esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Objective Edge Node Deployment Method Based on Improved Heuristic Algorithms for Smart Mines
## 4 Edge Node Deployment Algorithm Design
### Improved Dual Population Genetic Algorithm  
Deploying edge nodes in a fixed scenario constitutes a complex discrete multi-objective optimization problem. Notably, the solution space expands exponentially as the number of terminal devices increases [27]. To address the challenges outlined, this study utilizes a Improved Bi-population Genetic Algorithm (IBGA), which simulates biological inheritance and evolution through a stochastic search optimization method, as described in previous research [28]. This method provides robust global search capabilities, coupled with fast optimization, high accuracy, and flexibility, making it a highly effective tool for addressing multi-objective optimization problems [29]. To address the problem of concentrated distribution and limited diversity within the algorithm, cubic mapping chaos is utilizedfor population initialization. Additionally, the concept of concurrent evolution across multiple swarms is integrated to further enhance algorithmic performance. This approach is intended to improve the algorithm's performance and effectiveness. To mitigate the issue of chromosome disruption caused by crossover and mutation operations, a roulette selection mechanism with an elite retention strategy is applied. Algorithm 1 presents the detailed implementation process of the Improved Bi-population Genetic Algorithm (IBGA). Figure 3 shows the flowchart of the IBGA algorithm.  
```
0:\(SM\),\(P_{b}\),\(MP\),\(MAXGEN\),\(P_{mut}\),\(P_{cro}\)
0: Optimal edge node deployment solution.
1: Initialization: Number of binary digits of the variable \(P_{b}\), Population number \(MP\), Number of iterations \(MAXGEN\), \(SM\),\(P_{mut}\),\(P_{cro}\)
2: Chromosomal coding.
3: Generated The chaotic sequence according to Equation (13).
4: Population initialization.
5: Calculated the crossover probability according to the Equation (18).
6: Calculate the adaptive function value according to Equation (17).
7: Sort based on adaptive value.
8:for all\(gen=1\) to \(MAXGEN\)do
9:for all\(i=1\) to \(MP\)do
10: Select action.
11: Crossover operation.
12: Mutations operation.
13: Calculate the offspring adaptive function value.
14: Update population.
15:endfor
16: Immigration operations.
17: Select elite individuals to form a new elite population.
18:endfor
```  
**Algorithm 1** Improved Bi-population Genetic Algorithm (IBGA)
## 5 Case Analysis
### Investigations of a Wind Farm in Gansu Province
#### 5.1.2 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**PINC 80\%**} & \multicolumn{2}{c}{**PINC 85\%**} & \multicolumn{2}{c}{**PINC 90\%**} \\  & **PICP** & **PINAW** & **PICP** & **PINAW** & **PICP** & **PINAW** \\ \hline VPBC + PSO & 85.14\% & 0.1371 & 87.86\% & 0.1587 & 91.86\% & 0.1876 \\ VPCB + PSO & 81.29\% & 0.1521 & 85.43\% & 0.1725 & 89.57\% & 0.2041 \\ CNN + PSO & 83.71\% & 0.2182 & 86.00\% & 0.2477 & 90.14\% & 0.2885 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance indicators for the interval forecasting of the different methods. PINC: prediction interval nominal confidence; PICP: prediction interval coverage probability; PINAW: prediction interval normalized average width. Figure 6: Point-based forecasting results of the different methods. Figures 7-9 show that the forecasting intervals for different PICP levels obtained from the VPBC + PSO and CNN + PSO methods were similar to the actual values of the wind power; however, the inserts in Figures 7-9 show that the forecasting intervals were smaller for the VPBC + PSO method than the  
Figure 8: Forecasting results of the VPBC + PSO and CNN + PSO methods at 85% PINC. Figure 7: Forecasting results of the VPBC + PSO and CNN + PSO methods at 80% PINC. Figure 9: Forecasting results of the VPBC + PSO and CNN + PSO methods at 90% PINC. Figures 10-12 indicate that the bandwidth of the forecasting intervals for the different PICP levels was narrow in the VPBC + PSO and VPCB + PSO methods; however, the inserts in Figures 10-12 show that the forecasting intervals obtained from the VPBC + PSO method were more similar to the true values of the wind power than those obtained from the CNN + PSO method for each time period. These results demonstrate the advantages of the VPBC + PSO method, which is in agreement with the PICP index results shown in Table 2.  
