esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis
### Experimental Results and Discussion
#### 4.4.6 Different Sentence Vector Strategies  
To investigate the effect of different sentence vector strategies on our proposed model, four commonly employed sentence vector strategies are set up in the experiment: CLS, All, Last, and First-Last. Specifically, CLS is adopted to represent the sentence-level information of the output in the pre-trained model, the average pooling of the hidden states is denoted by All, the mean pooling of the hidden states in the last layer is indicated by Last, and First-Last is applied to denote the average pooling of the first and last hidden states.  
The comparison results for different sentence vector strategies on CCKS2020 and CCKS2019 are illustrated in Figures 8 and 9, respectively. It can be concluded that the CLS strategy achieves the best results on both CCKS2020 and CCKS2019 datasets, and the F1 score of other strategies is decreased with different degrees, so the CLS strategy is adopted as the sentence vector representation of our proposed model.
## 8 Simulation Setup and Results
### Performance Analysis
#### 8.2.2 Test: 2 Packet Throughput, PDR, and E2E Delay under Black Hole and Zigzag Attacks  
In this part, first, we carried out a performance analysis between our proposed scheme TER and AODV. Under the malicious attacks, the performance of these schemes varies. From Figure 7, it is clear that TER outperforms original AODV schemes. At start of simulation our scheme achieves 350 (bits/s), while AODV gives 270 (bits/s), please refer Figure 6(a). In Figure 6(b), zigzag attacks frequently change its pattern as good or bad nodes, so our scheme enabling trust mechanism outperforms AODV and achieves better throughput. TER and AODV have an average throughput of 300 (bits/s) and 230 (bits/s) respectively under zigzag attack.  
Second, under the black hole attack, as the start of the simulation, both schemes have lower packet loss ratio, after a few seconds, the packet loss of AODV is 45%, and our scheme has 20%, see Figure 6(c).  
Figure 7: Packet throughput, PDR, and E2E delay under Black Hole and zigzag attacks.  
In Figure 7d, zigzag attacks frequently change its pattern as on-off, our scheme outperforms AODV scheme and achieves minimum packet loss. TER minimizes this effect using trust information exchange; after a few seconds, TER gives 30% of packet loss. However, the original AODV has no mechanism of trust-based information; it simply routes packets, so it gives a 50% packet loss ratio.  
Third, at the start of the simulation, both schemes have less E2E delay, after a few seconds, the E2E delay of AODV is 3 (s), and TER has a delay of 1 (s), see Figure 7e. In Figure 7f, zigzag attack frequently changes its pattern as on-off, our scheme outperforms the AODV scheme and achieves minimum E2E delay. TER minimize this effect using trust information exchange; after a few seconds, TER gives 0.5 (s). The original AODV has no mechanism of trust-based information, and it simply routes the received packets, so increases E2E delay as 2 (s).
# Surface Defect Detection Methods for Industrial Products: A Review
## 4 The Key Problems
### Unbalanced Sample Identification Problem
#### 4.4.2 Model Level  
(1) Cost sensitive: The idea of cost-sensitive is to increase the loss value of the misclassified samples of small categories in the objective function and adjust the model's attention on the samples of small categories by optimizing the objective function. There are two main cost-sensitive methods: reconstruct the training set or introduce cost-sensitive factors.  
Reconstruct the training set: Without changing the existing algorithm, weights are assigned to each sample in the training set according to the different misclassification costs of the samples, and the original sample set is reconstructed according to the weight.  
Introduce cost-sensitive factors: Assign higher costs to small-class samples and lower costs to large-class samples to balance the difference in the number of samples. Cost sensitive factor includes cost sensitive matrix and cost sensitive vector, and cost sensitive method requires to specify cost sensitive matrix (or cost sensitive vector) before processing. In actual situations, the specific value of the misclassification weight in the cost-sensitive matrix (or cost-sensitive vector) can usually be specified based on information such as the ratio between samples and the confusion matrix of the classification result.  
(2) Integrated learning: There are two main ways of using ensemble learning for defect detection, respectively:  
A: Integrated learning + data preprocessing: the typical algorithms include Smote bagging, Smote boost, Easy Ensemble and Balance cascade;  
B: Integrated learning + cost sensitive: the typical algorithm is AdaCost [102], Rare Boost.  
(3) Converted to anomaly detection problem: When the sample classes are extremely unbalanced, the defect detection problem can be regarded as an anomaly detection problem, and the anomaly detection algorithm (such as One-Class SVM, SVDD, etc.) can be used to establish a single classifier to detect the anomaly points (i.e., samples of small categories).
