esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study investigated the potential of biomass fuels to replace coal during clinker calcination, examined their combustion characteristics, and explored the effects of their ash on clinker formation. The insights gained provide guidance for selecting suitable alternative fuels and optimizing kiln operations, which can improve energy efficiency and clinker quality in cement manufacturing while promoting sustainable fuel use. The following conclusions were drawn:  
1. The co-combustion of biomass fuel and coal can significantly reduce the ignition point of the mixed fuel by up to 54 \({}^{\circ}\)C, initiating the combustion reaction earlier and lowering the combustion temperature by approximately 62-93 \({}^{\circ}\)C. This facilitates efficient combustion and enhances the overall combustion rate;
2. High alkaline conditions, introduced by K\({}_{2}\)CO\({}_{3}\) and Na\({}_{2}\)CO\({}_{3}\), stabilized C\({}_{2}\)S and inhibited its conversion to C\({}_{3}\)S. Alkalis also reacted with SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\) to form alkali silicates and sulfates, consuming SiO\({}_{2}\) and CaO needed for C\({}_{3}\)S, thus reducing its content and increasing f-CaO in clinker. However, alkalis do not always have adverse effects. In this study, at a 20 wt% substitution level, the high-alkali ash from corn cobs promoted free CaO dissolution and reaction, likely due to improved combustion uniformity induced by moderate alkali content;  
Figure 11: SEM analysis of hydration products from cement paste (H\({}_{YMO2}\)) at a curing age of 28 days.  
At the studied substitution ratios, all biomass fuels affected clinker mineral phases but had little impact on mechanical strength and hydration products. High-silicon fuels like rice straw promoted calcium silicate intermediate formation. Although 3-day flexural strength was slightly lower (within 5% fluctuation), longer hydration led to formation of C\({}_{2}\)S, Ca(OH)\({}_{2}\), and C-S-H, compensating early strength loss.  
Conceptualization, SH; methodology, Z.W; software, Y.Z.; validation, Z.W., Y.Z. and D.Z.; formal analysis, Z.W.; investigation, Z.W.; resources, S.H; data curation, Z.W; writing--original draft preparation, Z.W.; writing--review and editing, Z.W.; visualization, D.Z.; supervision, Z.W.; project administration, D.Z.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the support of the Social Development of Science and Technology Department of Jiangsu Province-Major Science and Technology Demonstration Project (BE2022608).  
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 conflicts of interest.
# Uncertainty in Visual Generative AI
## 2 Background
### Text Evaluation Methods  
Evaluation methods and metrics are needed to determine the validity of auto-generated captions [63; 67]. Popular evaluation metrics are shown in Table 3, but more extensive reviews currently exist in the literature [63; 87]. The MS COCO Dataset Challenge uses BLEU, ROUGE, METEOR, CIDEr, and SPICE to evaluate performance, so these have become the status quo for evaluating the similarity between texts [74]. Though not a text-to-text evaluation method, in the realm of automated image captioning, CLIPScore is worthy of mentioning due to being \"reference-less\" [88]. CLIPScore, based on the CLIP model originally proposed in [30], allows for the direct comparison of an image to its candidate caption via CLIP model embeddings [88]. As text evaluation metrics have evolved, text mining has inspired the usage of cosine similarity metrics to measure how alike two texts may be. Over the past decade, word embedding models have become increasingly popular within the natural language processing field ever since the release of the vector space model Word2 Vec in 2013 [97; 98]. Though vector space models existed before 2013 (see [99]), the Word2 Vec neural network approach to transforming varying lengths of text into a multi-dimension single vector was particularly exciting because of its ability to quantify semantic and syntactic information in a comparatively low-dimensional space. Vector space models allow for any word, sentence, or document to be represented and compared on a mathematical basis, usually to determine similarity or dissimilarity based on the cosine similarity metric [100]. As an alternative to the machine translation metrics above, cosine similarity is another evaluation metric of interest when comparing two texts. For two vectors, \(A\) and \(B\), their cosine similarity is given by  
\[\textit{CosineSimilarity}(A,B)=\frac{A\cdot B}{\|A\|\|B\|}. \tag{1}\]  
