esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To deal with the non-diffuse conditions in the model presented in this paper, an estimation of the surface impedance of the ceiling is used. The reason is to take into account the angle-dependent properties of the ceiling absorber. This is a major difference to the other energy models referred to above. Another difference to the referred models is the handling of the scattering effect of interior objects such as furniture. In rooms with absorbent ceiling treatments, the directional scattering effect of objects is important. A method for estimation of the directional scattering effect is suggested as an outcome of the model formulation. When evaluating the reverberation \(T_{20}\) or \(T_{30}\) according to ISO 3382-2 [2], the dynamical ranges \(-5\) to \(-25\) dB and \(-5\) to \(-35\) dB are used, respectively. This means that the early reflections of the impulse response are neglected. Therefore, \(T_{20}\) and \(T_{30}\) are often referred to as late reverberation times. In a room with absorbent ceiling treatment, the late reverberation times are often related to energy travelling in the horizontal plane, comprising grazing waves in relation to the absorbent ceiling. The importance of early reflections for design of auditoria was already observed by [PERSON] and [PERSON] [50]. [PERSON] et al. [51] has investigated the subjective influence of early diffuse reflections on speech intelligibility and spatial perception. In [52], [PERSON] et al. show the importance of early reflections for speech intelligibility both for normal- and hearing-impaired listeners. These investigations show the benefits of using parameters incorporating the early reflections such as speech clarity. The examples in the text above show that public rooms with acoustic ceiling treatment comprise a large and important group of rooms that deserve closer examination. This involves investigation into how different acoustical treatment affects the sound field and how this impact can be predicted in a more accurate way than by the classical diffuse field assumption. Further, elucidate the limitations related to only using reverberation time as a descriptor characterising the acoustics. This paper presents a model that considers the special features of rooms with ceiling treatment and gives an estimation of several room acoustic parameters that are important for the subjective perception of the acoustics. The model takes into account the mounting height of ceiling absorbers and absorbent wall panels, as well as the scattering effect of furnishing, diffusers, or other objects. The purpose is to serve the user with a model that gives an estimation of room acoustic parameters that are reasonably consistent with measurements in rooms with ceiling treatment and thus, also to emphasize phenomena that influence the subjective perception of the acoustics.
## 2 Results
### APC/C\({}^{\text{Cdh1}}\) Acts as a Negative Regulator of FAH Protein Stability  
To empathize the physiological role of APC/C\({}^{\text{Cdh1}}\) in regulating the _FAH_ gene, we designed two sgRNAs specific to the exon 10 region of Cdh1 by using a clustered, regularly interspaced, short palindromic repeats/CRISPR-associated-9 (_CRISPRPCsa9_) knockout system through the Genetic Perturbation Platform (GPP) sgRNA designer (www.broadinstitute.org) website (Figure 4A). The gene disruption efficiency of the sgRNAs was estimated through the high indel percentage obtained from the T7E1 assay and Western blot analysis. The efficiency of sgRNA-1 showed high indel percentage than sgRNA-2 targeting the Cdh1 gene (Figure 4B). Then, we assessed the effect of sgRNA-1 and sgRNA-2 targeting Cdh1 on expression of FAH exogenously. The results showed that the sgRNA-1 targeting Cdh1 upregulated FAH protein when compared to mock (Figure 4C, lane 3). However, the combined effect of both sgRNA-1 and sgRNA-2 showed high upregulation of FAH protein when compared with sgRNA-1 alone targeting the Cdh1 gene (Figure 4C, lane 5). A similar experiment was conducted in HHSteCs in which the sgRNA-1 expression was upregulated in FAH protein (Figure 4D, lane 2) and the combined effect of both sgRNA-1 and sgRNA-2 showed an increase in the endogenous FAH protein level (Figure 4D, lane 4). To validate the specificity of Cdh1 on the FAH protein stabilization, Cdh1-depleted cells were reconstituted with Cdh1 and the expression of FAH was analyzed. Our results showed stabilization of FAH protein in Cdh1 depleted cells when compared with mock (Figure 4E, lane 4). However, FAH protein stabilization was reversed when Cdh1-depleted cells were reconstituted with ectopically expressed Flag-Cdh1 (Figure 4E, lane 6). Similarly, endogenous FAH protein stabilization effect was reversed when Cdh1-depleted cells were reconstituted with ectopically expressed Flag-Cdh1 in HHSteCs (Figure 4F, lane 4) when compared with endogenous expression of FAH in Cdh1-depleted cells (Figure 4F, lane 2). Our results prove that Cdh1 is a specific E3 ligase for FAH protein.
