esa-sceva/satcom-chunk-collection huggingface dataset:
# Historically Based Room Acoustic Analysis and Auralization of a Church in the 1470s
## 4 Room Acoustic Analysis
### Acoustics within the Choir and Gallery
A time series illustrating a raytracing model of the energy distribution over time is shown in Figure 12.  
On the left hand side in Figure 12, the energy distribution over time for a sound source located in the choir is shown. The energy emitted from the source travels upward as time progresses, to be reflected from the ceiling vaults. By 80 ms, the third figure from the top, the reflected wavefront can be seen in the space above the choir. Consequently, it has not reached the receiver by 80 ms, and thus has a detrimental effect on \(C_{80}\). On the right hand side in Figure 12, the corresponding time series for a source and receiver in the gallery is shown. As the gallery is closer to the ceiling, the strong reflected wavefront from the ceiling has already reached the receiver in the gallery by 80 ms, thus improving \(C_{80}\). The G within the gallery and choir can be seen in Figure 10. As shown in the graph, there are no or very small differences between the gallery and the choir. The results in both spaces are, however, significantly better than outside these spaces. This can be explained by strong reflections, from the ceiling in the gallery and by the walls in the choir. The positions of these surfaces ensure that a significant amount of sound energy is reflected back to the space, thus increasing the total G.  
Figure 11: Energy decay curves in the (a) nuns’ gallery and (b) monks’ choir in the reconstructed abbey. The small insets show the early parts of the decay. Each line corresponds to the results in one octave band. Finally, some brief comments are made regarding sound transmission between the gallery and choir, corresponding to the acoustic perception of monks from the nuns' position and vice versa. It is found that acoustic transmission between the two locations is very similar to the transmission between choir and nave. As those results are presented in Section 4.2, no further comments are made here.
# Deep Neural Networks for Document Processing of Music Score Images
## 3 Framework  
Although rarely formulated in this way, the document processing of music score images can be seen as a pixel-level problem. For instance, for the input image depicted in Figure 1(a), we want to know whether each pixel belongs to the foreground or to the background (Figure 1(b)). This corresponds to the document image binarization step, from which the background layer is ignored. Hence, those classified as foreground are further categorized into text or music elements, because they shall be processed by different automatic algorithms (Figure 1(c)). In the latter case, we want to know whether a pixel belongs to a staff line or not, as this information is valuable for several reasons such as the segmentation of single notes as well as determining the pitch (Figure 1(d)). In the framework described in this paper, we propose to tackle all these processes in a single step.  
The document processing can be reformulated as a supervised classification task in which a model is trained to distinguish the category to which every pixel belongs. Formally, our approach considers a model that categorizes a given pixel into four possible classes: _background_, _text_, _staff line_, and _symbol_. Although we instantiate our approach to this specific label set, the framework is rather general and can be used for any number of categories.  
In our framework, this classification process is carried out by means of Deep Learning. Recently, Deep Neural Networks (DNN) have shown a remarkable leap of performance in pattern recognition. Specifically, CNN have been applied with great success for the detection, segmentation, and recognition of objects and regions in images, approaching human performance on some of these tasks [7].  
CNN are composed of a series of filters (i.e., convolutions) that obtain several intermediate representations of the input image. These filters are applied in a hierarchy of layers, each of which represent different levels of abstraction; while filters of the first layers may enhance details of the image, filters of the last layers may detect high-level entities [6]. The key to this approach is that, instead of being fixed, these filters are modified through a gradient descent optimization algorithm called backpropagation [39].  
One of the main advantages of CNN is its ability to learn the representation that is needed for the classification task without any human intervention, achieving greater generalization and adaptability when applied to different manuscripts. In other words, these networks learn a suitable representation from raw data, without the need of performing a feature extraction process. Since collections of music documents are a rich source of highly complex information--often more heterogeneous than other types of documents--a framework based on CNN is promising.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 3 Results
### West Jakarta  
The urban green space percentage of West Jakarta is around 30.47% (38.1 km\({}^{2}\)). The largest urban green space is grass (12.43 km\({}^{2}\)), followed by mixed areas (10.28 km\({}^{2}\)), shrub (8.33 km\({}^{2}\)), and forest (6.22 km\({}^{2}\)) (Figure 6). A small part of the urban green space of West Jakarta is agriculture, and it consists of paddy fields (Figure 7).  
