esa-sceva/satcom-chunk-collection huggingface dataset:
# EC-GAN: Emotion-Controllable GAN for Face Image Completion
## 3 Proposed Approach
### EC-GAN Overall Image Completion Framework  
The overview architecture of EC-GAN is illustrated in Figure 1. The content completion module (with a pale green background) contains an encoder \(E\) and a generator \(G\). The main network of our generator is similar to PIC-Net [13], but we modify the encoder and generator to enable them to deal with extra-label information. The encoder maps the visible parts \(I_{m}\) into the native latent space, and the corresponding latent codes are denoted by \(z\). The generator reconstructs the image from \(z\).  
To disentangle the emotion semantics from the native latent space, we propose the emotion inference module (with a light orange background in Figure 1), which contains two parallel paths, one for the emotion semantics path and one for the image content path. The emotion semantics path designs an inference network to infer the emotion labels \(c_{gt}^{infer}\) of the ground truth and calculates the information entropy of the emotion noted as \(z_{emo}^{infer}\), while the image content path maps the ground truth \(I_{gt}\) to the latent codes of content \(z_{img}^{c}\). Once the generator \(G\) produces a result sample \(I_{g}\), the emotion semantics path maximizes  
Figure 1: The framework of Emotion-Controllable GAN (EC-GAN).  
the mutual information between the inferred truth emotion labels \(c_{gt}^{infer}\) and the generated sample \(I_{g}\), resulting in the disentanglement of the emotion semantics from the latent space (disentanglement between \(z_{emo}^{c}\) and \(z_{img}^{c}\)).  
Then the final latent codes \(z^{c}\) are obtained by combining \(z_{emo}^{c}\) and \(z_{img}^{c}\) together in the emotion control module (with a pale blue background Figure 1). Therefore, our model can calculate the emotion vectors \(n_{c}\), which are utilized to move the latent codes in the content completion module from \(z\) to \(z_{new}\) by the emotion control module. More details will be introduced in Section 3.3.  
The encoder \(E\) in the content completion module is optimized by minimizing the following loss function:  
\[L_{\text{CVAE}}=-E_{q(z|x)}[\log p(c|x,z)]+\text{KL}[q(z|x,c)||p(z|x)] \tag{2}\]  
where \(x\) is the data sample from the generative distribution \(p(x|z)\) and \(z\) is the latent variable extracted from the prior distribution \(p(z|x)\). In our EC-GAN model, the input data sample \(x\) is the truth image sample \(I_{gt}\), and the label \(c\) is the inferred truth emotion label \(c_{gt}^{infer}\). The generator \(G\) and the discriminator \(D\) of GAN try to minimize the loss functions \(L_{G}\) and \(L_{D}\), respectively:  
\[L_{D}=-E_{x\sim P_{data}}[\log D(x)]-E_{z\sim P_{z}}[\log(1-D(G(z)))], \tag{3}\]  
\[L_{G}=-E_{z\sim P_{z}}[1-\log D(G(z))]. \tag{4}\]  
Therefore, the combined loss function of our content completion module is:  
\[L_{\text{VG}}=L_{\text{CVAE}}+L_{D}+L_{G}. \tag{5}\]
## 3 Results
### sEMG Differences across Conditions  
Figure 3 represents the percentage of change in normalised sEMG muscle activity (\(\Delta\)%MVIC) across the different plank variations for all core muscles examined. The RA showed higher sEMG activation in _flexion_ compared to _neutral_ (\(p\) = 0.012, ES = 0.784) and to _extension_ (\(p\) = 0.013, ES = 0.730). However, no significant differences were observed for the RA between _neutral_ and _extension_ conditions (\(p\) = 0.962, ES = 0.000). The LES showed no significant differences across all experimental conditions (_neutral_-_flexion_, \(p\) = 0.163, ES = 0.620; _neutral_-_extension_, \(p\) = 0.110, ES = 0.620; _flexion_-_extension_, \(p\) = 0.835, ES = 0.000). For the LEO, _flexion_ elicited a greater sEMG response compared to _neutral_ (\(p\) = 0.047, ES = 0.694), but not compared to _extension_ (\(p\) = 0.079, ES = 0.554). Also, no significant differences between _neutral_ and _extension_ conditions were found (\(p\) = 0.802, ES = 0.446). For the REO, no statistical differences for _flexion_ were observed when compared to _neutral_ (\(p\) = 0.120, ES = 0.488) or when compared to _extension_ (\(p\) = 0.284, ES = 0.312). Likewise, no significant differences between _neutral_ and _extension_ conditions were detected (\(p\) = 0.622, ES = 0.168). Both the LIO and RIO elicited higher sEMG activation levels in _flexion_ in comparison with _neutral_ (for the LIO: \(p\) = 0.001, ES = 1.267; for the RIO \(p\) = 0.002, ES = 0.999) and _extension_ (for the LIO: \(p\) = 0.015, ES = 0.716; for the RIO \(p\) = 0.011, ES = 0.761). However, no significant differences were found on the _neutral_-_extension_ comparison (for the LIO: \(p\) = 0.268, ES = 0.380; for the RIO \(p\) = 0.485, ES = 0.252).
