esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A simple configuration dual-circularly polarized microstrip antenna with a 3 dB hybrid coupler, proximity-coupled feeding, and four slits on the antenna patch was presented in this paper for the standard LTE 43 of 5G mid-band applications. The antenna dimensions were 0.81\(\lambda_{\text{o}}\)\(\times\) 0.43\(\lambda_{\text{o}}\)\(\times\) 0.019\(\lambda_{\text{o}}\) at 3.7 GHz. The measured impedance bandwidth of the proposed design was 10.6% along the range (3.46-3.85 GHz), whereas the measured 3 dB ARBW was extended by 8.2% along the range (3.50-3.80 GHz). An acceptable realized gain value was achieved, i.e., a value larger than 1.3 dBi along the antenna operational bandwidth. The co- and cross-polarization patterns demonstrated a difference in values less than 3 dB in the broadside direction. For future work, the directivity and gain of the antenna can be optimized by using a reflector placed behind the patch antenna to suppress back radiation and redirect it toward the broadside direction, which significantly enhances the antenna gain. Also, array structures could be used for high directivity for 5G applications.  
Conceptualization, A.H.M. and F.M.A.; methodology, A.H.M.; investigation, A.H.M., F.M.A. and Y.I.A.A.-Y.; resources, A.H.M. and F.M.A.; writing--original draft preparation, A.H.M., F.M.A. and Y.I.A.A.-Y.; writing--review and editing, F.M.A. and Y.I.A.A.-Y.; visualization, A.H.M., F.M.A. and Y.I.A.A.-Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.
## 5 Experimental Results and Analysis
### Indicators for Model Evaluation
#### 5.1.4 Evaluation of Fully CNN Models  
Table 22 demonstrates that the average Accuracy was 84.54%, the average Precision was 83.04%, the average Recall was 73.57%, and the average F1 score was 68.09%. The average Loss value was 4.884, the maximum Loss value was 5.973, the minimum Loss value was 3.634, and the standard deviation of Loss value std was 0.421.  
From the specific values of the evaluation index of the fully connected neural network model, it can be found that the accuracy of the fully connected neural network model reaches 84%, and there is a decrease in the accuracy relative to that of the random forest model, because the deep learning model is more prone to overfitting due to the limitation of the amount of data, and the value of the F1 score index reaches 68%, and in general, as the most basic aspect of the deep learning field of the network model, the fully connected neural network model also shows better performance.
## 4 Results and Discussion
### Complexity of Carrier Phase Recovery Approaches  
The computational complexity is always a significant consideration and criterion for the DSP algorithms. Here the complexity of the one-tap normalized LMS, the block-wise average, and the Viterbi-Viterbi carrier phase recovery algorithms has been investigated in terms of the number of the complex multiplications per recovered symbol, which are shown in Table 1 (\(n\) is the level of modulation formats). It is found that the computational complexity of the one-tap normalized LMS algorithm is independent from the modulation formats, while the complexity of the block-wise average and the Viterbi-Viterbi CPR algorithms scales linearly with the level of modulation formats. Note that the computations in the pre-convergence of the one-tap normalized LMS algorithm also has to be considered in the practical applications.  
All the above analyses are based on the carrier phase recovery in the \(n\)-PSK coherent optical communication systems; however, all these discussions can be directly extended into the circular \(n\)-QAM transmission systems. Meanwhile, although the \(n\)-PSK signals have a lower tolerance to the ASE noise than the multi-amplitude signals (such as \(n\)-QAM signals), the \(n\)-PSK signals will have a better tolerance to fiber nonlinearities due to the constant amplitudes [54; 55].
# Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
## 2 Theoretical Conceptualization
### Sparse AutoEncoder  
Sparse AutoEncoders incorporate a sparsity penalty \(\Omega(\mathbf{h})\) into their cost function, as shown in Equation (2), to create sparser latent representations and increase network robustness by focusing on distinct data features. [PERSON] et al. (2016) [35] note that this sparsity enhances anomaly detection. Moreover, [PERSON] et al. (2023) [37] show that SAEs effectively detect behavioral changes in structural systems, simplifying data analysis and improving sensitivity to relevant patterns.  