In West Jakarta, there are approximately 202 small parks accessible to the public, including the city parks of Taman Hutan Kota Kampung Sawah, interactive parks, environmental parks, and a recreational park of Taman Kebun Bibit Srengseng. The rest of the urban green space is green lane roads and water banks, and there are twelve open spaces dedicated to cemeteries.  
One of West Jakarta's most renowned parks open to the public is Taman Cattleya. Besides offering a place for relaxation and exercise, this park also provides an opportunity for visitors to expand their knowledge by exploring its diverse range of plant species.  
Figure 6: The portion of urban green space in West Jakarta.
# Two Low-Complexity Efficient Beamformers for an IRS- and UAV-Aided Directional Modulation Network
## 5 Conclusions and Limitations
### Limitations  
The two methods proposed in this paper, the Max-SR-SLNR and the MRT-NSP-PA, did not jointly optimize the \(\mathbf{v}_{AN}\), \(\mathbf{v}_{CM}\), and \(\boldsymbol{\theta}\) with the criterion of maximizing the SR.  
Figure 9: Computational complexity versus \(N_{s}\).  
Therefore, the SR performance of both methods was not optimal. In addition, the power allocation (PA) factors \(\beta_{1}\) and \(\beta_{2}\) were fixed in the simulation parameters. If the PA factors were optimized, the SR of the system could be further enhanced.  
[PERSON] surveyed the algorithms based on the system and performed the simulations. [PERSON], [PERSON], [PERSON] and [PERSON] surveyed the directional modulation network, and the UAV- and IRS-aided directional modulation network. [PERSON] revised the paper and proposed the algorithms. [PERSON] and [PERSON] surveyed the IRS- and UAV-aided network. [PERSON] reviewed and edited the article. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Nos. U22A2002, 62071234, and 61972093), the Hainan Province Science and Technology Special Fund (ZDKJ2021022), the Scientific Research Fund Project of Hainan University under Grant KYQD(ZR)-21008, and the Fujian University Industry University Research Joint Innovation Project (No. 2022H6006).  
There are no data presented in the article.  
We thank the editor and the reviewers for their valuable comments.  
The authors of the article have no conflict of interest.  
The abbreviations used in the article are as follows:  
\begin{tabular}{l l} UAV & Unmanned aerial vehicle \\ LoS & Line of sight \\ IRS & Intelligent reflecting surface \\ DM & Directional modulation \\ SINR & Signal-to-interference plus noise ratio \\ PA & Phase alignment \\ SLNR & Signal-to-leakage-noise ratio \\ SR & Secrecy rate \\ Max-SR-SLNR & Maximum SR-SLNR \\ NSP & Null-space projection \\ MRT & Maximum ratio transmission \\ PSM & Phase shift matrix \\ AN & Artificial noise \\ CM & Confidential message \\ PL & Path loss \\ \end{tabular}
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 4 Comparison and Results
### The New Proposed Method
#### 4.3.2 Influence of Smooth Filtering Methods  
Accordingly, six types of smooth filtering functions and methods were used and compared regarding to the processed signal of S1, as shown in Figure 9. Explanatorily, the legend of original and moving represent the lowpass filtering signal of S1 which is to be further processed with its smooth filtering processed signals by moving average method. The legend of lowess and loess in the third and fourth line box means that the signals were processed by weighted linear least squares with first-order polynomial model and second-order polynomial model, respectively, while the sgolay means the smoothing signals processed by the Savitzky-Golay filtering method. Similarly, the rowess and loess  
Figure 8: Different weak signals processed by HHT and lowpass filtering method. (**a**) HHT and lowpass filtering method for wire rope signal S1; (**b**) HHT and lowpass filtering method for wire rope signal S2; (**c**) HHT and lowpass filtering method for wire rope signal S3.  
