esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Water-Soluble Solids (BRIX\({}^{\circ}\))  
As demonstrated in Table 2, the Brix values exhibited a narrow range, with a variation of less than 1 percentage point between 11.77% and 12.37%. The results of the statistical analysis confirmed a significant effect of the mineral-based clarification agent (F(2;101) = 250.692; \(p<0.001\)), the protein-based clarification agent (F(2;101) = 8.586; \(p<0.001\)), and the interaction between them as well (F(4;101) = 8.205; \(p<0.001\)). In industrial practice, a Brix value of 13% is considered standard for raw materials. However, this standard was not met by the samples under investigation, likely due to the ripening stage of the apples at the time of harvest.  
Within the Klar Sol and GranuBent groups, no significant differences were observed in the Brix values of the samples. However, differences were found between samples, where Seporit was used, in the case of SL-MM and SF-MM, as well as between SE-HH and SF-HH.  
Statistically, equivalent results to the control were found in the case of the following samples: GE-MM, GE-HH, GE-MH, GE-MH, GL-HM, GF-MM, GF-MH, SE-MM, SE-MH, SL-HH, SF-MM, SF-HH, SF-MH, SF-HM. In comparison to the control samples, the following samples exhibited a lower water-soluble solid content: GL-MM, GL-HH, GL-MH, GF-HH, GF-HM, SE-HH, SL-MM, SL-MH, SL-HM.  
Figure 1: Effect of clarifying agents on sediment volume in apple juice. MM (medium protein dosage and medium mineral dosage), HM (high protein dosage and medium mineral dosage), MH (medium protein dosage and high mineral dosage). E (gelatin), L (LittoFresh Liquid—plant-based protein), F (FloaClair—plant-based protein). Each value is expressed as mean \(\pm\) standard deviation. The type of mineral-based agent had no significant effect (F(2;101) = 1.408; \(p\) = 0.262). The type of protein-based clarification agent had a significant effect (F(2;101) = 60.417; \(p<0.001\)). The types of clarification agents had a significant effect (F(4;101) = 5.717; \(p<0.01\)). The presence of different letters indicates that the groups in question are significantly different. Lower-case letter: comparison of proteins at fixed mineral clarification agents and concentrations.
###### Abstract  
Pore structure is a fundamental parameter in determining the hydrocarbon storage capacity and flow characteristics of a reservoir. Mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR) are two commonly utilized techniques for characterizing rock pore structures. However, current studies indicate that disparities in testing methodologies due to distinct physical characteristics lead to a partial misalignment in pore size distributions. We conducted MICP (dynamic) and NMR (static) experiments on eight tight sandstone and eight shale samples and proposed a method to utilize information from the differences in MICP and NMR pore size distributions, aiming to enhance the accuracy of rock quality analysis. We observed that in rock cores where large pores are interconnected with smaller pore throats, MICP tends to overestimate the proportion of these smaller pores and underestimate the larger ones. Furthermore, we integrated information from both dynamic and static experimental processes based on physical significance and found that the fitting accuracy of the newly proposed method is superior to the Winland \(r_{\text{S}}\) equation. Compared to the Winland \(r_{\text{S}}\) equation, our new method significantly improves fitting accuracy, increasing the R-squared value from 0.46 to 0.93 in sandstones and from 0.80 to 0.87 in shales. This represents a potential high-precision, comprehensive tool for rock quality analysis, offering a new perspective for an in-depth understanding of rock properties.  
NMR; MICP; pore throat size distribution; shale; tight sandstone +
Footnote †: journal: Applied sciences
## 4 Applications of Multimodal Learning for Fault Detection Diagnosis
### Applications in Renewable Energy Systems  
Renewable energy systems, such as wind turbines and photovoltaic (PV) solar panels, rely heavily on sensors to monitor performance and detect potential faults in real time. While these sensors continuously collect vast amounts of data, the fusion of data from different modalities greatly improves fault detection, particularly under the dynamic and unpredictable conditions in which these systems operate. Recent advancements in machine learning (ML) and deep learning (DL) have significantly accelerated the development of more accurate and responsive diagnostic systems. For instance, wind turbines can benefit from predictive maintenance models that analyze data from sensors measuring vibration, temperature, and wind speed to identify potential issues before failures occur.  