## 6 Future Work, Challenges, and Limitations
### Map Construction and Route Planning  
The map construction technique collects the data of mapping from the physical space to the fingerprint space. It then trains the model and employs inverse mapping to estimate the location of the user or device. A unique challenge of map construction is that the measurements taken by training are not guaranteed to perform in the same physical space, and the measurements may also be obtained from different devices, resulting in an error [113]. On the other hand, route planning can play a critical role in determining the effective path to reach the end goal by considering various factors, such as distance, traffic, safety hazards, energy and time constraints, leading to an optimized journey with minimal cost to the device [114; 115].
# Phase Behavior and Rheological Properties of AES/CAPB/H\({}_{2}\)O Ternary System
## 3 Results and Discussion
### Phase Behavior of AES/CAPB/H\({}_{2}\)O Ternary System  
The single phases and transition region for the AES/CAPB/H\({}_{2}\)O system were identified through POM, viscosity, and SAXS and used to construct the ternary phase diagram of the AES/CAPB/H\({}_{2}\)O system at room temperature, as shown in Figure 5. Three single phases were observed: normal micellar phase (L\({}_{1}\)), normal hexagonal phase (H\({}_{1}\)), and lamellar phase (L\(\alpha\)). In addition, there is one transition region (L\({}_{1}\)\(\rightarrow\) H\({}_{1}\)) in the phase diagram.  
As can be seen from Figure 5, the normal micellar phase (L\({}_{1}\)) almost occupies the left half of the phase diagram. When the proportion of AES (70%) is lower than 10 wt.% or the proportions of AES (70%) and CAPB (38%) are both low, the viscosity of the L\({}_{1}\) phase is low (\(<\)10 Pa-s), which may be ascribed to the high water content in both AES (70%) (water content: 30 wt.%) and CAPB (38%) (water content: 62 wt.%). When the proportion of AES (70%) ranges from 10 wt.% to 40 wt.%, the viscosity of the L\({}_{1}\) phase is high (\(>\)10 Pa-s), which may be attributed to the formation of worm-like micelles.  
For liquid crystal phases, the H\({}_{1}\) phase occupies a significantly larger area than the L\(\alpha\) phase. The combination of medium-high proportions of AES (70%) (45-95 wt.%) and  
Figure 4: Small angle X-ray scattering spectra of (**a**) sample \(\alpha\), (**b**) sample \(\beta\), (**c**) sample \(\delta\), and (**d**) sample \(\varepsilon\) at 25 \({}^{\circ}\)C.  
low proportions of CAPB (38%) (0-20 wt.%) is easy to form the H\({}_{1}\) phase, and the same is true for the combination of medium-high proportions of CAPB (38%) (60-90 wt.%) and low-medium proportions of AES (70%) (10-40 wt.%). Moreover, when the proportion of H\({}_{2}\)O is low, the L\(\alpha\) phase appears for a wide CAPB (38%) proportion range of 0-60 wt.%, along the AES/CAPB binary axis.
# Enhancing Street-Crossing Safety for Visually Impaired Pedestrians with Haptic and Visual Feedback
## 3 Study 1: Haptic and Visual Feedback for Visually Impaired Pedestrians Crossing at Traffic Signals
### Experimental Design  
Our study utilized the Pico 4 headset for system development and designed two independent feedback channels for comparison: haptic feedback was delivered through a bHaptics Tactosy vest equipped with 20 haptic units, while visual feedback was provided via two floodlight strips, each containing six lighting units. Through a detailed system implementation process and experimental setup, we conducted an in-depth evaluation of the effectiveness of these two feedback methods under different parameter configurations in assisting visually impaired individuals to determine the safe crossing timing at traffic lights.