## 5 Challenges, Open Research Problems, and Future of WBANs
### Bandwidth Problems
#### 5.1.2 MAC Layer Challenges  
In addition to the usual problems of WSN MAC layer such as energy efficiency [84], there are still some critical and important issues that are specific to WBANs and health monitoring. To begin with, Quality of Service requirements of emergency traffic should be explored for health monitoring applications. [PERSON] _et al._[85] proposed a MAC scheme for healthcare applications which merge a preemptive service scheduling algorithm into the 802.11e Quality of Service MAC to provide the highest and preemptive channel access precedence for medical emergency traffic. The Quality of Service-aware MAC protocols [89] for low power wireless sensor networks such as 802.15.4 still need research from a healthcare specific perspective. WBANs and Healthcare monitoring applications require emergency event reporting besides periodic physiological data reporting.  
Under emergency conditions, the emergency data should be guaranteed to be delivered with a reasonable delay. For this purpose, emergency data prioritization mechanisms should be developed. Moreover, the fairness among different emergent situations should be considered. The event based fairness scheme proposed by [PERSON] _et al._[90] is an example for video sensor networks. The prioritization and fairness mechanisms for vital signs monitoring applications are open research issues.
# A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability
## 7 Discussion  
### Discussion about the Method  
In this work, we focus on developing an interpretable and accurate Grey-Box prediction model applied on datasets with a small number of labeled data. For this task, we utilized a Black-Box base learner on a self-training methodology in order to acquire an enlarged labeled dataset which was used for training a White-Box learner. Self-training was considered the most appropriate self-labeled method to use, due to its simplicity, efficiency and because is a widely used single view algorithm [4]. More specifically, in our proposed framework, an arbitrary classifier is initially trained with a small amount of labeled data, constituting its training set which is iteratively augmented using its own most confident predictions of the unlabeled data. More analytically, each unlabeled instance which has achieved a probability over a specific threshold is considered sufficiently reliable to be added to the labeled training set and subsequently the classifier is retrained. In contrast to the classic self-training framework, we utilized the White-Box learner as the final predictor. Therefore, by training a Black-Box model on the initial labeled data we obtain a larger and a more accurate final labeled dataset, while utilizing a White-Box trained on the final labeled data and being the final predictor, we are able to obtain explainability and interpretability on the final predictions.  
In contrast to [PERSON] et al. [12] approach, we decided to augment the labeled dataset utilizing the most confident predictions from the unlabeled dataset. This way we may lose some extra instances, leading to a smaller final labeled dataset, but instead we manage to avoid mislabeling of new instances and thus reduce the \"noise\" of the new augmented dataset. Thus, we addressed the weakness of including erroneous labeled instances in the enlarged labeled dataset which could probably lead the White-Box classifier to poor performance.  
[PERSON] et al. [19] proposed an interpretable mimic model which falls into the intrinsic interpretability category similar to our proposed model. Nevertheless, the main difference compared to our approach is that we aim to add interpretability on semi supervised algorithms (self-training) which means that our proposed model is mostly recommended on scenarios in which the labeled dataset is limited while a big pool of unlabeled data is available.
