esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion
## 3 Materials and Methods
### Algorithm Principle in DLLO-DRF
#### 3.2.1 Lifelong Learning Paradigm Based on Model Fine-Tuning  
A lifelong learning algorithm that is based on the model fine-tuning approach requires the condition that when new information is learned, only previous knowledge is tuned, instead of relearning all the information. This can significantly reduce computational complexity and allow the model to learn new information faster. The fine-tuning approach can take full advantage of the powerful generalization capabilities of deep learning neural networks, and can avoid the need to design new models and train from the beginning, converging faster and with better results. This offers many advantages, such as the model having a high initial performance before training, gaining faster improvement of the model during training, and having better convergence than the obtained model. This lifelong learning paradigm is universal, and it can be applied to all deep learning algorithms. The models trained through this paradigm also provide support for fusion. The lifelong learning paradigm based on model fine-tuning can be summarized as using data from the current stage of lifelong learning tasks, and using the task model of the previous stage as a pre-training model to train the task model of the current stage. The following paragraph provides a definition of the model-fine-tuning-based lifelong learning paradigm.  
The initial training dataset, noted as \(D\), is divided into \(n\) parts: \(D=\{d_{1},d_{2},\ldots,d_{n}\}\). The training dataset used in the \(n^{th}\) stage in lifelong learning is defined as \(D^{(n)}=d_{n}\). \(\Gamma\{A,B\}\) represents the model obtained by employing training dataset \(A\) to train pre-trained model \(B\) using algorithm \(\Gamma\). \(Model^{(n)}\) represents the model obtained in the \(n^{th}\) stage, and is defined as follows:  
\[Model^{(n)}=\left\{\begin{array}{ll}\Gamma\{D^{(n)},Model^{(other)}\},&n=1\\ \Gamma\{D^{(n)},Model^{(n-1)}\},&n\geq 2,\end{array}\right. \tag{1}\]  
where \(Model^{(other)}\) represents other pre-trained models that can be selected in the first stage.
# Multi-Range Sequential Learning Based Dark Image Enhancement with Color Upgradation
## 6 Result Analysis
### Image Denoising  
The performance of our proposed MSR-MIRNeT is represented for image denoising. The quantitative comparisons of denoising with respect to PSNR and SSIM measurement are summarized in Table 4. Compared to traditional data-driven denoising algorithms, our MSR-MIRNeT provides the standard result in PSNR.  
Compared to the CID dataset, our approach outperforms R2 RNet [9] and MIRNeT [5] by 2.19 dB. In the Extreme low-light denoising dataset ELD [20] our model provides standard PSNR results in comparison to the others; however, the deep Retinex-Net model provides slightly better results than our model. From Figure 9a, it can be seen that the denoising accuracy is almost 97.67 percent. From Figure 9b, it is also noticeable that the training loss is less than the validation loss, which defines our model run well.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**CID Dataset [21]**} & \multicolumn{2}{c}{**ELD [20]**} \\ \cline{2-5}  & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** \\ \hline (RED-Net) [12] & 34.02 & 0.901 & 33.67 & 0.917 \\ MSR [4] & 36.52 & 0.916 & 34.03 & 0.919 \\ R2 RNet [9] & 37.07 & 0.931 & 35.02 & 0.909 \\ MIRNeT [5] & 37.98 & 0.937 & 36.26 & 0.942 \\ deep Retinex-Net [11] & 38.17 & 0.926 & 37.87 & 0.959 \\ Proposed Model & 39.26 & 0.967 & 38.02 & 0.951 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of image denoising in the CID data set [21] and ELD [20] with respect to our model and existing methods.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**IELLc Dataset**} & \multicolumn{2}{c}{**ExDark [15]**} \\ \cline{2-5}  & **PSNR\(\uparrow\)** & **SSIM\(\downarrow\)** & **PSNR\(\uparrow\)** & **SSIM\(\downarrow\)** \\ \hline MSR [4] & 24.79 & 0.911 & 25.27 & 0.919 \\ (RED-Net) [12] & 25.67 & 0.917 & 26.57 & 0.911 \\ MIRNeT [5] & 26.17 & 0.949 & 27.01 & 0.950 \\ deep Retinex-Net [11] & 27.52 & 0.951 & 27.79 & 0.959 \\ Proposed Model & 29.73 & 0.963 & 30.07 & 0.952 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of the dark image enhancement performance of existing ExDark [15] and our preprocessed IELLc datasets concerning our model and existing methods.  
