esa-sceva/satcom-chunk-collection huggingface dataset:
# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 5 Experimental Evaluation
### The Experiment Description
#### 5.1.3 The Photostimulation Device  
Many researchers and developers of BCI systems use personal computer displays as sources of photostimuli. This imposes certain special requirements for the display. However, it is still possible for additional frequency components to occur, which could be a result of the screen refresh rate [54]. At the same time, in order to carry out research in this area, it makes sense, in the first approximation, to give preference to a special device that generates frequency-stable photostimuli, and is compatible with most of the EEG signal registration equipment.  
While exploring the available EEG equipment markets, we encountered a challenge in finding photostimulators that were compatible with most manufacturers of EEG equipment and also offered the ability to finely adjust the frequency and duration of photostimuli. As a result, we created our own photostimulator with all the necessary characteristics and functionalities, as shown in Figure 2.  
This device was based on an ATmega328P microcontroller with a maximum clock frequency of 20 MHz, which is more than sufficient for generating visible stimulations across the entire frequency range of interest. We utilized a 3D printer and additive technologies to produce the case and detachable elements (such as the stand, diffusing plates, and pattern-changing stencils) using PLA plastic.  
The photostimulator described is equipped with an LED matrix consisting of 60 white light LEDs. This matrix serves as the source of light emissions for generating flashes during photostimulation. The device is capable of generating photostimuli with a specified frequency ranging from 0.5 to 60 Hz, with an increment value of 0.25 Hz. Additionally, it can produce photostimuli with durations ranging from 1 to 90 s, with an incremental value of 0.5 s.  
To ensure uniform illumination, the photostimulator is equipped with a set of diffusing plates made of white PLA plastic, with a maximum diffusing surface area of 10 cm\({}^{2}\). These  
Figure 2: The model of the developed photostimulator used for generating photostimuli in this study.  
plates have varying thicknesses (ranging from 0.3 mm to 2.0 mm), which help in achieving consistent and evenly distributed light output.  
During the process of generating photostimuli, the stimulator's specialized software takes control and generates an auxiliary signal. This signal is of rectangular shape and has a frequency equal to the frequency of the photostimulus being produced. The amplitude of this auxiliary signal is also set equal to the frequency of the photostimulus. This auxiliary signal is streamed using the lab-streaming layer protocol, allowing it to be recorded in parallel with the EEG signal. This simultaneous recording enables accurate detection of specific fragments within the EEG signal that coincide with the photostimulation events.
# Reaction Forces and Apparent Thrust in Dual Oscillating Control Moment Gyroscopes
## 2 Materials and Methods
### Dynamic Equilibrium: Newton's Laws
#### 2.6.2 Exact Model: Variable Angular Velocity  
To meet the initial conditions where a gyroscope's frame must be _at rest_ when arriving at the ends of the oscillation, a promising approach is to interpolate the polar angle \(\theta(t)\) between the endpoint values \(\theta_{\text{min}}\) and \(\theta_{\text{max}}\) using Hermite polynomials:  
\[\theta(\xi)=(2\xi^{3}-3\xi^{2}+1)\theta_{\text{min}}+(-2\xi^{3}+3\xi^{2}) \theta_{\text{max}}\,, \tag{20}\]  
where \(\xi\) is a parameter which refers to the portion of elapsed time \(t\) such that  
\[\xi=\frac{t}{T_{\text{rise}}},\quad\text{with}\quad 0\leq\xi\leq 1\,. \tag{21}\]  
Therefore, the angular velocity is given by  
\[\omega(t) =\frac{d\theta}{dt}=\frac{d\theta(\xi)}{d\xi}\frac{d\xi}{dt}\] \[=\frac{(6\xi^{2}-6\xi)\theta_{\text{min}}+(-6\xi^{2}+6\xi)\theta_ {\text{max}}}{T_{\text{rise}}}\,, \tag{22}\]whereas its derivative is as follows:  
\[\dot{\omega}(t) =\bar{\theta}(t)=\frac{d\omega}{dt}=\frac{d\omega(\xi)}{d\xi}\frac{ d\xi}{dt}\] \[=\frac{(12\xi-6)\theta_{\min}+(-12\xi+6)\theta_{\max}}{T_{\rm rise}^ {2}}\,. \tag{23}\]  
One may easily verify that at the initial time (\(\xi=0\)) and final time (\(\xi=1\)) of the oscillation from \(\theta_{\min}\) to \(\theta_{\max}\), Equation (22) implies the desired stationary condition \(\omega=0\) and thus can be used for the purposes of this simulation.  
