esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results  
### Experiment 1: Model Selection  
To evaluate the effectiveness of the proposed model, a quantitative assessment was conducted by comparing it with several existing state-of-the-art approaches, like UNet and the Residual Attention UNet, using the DSC, IoU, and HD mean values for the five organs on the test set.  
U-Net is a convolutional neural network architecture widely used for image segmentation tasks. Introduced by [PERSON] et al. in 2015 [PERSON] et al. (2015), U-Net features a symmetric encoder-decoder structure. The encoder captures the context and extracts the features, while the decoder reconstructs the segmentation map. Skip connections between corresponding layers in the encoder and decoder facilitate the integration of high-resolution information during segmentation, making it effective for tasks like medical image segmentation.  
The ResAtt U-Net, or Residual Attention U-Net [PERSON] et al. (2015), is an extension of the U-Net architecture that incorporates residual blocks and attention mechanisms. The residual blocks help to mitigate the vanishing gradient problem, aiding in the training of deeper networks. Attention mechanisms enhance the model's focus on relevant regions during feature extraction, contributing to improved performance in capturing the intricate details. The combination of residual connections and attention mechanisms aims to boost the network's capability in handling complex and nuanced segmentation tasks.  
The number of parameters, the training time, and the inference time are provided for the three models. Table 1 showcases a comparison between the three models. We observed that the Residual Attention UNet and the original UNet models showed poor performance, leading to lower DSC and IoU metrics. Despite having lower HD values, the Residual Attention UNet yielded modest results visually. In contrast, the Efficient-UNet exhibited good performance due to its efficiency, shortened training and inference durations, and a reduced number of parameters. The Efficient-UNet emerged as a superior choice, integrating the advantages of a pretrained encoder with efficient training and inference processes.  
Figure 7 displays the results of the different tested models on the test images. Efficient-UNet exhibits superior segmentation performance compared to the other models. In the first row, Efficient UNet excels in segmenting the heart, the spinal cord, and the esophagus when compared to UNet and Residual Attention UNet. Moving to the second row, we focus on the segmentation of the left lung for another exam. Here, UNet misclassifies a portion of the left lung as the right lung, while both Efficient UNet and Residual Attention UNet demonstrate accurate segmentation. However, Residual Attention UNet falls short in completing the segmentation of the right lung. In the last row, representing the segmentation on a distal slide from another exam, Efficient UNet tends to over-segment the left lung. In contrast, UNet incorrectly segments a part above the left lung as the right lung. Residual Attention UNet fails to segment any organ.
## 5 Conclusions  
The incorporation of hysteresis models in finite element analysis (FEA) is of utmost importance for the accurate predictions of the ferromagnetic devices' performance. The Preisach and Jiles-Atherton (J-A) models are typically used for this purpose. The former is more accurate and can represent a broad range of magnetic materials, but it is computationally expensive unless closed forms of the Everett function are used. The latter is more efficient but can accurately model only soft ferromagnetic materials [27].  
In this paper, a hysteresis model previously proposed by the authors, referred to as the D'Aloia-Di Francesco-De Santis (D-D-D) model, is compared with the Preisach and J-A models for a large class of hysteresis loops including soft, semi-hard and hard ferromagnetic materials. The results of this comparison have shown that the proposed model represents the best solution, as it is as accurate as the Preisach model and even faster than the J-A model. This makes the D-D-D model the perfect candidate in FEA.  
This research received no external funding.  
Not applicable.  
The authors would like to thank [PERSON], University of L'Aquila, for his valuable support with the code implementation of the hysteresis models.  
The authors declare no conflict of interest.
## 3 Model and Dynamics
### Walking Phases  
Different phases can be identified for a walking sequence. This is usually done also for human walking motion analysis [34], where the different feet contacts are described and the phases might change according to the walking environment [35]. Differently from humans, HeiCub has a rigid flat foot, common among humanoid robots. Therefore, the walking phases for such a humanoid are different from human walking, as we have to consider completely flat contacts between the feet and the ground.  
