esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact Fuzzy Systems Based on Boolean Relations
## 6 Application Example of Architecture II: PID Control
### Hydraulic System  
The considered model is like the one used in [50], consisting of a hydraulic system displayed in Figure 21. The system variables and parameters values are shown in Table 19. As can be observed in Equation (64), the model has a square root that incorporates a nonlinear component in the system behavior. In addition, there is a limitation in the maximum tank inflow.  
The model of the hydraulic system is given by equation:  
\[q_{e}(t)=A\frac{dh(t)}{dt}+C_{o}\sqrt{h(t)} \tag{64}\]  
Figure 21: Tank filling system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Inflow & \(q_{e}(t)\) & 0.25 L/s or \(2.5\times 10^{-4}\) m\({}^{3}/s\) (max) \\ Height of the liquid & \(h(t)\) & 1 m (max) \\ Tank cross-sectional area & \(A\) & 0.126 m\({}^{2}\) \\ Output valve cross-sectional area & \(A_{0}\) & 0.317 cm\({}^{2}\) (\(3.17\times 10^{-5}\) m\({}^{2}\)) \\ Constant value of the valve & \(C_{v}=A_{0}\sqrt{2g}\) & \(1.4\times 10^{-4}\) m\({}^{5/2}\)/s \\ \hline \hline \end{tabular}
\end{table}
Table 19: System parameter values.  
Figure 20: Validation result.  
In addition, a delay time \(t_{0}\) of 0. s is associated with the valve operation; therefore, the relation between control action \(u\) and the input flow is \(q_{e}(t)=2.5\times 10^{-4}u(t-t_{0})\) where \(0\leq u\leq 1\). Figure 22 shows the plant response for different values of the action \(u(t-t_{0})\) considering that the maximum value of \(h\) is 1 m.
# Object and Event Detection Pipeline for Rink Hockey Games
## 3 Automatic Pipeline for Object and Event Detection in Rink Hockey Games
### Event Detection Rules Module  
Event detection follows object detection in the pipeline shown in Figure 2. The proposed specification of an event detection module, whose input is a timeline stream of detected visual objects, or the output of the object detection model, can comprise up to four stages, as follows:  
1. Filtering: due to the noisy nature of the object detection model, a filtering operator, such as a windowed spatio-temporal median filter, can be used as a pre-processing stage before the application of a rule-based detector.
2. Model-based Rule Detection: rule-based system that can detect predefined types of events.
3. Representation: the language or taxonomy used to represent the events. Event calculus is a formal method of event representation [39].
4. Revision: can be used to revise or update represented event beliefs due to new information available in the stream of detected visual objects.  
This module follows the object detection module and, consequently, depends on it according to the directed acyclic graph (DAG) presented in Figure 2. In the preliminary tests performed, a proof-of-concept approach was adopted for this module, and the detection of a unique type of event was considered for this article.
# Magnetic Field Effect of Near-Field Radiative Heat Transfer for SiC Nanowires/Plates
## 3 Results and Discussion
### Effect of Structure Parameters
#### 3.2.2 Gap Distance  
Figure 8 shows the heat transfer coefficient between SiC nanowires at several different vacuum gap distances. For SiC plates, the heat transfer coefficient of the plate under different magnetic field intensities did not change significantly at all gap distances. Hence, it is not shown here. This occurred because the dielectric constant of the SiC plates under the action of an external magnetic field had no obvious change, as shown in Figure 3.  
For \(f\) = 0.2, SiC nanowires can yield a super-Planckian behavior when the gap distance is below 3 \(\upmu\)m, whereas the heat transfer coefficient decreases with increasing gap distance. The heat transfer coefficient of the structure under a magnetic field was significantly higher than that of non-magnetic field when \(d\) was below 100 nm. The enhanced amplitude increased with increasing magnetic field intensity and decreasing gap distance. Evanescent waves were the main surface mode of the structure in this case. Therefore, the external magnetic field can significantly enhance evanescent waves in near field thermal radiation.