Similar approaches are applied to PV systems, where environmental factors such as temperature and solar irradiance heavily influence performance. Data-driven models now enable the detection of issues like panel degradation, electrical faults, and the impact of environmental conditions on PV efficiency [67].
## 3 Proposed Methods
### Stixels Generation Algorithm Based on Segmentation and Optimization (SGA-SO)
The optimization process is as follows: each row of the stixel is traversed from top to bottom, and the mean disparity of the current row is calculated. Determine whether the difference between the mean disparity of the current row and the previous row is within the disparity optimization threshold. The stixel is truncated if the difference exceeds the disparity optimization threshold. The optimized stixels are shown in Figure 10(b), where the disparity optimization threshold is five. It can be seen from the figure that the optimized stixels are truncated between two cars and  
Figure 11: Stixels segmentation result: (a) Horizontal segmentation; (b) Disparity optimization. and the area covered by the same stixel is basically the same obstacle, as shown in the black box in Figure 11b. In addition, the color of the optimized stixels also reflects the distance of different obstacles. Therefore, the optimized stixels can more accurately describe the traffic scenes, which are conducive to deepening the understanding of the scenes. The specific disparity optimization process is shown in Algorithm 2. ```
Input: S: List of \(\mathit{stixel}\); \(N\): Number of \(\mathit{stixel}\); \(i\): Number of current row in \(\mathit{stixel}\); \([\cdot]\_\)max_row: The maximum number of rows in the current \(\mathit{stixel}\); \(S[\cdot]\_\)min_row: The minimum number of rows in the current \(\mathit{stixel}\); \(\mathit{mean}(\mathit{stixel},i)\): The function to calculate mean depth of row \(i\) in input \(\mathit{stixel}\); \(\mathit{truncate}(\mathit{stixel},i)\): The function truncate \(\mathit{stixel}\) at row \(i\), generates and returns new \(\mathit{stixel}\) Output: S_opted: list of optimized \(\mathit{stixel}\) \(i=0\), \(k=0\), \(\mathit{dep}=0\), \(\mathit{dep}_{\mathit{last}}=0\), \(\mathit{S\_opted}=[]\), \(TH=5\) while\(k<N\)do while\(i<=(S[k]\_\)max_row\(-S[k]\_\)min_row\()\)do \(\mathit{dep}=\mathit{mean}(S[k],i)\) if\((\mathit{dep}-\mathit{dep}\_\mathit{last})>TH\)  S_opted.push\((\mathit{truncate}(S[k],i))\) elseif\(i+S[k]\_\)min_row\(==S[k]\_\)max_row  S_opted.push\((\mathit{truncate}(S[k],i))\) endif \(\mathit{dep}\_last=\mathit{dep},i++\) endwhile \(k++\) endwhile
```  
**Algorithm 2** Disparity Optimization
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 2 Materials and Methods
### Network Model
#### 2.1.3 Swin Transformer  
The Swin Transformer is a new network framework based on Transformer, which introduces the hierarchical construction method commonly used in convolutional neural networks (CNN) to build a hierarchical Transformer. Meanwhile, it solves the problem of scale and resolution of Transformer migration from the field of natural language processing (NLP) to computer vision (CV) by introducing the idea of the locality domain. It alternates the local attention module and the global attention module to reduce the attention computation.  
Firstly, the image is fed into the Patch Partition module for chunking, i.e., one patch for every \(4\times 4\) adjacent pixels, so that each patch has \(4\times 4=16\) pixels. Then it is flattened in the channel direction. If the input is a three-channel RGB image, each pixel has three values R, G and B, so there are a total of \(16\times 3=48\) pixels after spreading. Thus, the image shape changes from \([H,W,3]\) to \([\frac{H}{4},\frac{W}{4},48]\) after Patch Partition. A linear embedding layer then linearly transforms the channel data for each pixel from 48 to \(C\), i.e., the image shape changes from \([\frac{H}{4},\frac{W}{4},48]\) to \([\frac{H}{4},\frac{W}{4},C]\). A number of Swin Transformer blocks are applied to the embedded tokens and together with the linear embedding layer above form \"Stage 1\". To obtain a hierarchical representation, the output of \"Stage 1\" is then sent by patch merging, where \(2\times 2\) neighbouring patches are merged together. The resolution is also reduced by a factor of 2, the number of tokens is reduced by a factor of 4, and a linear  
Figure 2: The architecture of stacking.  
layer is applied to change the dimensionality from \(4C\) to \(2C\). As shown in Figure 3, multiple Swin Transformer blocks are joined together to form three identical stages through the patch merging layer. In addition, the output resolution is \(\frac{H}{16}\times\frac{W}{16}\) and \(\frac{H}{32}\times\frac{W}{32}\).  
