esa-sceva/satcom-chunk-collection huggingface dataset:
# ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
## 3 Methodology
### Dual-Channel Attention Fusion Module
To merge information from height and width directions, we transpose the dimensions of \(x_{w}\) to \((B,C,H,1)\), then concatenate it with \(x_{h}\) along spatial dimensions to obtain feature map y:  
\[y=\text{concat}(x_{h},x_{w}) \tag{5}\]  
After batch normalization and activation operations on the convolved feature map, y is split along spatial dimensions into two tensor representations, \(x_{h}\) and \(x_{w}\), as shown in Equations (6) and (7). \[y=\text{ReLU}(\text{BatchNorm}(y)) \tag{6}\]  
\[x_{h},x_{w}=\text{split}(y,[H,W]) \tag{7}\]  
Then, we pass \(x_{h}\) and \(x_{w}\) through \(1\times 1\) convolution layers to restore the channel number to C, and generate attention weights \(a_{h}\) and \(a_{w}\) through the Sigmoid activation function in Equation (8):  
\[ \begin{split} a_{h}&=\sigma(W_{h}(x_{h}))\\ a_{w}&=\sigma(W_{w}(x_{w}))\end{split}  \tag{8}\]These weights reflect the spatial importance of each position in the feature map. Finally, we multiply the generated attention weights \(a_{h}\) and \(a_{w}\) with the input \(X\) yield output feature map:  
\[M_{co}(X)=X\times a_{w}\times a_{h} \tag{9}\]  
The symbol \(\times\) in the formula denotes element-wise multiplication. To effectively integrate channel attention with coordinate attention, the DCAF module combines their outputs using adaptive weights. Initially, the coordinate attention and channel attention branches generate feature maps \(F_{coord}\) and \(F_{chan}\), respectively:  
\[F_{coord}=CoordAtt(X) \tag{10}\] \[F_{chan}=ChannelAtt(X)\]  
In Equation (10), \(CoordAtt(X)\) and \(ChannelAtt(X)\) refer to the outputs of the coordinate attention module and the channel attention module, respectively. Their relationship with Equations (3) and (9) is as follows: \(CoordAtt(X)\) calculates \(M_{co}\) obtained through the coordinate attention module, while \(ChannelAtt(X)\) calculates \(M_{ca}\) obtained through the channel attention module. Finally, \(F_{coord}\) and \(F_{chan}\) pass through the Gated Fusion Unit in Figure 4. This module combines the feature maps of coordinate attention and channel attention through an adaptive weighting mechanism, effectively enhancing the model's performance in medical image classification. Through this operation, the network can better focus on locally important information and exhibit stronger adaptability in complex medical images. The module fuses the outputs of the two branches using an adaptive weight \(\alpha\), resulting in \(Y\) in Equation (12):  
\[\alpha=\sigma(F_{coord}+F_{chan}) \tag{11}\]  
\[Y=\alpha F_{coord}+(1-\alpha)F_{chan} \tag{12}\]  
where \(\alpha\) is the adaptive fusion weight and Y is the final output feature map. The detailed pseudocode description of the DCAF module can be found in Algorithm A1 in Appendix A.
# Automatic Wireless Signal Classification: A Neural-Induced Support Vector Machine-Based Approach
## 5 Conclusions  
In this paper, a novel feature-based classifier NSVM has been proposed for Automatic Classification of Wireless Signals, in which the features were learned automatically from raw input signals by using Convolutional Neural Networks (CNN). The output of NSVM is given by a Gaussian Support Vector Machine (SVM), which takes the features learned by CNN as its input. Therefore, the proposed schemes NSVM learned to minimize a margin-based loss instead of a cross-entropy loss. The proposed scheme outperformed the traditional softmax-based CNN classifier by achieving faster convergence of accuracy and loss curves during training/validation. Furthermore, the robustness of the NSVM classifier was verified by extensive simulation experiments under the presence of several non-ideal real-world channel impairments over a range of signal-to-noise values. The performance of the NSVM was remarkable in classifying wireless signals, for example, at a low signal-to-noise ratio (SNR), the overall averaged classification accuracy was \(>\)97% at SNR = \(-\)2 dB, and at a high SNR, it achieved an overall classification accuracy of \(>\)99% at SNR = 10 dB. In addition to that, in the analytical comparison with other studies, the results showed that the performance of NSVM is superior over a range of settings.  