Cosine similarity ranges from 0, meaning completely dissimilar, to 1, meaning exactly alike; although a negative cosine similarity is mathematically possible, it is considered to be 0. Word2 Vec was followed by several other vector space models, with Global Vectors (GloVe) (see [101]) and FastText (see [102]) being the most prominent [103]. The embeddings of vector space models are static, meaning there is no variation for words with multiple meanings; however, this was addressed in more recent word embedding models that have contextualized vectors, such as Embeddings from Language Models (ELMo) [104], XLNet [105], and the Bidirectional Encoder Representations from Transformers (BERT) family of models [106]. BERT was released in 2018 (see [107]) and was soon followed by the Robustly optimized BERT pre-training Approach (RoBERTa) (see [108]), A Lite BERT (ALBERT) (see [109]), and a distilled version of BERT and RoBERTa (DistilBERT and DistilRoBERTa, respectively) (see [110]) [106]. Based on different pre-training data and international architecture, each word embedding model yields a different vector
## 3 Results and Discussion
### SiOC Layer Properties
The optical constants (n, k) of the deposited SiOC film are shown in Figure 6. The extinction coefficient k is less than \(10^{-4}\) (this value being the minimum detectable value for the ellipsometer) in agreement with previous observations [14] and is not visible in Figure 6. The ordinary axis refractive index n\({}_{\rm o}\) is determined to be 1.554 while it is 1.578 for extra-ordinary axis n\({}_{\rm e}\) at the wavelength \(\lambda\) = 1550 nm. The values of dispersion constants A and B of Cauchy model (Equation (2)) are 1.5528 and 4.0353 \(\times\) 10\({}^{-3}\), respectively. Figure 5: Ellipsometric (**a**) psi (\(\rm\upmu\)) and (**b**) del (\(\Delta\)) data acquired at three angles of measurements (65\({}^{\circ}\), 70\({}^{\circ}\), and 75\({}^{\circ}\)) and their respective model curves (solid red lines) calculated with a four-layer film/substrate optical stack. Figure 6: Ordinary (n\({}_{\rm o}\)) and extra-ordinary (n\({}_{\rm e}\)) refractive index curves of the deposited SiOC film extracted from measured ellipsometric data.
## 4 Conclusions  
We have experimentally characterized the induced amplitude and phase noise due to the RIN of the Raman pump and studied their impact on the system performance of an unrepeated 224 Gbit/s DP-16 QAM link. The results show that a Q factor above the 8.5 dB HD-FEC limit after 200 km SSMF can be achieved with a bidirectional pumping scheme, using an optimized pump power and a small fraction (5%) of the pump in the forward direction. At low pump powers, additive noise due to a low OSNR is the dominant noise source, while at high pump powers, the induced noise from the Raman pump dominates. Additionally, the noise enhancement in both the amplitude and phase was observed to concentrate on the low-frequency regime, while the high-frequency regime was not as influenced. This indicates that the system performance may be further improved at high pump powers by suppressing the RIN transfer or by using advanced higher-order pumping schemes, or with certain digital mitigation methods focusing on low-frequency noise suppression. Further experimental studies for various types of impairments and their balance is required for the optimal design of the advanced multi-level modulation format signal-based unrepeated transmission systems using higher-order DRFA. On the other hand, RPN impairment mitigation with more powerful DSP algorithms is worth investigating.  
**Acknowledgments:** This work was supported by EU projects ICONE, gr. # 608099, GRIFFON, gr. # 324391 and Vetenskapsradet PHASE, gr. #2016-04510. The equipment was funded by the Knut and Alice Wallenberg Foundation.  
**Author Contributions:** [PERSON], [PERSON], [PERSON] initiated the study, [PERSON], [PERSON], [PERSON] proposed the concept, [PERSON], [PERSON], [PERSON], designed and conducted the experiment, [PERSON], [PERSON] processed the data and analyzed the results, [PERSON] carried out the interpretation and graphical representation of the results, [PERSON], [PERSON], [PERSON], [PERSON] guided the studies, [PERSON], [PERSON], [PERSON] wrote the manuscript. [PERSON], [PERSON], [PERSON], [PERSON] edited the manuscript. All authors reviewed the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Study on the Ash Deposition Characteristics for Co-Combustion of Zhundong Coal with Cotton Stalk
## 2 Structure of the Boiler and Computational Model
### Fuel Properties and Simulation Conditions  
The fuels used were Zhundong coal (ZD) and cotton stalks (CTs). The proximate analyses and ultimate analyses of the Zhundong coal and cotton stalks are shown in Table 1, and the ash compositions are shown in Table 2. It can be seen that cotton stalks exhibited an extremely high volatile content of 78.36%, while the fixed carbon content was only 27.64%. In contrast, the volatile content of Zhundong coal was 35.65%, significantly lower than that of the cotton stalks. Analyses of the ash compositions of both samples revealed that Zhundong coal ash contained relatively low levels of SiO\({}_{2}\) (0.28%) and Al\({}_{2}\)O\({}_{3}\) (4.58%) but a notably high Na\({}_{2}\)O content of 6.41%. In contrast, the primary component of cotton stalk ash was K\({}_{2}\)O, accounting for 51.50%, along with 9.87% Na\({}_{2}\)O. The relatively high alkali metal content (Na and K) will lead to severe slagging and fouling issues during the co-combustion of Zhundong coal and cotton stalks.  