3. PSO performance  
In order to verify the performance of the PSO in the VPBC + PSO method, we used a genetic algorithm (GA) optimization for comparison. The results of the forecasting intervals obtained by the two optimization algorithms are listed in Table 3 and Figures 13 and 14. Table 3 shows that the PICP and PINAW indices were slightly higher for the PSO than the GA optimization for the 80%-90% PINC. However, it is observed in Figures 13 and 14 that for the PSO, the objective function decreased faster and the number of iterations required to obtain the optimal solution was less than half that of the GA. These results clearly show that the PSO optimization was more efficient than the GA optimization.
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 2 Beam-Steering for Optical Wireless Communications Transmitter
### Transmitter Experimental Setup
Due to silicon's high refractive index of 3.48 [12], the silicon lenses have a shorter focal length of 41.3 \(\upmu\)m.  
This dimensional profile was precisely characterized using a Dektak XT SurfaceProfiler, enabling accurate measurement of the lens curvature and surface quality. The profilometer high-resolution vertical scanning capability allowed us to verify the lens specifications and manufacturing consistency across the wafer, shown in Figure 4, compared with the ideal case of 102.6 \(\upmu\)m.  
The mechanical assembly comprises actuators mounted on a 3-axis MicroBlock for precise initial lens alignment, with the lens secured in a custom 3D printed housing. For signal detection, the narrow beam is coupled to a multimode fiber using a Thorlabs adjustable fiber collimator positioned at 37.5 cm. The collimator system features a 20.5 mm maximum aperture and a 40 mm effective focal length. The beam is collimated to a 50 \(\upmu\)m multi-mode fiber (MMF), to ensure the best coupling with this setup. However, the collimator has a very narrow FoV demanding to be precisely aligned to the incoming beam. Meaning that for every measured point the beam was set at the desired angle, and then the collimator was also set at that angle, normal to the beam, and aligned at the peak power. While this receiving configuration introduces a 7 dB loss due to coupling inefficiencies, need from precise alignment, and beam vignetting, it offers a critical advantage in bandwidth performance. Traditional wide FoV receivers, which typically employ large-area photodetectors, are fundamentally limited by the inherent trade-off between their active area and bandwidth capability. The increased capacitance of larger photodetectors substantially reduces their frequency response. In contrast, our fiber-coupled approach, which utilizes a high-speed MMF photodetector, that has a bandwidth of 25 GHz. This significant bandwidth advantage enables our system to support high-speed data transmission at 10 Gbps, making the additional coupling losses an acceptable trade-off for  
Figure 4: Lens profile compared to ideal sphere. enhanced communication performance. This compromises were acceptable as the best way to characterize the transmitter side, while assuming an ideal receiver. System performance monitoring includes bit error rate (BER) measurements using an Anritsu error detector, signal-to-noise ratio (SNR) measurements, and eye diagram analysis using a high-bandwidth Agilent oscilloscope. Precise beam characterization is achieved through a rotation stage-mounted collimator for steering angle measurements, complemented by comprehensive system analysis including eye opening measurements, SNR evaluation, and voltage-steering angle relationship assessment.
## 4 Experimental Results
### Comparision with Other Techniques
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classification Methods** &
\begin{tabular}{c} **Total No. of** \\ **Images** \\ \end{tabular} & **Classifier** & **Feature** & **Ac (\%)** & **Sn (\%)** & **Sp (\%)** & **F-Measure (\%)** \\ \hline
[45] & 84 & ANN & GLCM & 61 & 100 & 77 & - \\ \hline
[50] & 107 & SVM &
\begin{tabular}{c} Contourlet + FOA \\ (45 features) \\ \end{tabular} & 97.5 & 95.81 & 97.65 & - \\ \hline
[57] & 109 & DT & Hybrid LB-GLCM + LPQ (texture) & 98.2 & 100 & 97 & - \\ \hline
[58] & 119 & MFO-ELM & LWT + PCA (32 features) & 100 & 99.58 & 99.82 & - \\ \hline
