esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Test Results
### Load-Strain Relationship  
As shown in Figure 3b, the position with the maximum compression strain is marked as position 1.  
With regard to the specimens with length-diameter ratios \(\lambda\) equal to 2.6 and 3.5, the changes of strain in four positions were relatively consistent, indicating that it was an ideal axial compression state, and there was no instability phenomenon before failure. Taking a group of specimens with \(\lambda\) equal to 2.6 (PT300-2, BZ300-1, BZ300-2, BZ300-3) for example, the relationship curve of the average strain of the four positions with the change of load is shown in Figure 6. At the elastic stage, the ratio of hoop strain to longitudinal strain \(\mu_{s}\) varied from 0.26 to 0.29, with an average of 0.281. After entering the elastic-plastic stage, the value of \(\mu_{s}\) increased gradually and reached 0.52 when entering the limit state.  
Figure 5: The load–displacement curve: (**a**) \(\lambda=2.6\); (**b**) \(\lambda=3.5\); (**c**) \(\lambda=4.4\); (**d**) \(\lambda=5.3\).  
Figure 6: \(\lambda=2.6\) load–strain relationship.  
With regard to the specimens with length-diameter ratios \(\lambda\) equal to 4.4 and 5.3, the strains at the four locations were relatively close at the start of loading, indicating that the specimen installation was correct. With the increase of load, the strain gap at the four locations became larger and larger. Soon after entering the elastic-plastic stage, the strain gap at the four locations suddenly multiplied, indicating that the overall instability of the specimen occurred. Limited by space, this paper only gives the load-strain relationship curve of sample QZ600-3, as shown in Figure 7. In the case of instability, the rate of longitudinal compression strain at position 1 suddenly increases; meanwhile, the longitudinal compression strain at the opposite position (position 3) suddenly decreases and then turns to tensile strain. The strain changes at position 2 and 4 are not obvious. The ratio of hoop strain to longitudinal strain \(\mu_{s}\) changes from 0.25 to 0.30 at the elastic stage. From the elastic-plastic stage to the final failure, the \(\mu_{s}\) at position 1 and position 3 increase to about 0.46, and the \(\mu_{s}\) at position 2 and position 4 have no noticeable change.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Classification of MFCs
#### 3.2.4 Terrestrial MFCs  
Efforts have also been given to develop MFCs that can operate outside waterbodies and are often referred to as terrestrial MFCs [147] where the cathode was air facing, and the anode burying soil acted as both electrolyte and proton exchange membrane. [PERSON] et al. [148] reported their work where they used an insertion-type MFC for remediation of phenol in waterlogged soil and electricity generation. [PERSON] et al. [2] reported use of a terrestrial MFC to operate a wireless sensor network for land monitoring and precision agriculture where no water-soil interface was present and the anodes were buried at 8 cm depth. A similar application was reported by [PERSON] et al. [141] reported the application of a terrestrial MFC into a transportable bio-battery where they used a solid anolyte soil in addition to a small water reservoir for keeping the anodes functional. [PERSON] et al. [149] reported a soil-based, single chamber MFC with carbon felt electrodes where performance of the substrate (soil) was improved after urine treatment.  
Another promising and emerging concept is integration of terrestrial MFCs with green infrastructure for achieving a cleaner environment in urban landscapes [150,151]. Green roofs can be of particular interest [151, 152, 153] for this purpose since in addition to electricity generation, they also provide cooling impact and thus reduce additional energy demand. An additional smart agricultural application should also be noted where MFC integrated power generation systems are utilized for plant heath monitoring applications [154; 155].
# Deterministic Discrete Fracture Network Model and Its Application in Rock Mass Engineering
## 2 Acquisition of Structural Surface Parameters
### Structural Surface Parameters Retrieval  
For the extracted dominant structural surfaces, key parameters are obtained, including orientation, spacing, and traces. After obtaining the raster data corresponding to the structural surface areas, the first step is to extract the orientation using the arithmetic average method. Next, the average orientation of structural surfaces within the same group is calculated, and this average value is assigned to each structural surface in the group to ensure that they are mutually parallel. Geometric calculations are then used to determine the spacing between planes within the same group [25]. Finally, the raster data is converted into vector data, which is then used as a mask to extract the point cloud data corresponding to the structural surface areas. The intersection line between the structural surface and the rock surface is defined as the trace, and by extracting its two endpoints, the trace length and the coordinates of the trace center can be calculated.
