esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper applies a deep learning approach to model the mechanism of path loss based on the path profile in urban propagation environments for 5G cellular communication systems. The proposed method combines the log-distance path loss model for line-of-sight propagation scenarios and a deep-learning-based model for non-line-of-sight cases. Simulation results show that the proposed path loss model outperforms the conventional models when operating in the 3.5 GHz frequency band. The standard deviation of prediction error was reduced by 34% when compared to the conventional models. To explain the internal behavior of the proposed deep-learning-based model, which is a black box in nature, eight relevant features were selected to model the path loss based on a linear regression approach. Simulation results show that the accuracy of the explanatory model reached 72% when it was used to explain the proposed deep learning model. Furthermore, the proposed deep learning model was also evaluated in a non-standalone 5G New Radio network in the urban environment of Taipei City. The real-world measurements show that the standard deviation of prediction error can be reduced by 30-43% when compared to the conventional models. In addition, the transparency of the proposed deep learning model reached 63% in the realistic 5G network.  
path loss model; path profile; deep learning
## 2 Materials and Methods  
This study followed the principles laid down by the World Medical Assembly in the Declaration of Helsinki 2008 on medical protocols and ethics, it was approved by the Ethical Committee of the University of University of Rome \"Tor Vergata\" (protocol number: 257/21), and informed consent was obtained from the patients' parents.  
Digital dental casts of 32 children consecutively treated either with RME (RG group: \(n=17\), 8 males, 9 females; mean age 8.1 \(\pm\) 0.8 years) or the Clear aligners (CAG group: \(n=15\), 7 males, 8 females; mean age 8.4 \(\pm\) 1.1 years) were collected. Study subjects were retrieved from the records of patients treated at the Department of Orthodontics at the University of Rome \"Tor Vergata\".  
The inclusion criteria consisted of European ancestry, posterior transverse discrepancy between maxillary and mandibular arches of up to 6 mm [10], mixed dentition, mesial step or flush terminal plane molar relationship, fully erupted first molars, and a high level of compliance. The posterior transverse inter-arch discrepancy was obtained by calculating the difference between the maxillary interpolar width (distance between the central fossae of right and left first maxillary molars) and the mandibular interpolar width (distance between the mesio-vestibular cusps of right and left first mandibular molars) [10].  
The exclusion criteria included multiple and/or advanced caries, tooth agenesis, supernumerary teeth, cleft lip and/or palate, Class III malocclusion, and oral breathing.
# APT Detection via Hypergraph Attention Network with Community-Based Behavioral Mining
## 9 Evaluation
### Ablation Experiment  
In the first experiment, we compared attention-based network models similar to the hypergraph attention mechanism. In the \"w/o attention\" operation, convolutional operations were used to replace the node-level and edge-level attention mechanisms. In the \"w/o behavior semantic\" operation, the adaptability calculation for overlapping communities was based on the in-degree and out-degree of nodes to construct the hypergraph. In the experiment, we also added GCN and GAT as baseline methods for comparison.  
The experimental results are shown in Table 6. The experiment was conducted on the Theia and Cadets datasets. The provenance graph contains the host's behavior features, which are crucial for accurate classification.  
HyperGAT significantly outperforms the other four experimental groups. It achieved accuracy scores of 0.9773 \(\pm\) 0.0018 on the Theia dataset and 0.9253 \(\pm\) 0.0006 on the Cadets dataset, far surpassing \"w/o attention\" and \"w/o behavior semantic\", which demonstrates its distinct advantage in handling hypergraph classification tasks.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Accuracy** & **Recall** & **F1-Score** & **FPR** & **AUC** \\ \hline StreamSpot & 0.764 & 0.891 & 0.871 & 0.216 & 0.797 \\ ThreatTrace & 0.875 & 0.874 & 0.885 & 0.152 & 0.863 \\ Ours & **0.903** & **0.915** & **0.898** & **0.092** & **0.914** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of Related Work on Abnormal Nodes Detection.  
Further analysis reveals the pivotal roles of the attention mechanism and the constructed hypergraph's semantic information in the recognition process. HyperGAT's unique dual-attention mechanism enables it to learn features of nodes and edges within hyper-edges, thus better capturing node relationships and behavior patterns for accurate behavior recognition. Meanwhile, graph contextual semantics help the model understand the position and meaning of nodes and edges in the overall graph structure, further improving classification accuracy.  
