esa-sceva/satcom-chunk-collection huggingface dataset:
# Untargeted Urinary Volatilomics Reveals Hexadecanal as a Potential Biomarker for Preeclampsia
## 2 Results
### Enrichment Analysis  
Enrichment analysis is a statistical method for identifying metabolic pathways and metabolic groups of metabolites that are particularly important in a specific biological context based on data from metabolomic studies. In this study, enrichment analysis was used to identify important metabolic pathways that are significantly enriched in the urine samples of pregnant women with PE compared to control samples. The results of the enrichment analysis are shown graphically in Figure 3. The graph shows the enrichment of metabolic sets, with two major metabolic pathways identified as follows: sphingolipid metabolism and sulfate/sulfite metabolism.  
Figure 3: _Cont._  
Figure 2: ROC curve for hexadecanal.  
Sphingolipid metabolism stands out with an enrichment ratio higher than 4, highlighting it as the dominant metabolic pathway associated with the analyzed samples and significant metabolites. This level of enrichment suggests that alterations in sphingolipid metabolism may be a key factor in the pathophysiological processes within the placenta associated with PE, which is also confirmed by a very low \(p\)-value (less than 0.02). Although less pronounced, sulfate and sulfite metabolism also show a significant enrichment, with an enrichment ratio of about 3.5 and \(p\)-values between 0.04 and 0.06. Although the statistical significance is lower compared to sphingolipid metabolism, the results still indicate a possible link between this metabolic pathway and PE.  
The metabolite set enrichment analysis, shown in Figure 3, not only highlights the major metabolic pathways but also emphasizes the cellular locations where these pathways are predominantly active. This cellular localization highlights specific regions within the cell that may be particularly affected by the pathophysiology of PE and provides insight into how oxidative stress and metabolic dysfunction manifest in both the intracellular and extracellular spaces.  
Among the most enriched metabolic pathways are those associated with the endoplasmic reticulum (ER) and the extracellular space. These results suggest that the ER, an important site for protein folding, lipid metabolism and cellular stress responses, is significantly involved in the metabolic disturbances seen in PE.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments
#### 4.3.5 Evaluation and Analysis of Overall Algorithm Performance
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{**Stream**} & \multicolumn{8}{c}{**Supervised Learning Algorithms**} & \multicolumn{8}{c}{**Active Learning Algorithms**} \\ \cline{3-11}  & & **Types** & **LB** & **MOOB** & **ARFre** & **SRP** & **CSARF** & **ROSE** & **ROALE-DI** & **CALMID** & **MicFool-E** & **AdaAL-MID** \\ \hline \multirow{4}{*}{Recall} & Fixed & 4.78 & 7.67 & 4.78 & 7.89 & 5.00 & 2.67 & 10.00 & 3.11 & 7  
Figure 9 presents the one-to-one comparison results of the AdaAL-MID against other benchmark algorithms across all data streams. The threshold for the difference in each metric was set at 0.2%. If AdaAL-MID outperformed the benchmark algorithm by a margin greater than this threshold, it was marked as a win (green); if the margin was below the threshold, it was marked as an advantageous tie (light green). If the benchmark algorithm outperformed the AdaAL-MID and the margin was below the threshold, it was marked as a disadvantageous tie (yellow); if it exceeded the threshold, it was marked as a loss (red). As can be seen from the figure, compared to the supervised learning algorithms, the AdaAL-MID had more losses on the five metrics only against ROSE, while it had some losses against other supervised algorithms on certain metrics, and it significantly outperformed LB and MOOB. Additionally, compared to the other three active learning algorithms, the AdaAL-MID leads by an absolute margin in every metric.