## 2 Data and Methods
### Data
#### 2.1.1 Population Redistribution  
The Gridded Population of the world (GPW) version 4 [33] is a gridded data product that facilitates the integration of population data with earth science data. It models the distribution of human populations on a continuous global surface [34]. The data source is based on census data from all countries in the world, and the statistical unit of census data varies from country to country ([[https://sedac.ciesin.columbia.edu/data/collection/gpw-v4](https://sedac.ciesin.columbia.edu/data/collection/gpw-v4)]([https://sedac.ciesin.columbia.edu/data/collection/gpw-v4](https://sedac.ciesin.columbia.edu/data/collection/gpw-v4))). The population data of Mexico and Ethiopia discussed in this paper were based on census data of cities and towns (administrative units below the state level). The identification degree of its census data was about 15 min (about 30 km), and some urban areas had a higher identification degree. Population redistribution (PopReDist) in this paper was the spatial population change over decades, which is the absolute change for each grid with the resolution of 0.25 degrees. This database provides variables of spatial distribution of population (Pop) for the years 1990, 2000, and 2010 and spatial population redistribution (PopReDist) for 1990-2000 and 2000-2010 by subtracting data of the previous year from the last year.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Variable** & **Abbreviation** & **Database** & **Institution** \\ \hline Dependent variable & Population redistribution & PopReDist & GPW 1  & CIESIN 2  \\ \hline  &
\begin{tabular}{c} Crop yield deviation \\ Water flow deviation \\ Maximum number of consecutive \\ years below median crop yield \\ Maximum number of consecutive \\ years below median water flow \\ \end{tabular} & MeanYield & ISMIIP 3  \\ Independent variable & Maximum number of consecutive \\ years below median water flow \\ \end{tabular} & MeanYield & ISMIIP 3  \\ \hline Control Variables & Built-up Population & Built-up & GHS 5  \\ \hline \hline \end{tabular}  
* Gridded Population of the World. 3 The Inter-Sectoral Impact Model Intercomparison Project. 4 Potsdam Institute for Climate Impact Research. 5 The Global Human Settlement. 6 the European Commission.[ENDFOOTNOTE]  
\end{table}
Table 1: Potential factors influencing population redistribution and their data sources.
## 5 Discussion  
This study underscores the effect of OWA used as an alternative to cement on the physical, mechanical, and water absorption properties of cement paste for paving block applications. The findings indicate that although OWA reduces compressive strength and increases water absorption, incorporating 10% OWA provides an optimal balance between strength and durability.  
In terms of strength, the drop in compressive strength is primarily attributed to the loss of hydration products such as C-S-H gel, which plays a vital role in strength gain. Nevertheless, incorporating 10% as cement replacement exhibits a minimal reduction in compressive strength due to the achievement of sufficient pozzolanic activity that compensates for the removal of cement. This demonstrates that 10% is the optimal replacement level, maintaining the mechanical performance. Similarly, replacing cement with rice husk ash and bamboo leaf ash slightly reduces compressive strength at lower percentages [59,60].  
In terms of durability, OWA contributes to a progressive increase in water absorption measurements. This growth is likely related to the fact that OWA is a porous body that leads to the introduction of more voids in the mixture. Among all OWA-based cement pastes, 10% OWA presents the lowest increase percentage in water absorption, ensuring tolerable durability performance in paving blocks.  
In terms of practical implications, integrating OWA into paving blocks seems beneficial in limiting cement consumption and valorizing agricultural-based wastes. Furthermore, the lower density of OWA-based cement pastes leads to the production of lighter paving blocks, facilitating transportation and accelerating construction. Nonetheless, the augmentation in water absorption can be treated by providing additional sealant and coatings, especially in regions highly vulnerable to moisture.  
Figure 14: Correlation between compressive strength and: (a) flexural strength; (b) density; (c) TWA; (d) CWA.  