# DExter: Learning and Controlling Performance Expression with Diffusion Models
## 5 Evaluation
### Expressive Steering with Perceptual Features  
Our framework of conditioned performance generation allowed us to explore the conditioning of the performance generation using additional features. As described in Section 4.1, we used mid-level perceptual features (encoded as c_codec) as steering inputs to guide the expressive character of the generated performance.  
To gauge DExter's sensitivity to changes in these features, we used the perceptual feature recognition model of [5] as a proxy for human perception. However, as that model had originally been trained on an audio input, we wanted to eliminate the effect of acoustic artifacts introduced by rendering MIDI to audio; we decided to fit a MIDI-to-perceptual-features model to serve as the proxy instead. Details on this proxy model are given in Appendix A.  
Steering performance generation is achieved by manipulating the individual dimensions of perceptual features, aiming to induce measurable corresponding effects in the resulting outputs. For each test sample and across all seven perceptual attributes, we first generated performances using the unmodified target \(c_{c}\). These target perceptual-feature values could be randomly initialized in practice or derived from an actual performance. We took the feature values derived from actual performances and modified the values to steer the generation, in particular expressive directions, thus generating 'alternate' performances of the original performance. We either halved one feature, \(\frac{1}{2}c_{c}\), or doubled one feature, \(2c_{c}\), at a time.  
Figure 3 displays the proxy model's predictions of the generated outputs. We observed that, for the first four features _melodiousness, articulation, rhythm complexity, and rhythm stability_, the adjustments applied to the input conditions manifested as anticipated directional changes (the \(c_{c,double}\) group led the \(c_{c,half}\) group at 12.2% in terms of their absolute values), providing evidence of the model's responsive behavior to the controlled feature alterations.  
The other three dimensions--notably, _dissonance_--exhibited less consistent patterns in alignment with the input modifications. That seems reasonable, as harmonic and tonality-related properties are more functions of a piece itself, rather than any specific interpretation of it.
# Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
###### Abstract  
Detailed information about mangroves is crucial for ecological and environmental protection and sustainable development. It is difficult to capture small patches of mangroves from satellite images with relatively low to medium resolution. In this study, high-resolution (0.8-2 m) images from Chinese GaoFen (GF) and ZiYuan (ZY) series satellites were used to map the distribution of mangroves in coastal areas of Guangdong Province, China. A deep-learning network, U\({}^{2}\)-Net, with attention gates was applied to extract multi-scale information of mangroves from satellite images. The results showed that the attention U\({}^{2}\)-Net model performed well on mangrove classification. The overall accuracy, precision, and F1-score values were 96.5%, 92.0%, and 91.5%, respectively, which were higher than those obtained from other machine-learning methods such as Random Forest or U-Net. Based on the high-resolution mangrove maps generated from long satellite image time series, we also investigated the spatiotemporal evolution of the mangrove forest in Shuidong Bay. The results can provide crucial information for government administrators, scientists, and other stakeholders to monitor the dynamic changes in mangroves.  
m  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping. _Appl. Sci._**2023**, 13, 8526. [[https://doi.org/10.3390/appl3148526](https://doi.org/10.3390/appl3148526)]([https://doi.org/10.3390/appl3148526](https://doi.org/10.3390/appl3148526))  
Academic Editor [PERSON]  
Received: 19 June 2023  
Received: 13 July 2023  
Accepted: 20 July 2023  
Published: 24 July 2023  
**Copyright**\(\copy\) 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Cluster-Based Approaches toward Developing a Customer
## 2 Related Research
### Loyalty Programs and Data Science Approaches  
Segmenting customers and implementing more successful loyalty programs has become easier and useful in recent years thanks to advances in data mining techniques [2]. One of the goals of segmentation is to ascertain the attitudes of individual customer groups toward a specific product or service [10].  