# A Literature Review of Adhesive Systems in Density: Key Components and Their Clinical Applications
## 4 Effect of Dentin Structure on Resin-Dentin Bonding  
The main goal of any bonding agent is to achieve a firm and enduring adaptation of the restorative material to dental substrates [9]. The major challenge for any new dental adhesive is to be equally effective on both enamel and dentin surfaces [33]. To accomplish this goal, several obstacles must be overcome [9]. Bonding efficiently to the organic and wet dentin is a baffling task in adhesive dentistry. Nevertheless, bonding to enamel can be consistently achieved through the micro-mechanical interlocking of the resin tags (RTs) within the arrangement of micro-porosities of demineralized enamel, which successfully seals the margins of the restoration against leakage [3, 7, 33].  
It has long been argued that the base of adhesion to dentin is imperfect [67]. On a nanoscale level, completely enveloping the collagen fibrils by monomers to fill up all the spaces is practically impossible [68]. Furthermore, methacrylic acid (MA)-based resin monomers are required for a higher possibility of penetration into the interfibrillar spaces of the collagen matrix because of this monomer's low viscosity and relative hydrophilic nature. However, this may lead to hydrolysis [69, 70]. Ultimately, the presence of an exposed collagen, in addition to water empty spaces which are naturally present within the dentin collagen, can result in both hydrolytic and enzymatic degradation simultaneously [33, 71]. These reasons led [PERSON] et al. [72] to determine that a suitable resin-dentin network is an important pre-requisite for a successful adhesion on a molecular level.  
Acid etching is a process occurring as part of the mechanism of dentin binding. This procedure is used to demineralize between 5 and 8 \(\upmu\)m of the ITD network in an effort to generate porosities at a nanoscale level in the collagen matrix. This results in the opening of dentin tubules, exposure of collagen fibers, and modification or elimination of the smear layer [3, 10, 17, 73].  
Thus, it is crucial to fully grasp the knowledge behind the behavior and properties of the smear layer in bonding mechanisms [74].
## 3 Methodology and Cooperative Game Theory Models
### Listing Cooperative Game Strategies and Computing Their Returns  
The goal of cooperation is to achieve greater benefits/returns. In order to proceed with the analyses, cooperative game models require all possible strategies be listed and all associated benefits/returns be found. In other words, before applying Models I-IV, the prerequisite steps necessary to proceed are: (1) initially listing all possible individual (\(i\)), subgroup (\(S\)), and grand coalition (\(N\)) strategies and then (2) computing the associated benefit parameters of these strategies, namely the individual (\(b_{i}\)), subgroup (\(b_{S}\)), and grand coalition (\(b_{N}\)) benefits. These parameters are used to determine the availability and extent (\(Z\)) of the core, as well as the upper and lower bounds of player benefits (\(x_{i\_max}\), \(x_{i\_min}\)) by applying Models II-IV. The following sub-sections involve the techniques designed to list the strategies and procedures for computing their associated returns.
###### Abstract  
Orthogonal frequency division multiplexing (OFDM) has been widely adopted in underwater acoustic (UWA) communication due to its good anti-multipath performance and high spectral efficiency. For UWA-OFDM systems, channel state information (CSI) is essential for channel equalization and adaptive transmission, which can significantly affect the reliability and throughput. However, the time-varying UWA channel is difficult to estimate because of excessive delay spread and complex noise distribution. To this end, a novel Bayesian learning-based channel estimation architecture is proposed for UWA-OFDM systems. A clustered-sparse channel distribution model and a noise-resistant channel measurement model are constructed, and the model hyperparameters are iteratively optimized to obtain accurate Bayesian channel estimation. Accordingly, to obtain the clustered-sparse distribution, a partition-based clustered-sparse Bayesian learning (PB-CSBL) algorithm was designed. In order to lessen the effect of strong colored noise, a noise-corrected clustered-sparse channel estimation (NC-CSCE) algorithm was proposed to improve the estimation accuracy. Numerical simulations and lake trials are conducted to verify the effectiveness of the algorithms. Results show that the proposed algorithms achieve higher channel estimation accuracy and lower bit error rate (BER).  
c 2021 15 July 2021 18 July 2021 18 July 2021
# Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maximly Sinus Surgery
## 4 Results and Analysis
### Fault Detection, Estimation and Identification  
Figure 14 illustrates the residual signal for the surgical robot for the sinus and force torque signals in the normal condition. Based on Figure 14a, this system has a spike at various times, such as 5, 10, 12, 15, and 17 s. These signals are not errors, and the signal rise and fall times are very short.  