represent the signals processed by robust lowess and loess methods, separately. Obviously, the signals processed by the moving average method become smoother in the amplitude and shape, and the micro fluctuation of the interference signals become smaller, as well as the signals processed by local regression techniques of lowess and loess. However, the signals processed by rlowess methods shown in the penultimate line box indicate that the signal amplitudes become weaker compared with these signal processing results mentioned above. The signals processed by rloess method expressed in the last line box were also featured with high SNR and distinct signal peaks. According to these signal processing results filtered by smoothing techniques, it could be found that almost all the smoothing and averaging methods were available for the original lowpass filtered signals except the rlowess method. Consequently, the typical moving average method was chosen as the smooth filtering technique in the proposed algorithm.
### Ex Vivo Permeation Studies
#### 2.7.3 Permeation of Insulin  
For the control group, an insulin solution (7 mL) with a concentration of 1 mg/mL of insulin in KRB was placed in each apical chamber of 6 Sweetana-Grass diffusion cells (\(n=6\)). Samples (200 \(\upmu\)L) were withdrawn from the basolateral side at time intervals of 10, 20, 30, 60, 90, and 120 min and placed in vials to be analysed by means of HPLC. The samples withdrawn were replaced with the same volume of pre-heated KRB at every time interval.  
For the experimental groups, a sufficient quantity of each microparticle formulation was dispersed in KRB to provide a suspension with an equivalent concentration of 1 mg/mL of insulin based on the insulin content of each microparticle formulation as determined by the assay. A suspension of each microparticle formulation (7 mL) was placed in each apical chamber of 6 Sweetana-Grass diffusion cells (\(n=6\)). At time intervals of 10, 20, 30, 60, 90, and 120 min, samples (200 \(\upmu\)L) were withdrawn from the basolateral side and placed in vials to be analysed by HPLC. After each sample withdrawal, a volume of 200 \(\upmu\)L pre-heated KRB was added to the basolateral side to maintain the volume [24].  
The apparent permeability coefficient (P\({}_{\text{app}}\)) values were calculated by using the following equation [36]:  
\[\text{P}_{\text{app}}=\frac{\text{dQ}}{\text{dt}}\times\frac{1}{\text{A.C}_{0.60}} \tag{4}\]  
where P\({}_{\text{app}}\) is the apparent permeability coefficient value (cm.s\({}^{-1}\)), dQ/dt (\(\upmu\)g.s\({}^{-1}\)) is the amount of insulin that permeated into the receiver chamber over time (slope of the plot of insulin permeated as a function of time), A is the effective surface area of the olfactory/respiratory tissue (cm\({}^{2}\)) between the apical and basolateral chambers, and C\({}_{\text{o}}\) is the initial insulin concentration that was placed in the apical chamber (\(\upmu\)g.cm\({}^{-3}\)).
## 1 Introduction  
### The Necessity of Optimization in Architecture  
Architectural design problems have an excessive number of design parameters. All possible combinations of design parameters correspond to thousands of different design alternatives. It is difficult to choose the desirable design within such a big search space. In addition, architectural design requires different performance aspects to be satisfied as design objectives focus on various topics (e.g., social, economic, physiological, health, safety, structural, cultural, sustainability, etc.) [1]. Some of these performance aspects (e.g., energy, and daylight) require non-linear equations, which increase the level of the complexity. All these refer that decision-making is highly important in the early stages of the design process. Because, the decisions that are taken in the early design phases have a great impact on the following design stages. As a result, they influence the overall performance and the appearance of the constructed building. At this point, computational optimization techniques became a necessity in architectural design.  
Regarding the computational optimization techniques, metaheuristic algorithms can play a vital role for not only presenting promising design alternatives but also for dealing with complexity [2]. On the other hand, these algorithms do not guarantee to finding the global optimal solutions [3]. However, they can present near-optimal results within a reasonable time. For a decision maker, providing a near-optimal solution in a reasonable time can be more advantageous than presenting the optimal solution within extremely long time. To discover desirable solutions, metaheuristics are usually inspired by the natural processes (such as interactions within swarms and evolution over the generations). Some of these metaheuristic algorithms are harmony search (HS) [4], particle swarm optimization (PSO) [5], differential evolution (DE) [6; 7], genetic algorithm (GA) [8], ant colony optimization (ACO) [9], simulated annealing (SA) [10], and evolutionary algorithm (EA) [11]. According to current state of the art, evolutionary computation and swarm optimization algorithms are the most popular metaheuristics in architectural domain [2].