Clustering techniques are the most suitable methods enabling companies to identify segments or groups of customers with the aim of reaching a potential user base [7]. Customer-related data typically contain thousands of observations. One of the most employed techniques for finding similar customers and grouping them into homogeneous segments is referred to as cluster analysis.  
According to [PERSON] et al. [4], companies have generated more valuable data to enhance customer segmentation using the above-mentioned techniques, depending on their data analysis and interpretation capabilities. The RFM technique, which is based on recency, frequency, and monetary value characteristics, has been in use for over 50 years for customer segmentation purposes [4]. RFM can be utilized to calculate customer lifetime value (LTV) [8]. According to [PERSON] et al. [16], LTV can be considered a significant factor in every decision aimed at improving customer relationships and profitability. The author further asserts that LTV is associated with predicting future customer behaviors.  
The current literature presents numerous different approaches to customer segmentation and loyalty program creation. [PERSON] and [PERSON] [10] applied the k-means algorithm in the banking sector to create three clusters of loyal borrowers: \"platinum\", \"gold\", and \"silver\". In this study, the authors employed three variables: the total Euro value of a loan requested, customer tenure in months, and the quantity of \"non-payments\" by customers in the past 12 months. The same k-means approach was utilized in the study conducted by [PERSON] et al. [17]. This study identified four groups of influencing factors for selecting a specific service provider in the telecommunications industry. Personal data such as age and occupation; monetary data like recurring monthly expenditure; and behavioral data were used for segment creation.  
The hierarchical clustering technique is evident in a study conducted by [PERSON] et al. [18] and in another study by [PERSON] et al. [19]. The former applied the mentioned method to identify commercial customers of an international original equipment manufacturer. Variables derived from the RFM model were used, such as \"loyalty score\", \"average annual demand\", \"long-term relationship potential\", \"average percentage change in annual sales revenue\", and \"average percentage change in annual demand\". The latter study identified four loyalty segments indicative of customer behavior in the casino industry. To achieve this, the authors utilized behavioral and atttitudinal dimensions.
# The Structure-Activity Relationship of the Antioxidant Peptides from Natural Proteins
## 3 The Relationship of Chemical Structure and the Antioxidant Ability of Peptides  
The characteristic peptide chemical structures may be the main factors that influence their antioxidant activities. The precursor protein structures and their hydrolytic process may also affect peptide antioxidant activities.  
Effect of the Structure of Precursor Proteins and the Hydrolytic Process on the Antioxidant Activities  
By using a bioinformatic database of 745 sequences, [PERSON] _et al._ predicted that the bioactive fragments released from the precursor proteins are usually located on protein surfaces with hydrophilic surroundings [5]. The structural properties of bioactive fragments were dominated by random coils (46%) and \(\beta\)-turns (30%) rather than \(\beta\)-sheets and \(\alpha\)-helices. The prediction has been confirmed that \(\beta\)-sheet structures are found predominantly in \(\beta\)-lactoglobulin from bovine milk and proteins from legume seeds, which are possibly responsible for the on-release of bioactive peptides during gastrointestinal digestion [34].  
[PERSON] _et al._ reported that antioxidant peptides that could be released by the pepsin hydrolyzed whey and casein fractions of goat milk, but were not found in the hydrolysates of whole defatted goat milk using bacterial proteases [35]. [PERSON] _et al._ compared the peptide generation capacity of protein extracted from soybean seeds with that obtained from untreated soy milk using _in vitro_ gastrointestinal digestion. The numbers of potential bioactive peptides identified in extracted protein soybean seedsand soy milk, and untreated soy milk samples were 1173, 1422 and 1364, respectively. Obviously, there is significant gap in bioactive peptide generating capacity between soy seeds and its processed forms (soy milk) [36]. A reasonable explaination for the former is that it may be due to some changes in the molecular structure of precursor proteins during soy milk processing, while the impurities in unextracted soy milk proteins may affect peptide activities due to enzymolysis [36]. Furthermore, after _Amaranthus mantegazzianus_ proteins were hydrolyzed with alcalase, the scavenging activities of the fractions increased, especially at a high degree of hydrolysis. One fraction called GlobPs with no initial activity exhibits the highest scavenging capacity after hydrolysis. However, the linoleic acid oxidation inhibitory capacity of GlobPs fraction that existed prior to enzymatic hydrolysis, due to the presence of naturally-occurring peptides/polypeptides, was partially lost after enzymatic hydrolysis [37].  