Also, false positives (FPRs) are crucial in graph-level detection tasks. Experimental results show HyperGAT has low FPRs of 0.0012 \(\pm\) 0.0010 on Theia and 0.0016 \(\pm\) 0.0011 on Cadets, rarely misclassifying negative instances and effectively controlling false positives. In contrast, \"w/o attention\" and \"w/o behavior semantic\" have higher FPRs, being more prone to misjudging positive and negative instances. The lower FPR makes HyperGAT more reliable and practical in real-world applications, delivering more accurate results for provenance graph classification.
# Proteomics in Forensic Analysis: Applications for Human Samples
## 3 Applications of Forensic Proteomics for Human Samples
### Other Applications of Forensic Proteomics for Human Samples
#### 3.4.6 Decomposition Fluid  
A recent study reported a targeted proteomic analysis of pig decomposition fluid for the estimation of PMI [33]. A total of 29 peptides showing differences in the mean peak areas were identified by comparing samples collected in the early and late periods of decomposition in both summer and winter. Among them, eight peptides had significantly different fold changes between early and late PMI, including GHLDDPGAL (from hemoglobin subunit alpha), ESFGDLSNADAVMGNPRVK, FGDLSNADAVMGNPRK, GDL-SNADAVMGNNPKVK (from hemoglobin subunit beta), KDLFDPIIQDR (from creatine kinase), IVGDDLTVTNPK (from beta-enolase), DLQHGSLF, and ILGQNGBSDVVKV (from lactate dehydrogenase). Thus, PMI can also be predicted using proteomic analysis of the decomposition fluid. An untargeted method can also be used to obtain a full profile of protein degradation in post-mortem samples. It is also necessary to increase the sample size in future studies.  
In addition to decomposition fluid, other types of human samples have been used for estimation of PMI, such as bone, muscle, brain, and cerebrospinal fluid (as discussed in Section 3.2, Section 3.4.3, and Section 3.4.4). Estimation of PMI (i.e., the time elapsed since death) is a crucial task in forensics to investigate unidentified human remains. A frequently used method is to assess gross morphological changes of the body visually. However, the rate of these changes is highly variable, and the reliability depends on the method and experience of investigators [171; 172]. Biochemical techniques have emerged as precise and accurate methods to estimate PMI via analyses of proteins, metabolites, and RNA. These methods need to be validated for applications in forensic contexts [173; 174; 175]. Among the samples to be used, bone is a promising one for the development of PMI estimation methods with high accuracy, preciseness, and objectiveness. In bone, proteins are relatively stable and can be successfully extracted for proteomic analysis, whereas the analysis requires only a small amount of sample (microgram to sub-microgram of protein extract). Thus, it is possible to estimate late-PMI (e.g., >100 years) [41]. Other samples such as muscle, brain, cerebrospinal fluid, and decomposition fluid can be used for early and mid-PMI. Proteomic methods to estimate PMI have been developed and evaluated by many researchers. Some methods, as discussed above, were successfully applied in forensic contexts.
## 7 Results and Discussion
* Predicted anomaly: This indicates the start of the predicted anomaly. On the right-hand side of the graph, the time series values are presented, with the following legend:  
* Effective time series: This represents the actual trend of the time series. * Predicted time series: This shows the predicted trend of the time series. * Actual anomaly: This indicates the start of the actual anomaly. * Predicted anomaly: This signals the start of the predicted anomaly. This visual representation provides a clear comparison between the two models in terms of standard deviation and offers a detailed overview of the temporal trend of the time series, highlighting both actual and predicted anomalies. The analysis begins by focusing on the system current as the first feature. As highlighted in Figure 13, although both models show a similar trend in the time series, the DeepESN presents timelier error detection. The prediction of the time series by DeepESN has a slightly more indented behavior than the LSTM model. DeepESN allows us to find the fault before LSTM because after the effective fault, the prediction starts to look very different from the effective time series. Figure 12: Development of the accuracy, F1 score, time, and CO2 emissions metrics for each epoch of LSTM model training compared with the one-shot DeepESN results. A further indicator used to evaluate the performance of the proposed model is the confusion matrix (CM), which allows for the calculation of metrics such as precision, recall, and accuracy, providing a detailed view of the classification errors. As shown in Table 4, the percentage of \(FN\) is higher than \(FP\), confirming the lower value of recall with respect to precision. Another useful indicator is the receiver operating characteristic (ROC) curve shown in Figure 14. It shows that the model is effective in distinguishing between positive and negative instances. Indeed, the area under the curve (AUC) score, equal to 77%, further quantifies its performance, showing that the proposed model correctly ranks a randomly chosen positive instance higher than a negative instance 77% of the time. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Actual Positives** & **Actual Negatives** \\ \hline Predicted Positives & 37.62\% & 15.98\% \\ \hline Predicted Negatives & 7.22\% & 39.18\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: DeepESN confusion matrix. Figure 14: DeepESN receiver operating characteristic (ROC) curve. Figure 13: Current system anomaly detection.
