esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Dot-Based White Organic Light-Emitting Diodes Excited by a Blue OLED
###### Abstract  
In this study, white organic light-emitting diodes (OLEDs) consisting of red quantum dots (RQD) and green quantum dots (GQD) were investigated. These are the most exciting new lighting technologies that have grown rapidly in recent years. The white OLED development processes used consisted of the following methods: (a) fabrication of a blue single-emitting layer OLED, (b) nanoimprinting into QD photoresists, and (c) green and red QD photoresists as color conversion layers (CCL) excited by blue OLEDs. To fabricate the blue OLED, the HATCN/TAPC pair was selected for the hole injection/transport layer on ITO and TPBi for the electron transport layer. For blue-emitting material, we used a novel polycyclic framework of thermally activated delayed fluorescence (TADF) material, \(\
u\)-DABNA, which does not utilize any heavy metals and has a sharp and narrow (FWHM 28 nm) electroluminescence spectrum. The device structure was ITO/HATCN (20 nm)/TAPC (30 nm)/MADN: \(\
u\)-DABNA (40 nm)/TPBi (30 nm)/LiF (0.8 nm)/Al (150 nm) with an emitting area of 1 cm \(\times\) 1 cm. The current density, luminance, and efficiency of blue OLEDs at 8 V are 87.68 mA/cm\({}^{2}\), 963.9 cd/m\({}^{2}\), and 1.10 cd/A, respectively. Next, the bottom emission side of the blue OLED was attached to nanoimprinted RQD and GQD photoresists, which were excited by the blue OLED in order to generate an orange and a green color, respectively, and combined with blue light to achieve a nearly white light. In this study, two different excitation architectures were tested: BOLED\(\rightarrow\)GQD\(\rightarrow\)RQD and BOLED\(\rightarrow\)RQD\(\rightarrow\)GQD. The EL spectra showed that the BOLED\(\rightarrow\)GQD\(\rightarrow\)RQD architecture had stronger green emissions than BOLED\(\rightarrow\)RQD\(\rightarrow\)GQD because the blue OLED excited the GQD PR first then RQD PR. Due to the energy gap architectures in BOLED-GQD-RQD, the green QD absorbed part of the blue light emitted from the BOLED, and the remaining blue light penetrated the GQD to reach the RQD. These excited spectra were very close to the white light, which resulted in three peaks emitting at 460, 530, and 620 nm. The original blue CIE coordinates were (0.15, 0.07). After the excitation combination, the CIE coordinates were (0.42, 0.33), which was close to the white light position.  
organic light-emitting diodes; thermally activated delayed fluorescence; quantum dots; photoresist; color conversion layer; nanoimprint; blue OLED; white light
# Topology-Embedded Temporal Attention for Fine-Grained Skeleton-Based Action Recognition
## 4 Experiments
### Visualization of Learned Attention  
The attention learned by the TE-TAM is visualized to directly demonstrate the effectiveness of the TE-TAM. As different layers of the network are not consistent in the temporal dimensions, for display convenience, we only showed the attention of the first thirty frames of all layers in the network. Figure 5 shows the learned attention of the TE-TAM at each layer. The attention learned by layers 5, 6, 7, 8, and 9 was relatively more discriminative in the first thirty frames. The attention score ranged from 0 to 1. The closer the value was to zero, the less weight was assigned to the frame, and the frame's features were weakened. The closer the value was to one, the more weight was assigned to the frame, and the features within the frames were magnified. Small differences in the temporal dimensions can thus be captured, making features more discriminative.  
Figure 5: Visualization of learned attention by CTRGCN paired with TE-TAMs in different layers. We only show thirty frames of the first data sample in _TestSel\({}_{3}\)_.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(\mathbf{\theta}\) & **r** & \(\mathbf{\sigma}\) & \(\mathbf{\sigma_{1}}\) & **Top-1 Acc (\%)** \\ \hline
**Baseline** & **-** & **-** & **-** & **-** & **59.8** \\ \hline \(I\) & \(\theta_{1}\) & 2 & sigmoid & relu & \(\mathbf{61.8^{\dagger 2.0}}\) \\ \(J\) & \(\theta_{2}\) & 2 & sigmoid & relu & \(57.8^{\dagger 2.0}\) \\ \hline \(K\) & \(\theta_{1}\) & 4 & sigmoid & relu & \(54.6^{\dagger 5.2}\) \\ \(L\) & \(\theta_{1}\) & 8 & sigmoid & relu & \(58.5^{\dagger 1.3}\) \\ \hline \(M\) & \(\theta_{1}\) & 2 & sigmoid & tanh & \(59.4^{\dagger 0.4}\) \\ \(N\) & \(\theta_{1}\) & 2 & sigmoid & sigmoid & \(56.4^{\dagger 3.4}\) \\ \(O\) & \(\theta_{1}\) & 2 & relu & relu & \(59.8^{-}\) \\ \(P\) & \(\theta_{1}\) & 2 & tanh & relu & \(57.8^{\dagger 2.0}\) \\ \hline \hline \end{tabular} Note: Parameter is set: \(K_{M}=(3,3)\) and with ten TE-TAM blocks.  