## 4 Discussion  
This study describes the sequencing, assembly, and annotation of the \(H\). _coralloides_ genome, finding that this genome is 43.7 Mb in size, with 13 chromosomes and an N50 of 3.60 Mb. The effect of chromosome assembly evaluated by BUSCOs was 96.9%. Compared with the previously described ASM367540v1 genome, the \(H\). _coralloides_ genome had higher degrees of continuity and integrity. Many genes associated with terpenoid biosynthesis were identified by homologous comparison and KEGG enrichment analysis. Geranylgeranyl pyrophosphate synthase (GGPPS) and farnesyl diphosphate synthase (FDPS), encoded by the _Her011461_ and _Her008335_ up-regulated genes in the terpenoid backbone synthesis pathway (ko00900), respectively, are key enzymes in the biosynthesis of GGPP, a common precursor of diterpenes. In addition, the expression level of these genes, _Her011461_ (Geranylgeranyl pyrophosphate synthase), _Her011465_ (NAD binding protein), and _Her011467_ (UDP-glycostransferase) in DK was significantly up-regulated compared with FB-A, FB-B, and FB-C in the diterpen biosynthesis pathway. This indicated that the content of diterpenoids in DK might be higher than FB-A, FB-B, and FB-C. A total of nine presumed STS genes were identified in the sesquiterpene synthesis pathway (_Her003832_, _Her1003788_, _Her005338_, _Her002337_, _Her005433_, _Her006723_, _Her006724_, _Her010605_, and _Her010608_). Most of the nine STS genes were down-regulated in the comparison groups. In addition, genes involved in sesquiterpene biosynthesis were found and dispersed in the \(H\). _coralloide_ genome. In general, these genes show different levels of expression in different tissues. Transcriptome analysis showed that the genes responsible for terpenoid backbone and diterpenoid biosynthesis were highly expressed in DK, whereas all nine genes responsible for sesquiterpenoid biosynthesis were under-expressed in DK (Table 7), suggesting that gene expression may be related to tissue-specific biosynthesis of active compounds. These results also indicated that, under our culture conditions, \(H\). _coralloide_ may produce very small amounts of sesquiterpenoids. The eight gene clusters involved in terpenoid biosynthesis were identified using anti-SMASH software, with most of the predicted genes in these clusters being involved in sesquiterpene biosynthesis. In contrast, these clusters did not include genes involved in terpenoid backbone or diterpene biosynthesis. These findings indicated that the two methods of predicting terpenoid synthesis gene clusters yielded different results, suggesting that both the homology search and antiSMASH software methods were required to identify genes associated with the terpenoid backbone, diterpenoid, and sesquiterpenoid biosynthesis. In summary, the results of this study provide additional genomic information on \(H\). _coralloides_, including the identification of key genes involved in terpenoid biosynthesis. These findings not only increase understanding of \(H\). _coralloide_ biology, but suggest the possible commercial application of terpenoid metabolites from \(H\). _coralloide_.
# Optimization of Stop Plan for Skip-Stop Operation on Suburban Railway Line
###### Abstract  
A combination of express and local trains (E/L mode) is generally used to operate a suburban rail service, it can meet the rapid and direct service needs of long-distance travelers as well the needs of short-distance travelers. Generally, a stop plan is the core of the E/L mode. A stop plan optimization model in E/L mode, which aims to minimize the total passenger travel time and the number of operating trains during the peak period with the safe headway and departure frequency constraints, is proposed in this study. Meanwhile, an algorithm based on a genetic algorithm is designed to solve the proposed model. A case study of the Jiangjin Line, a suburban railway in Chongqing, China, is carried out. The results show the efficiency and feasibility of the proposed method. The calculation results also show that the total passenger travel time under E/L mode with the overtaking condition is significantly reduced compared with the all-stops (AS) mode and E/L mode without overtaking condition. The superiority of the E/L mode can be enhanced by reducing the dwell time at stations and adopting the overtaking condition.  
