band (5.5 GHz), satellite communication X-band (7.5 GHz), and amateur radio band (10.5 GHz). The simulated and experimental results show good agreement with each other. The antenna possesses a high gain, super-wide impedance bandwidth, and omni-directional radiation patterns.  
compact; monopole; notched-band; planar; super-wideband +
Footnote †: journal: Electronics Electronics
# Investigations of Mixing Technique on the Rheological Properties of Self-Compacting Concrete
## 4 Conclusions  
Systematic experiments have been carried out to characterize the effects of mixing procedures, including charging sequence, mixing time, and mixing speed, on the rheological properties of self-compacting concrete (SCC). The slump-flow, yield stress, plastic viscosity, and shear thickening behavior have been analyzed and discussed in detail. As a result of this investigation, the following conclusions are derived.  
1. The shear thickening of SCC is reduced by first mixing the aggregates with water and then adding other raw materials. On the other hand, the direct contact between aggregates and water results in a large amount of free water to be adsorbed by the aggregate system and therefore increases the yield stress of SCC.
2. Short mixing time within 4 min leads to heterogeneous distribution of various components, resulting in high yield stress and plastic viscosity of the SCC mixtures. Prolonging the mixing time weakens the shear thickening of SCC. A critical mixing time of SCC from shear thickening to shear thinning is found to be 5.9 min. Over mixing results in a poor fluidity of SCC. The mixing time is recommended to be controlled at 4-5 min in order for favorable rheological properties of SCC.
3. Increasing the mixing speed weakens the shear thickening behavior of SCC. The yield stress and plastic viscosity of the SCC will be simultaneously reduced. In order to ensure an excellent  
Figure 10: Effect of mixing speed on the shear thickening behavior of SCC.  
fluidity and a low shear thickening behavior, the mixing speed of SCC should be controlled within the range of 30-45 r/min.  
Writing--original draft preparation and conceptualization, H.L.; formal analysis and data curation, F.H.; investigation and resources, Z.Y. ([PERSON]); visualization and validation, Z.W.; supervision, writing--review and editing, Y.Z.; methodology and project administration, Z.Y. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Plan of China (Grant No. 2017 YFB0310100), the National Natural Science Foundation of China (Grant No. U1934206 and 51578545), and the Technological Research and Development Programs of China Railways Corporation (Grant No. 2017G006-J, N2018G029 and J2017G001).  
The authors are grateful to the State Key Laboratory of High Speed Railway Track Technology and Fujian Provincial University Research Center for Advanced Civil Engineering Materials. Both provided technical support for the experimental work.  
The authors declare no conflict of interest.