esa-sceva/satcom-chunk-collection huggingface dataset:
# Polarimetry for Photonic Integrated Circuits
## 4 Discussion  
The polarimetric approach proposed in this work makes use of a complete polarization model of our measurement setup. Other setup configurations may be implemented, but for our particular setup, the three-step calibration technique proves successful. The first step involves free space optics to distinguish the input path from the output path. The second step is to bring the setup into its final configuration. Both steps involve a considerable amount of measurement data. In the third step, we use a numerical least-squares fit for the 18 open parameters. During this work, we find this amount of parameters necessary to achieve the highest accuracy possible. We also find that the calibration remains stable over the duration of two weeks, if one takes care fixating all fibers to the optical table. To validate the stability, we rerun the measurement as in Figure 6 after two weeks. The maximum error increased from below 3\({}^{\circ}\) to below 5\({}^{\circ}\). This excellent stability is achieved by carefully fixating all SMFs, allowing a robust measurement schedule.  
As shown in Section 3.2, polarimetry allows the extraction of a wide variety of device parameters of integrated devices. We could retrieve retardance, birefringence, fast axis, PDL, and depolarization of a given DUT. Under the assumption of strong polarization dependence (\(\Delta n_{TE}\gg\Delta n_{TM}\)) the chirp parameter can be extracted. New insights into device performance can be gained this way. For instance, we find that the depolarization of the EAM decreases with stronger bias voltages at most wavelengths. This could be attributed to the fact that photocarriers are depleted faster, decreasing their probability to recombine radiatively, and therefore to contribute to spontaneous emission noise. However, this needs further investigation. Furthermore, since the fast axis of the DUT is readily accessible, any polarization conversion can be revealed and analyzed.
# Universal Local Attractors on Graphs
## 6 Discussion
### Requirements to Apply ULA  
Here, we summarize the conditions needed to employ ULA in new settings while benefiting from its local attraction property. We consider these mild, in that they are already satisfied by many settings. In addition to checking these conditions in new settings, make sure to follow the necessary implementation details of Section 4.3, as seen in our publicly available implementation at the experiment repository.  
1. The first condition is that processed graphs should be unweighted and undirected. Normalizing adjacency matrices is accepted (we perform this in experiments--in fact it is necessary to obtain the spectral graph filters we work with), but structurally isomorphic graphs should obtain the same edge weights after normalization; otherwise, Theorem A3 cannot hold in its present form. Future theoretical analysis can consider discretizing edge weights to obtain more powerful results.
2. Furthermore, loss functions and output activations should be twice-differentiable, and each of their gradient dimensions should be Lipschitz. Our results can be easily extended to differentiable losses with continuous non-infinite gradients by framing their linearization needed by Theorem A1 as a locally Lipschitz derivative. However, more rigorous analysis is needed to extend results to non-differentiable losses, even when these are differentiable almost everywhere.
3. Lipschitz continuity of loss and activation derivatives is typically easy to satisfy, as any Lipschitz constant is acceptable. That said, higher constant values are expected to create architectures that are harder to train, as they require more fine-grained discretization to separate between graphs with similar attributes. Note that _the output activation is considered part of the loss_ in our analysis, which can drastically simplify matters. Characteristically, passing softmax activation through a categorical cross-entropy loss is known to create gradients equal to the difference between predictions and true labels, which is a 1-Lipschitz function, and therefore, accepts ULA. Furthermore, having no activation with mabs or L2 loss is admissible in regression strategies, as this loss has a 1-Lipschitz derivative. However, employing higher powers of score differences or KL-divergence is not acceptable, as their second derivatives are unbounded, and thus, their first derivatives are not Lipschitz.
4. Finally, ULA admits powerful minimization strategies but requires positional encodings to exhibit stronger expressive power. To this end, we suggest following progress in the respective literature.
## 3 Analysis of Test Results
### Single-Factor Analysis
#### 3.2.5 Setting Time  
Based on Equation (6), the relationship curve among various factors and the slurry setting time is shown in Figure 10. Notably, Equation (6) considers only the water-binder ratio, binder-sand ratio, and soil-binder ratio, suggesting that the influence of other factors on the slurry setting time can be neglected [53; 54]. The soil-binder ratio has a more pronounced effect on the slurry setting time compared to the water-binder ratio and binder-sand ratio. As the soil-binder ratio and muck content increase, the slurry setting time significantly decreases due to the clay in the muck's enhanced ability to bind water molecules [31; 51], bringing the slurry closer to a solid state and shortening its setting time. An increase in the water-binder ratio prolongs the slurry setting time, while a slight decrease is observed with an increase in the binder-sand ratio. The larger the water-binder ratio, the better the flowability of the slurry, the more water molecules in the slurry, and the longer the slurry setting time.  
Figure 10: Relation curve between each factor and slurry setting time.  