## 3 Methodology  
Let \(U=\{u_{1},...,u_{N}\}\) be the set of \(N\) users, \(V=\{v_{1},...,v_{M}\}\) be the set of \(M\) items. The users' feedback for the items can be represented by an \(N\times M\) preference  
similar text descriptions with a CS item, the CS item's rating can be derived from the ratings of those similar non-CS items. Therefore, this study's first goal is to develop a CS item rating generator based on ratings of similar non-CS items. The derived ratings of CS items are then added to the original preference matrix \(\mathbf{R}\) and formed an updated preference matrix \(\mathbf{R}^{\prime}\in\mathbb{R}^{N\times M}\). Based on \(\mathbf{R}^{\prime}\), the second goal of this study is to develop a robust recommendation model that can deal with sparse preferences and accurately predict the user's preference.  
To fulfill the above goal, this research proposes a two-stage CS item recommender. The major components of the proposed recommender include the denoising autoencoder-based CS item rating (DACR) generator and the neural network-based collaborative filtering (NNCF) predictor. In the DACR generator, textual descriptions of items are collected and used to generate items' content features. Through the preprocessing tasks of tokenization, stop-words removal, and stemming, a set of meaningful terms is derived from the textual description of all items. Each item is then represented as the vector format, where each entry in the vector represents the occurrence frequency of the term. Next, the DAE, a neural network-based dimension reduction method, is applied to extract compact content features from the high-dimensional vectors. Based on the compact content feature vectors, a CS item's rating can be derived from the ratings of similar non-CS items in a more efficient way. In the second stage, the updated user-item rating matrix \(\mathbf{R}^{\prime}\) is used to train the NNCF predictor. First, the unique identifications of users and items are converted to vectors format through one-hot encoding. The long sparse vectors are then projected to a dense latent vector in the embedding layer. Next, latent vectors are fed into multilayer perceptron (MLP) layers for user-item matrix learning. The objective of the NNCF predictor is to minimize the loss among the predicted ratings and real ratings. When the user ID is specified, the trained NNCF predictor will return the ratings of all items. After sorting the ratings, the Top-N item suggestion will be returned to the user. Figure 1 illustrates the framework of the proposed cold start item recommendation system.
## 5 Results and Discussion
### Accuracy Results on Different Grid Spacings and Size of the Activation Field  
The larger grid spacing grid neurons are those that fire from 2-5 sites in an environment. The grid neuron fires at more than five sites are considered as smaller grid spacing grid neurons. We checked different numbers of smaller and larger grid neurons that are defined as a value, which is a ratio of the number of larger grid spacing grid neurons to the total number of grid neurons. The results for the localization accuracy with respect to the ratio value are shown in Figure 25. The figure has shown poor accuracy at lower ratio value. The poor accuracy is observed due to higher ambiguity in the smaller activation field size grid neuron. In a small activation region, the firing pattern repeats several times in an environment, so it fails to predict the correct sub-area in the environment. On a higher ratio value, the accuracy has reached to its optimum level. The reason behind the optimal accuracy is the correct prediction of the sub-area by the larger grid spacing neurons and the correct location in the sub-area by the smaller grid spacing grid neurons. On the further increase of the ratio value, the accuracy has decreased from the optimal level but not much compared to the lower ratio value. The reason behind this phenomenon is the large grid spacing grid neurons. The large grid spacing neuron predicts the correct sub-area but fails to locate the correct location in a sub-area. Finding the correct location in a sub-area is the task of smaller grid spacing neurons.
## 4 SAR Decomposition
(**a**) Single bounce component; (**b**) Double bounce component from co-pol; (**c**) Double bounce component from cross-pol; (**d**) Double bounce component from both the co- and cross-pol; (**e**) Volume scattering component; (**f**) Color composite image using our decomposition: blue = single bounce, red = double bounce (both from the co- and the cross-pol), and green = volume scattering. observations [36]. The decomposition results from our previous research with Radarsat-2 data over Tarpon Bay showed dominant volume scattering throughout the image [36], and most of the double bounce scattering occurred over sawgrass and some mangroves. Hence, we roughly could distinguish sawgrass from scrub with relatively low resolution. However, the decomposition of the TSX dataset shows large color variability resulting from the shorter wavelength of the X-band SAR signal, which has more sensitivity for vegetation variation with high resolution [37]. These newer and more detailed features of vegetation distribution can be useful for classifying wetland vegetation.