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 3 System Model
### Power Consumption Model
#### 3.3.1 Energy Consumption of Boost Converter  
In order to facilitate low-power operation, all the power converters have been made to operate in the discontinuous conduction mode (DCM) in this work. As demonstrated by [30], the Boost converter operating in fixed-frequency DCM has a constant input equivalent resistance on average, given by:  
\[R_{IN}=\frac{2L_{1}T_{Boost}}{t_{1}^{2}} \tag{7}\]  
where \(T_{Boost}\) is its working period, \(L_{1}\) is the inductance value, \(t_{1}\) is the time of inductor current ramps up from zero to peak. Therefore, the fixed resistance value is only dependent on \(L_{1}\) and two time parameters. Choosing the values of \(T_{Boost}\), \(L_{1}\) and \(t_{1}\) seemingly has three degrees of freedom. However, there is still the following constraint to satisfy:  
\[R_{IN}=R_{Optimal}=400\ \Omega \tag{8}\]  
The above Equation (8) implies that two of the three parameters can be set randomly. Nevertheless, the best way of fully utilizing this possibility is to choose the values that could minimize the power dissipation of Boost converter, as well as maximize the energy conversion efficiency.  
Commonly the power dissipation in DC-DC converter is composed of two parts:  
\[P_{dcdc}=P_{conduction}+P_{switch} \tag{9}\]  
\(P_{conduction}\) means the power dissipation due to the electric current flows through the equivalent resistance of all elements in the converter. \(P_{switch}\) is the dissipation of gate capacitance of MOSFET switches: opening and closing switches by turns requires repeated charging of the gate capacitances. Hence, we can deduce the conduction consumption of this PWM converter:  
\[P_{cond\_Boost}=l_{O1}^{2}\cdot[D_{1}\cdot R_{N1}+R_{L1}]+\frac{1}{3}\cdot( \frac{\Delta l_{L1}}{2})^{2}\cdot[D_{1}\cdot R_{N1}++R_{L1}+R_{SC}] \tag{10}\]where \(I_{O1}\) is the output current (i.e., the supercapacitor charging current) of Boost converter. \(R_{N1}\), \(R_{L1}\) and \(R_{SC}\) are the turn-on resistance of NMOS1, the equivalent series resistance of inductor \(L1\), and the equivalent series resistance of supercapacitor. \(D_{1}\) is the duty ratio and \(\Delta I_{L1}\) is the ripple of electric current flowing through \(L1\), which could be described as follows:  
\[D_{1}=1-\frac{V_{In}}{V_{SC}}=\frac{t_{1}}{T_{Bost}} \tag{11}\]  
here \(V_{SC}\) is the output supercapacitor voltage, and \(V_{In}\) is the input voltage of Boost converter.  
Then the gate-switch power dissipation can be defined as the product of the input voltage, the switching frequency, and the gate charge of NMOS1, as shown below:  
\[P_{Switch1}=V_{In}\cdot f_{NMOS1}\cdot Q_{SW1} \tag{12}\]  
\(Q_{SW1}\) is just the gate charge of NMOS1, \(f_{NMOS1}\) is its switching frequency.
# Accelerating High-Resolution Seismic Imaging by Using Deep Learning
## 2 Methods
### Data Set  
We train and validate the proposed method with real 3D field data. The field data set includes nearly 3000 shots, with a shot interval of 12.5 m, and receiver interval of 12.5 m. Each shot has 2688 receivers. The time sampling interval is 2 ms. The complete fold time is 200. After the pre-processing, the volume is migrated using the conventional PSTM method. Four lines from the migrated volume are used as the network input data. Simultaneously, high-resolution imaging corresponding to these four lines is performed, and the results are used as the predication target output. Three of the four lines are used to train and test the network. The remaining line is used to demonstrate the effectiveness of the proposed method. Under the considered measurement configuration, the output profile of the remaining line involves 601-CDPs and 1001 time samples. All the profiles are divided into small patches sized \(64\times 128\). For the three profiles, we obtain 3564 samples in total. To overcome the over-fitting problem [47] and improve the generalization ability, each patch is flipped up/down and left/right and rotated by 90\({}^{\circ}\). Consequently, we obtain 14,256 input patches and the corresponding high-resolution patches. From these patches, we select 80% randomly as the training data set, and the remaining 20% of the patches are used as the validation samples.