Figure 13: Fault-tolerant control for fuzzy feedback linearization method, observation-based feedback linearization method, and proposed method for a surgical robot for the sinus.  
Based on Figure 14, in the normal condition, the level of the position residual signal and the torque residual signal should be lower than the normal threshold value. Figures 15-17 illustrate the fault detection in actuator fault, sensor fault, and sensor-actuator fault for the proposed fuzzy ARX-Laguerre T-S fuzzy robust feedback linearization observer, fuzzy ARX-Laguerre robust feedback linearization observer, and fuzzy ARX-Laguerre feedback linearization observer. Based on Figure 16 and Equation (32), in an actuator fault, the position residual signal should be higher than the normal, and the actuator thresholds and torque residual signal should be higher than the normal threshold but lower than the sensor threshold value.  
Figure 16: (**a**) Position residual signals and (**b**) torque residual signals for sensor fault identification based on the fuzzy ARX-Laguerre feedback linearization method, fuzzy ARX-Laguerre robust feedback linearization method, and fuzzy ARX-Laguerre T-S fuzzy robust feedback linearization method for the surgical robot for the sinus.  
Figure 14: Normal condition residual signals: (**a**) torque and (**b**) joint position variable.  
Figure 15: (**a**) Position residual signals and (**b**) torque residual signals for actuator fault identification based on the fuzzy ARX-Laguerre feedback linearization method, fuzzy ARX-Laguerre robust feedback linearization method, and fuzzy ARX-Laguerre T-S fuzzy robust feedback linearization method for the sinus.  
Based on Figure 15, the delay time for fault identification in the proposed algorithm is better than with the other techniques. Figure 16 shows the power of sensor fault identification by three methods for the surgical robot for the sinus. Regarding Figure 16a, the classical feedback linearization observer has drawbacks in fault identification in this type of fault. As shown in this figure, the proposed and robust feedback linearization observers have reliable performance compared with the classical feedback linearization observer. Figure 17 illustrates the actuator-sensor fault identification efficiency for the surgical robot for the sinus based on three different algorithms. Regarding this figure, the delay time to fault identification in the proposed method is less than with the other two methods.
###### Abstract  
The thermal conductivities of most commonly used phase change materials (PCMs) are typically fairly low (in the range of 0.2 to 0.4 W/m\(\cdot\)K) and are an important consideration when designing latent heat energy storage systems (LHESSs). Because of that, material scientists have been asking the following question: \"by how much does the thermal conductivity of a PCM needs to be increased to positively impact the design and performance of a LHESS?\" The answer to this question is not straightforward as the performance of a LHESS depends on the PCM's thermal conductivity, other PCM thermophysical properties, the type of heat exchange system geometry used, the mode of operation, and the targeted power/energy storage of the LHESS. This paper presents work related to this question through a numerical study based on a simplified 2D model of an experimental setup studied previously in the authors' laboratory. A model created in COMSOL Multiphysics, based on conduction and accounting for the solid-liquid phase change process, was initially validated against experimental results and then used to study the impact of the PCM's thermal conductivity (dodecanoic acid) on the discharging power of the LHESS. The results show that even increasing the thermal conductivity of the PCM by a factor of 50 only leads to a maximum instantaneous power increase by a factor of 2 or 3 depending on the LHESS configurations.  
phase change material (PCM); thermal conductivity; thermal energy storage (TES); latent heat energy storage system (LHESS); solidification power; thermal performance
# Nanotechnology, Nanomedicine, and the Kidney
## 3 Renal Clearance of Nanoparticles
### Stiffness  
The use of nanomaterials that are not rigid and inert allows for clearable NPs with a larger HD. Many \"soft\" and biodegradable nanosystems have been developed to facilitate renal clearance. For example, [PERSON] et al., developed DNA origami nanostructures of different forms [19]. Besides dimensions over at least ten times higher than the size-dependent threshold (rectangular with dimensions of 60 \(\times\) 90 nm; triangular with 120 nm long sides; 400 nm long tube), the nanostructures exhibit a preferential renal uptake and a whole-body clearance 24 h after injection.