Although, the proposed approach may not always perform well for all modulations under different conditions, so there is still room for improvement. As NSVM is not dependent on N, a parallel computation can be introduced to train deeper models because the classifier performance is greatly dependent on the architecture and the depth of the feature extraction module. Extended implications of the proposed scheme also include increasing the number of modulation schemes to classify.  
Conceptualization, A.H.W. and Y.W.; methodology, A.H.W. and Y.W.; validation, A.H.W., Y.W. and R.C.; writing, review and editing, A.H.W. and Y.W.; funding acquisition, L.C.  
This work was support in part by National Science and Technology under grant 2016 ZX03002003-003 and in part by the Beijing Science and Technology project under grant Z171100001117147.  
The first Author, [PERSON], hereby acknowledges the University of Chinese Academy of Science (CAS) and The World Academy of Sciences (TWAS) for financial support of Ph.D. studies under the CAS-TWAS Fellowship.  
The authors declare no conflict of interest.
## 4 GNN-Based Digital Twins for Next-Generation Networks
### Major Benefits of Using GNN-Based Digital Twins in Next-Generation Networks
#### 4.1.3 Predictive Maintenance  
The capabilities of a GraphNDT to analyze system states over time can be leveraged for predictive maintenance, including predicting failure points, tracing back to root causes, and making maintenance decisions [28; 31; 43; 44]. By providing two-way communication, an NDT not only allows synchronizing the digital twin with the actual network infrastructure, but also provides a means to apply remote reconfiguration, simulate potential changes or maintenance activities, and validate their impact on network performance. Additionally, GraphNDT can facilitate the detection of anomalies or attacks by applying data-driven methods to time series data combined with network topology (see Section 3.2.3). After that, the graph structure, representing the communication between network elements, is crucial for performing root cause analysis. GNNs are well-suited for exploiting this structural information and tracing back to the root cause of anomalies detected within the network. Therefore, the combination of an NDT and GNNs empowers predictive maintenance, enabling a comprehensive understanding of network behavior and proactive maintenance decision making.
## 3 Model
### Attention Layer  
The self-attention mechanism can learn the internal structure of sentences and can learn the weight between every two nodes according to the correlation information between words. However, the weight vector usually obtained by the self-attention mechanism can only represent one-sided information of the sentence. Medical texts have a high entity density distribution, and for a sentence, there may be multiple aspects of semantic information that together constitute the overall information of the sentence. To be able to capture the dependency information between each node in the graph structure in a multi-dimensional way, this model uses a multi-head attention mechanism to learn the weight information between nodes from different semantic spaces.  
The calculation of the attention mechanism involves a set of vectors \(Q,K,V\). Firstly, the current input \(h\) is multiplied by three independent parameter vectors \(w^{q},w^{k},w^{p}\). Then, the attention is calculated by zooming the dot product, as shown in Equation (6):  
\[Attention(Q,K,V)=softmax(\frac{Q^{T}K}{\sqrt{d_{k}}})V \tag{6}\]  
Figure 6: GCN local convolution graph.  
where \(Q,K,V\) represent the query matrix, key matrix, and value matrix, respectively, \(\frac{1}{\sqrt{d_{k}}}\) is used as the scaling factor, and \(softmax\) normalizes the result of \(Q^{T}K\).  
The multi-head attention module contains multiple heads for parallel computation, and \(N\) is the number of heads. \(Q,K\), and \(V\) will be mapped \(N\) times independently using different parameter matrices, and then will be input to \(N\) parallel heads for attention computation. Finally, the attention results of the N heads are stitched together and then linearly transformed to obtain the final output. The specific calculation process is shown in Equations (7) and (8).  
\[head_{i}=Attention(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V})\quad i\in[1,N] \tag{7}\]  
\[at_{t}=Multihead(Q,K,V)=concat(head_{1},head_{2},...,head_{n})W^{o} \tag{8}\]  
where \(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V},W^{o}\) are the parameter matrices used in the linear mapping, \(head_{i}\) denotes the \(i\)-th head attention module, and \(concat\) is the splicing multi-head operation.
## 4 BIM Element Classification
### Results and Discussion
#### 4.3.2 Comparison of Classification Results in Single Different Dataset  
The main purpose of this section is to verify the effectiveness of the machine learning algorithm for the classification of BIM elements on a single different data set, using the grid search method to find the optimal super parameters. The particular single data set selected was ON, NI, and EL, respectively, all of which contain four types of elements: BEAM, DOOR, MEMBER, and PLATE. Due to the large difference in the number of various elements, the resampling method was adopted to ensure that the samples ON each data set are balanced. SVM, ANN, decision tree and random forest were used to obtain the best classification results of these methods on the above three data sets. The parentheses following the dataset name in Table 10 represent the number of elements to be classified in the dataset.  