\end{table}
Table 3: Comparison of Top-1 Acc with different parameter settings of \(\theta,\tau,\sigma,\sigma_{1}\).
## 6 Results and Discussion
### Performance Metrics
#### 6.1.1 Confusion Matrix  
It is a table that is used to visualize and describe the performance of a classification model on a testing set of data as shown in Figure 12. It is a summary of prediction results in a classification problem [43]. In confusion matrix, values of True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN) are represented by assuming \(\mathrm{C_{i}}\) one of the four classes in our dataset.  
Figure 11: Neural Network Architecture.  
* TP (C\({}_{i}\).) = All the instance of C\({}_{i}\). that are classified as C\({}_{i}\).
* TN (C\({}_{i}\).) = All the non-Ci. instances that are not classified as C\({}_{i}\).
* FP (C\({}_{i}\).) = All the non-Ci. instances that are classified as C\({}_{i}\).
* FN (C\({}_{i}\).) = All the Ci instances that are not classified as C\({}_{i}\).
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
### Prevalent Methodologies
To selectively enhance useful information [25], our improved Multi-feature Attention (MFA) module is added after each residual block. On the other hand, inspired by Atrous Spatial Pyramid Pooling (ASPP) in DeepLab_v2 [30], this paper proposes Feature Pyramid Pooling (FPP) to better solve the segmentation problems on contextual multi-scale objects. The experimental results show that the inference speed has been greatly improved without reducing the segmentation accuracy. The work of this paper is essentially the segmentation task; that is, the segmentation of buildings from remote sensing images. The main innovations of this work are as follows: To reduce a large number of parameters, we introduce depthwise separable convolution to improve efficiency. At the same time, a separable residual module is designed to complete the downsampling work. Moreover, in order to obtain multi-scale information for constructing remote sensing images, we also design a feature pyramid pooling module to focus on context more effectively and extract multi-layer features more efficiently.
## 2 Structure of PV Panel and Causes of Power Loss
### Power-Loss Factors of PV Panels
#### 2.3.1 Module Mismatch Losses and Application of Inverters  
The required output of PV panels is generated by arranging them into strings, i.e., connecting several panels in series or parallel (arrays), rather than using the panels individually. During operation, shading effects due to obstruction factors such as clouds or fallen leaves may cause mismatch losses, which could lead to a decrease in the power output. Several methods to decrease the module mismatch losses have been developed, including the use of built-in module inverters, string inverters, and module built-in DC-DC converters [16; 17; 18; 19; 20]. However, these methods require a separate circuit for compensating for the voltage loss due to shadowed strings. For the implementation of these methods, the cost of the additional modules to be installed, and the power losses due to the presence of these modules should be analyzed.