Similar considerations may be assumed for the reset phase as well. Then, the initial state is \(\theta_{\max}\), and the final state is \(\theta_{\min}\), whereas the elapsed time is \(T_{\rm reset}\).  
In the general case of a variable angular velocity \(\omega(t)\), the reaction forces become  
\[F_{\rm front}=\frac{l_{2}}{l_{1}+l_{2}}F_{g}+\frac{l_{2}}{l_{1}+l_{2}}2 mr\Big{(} \dot{\omega}\cos\theta-\omega^{2}\sin\theta\Big{)}+\frac{2}{l_{1}+l_{2}}L_{ \rm rotor}\omega\sin\theta\,, \tag{24}\]  
and  
\[F_{\rm rear}=\underbrace{\frac{l_{1}}{l_{1}+l_{2}}F_{g}}_{\rm Dead-weight\ term}+\underbrace{\frac{l_{1}}{l_{1}+l_{2}}2 mr\Big{(}\dot{\omega}\cos \theta-\omega^{2}\sin\theta\Big{)}}_{\rm Dean-drive\ term}-\underbrace{ \frac{2}{l_{1}+l_{2}}L_{\rm rotor}\omega\sin\theta}_{\rm Gyroscopic\ term}\,. \tag{25}\]
# Performance of API Design for Interoperability of Medical Information Systems
## 3 Design of the API Proposal
### Research Limitations and Future Trends  
This proposal addresses the pressing need for interoperability in medical information, which is particularly underscored in the post-COVID-19 era, where its pivotal role in facilitating accurate diagnoses and effective treatments for patients is widely acknowledged. The primary objective was to enhance the architecture of the new medical interoperability API software through the utilization of an object-oriented approach and the seamless integration of design patterns. The emphasis was placed on refining the design to accommodate devices with limited memory capacity. Furthermore, this initiative laid down a robust framework for early evaluation and enhancement of the software architectural design, thus mitigating costs in the API development lifecycle. However, a significant challenge arose during the design phase that stemmed from the difficulty in assessing the practical effectiveness of the proposed architecture in real-world scenarios. The evaluation predominantly relied on static methods using predictive models and formal techniques. Additionally, practical implementation may face hurdles due to resource constraints, compatibility issues with existing platforms, and the intricate nature of integration with established healthcare systems.  
Among the programming languages where the proposed architecture was suggested to be implemented, Java stood out as a strong candidate. It was designed from its inception to be interoperable, promoting portability and integration across different systems and services through standards, abstractions, and a common virtual machine. During the testing phase, the analysis model utilizing the proposed GCA acted as a guiding framework. This model, coupled with functional analysis weighted with design metrics, delineated the operational sequences of the API, offering early insights for crafting the test matrix.  
We worked with the HL7 CDA standard [13], which is based on specific clinical documents. Its data model is structured around segments and fields, and the standard specifies how they should be organized and transmitted in messages. However, CDA's rigid structure limits its adaptability to different contexts and data requirements, particularly when handling large volumes of information. Other standards are also widely utilized and offer complementary features in managing health information, such as OpenEHR. This standard focuses on creating and managing electronic health records (EHRs) and semantically representing clinical information. OpenEHR utilizes a data model based on archetypes, which are semantic constructs that define the structure and meaning of clinical information. This enables a more robust and structured representation of health data. In this scenario, the API must integrate with other standards to function effectively.
## 4 Experimental Components and Setup  
A detailed experimental setup is presented in this section (as shown in Figure 2). The experimental setup consists of thermoelectric cooling (manufactured by Tellurex, Traverse, MI, USA) module to create a cooling effect, a cold plate (model: CP15G05, manufacturer: LYTRON, Woburn, MA, USA) to remove heat from the hot side of the TEC module, copper tube fin heat exchanger (model: LC200, manufacturer: TE Technology, Traverse; MI, USA) to remove heat from the liquid to deliver cool liquid to the chiller (manufacturer: Polystat, Montreal, QC, Canada) with precision temperature controlled to supply water to the cold plates, K-type thermocouple (manufacturer: Omega, St-Eustache, QC, Canada) and 16 channel NI-DAQ system (manufacturer: National Instruments, Austin, TX, USA). The main aim of the proposed design is to supply the ambient air to the heat sink sandwiched by TEC module on both the side of the heat sink to generate cool air. Furthermore, the hot liquid from the cold plate is passed to the copper heat exchanger, which is inside in an insulated closed room made from styrofoam and supply cool liquid back to the chiller. Therefore, the temperature at various points is monitored during the operation of the system (see Figure 2). A detailed description of each component is given in the following sections.