From the horizontal comparison shown in Table 10, we can observe that, in general, all four methods have the highest classification accuracy with the data set \"ON\", the lowest with NI, and EL is intermediate, indicating that the main reason for this observation is the data set. In other words, the data set \"ON\" has the best separability, while \"NI\" has the worst. The reason is that the data set \"NI\" is much larger, which increases the complexity of the data set. In machine learning, the features to be \"learned\" are more complex. However, that does not mean that the greater the number of elements, the worse the separability, as can be confirmed by the classification results with data sets \"ON\" and \"EL\".  
From a vertical comparison of the classification results obtained with different methods on the same data set, the accuracy of the RANDOM forest method seems to be the highest, with the test accuracy of \"ON\", \"NI\" and \"EL\" reaching 1, 0.9454 and 0.9767, respectively. The difference in accuracy between the random forest and the other three methods is within two percent of each other. Furthermore, the accuracy obtained on the test sets are all above 0.94, which proves the feasibility of BIM element classification for different data sets using these four machine learning methods. This provides an answer the question in Section 3.1.
# Network Attack Classification in IoT Using Support Vector Machines +
## 3 Methodology for IDS Using SVM
### Cross-Validation and Grid Search  
The SVM and RBF kernel use the free parameters \(C\) and \(\gamma\) for the Equations (2) and (9). The parameters \((C,\gamma)\) are used to maximize the margin of the SVM hyperplane. It is an important step to avoid over-fitting the data. SVM aims in maximizing the margin from the hyperplane to avoid misclassification errors [7]. The parameters are chosen through the cross-validation and grid search methods that extensively evaluate the \((C,\gamma)\) values against the input data set. The cross validation technique splits the data to training and evaluation. The grid search evaluates pairs of \((C,\gamma)\) against the input data to find the best pair for the best hyperplane margin.
# Antioxidant and Antimicrobial Properties of Selected Red Seaweeds from Central Portugal
## 2 Materials and Methods
### ABTS Radical Scarenging Activity Assay  
The 2,2'-azino-bis (3-ethylbenzothazoline-6-sulfonic acid) (ABTS) radical scavenging activity was measured according to [PERSON] et al. [64] with a few modifications. Briefly, an ABTS stock solution was prepared by mixing equal volumes of 7 mM of ABTS and 2.45 mM of potassium persulfate solutions (both previously prepared in ultra-pure water), and was allowed to stand at room temperature for 12 to 16 h. Afterwards, this stock solution was further diluted with ultra-pure water until reaching an absorbance value of 0.72 \(\pm\) 0.02 at 734 nm; the new dilution was considered our ABTS working solution. The aqueous extracts were dissolved into different concentrations (20, 40, 60, 80 mg\(\cdot\)mL\({}^{-1}\)).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Species** & **Order** & **Code** & \
\begin{tabular}{c} **Harvest** \\ **Season** \\ \end{tabular} & **Coordinates** \\ \hline _Porphyna umbilicalis_ (Linnaeus) J. Agadh & Bangiales & PoUm & Winter &
\begin{tabular}{c} 39\({}^{\circ}\)19\({}^{\prime}\)10\({}^{\prime}\) N, \\ 9\({}^{\circ}\)21\({}^{\prime}\)24\({}^{\prime}\) W \\ 39\({}^{\circ}\)19\({}^{\prime}\)27\({}^{\prime}\) N, \\ 9\({}^{\circ}\)21\({}^{\prime}\)  
The reaction was adapted to a microscale, being carried out in a 96-well plate containing 2 \(\mu\)L of the aqueous extract (either at 20, 40, 60, 80 and 100 mg\(\cdot\)mL\({}^{-1}\)) and 198 \(\mu\)L of ABTS working solution. Blank reactions containing 2 \(\mu\)L of ultra-pure water instead of extract, as well as a control made with acid ascorbic (10 mg\(\cdot\)mL\({}^{-1}\)), were also performed. The plate was incubated in the dark for 6 min at room temperature, and the absorbance was measured afterwards at 734 nm (Biotek, Epoch2). The ABTS radical scavenging activity was calculated according to Equation (1).  