In this study, the blending ratio of cotton stalks was defined as the ratio of the heat contributed by cotton stalks to the actual heat required of the boiler. Due to the significant difference in calorific value between Zhundong coal and cotton stalks, the selected blending ratios in this study were 10%, 15%, 20%, and 25%. In our previous research, it was found that adding cotton stalks through the uppermost burner nozzle had a minimal impact on the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Proximate Analysis/wt.\%**} & \multicolumn{4}{c}{**Ultimate Analysis/wt.\%**} & \multirow{2}{*}{\(\mathbf{\Omega_{\text{net,ar}}}\)**/(kJ/kg)**} \\ \cline{2-3} \cline{5-10}  & **M\({}_{\text{ar}}\)** & **A\({}_{\text{ar}}\)** & **V\({}_{\text{daf}}\)** & **FC\({}_{\text{ad}}\)** & **C\({}_{\text{ar}}\)** & **H\({}_{\text{ar}}\)** & **O\({}_{\text{ar}}\)** & **N\({}_{\text{ar}}\)** & **S\({}_{\text{t,ar}}\)** & **Q\({}_{\text{net,ar}}\)**/(kJ/kg)** \\ \hline ZD & 11.69 & 21.10 & 35.65 & 44.99 & 50.12 & 4.74 & 10.95 & 0.83 & 0.62 & 20,800.0 \\ CTs & 11.76 & 9.31 & 78.36 & 27.64 & 43.01 & 4.29 & 30.62 & 0.71 & 0.30 & 14,348.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximate analyses and ultimate analyses of Zhundong coal and cotton stalks.  
Figure 1: Overall structure of the boiler.  
combustion characteristics of the boiler. Therefore, this blending method was also adopted in this study. Detailed simulation parameters can be found in our previous research.
## 2 Theoretical Foundations and Development of Hypotheses
### Measurement Model
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Loadings** & \
\begin{tabular}{c} **Cronbach** \\ **Alpha** \\ \end{tabular} &
\begin{tabular}{c} **Composite** \\ **reliability** \\ \end{tabular} & **AVE** \\ \hline \hline
**Economic Impact** & & **0.898** & **0.922** & **0.663** \\ \hline
**Tourism has generated employment in the city & 0.814 & & & \\
**Tourism development attracts important brands to Seville & 0.821 & & & \\
**All the economic sectors are favored by the arrival of tourists & 0.774 & & & \\
**Tourism is attracting investments for the city & 0.854 & & & \\
**Tourism has contributed to the creation of new firms & 0.855 & & & \\
**Tourism has increased the number of self-employed workers & 0.764 & & & \\ \hline
**Environmental Impact** & & **0.924** & **0.935** & **0.618** \\ \hline
**Tourists generate a high volume of waste & 0.657 & & & \\
**Tourists use too much energy & 0.777 & & & \\
**Tourists use too much water & 0.807 & & & \\
**Tourists negatively affect the environment of Seville & 0.819 & & & \\
**Tourism negatively affects the Sevillian lifestyle & 0.817 & & & \\
**Tourists have worsened the acoustic contamination (generated by noise) & 0.794 & & & \\
**Tourism negatively affects traffic congestion in Seville & 0.731 & & & \\
**Tourism is degrading the city & 0.857 & & & \\
**The current tourism model is not sustainable in Seville & 0.799 & & & \\ \hline
**Social Impact** & & **0.868** & **0.896** & **0.519** \\ \hline
**Tourism has boosted the protection of the cultural heritage & 0.670 & & & \\
**The improvement of cultural, educational, and recreational & & & & \\ means is a consequence of tourism & & & & \\
**Tourism has contributed to the recuperating of traditional crafts & 0.790 & & & \\
**Tourism has contributed to the recuperating of local traditions & 0.738 & & & \\
**The educational level of the population has improved through tourist contact & 0.710 & & & \\
**Tourism is stimulating local trade & 0.707 & & & \\
**Tourism has boosted accessibility for disabled people in the city & 0.709 & & & \\
**Tourism has contributed to attracting qualified workers to our city & 0.738 & & & \\ \hline
**Satisfaction with the Public Administration of Tourism** & & **0.802** & **0.862** & **0.558** \\ \hline
* I am satisfied with the participation and influence of the citizens & & & & \\ in the tourist planning and development of Seville & & & & \\
**I am satisfied with the public administration of tourism in & & & & \\ Seville & & & & \\
**In general, the arrival of tourists is positive for the city & 0.801 & & & \\
**I am satisfied with the tourism that the city receives & 0.804 & & & \\
**I am satisfied with the level of development of the tourism & & & & \\ sector in the city & & & & \\ \hline \hline Source: * Adapted from European Commission (ETIS) [18]; ** adapted from [PERSON] and [PERSON] [20]; *** prepared ad-hoc. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the measurement model.