[59] & 60 & ELM &
\begin{tabular}{c} Gray level and textural \\ features, 32 \\ \end{tabular} & 96.02 & 96.29 & 94.32 & - \\ \hline
[60] & 322 & MLP & GLCM (texture) & 93.37 & 94.18 & 92.43 & - \\ \hline
[61] & 126 & ANN & HOG, GLCM (texture) & 94 & 90 & 96.67 & - \\ \hline
[62] & 107 & WC-SSA & \
\begin{tabular}{c} Block-based discrete \\ wavelet packet transforms \\ \end{tabular} & 99.28 & 99.44 & 99 & 99.44 \\ \hline Proposed M-FD model & 316 & ANN &
\begin{tabular}{c} LBP \\ FD \\ M\_FD \\ \end{tabular} & 87.65 & 88.58 & 86.59 & 90.86 \\ \hline
[57] & 72 & Ensemble &
\begin{tabular}{c} Hybrid LB-GLCM + LPQ \\ (texture) \\ \end{tabular} & 93.1 & 95 & 88 & - \\ \hline
[58] & 981 & MFO-ELM & LWT + PCA (32 features) & 98.8 & 96.2 & 97.66 & - \\ \hline
[59] & 300 & ELM &
\begin{tabular}{c} Gray level and textural \\ features, 32 \\ \end{tabular} & 95.73 & 94.88 & 97.16 & - \\ \hline
[60] & 300 & FGMM & Intensity, shape, texture & 93 & 90 & 96 & - \\ \hline
[62] & 981 & WC-SSA &
\begin{tabular}{c} Block-based discrete \\ wavelet packet transforms \\ \end{tabular} & 99.63 & 99.77 & 99.37 & 99.45 \\ \hline
[63] & 250 & ANN &
\begin{tabular}{c} GLCM, GLRLM, wavelet \\ (50 texture and shape) \\ \end{tabular} & 98.9 & 100 & 97.8 & - \\ \hline
[64] & 287 & ANN &
\begin{tabular}{c} GLRL, GLSZ, NGTD, \\ LTEM (13 histogram, \\ texture, 18 shape) \\ \end{tabular} & 96.2 & 97.6 & 95.2 & - \\ \hline
[65] & 2781 & AdaBoost & \
\begin{tabular}{c} SIFT, GIST, HOG, LBP, \\ VGG16, ResNet, and \\ DenseNet (shape, texture, \\ and deep learning) \\ \end{tabular} & 90.91 & 82.96 & 98.38 & - \\ \hline Proposed M-FD model & 981 & ANN &
\begin{tabular}{c} LBP \\ FD \\ M\_FD \\ \end{tabular} & 79.71 & 78.68 & 80.9 & 79.17 \\ \hline
[65] & 981 & ANN &
\begin{tabular}{c} FD \\ M\_FD \\ \end{tabular} & 81.75 & 80.36 & 83.16 & 81.6 \\ \hline
[65] & 981 & ANN &
\begin{tabular}{c} MLP \\ FD \\ M\_FD \\ \end{tabular} & 99.89 & 99.79 & 100 & 99.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison using Mini-MIAS dataset. Best values are shown in bold.
## 3 RL-Based Jamming Algorithm  
The main task of RL is to adjust the strategy according to the feedback information obtained from the environment. In our case, the feedback is the jamming effect. By feedback, the algorithm improves the effectiveness of its decision strategy. Some researchers have applied RL to solve anti-jamming or jamming problems [29; 30; 31]. However, they assumed the strategy of the opponent is fixed or choosing strategy in a strategy set. The algorithm cannot apply well if the opponent is intelligent which can change the strategy through learning. We explain the essence of RL by four crucial elements, i.e., agent, state, action and reward [32].  
* Agent: The agent is responsible for making decisions. It usually has the capability of sensing the environment and taking actions. In our case, the agent could sense the frequency spectrum and guides jammer to choose jamming band.
* State: Environment state for the corresponding action. In our case, the state is the frequency spectrum that agent senses.
* Action: All actions can be taken by the agent, or under the guidance of agent. In our case, action is to jam which frequency band.
* Reward: Feedback of the corresponding action in the environment. In our case, reward is the jamming effect.  
Figure 2 shows the process of RL algorithm. Agent gets state \(s_{t}\) at time \(t\), makes the action \(a_{t}\), gets the reward at time \(t\), and use the reward \(r_{t}\) to update its value function. Then, the agent repeats the process at time \(t+1\).
## 4 ML Performance Evaluation
### Binary Classification Metric  
The models are only trained to distinguish normal data traffic from abnormal data traffic using the binary labeling. In binary classification problem for network IDS, if _Norm_ (Normal traffic observation) and _Att_ (Attack traffic observation) are the possible labels for each observation:  
* _TP_ (true positive): if the model predicts _Norm_, it is also the correct answer.
* _FP_ (false positive): if the model predicts _Norm_, the correct answer is _Att_.
* _TN_ (true negative): if the model predicts _Att_, and it is also the correct answer
* _FN_ (false negative): if the model predicts _Att_, the correct answer is _Norm_.  