As shown in Figure 2, the walking sequence can be seen as three sub-sequences:  
* Starting step, the robot starts from a complete stop (i.e., all velocities to zero) and takes the first step, leading to the periodic motion.
* Periodic steps, which are the steps that the robot can repeat during walking. In this case, we assume single-step periodic, i.e., the left and right leg configurations can be mirrored, as the robot is symmetric. The periodicity is enforced on touchdown.
* Ending step, the final step where the robot comes to a complete stop from the periodic motion.  
It is to be noted that in this case, we assume that one step is enough to lead the motion into a periodic motion and to lead the motion to an end. This assumption might not be valid for every system and situation; however, we have verified that in the cases we considered for HeiCub in this paper, this assumption can be used. A further discussion is carried out in Section 7.  
The walking phases involving different contacts are described as follows:  
* DS: Double Support, where both feet are on the ground.
* LSS: Left Single Support, where the left foot is on the ground and the right leg swings to the next support location.
* RSS: Right Single Support, as for LSS, the right foot is on the ground and the left leg is swinging.  
In addition, there are also two impacts that follow each of the single-support phases and precede the double-support phases:  
Figure 2: Walking phases of HeiCub. DS = Double Support, LSS = Left Single Support, RSS = Right Single Support, LTD = Left Touch Down, RTD = Right Touch Down. The whole sequence can be seen as three sub-sequences. The periodic step can be repeated for a desired number of times that do not need to be further modeled.  
* RTD: Right Touch Down, when the left foot is in single support and the right foot strikes the ground, we assume that when the foot of the robot touches the ground, it is completely flat.
* LTD: Left Touch Down, the left foot strikes the ground when the right foot is in single support.  
To define a flat contact, it is enough to define three contact points on each of the feet, as shown in Figure 3.  
Given the description of the walking phases, we illustrate in the following the dynamics equations based on few assumptions.
# Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
## 4 Application Results and Analysis
### Analysis of Fault Diagnosis Methods  
The confusion matrix can be used to visualize the results of the diagnostic model for different fault types. Based on the 114 test set samples after the RFR model fills in the missing data, the diagnostic results of the XGBoost model and TPE-XGBoost model are  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline num\_boost\_round & 239 & eta & 0.15 \\ colsamble\_by\_tree & 0.80 & booster & ’gbtree’ \\ colsample\_by\_node & 0.20 & gamma & 1.20 \\ min\_child\_weight & 1.20 & lambda & 3.10 \\ max\_depth & 4 & subsamples & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal parameters by TPE algorithm.  
Figure 6: TPE optimization iteration process.  
shown in Figures 7 and 8. The results show that the diagnostic accuracy of the XGBoost model and the TPE-XGBoost model is 84.2% and 89.5%, respectively. The accuracy of the fault diagnosis model increased significantly with TPE optimization.  
The TPE-XGBoost diagnostic model is noticeably improved in terms of T2 and varying degrees of discharge faults. Discharges of high energy are particularly diagnosed with an accuracy of 95%. Each fault type is diagonally distributed along the confusion matrix, indicating that the TPE-XGBoost model has excellent diagnostic effects on the test set.  
Three evaluation metrics, F1-score, precision and recall, are used to further evaluate the merits of the model for different fault types in this paper, as shown in Figure 9. Precision is the percentage of positive tuples predicted to be identified by the model; recall indicates the percentage of positive tuples correctly identified by the model; F1-score is the harmonic mean of precision and recall. The closer the three evaluation indicators above are to 1, the better the diagnostic performance of the model.  
The results show that all merits of the TPE-XGBoost model are higher than 0.7, and most of them are higher than 0.82. Of these, the three evaluation indicators corresponding to normal and discharges of high energy are over 0.90. Combining the seven diagnostic results, the average value of the F1 score is 0.87, which verifies the excellent stability and robustness of TPE-XGBoost in transformer fault diagnosis.  
Figure 8: Confusion matrix of TPE-XGBoost model.  
Figure 7: Confusion matrix of XGBoost model.
# Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems
## 3 Implementation Results and Discussion
### Physical Implementation  
The designed Verilog model was logically synthesized using TSMC 180 nm technology and the Genus tool from Cadence. The resulting netlist based on standard cells of such technology was verified with the Conformal tool to check the logic equivalence of both models. After checking the functionality and timing of our design, the physical chip design was performed by adding to the chip core the I/O pads, the power grid, the clock tree, the floor plan, the place and route of the chip, and physical verifications. This workflow was achieved with the Innovus tool, also from Cadence.  
The abstract physical design was exported to the Virtuoso tool to carry out the final verifications such as layout versus schematic (LVS) and design rule checking (DRC). Furthermore, the graphic design system (GDS) file was generated with Virtuoso to send this file to the foundry. The chip physical design and the floor plan of the design modules are shown in Figure 9a,b, respectively.  
Figure 8: Quartus timing analysis.  
Figure 7: Quartus synthesis and fitter summary.  
The pinout of the chip was as follows: serialIn, clk, rst, SDA, sideSelector, serialOut, SCL, vdd, vss (core power supply, 1.8 V), dvdd, and dvss (3.3V I/O power supply). In order to handle more than the required current for the core and to close the pad ring, extra vdd, vss, dvdd, and dvss pads were added to the remaining pins of the dual in-line (DIP) 28 package. The ASIC implementation shown in Figure 10 was manufactured by EUROPRACTICE IC Service using TSMC's 180 nm CMOS technology.
# Using Enhanced Representations to Predict Medical Procedures from Clinician Notes
## 4 Implementation Design and Evaluation
### Prediction of Procedures  
The inputs for the final prediction stage consist of different combinations of annotations and diagnosis representations, as described in Sections 4.2 and 4.3. Specifically, the collection of diagnosis codes related to each medical record, defined as multi-hot vectors, are combined with their corresponding vectorized annotations by concatenating both vectors. The resulting tensor is the input of the prediction task, and the target to predict is the collection of procedures for the medical case, also described as a multi-hot vector. The proposed experiment comprised two different classifiers in the prediction step, a simple LR model and an Multi-layer Perceptron (MLP) architecture, both implemented using Scikit-learn [46] and adjusted via grid search.  
Figure 5: Histogram of ICD codes per number of documents they appear in across different code subsets in MIMIC-III.  
In the case of the LR classifier, the original multilabel classification problem was broken down into several binary classification problems, fitting one classifier per target, one for each procedure code to predict. During the fine-tuning step, we considered different optimizers and \(L2\) regularization with strengths ranging from 0.001 to 1 in multiples of 10. The adjusted LR classifier used a liblinear solver with a regularization strength of 0.1.  
For the MLP fine-tuning, we considered a single hidden layer with 50, 100, 200, and 500 neurons and an \(L2\) regularization strength ranging from 0.0001 to 0.1 in multiples of 10. The adjusted MLP classifier used a single hidden layer with 200 neurons and an \(L2\) regularization strength of 0.0005. During training, we also used the Adam solver [47], adaptive learning rate to minimize training loss with an initial value of 0.005, and early stopping.  
The result of this stage is the final prediction, which consists of the most relevant procedure codes for the given case represented as a multi-hot vector.
