esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 3 Material & Methods
### Dataset  
A basic intrusion detection system's internet traffic records are contained in the dataset known as NSL-KDD [49]. These recordings are the phantoms of the traffic that a real IDS would have seen, leaving only traces of its former existence. There are 41 attributes linked to the traffic input for each record in the dataset, along with one label indicating the type of assault, for example, denial of service, often known as \"DoS\", where the four sorts of attacks that make up the target variable \"Class\" are probe (which stands for surveillance or a similar type of probing), U2R, U2R, and R2L. The KDD dataset's high volume of redundant records is one of its most severe weaknesses, since it pushes learning algorithms to prioritize learning frequent records over learning less frequent archives, which are frequently very detrimental to systems such as R2L and U2R assaults.  
Figure 3: Overview of the proposed approach.  
The data in Figure 4 provide a breakdown of traffic records, both legitimate and malicious. The majority of the records, at 53.4%, correspond to regular, non-malicious traffic, while only a small percentage correspond to specific types of attacks. For example, probe attacks account for just 9.25% of the records, R2L attacks make up only 0.79%, and U2R attacks are the least common, at 0.041%. Normal traffic refers to legitimate network traffic that includes data packets transmitted between network nodes for regular communication purposes such as web browsing, email, and file transfer. Conversely, DoS attacks are malicious attempts to disrupt the normal functioning of a network or website by overwhelming it with a flood of traffic or requests, making it unavailable to its intended users. Probe attacks are focused on gathering information about a target system by sending a series of probes or requests to identify open ports, operating system information, and other details that can be used in further attacks. R2L attacks, also known as \"remote-to-local\" attacks, involve an attacker attempting to gain unauthorized access to a local system from a remote location by targeting vulnerabilities in remote access protocols or software applications. Finally, U2R attacks are attempts by an attacker to gain elevated privileges or \"root\" access on a target system by exploiting vulnerabilities in software applications or operating system components.
## 1 Introduction
We firstly selected five PM\({}_{2.5}\) time series from 18 available datasets and separated those series into two groups, namely, G1 with a mild concentration and G2 with a severe one. In terms of seasonal statistics and initial examination, G1 shows less seasonality while G2 shows more seasonality (see Figure 3). In order to quantitatively examine the impact of seasonal outliers attributable to China in terms of PM\({}_{2.5}\) mass concentrations in the Korean peninsula, we applied the conventional DFA method to the series belonging to different groups, i.e., G1 and G2. In DFA analysis, a crossover behavior can be observed depending on the magnitude of outliers [19]. In our study, although the effect of China's air pollutant influx is usually nationwide, each monitoring site has unique features in geographical, topographical, and meteorological conditions. Thus, idiosyncratic behaviors appear according to monitoring stations. Since the crossover in DFA analysis means that there are different dynamics working over different scales, we confirmed the heterogeneity in auto- and cross-correlation structures between G1 and G2 by checking the DFA analysis results. Also, periodic trends in a time series can lead to crossover behavior in the Hurst exponent based on the DFA analysis [18]. Thus, in order to reproduce the auto-correlation features produced from real PM\({}_{2.5}\) time series, we present a stochastic model, namely, the ARFIMA process with periodic trends, and we succeed in qualitatively explaining the spatial variations on the crossover behavior of DFA exponents by adjusting the amplitude of a periodic trend. We also conducted DCCA analysis on both groups in order to confirm the heterogeneity in their dynamics, and we discovered that two series belonging to the same group showed increasing coupling or synchronization, while two series belonging to different groups showed decoupling behavior. The organization of this paper is as follows: in Section 2, the authors give a close description of the dataset and present several seasonal and spatial statistics of PM\({}_{2.5}\) mass concentrations. This section also provides a detailed description of the methodology applied to this study. Section 3 derives and interprets various analytical results using DFA, MFDFA, and DCCA methods. Finally, Section 4 gives the discussion and conclusion of this study.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
###### Abstract  
In space, high-energy particle radiation poses a serious threat to the data stability and reliability of SRAM. Existing radiation-tolerant techniques, such as Triple Modular Redundancy (TMR) and Error Correction Code (ECC), have disadvantages such as large area, high power consumption, and additional delay, making them unsuitable for small satellite systems. To overcome these limitations, this paper proposes a 16-transistor-based radiation-tolerant SRAM cell, WRTU-16T, which applies a read-decoupled structure and a charge-sharing suppression mechanism. The proposed structure effectively isolates the storage node from external disturbances and improves the recovery capability for single-event inversion (SEU) and multiple-node inversion (SEMNU) by reducing charge loss. WRTU-16T shows superior performance in terms of write delay, charge recovery capability (Qc), hold power, and word line write threshold voltage (WWTV) compared to existing radiation-tolerant SRAM designs. The integrated circuit is implemented using a 90 nm CMOS process and has an operating voltage of 1V.  