## 7 Results and Discussions
### The Impact of LPSP on the System Efficiency  
The Ni-Fe-battery-based IRES with the LPRAHC-based scenario was identified as the optimal configuration for the electrification of the study area at 0% LPSP. The proposed SSA algorithm was used to analyze this optimal configuration over a range of LPSP values from 0 to 5%, and the results are presented in Table 4.  
Table 4 and Figure 14 show a significant difference in optimal values obtained with 0% and 1% loss of power supply probability values. The LCC value is reduced by 10%; the number of Photovoltaic (PV) panels is reduced from 852 to 737, and the number of batteries is reduced from 496 to 452 when a 1% LPSP value is assumed. With 1% power loss, the total number of power lost hours is only 173 out of 8760. Furthermore, increasing the LPSP values from 2% to 5% further reduces the achieved LCCs and component values, but with a small increase in the number of lost power hours. Hence, the Ni-Fe-battery-based IRES with an LPSP value of 1% can be used to provide power supply to the study area with notable cost savings at minimal loss of power hours when compared to the optimal values at 0% LPSP.  
Figure 12: Curves of convergence for the nickel–iron-battery-based IRES with the MPRAMC scenario.  
Figure 13: Convergence curves of the Ni–Fe-battery-based IRES with LPRAHC scenario.
## 4 Discussion  
### Analysis of Results  
The results show that the total area of slightly sensitive areas and moderately sensitive areas in the Tumen River Basin account for 99.41%. This indicates that the ecological sensitivity of the study area is below average, with relatively strong ecosystem stability and ecological carrying capacity. This situation is closely related to the good ecological environment in the study area. As a part of the Changbai Mountain Forest Ecological Function Area, this region has high forest cover, and ecological protection policies and ecological management measures in recent years have played an important role in maintaining the stability of the ecosystem. Nevertheless, moderately sensitive areas (54.12%) are also widely found in the central part of the study area in and around densely populated areas and on both sides of roads and rivers where human activities are frequent. Among the three types of ecological sensitivity factors, the influence of human disturbance is significantly stronger than that of the natural environment and soil erosion. The weight of land use type and distance to construction land is the highest among all the factors in the ecological sensitivity evaluation of the study area. The highly sensitive area is dominated by water bodies, because the water body has a high score in the land use factor layer. Therefore, this result confirms the importance of land use type factors in the comprehensive evaluation of sensitivity, which is similar to some previous studies [19; 42; 43].  
According to the results of the study, there are 9 insensitive and extremely sensitive areas out of the 11 impact factors analyzed. After the comprehensive multi-factor analysis, there are no insensitive areas and extremely sensitive areas in the study area. In related studies, some researchers use the extreme value method for comprehensive ecological sensitivity evaluation, i.e., the maximum value of sensitivity evaluation results of each factor is used as a comprehensive evaluation result [3; 38; 44]. This will inevitably lead to an increase in areas of higher sensitivity. In contrast, this study avoids such extreme results as \"buckets effect\", and can better reflect the comprehensive influence of various factors on regional ecological sensitivity.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 6 PHY Deployment on RFNoC Platform
In addition, variation in the execution time of packets is lower in RFNoC compared to GNU Radio, thereby offering stable and reliable performance. Figure 16: Execution time comparison for PSS detection on GNU Radio and RFNoC for different downsampling factors. Here we consider single SS burst comprising of 8 SS signals over two frames. Figure 15: Realization of the CS transmitter and receiver PHY using GNU Radio based RFNoC framework on X310 USRP platform.
# A Survey on Network Optimization Techniques for Blockchain Systems
## 5 Network Optimization Techniques Used in Blockchain Networks
### Consensus Layer
#### 5.2.1 Selective Verification  
[PERSON] et al. [14] proposed to reduce the average network latency by reducing the amount of time required to verify blocks using a probabilistic verification method. They introduced a validation degree metric, which other nodes used to determine whether or not to validate a block. According to the metric, if a block does not require validation, then it can be transmitted immediately to other peers. This probabilistic verification leads to an increase in transactional throughput and also has good scalability. However, since not all peers will validate transactions, the authors should have factored in the trustworthiness of peers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **PC** & **PR** & **BI** & **LY** & **TT** & **BW** & **Main Contributions** & **Limitations** \\ \hline
[14] & & & & 10\% & & Probabilistic verification algorithm to determine when to validate blocks & Trustworthiness of nodes not checked \\ \hline
[85] & ✓ & & \(>\)100\% & & Trust-based verification Guarantor selection algorithm & \\ \hline
[86] & & & & & Reputation-based block verification & \\ \hline
[87] & ✓ & & \(>\)100\% & & Byzantine Agreement consensus algorithm with two rounds of communication communication & Performance degrades with increase in peers. \\ \hline
[88] & ✓ & & & & HSBFT consensus algorithm & No evaluation performed for the technique \\ \hline
[89] & ✓ & & & & 30\% & Multi-Raft consensus algorithm & \\ \hline
[28] & ✓ & ✓ & \(>\)100\% & 68.75\% & SPBFT consensus algorithm & Evaluated against a network with a few nodes \\ \hline
[90] & ✓ & & \(>\)100\% & & Multi-leader PBFT algorithm & \\ \hline
[91] & ✓ & & ✓ & & A closed-loop algorithm to determine the optimum blocks to mine & Higher occurrences of forking at high scale \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of Consensus Layer Techniques. PC = Public Blockchain, PR = Private Blockchain, BI = Blockchain-IoT, LY = Latency, TT = Transactional Throughput, BW = Bandwidth = Reduction in Total Network Bandwidth due to the Minimization of Duplicates or Reducing Overhead Caused by Distributed P2P Topology Management.  