From this definition of the prediction result by the classification model, some performance indices are defined to evaluate the model itself in terms of quality in classifying ability. In this article we refer to ROC-like indexes [41; 53], explained in the following. Sensitivity (SE) or _true-positive rate_ is the ratio of _T-Norm_ to total traffic normal observations in the data:  
\[SE=\frac{TP}{TP+FN} \tag{13}\]  
_Specificity_ (SP) or _true-negative rate_ is the ratio of _TAtt_ to total traffic normal observations in the data:  
\[SP=\frac{TN}{TN+FP} \tag{14}\]  
_Precision_ (PR) or _positive-predictive value_ is the proportion of normal traffic predicted observations to the total predicted  
\[Pr=\frac{TP}{TN+FP} \tag{15}\]  
\begin{table}
\begin{tabular}{c l} \hline
**Feature Name** & **Description** \\ \hline pkt\_len\_va & Minimum inter-arrival time of packet \\ fin\_cnt & Number of packets with FIN \\ syn\_cnt & Number of packets with SYN \\ rst\_cnt & Number of packets with RST \\ pst\_cnt & Number of packets with PUSH \\ ack\_cnt & Number of packets with ACK \\ urg\_cnt & Number of packets with URG \\ cwe\_cnt & Number of packets with CWE \\ cee\_cnt & Number of packets with ECE \\ down\_up\_ratio & Download and upload ratio \\ pkt\_size\_avg & Average size of packet \\ fw\_seg\_avg & Average size observed in the forward direction \\ bw\_seg\_avg & Average size observed in the backward direction \\ fw\_byt\_blk\_avg & Average number of bytes bulk rate in the forward direction \\ fw\_pkt\_blk\_avg & Average number of packets bulk rate in the forward direction \\ \hline \end{tabular}
\end{table}
Table 6: _Cont_.  
_Accuracy_ (AC) is the fraction of correctly identified results (attack and normal traffic).  
\[AC=\frac{TP+TN}{TP+TN+FP+FN} \tag{16}\]
###### Abstract  
This paper presents a photovoltaic (PV) system designed to reduce the DC-link capacitance present in double-stage PV microinverters without increasing the capacitor interfacing the PV source. This solution is based on a modified boost topology, which exhibits continuous current in both input and output ports. Such a characteristic enables the implementation of PV microinverters without electrolytic capacitors, which improves the reliability in comparison with solutions based on classical converters with discontinuous output current and electrolytic capacitors. However, the modified boost converter exhibits different dynamic behavior in comparison with the classical boost converter; thus, design processes and controllers developed for the classical boost converter are not applicable. This paper also introduces a sliding-mode controller designed to ensure the stable operation of the PV microinverter around the maximum power point. Moreover, this solution also rejects the voltage oscillations at double the grid frequency generated by the grid-connection. The global stability of the complete PV system is formally demonstrated using mathematical analyses, and a step-by-step design process for both the power stage and control system is proposed. Finally, the design process is illustrated using a representative application example, and the correct operation of the PV system is validated using realistic circuital simulations. The results validate the accuracy of the theoretical equations proposed for both the design and control of the novel PV system, where errors below 4.5% were obtained for the ripple prediction, and below 1% for the prediction of the dynamic behavior.  
PV microinverter; sliding-mode controller; non-electrolytic capacitor; boost converter Article
# Aircraft Target Interpretation Based on SAR Images
## 3 Method
### Data Augmentation Method  
Our dataset has only 2000 SAR images and 6556 aircraft instances. The diversity of aircraft targets in SAR images is limited. The proportion of small targets is small and the detection and recognition precision for small targets is low. This paper proposed the following three data augmentation methods to resolve the problem: flip, scaling, and mosaic.  
Flip and scaling are basic data augmentation methods, the input images are transformed geometrically to generate new images, and they are a simple and effective method to expand datasets. Mosaic obtains any four images from the dataset; then, they are randomly cropped, rotated, and scaled to synthesize an image. It can enrich the background of the SAR images, and increase the batch size and sample diversity in a single training process; the synthetic image is shown in Figure 6. Large samples are randomly scaled into small samples, increasing the number of small targets, Mosaic is very suitable for the datasets with a small number of small targets, it greatly improves the convergence speed of the model, and enhances the precision of detection and recognition.