CMOS; critical charge; integrated circuit; radiation robustness; single-event upset; static random access memory +
Footnote †: journal: Applied Sciences  
1
# Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication
## 4 The D2D Loosely Coupled Model
### Adding a User to the Network  
To join a D2D network, a new user (NU) starts by sending a PSCCH message to discover his neighborhood. Based on the PC1 interface, the ProSe Application Server should provide the NU with a global description of the participating UE including an identification of interconnection links. Based on this information, the NU should be able  
Figure 6: Overview of some D2D underlay network topology models: (**a**) loosely coupled, (**b**) fully coupled, and (**c**) tightly coupled.
## 2 Literature Review
### Machine Learning Methods  
With the advancement in computational power, machine learning methods have increasingly been applied in parking prediction, effectively addressing the shortcomings of statistical methods by handling complex nonlinear time series data. machine learning algorithms such as decision tree (DT) [18], least absolute shrinkage and selection operator (LASSO) [19], and K-nearest neighbors (KNN) [20] have been employed for parking prediction. However, most machine learning studies fail to consider the impact of contextual features in parking data [2]. In a study by [PERSON] et al. [21] a combination of long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) methods was used to predict the occupancy status of parking lots.  
Similarly, [PERSON] et al. [22] proposed a majority voting ensemble model that combines the outputs of seven different forecasting methods for parking prediction. On the other hand, [PERSON] et al. [23] conducted a further analysis of parking space availability using a combination of four regression methods (bagging, random forest, adaptive boosting, and gradient boosting). [PERSON] et al. [24] designed a model based on the LASSO regression analysis machine learning method, improving prediction accuracy by performing variable selection and regularization to capture the nonlinear characteristics of parking events. [PERSON] et al. [25] highlighted the differences between models using LSTM and bidirectional LSTM, feature correlation (baseline, weather, traffic, etc.), and the impact of seasonality on predictions through feature correlation analysis using interpretable artificial intelligence techniques based on gradients and integrated gradients. [PERSON] et al. [26] proposed the PewLSTM model, which leverages a periodic weather-aware LSTM module to forecast parking behavior. [PERSON] et al. [27] developed a parking availability prediction model by incorporating wavelet transform (WT) and bidirectional long short-term memory (Bi-LSTM). The model utilizes WT for denoising historical parking data and then employs a Bi-LSTM network to learn the time correlation of parking data.  
However, the aforementioned studies mostly focus on the temporal correlation of parking data, neglecting the spatial correlation between parking lots. [PERSON] et al. [28] proposed a bi-convolutional long short-term network with dense convolutional layers that uses a CNN to extract spatial features of parking lots by partitioning grids. However, parking lots often exhibit non-Euclidean structures (as shown in Figure 1), making them less suitable for modeling spatial dependencies using a CNN.