# Reducing Videoconferencing Fatigue through Facial Emotion Recognition
## 3 Data and Methods
### Data Pre-Processing  
We applied different pre-processing steps to clean up the recorded data and to arrange them in a suitable form for subsequent analysis. Each recording was first divided into eight sequences and each of them contained the presentation of one group. We then converted each video presentation into a sequence of images. More specifically, we extracted one image (frame) per second from the video using Moviepy ([[https://zulko.github.io/moviepy/](https://zulko.github.io/moviepy/)]([https://zulko.github.io/moviepy/](https://zulko.github.io/moviepy/)), accessed on 10 May 2021). As each image contained a grid of up to 49 faces, we localized and extracted individual faces from each image using face-recognition ([[https://github.com/ageitgey/face_recognition](https://github.com/ageitgey/face_recognition)]([https://github.com/ageitgey/face_recognition](https://github.com/ageitgey/face_recognition)), accessed on 10 May 2021), which is a python li brary for detecting faces in a given image. Moreover, we utilized the same library to divide the individual faces into three groups, namely: audience, presenter, and supervisor. The library automatically detects similar faces given an example. Hence, we manually selected sample images for supervisor and presenter, respectively, and stored them separately using face-recognition. Note that in some presentations, neither the presenter nor the supervisor could be identified due to poor lighting conditions or due to the person of interest not being recorded at all. We then discarded images with low quality and this resulted in a total of 41 presentations with the number of individual faces ranging from 3600 to 15,863 for each presentation. Finally, we converted each image to greyscale and reshaped its size to 48 \(\times\) 48 pixels. During model training, we used data augmentation, that is, randomly flipping the images horizontally (see Figure 1 for an illustration).
## 4 Experiments and Results
### Results with the ACDC Dataset
#### 4.3.6 Comparison with Related Works
Although this method does not have a specific stage for post-processing, it obtains Dice ED and ES values that are very close to those achieved by our previous work [44] and [PERSON] et al. [59], which are approaches that use FCNs to refine the RV segmentation. The first, surpassed in Dice ED, uses the U-Net-based reconstruction module, and the latter uses an adversary autoencoder (AVAE). However, these strategies add more FCNs to the method's pipelines, increasing their complexity. Lastly, regarding RV segmentation, the proposed method produces a lower result than those obtained for the LVC and MYO. However, it still achieves an interesting placement, being competitive with other approaches.
## 5 Conclusions  
In this study, we combined an SE-CNN network and the MCMC method to solve the inversion problem of cross hole ground-penetrating-radar data. We replaced the forward model in the MCMC inversion process with an SE-CNN network and used travel time data to invert the underground velocity model. The inversion results indicate that as the training size increases, the accuracy of the inversion results improves.  
The advantage of Monte Carlo method is that it can perform statistical analysis on a forward model and explain model errors during the inversion process, which can reduce the impact on the inversion results. By comparing neural network models with different training sizes with the ray method, this article reveals that as the training size of the neural network increases, the accuracy of the forward model increases, and the corresponding model error can be even smaller than the ray method. The error of the neural network model trained 20,000 times in this article is smaller than that obtained using the ray method. In terms of computational efficiency, the forward models based on neural networks and the ray method have the same order of magnitude of efficiency, i.e., three orders of magnitude faster than the accurate forward model. Introducing a SE-CNN network into the MCMC method is an effective way of solving inversion problems, ensuring computational efficiency while obtaining more accurate inversion results.  