\[ABTS\ Radical\ Scavenging\ Activity\ (\%)=\frac{AbsC-(AbsS-AbsB)}{AbsC}\times 100 \tag{1}\]  
where \(AbsC\) is the absorbance of the control at 734 nm (ultra-pure water and ABTS), \(AbsS\) is the absorbance of the sample at 734 nm (extract and ABTS) and \(AbsB\) is the absorbance of the blank at 734 nm (sample and ultra-pure water). From the results obtained from Equation (1), the Half Maximal Effective Concentration (EC\({}_{50}\)), defined as \"the concentration of a drug that gives half-maximal response\" [65], was calculated. The results were interpreted considering: the lower the EC\({}_{50}\) value, the higher antioxidant activity the respective extract holds.
# Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors +
## 5 Conclusions and Future Work  
The application of Trusted Autonomous Satellite Operations (TASO) is feasible in Distributed Satellite Systems (DSS) owing to the incorporation of reactive elements in the management of various types of Earth Observation (EO) missions. The applicability to a wildfire sensing and management mission is demonstrated in this research work, focusing on real-time/near-real-time operations. Since the results are promising, the same approach can be used for other EO/disaster management events such as flood detection, hazardous zone monitoring, and volcanic eruption. This is made possible by the DSS's autonomous reconfiguration and predictive/reactive integrity features, which can be provided by AI-based software algorithms. With the inclusion of such features, the overall system architecture becomes an intelligent DSS (iDSS), which exhibits high levels of flexibility, resilience, and trusted autonomy in several practical applications, also beyond EO. In future research, the suitability of iDSS to detect/analyse rare events in astronomy and astrophysics-based missions will be investigated. Furthermore, heterogeneous satellites and sensors will be considered, and effective scheduling and planning strategies will be examined for autonomous navigation and mission management tasks. As TASO capabilities evolve, humans will play a supervisory role in iDSS operations, shifting from human-in-the-loop to human-on-the-loop mission concepts. This will place emphasis on iDSS flexibility and resilience (i.e., the ability of such systems to properly react/adapt to both changing requirements and hardware/software malfunctions), enabling a timely and effective reconfiguration of the iDSS to successfully accomplish space missions with no or little performance degradations.  
Conceptualisation, K.T., D.S. and R.S.; methodology, K.T. and D.S.; software, K.T., D.S. and S.A.; formal analysis, K.T. and D.S.; investigation, K.T., D.S. and S.A.; validation, K.T., D.S. and S.A.; writing--original draft preparation, K.T. and D.S.; writing--review and editing, S.A., R.S., H.F., P.M., L.A., P.S. and N.L.; visualisation, K.T. and D.S.; supervision, S.A., D.S., R.S., H.F., P.M., L.A., P.S. and N.L. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the SmartSat Cooperative Research Centre (CRC) for their support of this work through the collaborative research project No. 2.13s.  
Not applicable.  
The code and data are available here: [[https://github.com/[PERSON]/Tutorial_PRISMA_IGARSS_wildfires](https://github.com/[PERSON]/Tutorial_PRISMA_IGARSS_wildfires)]([https://github.com/DarioSpiller/Tutorial_PRISMA_IGARSS_wildfires](https://github.com/DarioSpiller/Tutorial_PRISMA_IGARSS_wildfires)), accessed on 26 January 2023.  
FRISMA L1 and L2 data are available free of charge under PRISMA data license policy and can be accessed online at the official Italian Space Agency website www.prisma.asi.it, accessed on 15 August 2022.  
The authors declare no conflict of interest.
## 4 Evaluation Methodology
### Experiment Result of Feature Selection with Permutation Decoding
#### 4.4.4 Result of ETS, CSI, PAG, ACC  
The results of ETS/CSI/PAG/ACC are shown in Table 5 and expressed graphically in Figure 10 (Proposed vs. wrapper-DR) and Figure 11 (Proposed vs. PCA). The ETS, CSI, PAG, and ACC results show that the proposed algorithm performs better than PCA and wrapper-DR methods in all cases, with 0.9160, 0.9920, 0.9940, and 0.9927 at 10%; 0.9279, 0.9929, 0.9950; and 0.9935 at 15%, and 0.9318, 0.9933, 0.9951, and 0.9942 at 20%. ETS and CSI score are reflected, as that algorithm solves the imbalance class problem and searches for the optimal forecast model. In these results, we find that the proposed algorithm performs better than the others in searching for optimal detection models. In the boxplot (Figure 10) analysis, it can be seen that the proposed algorithm shows less deviation between repeated experiments. This can be considered as showing consistent performance indicators compared to wrapper-DR.