## 3 Proposed ESN-MPC Strategy for PMSM
### Computational Complexity Analyze  
In conventional FCS-MPC strategies, the computational burden primarily depends on the complexity of the system model, prediction horizon length \(S\), the number of candidate switching states, and the cost function. In this paper, the inverter has eight candidate switching states. The computational complexities attributable to the prediction model and the cost function evaluation are denoted as \(C_{\text{P}}\) and \(C_{\text{CF}}\), respectively (in this paper, \(C_{\text{P}}\) = 30 and \(C_{\text{CF}}\) = 4). Thus, at each control cycle, the computational burden of conventional FCS-MPC with prediction horizon \(S\) can be expressed as  
\[\text{FLOPs}_{\text{MPC}}=8^{S}.(C_{\text{P}}+C_{\text{CF}}) \tag{29}\]  
For the proposed ESN-MPC controller, the computational complexity consists of two main parts. The first is associated with reproducing the mathematical relationship through the trained ESN model. The complexity of the ESN primarily depends upon the network structure (layer number and neuron quantities) and the activation function complexity, denoted \(C_{\text{tanh}}\) (\(C_{\text{tanh}}\) = 4 in this paper). This article defines the size of the input layer, reservoir, and output layer as \(\text{N}_{\text{I}}\), \(\text{N}_{\text{H}}\), and \(\text{N}_{\text{O}}\), respectively (\(\text{N}_{\text{I}}\) = 4, \(\text{N}_{\text{O}}\) = 2 in this paper). Moreover, in practical implementation, the number of candidate switching states can easily be pre-selected and reduced to 3 according to the error between reference and current value. In this way, the computational complexity of the ESN can considerably simplify and expressed as  
\[\text{FLOPs}_{\text{ESN}}=3\cdot(2\cdot(N_{\text{I}}N_{\text{H}}+N_{\text{H}}N _{\text{H}}+(N_{\text{H}}+N_{\text{I}})N_{\text{O}})+N_{\text{H}}+C_{\text{ tanh}}N_{\text{H}}) \tag{30}\]  
Activating the online learning algorithm introduces an additional computation burden, related primarily to the network reservoir size \(\text{N}_{\text{H}}\). While increasing the reservoir size may enhance modeling and prediction accuracy, it will increase the complexity, potentially hindering real-time applications. Hence, properly determining the reservoir size is crucial for effectively balancing computational efficiency and prediction performance. Table 1 compared the estimated computational complexity during the implementation of conventional FCS-MPC and the proposed ESN-MPC. Considering a comprehensive trade-off between simplicity and prediction accuracy, the reservoir size is eventually set to \(\text{N}_{\text{H}}\) = 15 in the succeeding tests. In this reservoir size, the proposed ESN-MPC can achieve a modeling accuracy of approximately 90.2%. The following section will verify the control performance of the proposed strategy and parameter robustness under changing load and reference tracking conditions.  
Figure 4: Control and online learning process of proposed ESN-MPC.
## 4 Experiment Results and Analysis  
In order to verify the feasibility of this method, the vertical axis integrated blade turntable is used in this experiment, as shown in Figure 21. The speed of the blade is controlled by a servo motor. The data collector collects the voltage changes perceived by the blade tip when it passes through the sensor and processes them to obtain BTC. When the integrated blade with a blade length of 40 mm and a disk radius of 40 mm works at a speed of 3000 rpm, four BTC sensors and a speed sensor are used for monitoring. The installation angles of the four BTC sensors are \(0^{\circ}\), \(15^{\circ}\), \(29^{\circ}\), \(43^{\circ}\). The BTC values measured by each BTC sensor are shown in Table 16. Using the parameter identification method introduced in this paper, the results are shown in Table 17 and Figure 22.  
Figure 19: Constant bias error.  
Figure 20: Number of times Ne was accurately identified.  
The above data show that the method can identify the synchronous vibration parameters of the constant-speed rotating linear blade, and the identified vibration parameters are within a reasonable range. When using this method, we need to obtain the deformation of the blade in advance. Due to the limitations of experimental equipment, we cannot obtain the actual deformation of the blade. Therefore, we obtain the deformation of the blade by calculation. If the actual deformation of the blade can be obtained, the method proposed in this paper can obtain more accurate identification results. Therefore, the vibration parameter identification method proposed in this paper is feasible in the synchronous vibration parameter identification of a blade with constant speed.