# Influence of Noble Metals on the Microstructure and Properties of Biodegradable Mg-Nd-Zr Alloy
## 2 Materials and Methods  
The biodegradable Mg-based alloy of the following chemical composition was studied: 0.9 wt.% Zr, 2.6 wt.% Nd, 0.6 wt.% Zn, Mg is a balance. The alloy was smelted in a crucible induction furnace of 0.5-ton capacity and 140 kW power and also in a gas-protected furnace of 150 kg capacity. Preheated charge materials were loaded into the crucible furnace and, after melting, the melt was poured into removable crucibles at a temperature of 650-730 \({}^{\circ}\)C. Removable crucibles were installed in holding furnaces, in which the chemical composition of the alloy was adjusted followed by the flux refining (38-46 wt.% MgCl\({}_{2}\), 32-40 wt.% KCl, 5-8 wt.% BaCl\({}_{2}\), 3-5 wt.% CaF\({}_{2}\)) at 740-760 \({}^{\circ}\)C. After this, Ag and Au additives were added to the melt (0; 0.05; 0.1; 0.2 wt.%--as calculated), with subsequent heating of the melt, holding and pouring it at 730 \({}^{\circ}\)C into molds to obtain samples and workpieces for mechanical testing and metallographic control.  
Heat treatment of cast billets was carried out in a 112 kW Bellevue-type thermal shaft furnace and also in a thermal furnace PAP-4M type. The metal was examined after heat treatment according to the following regime: heating to 540 \(\pm\) 5 \({}^{\circ}\)C, holding for 15 h, air cooling to room temperature and aging at 200 \(\pm\) 5 \({}^{\circ}\)C for 8 h with subsequent air cooling.  
The quality of cast samples and workpieces made of Mg-based alloys was determined by X-ray control. Optical microscopy was used for the observation of the macro- and microstructure of samples (OLYMPUS IX 70). The grain size was measured according to the intercept method using the built-in software for the optical microscope. The specimens for microstructure analysis were mirror polished according to standard procedure and etched by the reagent consisting of 1% nitric acid, 20% acetic acid, 19% distilled water and 60% ethylene glycol. The microhardness of the structural components of the alloys was measured with Buehler and LM-700 AT microhardness testers at a 0.1 N load.  
The fractured surface of the samples was observed using a Jeol JSM-6360 LA scanning electron microscope. Local chemical analysis of the structural components was performed using REMMA 202M and REM 16I scanning electron microscopes equipped with an energy-dispersive microanalyzer.  
The mechanical properties of the samples were determined using an INSTRON 2801 tensile testing machine. The tensile testing was performed for as-prepared samples and for samples held in an artificial blood substitute for one, two and three months at 36 \(\pm\) 1 \({}^{\circ}\)C. The temperature steadiness was regulated by the thermostat UT-15. The artificial blood substitute was a Gelofusine(r) solution. Before immersion in blood substitute, the samples were degreased by ethanol. After immersion, the samples were cleaned by chromic anhydride at 18-25 \({}^{\circ}\)C for 3 min to delete the corrosion products and, afterwards, they were washed in distilled water and air dried.
## 2 Results
### Principal Component Analysis (PCA)  
Principal component analysis (PCA) of samples (including quality control samples) provides a preliminary understanding of the overall metabolite difference between groups and the degree of variability between samples within the group. [PERSON] et al. initially showed that the secondary metabolites in black sesame and white sesame were significantly different by PCA scores [36]. [PERSON] et al. analyzed by PCA that the effects of cultivation conditions were greater than the species and sampling sites on metabolites in leaf lettuce [37]. Based on the Scree plot of cumulative eigenvalues (Figure 2), the first two principal components clearly separated all samples, and explained 56.69% of the total variation. In the PCA plot, the quality controllsamples grouped together, which indicated that the quality control samples had similar metabolic profiles and that the entire analysis was stable and repeatable. Vanilla beans from the same curing stages were clustered together, indicating that the metabolites were generally similar. On the contrary, vanilla beans from the different curing stages (FVB, BVB, SVB, DVB, CVB) were scattered, implying that their metabolites were quite different. Particularly, FVB showed significant differences from CVB.  