## 3 Finite element model (FEM) Analysis of Sound Transmission Loss of the ABH Circular Plate
### Results of TL Simulation  
The TL of an ABH circular plate with a diameter of 60 mm embedded in the center was calculated and compared with the sound transmission loss of the uniform circular plate, as shown in Figure 4. It can be seen from Figure 4 that, below the first-order resonance frequency, the TL of the circular plate embedded with the two-dimensional (2D) ABH is improved compared with the uniform circular plate and both the TLs of the two plates decrease as the frequency increases. At the first-order resonance frequency, the acoustic loss of the circular plate embedded with the acoustic black hole is greatly improved compared with the uniform circular plate. The difference between the TL of the ABH circular plate and the uniform circular plate at this frequency is 21.13 dB. In the mass-controlled region, the TL of the circular plate embedded with ABH is larger at some frequencies, while at other frequencies, the TL of the uniform circular plate is larger, indicating that the sound insulation performance of the ABH circular plate is not improved overall in this characteristic region. At the coincidence frequency, the TL of the ABH circular plate is 8.31 dB higher than that of the uniform circular plate. While in the damping-controlled region, the TL of the ABH circular plate is not improved overall, but at a certain frequency; for example, 8 kHz, there is an increase of 20 dB for the TL of the ABH circular plate.
# A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception
## 3 Robust Whitening
Use \(\mathbf{C}^{\star}\) to denote the final derived positive definite matrix. The eigenvalue decomposition of \(\mathbf{C}^{\star}\) is performed to obtain the eigenvalues and eigenvectors, respectively. The \(K\) large eigenvalues and the corresponding eigenvectors are selected and denoted as \(\mathbf{\Lambda}_{s}=diag(\sigma_{1}^{2},\sigma_{2}^{2},\cdots,\sigma_{K}^{2})\) and \(\mathbf{U}_{s}=[\mathbf{u}_{1},\mathbf{u}_{2},\cdots,\mathbf{u}_{K}]\), respectively, which leads to the following robust whitening matrix. \[\tilde{\mathbf{R}}_{x}(\tau)=\frac{1}{2}\Bigl{(}\mathbf{R}_{x}(\tau)+\mathbf{R}_{x}^{H}( \tau)\Bigr{)} \tag{15}\]0  
The whitening signal after robust whitening is expressed as  
\[\tilde{\mathbf{R}}_{x}(\tau)=\frac{1}{2}\Bigl{(}\mathbf{R}_{x}(\tau)+\mathbf{R}_{x}^{H}( \tau)\Bigr{)} \tag{15}\]1  
The above method can not only attenuate the effect of noise, but also achieve dimensionality reduction and reduce the amount of computation.
## 3 Data Processing  
In this section, we introduce the channel characteristic parameter extraction method based on the electronic wave band channel data from Wireless InSite(r) and the VLC channel data from Zemax(r). Three types of channel characteristics will be presented, including the path loss, RMS DS, and AS.  
For the electronic wave band, the CIR can be given by  
\[h(\tau)=a_{1}\delta(\tau-\tau_{1})+\sum_{n=2}^{N}a_{n}e^{i\phi_{n}}\delta(\tau- \tau_{n}), \tag{1}\]  
where \(a_{1}\) presents the deterministic (typically LOS) component with the excess delay \(\tau_{1}\). The parameters \(a_{n}\), \(\tau_{n}\), and \(\phi_{n}\) are the amplitude, excess delay, and initial phase of the nth stochastic (typically NLOS) component, respectively. N denotes the number of multiple paths.  