Figure 9: (**a**) PSNR VS Epoch Curve, (**b**) Loss VS Epoch Curve [\(\beta_{1}=0:82\) and \(\beta_{2}=0:99\), Adam optimizer, 21 batches, 150 Epoch].
###### Abstract  
The use of Topology Optimization techniques has seen a great development since the last decade. The principal contributor to this trend is the widespread use of Additive Manufacturing technologies to effectively build complex and performant structures over different settings. Nevertheless, the use of Topology Optimization in Design for Additive Manufacturing processes is not simple and research aims to fill the gap between theory and practice by evolving at the same time both approaches, workflows, and design software that allow their implementation. Since a strong connection between methodologies and tools exists, this work proposes a method to assess computer-aided design tools or platforms. This can be applied to sustain the key phase for selection and adoption of the computer-aided tools in industrial settings embracing Additive Manufacturing. The workflow for Topology Optimization implementation, the structure of the proposed evaluation approach, and its application, are presented to demonstrate effective usability. The automotive case study is the redesign of internal combustion engine piston to benefit of metal Additive Manufacturing based enhanced product performance. A preliminary finite element model is defined and a Topology Optimization based redesign is concurrently set up through four different commercial computer-based platforms. The method accounting for the assessment of required operations for the design optimization is applied to perform the tools selection phase.  
topology optimization; computer aided design tools; design for additive manufacturing; design methods; automotive +
Footnote †: journal: Applied Sciences
# Ship Detection from X-Band SAR Images Using M2 Det Deep Learning Model
## 3 Methods
### SAR Image Preprocessing
#### 3.1.3 Land Masking  
In the land area, because many objects, for example buildings and containers, can cause double scatterings, most land areas show a high backscattering coefficient on the image. Because a high backscattering coefficient can lead to false detection, many ship detection studies perform a land masking before ship detection [1, 33, 34]. In this study, a digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) with a 30 m resolution was used to mask the land area. To remove the land area from the SAR image using the DEM, the geometry of the image and the DEM were matched using intensity cross-correlation. However, reclaimed area after DEM was created still remain on image. To cover this area with DEM, we made a buffer area on the DEM, then removed it.
###### Abstract  
This research presents a high-performance design for a multiple-input multiple-output (MIMO) antenna intended for operation within the 28 GHz band. The four-port MIMO antenna configuration, featuring 1 \(\times\) 8 series-fed arrays for each port, has demonstrated peak gains of 15.5 dBi and bandwidths of 2 GHz. This improved antenna performance results from carefully optimized antenna spacing and a decoupling approach involving well-designed metamaterial cells, effectively minimizing interference between antenna elements. The system exhibits remarkably low mutual coupling, measuring below \(-\)40 dB, with envelope correlation coefficients of 0.00010, diversity gains nearing 10 dB, and a channel loss capacity of 0.11 bit/s/Hz across the frequency spectrum under investigation. Experimental evaluations have confirmed these improvements, establishing the proposed design as a robust candidate suitable for a wide range of millimeter-wave communication systems.  
m m mWave; antennas; MIMO systems; high isolation; 5G communications 202525251036  
2521 January 2025
## 2 Research Method  
### Establishment of the Finite Element Model  
The 3D FEM model of the novel non-circular spinning method is shown in Figure 1. Simulations were performed in ABAQUS 2016/Explicit. To shorten the simulation time, a simplified model was adopted. The rotation of the workpiece and the principal axis of the roller were first transformed into a stationary workpiece, with two rollers arranged symmetrically on the principal axis of the roller which rotated and simultaneously revolved around the workpiece.  
In the 3D model, a clamp with a width of 20 mm was affixed, and the workpiece was connected to the clamp using the tie function. Deformation of the clamped section of the workpiece was constrained, while other areas of the workpiece deformed during the spinning process. The rollers and the clamp were defined as rigid bodies, and the centers of the rollers were connected with the center of the roller principal axis using the hinge function.  