## 2 Spatial Polarimetric Time-Frequency Distribution
### Spatial Polarimetric Time-Frequency Distributions
\right], \tag{16}\]is the signal polarization characteristic vector, where  
\[\mathbf{q}^{[v]}=[\cos(\gamma_{1}),\ldots,\cos(\gamma_{N})]^{\mathrm{T}},\mathbf{ Q}^{[v]}=\mathrm{diag}\Big{(}\mathbf{q}^{[v]}\Big{)}, \tag{17}\]  
\[\mathbf{q}^{[h]}=\Big{[}\sin(\gamma_{1})e^{j\eta_{1}},\ldots,\sin(\gamma_{N})e ^{j\eta_{N}}\Big{]}^{\mathrm{T}},\mathbf{Q}^{[h]}=\mathrm{diag}\Big{(} \mathbf{q}^{[h]}\Big{)}. \tag{18}\]  
Then,  
\[\mathbf{B}(\mathbf{\Phi})\mathbf{Q}= \begin{bmatrix}\mathbf{a}^{[v]}(\phi_{1}) c_{1v}&\cdots&\mathbf{a}^{[v]}(\phi_{n})c_{Nv}\\ \mathbf{a}^{[h]}(\phi_{1})c_{1h}&\cdots&\mathbf{a}^{[h]}(\phi_{n})c_{Nh}\\ \end{bmatrix} = \begin{bmatrix}\widetilde{\mathbf{a}}(\phi_{1})&\cdots& \widetilde{\mathbf{a}}(\phi_{N})\end{bmatrix} . \tag{19}\]  
The matrix (19) can be considered as the extended mixing steering matrix, with \(\widetilde{\mathbf{a}}(\phi_{n})\) representing the spatial polarimetric extended characteristic vector. The spatial polarimetric characteristic extended vector of the \(n\)th signal is  
\[\widetilde{\mathbf{a}}(\phi_{n})= \begin{bmatrix}\mathbf{a}^{[v]}(\phi_{n}) \cos(\gamma_{n})\\ \mathbf{a}^{[h]}(\phi_{n})\sin(\gamma_{n})e^{j\eta_{n}}\\ \end{bmatrix} . \tag{20}\]  
Compared to a single-polarization array, the dual-polarization array doubles the vectors' spatial dimensionality. It is now possible to combine the spatial, polarized and T-F information of the signals incoming to the array. For the dual-polarized data vector, the SPTFDs matrix is described as  
\[\mathbf{D}_{\mathbf{xx}}(t,f)=\iint\varphi(t-u,\tau)\mathbf{x}\Big{(}u+ \frac{\tau}{2}\Big{)}\times\mathbf{x}^{\mathrm{H}}\Big{(}u-\frac{\tau}{2} \Big{)}\mathbf{e}^{-j2\pi f\tau}\mathrm{d}u\mathrm{d}\tau. \tag{21}\]  
This matrix serves as a general framework where classical issues in radar array processing can be resolved, for instance, DOA estimation. When the noise effect is neglectful, the SPTFDs matrix is in connection with the signal TFDs matrix via  
\[\mathbf{D}_{\mathbf{xx}}(t,f)=\mathbf{B}(\mathbf{\Phi})\mathbf{Q}\mathbf{D}_{ \mathbf{ss}}(t,f)\mathbf{Q}^{\mathrm{H}}\mathbf{B}^{\mathrm{H}}(\mathbf{\Phi }). \tag{22}\]
###### Abstract  
Karst aquifer recharge areas are usually difficult to identify because of the complexity of these aquifers' characteristics. On the other hand, their identification is very important in the aim of protecting the groundwater resources that these aquifers host. Regarding this topic, this paper presents an approach aimed at identifying karst aquifer recharge areas by the application of oxygen-18 and deuterium isotopes composition of groundwater coupled with hydrological features. Oxygen-18 and deuterium isotope composition of Capodacqua di Spigno Spring, in the South of the Latium Region, has been applied with rainfall and discharge values related to the feeding aquifer of this spring. As \(\delta^{18}\)O and \(\delta^{2}\)H values of groundwater samples are natural tracers of the recharge area's elevation, we propose a model, based on the distribution of the basin surfaces involved as recharge areas, in relation to elevations. The model estimates, for any discharge value, the percentage of the topographic area involved in the aquifer recharge. The setting up of this simulated distribution is supported by a Weibull cumulative probability function. The results show that the measured discharges increase as larger areas with lower elevations are involved in the recharge process.  