## 5 Simulation results and performance comparison  
First, we make a simulation of the location and tracking of DOA estimator of DILL. We set the antenna array elements of the system as 16 with a distance of half a wave length and the SNR as 5 dB. Assume a target signal source A entering in the antenna system with 20\({}^{\circ}\) direction, it is then located and tracked in the DILL after reorganization by spatial filter. As shown in Figure 4, we make estimated tracking of DOA without UKF filtering, we then can know that the approximation of the DOA fluctuated around the real value while the fluctuation range is relatively large and the estimated accuracy is low due to the influence of non-linear system and noise.  
In Figure 5, after UKF filtering, it reduces the influence of nonlinear and noise on DOA estimation, making the estimated value \(\hat{\theta}_{k}\) more close to the actual value \(\theta_{k}\). To analyze the effect of UKF filtering we draw the estimation error \(\epsilon_{k}\) of DOA before filtering. Just like Figure 6, \(\epsilon_{k}\) fluctuates around 0\({}^{\circ}\) and closes to 0\({}^{\circ}\) with a relatively small range after UKF filtering, improving the DOA accuracy. When the target moves slowly, the tracking error is between [\(-1.5^{\circ}\), \(1.5^{\circ}\)]. However, the error increases and could surpass \(\pm 2^{\circ}\) and the location performance decreases when the target moves faster, implying that the real-time performance of the system needs improvement.  
Figure 4: Estimated tracking of Direction of Arrival (DOA) in DILL.  
Figure 5: Location and tracking of DILL-Unscented Kalman Filtering (UKF).  
We add another mobile signal source B in the direction of \(-57^{\circ}\) to do uniform circular motion with a slow angular velocity just like that shown in Figure 7. We can know that the DOA change of B is a straight line with a low angle change rate. Compared to A, the location of B is much better. Therefore, the system can locate and track several targets with more accurate and reliable performance.
# Highly Integrated Wideband Transmit/Receive Module for X-Band SAR Applications
## 3 Radio Frequency Design  
This section reviews the design of those interconnects and devices of major importance, since they are laid out between the SCFE and the antenna connector. Thus, they should ensure low loss for a minimal degradation of the noise figure (NF) when the TRM works in receiving mode, and to avoid power loss in transmit mode, there should be good impedance matching for avoiding the high power of the transmitted signal bouncing between the SCFE and the antenna, thus degrading the antenna gain.  
The TRM portion of interest between the SCFE and the antenna connector is shown in Figure 3. Each single RF line to be accurately designed is enclosed in a yellow rectangle and identified by a specific number from one to four:  
1. Directional coupler for reaching the calibration port,
2. Microstrip (red)-stripline (purple)-microstrip (red) transition from the SCFE output to the coaxial connector toward the antenna,
3. Wilkinson power divider in order to have only one Cal Connector for two TRMs,  
Figure 2: Compact packaging solution for hosting the 4 TRMs. (**a**) Top view. (**b**) Bottom view.  
(4) Stripline (purple) to coaxial connector transition toward the Cal Port. This transition is similar to the one designed for the TXin and RXout ports at the TRM input, as defined in Figure 1, although the latter involve the transition between the microstrip and the connector on the opposite side of the stack-up.  
Since the two TRMs are placed next to each other, the same transitions 1 and 2 belong to both adjacent modules. Such transitions for the TRM2 are enclosed by yellow dashed rectangles.  
The design of the RF transition was carried out by combining the advantages of a circuit simulator (Keysight Advanced Circuit Simulator ADS [34]), such as simulation speed and possibility of optimization process based on the simultaneous tuning of multiple geometrical variables, and the accuracy of a full-wave simulation using CST Microwave Studio [35].