# Low-Temperature and Low-Pressure Silicon Nitride Deposition by ECR-PECVD for Optical Waveguides
We characterized the thin film optical loss across a broad wavelength range including visible and telecommunication bands from 638 to 1640 nm using prism coupling and observed low surface roughness measured through atomic force microscopy (AFM). Low losses are achieved without post-fabrication annealing treatment, which is typically required to reduce significantly higher losses in the telecommunication bands due to hydrogen bonds [27; 28; 29; 30]. These characteristics demonstrate ECR-PECVD to be a highly promising method for planar waveguide fabrication.
## 2 Materials and Methods
### Data Collection Process and Data Items  
Two reviewers (I.G.L; M.S.) independently performed the data extraction manually. The primary study characteristics were identified and organized on Excel spreadsheets (Excel 2010(r), Microsoft(r), USA), including first author, year of publication, the country where the research was conducted, study design, sample type, sample size, eye bank, distinct exposition media, storage time, storage temperature, assessment/methods of analysis, qualitative and quantitative parameters for cornea preservation after cold storage in Optisol-GS. Reported cornea quality parameters, main results, and conclusions were also collected from the included studies. The outcomes of interest were reported exclusively for the storage period at single or multiple experimental times.
## 1 Introduction  
Obesity is a serious civilization-related health problem in the 21 st century. This disease affects a variety of other conditions, including cardiovascular diseases, diabetes, osteoporosis, and alimentary tract diseases [1]. An increase in body mass is a typical feature of obesity, which is dependent on an increase in the size of a single adipocyte, termed as hypertrophy, or an increase in the number of adipocytes, termed as hyperplasia [2].  
Adipogenesis, or the generation of mature adipocytes from pre-adipocytes, accelerates lipid accumulation, and the research basis for adipogenesis has been established using the 3T3-L1 cell line of murine pre-adipocytes in vitro [3; 4; 5]. Pre-adipocytes exposed to differentiation-induction substances demonstrated the full maturation of adipocytes [6]. When the differentiation of pre-adipocytes begins, the specific transcriptional factors CCAAT/enhancer-binding protein delta (C/EBP \(\delta\)) and C/EBP\(\beta\) are combined to induce the expression of peroxisome proliferation-activated receptor gamma (PPAR\(\gamma\)and C/EBP\(\alpha\)[7, 8]. PPAR\(\gamma\) and C/EBP\(\alpha\) regulate various adipogenic genes, such as fatty acid-binding protein 4 (FABP4), adiponectin, leptin, and phosphoenolpyruvate carboxykinase 1 (PCK1) [9, 10]. The action of high-carbohydrate absorption and insulin signaling activates lipogenesis in adipocytes. Acetyl coenzyme A (acetyl-CoA), which migrates from the intracellular mitochondrial membrane into the cytoplasm, is involved in fatty acid (FA) biosynthesis and is converted to malonyl-CoA by the multifunctional enzyme acetyl-CoA carboxylase (ACC) [11].  
Indian gooseberry (IG), or _Emblica officinalis_, has been widely used for thousands of years as a therapeutic substance in Southeast Asia. IG is a rich source of vitamin C and is known to contain phenolic compounds, tannins, and alkaloids. Other known active materials include gallic acid, ellagic acid, quercetin, and kaempfer [12, 13]. IG has been used in several studies modeling various diseases, including hyperlipidemia, diabetes, and cancer [14, 15, 16, 17]. Barley sprout (BP), or _Hordeum vulgare_ L, is a young plant (10-20 cm after sowing) that is cultivated around the world [18]. BP is rich in dietary fiber and contains strong antioxidants, as well as other active compounds, including vitamin C, vitamin E, carotenoids, saponarin, and lutonarin [19, 20]. In addition, BP is known to be effective for anti-inflammatory, lipid metabolism, and blood sugar control [21, 22, 23]. We already confirmed the lipolytic effects of IG, BP, and an IGBP mixture in a previous in an in vitro screening experiment. Therefore, in the present work, we studied whether the IGBP mixture has an inhibitory effect on adipogenesis and lipogenesis in vitro.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 3 The Proposed Scheme
### Information Extraction and Lossless Recovery  
In order to obtain a patient's information for remote treatment, the recipient needs to extract the embedded secret message from the encrypted shared image they receive. The medical image must be protected using the lossless recovery method for data recovery. Figure 5 illustrates the information extraction and lossless recovery scheme using the example of ShareA\". Firstly, an authentication bit is extracted from ShareA\" for authentication bit detection. If it is not tampered with, the receiver extracts the secret message and completes image recovery. If it is detected that the Share has been tampered with, only the tampering recovery operation is performed. The same operation is performed for ShareB\".  