The Swin Transformer is constructed by replacing the standard Multiple Self-Attention (MSA) module in the Transformer block with a shift window-based module, leaving the other layers unchanged. As shown in Figure 3, a Swin Transformer block consists of a shift-window based MSA module and a two-layer MLP, which is connected nonlinearly via GELU. A LayerNorm (LN) layer is applied before each MSA module and each MLP, and a residual is applied after each module to connect them, as shown in Equation (1).  
\[\hat{Z}^{l}=W-\text{MSA}(\text{LN}(Z^{l-1}))+Z^{l-1}\]  
\[Z^{l}=\text{MLP}(\text{LN}(\hat{Z}^{l}))+\hat{Z}^{l}\]  
\[\hat{Z}^{l+1}=\text{SW}-\text{MSA}(\text{LN}(Z^{l}))+Z^{l}\]  
\[Z^{l+1}=\text{MLP}(\text{LN}(\hat{Z}^{l+1}))+\hat{Z}^{l+1} \tag{1}\]
# Bird Detection on Power Transmission Lines Based on Improved YOLOv7
## 6 Conclusions  
This paper primarily addresses the limitations of existing bird detection models on power transmission lines, specifically their poor performance in detecting small and densely packed  
Figure 10: Extremely small object image. (**a**) YOLOv7; (**b**) the algorithm in this paper.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparative Models** & **mAP0.5** & **mAP0.5:0.95** & **Precision** \\ \hline YOLOv7 & 75.84 & 45.42 & 71.22 \\ YOLOv8 & 75.13 & 43.27 & 71.87 \\ YOLOv5 & 74.3 & 43.41 & 70.69 \\ YOLOv & 77.5 & 44.56 & 72.35 \\ The algorithm in this paper & 78.42 & 46.14 & 73.56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance comparison.  
packed targets. To address these obstacles, this research incorporates the ODConv module into the backbone of YOLOv7, amplifying the capability to detect small objects effectively.  
Building upon this foundation, the IoU loss function has been optimized by incorporating the Alpha_GIoU loss function to enhance the network's localization capability. This, in turn, improves the network's detection accuracy and reduces instances of false positives and false negatives in the detection of small objects.  
Additionally, the data augmentation is utilized to enhance the dataset, thereby improving detection accuracy and reducing false positives and missed detections for small targets. It is also expanded to alleviate the issue of insufficient data collection.  
In real-world application scenarios, the resolution of bird images on power transmission lines can vary significantly due to changing imaging conditions. Convolutional neural network models frequently exhibit reduced recognition accuracy when dealing with low-resolution targets. Nevertheless, the experimental findings offer strong evidence that the proposed enhanced model surpasses both the original YOLOv7 model and conventional algorithms in the detection of small and closely positioned targets. This underscores the superior detection capabilities of our model in practical, real-world scenarios.  
By addressing the limitations of existing models and introducing novel improvements, this study provides valuable insights for enhancing bird detection on power transmission lines, particularly in scenarios involving small and densely packed targets.  
Our model has addressed the issue of detecting small targets, but the detection performance remains suboptimal in situations with extremely dense concentrations. Our future work will be focused on addressing this problem.  