In order to realize movement of the rollers in a hypocycloid manner, the boundary conditions were defined as the angular velocity of the rollers' principal axis around the workpiece and the rotational velocity of the rollers' principal axis. The feed rates of the rollers in the axial direction were also input.  
The total length of the workpiece was set to 110 mm, the diameter was set to 80 mm and the thickness was set to 3 mm. The workpiece was then discretized into eight node hexahedral elements. In order to improve simulation accuracy, the deformed section of the workpiece was refined, with an element length of 1.25 mm along the axial direction and 0.75 mm along the thickness direction, and the angle between the two sides of the mesh was set to 1.8\({}^{\circ}\) along the circumferential direction. In the clamped section of the workpiece, the element length was 2 mm along the axial direction, and the other two dimensions were the same as those in the deformed section. The total number of elements was 70,752, and the number of nodes was 89,445.  
The workpiece materials were composed of 6061 aluminum alloy, and the mechanical properties are presented in Table 1. The relationship between the true stress and strain was Y = 229.88\(\in^{0.25}\). The Coulomb friction coefficient between the workpiece and the rollers was 0.1.
# A Specification-Based IDS for Detecting Attacks on RPL-Based Network Topology
## 3 A specification-Based IDS for RPL to Detect the Topology Attacks
### Design and Implementation of the Specification-Based IDS for RPL-Based Network
#### 3.2.3 Detection at the IDS Agent  
Algorithm 3 shown a detection algorithm that we developed based on the RPL profile discussed in Section 3.1 and the collected data given in Section 3.2.2. The detection algorithm would be executed as an IDS agent at Cluster head. The algorithm consists of 5 modules:  
* Module 1: Checking the DIS message, alarm if the received DIS is fake or sending too much.
* Module 2: Checking the sequence of DIO message, alarm if the received DIO is fake
* Module 3: Checking the rank consistency, alarm if the rank of the member is different to the rank reported by its neighbors or the cluster head, given the same DIO sequence. Penalize the neighbors if they do not have the latest DIO message. Alarm if there is any DIO message reported by the neighbors or cluster head that has newer DIO sequence than the member itself
* Module 4: Check the rank rule between every pair of parent and child
* Module 5: Check the instability of the network area around a member through the relevant states and observations in Section 3.1. Penalize if there is any instability.  
The detail algorithm is as below.  
```
1:On receivingDIS{
2:recordSourceID,DIS_seq_new;
3:DIS_count[SourceID]++;
4:ifDIS_seq_new\(\leqslant\)DIS_seq_currentthen
5:alarmfakeDIS;
6:elseDIS_seq_current\(=\)DIS_seq_new
7:endif
8:ifDIS_count[SourceID]\(>\)threshold\({}_{\text{DIS\_count}}\)then
9:alarmDISattack;
10:endif{ }
```  
**Module 2:** Check whether there is any fake DIO  
```
1:On receivingDIO{
2:recordSourceID,DIO_seq_new,rank;
3:DIO_count[SourceID]++;
4:ifDIO_seq_new\(\leqslant\)DIO_seq_currentthen
5:alarmfakeDIO;
6:elseDIO_seq_current\(=\)DIO_seq_new
7:endif }
```  
**Module 3:** Check the rank inconsistency  
```
1:After receiving reports from all of the members {
2:for eachMember in Clusterdo{
3:ifMember.DIO_seq < CH.Member.DIO_seq then
4:alarmfakeDIO;
5:endif
```  
**Algorithm 3** Detecting topology attacks from cluster head view ```
1:for each Member in Clusterdo
2:ifMember.rank + MinHopRankIncrease < Member.parent.rankthen
3:alarm rank attack;
4:endif
5:for each Neighbour in Member.Neighbourdo{
6:ifMember.DAO.parent == 1 then
7:ifMember.rank - MinHopRankIncrease > Member.child.rankthen
8:alarm rank attack;
9:endif
10:endif
11:endfor
12:endfor }
```

**Module 5:** Check the stability of the network part which relate to a cluster member

``` //Setting the initial stability evaluation for each member in cluster
1:for each Member in Clusterdo
2:Member.stability = threshold\({}_{stability}\)
3:endfor //Penalise if stability condition is observed to be not satisfied
4:for each Member in Cluster {
5:ifIsRepairAfterPeriodthen
6:Member.stability == 2 // penalised -2 on stability
7:endif
8:ifIsChangeAfterPeriod(Member.DIO) | | IsChangeAfterPeriod(Member.DAO)
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 2 Literature Review and Applications in Disaster Management
### Applications
#### 2.2.3 Situational Awareness  
Situational awareness implies gathering of information regarding development in a disaster situation, rescue efforts, and the affected people. Situational awareness plays a critical role in decision making involved in disaster management and reduces the response time of rescue operation. Aerial assessment of disaster area using UAVs provides accurate and real-time situational assessment. In [5], a lack of situational awareness in disaster scenarios is studied and it is recommended that the deployment of UAVs along with sensor network in the affected area can provide accurate and timely information required during various phases of disaster management such as planning and disaster studies. Cooperation between the sensor network and UAVs is also considered in [42] to improve the disaster response through timely detection of events and reliable transmission to the data sink. The authors also presented a reliable routing protocol that can dynamically respond to the network topology changes. In [43], a UAV-based remote sensing system is exploited for effective decision support in an emergency situation such as a natural disaster.