Figure 4: Double embedding based on the POB system. (**a**) Secret message embedding based on the POB system. (**b**) Authentication information embedding based on the POB system.
# A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System
## 4 Numerical Simulation Results
Besides, we can also draw the conclusion that the size of the channel estimation error also has a relatively large impact on the system security rate, i.e., larger channel estimation error \((\sigma_{e}^{2})\) corresponds to lower security rate. Even under the premise that the channel estimation error is relatively large, the IANT scheme proposed in this paper is still superior to the contrast scheduling scheme in the entire area after adopting the noise elimination method. Through the numerical simulation results, we find that the number of tags has no obvious effect on the channel estimation error, but we find that in the first sub-slot, the signal length \(N_{0}\) transmitted by each tag has a strong correlation with the channel estimation error. As shown in Figure 7, the horizontal axis represents the different signal length \(N_{0}\) sent by tag in the first sub-slot. We set the channel signal-to-noise ratio to 0, 5, and 10 respectively, and showed the channel estimation error under different channel conditions. We can conclude that the channel estimation error keeps decreasing as the signal length increases, but when the signal length \(N_{0}\) is greater than 20, the change rate of the channel estimation error slows down significantly. Moreover, when the channel quality is relatively good, a smaller channel estimation error can be obtained. Figure 5: The influence of the number of tags on the achievable secrecy rate. Figure 6: The effect of different channel estimation errors on system achievable secrecy rate.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 5 Experiments Study
### Deployment Environment  
We deployed the large-scale \(uw\)-Cellular network in the northern Bohai Strait, Liaoning, China. The field experiment lasted for about 2 months from April to May, 2021. Figure 10 illustrates the area of the network nodes deployed based on the GPS information collected during the experiment. The average water depth of the implement experiment is 40 m. The modems were deployed about 20 m below the sea surface. As the experiments were  
Figure 9: \(ERSR\) of different hops.  
deployed in a shallow marine environment, severe multi-path effects and propagation losses of underwater acoustic transmission will occur in such environments. So, the distance between the surface gateway node and each L1-BS node does not exceed 3500 m. Similarly, all terminal nodes, except the AUVs, are deployed within 3500 m of the L1-BS nodes. The AUVs can move freely throughout the network coverage area. The entire network area covers over 100 km\({}^{2}\). The weather was rated from moderate to rough during the experiment with wave heights between 1.25 and 2.5 m. The records of the field test are shown in Figure 11.
## 6 Results and Discussion
### Feasibility Analysis  
The algorithm proposed in this paper was implemented in simulation and is based on the traditional energy detection algorithm, which is one of the spectrum-sensing algorithms with a wide range of applications and low algorithm complexity. In addition, the transmitted signal is synthesized to pass through the simulated hydroacoustic channel to obtain the received signal, and the values of the relevant parameters are given. However, in the actual hydroacoustic channel, the actual parameter values will be different from those set during simulation, such as temperature and humidity. Moreover, these factors will affect the transmission of acoustic waves. Thus, the proposed algorithm framework in this paper can be implemented in a near real-time manner, but the simulation experimental results will be slightly different from the actual experimental results.
# Audio Time Stretching Using Fuzzy Classification of Spectral Bins
## 3 Novel Time-Scale Modification Technique
### Proposed Phase Propagation
\(A_{n}[m,k]\) is the phase randomization factor, which is based on the estimated noisiness of the bin \(R_{n}[m,k]\) and the TSM factor \(\alpha\):  
\[A_{n}[m,k]=\frac{1}{4}\big{[}\tanh(b_{n}(R_{n}[m,k]-1))+1\big{]}\big{[}\tanh(b _{n}(\alpha-\frac{3}{2}))+1\big{]}, \tag{13}\]  
where constants \(b_{n}\) and \(b_{n}\) control the shape of non-linear mappings of the hyperbolic tangents. The values \(b_{n}=b_{n}=4\) were used in this implementation. The phase randomization factor \(A_{n}\), as a function of the estimated noisiness \(R_{n}\) and the TSM factor \(\alpha\), is shown in Figure 5. The phase randomization factor increases with increasing TSM factor and noisiness. The phase randomization factor saturates as the values increase, so that at most, the uniform noise added to the phases obtains values in the interval \([-0.5\pi,0.5\pi]\).