Figure 1: Liquid chromatography–tandem spectrometry (LC–MS/MS) base peak intensity (BPI) profiles of metabolites different processed vanilla beans (FVB (fresh vanilla bean): Blue, BVB (blanched vanilla bean): Red, SVB (sweating vanilla bean): Green, DVB (drying vanilla bean): Gray, and CVB (cured vanilla bean): Light blue).
# CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions
## 4 Experiments
### Results and Discussion
#### 4.3.3 FMNIST  
Because the size of FMNIST is 28 \(\times\) 28, which is not the same as CIFAR-10 and SVHN, when we transposed the features from blocks 3 and 4, the feature size of TC layers was not equal to that of previous layers, so we used additional layers with stride 2 and 0-padding to adjust the output size to equal that of previous features that will lose some boundary information. The experimental results are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Baseline** & **Training Loss** & **Training Accuracy** & **Test Loss** & **Test Accuracy** \\ \hline CDT\_C & VGG16 & 25.55\% \(\downarrow\) & 9.83\% \(\uparrow\) & 18.14\% \(\downarrow\) & 9.13\% \(\uparrow\) \\ CDT\_C & VGG19 & 68.83\% \(\downarrow\) & 58.73\% \(\uparrow\) & 62.85\% \(\downarrow\) & 54.81\% \(\uparrow\) \\ CDT\_C & ResNeXt & 42.16\% \(\downarrow\) & 5.54\% \(\uparrow\) & 40.8\% \(\downarrow\) & 17.49\% \(\uparrow\) \\ CDT\_C & DenseNet & 13.84\% \(\downarrow\) & 5.92\% \(\uparrow\) & 17.68\% \(\downarrow\) & 3.82\% \(\uparrow\) \\ CDT\_A & VGG16 & 23.3\% \(\downarrow\) & 7.71\% \(\uparrow\) & 15.98\% \(\downarrow\) & 6.79\% \(\uparrow\) \\ CDT\_A & VGG19 & 67.89\% \(\downarrow\) & 55.67\% \(\uparrow\) & 61.87\% \(\downarrow\) & 51.48\% \(\uparrow\) \\ CDT\_A & ResNeXt & 40.41\% \(\downarrow\) & 3.50\% \(\uparrow\) & 39.24\% \(\downarrow\) & 14.97\% \(\uparrow\) \\ CDT\_A & DenseNet & 11.24\% \(\downarrow\) & 3.87\% \(\uparrow\) & 15.51\% \(\downarrow\) & 1.59\% \(\uparrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific compared results on SVHN.  
Figure 8: Compared results on SVHN.  
As seen in Figure 9, the additional 0-padding convolution may lose some boundary information, and the training loss and training accuracy of VGGs are better than those of CDTNet at the beginning, but the performance of CDTNet turns the tide after epoch 28, and the test accuracy of CDTNet is also better than VGG16, VGG19, ResNeXt, and DenseNet. The test accuracies of all models are shown in Table 3.  
The CDTNet has better performance in the training stage and improves the test accuracy. CDT_C improves the test accuracy by 1.35%, 1.41%, 1.80%, and 2.23% compared to VGG16, VGG19, ResNeXt, and DenseNet, respectively. CDT_A improves the test accuracy by 1.28%, 1.35%, 1.73%, and 2.17% compared to VGG16, VGG19, ResNeXt, and DenseNet, respectively.
## 2 Method
### Magnetic Anomaly Inversion
#### 2.4.2 The Construction of the Inverse Problem  
The objective function of \(l_{p}\) norm magnetic anomaly inversion can be expressed as follows,  
\[\begin{array}{l}\min\ \Phi(\mathbf{m})=\Phi_{d}(\mathbf{m})+\lambda\Phi_{\mathbf{m}}(\mathbf{m}) \\ s.t.\ \mathbf{m}_{a}\leq\mathbf{m}\leq\mathbf{m}_{b},\end{array} \tag{21}\]  
\(\lambda\) is the regularization coefficient; \(\Phi_{d  
There are many depth weighting methods, and the method proposed in [6] is more commonly used. The depth weighting function can be expressed as,  
\[W_{z}=(z+z_{0})^{-\beta}, \tag{27}\]  
where \(\beta\) is the attenuation index, for 2D magnetic inversion, \(\beta=2\)[10]; \(z_{0}\) is a constant, which is related to the observation height of the measuring point; z is the depth of the element's center. When p-norm is considered, the weighting coefficient of element \(i\) can be obtained by applying Equation (27):  
\[W_{z,i}=(z_{i}+z_{0})^{-\frac{\beta}{\beta}}, \tag{28}\]  
(2) Weighting of element area  
The area weighting matrix is a diagonal sparse matrix formed by the area of the element. For any element \(i\), if the size of it is known to be \(\Delta y_{i},\Delta z_{i}\), then,  
\[W_{V,i}=\Delta y_{i}\cdot\Delta z_{i} \tag{29}\]  
When the element size of the inversion space is uniform, the area of each element is the same, which makes no sense to employ area weighting. However, when the inversion element was non-uniform, an area weighting matrix was necessary.  