## 4 Related Works
The paper evaluates different optimization algorithms, including distance, Cost Function, and Fuzzy Logic, in terms of ping-pong handover probability (PPHP), radio link failure (RLF), and spectral efficiency (SE). The results indicate that the distance algorithm demonstrates superior performance, significantly reducing PPHP, RLF, and SE across different mobile speeds. This study underscores the importance of optimizing HCPs based on user location and mobility, providing valuable insights for improving load balancing and handover management in 5G and future mobile networks. [PERSON] et al. [23] comprehensively explored the future of UAV cellular communications, spanning from current 5G capabilities to anticipated 6G advancements. The authors start by reviewing the current state of UAV communications from an industrial standpoint, highlighting new 5G NR features that support aerial devices. They analyze the potential and limitations of these features, demonstrating how sub-6 GHz massive MIMO can address cell selection and interference challenges, and showcase encouraging mmWave coverage evaluations in urban and suburban/rural settings. The paper also examines direct device-to-device communications in the sky and previews next-generation UAV communications, listing use cases envisioned for the 2030s. The authors identify the most promising 6G enablers for UAV communication, such as non-terrestrial networks, cell-free architectures, artificial intelligence, reconfigurable intelligent surfaces, and THz communications, discussing their benefits and technological hurdles. The paper concludes with a series of original results and key takeaways, emphasizing the need for further research to address open problems in UAV-specific opportunities and challenges. This work serves as a valuable resource for understanding the trajectory of UAV cellular communications and the technological advancements required to support their integration into future networks.
## 4 Experimental Evaluation
### Influence of Camera Resolution on the Identification and Detection Processes
In the last part of the experiments, we investigated the influence of the image quality on the time performance of bee identification and _V. destructor_ detection processes. Experiments were performed for the same four resolutions (5 Mpx, 1.2 Mpx, 0.3 Mpx, and 0.1 Mpx) and the same initial test set of 20 video frames. The analysis time (covering bee segmentation and varroosis detection) was measured for each image separately and averaged, first for all pictures of bees segmented from the image (ROIs) and then for all pictures of bees obtained for a particular resolution. Results are presented in Figure 12.  
We observed that the whole execution times for images taken with camera resolutions of 5 Mpx are 5-30% higher than for images in the resolution 0.1 Mpx. However, the number of correctly segmented bees on images in the resolution 5 Mpx is higher than for the resolution 0.1 Mpx. When looking at the average time per segmented bee (Figure 12), we can see that for the lowest resolution 0.1 Mpx, the average processing time of a picture was around 296 ms, which was the highest of all results. For the other three resolutions, average times were similar--about 277 ms. Taking into account the results from previous experiments, it seems that analyzing video streams with a resolution of 5 Mpx would be the best choice regarding both efficiency and quality of bee segmentation and varroosis detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Camera Resolution (Mpx)** & **TPR** & **TNR** & **PPV** & **ACC** & **F1** \\ \hline
0.1 & 1.000 & 0.000 & 0.185 & 0.185 & 0.313 \\ \hline
0.3 & 1.000 & 0.000 & 0.167 & 0.167 & 0.286 \\ \hline
1.2 & 1.000 & 0.043 & 0.185 & 0.214 & 0.313 \\ \hline
5.0 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effectiveness of _V. destructor_ detection depending on camera resolution (TPR—sensitivity, TNR—specificity, PPV—precision, ACC—accuracy, F1 score). Figure 11: F1 score obtained for the bee identification process for different camera resolutions.
# Automatic Detection of Sensitive Data Using Transformer-Based Classifiers
## 2 General Data Protection Regulation  
The General Data Protection Regulation [1] is a European Union regulation regarding privacy and data protection, whose goal is to secure personal data of citizens and residents in the EU, both inside and outside its borders. This regulation applies to companies, businesses and organizations, inside and outside the EU that process personal data of EU residents. The GDPR provides a common base of rules shared by all EU countries, making it easier for companies to comply with its standards; this simplification has come at the price of a stricter data protection, with sanctions amounting up to 4% of a company's global turnover.  
According to the European Commission, personal data consist of any information related to an individual in private, professional or public ambit. This information can exist in different forms: text, images, videos, audios, etc. This new regulation expands and characterizes the data definitions in the old directives, as well as adds new ones.  