## 3 Design
### System Technical Requirements
#### 3.1.1. Mosaic Coating
Under the premise of maintaining the optical aperture spacing, the system uses a CMOS image sensor, which has a pixel size of 3.2 um and an effective resolution of \(16556(H)\times 9200(V)\). According to this, the calculated image height d of each image can be deduced to be 12.23 mm.
## 4 Experimental Evaluation
### Scenario 1: Antenna Direction
#### 4.2.3 Results of Wireless Network Layer in Scenario 1  
If the communication performances of the IP network layer have some degradation, the communication performance of the wireless network layer should be examined to check the causes of the degradation. Here, we investigate the results of the wireless network layer when the communication performance is considered to be adequate. Understanding the reference characteristics is helpful for later discussions and comparisons.  
Since three measurement tools are simultaneously run to collect the logs in our methodology, we can assess the communication performance from the _iw_ results for the wireless network layer as needed. The methodology focuses on values of RSSI and Tx bitrates in the information that the _iw_ provides. Although RSSI is a familiar indicator for showing wireless conditions, it is difficult to estimate the communication quality from the RSSI value. Furthermore, even if the RSSI value is high, it cannot detect performance degradation due to radio interference and other factors. Thus, to supplement it, our methodology employs Tx bitrates that show the logical transmission speed by the rate adaptation algorithm implemented in IEEE 802.11 series products as an additional indicator. For instance, the IEEE 802.11 ad interface that we used supports eight bitrates: 27.5, 385.0, 770.0, 962.5, 1155.0, 1540.0, 1925.0, and 2310.0 Mb/s.  
Figure 6 shows the RSSI results. From the graphs, we can see that the RSSI fluctuates to some extent, but estimating the actual communication quality, such as throughput, packet loss, RTT, and jitter, is difficult from these values. On the other hand, Table 3 shows the percentage of each Tx bitrate observed by the _iw_ every 500 ms. Note that the values in bold in the table indicate the mode of the Tx bitrate at each distance. Since the Tx bitrate changes quickly in intervals shorter than 500 ms, collecting actual Tx bitrates for all transmissions is impossible. However, since the table shows that a Tx bitrate of 2310 Mb/s is statistically used in more than 95% of the transmissions, we can assess that the wireless link is relatively stable. In addition, there were no transmission failures during the measurement. On the contrary, if many low Tx bitrates are used, the communication quality is assessed to be  
Figure 4: Jitter of UDP communication on receiver side (Scenario 1).  
degraded. Hence, RSSI can check primary wireless conditions, and Tx bitrate helps confirm the occurrence of communication quality degradation.
# A Hybrid Artificial Intelligence Model for Detecting Keratoconus
## 3 Background
### \(K\)-Medoid  
\(k\)-medoid is another clustering algorithm based on partitions. It selects the actual dataset and identifies the clusters based on the medoids. A medoid reflects the samples in the data collection that is most centrally located. The primary objective of the \(k\)-medoid is to minimize the dissimilarity among the samples in the same cluster and maximize the dissimilarity among the rest of samples in others clusters based on the medoid.  
The mechanism of the \(k\)-medoid algorithm is like the \(k\)-means algorithm. It also starts with a random selection of sample as initial medoids to from the clusters. The sample with the closest medoid are assigned to the same cluster. A new medoid then represents the cluster. The samples are iteratively allocated to the clusters with the closest medoid--the medoids' positions changes with each iteration. The \(k\)-medoid seeks to reduce the number of differences between each sample and its corresponding medoid [42]. As the \(k\)-means algorithm, the \(k\)-medoid process is stopped once the predefined number of iterations is meet or until no more changing of the clusters medoids values. Ultimately, \(K\) clusters are created with center on medoids, and all samples are placed into the fitting cluster on the basis of the nearest medoid. The main steps of \(k\)-medoid process are implemented in Algorithm 2.  