## 3 Results
### Scanning Electron Microscopy (SEM)  
The SEM micrographs of fractured surfaces of raw and cooked _Gnocchi_ prepared with tap and sea water are reported in Figure 3. _Gnocchi_ samples were cooked in unsalted water, in order to respect the right balance of dispersed inner ions and to correctly evaluate the morphological structure of GSW and GTW fractured surfaces. The micrographs related to raw (Figure 3a) and cooked (Figure 3b,c) GSW samples evidenced a tight structure of interconnected polymer regions homogeneously distributed. In fact, strong physical interactions occurred between the polar residues of starch, protein, and the ions naturally present in sea water, resulting in an optimal adhesion and stickiness of all the components, substantially different from the weak and labile network of samples cooked in salty water. It can be concluded that the sea water, used as a new ingredient of gnocchi dough improved their textural characteristics. Moreover, it is amazing to highlight that no substantial structural differences were observed before and after _Gnocchi_ cooking, thus meaning that the physical binding resulted in a three-dimensional stable network in which mostly sea water macro-micro nutrients were preserved also after cooking at high temperature. This outcome both confirms the effectiveness of sea water as a protective agent of _Gnocchi_ morphology, appearance and texture after the thermal treatment, and supports the previous findings of GSW higher thermal stability. Nevertheless, some small circular voids were observed both in raw and in cooked GSW samples, as evidenced in Figure 3c. They were likely due to the formation of some air bubbles during _Gnocchi_ kneading and preserved also after cooking, further confirming the strength of the GSW polymer network. As far as GTW samples are concerned, in Figure 3d-f, the micrographs of raw (Figure 3d) and cooked (Figure 3e,f) samples are reported. A more heterogeneous and weaker network with the presence of irregular voids dispersed throughout the inner delaminated surface could be highlighted [47]. In particular, from the higher magnification of cooked GTW samples of Figure 3f, discrete domains of higher material agglomeration were found, typical of a less tight and uneven polymer network [48]. In fact, during cooking, starch gelatinization and protein coagulation, occurring nearly at the same temperature, causes major structural changes resulting in textural hardness and stickiness loss.  
Figure 3: SEM micrographs of _Gnocchi_ fractured surfaces: raw (**a**) and cooked in saline water (**b,c**) and raw (**d**) and cooked in tap water (**e,f**).  
Figure 2: (**a**) TGA thermograms and (**b**) DTG curves of _Gnocchi_ GSW before and after cooking.
## 2 Materials and Methods  
### Materials  
Acetic acid, acetonitrile (ACN), methanol (MeOH), sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)) were purchased from Sigma-Aldrich (Milan, Italy). The following commercial compound as analytical standards are used: gallic acid, oleuppein and p-hydroxyphenylacetic acid, purchased from Sigma-Aldrich (Milan, Italy); tyrosol, hydroxytyrosol, caffe acid, p-coumaric acid, purchased from TCI (Zwijndrecht, Belgium); luteolin-7-O-glucose, apigenin-7-O-glucose and rutin, purchased from Extrasynthese (Lyon, France). Folin-Ciocalteu reagent was purchased from Merk (Darmstadt, Germany). Phosphate-buffered saline (PBS) was purchased from Sigma-Aldrich (Milan, Italy) and for each analysis was diluted 10 times. Chloroform (code: 102442), 2-butanol (code: 109630), lithium bromide (LiBr\({}_{2}\)), acetic acid (code: 33209), ethanol (EOH), PHBHV (HV content 12% \(w/v\)) and Dulbecco's phosphate-buffered saline (DPBS) were provided by Sigma-Aldrich (Milan, Italy). Im-mortalized human keratinocytes, HaCaT cell line was purchased from CLS--Cell Lines Service, Eppelheim, Germany. MgCl\({}_{2}\), Dulbecco's Modified Essential Medium (DMEM), L-glutamine, penicillin, streptomycin and fetal calf serum were purchased from Invitrogen, (Carlsbad, CA, USA). AlamarBlue was bought from Thermo Fisher Scientific (Waltham, MA, USA). LC Fast Start DNA Master SYBR Green kit was obtained from Roche Applied Science (Eurolone S.P.A., Pero, Italy). P(3 HB) and P(3 HO-3 HD) were supplied by Roylab, University of Sheffield, UK.