Figure 9: Relation curve between each factor and slurry consistency.
# FF-MR: A DoH-Encrypted DNS Covert Channel Detection Method Based on Feature Fusion
## 4 Method Design  
In this section, we design a DoH-encrypted DNS covert channel detection method named FF-MR based on features fusion. FF-MR, including a Multi-Head Attention mechanism and a Residual Neural Network, fuses statistical features and byte sequence features. Its framework is shown in Figure 2.  
FF-MR is mainly divided into three parts: data preprocessing, statistical features and session representation extraction, and a DoH-encrypted DNS covert channel detection model based on Multi-Head Attention and Residual Neural Network. The proposed method achieves HTTPS (i.e., non_DoH), normal DoH (i.e., benign_DoH), and three kinds of malicious DoH traffic (i.e., iodine, dnscat2, and dns2 tcp) in five categories. Iodine, dnscat2, and dns2 tcp are three kinds of malicious DoH traffic generated by encrypted DNS covert channel tools, that is, DoH-encrypted DNS covert channel traffic.  
Firstly, the data preprocessing module splits and reorganizes the raw pcap file into sessions, and then, we clean these sessions for filtering and anonymization.  
Secondly, session representation and statistical features are extracted, and after standardization and normalization, they are used as the input of the DoH-encrypted DNS covert channel detection model.  
Finally, in the DoH-encrypted DNS covert channel detection model based on the Multi-Head Attention and Residual Neural Network (MHA-Resnet), byte sequence features are extracted by the Residual Neural Network, and the Multi-Head Attention mechanism calculates the weighted fusion of session statistical features and byte sequence features so that the distinction between features of different traffic sources is more pronounced. Moreover, the classification of four kinds of DoH and HTTPS traffic is achieved by a Multilayer Perceptron (MLP) to detect and identify DoH-encrypted DNS covert channels.
# Tuning the Cell and Biological Tissue Environment through Magneto-Active Materials
## 6 Magneto-Active Polymers in Biomedical Applications
### MAPs for Neuronal Regeneration  
When developing engineered tissue in vitro, one of the major challenges and requirements is the integration of its vascularisation to provide correct viability and promote growth when implanted in vivo [189]. To overcome these issues, micro-ring scaffolds with magnetic alginate microfibers offer a timely solution, promoting microvascular-like structures [232]. These problems are also present in neuronal regeneration. In recent years, motivated by the lack of natural repair of the central nervous system (reducing life quality of the patients), the development of magneto-responsive biomaterials is being investigated to promote neuronal regeneration [189; 192]. These systems have demonstrated an improvement in neuronal network formation, growth and organisation when subjecting magnetic microbead scaffolds to a controlled magnetic field [233].
# COVIDNet: Implementing Parallel Architecture on Sound and Image for High Efficacy
## 4 Results and Discussion
### Diagnosing COVID-19 Using CXR Image and Cough Audio  
In an effort to build a reasonable method for less experienced medical personnel to make preliminary diagnoses on both CXR and cough files collected from patients, we developed COVIDNet that allows for less experienced medical personnel to make preliminary diagnoses on both CXR and cough files (datasets). CXR image data and cough Mel-spectrogram images can be used to train the convolutional neural network architecture, which can give rapid diagnosis. The researcher converted the lung sound signals to spectrograms using time-frequency method which is basically the short time Fourier transform (STFT). The researcher believed that using deep learning algorithms would be a better approach whereby the first approach is using a pre-trained deep CNN model for feature extraction and a SVM classifier to classify the lung sounds.  
Based on Figure 12, COVIDNet can detect if an individual has COVID-19. In the system below the user has the opportunity to know the area of interest such as the pneumonia growth based on the patient's CXR image.  
In the CXR Image which is displayed it can be seen there is a rectangle shape over the lung highlighting the condition of the lungs and two circles around the bronchus and bronchioles indicating the presence of pneumonia growth if it exists.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Category** & **Result (\%)** \\ \hline Accuracy & 91 \\ Precision & 84 \\ Recall & 81 \\ Sensitivity & 81 \\ Specificity & 100 \\ F1-score & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 6: COVID-19 Cough Model Performance Table.  
Figure 11: Cough Model Loss Graph.  
On the other hand, for the cough audio file, in the top right, the audio graph is plotted with the soundwave of the input based on the audio file. On the bottom right, the Mel-spectrogram is used and passed to the model to obtain the predicted label and some arrows are used to highlight the parts where the clarity of the air intake after cough is visualized.  
At the bottom of the screen in the result section below, the system states whether the CXR image is either COVID-19 positive, non-COVID-19 pneumonia positive, or healthy lungs, and highlights to make it distinctive depending on the predicted label. The inspiration for the design of the UI is obtained from Jordan Micah Bennett Project [17].