## 3 Results and Discussion
### \({}^{1}\)H-NMR Measurement  
Generally, the spectral area obtained by \({}^{1}\)H-NMR measurement is proportional to the number of protons, which is used for qualitative analysis. We measured \({}^{1}\)H-NMR to comprehensively analyze the components and contents of each rice wine (Figure 2). Signals around 1 ppm were attributed to branched-chain amino acids, around 3-5 ppm to carbohydrates, amino acids, and organic acids, and around 7-8 ppm to aromatics. In the 7-8 ppm range, signals were observed at A: 8.03, 8.06, and 8.51 ppm; B: 7.73, 7.95, 8.06, and 8.51 ppm; and C: 7.73, 7.83, and 8.51 ppm, confirming the presence of various aromatic compounds in these samples. The signal area at 2.45 ppm was taken to be 1, and the integral ratios at each chemical shift for A, B, and C around 1 ppm were 75.62, 29.60, and 42.40, respectively; at 3-5 ppm, they were 473.57, 440.2, and 437.92, respectively; and at 7-8 ppm, they were 1.02, 0.69, and 0.83, respectively. Although fragmentary, the NMR spectra revealed differences in the contents of carbohydrates, amino acids, organic acids, and aromatic compounds in the samples.  
Figure 1: Viscosity depends on temperature increase (**X**). The logarithmic value of viscosity (**Y**) on the vertical axis and the temperature on the horizontal axis of Samples A, B, and C. (**a**) A, (**b**) B, (**c**) C, and (**d**) A, B, C systems.
## 3 Result and Discussion
### Power Capacity Measurement
#### 3.5.3 The Energy Capacity  
Sensor-node energy capacity was obtained according to the sensor node's power-consumption calculation and time-consumption measurement data. The energy capacity was calculated in one-cycle sensor-node operation with the interval between cycles set to 10 s. The sensor-node energy capacity is presented in Table 6. The sensor node consumes the highest power of 392.12 mW in CO\({}_{2}\) transmitting process and the lowest power of 106.90 mW in the sensor-node sleep stage. However, the highest energy capacity sensor node is in the sleep stage, which requires 0.2970 mWh (66.94%), as described in Figure 15. This is because the sleep stage was set with the longest duration among other processes, with a duration of 10 s. From Table 6, the total energy capacity of the sensor node is 0.4436 mWh for one-cycle sensor-node operation, including measurement time interval. These results indicate that a supercapacitor has the potential to be used as power storage for energy harvester in further study.  
Figure 14: The time consumption of sensor node process.  
Figure 15: The percentage of energy capacity requirement.
## 4 Experiments and Results Discussion  
### Experiment Settings  
We conducted a secondary development based on the Ethereum source code of gotheteum-1.9.25, and established a prototype of the OSCMS in the environment of Go 1.14.2. Based on the modified Ethereum code and solidity 0.4.17, we implemented a consensus and all the functions introduced in this paper, and simulated an oracle service with node.js 10.19.0 to monitor and respond to requests from smart contracts. The prototype and experiments ran on four Ubuntu 20.04.1 (64-bit) virtual machines, an Intel Xeon 5117 @2.0 GHz processor with 14 cores and 32 GB RAM. Each node independently operated the protocol of Ethereum and our system on Docker.  
Our experiments explored the feasibility, scalability, reliability and performance superiority of the OSCMS under different parameters, architectures and consensus algorithms. The consumption and steps of different transactions are different. For example, the sub  
Figure 8: Mutual confirmation among three blockchains during epoch switching.  
mission of OI is divided into two steps. After the submission transaction is processed and exists in a valid block, the oracle will submit the verification result in a later block through the callback function, thus updating the OI status. Other transactions involving oracle verification have similar two steps. Our simulation oracle service calls the callback function around 3 s after listening to related events in the network. The speed of real oracle is affected by many factors, which are beyond the scope of our experiments. We select representative submission transactions or query transactions of each blockchain according to a certain ratio. The settings of some conditions and parameters are shown in Table 1.