\[L_{SAE}=L(\mathbf{x},g(f(\mathbf{x})))+\Omega(\mathbf{h}) \tag{2}\]  
As described in [35], the Mean Squared Error (MSE) in Equation (3) computes the average of the squared differences between the original values (\(x_{i}\)) and their reconstructions (\(y_{i}\)). Its quadratic penalization of larger errors makes it particularly effective for detecting anomalies, where identifying significant deviations is fundamental.  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-x_{i})^{2} \tag{3}\]  
A typical SAE network (Figure 1) features a symmetric arrangement of two encoding and two decoding layers, with a central layer defining the latent representation \(\mathbf{h}\). Both the first (encoder) and last (decoder) layers contain \(M\) neurons, matching the length of each sampled signal, while the central layer has \(k\) neurons \(\left(k\leq M\right)\), thus reducing the dimensionality. The feature vector \(\mathbf{h}\) captures essential information about structural behavior and is subsequently used as input to the \(T^{2}\) Control Chart for structural novelty detection.
## 2 Experimental Setup and Methods
### Measurement Parameters
#### 2.3.2 Bedload Transport Intensity and Flow Intensity  
The bedload transport rate during the experiment was measured using test pits. The sand captured by the funnel was considered as the bedload particles. The streamwise bedload transport rate (\(g_{b}\)) was calculated using the following formula:  
\[g_{b}=\frac{m}{tL} \tag{1}\]  
where \(g_{b}\) denotes the streamwise bedload transport rate, kg/m/min; \(m\) denotes the mass of the bedload falling into the funnel within the time interval t, kg; \(t\) denotes the time interval, min; and \(L\) indicates the width of the flume, m.  
\[\Phi=\frac{g_{b}}{r_{s}}(\frac{r}{r_{s}-r})^{1/2}(\frac{1}{gD^{3}})^{1/2} \tag{2}\]  
where \(\Phi\) denotes the bedload transport intensity, N/(s\(\cdot\)m); \(r_{s}\) denotes the specific gravity of experiment sand, N/m\({}^{3}\); \(r\) denotes the specific gravity of experiment water, N/m\({}^{3}\); and \(D\) denotes the experiment sand particle size, m.  
\[\psi=\frac{1}{\theta} \tag{3}\]  
where \(\Psi\) denotes the flow parameter; \(\theta\) denotes the Shields parameter, which represents the flow intensity and is expressed as \(\frac{\tau_{0}}{(r_{s}-r)D}\); and \(\tau_{0}\) denotes shear stress on the bed. This is expressed as \(\tau_{0}=\rho gRJ\), N/m\({}^{2}\).  
The parameters \(\Phi\) and \(\theta\) are well-known Einstein parameters for the bedload transport rate. In this study, the calculation formulas for these two parameters are provided for an thorough explanation of the experimental process.
_applied sciences_  
_Article_  
**Healing Capacity of Bone Surrounding Biofilm-Infected and Non-Infected Gutta-Percha: A Study of Rat Calvaria**  
**[PERSON] 1,2,t**, [PERSON] 3,t**(c), [PERSON] 4, [PERSON] 5 and [PERSON] 1,2,6,*  
There is consensus that microorganisms, mainly bacteria, cause primary apical periodontitis, but the reasons for persistent apical periodontitis lesions are debatable [7; 8]. Secondary factors, such as foreign material in the periapical area of a tooth with apical periodontitis, may contribute to additional irritation and/or inflammatory reactions [8]. [PERSON] (2008) claimed that extruded root canal filling might cause a foreign body reaction [9], but [PERSON] et al. (2008) suggested that a primary role for such factors, without the continued presence of bacteria, is lacking [8].  
It has been suggested that this adverse effect (i.e., the foreign body reaction) could be a result of over-instrumentation and the subsequent transportation of contaminated debris periapically [10; 11; 12]. [PERSON] (2001) stated that failure associated with overfilled teeth is usually caused by a concomitant intraradicular and/or extraradicular infection [10]. Over-instrumentation usually precedes overfilling and induces the displacement of infected debris into apical tissues in teeth with infected necrotic pulps [10; 11; 12; 13].  
The prevention of contamination becomes a problem when GP cones are used since this material does not readily lend itself to sterilization by moist or dry heat [14]. A one-minute immersion in 5.25% sodium hypochlorite may be used in clinical practice to sterilize GP cones and avoid this possible source of exogenous contamination [15]. Bacteria adherent to GP and growing as a biofilm may play a role in the delayed healing of apical tissues, but few studies have examined the biofilm potential on GP points as a cause for persistent apical periodontitis after endodontic treatment [16; 17].  