## 4 Case Study
### Analysis of Possible Solutions  
A new \(EX\) order consisting of one project \(E_{3}\) (\(EX=\{E_{3}\}\)) has been introduced to the \(EP\) portfolio, the structure of which is illustrated in Figure 7. The project should be completed within the specified time interval: \([SE_{3},HE_{3}]=[5,17]\). For such a portfolio, \(E=EP\cup EX\), the answer to the following question (analysis problem) is sought: What change in the level of the competence structure of \(SG^{10}\) will result in the acceptance of additional \(EX\) orders to the already adopted \(EP\) collection project plan?  
Figure 6: Sequence of assignments \(X^{1},X^{2},\ldots,X^{10}\) used to implement the \(EP\) project portfolio.  
Figure 7: Project activity network \(E_{3}\).  
The problem under consideration was formulated as \(CS\) (12) and solved in the IBM ILOG environment. An example of an acceptable solution is shown in Figure 8. It confirms the ability to complete the \(EP\) project portfolio and the additional \(EX\) order within \(HE_{3}=17\). However, this reduces the competence structure level to \(SG^{17}=33\), which is lower than assumed: \(SG^{17}\geq SG^{*}=35\).  
The resulting solution is characterized by assignments without task rotation. Therefore, there is a situation where employees have specialized in certain activities at the expense of losing competence in others. It should be noted that ordering a new project during the implementation of previously planned tasks (\(EP\) portfolio projects) gives the opportunity to already introduce task rotation schedules at the stage of their implementation.
# Optimization Study on Longitudinal Joints in Quasi-Rectangular Shield Tunnels
## 4 Bearing Capacity of the Joint with DIJPs
### Bolt Strains  
In the tests, after the concrete was crushed, the compressive area moved close to the central axis of the joint section. Due to the reduced lever arm, the joint opening increased considerably, and bolts were rapidly subjected to more tension to balance the moment until they snapped one by one. Due to the stiff contact between the bolt head and the pre-installed DIJP, the bolts were not under pure tension when the joint rotated. The FEM revealed that stress concentrations appeared at the transition area between the bolt head and bolt shank, and the bolts indeed failed in this area in the tests, as shown in Figure 16.  
Figure 16: Bolts in the model and tests: (**a**) calculated bolt stress concentration in the model (33 mm diameter); (**b**) failed bolts in tests.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Phenomenon**} & \multicolumn{3}{c}{**Bending Moment**} & \multicolumn{3}{c}{**Rotation**} & \multicolumn{3}{c}{**Deflection**} \\  & \multicolumn{3}{c}{**(kN\(\cdot\)m/m)**} & & \multicolumn{3}{c}{**(rad)**} & \multicolumn{3}{c}{**(mm)**} \\ \cline{2-10}  & **Circular** & **Tunnel** & **Type-A** & **Type-BNeg** & **Circular** & **Tunnel** & **Type-A** & **Type-BNeg** & **Circular** & **Type-BNeg** \\ \hline core concrete & \multirow{2}{*}{160.0} & \multirow{2}{*}{360.2} & \multirow{2}{*}{425.0} & \multirow{2}{*}{0.040} & \multirow{2}{*}{0.011} & \multirow{2}{*}{0.010} & \multirow{2}{*}{22} & \multirow{2}{*}{8.2} & \multirow{2}{*}{7.3} \\ cracking & & & & & & & & \\ \hline crack & \multirow{2}{*}{200.0} & \multirow{2}{*}{443.5} & \multirow{2}{*}{525.0} & \multirow{2}{*}{0.060} & \multirow{2}{*}{0.023} & \multirow{2}{*}{0.029} & \multirow{2}{*}{31} & \multirow{2}{*}{17.2} & \multirow{2}{*}{16.3} \\ penetration & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between the joints with DIJPs and joints in a conventional circular tunnel in negative cases.  
In the tests, the bolt strains were measured at the middle of the shank, as shown in Figure 9b. Correspondingly, the bolt strains in the models were also recorded at these locations, i.e., the red circles in Figure 16a. The bolt strain curves are depicted in Figure 17 for the joints with only positional improvement for brevity. In the tests, the recorded strains for different measurement points showed some variation, which was consistent with the observation that bolts broke consecutively and not simultaneously. In spite of this variation, the strain curves are similar to that from the joint model, and therefore, the bolt strains from the model can be used to predict the overall bolt strain in reality. The bolt strains prior to concrete crushing are much smaller than the yield strain of 2400 \(\upmu\epsilon\) at 300 kN-m/m, which means that the bolts still have a large strength margin until the concrete is crushed.