The GDPR defines three types of data that must be protected:  
* **Personal data:** in the GDPR, article 4, paragraph 1: _'personal data' means any information relating to an identified or identifiable natural person ('data subject'); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person;_ Therefore, personal data involve any information that can lead to the identification of a natural person. The limits of this definition are still subject to debate: according to it, even a generic physical description of an individual, if too specific, could be treated as personal data. Direct information is the most common type of personal data, and it is easier to detect than its indirect counterpart.
* **Sensitive data:** a special category of personal data that requires strict protection, as stated in the GDPR, article 9, paragraph 1: _Processing of personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, and the processing of genetic data, biometric data for the purpose of uniquely identifying a natural person, data concerning health or data concerning a natural person's sex life or sexual orientation shall be prohibited._ The definition of sensitive data has been intentionally kept generic to include a broader group of information types.
* **Personal data relating to criminal convictions and offences:** the processing of this personal data type must be kept under control of an official authority or authorised by Union or Member State laws providing for appropriate safeguards for the rights and freedoms of data subjects.  
In this work, we are going to focus only on personal and sensitive data, since they are the most common data types saved in companies' databases.
# Modeling of Fuzzy Systems Based on the Competitive Neural Network
## 4 Experimental Results
### Comparative Analysis  
A summary of the results is presented in Tables 11 and 12. Table 11 describes the comparative analysis achieved by the different MFs when implementing Mamdani FIS and Table 12 presents the results of Sugeno FIS. According to these values, we can notice that in Mamdani FIS, the configuration using the Gaussian MF was better for all datasets. Additionally, in Sugeno FIS, the behavior had a slight variation; for the Iris dataset, the Gaussian MF also presents better results, but in the case of Wine, the best precision was obtained with the Triangular MF and the best average with the Gaussian MF, and for WDBC dataset, the performance was improved with the Trapezoidal MF.  
Analyzing the general performance of Mamdani FIS against Sugeno FIS, we can conclude that Sugeno was more stable than Mamdani, achieving a better precision in two of the three datasets (Wine and WDBC). On the other hand, comparing the results with respect to the MFs, the use of Gaussian MF presents greater stability in most results. In Mamdani FIS, it is better in all cases, and in Sugeno FIS, it is better in the Iris and Wine datasets.  
In order to evaluate the performance of the proposed CNNT1 FL method versus other state of the art methods, we performed a comparison with Interval Type-2 Fuzzy C-Means (IT2 FCM) [6] using only the Iris data. This analysis comparison is described in Table 13.  
The comparison between both methods, CNNT1 FL and IT2 FCM, comprises analyzing the best percentage classification of Mamdani FIS and Sugeno FIS for the Iris database, since it is the only dataset tested by the authors.  
As we can notice in Table 13, with the three variations of membership functions in Mamdani FIS, the proposed method CNNT1 FL obtained better results than IT2 FCM; however, for the Sugeno FIS, the results of CNNT1 FL were not good compared to IT2 FCM.  
It is important to mention that one of the disadvantages of CNNT1 FL is that it uses all features for each dataset of the databases, on the contrary, IT2 FCM did not specify how many and which features are used.
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 3 Methodology
### Simulation and Dataset  
The CHB-MIT Scalp EEG Database [25] was utilized to examine the proposed algorithm in terms of its compression rate. The standard EEG pattern is recorded in European Data Format (edf) containing one uninterrupted digitized polygraphic recording. The original EEG signals in 23 channels were sampled at 256 samples per second with 16-bit resolution and 1 hour duration. A software program was used to extract the information from the edf file as well as to restore the original EEG pattern. The extracted data from the edf file was compressed into a bitstream of data by using the different prediction and entropy encoders. The resulting encoded bitstream of data was decoded to verify that it has the same data as that of the original data extracted from the edf file. The compression rates were computed by getting the ratio of the size of the original data and the size of the encoded bitstream.  
The proposed lossless EEG encoding algorithm was implemented using Verilog code and was simulated using the ncevilog tool. The VLSI architecture was synthesized using a design compiler with a TSMC 0.18 \(\upmu\)m CMOS generic logic process technology.