# Laser Finishing of Ti6 Al4V Additive Manufactured Parts by Electron Beam Melting
## 2 Materials, Equipment, and Methods
### Laser Finishing Setup  
A 450 W fiber laser (IPG YLR-450/4500-QCW-MM-AC-Y14) was used to finish the surface of the samples. The system can operate either in continuous wave (CW) or in a modulated wave known as \"quasi continuous wave\" (QCW). The laser radiation is transferred via 50 \(\upmu\)m core fiber, with a length of 5 m, to a collimator with a lens of 85 mm, mounted onto a micro cutting head (IPG FLC D 25) with a focusing lens of 100 mm. Thus, a beam diameter of about 60 \(\upmu\)m is achieved. The main laser source characteristics are reported in Table 2.  
During all tests, the CW mode was adopted in order to achieve a smoother surface. Nitrogen gas with a flow rate of 12 l/min at 2 bar was adopted to shield the samples, thus avoiding oxidation. The samples were rotated using a stepping motor (SANYODENKI 103F/126) at 200 rpm, while the laser was switched on outside the sample. The laser beam was then moved onto the samples at different speeds, with a length of about 50 mm, as reported in Figure 1. After the laser treatment, the samples were sandblasted with pink corundum (mesh size 120) at a pressure of 4 bar, with the aim to remove any incoherent material, and then cleaned in an ultrasonic bath with acetone to remove any traces of sand.
# ICN-Based Enhanced Content Delivery for CDN
## 1 Introduction
* We quantify the optimization goals of content delivery network architecture by formulating a mathematical model derived from the Multi-Commodity Flow (MCF) problem, which jointly considers the QoS demand and bandwidth capacity constraint. * Based on the optimization model, we use a real-world network topology to gauge the performance improvements of our proposed design by conducting a series of experiments. The results demonstrate our design's benefits in terms of reduced delivery latency, decreased network bandwidth consumption and better resilience to traffic surge. The rest of this paper is organized as follows. In Section 2, we first introduce the background of the ICN cache and content discovery mechanism, and then review the related research, which jointly studies ICN and CDN. In Section 3, we present our design of the ICN-enhanced CDN architecture, called IECDN. In Section 4, we describe the formulation of the optimization model in detail. Then we conduct a series of experiments and analyze the results in Section 5. Finally, the conclusion is drawn in Section 6.
# On the Optimal Size of Candidate Feature Set in Random forest
## Motivation  
### Random Forest  
Given a training dataset, Random forest uses bootstrapped samples at each stage of ensemble. The main difference from Bagging is that it uses different random subset of \(m\) features (predictors) instead of using all \(p\) features at each node of trees. The best partitioning rules for nodes are selected only from the candidate feature set. Using only a subset of features rather than all, Random forest generally produces many diverse and un-correlated trees. Averaging many un-correlated trees has resulted in a significant reduction in classification variance, thus leads to boost the classification accuracy [9]. It has been shown that the predictive accuracy of Random forest is significantly better on average than that of Bagging [14]. Random forest is a popular method in various fields because it is fast both in training and prediction, robust to label noise and outliers, has an inherent multi-class capability, suitable for parallel processing, and has good performance for high dimensional data. Algorithm 1 provides the algorithm of Random forest. For more details, refer to [PERSON] [10].  
```
Training Phase: Given: - D: training set with \(n\) instances, \(p\) variables and target variable. - K: number of classes of target variable. - B: number of classifiers in Random forest. Procedure: For \(b=1\) to \(B\)  1. Generate bootstrapped sample \(D_{b}^{*}\) from the training set \(D\).  2. Grow a tree using a candidate feature set from bootstrapped sample \(D_{b}^{*}\).  For a given node \(t\),  (i) randomly select \(m\) features. Typical choices are \(m\approx\sqrt{p}\) in classification or \(m\approx p/3\)  in regression.  (ii) find the best split features and values using the candidate feature set.  (iii) split a node using the best split features and values.  Repeat (i)-(iii) until stopping rules are met.  3. Construct trained classifiers \(C_{b}\). Test Phase:  Aggregate the \(B\) trained classifiers using simple majority vote.  For a test instance \(x\), the predicted class label from classifiers \(C_{b}\) is: \(C_{B}(x)=\operatorname{argmax}_{j}\sum_{b=1}^{B}I(C_{b}(x)=j)\), for \(j=1,\ldots,K\)
```  
**Algorithm 1**The algorithm of Random forest