Figure 8: Semi-Distributed Topology Management Scheme used by [PERSON] et al. [84].  
However, unlike [PERSON] et al., the authors in [85; 86] checked and used the trustworthiness or reputation of peers to decide whether or not to verify new blocks. Their approaches have good scalability, smaller latency and increased transactional rate when compared to the bitcoin network.
## 3 Results and Discussion
### Quick Identification of DG Locations  
Traditionally, the identification of DG placement is usually carried out using an iterative-based power-flow solution method. This method is associated with various challenges that can hinder the quick identification of locations where DGs could be optimally placed. One of the major challenges associated with iterative-based procedures, which has been of serious concern to power systems researchers, is the convergence issue. Oftentimes, a local optimal solution is obtained instead of a global optimal solution. The information provided by such a solution can be misleading. Another issue associated with the load-flow-based solution is computational and space complexity problems. In order to provide effective solutions to these problems, an alternative coupling factor (CF) method is suggested. Considering the base case scenario, the results obtained using the novel approach of the coupling factor (CF) method for identifying weak points within a system are presented in Table 1.  
It can be seen from the results that bus 16 presented the lowest value of CF and, as such, its CF is ranked first. The implication of this is that bus 18 was the weakest bus through which DG integration or placement could be identified to benefit system operations. The next bus, whose value is ranked second, was bus 33, as seen in the results presented in Table 1, and next to bus 33 was bus 32 followed by bus 31 and buses 30, 17, 16, etc. In order to compare the results, the results obtained through the load-flow-based solution are presented in Table 2. As presented in Table 2, the voltage magnitudes are ranked in order of increasing magnitudes. By comparing the results obtained using the two methods as presented in Tables 1 and 2, it can be seen that these results compare well for the first seven buses (i.e., buses 18, 33, 32, 31, 32, 30, 17 and 16).
# Analysis of Cyclone Spinning Effect with Different Guide Vane Heights
## 3 Numerical Calculations and Validation of Results
### Model Selection  
We used Fluent 2022 R1 commercial software to simulate the cyclonic flow characteristics inside the cyclone. A k-\(\varepsilon\) turbulence model was chosen for the simulations. k-\(\varepsilon\) models include standard k-\(\varepsilon\), RNG k-\(\varepsilon\), and realizable k-\(\varepsilon\)[27]. Their advantages and disadvantages are shown in Table 3.  
Near the cyclone guide vane, particularly at the trailing edge and where it joins the tube's inner wall, the flow dynamics are intricate, and the streamlines undergo significant alterations. The RNG k-\(\varepsilon\) model is adept at capturing moderately intricate flow phenomena, including jet impingement, flow separation, secondary flow, and swirling motion. We selected the RNG k-\(\varepsilon\) model for our simulation studies.
## 1 Introduction
### Improving the Symmetric Encryption System  
By taking advantage of the security advantages of the ECDH protocol, a mechanism has been implemented in the process of the presented protocol, so that the communication parties can generate the needed symmetric key. This key is used to encrypt the messages being exchanged in the protocol process and is used instead of the fixed symmetric key selected by the network. The symmetric key generated by the communication parties is unique and its security is guaranteed by the ECDH protocol.
## 5 Linear Methods
### Production Planning and Process Optimization  
Linear methods are also instrumental in optimizing production planning and scheduling in the food industry. By modeling production processes as linear systems, these methods ensure efficient resource allocation, minimize downtime, and enhance overall productivity.  