urban rail transit; suburban rail; skip-stop; stop plan; overtaking
# Hybrid Quantum Neural Network Image Anti-Noise Classification Model Combined with Error Mitigation
## 5 Conclusions  
In this study, we successfully developed a hybrid variational quantum neural network architecture to tackle the challenge of noise interference in quantum image classification. Our architecture utilizes a convolutional autoencoder for feature extraction, offering robust support for subsequent variational quantum circuits by capturing essential image features. The use of amplitude coding simplifies the model by directly converting image information into a quantum state, reducing complexity and enhancing the model's resistance to noise. To further improve the system's anti-noise capabilities, we introduce a quantum autoencoder for error mitigation. Experimental results demonstrate a substantial improvement in image classification accuracy in noisy environments, achieving a high level of 92%. Compared to other traditional quantum algorithms, our method exhibits superior robustness, confirming the excellent performance of our hybrid quantum neural network classifier in addressing noise interference in image classification tasks.  
This study introduces a novel approach to advancing quantum image classification, showcasing significant potential with the mixed variational quantum neural network architecture, especially in noisy environments. The experimental demonstration, using quaternary classification, serves as an illustrative example of our algorithm's performance. Future research directions may involve exploring the method's efficacy in more complex datasets and practical applications, along with gaining a deeper understanding of the integration between quantum computing and machine learning. While there may still be some disparity between our model and its classical counterpart, ongoing efforts will concentrate on refining our model to minimize resource consumption and ensure its practical utility in future quantum computers. We are optimistic that our model will play a  
Figure 10: (**a**) The classification performance of the three classification methods of QNN, QCNN, and this model in the environment of different noise values of MNIST datasets. (**b**) Comparison with FashionMNIST.  
pivotal role in supporting the widespread application of quantum computing in the field of image processing.  
Writing--original draft, N.J. and R.B.; data curation, N.J. and R.B.; formal analysis, Z.C. and Y.Y.; methodology, R.B. and H.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2021 MF049), and Joint Fund of Natural Science Foundation of Shandong Province (Grant Nos. ZR2022 LLZ012).  
Not applicable.  
The data are contained within the article.  
The authors declare no conflicts of interest.
# Overview of the Development of 3D-Printing Concrete: A Review
## 4 Key Preparation Technology
### Admixture  
Admixtures are an important part of 3D printed concrete materials. They can significantly adjust and improve the fluidity, setting time, and mechanical properties of concrete. Adding admixtures to 3D printed concrete appropriately will get a small amount, but have a big effect. [PERSON] et al. [76] found that by increasing the amount of water reducing agent, the total pore volume of the printed sample was reduced, and the content of nanopores was increased. It also caused an increase in the compactness of the sample, and the drying and shrinkage reduced. [PERSON] et al. [15] found that xanthan gum and Brunei gum thickeners can significantly improve the moldability of the accumulated layer of printed concrete test strips. [PERSON] et al. [16] used 1% water-reducing agent gelling materials to improve fluidity and realized the construction of specimens with the highest 61 layers.  
Figure 14: Tensile strength of specimens with various fiber ratios. Reprinted with permission from ref. [73]. Copyright 2020 Elsevier Ltd.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fiber Material Name** & **Effect** & **Reference** \\ \hline Polypropylene fibers & Anti-flaking, make the extrusion more uniform and continuous & [70] \\ \hline GFRP (Glass fiber reinforced plastic) fiber & Improve axial load carrying capacity and ductility & [71] \\ \hline Carbon fiber & Improve bending strength and deformation ability & [72] \\ \hline PE (Polyethylene) fiber & Significantly improve the bending strength in & [73; 74] \\ Steel fiber & different directions & [75] \\ \hline Basalt fiber & Improve anisotropy and basic mechanical properties & [75] \\ \hline \hline \end{tabular}
\end{table}
Table 3: The effect of different fiber materials on components.