## 6 Current Challenges and Future Prospects
### Anticipated Advancements and Research Directions
While this perspective holds some truth, a deeper examination of each technology reveals a significant emphasis on human inclusion, environmental awareness, and social responsibility [13; 185]. The European Commission's document emphasizes three central drivers of the new industrial paradigm, Industry 5.0 [32; 183]:  
1. _Human-centric approach_: This approach puts human needs at the forefront of the production process, focusing on how technology can support and benefit workers. 2. _Sustainability_: This approach highlights the importance of reusing, repurposing, and recycling materials, in addition to cutting down on waste and lowering environmental harm. 3. _Resilience_: This involves bolstering industrial production with robustness, which ensures flexibility in processes and adaptability in production capacities, particularly during times of crisis. The main idea behind Industry 4.0 and Industry 5.0 is using technology to gather, process, and analyze data from different sources to improve how things are done in industries. This includes making decisions based on these data and learning from it. Various technologies like computing and digital tools work together to achieve this goal [185]. Table 5 summarizes the basic concepts underlying Industry 4.0 and Industry 5.0. \begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Concept** & **Definition** \\ \hline Internet of Things (IoT) & IoT links computing power and network connections to sensors and objects, using different ways of communication such as between devices or from a device to a gateway. To fully tap into what IoT can do, it is important to deal with issues related to security and privacy [186; 187]. \\ \hline Artificial intelligence (AI) & Artificial intelligence is about computers copying smart actions with minimal help from humans, commonly seen as the science of making machines that can decide things on their own without people telling them what to do [188; 189]. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Common concepts of Industry 4.0 and Industry 5.0 adapted from references [14; 32; 185]. Figure 6: Technologies of Industry 5.0.
# Lunar Calendar Usage to Improve Forecasting Accuracy Rainfall via Machine Learning Methods
## 2 Materials and Methods  
The techniques used for rainfall prediction in this study include long short-term memory (LSTM) and gated recurrent unit (GRU) models. Specifically, the models employed are vanilla LSTM and GRU, 2-stacked-LSTM and 2-stacked-GRU, dual-directional LSTM and two-directional GRU, and 2-stacked-biLSTM and 2-stacked-biGRU.  
The main difference between LSTM and GRU lies in the number of gates and memory cells. LSTM has three gates and two memory cells, while GRU has two gates and one memory cell. BiLSTM, similar to LSTM, uses the same number of gates and memory cells but incorporates two processes: forward and backward. Likewise, BiGRU, which is based on GRU, has two directional processes: forward and backward. The stacked BiLSTM and stacked BiGRU architectures involve a concatenation process, further enhancing their performance.
# Characterization and Finite Element Modeling of Microperforated Titanium Grade 2
## 2 Materials and Methods  
### Tensile Testing  
Tensile testing is a fundamental mechanical test widely used to determine the behavior of materials under axial forces. During this test, a specimen is subjected to a controlled tension until it fractures, allowing the measurement of material properties such as ultimate tensile strength, yield strength, elongation, and Young's modulus. Initially, the sample experiences elastic elongation as it is pulled. With increasing stress, the specimen begins to undergo permanent deformation, known as plastic strain. The yield strength is the stress required to induce noticeable plastic deformation, typically defined at 0.2% strain. Once yielding begins, a significant dislocation motion occurs within the metal grains. It is expected that the presence of microperforations in the titanium specimens influences stresses, strains, and dislocations, which alters the mechanical properties.  
The tensile tests were carried out with the aid of a Zwick 1484 (200 kN) testing machine (made by ZwickRoell GmbH & Co. KG, Ulm, Germany). The VideoXtens 1-120 was used for the strain measurement and the TestXpert III software (version 1.8) was employed to control the tests and collect data. The tests were performed at room temperature and the crosshead speed was set to 12 mm/min (which corresponds to the strain rate of 0.005 s\({}^{-1}\)).  
A sheet of titanium grade 2 with a thickness of 0.8 mm was microperforated and then cut into specimens by means of waterjet cutting. The geometry of the tested specimens is presented in Figure 1. They were then milled to ensure edge quality and to position microperforation exactly in the center of the specimen.  
A microperforation squared pattern of 1 mm was introduced into some specimens to study how mechanical properties change in the presence of holes. These holes have a conical shape with a small diameter in one face (50.0 \(\pm\) 3.0 \(\upmu\)m) and a bigger diameter in the other (120.0 \(\pm\) 6.0 \(\upmu\)m). In all cases, microperforation pitch is aligned according to the rolling direction. Several microholes were observed by using an optical microscope and it was found that they are not exactly round, and the diameter varies from hole to hole. The authors assume that this fact does not affect the results.  
Both perforated and non-perforated specimens were tested in different orientations: longitudinal (rolling direction), transversal, and diagonal. Moreover, all cases were repeated three times. In summary, 18 samples were tensile tested: 3 orientations \(\times\) 2 materials \(\times\) 3 repetitions. Perforated (PM) and base material (BM) are considered here as different materials.
# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 2 Materials and Methods
### Statistical Indices  
The IMERG products were evaluated using two types of statistical comparative metrics to assess their performance (Table 1), using the radar product as reference. The basic statistical indices included Pearson's correlation coefficient (CC), relative bias (RBIAS), and root mean squared error (RMSE). These statistical parameters were calculated to examine the consistency of the data in contrast to the precipitation radar data. The probabilistic statistical indices: probability of (rainfall) detection (POD), false alarm ratio (FAR), critical success index (CSI), and Peirce skill score (PSS) were calculated to obtain information about the probability and accuracy of precipitation detection by the IMERG products compared to the reference radar products. Probabilistic statistical indices are widely used to assess the probabilistic quality of satellite products in this category [9; 15]. The probability of rainfall detection by IMERG satellites during the precipitation reporting of the radar data is evaluated with POD. The FAR index provided information on the inconsistencies between the IMERG satellites falsely detecting rainfall while the radar products were without detection. CSI index aggregates all reports captured by both satellite and radar. The CSI index provides more harmony over the precipitation reporting of both radar and satellite products over the study area, while also providing more insight into the satellite data in various spatiotemporal resolutions [15]. The system's overall accuracy in capturing rainfall events was indicated by PSS, which is the difference between the probability of detection and the probability of false detection. The Kling-Gupta model efficiency coefficient is another powerful statistical metric that measures the goodness-of-fit of a model. [PERSON] et al. (2016) developed this method as an alternative to the mean squared error (MSE) and Nash-Sutcliffe efficiency (NSE) in the context of hydrological modeling. This coefficient has three main components: correlation, bias, and variability. In this study, the updated version, i.e., [PERSON] et al. (2016) was used to ensure the variability and bias components were not cross-related. This means the coefficient of variation was used as a measure of variability instead of standard deviation. Table 1 lists all the parameters with their formulae, ranges, and perfect values.
## 2 Materials and Methods
### Risk Exposure  
The pesticide content in cereal and legume samples was evaluated by interpolation on the corresponding external standard calibration curves. Most of the analyzed samples showed no quantifiable residues of pesticides, therefore the risk exposure was studied considering only the pesticides detected with content above the corresponding quantification limit found in wheat (durum and soft) and oats samples, i.e., chlorpyrifos, fenvalerate, cyfluthrin, phenothrin, deltamethrin, cypermethrin, and pirimiphos-methyl. Regarding the food type and the related human mean consumption, the following cereal-based products were considered: pasta for durum wheat sample, bread for soft wheat, and breakfast cerealsfor oats samples. Then, the data related to the pesticide occurrence in cereals were corrected on the basis of the mean percentage of the raw material in the product. The following corrective percentages were then applied: 65% for soft wheat in bread, 85% for durum wheat in pasta, and 90% for oats in breakfast cereals [10,11].  
Considering the specific normative [12-19], the risk exposure was evaluated taking into account the relevant 2-years no observed adverse effect level (NOAEL) and the admissible daily intake (ADI). The toxic effect of different active substances, and the corresponding NOAELs and ADIs, were studied on rats/mice: therefore, both parameters were elaborated following the indications reported in the official European Food Safety Authority document [20]. In this report, the default values to be used in the absence of actual measured data are proposed. The conversion factors applied in the present study were as follows: body weight for children and adolescents = 12 kg; body weight for adults and elderly = 70 kg; 0.05 is the default factor to convert a feed concentration of test substances (mg kg\({}^{-1}\)) into a daily dose for rats (mg kg\({}^{-1}\) b.w.) for chronic studies.  
Regardless of their country of origin, all the samples monitored during this survey were collected in Italy. The reference data related to Italian food consumption were found in the INRAN-SCAI 2005-06 report [21], where the mean consumption of the three categories of cereal-based products (pasta, bread, and breakfast cereals) by this population is provided. In this document, data are divided into five population subgroups: infants (0-2 years), children (3-9 years), adolescents (10-17 years), adults (18-64 years), elderly (65-97 years). Anyway, in the present risk assessment study, only the last 4 subgroups were taken into account, since data available for the infants were considered not representative (n < 30) [22,23].  
The percentage of NOAEL and ADI were calculated according to the following Equation (1):  
\[\%\ \text{NOAEL}\ (\text{or}\ \%\ \text{ADI})=\frac{x\cdot p\cdot c}{1000 \cdot NOAEL\ (or\ ADI)}\cdot 100 \tag{1}\]  
where:  
\(x\) is the pesticide concentration quantified in the sample  
\(p\) is the conversion factor related to the cereal percentage in the product  
\(c\) is the mean food consumption according to INRAN-SCAI 2005-06 report