T.J.: formal analysis, funding acquisition, investigation, project administration, and resources. J.Z.: conceptualization, data curation, methodology, software, supervision. M.W.: validation and visualization. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Single Bragg-Dip Profile-Fitting Analysis Method
### Principle of Extinction Effects  
In this section, the principle of extinction effect is described. The extinction effect should be considered when the integrated intensity information of Bragg-dip is evaluated. The extinction effect is the effect when the diffracted neutrons are diffracted again in the same crystallite or other crystallites. As a result, the intensity of diffracted neutrons decreases and the neutron transmission increases. In other words, because of the extinction effect, the coherent elastic scattering cross-section decreases; the microscopic cross-section does not really change, but the observed macroscopic cross-section is reduced due to the multiple diffraction. Here, the crystallite means the perfect crystal block or the mosaic block. The crystalline grain consists of plural crystallites or mosaic blocks. There are two kinds of extinction effects. One is the primary extinction effect, and it is the effect when the diffracted neutrons are diffracted again in the same crystallite. The other is the secondary extinction effect, and it is the effect when the diffracted neutrons are diffracted again in other crystallites. The occurrence probability of the primary extinction effect depends on the crystallite size. In contrast, the occurrence probability of the secondary extinction effect depends on the crystalline grain size. In polycrystals, only the primary extinction effect should be considered. However, in single crystals, not only the dominant primary extinction effect but also the secondary extinction effect should be considered [23]. Here, there are some considerable issues concerning the secondary extinction effect. Firstly, both extinction effects cannot be separately evaluated currently. In addition, if we use a high-spatial resolution detector, neutrons re-diffracted at the different crystallite may be detected by the different neighboring pixel from a pixel positioned along the direction of incident neutrons. Such a considerable phenomenon has not been investigated yet at all. Therefore, the secondary extinction effect cannot be quantitatively evaluated in the present study.  
In any case, the integrated intensity should be constant in a crystalline grain ideally. However, in actuality, it changes due to the extinction effects. In this study, by considering the extinction effect, the existence of a new mosaic block reflecting crystal defects was observed finally.
## 1 Introduction
Several research facilities and industry-related techniques are already known to use it for the delivery of different medicines [11, 12, 13, 14]. The advantages of the bovine serum albumin (BSA) encompass the presence of multiple binding sites for the drugs, lower cost, non-immunogenicity, and naturally biodegradable property that has allowed its implementation as a perfect carrier for nanoparticles-based drug delivery systems [15,16]. These properties could result in developing promising delivery systems for biopharmaceutical challenging medications and improving their delivery and bioavailability in the body [17,18]. Carbonic Anhydrase (CA) is a family of zinc metalloenzyme involved in the reversible conversion of carbon dioxide to ionic bicarbonate form, producing a proton subsequently. Thus, it has a role in ion exchange and pH balance in the physiological system [19]. Of this family of receptors, CA IX is the most common and is overexpressed on the surface of most cancer cells [20,21] in hypoxic conditions developed when the rapidly growing tumor cells that overuse the oxygen supply. For this reason, it has emerged as the primary target for most cancer remedies [22, 23, 24]. Sulfonamides, like Acetazolamide, are a subset of antibacterial agents that recently showed antitumor activity [25,26] via inhibition of carbonic anhydrase activity [27,28]. Our experiments use paclitaxel (PTX), a hydrophobic drug as the experimental drug due to its unique chemical structure and ability to polymerize tubulin to stable microtubules. It is also known to operate by inhibiting mitochondrial apoptosis inhibitor protein B-cell Leukemia 2 (Bel-2) [27,28]. Hence, we believe that the combination of the bio-safe BSA, the targeting ligand ATZ specific for the hypoxia receptor CA IX, and PTX with its unique anti-cancer properties is a powerful system to target TNBC. The delivery system itself has the potential to be applied to a varied range of cancers apart from TNBC.
## 3 Results and Discussion
### Comparison between the Extraction Efficiencies of the Biosorbent and Commercial Coatings  
A comparison between the extraction efficiencies using the proposed fiber and commercial fibers (PDMS/DVB and PDMS) was performed. The conditions for the extractions using commercial fibers were optimized (data not shown) as extraction time of 70 min at 80 \({}^{\circ}\)C. These values are much closed to those mentioned in the literature (extractions of 60 min at 70 \({}^{\circ}\)C) [22; 23; 24]. Figure 4 shows this comparison through bar graph using normalized peak area and considering the film thickness of each fiber. The normalization of peak areas for each analyte was made using the highest chromatographic peak areas as 100% for each analyte.  
It can be observed in Figure 4 that the extraction with the PDMS/DBV coating showed good performance for acenaphthylene, fluorene, phenanthrene, and anthracene, but for the other analytes the results were not as promising. The PDMS fiber gave values below 20%, except for pyrene, and it is not efficient for this application. Taking this into account, the proposed fiber demonstrated very satisfactory performance for PAH extraction when compared to the commercial fibers, with the exception of acenaphthylene, fluorine, and phenanthrene.  