recharge areas; karst aquifer; stable isotope; isotopic tracers; spring discharge +
Footnote †: journal: Applied Sciences  
Article  
Karst Spring Recharge Areas and Discharge Relationship by Oxygen-18 and Deuterium Isotopes Analyses: A Case Study in Southern Latium Region, Italy
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
###### Abstract  
Background: Epilepsy is a chronic neurological disorder that affects individuals across all age groups. Early detection and intervention are crucial for minimizing both physical and psychological distress. However, the unpredictable nature of seizures presents considerable challenges for timely detection and accurate diagnosis. Method: To address the challenge of low recognition accuracy in small-sample, single-channel epileptic electroencephalogram (EEG) signals, this study proposes an automated seizure detection method using a micro-capsule network. First, we propose a dimensionality-increasing transformation technique for single-channel EEG signals to meet the network's input requirements. Second, a streamlined micro-capsule network is designed by optimizing and simplifying the framework's architecture. Finally, EEG features are encoded as feature vectors to better represent spatial hierarchical relationships between seizure patterns, enhancing the framework's adaptability and improving detection accuracy. Result: Compared to existing EEG-based detection methods, our approach achieves higher accuracy on small-sample datasets while maintaining a reduction in computational complexity. Conclusions: By leveraging its micro-capsule network architecture, the framework demonstrates superior classification accuracy when analyzing single-channel epileptiform EEG signals, significantly outperforming both convolutional neural network-based implementations and established machine learning methodologies.  
epileptic; automatic detection; micro-capsule networks; electroencephalogram (EEG); classification performance
## 2 Materials and Methods
### Experimental Setup  
To create the primary dataset, a Python script was written to extract the features based on URL, website content, SSL certificate, domain registration date, and public ranking and reviews. Python standard libraries (version 3.9.13) were used:'re', 'urllib','ssl','socket', and'requests'. Furthermore, the libraries 'whois' (version 0.7.2), 'td' (version 0.13), and 'Tranco' (version 0.6) were used, which are not included in the standard Python distribution. Finally, 'BeautifulSoup' (version 4.11.1) was used to scrape the source code of online shops, because this library has good documentation and code examples. On the other hand, other existing scraping libraries such as 'lxml', 'parsel' or 'Scrapy' could also be used, since only basic text parsing functionality is required to extract domain name, email, and payment methods. To create secondary datasets with different combinations of features, we used a 'combinations' function from 'itertools' library. This function constructs m-length tuples with no repeated elements. The secondary datasets were divided into training and testing sets, applying different splitting strategies as described in Section 2.1. These datasets were used to train and evaluate the machine learning classifiers from the Scikit-learn library (version 1.0.2) ([PERSON], 2015), namely Decision Tree, Random Forest, Stochastic Gradient Descent, Logistic Regression, Gaussian Naive Bayes, and Multilayer Perceptron. According to ([PERSON], 2015), the Scikit-learn library is the most popular Python library for machine learning, which has the largest number of algorithms implemented in most categories. In addition, this library has extensive documentation and many source code samples available for reuse, therefore it was used as the main machine learning framework. The XGBoost library (version 1.7.3) ([PERSON], 2015) was used for the implementation of the Extreme Gradient Boosting classifier. The configuration parameters for each classifier are given in Table 2.
## 4 Experimental Analysis
### TCReC's Reconstruction Ability Employing A Different Classifier  
It is well known that CNN has specialties in spatial feature processing, while LSTM has advantages in temporal feature processing. In this experiment, we employ TCReC with different classifiers, CNN and LSTM, to compare whether there is a difference in the reconstruction ability of TCReC in spatial features and temporal features.  
In order to avoid the bias of results caused by differences in input data, we conduct three sets of comparative experiments:  
1. CNN or LSTM is used as the classifier to classify and identify a set of raw data randomly selected from the CIC-IDS-2017 dataset, and the result is recorded as \"Original Data\".
2. Using the same dataset, randomly delete 70% data in bytes to simulate the scenario of packet loss, and then use CNN or LSTM to classify and identify different data flows, the result is recorded as \"Packet Loss\".
3. Finally, TCReC is used to reconstruct the features of the simulated packet loss data, and then input into CNN or LSTM for classification and recognition, and the result is recorded as \"TCReC\".  
The experimental data show that TCReC has good ability in the reconstruction of spatial and temporal features. It should be noted that in a few cases, TCReC may display different performance under different detection task scenarios. The most prominent example is SSH_Patator. By comparing the data in the table, it can be found that in the case of a packet loss of 70%, TCReC cannot help the detection model based on CNN to accurately identify SSH_Patator. However, the reconstruction ability of TCReC is well reflected when LSTM is used as the classifier.  
The experimental results are shown in Table 4.  
According to our analysis, the spatial characteristics of SSH_Patator traffic are fewer than the temporal characteristics. When packet loss occurs, the detection model based on spatial feature analysis will be more affected. This can also be seen in the comparison of \"packet loss\" in Table 4. Therefore, the main reason for the difference in TCReC performance is the differences in the identification ability of the detection models toward the network data traffic characteristics, rather than the reconstruction ability of TCReC. In a sense, TCReC can mitigate this difference.  