## 1 Introduction
Only when \(k_{z}\) is real does the wave with a propagating mode advance in the \(+z\) direction. From Equation (4), for the Order \((n_{x},n_{y})\) wave, we may see that the cut-off angular frequency is  
\[p_{n_{x},n_{y}}(x,y,z,t)=A_{x,y}\cos(k_{x}x)\cdot\cos(k_{y}y)\cdot e^{i(\omega t -k_{z}z)} \tag{2}\]0  
If \(\omega<\omega_{n_{x}n_{y}}\), then \(k_{z}\) is pure imaginary, that is, \(k_{z}=\pm i\sqrt{\beta_{n_{x},n_{y}}^{2}-\left(\frac{\omega}{c}\right)^{2}}\). The minus sign must be taken on the physical grounds so that \(p_{n_{x},n_{y}}\to 0\) as \(z\rightarrow\infty\). Therefore, we have  
\[p_{n_{x},n_{y}}(x,y,z,t)=A_{x,y}\cos(k_{x}x)\cdot\cos(k_{y}y)\cdot e^{i(\omega t -k_{z}z)} \tag{2}\]1  
where  
\[p_{n_{x},n_{y}}(x,y,z,t)=A_{x,y}\cos(k_{x}x)\cdot\cos(k_{y}y)\cdot e^{i(\omega t -k_{z}z)} \tag{2}\]2  
This is an evanescent wave [17] that attenuates exponentially with \(z\). From Equation (8), we may observe \(\omega_{00}=0\). If \(\omega<\min(\omega_{01},\omega_{10})\), then all order of waves except Order \((0,0)\) are the evanescent waves, that is, the waves will disappear after traveling a certain distance. This also suggests that a sound source definitely generates some evanescent wave appearing in a certain range. Taking the tire acoustic cavity of a 185/60R15 tire as an example, according to the actual tire size, the parameters in the simplified tire acoustic cavity model are as follows: outer radius of 290 mm, inner radius of 200 mm, width of 155 mm, and circumference of the tire acoustic cavity cross-section center of \(L_{c}=1539.4\) mm (see Figure 1b). Therefore, the tire cross-section is approximately a rectangular cross-section of \(90\times 155\) mm (\(l_{x}=0.09\) m, \(l_{y}=0.155\) m). The sound velocity in the tire acoustic cavity is set as \(c=340\) m/s, and the sound pressure amplitude of sound source is assumed to be \(P_{0}=1\) Pa in order to facilitate calculation in the formula. According to Equation (8), \(l_{y}>l_{x}\) leads to \(\omega_{10}>\omega_{01}=6891\) rad/s, which means that if the angular frequency (\(\omega\)) of the sound source is less than \(\omega_{01}\) (\(\omega<\omega_{01}\)), then only an Order \((0,0)\) wave (plane wave) can propagate down the waveguide and other waves are evanescent. \(\omega<\omega_{01}\) leads to \(\lambda>2l_{y}\). That means that, as long as the wavelength is more than twice the cross-section characteristic size, only the plane wave is in the propagating mode. Figure 2 shows the amplitude distributions of sound pressures on the cross-sections away from the sound source in the tire acoustic cavity when \(f=250\) Hz (\(\omega<\omega_{01}\)), \(n_{x}=1\), and \(n_{y}=2\). Since the tire acoustic cavity resonance frequency ranges from 200 to 250 Hz  
generally, \(f=250\) Hz is chosen for the sake of calculation result reliability (shown in Section 3).
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 4 Experimental Setup and Result
### Datasets and Evaluation Metrics  
The experiments were conducted on the DOTA-v1.5 dataset [24], a benchmark specifically designed for oriented object detection in aerial imagery. DOTA-v1.5 consists of high-resolution aerial images containing objects with diverse orientations, scales, and densities. It includes 15 object categories, such as airplanes, ships, and vehicles, with annotations in the form of oriented bounding boxes. This dataset is well-suited for evaluating both rotational invariance and multi-scale feature fusion capabilities.  
The primary evaluation metric used in these experiments is the mAP, calculated for Intersection over Union (IoU) [25] thresholds ranging from 0.5 to 0.95. This metric provides a comprehensive measure of detection accuracy across all object classes and is particularly useful for assessing performance in scenarios with challenging orientations and scales.