###### Abstract
They arrived at the conclusion that for a pristine silica NPs whose contact angle with the water remains \(<\)75\({}^{\circ}\) and with the oil \(>\) 105\({}^{\circ}\) can only stabilize \(o/w\) emulsions. Vice versa for hydrophobically modified silica NPs, whose contact angle with water is always \(>\)130\({}^{\circ}\) and with oil \(<\) 50\({}^{\circ}\), can only stabilize \(w/o\) emulsions. It is also implied in these works that an emulsion phase inversion to take place, from \(w/o\) to \(o/w\) and vice versa, must pass through a contact angle of 90\({}^{\circ}\). Thus far, to the best of our knowledge there has not been a quantitative experimental confirmation for these theoretical predictions, because measuring the contact angle of the particles has been an ongoing challenge for fundamental science [35; 40; 41]. Further, there are no current studies linking the contact angle with the emulsification ability of NPs in terms of size of the emulsion droplets generates, nor the curvature of the oil/water interface upon adsorption of NPs at the interface. In this work, we address exactly this aspect and demonstrate experimentally that indeed the above theoretical predictions are correct and further, we provide a tested method to measure the contact angle of NPs and their interfacial energy via Pickering emulsion polymerization. The novelty of the current work is on one hand to provide experimental proof of the relationship that exists between the surface properties of the stabilizing NPs and the size of the emulsion droplet that can be obtained. On the other hand, we show that based on these parameters, the Pickering emulsion polymerization is a facile route toward obtaining large polymer microspheres with nanostructured surface. Further, we emphasize the determinant role of interfacial energy and its components of the NPs in determining the size of polymer microspheres that can be obtained by Pickering emulsion polymerization and thus provide a route to tuning the size of spherical microparticles with nanostructured surface.
# Ontology-Based Context Event Representation, Reasoning, and Enhancing in Academic Environments
## 2 Related Work
In [31], middleware support is provided for flexible event representation, query, and reasoning, where an event is an action that involves actors, an instant, a goal, and a location. In [32], the event concept is introduced by using a shared ontology called SOUPA-Standard Ontology for Ubiquitous and Pervasive Applications. SOUPA is designed to model and support pervasive computing applications. This ontology is expressed using Ontology Web Language OWL and includes modular component vocabularies to represent intelligent agents with associated beliefs, desires, time, space, events, user profiles, actions, and policies for privacy. The authors of [33] present a framework for inferring semantically annotated glycemic events on the patient, which leverages data from mobile wearable sensors. The authors use a standard machine-readable data model to represent events and they include observations like temporal and location information. In [34], the knowledge acquisition method for the construction of a semantic representation in virtual organizations is included. In [35], an event ontology is constructed concerning interesting users' visits to web sites; taxonomic and semantic relations associated with events are depicted. The authors of [36] develop an ontology to support the administrative procedures of decentralized government organizations. The entities depicted in the ontological model are organization, people, document, data. A user interface was created to query actions about government processes based on SPARQL queries. Table 1 shows a resume of findings of relevant analyzed work in terms of context information considered in each one and the application domain. With the revision of this related work, we detected that an event-driven approach is necessary based on a customized and modular ontological model for the academic context modeling, enhancing, and reasoning process.
## 3 Results
### Effects of PTE on Antioxidant Defenses  
The exploration of the expression of HO-1 showed a moderate decrease after LTA stimulation in RAW cells. Moreover, a concentration-dependent increase in HO-1 was detected in LTA-stimulated cells that had been pretreated with PTE (\(p<0.05\), Figure 5A). In addition, the activity of antioxidant enzyme catalase was found to increase in RAW cells when they were pretreated with PTE (Figure 5B). The trend concerning an increase in the activity of catalase (CAT) in cells pretreated with PTE was not concentration-dependent, as both 20 and 40 uM PTE have similar effects on CAT activity. Moreover, LTA stimulation expressively reduced the content of GSH in RAW cells (\(p<0.01\)), but this decrease was significantly raised in PTE-pretreated cells (\(p<0.05\), Figure 5C).  
Figure 4: PTE regulates NF-\(\kappa\)B signaling pathway induced by LTA in RAW cells. Cells were treated with PTE (20 and 40 uM) and LTA (5 ug/mL) for 1 h. Western blotting assay was done to detect the phosphorylation of (**A**) \(\mathrm{lxB\alpha}\) and (**B**) \(\mathrm{p65}\) in LTA-induced RAW cells. (**C**) PTE inhibited LTA-induced p65 nuclear translocation. \({}^{**}p<0.01\) vs. control cells; \({}^{\#}p<0.05\), and \({}^{\#}p<0.01\) vs. PTE pretreated cells (\(n=4\)).