Figure 10: t-SNE plot of six types of traffic.  
Based on the aforementioned experimental results, we computed the Reconstruction Ability Index (RAI) to mitigate the influence of different models on the experimental data, with the results presented in Table 5.  
As shown in Table 5, the RAI of TCReC is significantly higher than that of the other models, and there is little difference when TCReC is combined with different classifiers. However, the experimental results also show that the ability of the classification model cannot be ignored.
## 7 Discussion  
Based on the aforementioned simulation results, it can be deduced that the droplet deposition diffusion rate, which is influenced by process variables such as nozzle aperture, pulse width, inlet velocity, and liquid viscosity, directly affects the mechanical properties and dimensional accuracy of the printed sand model.  
Stretching, neck contraction, column breakdown to produce microdroplets, and steady motion are the typical steps in the droplet production process, which is controlled by the physical properties of the printer nozzle. As these stages change, satellite droplets are created. The paper's quality might be impacted by satellite droplets. Therefore, it is possible to combine satellite droplets by slowing down or speeding up their drop rate.  
The minimum separation distance must be greater than the satellite droplet convergence distance, per the simulation of droplet flight. According to the findings of the effects of nozzle radius on droplet generation and injection, changing the nozzle's radius will affect the time when the satellite droplet contacts the main droplet. The satellite droplet gradually disappears when the nozzle's radius is decreased. However, a particular drop distance is required for the interaction of satellite and primary droplets. The modeling results indicate that satellite and main droplets can merge when the injection distance exceeds 1.24 mm. Consequently, a minimum printing distance of 1.24 mm must be maintained between the printing head nozzle and the substrate during actual manufacturing.  
Additionally, the viscosity of the jet fluid is the most influential factor on jet performance. The fluid's viscosity must fall within the acceptable range of the printer. The lower the fluid's viscosity, the more challenging it is to achieve consistent injection control. Under the condition of suitably increasing viscosity, droplets formed under identical conditions become more uniform.  
The result processing graph demonstrates that the driving voltage during the simulation experimental investigation is positively correlated with the droplet velocity, and the voltage is also positively correlated with the microdrop volume; therefore, the driving voltage can be considered to be positively correlated with the microdrop mass. Therefore, the simulation is consistent with the study law of the conversion velocity.  
As a reference for the optimization and design of droplet injection technology, the purpose of this study is to propose a more direct and efficient method for optimizing the droplet injection parameters. However, this research does have certain limitations. In this paper, just four influence parameters are selected for optimization and sensitivity analysis. Other determining parameters, such as driving signal amplitude and nozzle structure size, require additional study. The findings of the sensitivity analysis of the influencing factors show that the ink density has the greatest impact, but it appears that this result requires additional verification and analysis based on the entire impact mechanism. Future research will investigate these limits in greater depth.
## 9 Lessons Learned
### The Technological Perspective  
#### 9.3.1 Clarity on Model LOD  
In the BRDF expansion project, the absence of specific BIM requirements and a lack of clarity regarding the level of detail (LOD) in the models led to the inclusion of unnecessary information in the disciplinary models. This not only increased the modeling workload but also added to project's expenses. Interestingly, even the VDC service provider was uncertain about the necessary level of detail for these models, resulting in an excessively large central model. This, in turn, posed challenges in terms of model development and management. Specifically, navigating these complex models on personal computers or mobile devices on-site became problematic, necessitating a stable internet connection to access the models hosted on the VDC service provider's computers. Facing such technological challenges due to lack of clarity on LOD has been a great motivation for numerous research projects. This is why the clarity of models' LOD is repeatedly mentioned in various research works as a key technological factor in successfully implementing BIM in construction projects [11; 13; 74; 75].
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 5 Results
### Training and Testing of Detection Model  
In this methodology, the dataset is split into training (70%) and testing (30%) subsets. The model training includes the utilization of a Gaussian kernel function to ensure effective representation of data in the kernel space for OCSVM. The initial value of the \"nu\" parameter (v) is set to 0.5 and adjusted through cross-validation, resulting in \"nu\" being set to 0.95. Early stopping criteria are implemented in dAE to determine the optimal epoch count. Additionally, ReLU serves as the activation function, while Adam optimization with a learning rate of 0.001 aids in model optimization during training.