## 4 Experimental Evaluation
### Selection of Evaluation Metrics  
To analyze the performance of the proposed algorithm, we used the root mean square error (RMSE), mean absolute error (MAE), R\({}^{2}\) score, index of agreement (AI), Theil inequality coefficient (TIC), minimum error (MIN), and maximum error (MAX), as defined in Table 2[53]. We used the RMSE and MAE metrics to represent the average error between the estimated and actual values. The lower the values of these measures, the higher the efficiency of the autoencoder.  
The R\({}^{2}\) score is a statistical measure that is defined on the domain \([0,1]\), where a value of one indicates perfect prediction and zero an inefficient model [54]. In contrast, the TIC metric indicates the accuracy capability of the proposed system, with a value between zero and one. The closer the value is to zero, the higher the predictive efficiency of the ELM-AE method. Likewise, the IA prediction index can provide external statistical information on the predictive ability of the model. To verify the efficiency of the ELM-AE, each experiment was repeated 10 times, and the average values of the training times and the evaluation criteria represented in Table 2 were recorded.
## 3 Results  
### Identification of a Fourth Rsm sRNA in P. brassicacarum  
We identified a fourth Rsm sRNA by performing a BLAST search against the _P. brassicacarum_ NFM421 genome and using the upstream activating sequence (UAS; TATAGCGAAAACGCTTACA) recognized by GacA as query sequence. This revealed an additional UAS located upstream the gene encoding a putative sRNA (Figure 1A), which shares 75% similarity with _rsmX_ gene, while the UAS sequence is almost identical (Figure 1B). We renamed RsmX to RsmX-1, and the novel Rsm sRNA was named RsmX-2. The secondary structure of RsmX-2 was predicted with RNAfold and visualized using vaRNA software [31]. At least three potentially mimicked recognition motifs of RsmA/E (i.e., ANGGA or AGGA motifs) [32], are exposed within a hairpin loop, suggesting that RsmX-2 could sequester these proteins (Figure 1C).  
To determine, the relationship and evolutionary history, we performed a phylogenetic analysis of Rsm sRNAs genes. Phylogenetic tree Unweighted Pair Group Method with Arithmetic Mean (UPGMA) tree was constructed using MAFFT toolkit with a modified version of UPGMA. Rsm sRNAs exhibit a remarkably uniform distribution among _Pseudomonas_ species. The UPGMA phylogenetic tree shows that _rsmX_, _rsmY_ and _rsmZ_ are sorted into three clades (Figure 2).  
Figure 1: _rsmX_-2 identification and predicted structure. (**A**) Organization of the _rsmX-1_ and _rsmX-2_ genomic region of _P. brassicacarum_. The upstream activating sequence (UAS) of the GacA protein is represented within the box. GGA motifs involved in the sequestration of RsmA/E proteins are underlined. The transcriptional start has been determined by 5\({}^{\prime}\)RACE method. _rsmX_-2 gene sequence is indicated in bold; (**B**) Alignment of _rsmX-1_ and _rsmX-2_ UAS sequence; (**C**) _rsmX-2_ secondary structure predicted using RNAfold and visualised using vaRNA.  