In summary, we have considered two kinds of model weight information, the weight coefficient of element \(i\) is,  
\[\begin{array}{c}W_{m,i}=\frac{W_{V,i}W_{z,i}}{\sum W_{V,i}W_{z,i}}\\ W_{m}=[W_{m,1},\ W_{m,2},\cdots,W_{m,D}]^{T}\end{array} \tag{30}\]  
where \(W_{m}\) is the weight vector of the model. For example, the effect of depth weighting was to suppress the shallow elements and avoid model concentration near the surface.
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 3 Proposed Model
### Public and Private Data Access Control  
To maintain both transparency and confidentiality, our system separates data access pathways based on data sensitivity. Public data, such as mission logs or general telemetry, is stored on the public blockchain and is accessible to all users. Users can retrieve data integrity proofs from the public blockchain and validate them independently via smart contracts without restrictions.  
In contrast, private data--such as astronaut health records or mission-critical configurations--is stored on the private blockchain with strict access controls. We implement a role-based access control system to manage authorization. Under RBAC, users are assigned specific roles, and each role is granted a defined set of permissions. Access to private data is permitted only if the requester's role matches the access policy defined for that data category.  
Access control is enforced using smart contracts deployed on the private blockchain. Each access request triggers a chaincode invocation, during which the user's identity and role are verified using the Membership Service Provider. The access control logic checks the user's role against the required permission level before authorizing data retrieval.  
Example Access Logic:  
* If a user requests data labeled as \"eva_log_secure\", the smart contract verifies:
* Is the user's role in [MissionCommander, EVAController]?
* Has the user's identity been cryptographically validated via X.509 certificates?
* If so, grant access and log the event; otherwise, deny access.  
This approach ensures that only authorized personnel can access sensitive information while keeping audit logs for traceability. All successful and failed attempts are recorded in the Fabric ledger for post-mission audit analysis.  
Additionally, we store only cryptographic proofs for private data on the public blockchain. This enables third-party auditors to verify data integrity without accessing the actual content, preserving confidentiality while ensuring transparency.
###### Abstract  
This research aimed to optimize the extraction conditions of phenolic compounds by ultrasound-assisted extraction (UAE) from _Cornulaca monacantha_ Del., a species of the Chenopodiaceae family, using response surface methodology (RSM). A three-level Box-Behnken Design was used to investigate the following three factors of extraction conditions: solid-to-liquid ratio (\(X_{i}\)), extraction temperature (\(X_{j}\)), and extraction time (\(X_{k}\)). The optimized UAE extraction conditions obtained were: (\(X_{i}\)) = 0.5:10 g/mL, (\(X_{j}\)) = 45 \({}^{\circ}\)C, and (\(X_{k}\)) = 30 min. Once the extraction conditions of the phenolic compounds had been optimized, this protocol was applied to another plant of the same family, _Anabasis articulata_ (Frossk.) Moq. The optimum values of extraction yield, total polyphenol content (TPC), and total flavonoid content (TFC) were respectively 14.68%, 37.27 (ug GAE/mg DE), and 7.21 (ug QE/mg DE) for _Cornulaca monacantha_ Del., and 13.56%, 58.38 (ug GAE/mg DE), and 6.44 (ug QE/mg DE) for _Anabasis articulata_ (Frossk.) Moq. _Anabasis articulata_ (Frossk.) Moq. has a significantly higher antioxidant potential than _Cornulaca monacantha_ Del. due to its high content of phenolic compounds (TPC). The high concentration of these plants in phenolic compounds validates their potential for traditional medicinal use.  
Chenopodiaceae family; Box-Behnken design; ultrasound-assisted extraction; phenolic compounds; antioxidant activity +
Footnote †: journal: Applied sciences