```
1:Input: Number of clusters \(k\), row data (samples) \(S\).
2:Output: Matrix contains \(S\) assigned to \(K\).
3: Select random from \(S\) as for each \(K\).
4:while the number of iterations is not meet or no more changing of the clusters centroids values do
5: Calculate the similarity between the clusters medoids and \(S\).
6: Assign \(S\) to the closest cluster medoids.
7: Calculate the clusters medoids.
8:endwhile
```  
**Algorithm 2** Psudo code of \(k\)-medoid clustering heuristic algorithm.
## 5 Results and Analyses
### Error Analysis  
We conduct an error analysis on the CodRED development set to understand the limitations of our approach. The analysis reveals two primary failure modes: (1) insufficient commonsense reasoning capabilities, and (2) susceptibility to noise in multi-path scenarios with sparse positive evidence. For samples containing only one positive document path among multiple negative paths (approximately 8.6% of validation samples), the model struggles to maintain confidence in the correct relation when the positive evidence requires implicit reasoning steps that depend on domain-specific knowledge, such as cast continuity in film series. In such cases, the distance-weighted attention mechanism successfully identifies semantically relevant paths but may still be influenced by the predominance of negative evidence when positive signals are weak or require external knowledge for completion. These findings suggest that future improvements could benefit from incorporating external knowledge bases or commonsense reasoning modules to strengthen inferential capabilities, particularly for relation extraction scenarios requiring multi-hop reasoning with incomplete textual evidence.
# ISAR Image Quality Assessment Based on Visual Attention Model
## 2 Proposed Method
### Gramin Transformer  
The Gramin Transformer (Gram-T) is designed to extract global information from images and strengthen the association between features and the image quality score. Gram-T comprises three principal components. The first is the linear projection of flattened patches, transforming image patches into embeddings. The second part is the Transformer encoder that extracts features from embeddings. The third module is Gram attention, which obtains score tokens by computing the Gramin matrix.  
The Gramin matrix is computed according to the output features of the Transformer encoder, and the output features are concatenated by the (7,8,9,10)th layer of the transformer encoder. Figure 1 shows that the score token is assigned through Gram attention. We leverage the Gramin matrix to obtain a pairwise feature similarity in order to directly increase the interaction between features and score tokens in the process of feature extraction. Then, the score token is introduced to the final MLP stage to avoid significant information loss. On the one hand, we can promote agglomeration of features attributed to similar quality levels and diversity of features attributed to different quality levels. On the other hand, the loss function can directly propel the optimization of the score token when the score token makes an immediate impact on loss. Ultimately, our model can obtain more discriminative features. We obtain the projected feature \(V_{X}=XW_{C}\) from the output feature \(X\in\mathbb{R}^{N\times C\times L}\), where \(W_{C}\in\mathbb{R}^{L\times S}\). Thus, the Gramin matrix is computed as \(Gram_{X}=(V_{X})^{T}V_{X}\), which is used to measure the pairwise similarity of features. Finally, our method introduces the score token, as follows:  
\[score_{token}=((V_{X})^{T}V_{X})\cdot\text{Softmax}(Q\cdot K^{T}/\beta) \tag{1}\]  
Figure 1: The architecture of the proposed model. The image is partitioned into 8 \(\times\) 8-sized patches. Then, the linear projection layer performs a convolution operation on these patches to acquire patch embeddings, which can be processed by the Transformer encoder. The Gram–T model computes the Gramin matrix of extracted features to obtain score tokens through one attention layer. Moreover, the CAB and IRAB strength interactions between channels and regions within features are output by the Transformer encoder. Finally, the prediction score from the IRAB is added by a score token from Gram–T to obtain the final score.  
where \(K=XW_{K}\) and \(Q=XW_{Q},W_{K},W_{Q}\in\mathbb{R}^{L\times S}\). This approach ensures that the score token is consistent with the feature, and its parameters are no longer randomly initialized to directly affect the model parameters during the training process.