This study aimed to examine the healing capacity of bony lesions around biofilm-infected and non-infected GP points in a rat calvaria model. Our main hypothesis was that biofilm and GP would induce bone destruction, whereas bone defects alone would not.
# Prognostics and Health Management Based on Next-Generation Technologies: A Literature Review
## 6 Challenges and Potential  
### Major Challenges  
PHM has become an academic research priority due to its sophistication in prediction and diagnosis, which is likely to improve industrial military equipment's safety and reliability significantly.  
1. As PHM systems become progressively more complex, the data required become syntactically accurate and significant, which requires algorithmic models that can be afforded to be trained in computing and computational hardware. Although deep learning and transfer learning algorithms are improving, they still need help being made operational and in collecting fault parameters from large and complex equipment, and more importantly, the data are often abnormal, scarce, incomplete, and untagged.
2. In addition, more advanced theories and devices that can be applied to improving PHM systems are needed.
3. One of the problems with PHM is the need for greater interpretability of the algorithmic data, which reduces the trust in their use, especially for safety-critical applications. This has led to the need to find ways to improve transparency and interpretability in order to more clearly understand what and how models predict and ultimately build trust in their use.
4. Digital twins (DTs) enable full life cycle inspection and maintenance of installations through advanced simulation, optimization, prediction, and decision-making capabilities. The problem is that the complexity of standardizing DTs has yet to be eliminated due to the limitations of overlaying multiple technologies with multiple data. The implementation of DTs involves support from multiple disciplines, multiple industries, and multiple software. In order to realize full-cycle PHM for digital-twin-to-device pairing, a large number of sensor detections are still required, which will not only consume a large amount of money but also be less efficient. Although there many scholars have optimized and applied machine learning to data detection and transmission for digital twins, there are still some shortcomings that affect the real-time accuracy of PHM.
5. Augmented reality (AR) can solve complex equipment situations with few maintenance personnel and technical difficulties and in harsh environments. However, SLAM technology applied in AR, the mainstream visual tracking technology nowadays, has not been able to build an accurate virtual model by extracting features when the image features are insufficient. Through the literature research, we found that the technology and equipment in human-computer interaction have made significant progress. However, there are still problems, such as the interaction dimensions needing to be higher, the realism not being high, and low user acceptance. In terms of equipment, there are even problems such as the significant weight, large size, and cumbersome calibration of AR-supporting equipment, which hinder the popularization of AR.
6. The combination of AR and DT opens up a challenging path for PHM, which has the disadvantage of a single technology logic in the currently existing AR-DT-supported PHM.
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 4 Implementation of SD6 WSN on the Contiki Platform
### Hardware Architecture
#### 4.2.4 Native IEEE 802.15.4 and 6 LoWPAN in Linux Kernels  
Some projects, such as the \"IEEE 802.15.4 Stack for Linux\", aim to implement the IEEE 802.15.4 and 6 LoWPAN stacks for Linux, but are still in the \"alpha\" development stage. This option leaves all the processing of the MAC, RDC, 6 LoWPAN, IPV6 and RPL layers to Linux. The external hardware only implements the physical layer, with the radio communicating with the computer by the SPI interface.