For instance, in beverage production, LP models have been used to optimize bottling schedules, balancing production capacity and demand variability. A case study in a soft drink manufacturing facility demonstrated that linear optimization reduced production costs by 10% and improved on-time delivery rates by 15% [53].  
In food processing, LP models facilitate energy optimization by identifying the most efficient allocation of heat and power across different stages. For example, an LP-based energy management system in a food processing plant reduced energy consumption by 12%, contributing to cost savings and environmental sustainability [54].
## 4 Buck Converter
### Determination of Generator-Supplied Current  
We are going to analyze the behavior of the converter in the time intervals considered in (11). Now, in both operation modes, the mean current supplied by the generator within a given time period \(T\) (see Figure 2 and (11)) is  
\[I_{g}=\frac{1}{T}\int\limits_{0}^{T_{ON}}i_{g}\ dt=\frac{1}{T}\int\limits_{0}^{T_{ON }}i_{L}\ dt. \tag{66}\]  
A similar analysis to Section 3 will then be carried out.  
\(\mathbf{0}\leq\mathbf{t}\leq T_{ON}\)  
In this time interval current through the inductor grows, the diode is reversing biased and its branch is disconnected. Figure 8 shows the equivalent circuit.  
Figure 8: Equivalent buck converter circuit for \(0\leq t\leq T_{ON}\).  
Figure 7: Buck converter circuit including its main non-idealities (losses).  
Reasoning in Figure 8 like from (13) to (14), but now for \(\Delta V=V_{g}-V_{0}\),  
\[I_{g}=\frac{\Delta V}{r+R_{S}}\delta-\frac{f\cdot L}{r+R_{S}}\Delta I. \tag{67}\]  
Now, operating like from (15) to (19) it comes to  
\[I_{1}=\frac{\Delta V}{r+R_{S}}(1-e^{\gamma_{1}})+I_{2}\,e^{\gamma_{1}}. \tag{68}\]  
\(\mathbf{T_{ON}\leq t\leq T_{X}}\)  
Now the generator branch is disconnected. Figure 9 shows the equivalent circuit.  
Reasoning on Figure 9 like from (20) to (21),  
\[\frac{1}{T}\int\limits_{T_{ON}}^{T_{X}}i_{L}\ dt=-\frac{V_{0}+V_{\gamma}}{r+R_{ D}}\delta_{f}+\frac{f\cdot L}{r+R_{D}}\Delta I. \tag{69}\]  
Now, operating like from (22) to (26),  
\[I_{2}=-\frac{V_{0}+V_{\gamma}}{r+R_{D}}(1-e^{\gamma_{2}})+I_{1}\,e^{\gamma_{2 }}. \tag{70}\]  
At this point, as in the step to (27), the equation system formed by (68), (70), and (6) is suggested:  
\[I_{1}=\frac{\Delta V}{r+R_{S}}(1-e^{\gamma_{1}})+I_{2}e^{\gamma_{1}}. \tag{71a}\] \[I_{2}=-\frac{V_{0}+V_{\gamma}}{r+R_{D}}(1-e^{\gamma_{2}})+I_{1}\,e^{\gamma_{2 }}.\] (71b) \[\Delta I=I_{2}-I_{1}. \tag{71c}\]  
Solving (71) in the same way that from (27) to (31):  
\[\Delta I=-\beta\bigg{(}\frac{V_{0}+V_{\gamma}}{r+R_{D}}+\frac{\Delta V}{r+R_{S }}\bigg{)}, \tag{72}\]  
with \(\beta\) given in (32).  
Figure 9: Equivalent buck converter circuit for \(T_{ON}\leq t\leq T_{X}\).  
Now we can calculate the current supplied by the generator in the interval that \"sees\" the load. Therefore, substituting (72) into (67),  
\[I_{g}=\frac{\Delta V}{r+R_{S}}\delta+\frac{f\cdot L}{r+R_{S}}\beta\bigg{(}\frac {\Delta V}{r+R_{S}}+\frac{V_{0}+V_{\gamma}}{r+R_{D}}\bigg{)}. \tag{73}\]  
Taking into account \(k_{1}\) and \(k_{2}\) values from (18) and (25) respectively, \(\Delta V\) from (7) and considering (35),  
\[I_{g}=\frac{V_{g}}{r+R_{S}}(\delta+k_{1}\beta)-\frac{V_{o}}{r+R_{S}}(\delta-k \beta)+\frac{k_{2}\beta V_{\gamma}}{r+R_{S}}. \tag{74}\]