Conceptualization, H.Q.; Methodology, H.Q. and S.W.; Software, H.Q.; Validation, H.Q.; Formal analysis, S.W.; Investigation, H.Q. and C.L.; Data curation, S.W.; Writing--original draft, H.Q. and S.W.; Writing--review & editing, C.L.; Supervision, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic research funds of the Chinese Academy of Geological Sciences (JKYZD020329); China Geological Survey Program (AS2020J02); the China Geological Survey Project (DD20230010); the China Geological Survey Project (DD20230324); and the National Natural Science Foundation of China (41904065).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm
## 3 System Model
### Node Residual Energy and Average Energy  
Nodes in the edge-side WSN must ensure the life cycle of the nodes and the network during the cluster head selection process, so the remaining energy of the nodes needs to be considered during the selection process.  
In the edge WSN, it is necessary to save the power of sensor nodes as much as possible to ensure the energy balance of sensor nodes and prolong the life of the network. Cluster head nodes need to consume more energy than ordinary nodes. Therefore, we need to select nodes that have consumed less energy as cluster head nodes as much as possible. The energy consumption \(E_{i}^{r}\) of nodes is defined as  
\[E_{i}^{r}=\frac{E_{i}^{s}-E_{i}}{E_{i}^{s}} \tag{5}\]  
where \(E_{i}^{s}\) is the initial energy of the \(i\)-th sensor node; \(E_{i}\) is the remaining energy of the current node of the \(i\)-th node. The smaller the value, the more abundant the energy of the cluster head node, the more suitable it is as the cluster head node.  
In order to narrow the selection range of cluster head nodes and ensure that the cluster head nodes have a longer life cycle and can better assume the responsibility of data forwarding and aggregation, the edge layer nodes select the sensor nodes whose residualenergy is greater than the average energy \(E_{avg}\) of the selected nodes as candidate cluster head nodes and join the set of candidate nodes.  
\[E_{avg}=\frac{\sum\limits_{i\in N\wedge i\
eq G}E_{i}}{N-G} \tag{6}\]  
where \(E_{avg}\) is the average energy of all surviving nodes in the network, \(E_{i}\) is the current remaining energy of sensor node \(i\), and \(G\) is the number of dead nodes. When the remaining energy of the node is greater than \(E_{avg}\), we put it into the set \(H\).
# Generation of Monoclonal Antibodies for Sensitive Detection of Pro-Inflammatory Protein S100A9
## 3 Results  
### Expression of Soluble rhS100A9 with High Yield and Purity  
We produced a recombinant human S100A9 (rhS100A9) protein in _Escherichia coli_ (_E. coli_) to produce a sufficient amount of highly pure antigens that can be used to boost the generation of antigen-specific antibodies in mice. We cloned the N-terminally His-tagged rhS100A9-coding gene (NM_002965.3) into the pET-Duet protein expression vector and transformed the clones into _E. coli_. The proteins were bacterially expressed and then purified by nickel affinity purification, yielding 16 mg of soluble S100A9 protein per 100 mL culture, with over 99% purity (Figure 1a). We performed an indirect ELISA using a commercial mouse anti-hS100A9 antibody and HRP-conjugated goat anti-mouse IgG antibody to confirm the antibody-binding activity of the purified rhS100A9 antigen (Figure 1b). The resulting titer revealed higher dose-dependent binding activity between the recombinant antigen and antibody than that of the commercial hS100A9 protein (Figure 1c).
# A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging
## 2 Method Development
### Quantitative Analysis  
Even though some shadow removal datasets are available [29; 43; 44], they have been constructed for close-shot images. Currently, there is no dataset related to shadow removal in urban aerial images, mainly because of the higher cost of image capturing; this complicates the analysis of shadow removal results as no reference point could be taken. For the quantitative evaluation, in this work, we use the shadow standard deviation index (SSDI) proposed by [25]. The SSDI computing is carried out for each channel (\(R\), \(G\), and \(B\)) of the output image \(T\), defined by \(\sigma_{s-ns}\), as shown in Equation (5).  
\[\sigma_{s-ns}=\frac{1}{B}\sum_{b=1}^{B}\sqrt{\frac{1}{N}\sum_{i=1}N\big{(}F_{b, i}^{s}-\overline{F_{b}^{ns}}\big{)}^{2}} \tag{5}\]  
where \(b\) is the current channel of the image, \(B\) is the total number of channels at the corrected image, \(i\) is the pixel in the shadow regions, and \(N\) is the total number of pixels in the shadow regions. \(F^{s}\) is the corrected shadow region, and \(\overline{F^{ns}}\) is the means of the corresponding unshadowed sample set of the same channel. The SSDI is useful for measuring the variation of the corrected shadow regions regarding unshadowed regions. A low SSDI value specifies that the corrected shadow regions are consistent with the unshadowed re  
Figure 4: Color transfer result (**a**); color tuning result (**b**). Red rectangles focus on concrete areas and blue in vegetation or grass.  
gions, and a high SSDI value indicates that the corrected shadow regions are not consistent with unshadowed regions.