## 4 The Proposed ECHR Algorithm
### Energy-Aware Hierarchical Routing Algorithm
**Data:**\(templevel=1\),  
**while**\(s_{i}.energy\
eq 0\), \(s_{i}\in S\)  
\(s_{i}.level=0\);  
% Selecting the root node  
\(SelectRootNode(q_{r}\), \(O(s_{r})\), \(C(s_{r})\), \(d(s_{r}\), \(BS)\), \(\tau_{1}\), \(\tau_{2})\);  
% Assigning nodes to level 1  
**for** node \(s_{i}\), \(s_{i}\in S\)**do  
**if**\(s_{i}.level=0\) and \(d(s_{r}\), \(s_{i})\leq C_{r}\)**do  
\(s_{i}.level=1\);  
**end if**  
**end for**  
% Assigning nodes to the subsequent levels  
**while**\(s_{i}.level=0\)**do  
**for**\(s_{j}.level=templevel\), \(s_{j}\in S\)**do**  
% Determining whether node \(s_{j}\) is suitable to communicate with node \(s_{i}\)  
**if**\(d(s_{i}\), \(s_{j})\leq C_{r}\) and \(i\
eq j\)**do**  
\(s_{j}.level\leftarrow\)\(s_{i}.level+1\);  
**end if**  
**end for**  
_templevel_\(\leftarrow\)_templevel_ + 1;  
**end while**  
% Selecting parent nodes for each node  
**for** node \(s_{i}\), \(s_{i}\in S\)**do**  
% Determining whether node \(s_{p}\) is suitable to be the parent node of node \(s_{i}\)  
**if** node \(s_{p}\), \(s_{p}\in S\), \(d(s_{i}\), \(s_{p})\leq C_{r}\)**and**\(i\
eq p\)**do**  
\(SelectParentNode(d(s_{i}\), \(s_{p})\), \(q_{p}\), \(\lambda_{1}\), \(\lambda_{2})\);  
**end if**  
**end for**  
each node \(s_{i}\) transmits sensing data to its parent node;  
the root node \(s_{r}\) transmits sensing data of all nodes to \(BS\);  
**end while**  
**Function**\(SelectRootNode(q_{r}\), \(O(s_{r})\), \(C(s_{r})\), \(d(s_{r}\), \(BS)\), \(\tau_{1}\), \(\tau_{2})\)  
**begin**  
return the root node \(s_{r}\) with maximum \(\alpha_{r}\) by Equation (4) and Equation (6);  
**end**  
**Function**\(SelectParentNode(d(s_{i}\), \(s_{p})\), \(q_{p}\), \(\lambda_{1}\), \(\lambda_{2})\)  
**begin**  
return the parent node \(s_{p}\) with maximum \(\beta_{i,p}\) by Equation (7) and Equation (9);  
**end**  
Figure 2: Pseudo code of the proposed ECHR protocol.
# Separate Syntax and Semantics: Part-of-Speech-Guided Transformer for Image Captioning
## 4 Experiments
### Ablation Studies  
To validate the impacts of different modules and settings in our models on the captioning performance, we conducted extensive ablations including different numbers of encoding and decoding layers \(L\), different values of the hyperparameter \(\lambda\), and different PoS-guided attention mechanisms.  
(1) Effect of encoding and decoding layers: To investigate the impact of the number of encoding and decoding layers, we applied the single-attention-based PoS-Transformer (SAT-PoS-Transformer) model with different numbers of stacked blocks \(L\in\{1,2,4,6\}\) on Flickr30k, as well as the dual-attention-based PoS-Transformer (DAT-PoS-Transformer) model on MSCOCO and Flickr30k, respectively. For simplicity, the numbers of stacked blocks in the encoder and decoder were set to the same value. Table 1 shows the performance of SAT-PoS-Transformer and DAT-PoS-Transformer with different \(L\)'s on Flickr30k. We can observe that these two models achieved the best performance when using four encoding and four decoding layers. This was due to the fact that deeper layers enabledthe encoder of the captioner to represent more complicated relationships between objects and the decoder to provide more discriminative latent vectors for the prediction of words. However, if the number of layers becomes large, the risk of overfitting also increases. Table 2 reports the performance of DAT-PoS-Transformer with different \(L\)'s on the MSCOCO dataset. Similarly, we can see that the generated image captions by our proposed models reached the highest scores on all metrics when \(L=4\). Thus, all subsequent experiments used four layers.  
(2) Effect of the hyperparameter \(\lambda\): To analyze the impact of the hyperparameter \(\lambda\) on the captioning performance, we applied our PoS-Transformer models with different values of \(\lambda\) on MSCOCO and Flickr30k, respectively. The experimental results of SAT-PoS-Transformer and DAT-PoS-Transformer on Flickr30k are illustrated in Table 3. It can be seen that DAT-PoS-Transformer with \(\lambda=0.75\) had the highest scores on most metrics and a pretty high BLEU-4 and ROUGE-L scores (only slightly lower than the highest 0.287 and 0.492, respectively). For SAT-PoS-Transformer, it reached relatively optimal performance when \(\lambda=1.00\). As can be seen from Table 4, when the coefficient \(\lambda\) of the PoS loss function increased to 0.50, DAT-PoS-Transformer obtained the highest scores in terms of all metrics on MSCOCO.  