Even though the results are promising, this study presents certain limitations in distinct fields. The current investigation concentrates on the short-term strength and durability characteristics of cement paste containing OWA. Future studies must be conducted on the long-term behavior of OWA-incorporated paving blocks under severe environmental conditions, including freeze-thaw cycles and sulfate attacks. Moreover, it is essential to evaluate the effect of OWA on the volume stability characteristics of paving systems alongside microstructural analysis to deeply comprehend the modified hydration process and porosity caused by OWA.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
###### Abstract  
Based on economic feasibility, renewable generators can use pumped hydro storage (PHS) to improve their profitability by performing energy arbitrage under real-time pricing (RTP) schemes. In this paper, we present a new method to optimise the size of and manage utility-scale wind-PV systems using PHS with energy arbitrage under RTP. PHS is used to supply load consumption and/or energy arbitrage. Further, both load-supply and power-generating systems are considered, and a genetic algorithm metaheuristic technique is used to perform the optimisation efficiently. Irradiance, wind speed, temperature, hourly electricity price, component characteristics, and financial data are used as data, and the system is simulated in 15 min time steps during the system lifetime for each combination of components and control variables. Uncertainty is considered for the meteorological data and electricity prices. The pump and turbine efficiencies and available head and penstock losses are considered as variables (not fixed values) to obtain accurate simulations. A sample application in Spain is demonstrated by performing a sensitivity analysis of different locations, electricity prices, and costs. PHS is not worth considering with the present cost of components. In load-supply systems in Zaragoza (Spain), we found that PHS would be worth considering if its cost was lower than 850 EUR/kW (considering all PHS components except reservoirs) +20 EUR/m\({}^{3}\) for reservoirs (equivalent to 105 EUR/kWh with a 70 m head), whereas in Gran Canaria Island (with a considerably higher irradiation and wind speed), the required PHS cost is considerably lower (~350 EUR/kW + 10 EUR/m\({}^{3}\)). For power-generating systems, PHS required costs ranging from 400-700 EUR/kW + 15-20 EUR/m\({}^{3}\) for obtaining the optimal PV-wind-PHS system with economic results similar to those of the optimal power-generating system without PHS. Thus, the renewable-PHS system with energy arbitrage under RTP could be profitable for many locations globally given the wide range of the PHS cost; however, each case is different and must be evaluated individually. The presented model can be used for optimising the renewable-PHS system in any location with any costs and RTP schemes.  
Keywords:photovoltaic; wind turbines; pumped hydro storage (PHS); simulation; optimisation +
Footnote †: journal: Applied Sciences
# Construction of Color Prediction Model for Damaged Korla Pears during Storage Period
## 5 Conclusions  
With the increase in the storage time, the L* value of the pericarp of Korla fragrant pears decreased, but both the a* and b* values increased. The ANFIS model with a membership function of trimf achieved the optimal prediction effect of the pericarp colors of the fragrant pears (\(RMSE=0.1089\), \(R^{2}=0.9773\); \(RMSE=0.5894\), \(R^{2}=0.9853\); and \(RMSE=0.2360\), \(R^{2}=0.9772\)). The optimal model was also verified, which proved its best prediction performances in practice (\(RMSE=0.2303\), \(R^{2}=0.9685\); \(RMSE=0.7593\), \(R^{2}=0.9812\); and \(RMSE=0.5793\), \(R^{2}=0.9799\)). The peel color of the fragrant pears was accurately predicted and used as an important basis for measuring the storage quality of the fragrant pear. According to the storage quality, the pears with a short shelf life and intolerance to storage can be sold or processed in advance, which can reduce economic losses. The proposed prediction method provides a basis for the storage and scientific management of pears.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app13137885/s1](https://www.mdpi.com/article/10.3390/app13137885/s1)]([https://www.mdpi.com/article/10.3390/app13137885/s1](https://www.mdpi.com/article/10.3390/app13137885/s1)): Figure S1: Correlation between measured and predicted L* values of fragrant pears during the training phase and prediction phase by the ANFIS model with different membership functions; Figure S2: Correlation between measured and predicted a* values of fragrant pears during the training phase and prediction phase by the ANFIS model with different membership functions; Figure S3: Correlation between measured and predicted b* values of fragrant pears during the training phase and prediction phase by the ANFIS model with different membership functions.  
Methodology, Y.L. and R.Z.; software, X.J. and G.L.; data curation, Y.L.; writing--original draft preparation, R.Z. and S.L.; writing--review and editing, R.Z. and S.L.; visualization, X.F.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Innovation and Entrepreneurship Project of the Xinjang Production and Construction Group Special Commissioner for Science and Technology (Grant No. 2019 CB037), the Research Project of Double Employment Academician Work Funds Opening in Tarim University (Grant No. SPYS202002), the University President Fund Project (Grant No. TDZKCQ201902), and the \"Strong Youth\" Key Talents of Scientific and Technological Innovation (Grant No. 2021 CB039), the Innovation Research Team Project of the President's Fund of Tarim University (TDZKCX202203).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors thank [PERSON] from Tarim University for thesis supervision. The authors are grateful to the anonymous reviewers for their comments.  