## 3 Composite Laminates Toughened by Nano-Particles
### Three-Dimensional (3D)  
3D nanostructures are important materials owing to the large specific surface area and other superior properties arising from quantum size effect. Nanocarbon aerogels are 3D nanoparticles with a high electrical conductivity, high porosity, controllable pore structure and high specific surface area. Nanocarbon aerogels are used to improve toughness and impact performance of composite materials. Significant improvement in the fracture toughness of the relatively low (0.3 wt %) aerogel concentration composites are reported [82; 140]. The impact and CAI properties of the CFRP laminates were improved by adding the Nanocarbon aerogels and an optimum content was reported for the best performance [83].  
Crack pinning, crack deflection, and plastic void growth are reported as the mechanisms for the toughness improvement of the carbon aerogel/epoxy polymers [82]. These mechanisms are caused by the obstruction of crack propagation by agglomerated carbon aerogels (see Figure 16).
## 3 Results
### Performance Analysis on Monthly Basis
The most accurate and precise ET\({}_{0}\) estimates based on UMS and E5L occurs in July, which is also the month with highest ET\({}_{0}\) in the study region. UMS performances drop in April, May and slightly in September. When the aerodynamic term of Penman Monteith equation is prevalent over the radiation term, the uncertainty about VPD and wind speed exerts a major role in the estimation of ET\({}_{0}\). Hence, the performance of reanalysis models in April and May is influenced by the larger BIAS and RMSE (Figure 4).
# Scene Adaptive Segmentation for Crowd Counting in Population Heterogeneous Distribution
## 2 Related Work
### Deep-Learning-Based Methods  
Due to the stronger ability of deep learning to capture features, these CNN-based counting methods have been applied in fields that need to be counted. More and more researchers train models to continuously refresh the performance of the CNN-based crowd counting approach. [PERSON] et al. [13] utilized CNN that inputs an image and outputs directly the sum of a crowd. [PERSON] et al. [14] attempted to use a network of two columns for estimating density maps. [PERSON] et al. [15] employed a single-column fully convolutional network, while [PERSON] et al. [16] proposed senior-level information to boost the density prediction performance. The research results of [PERSON] et al. [4] exploring the MCNN were consistent with those of [PERSON] et al. [17]. In other words, the Hydra-CNN of scale and multi-column module was presented for estimating the density map. [PERSON] et al. [18] explored a multi-task structure to predict the classification of density while generating a senior-quality feature map. Motivated by the MCNN, [PERSON] et al. [5] selected the optimal regressor as input and adopted a network to acquire the same classification result of that input image in the prediction. [PERSON] et al. [19] adopted a single-column structure with deeper dilated CNN; the proposed CSRNet consisted of a 2D feature extraction network and a dilated convolution and adopted dilated convolution to replace pooling operations for obtaining larger reception fields. [PERSON] et al. [20] proposed a RANet that accounted for the interdependence of pixels to increase the self-attention mechanism. [PERSON] et al. [21] developed a scale aggregation network (SANet) performing encoder and decoder. The scale aggregation models as encoders grab different scale features, and the decoder uses a large number of transposed convolutions to build high-definition density estimation maps. [PERSON] et al. [22] exploited a consistent polarity deep attention network (PDANet), which incorporated attention into an emotion polarity constraint CNN. [PERSON] et al. [23] proposed a scale-driven CNN, which premised that features of heads were visible and dominant in any density of the crowd. [PERSON] et al. [24] designed the ScSiNet for extracting scale-invariant features, which integrated scale-invariant transformation.  
From the above, it can be seen that most CNN-based crowd counting methods compute the population by estimating density with _L2_ regression loss, whereas because the _L2_ is sensitive, the generating density map is punch-drunk. To tackle this problem, a novel dilated convolutional model that combines traditional convolutional and expansion convolutional layers is proposed, which can avoid the ambiguous density map caused by _L2_ loss.