(3) Effect of single attention and dual attention: As shown in Tables 1 and 3, the image captions generated by SAT-PoS-Transformer with \(L=4\) and \(\lambda=1.00\) reached the highest scores on most metrics. It can be also observed from Table 3 that DAT-PoS-Transformer significantly outperformed SAT-PoS-Transformer in terms of all metrics. Based on the dual attention mechanism, the best CIDEr score increased from 0.601 to 0.612 on the Flickr30k dataset, which validated the superiority of dual attention over single attention.
# The Significance of IL-36 Hyperactivation and IL-36R Targeting in Soriasis
## 1 Introduction
IL-36R-mediated signal transduction has been shown to induce a number of pathogenic pathways in different cell types of psoriatic skin, especially in resident skin cells such as keratinocytes and endothelial cells (Figure 1). In keratinocytes, IL-36s promote the release of pro-inflammatory cytokines and chemokines, upregulate AMP and proliferative mediators, and, importantly, interferes with differentiation/conrification processes [19; 20; 21]. IL-36R-dependent signaling also induces the expression of T-cell attracting and adhesion molecules by endothelial cells [21], as well as regulates the production of T-cell polarizing cytokines by mDC and macrophages, thus influencing Th17 and Th1 responses in psoriatic skin [15; 19; 20; 21; 22; 23]. Of note, IL-36 cytokines potently sustain neutrophil circuits in psoriatic skin, in particular in clinical variants of psoriasis where neutrophils are abundant and have a major pathogenic role, such as generalized pustular psoriasis (GPP) and palmoplantar pustolosis (PPP) [24]. Here, genetic mutations in IL-36 signature, and in particular in the _IL-36 RN_ gene, have been identified, confirming the pathogenic role of IL-36 axis in psoriasis development [25; 26; 27; 28]. Interestingly, a higher frequency of _IL-36 RN_ disease alleles has been found in GPP compared to the PPP patient population, as well as associated with an earlier age of pustular psoriasis onset [29]. In this review, we discuss on the molecular and cellular mechanisms leading to the dysregulated IL-36 expression in psoriasis, with the emphasis on consequences of the altered balance of IL-36 agonists/antagonists in psoriatic skin. We illustrate the multiple pathogenic effects of IL-36 cytokines on inflammatory responses of resident skin cells and immune cells, as well as their impact on differentiation/conrification processes of the epidermis. Collection of the information will provide a theoretical basis for the development of new therapeutic strategies for psoriasis based on IL36R-targeting.
# A Doppler Frequency-Offset Estimation Method Based on the Beam Pointing of LEO Satellites
## 2 Model Establishment
### Establishment of the Frequency-Offset Model
In the case of satellite-ground communication, the satellite antenna's azimuth angle \(\theta\) and elevation angle \(\varphi\) are the parameters that must be calculated by the satellite, so the angle \(\theta\) and \(\varphi\) parameters can be directly used in the calculation of Doppler frequency offsets without recalculation.
###### Abstract  
This paper tests the capability of a simplified model to predict major trends in the dynamic structural response of monopile offshore wind turbines. For this purpose, the results of two numerical models of different levels of complexity are compared: the advanced time-domain multi-physics tool OpenFAST and a simplified static-equivalent model based on beam elements and concentrated masses. The IEA-15-240-RWT reference wind turbine is considered as a benchmarking problem. The comparison between the two structural models is presented in terms of their fundamental frequencies and through the analysis of shear forces and bending moments under wind-only and combined wave and wind load scenarios. The results show that the simplified model can adequately represent the system's mass and stiffness characteristics, as well as the impact of soil-structure interaction effects on its fundamental frequency. Turbulence and wind velocity have a significant impact on internal forces and on the ability of the simplified model to reproduce their values. Despite the large differences obtained for highly turbulent scenarios, the acceptable accuracy obtained for relevant load scenarios and the conservative nature of the simplified model make it a viable option for preliminary large-scale studies that prioritize efficiency and efficacy over high-precision.  
offshore wind turbine; soil-structure interaction; monopile foundation; OpenFAST; static-equivalent analysis Article