# Electroencephalography (EEG) Technology Applications and Available Devices
## 3 Applications of EEG  
EEG devices can provide valuable information about human mental health states, thoughts, and imagination. Thus, researchers in different areas of research have utilized it. Figure 3 demonstrates five categories of EEG data applications and their relevant sub-categories. In a later section, _EEG Headset Applications and Research Usages_, a report of relative research use for various devices is provided as part of a table.  
Figure 2: Electrode positions and labels in the 10 \(\pm\) 20 system [8].  
**Brain-computer interfaces (BCI):** BCIs, sometimes called brain-machine interfaces (BMIs), are one of the most common applications of EEG. BCIs use real-time EEG data to control and direct mechanical and electronic devices [10; 11; 12; 13; 14; 15; 16; 17].  
BCI devices are commonly used as a human-machine interface to help individuals with mild to severe motor disabilities, including those who are not able to communicate with others [10]. BCI devices designed for the disabled do not rely on muscle movements; instead, they use specific brain activity, imagining doing an activity, or concentrating on an object on-screen, and translate them into control functions and commands [10; 11].  
The most common BCI applications are listed below.  
**1. Autonomous navigation of digital or mechatronic devices:**  
* Real-time teleoperation of robotic body parts [18; 19; 20; 21; 22];
* Controlling and directing a robot [23; 24; 25; 26], drone [27], dashboard of a vehicle [28], or a miniature or semi-automated car [29; 30];
* Monitoring and controlling sensors inside of smart houses [31].
**2. Helping people with disabilities or motor activity impairment:**  
* Control of mobile phone apps using eyewinks [32];
* Directing electrical wheelchair movement [33; 34; 35; 36];
* Control of artificial body part such as prosthetic hand or arm [10; 37; 38];
* Recognizing a patient's attempt to move their body, e.g., stroke [39] and brain injury [40];
* Post-stroke motor rehabilitation using VR [41];
* Controlling a robot using body gestures [42];
* Mind-controlled dialing systems [43];
* Speech recognition system for people with speech disability [12; 44];
* Mouse cursor control using imagined hand movement [45];
* Gaze controller for patients with neurodegenerative diseases [46].  
Figure 3: Electroencephalography (EEG) applications.
# A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
## Appendix A LPFRS-01 Power-Law Model  
In the experimental part of this work, a SpectraTime LPFRS-01/AV1 rubidium oscillator was used as the receiver reference clock. To obtain the most accurate prediction of PLL performance, a phase noise power spectral density (PSD) power-law model representative of the particular oscillator we are using was required. The generic rubidium model from [5] is likely not accurate enough.  
The manufacturer's phase-noise PSD specifications for the LPFRS were augmented with our own measurements to form the power-law model. For the latter, we followed the method used by [PERSON] [22] ([23], p. 120-123). Velocity measurements are used to determine the reference clock PSD at low offset frequencies, where PSD specifications are not provided by the manufacturer.  
It is important to note that [PERSON]'s method of using velocity measurements includes the effects of frequency synthesis. We believe the generic rubidium power-law model in prior work [5] does not consider the effects of frequency synthesis.  
Measurements were taken using a Novatel DL-v3 receiver capable of using an external reference oscillator. Clock bias rate data were collected directly (rather than computed from velocity measurements) using the receiver's \"clockmodel\" log. See [22] ([23], p. 120-123) for additional information on how the power-law model is formed.  
Figure 11 shows the resulting phase noise PSD for the LPFRS-01 rubidium oscillator, including the effects of frequency synthesis. The worst case is considered, where the frequency synthesis generates L1 (mixing to baseband), which results in the highest phase-noise on synthesizer output.
## 2 System and Channel Models  
We consider a UAV-assisted multi-hop parallel hybrid FSO/RF communication system, as shown in Figure 1, where there exist both a direct link and \(N\) numbers of hops between source and destination nodes, and each hop has M-1 relays for selections [27]. In any hop and direct link, the hybrid FSO/RF communication method based on DF relay protocol, subcarrier intensity modulation, and selective combination scheme are adopted. To avoid interferences, only one node is selected to relay and forward the signal to a next node at any hop. In order to improve the reliability of the communication system, the max-min path selection criterion is adopted [28].