In addition, reproducibility studies using two diatomaceous earth fibers were performed and the results showed no significant variation (data not shown). The repeatability obtained with the fibers was estimated comparing the results of the first extraction with those obtained after 115 extractions using the same fiber (data not shown). It was verified that there was no significant loss of extraction efficiency, confirming that the fiber produced with the biosorbent material can be used at least 115 times.  
These data demonstrate the high potential for diatomaceous earth fiber as a sorbent candidate for SPME. Moreover, diatomaceous earth is biodegradable, natural, and renewable. In addition, its chemical composition provides numerous possibilities of chemical interaction with a wide range of compounds. Diatomaceous earth has a microporous structure, which facilitates the extraction of the analytes through a physical process (adsorption mechanism).  
Figure 4: Comparison of extraction efficiencies of the biosorbent fiber, PDMS/DVB and PDMS coatings for determination of PAHs. analytes: (1) acenaphthylene; (2) fluorene; (3) phenanthrene; (4) anthracene; (5) pyrene; (6) benzo[a]anthracene; (7) chrysene; (8) benzo[b]fluoranthene; (9) benzo[k]fluoranthene; and (10) benzo[a]purene.
# Interaural Coherence Estimation for Speech Processing in Reverberant Environment
## 3 Numerical Experiment and Results
### Evaluation Metric  
For reverberant speech, the original speech is used to distinguish between bins with direct speech plus a relatively small amount of diffuse speech and bins with diffuse speech only. The time when the impulse starts from the impulse response corresponding to the specific reverberation environment is applied as a time delay to the original speech to align the start time of direct speech. STFT is applied to the time-aligned original speech, and a binary mask \(M_{\text{direct}}\) that suppresses the time-frequency bins lower than a threshold to reduce speech energy by 0.01 dB is determined. \(M_{\text{direct}}\) is a binary mask to suppress diffuse speech bins of reverberant speech. In contrast, a binary mask \(M_{\text{diffuse}}\) suppresses the time-frequency bins higher than the threshold. The perceptual evaluation of speech quality (PESQ) [18] between the original speech and \(M_{\text{direct}}\) applied original speech is about 4.0 compared to the maximum value of 4.5, which means that it extracts only direct speech. Thus, the IC values of direct plus a relatively small amount of diffuse speech and diffuse speech only are extracted by applying \(M_{\text{direct}}\) and \(M_{\text{diffuse}}\) to the IC of reverberant speech.  
To quantify the estimation performance, the mean accuracy of coherence (MAC) is defined as the average value of similarity between the ideal coherence (one for direct speech only and zero for diffuse speech only) and the estimated IC. When the IC of reverberant speech is \(\Gamma^{2}(n,f)\), the coherence accuracy of direct speech bin is set to \(\Gamma^{2}\), and the coherence accuracy of diffuse speech bin is set to \(1-\Gamma^{2}\). The MAC is calculated as  
\[\text{MAC}_{\text{direct}}=\frac{\sum_{n,f}\Gamma^{2}(n,f)M_{\text{direct}}( n,f)}{\sum_{n,f}M_{\text{direct}}(n,f)}, \tag{13}\]  
\[\text{MAC}_{\text{diffuse}}=\frac{\sum_{n,f}\bigl{[}1-\Gamma^{2}(n,f)\bigr{]} M_{\text{diffuse}}(n,f)}{\sum_{n,f}M_{\text{diffuse}}(n,f)}. \tag{14}\]  
Because the IC ranges from 0-1, the MAC also ranges from 0-1, and the closer it is to one, the better the estimation performance is. \(L_{\text{T}}\) and \(\beta\), where \(M_{\text{direct}}\) and \(M_{\text{diffuse}}\) have minimum values, are optimal parameters. They are calculated by multiplying a binary mask for the frequency interval to determine the parameters according to frequency. \(\text{MAC}_{\text{total}}\) is defined as  
\[\text{MAC}_{\text{total}}=\text{MAC}_{\text{direct}}+\text{MAC}_{\text{diffuse}}, \tag{15}\]  
which was used to compare the overall performance and determine \(L_{\text{T}}\) and \(\beta\) according to gender and frequency. The determined parameters were applied to the proposed method, and its performance was compared with that of the conventional estimation method.