For the visible light band, the CIR can be given by  
\[h(\tau)=\sum_{n=1}^{N}a_{n}\delta(\tau-\tau_{n}). \tag{2}\]  
In the following equation, the CIR is utilized to obtain the channel characteristics. The path loss is the reduction in the power density of a radio wave as it propagates, which is of high importance for coverage prediction and interference analysis. According to the definition of the path loss, it can be defined by [33]  
\[PL[dB]=-10\text{log}_{10}(\sum_{n=1}^{N}|h(\tau_{n})|^{2}), \tag{3}\]  
Figure 3: The relative spectral power distribution of the light source.  
The RMS DS describes the time dispersion of the MPCs, which determines the length of the guard interval in the OFDM system. It can be expressed by  
\[\tau_{RMS}=\sqrt{\frac{\sum_{n=1}^{N}(\tau_{n}-\tau_{mean})^{2}|h(\tau_{n})|^{2}} {\sum_{n=1}^{N}|h(\tau_{n})|^{2}}}, \tag{4}\]  
in which \(\tau_{mean}\) is the average excess delay, which can be derived by  
\[\tau_{mean}=\frac{\sum_{n=1}^{N}\tau_{n}|h(\tau_{n})|^{2}}{\sum_{n=1}^{N}|h( \tau_{n})|^{2}}. \tag{5}\]  
The AS can characterize the dispersion degree of MPCs in the angle dimension, which can be used to represent the gain brought by MIMO technology [34]. In this paper, we analyze the angle spread of the arrival for the azimuth dimension, which can be expressed by  
\[\sigma_{AS}=\sqrt{-2\text{ln}(\frac{\sum_{n=1}^{N}e^{i\theta_{n}}|h(\tau_{n})| ^{2}}{\sum_{n=1}^{N}|h(\tau_{n})|^{2}})}, \tag{6}\]  
where \(\theta_{n}\) is the angle of the MPCs in radians.
# Automatic Medical Image Segmentation with Vision Transformer
## 2 Related Works
### Combining Self-Attention Mechanisms into CNNs  
Numerous studies have explored the incorporation of CNNs and self-attention mechanisms, aiming to model global interactions between pixel-level features. For example, a non-local operator was designed, which could be inserted into multiple convolution layers [35]. Based on an encoder-decoder parallel structure, additive attention gate modules [36] have been proposed to inject self-attention into skip-connections.  
Unlike the existing approaches, our method employs transformers to embed global self-attention. In our framework, transformers enable the capture of long-range dependencies and global context that complement CNNs' local feature extraction capabilities, providing a novel and effective way of incorporating self-attention for medical image segmentation tasks.
# Management of Obstructive Sleep Apnea in Hospitalized Patients
## 3 Screening for Sleep Apnea in Hospitalized Patients  
### Screening Questionnaires  
Screening tools for obstructive apnea include the Berlin questionnaire [45], the Epworth sleepiness scale [46], and the STOP-BANG questionnaire [3; 47; 48]. Screening questionnaires have low diagnostic accuracy, and the American Academy of Sleep Medicine (AASM) discourages their use for diagnosis in outpatient settings [3]. However, screening questionnaires may be more useful in inpatient settings where sleep studies are challenging to obtain [3]. The Berlin questionnaire comprises 11 questions used to stratify OSA risk, but its length makes it less desirable for routine screening use [45]. The Epworth sleepiness scale assesses primarily daytime sleepiness, and due to its focus on symptoms, its utility is limited in hospitalized patients [46]. The questionnaire with the highest utility in hospitalized patients is the STOP-BANG, which includes eight screening questions (four clinical and four demographic) and was developed for screening pre-op patients for OSA risk [47; 48]. The STOP-BANG has been validated in both outpatient and inpatient populations [48; 49]. Higher scores correlate with a higher risk of OSA. STOP-BANG questionnaire has a higher sensitivity than other questionnaires based on pooled sensitivity analysis (sensitivity 0.93, specificity 0.36, and diagnostic accuracy 53%) [3].
## 3 Proposed Joint Traffic Prediction and Handover Design Framework  
We propose a unified framework that combines traffic prediction and a handover policy design for LEO satellite networks. As shown in Figure 2, the workflow begins with traffic forecasting using an LSTM network, followed by satellite load computation based on the predicted TTG traffic. This information, along with the satellite status, forms the MDP state space, which is then used by an attention\(-\)enhanced rainbow DQN to derive optimal handover policies.