The authors declare no conflict of interest.
## 2 Methodology
### Proposed Model Orga-Dete
#### 2.2.3 EMASlideLoss  
Pulmonary organoid detection also faces severe sample imbalance, characterized by a significant disparity between simple and hard samples. Simple samples tend to have high IoU scores due to greater alignment with ground-truth annotations. The IoU is defined as follows:  
\[IoU=\frac{A\cap B}{A\cup B} \tag{2}\]  
where \(A\) represents the predicted bounding box and \(B\) denotes the ground-truth bounding box. High-IoU samples often exhibit regular shapes and clear boundaries, making them easier to detect. In contrast, small, occluded, or morphologically irregular objects tend to have low IoU scores. Analysis reveals that the binary cross entropy (BCE) loss used in YOLOv11 focuses solely on classification confidence and lacks a mechanism to corre  
Figure 3: Network architecture diagram of MPCA.  
late localization accuracy with classification scores. It is also insensitive to object scale, leading to compounded localization errors for hard samples. To address this, we propose EMASlideLoss [37].  
EMASlideLoss is a novel loss function tailored for sample imbalance in object detection. It combines an exponential moving average (EMA) with a dynamic sliding threshold mechanism to adaptively weight hard and easy samples during training. Unlike conventional SlideLoss [38], which uses fixed thresholds, EMASlideLoss dynamically adjusts thresholds based on the mean IoU of predicted bounding boxes in each training batch:  
\[\mu_{t=\alpha}\cdot\mu_{t-1}+(1-\alpha)\cdot mean(IoU_{batch}) \tag{3}\]  
where \(\alpha\) is the smoothing factor, enabling the threshold to adapt to diverse data distributions. Based on the relationship between sample IoU and the dynamic threshold \(\mu\), different weights are assigned: samples with IoU below \(\mu\) are categorized as hard samples and assigned higher weights, while weights for easy samples with IoU above \(\mu\) are progressively decayed. This weighting strategy significantly enhances the model's localization capability for hard samples in scenarios involving small targets, dense occlusions, and complex backgrounds. The weighting function f(x) is defined as follows:  
\[f(x)=\left\{\begin{array}{l}1,x\leq\mu-0.1\\ e^{1-\mu},\mu-0.1<x<\mu\\ e^{1-x},x\geq\mu\end{array}\right. \tag{4}\]  
By applying EMA to the sliding threshold, the loss surface becomes smoother, reducing noise and improving generalization. This adaptive weighting mechanism allows the model to better focus on hard samples, making EMASlideLoss especially effective in complex tasks like organoid detection and small object recognition.
## 6 Discussion  
### Synergistic Effects of Using Multiple Modalities  
In our experiments, the MulDIC\({}_{TIC}\) model, which combines information from text, image, and code data, showed the significant performance improvement of 5.07-14.12% F1-score, compared to the Text Only model. The MulDIC\({}_{T1}\) model showed an F1-score improvement of 1.84-10.39%, compared to the Text Only model. On the other hand, the MulDIC\({}_{TC}\) model showed an F1-score improvement of 12.64% for the Kubernetes project, but showed a decrease of 0.42% for the VS Code project. The MulDIC\({}_{TIC}\) model significantly outperformed the MulDIC\({}_{T1}\) and MulDIC\({}_{TC}\) models across all projects. These results demonstrate the synergistic effect of a multimodal model. Here, the synergistic effect refers to the phenomenon where using different information sources together produces better results than using individual sources alone.  
Since the three modalities of an issue report contain different information, considering them together allows us to obtain more heterogeneous and rich information. To illustrate the information of three modalities, Figure 1 shows the contents of issue report #147154 ([[https://github.com/microsoft/vscode/issues/147154](https://github.com/microsoft/vscode/issues/147154)]([https://github.com/microsoft/vscode/issues/147154](https://github.com/microsoft/vscode/issues/147154)), accessed on 15 August 2023). In the issue report, the textual information specifies the conditions where the bug occurs and steps to reproduce. The code information provides the code lines typed by a reporter, when s/he faced the bug. The image information shows the buggy result in the situation. The MulDIC\({}_{TIC}\) model uses all of these different kinds of information for classifying an issue report and successfully classifies issue report #147154 as a bug.