Figure 2: Phylogenetic analysis of _rsm_ RNA sequences. _rsm_ genes from _P. brassicacarum_ NFM 421 are shown in bold.  
Typically, Pseudomonas contain single copies of RsmY and RsmZ, however, the copy number of RsmX is variable. _P. protegens_ CHA0 possesses one copy of RsmX [6], while _P. syringae_ contains up to five copies [8]. In this study we identified a fourth Rsm RNA in _P. brassicacarum_ NFM421 found in tandem with _rsmX_ with which it shares 75% of sequence identity. We also show a potential fourth Rsm sRNA in _P. fluorescens_ F113 and _Pseudomonas stutzeri_, which are in tandem with _rsmX_ and share 75% and 81% sequence identity respectively (Table 1).
# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 1 Introduction
Since it requires such human experts, artificial neural networks (ANN) show superior performance compared to other machine learning approaches in the automatic extraction of more complex data illustrations and dependencies at high levels of concept, which can also be used in predictive maintenance [9]. This study proposes a new multi-level selective maintenance approach based on a predictive model of a multi-component system by applying supervised ML tools, unsupervised ML methods and metaheuristic optimization methods. Our methodology aims to establish an efficient selective maintenance policy exploiting historical maintenance data. Firstly, a clustering algorithm is used to identify similar components having the same degradation behavior and then to determine the actual age for each component. Therefore, the ANN regression model was exploited to build the actual behavior model capable of predicting the real age of the component system based on the operational and real-world conditions such as number and type of maintenance actions and mean time between failures, etc. The predicted component age is further used in the proposed optimization model to create an optimal maintenance plan. Several intermediate maintenance strategies \(k_{i}\) (\(k_{i}\) = [1, 2,... \(K_{i}\)]) between minimal repair--As Bad As Old (ABAO)--and perfect maintenance--As Good As New (AGAN)--can be applied for each component \(i\). Thus, each maintenance policy has an age improvement value, a known maintenance cost and a duration. The resolution method adopted is based on the EDA approach (Extended Deluge Algorithm). The rest of the paper is structured as follows. In Section 2, we present a literature review on machine learning methods and mathematical models used for predictive maintenance. Problem statements and the optimization model are present in Section 3 which comprises details and notations used in this paper. A description of the proposed approach is provided in Section 4. An illustrative numerical example and results are given in Section 5. In Section 6, we summarize this work in the conclusion and we propose some future axis of research and perspectives.
# A Short-Circuit Current Calculation Model for Renewable Power Plants Considering Internal Topology
## 4 Results
As shown in Figure 9, under a certain fault condition, the terminal voltages of the individual wind turbines are not identical, and the voltage increases as the distance from the grid connection point increases, which is due to the presence of line impedance. The figure also reveals the drawback of the single-machine multiplication method, which overlooks the internal differences within the wind farm, resulting in significant calculation errors. From Table 2, it can be seen that the maximum error between the method proposed in this paper and the simulated voltage does not exceed 5%, demonstrating the accuracy of the proposed method. From Table 3, it can be seen that for the same fault location, as the fault impedance increases, the PCC voltage increases, and the SCC injected by the wind farm decreases. For the same fault impedance, the closer the fault point is to the PCC, the higher the PCC voltage and the smaller the SCC injected by the wind farm. Comparing the three conditions: \(\gamma=0.25\), \(Z=3\); \(\gamma=0.5\), \(Z=2\); and \(\gamma=0.75\), \(Z=1\), it is found that the PCC voltage is nearly the same in all three cases, and the SCC is also similar. This indicates that the SCC injected by the wind farm mainly depends on the PCC voltage. From Table 3, it can be seen that the SCC accuracy obtained by the method proposed in this paper shows a significant improvement compared to the single-machine multiplication method, which verifies the feasibility of the method discussed in Section 2. When the internal differences within the wind farm are small, such as when there are only a few wind turbines connected to a single collection line, the terminal voltage variations between individual turbines are negligible, and the single-machine multiplication method can be used. However, as demonstrated in the example, when there are 10 turbines on a single line, the method proposed in this paper reduces the error by nearly half. Compared to other iterative algorithms, the algorithm proposed in this paper has a time complexity that is linearly related to the number of wind turbines, without the issue of convergence. This significantly reduces the computational resources required.