These results suggest that the two objects of releasing certain fragments from proteins and maintaining its efficient domain must be taken into consideration during the exploition of and research on bioactive peptides. Comprehensively understanding the relationships between structure and the digestion of precursor proteins will shed light on the generation of novel antioxidant peptides.
## 4 Results  
### Dry Beach Length  
Eight images of the waterline area and six images of the boundary between the dry beach and the dam were selected. Figure 11 shows the partial identification results of the boundary according to the method proposed in this study. The recognition process is described in Section 3.2.1 and illustrated in Figure 6. The boundary between the water and dry beach is fuzzy, leading to some inaccuracies in the waterline-identification process, as shown in Figure 11a. However, these errors remain within the acceptable range. In contrast, the boundary between the dry beach and the dam is clearly defined, as illustrated in Figure 11b, resulting in a more accurate identification of the boundary.  
The K matrix and \([R|t]\) matrix can be obtained by the SfM process of 3D reconstruction, and the pixel coordinates can be obtained by the boundary-identification process. The three-dimensional coordinates of the identified boundary can be converted using Equation (1). The resulting 3D coordinates are displayed in the original point cloud, as shown in Figure 12. The boundary-identification method proposed in this study can accurately extract the boundary of the dry beach and provide coordinate data for the subsequent measurement of the dry beach length.  
Figure 11: Boundary identification result. (**a**) Panoramic view of the tailing pond. (**b**) Waterline. (**c**) The boundary between the dry beach and the dam.  
The three-dimensional coordinates of the boundary between the dry beach and the dam are projected to the horizontal plane (XY plane), and the fitted line equation is \(l:y=0.72x+2899860\). The 3D coordinates of the waterline are projected onto the horizontal plane (XY plane). The length of the dry beach is the distance between the projected waterline coordinates and the fitted line \(l\). The method proposed in this study can be used to measure the beach length at any point on the waterline. In this paper, five monitoring positions, A, B, C, D, and E, were selected to measure the length of the dry beach at each position, and the actual length of the dry beach, measured in situ, was used to verify the results, as shown in Figures 12 and 13. In this example, there are four positions where the error is less than 2.00%. The minimum error is 1.83%. Position E has the largest error of 2.25%. The boundary picture at position E is shown in Figure 14a. There are obvious reflective spots. The reflection is removed, and the boundary identified using the proposed method is shown in Figure 14b. It can be seen that the light spot caused by the reflection is not completely removed, resulting in the identified boundary receding towards the water surface, which results in a larger dry beach length and a larger error. Most of the literature in this field usually has an error range of 1.4% to 3.5% in the calculation of the dry beach length. Therefore, the measurement error of our method is within the acceptable range. This validates the feasibility of our method.  
Figure 12: Three-dimensional coordinate conversion.  
Figure 13: Measurement results of the dry beach length at the monitoring points.
## 4 Discussion
This is also visually confirmed by Figures 2 and 3, in which it is possible to observe how, below the cusps, G2 presents a significant negative \"impression\" of the bur's head, which surely impacts the precision in that area. Regarding G1's performance, the available literature contains limited data when it comes to interfacial adaptation of such technology, and results often appear to be inconsistent due to the rapid progresses in this field. The tested chairside printer uses tilting stereolithography (TSLA), which was proven to be efficient for producing definitive restorations [43]. Analogously, another study investigated the internal and external fit of 3D-printed occlusal veneers produced using lithography [43]. Comparably to the present study results, their findings indicated that the marginal adaptation and accuracy of the 3D-printed veneers were comparable to those of milled and heat-pressed restorations. Accordingly, another recent paper evaluated the accuracy of 3D-printed and milled inlay and onlay restorations, highlighting the high accuracy of both workflows in terms of marginal adaptation but the superior internal fit of the 3D-printed group [44]. Another study on 3D-printed wax crowns showed even higher accuracy of 3D printing compared to laboratory milling, but a similar protocol in another study revealed no differences between printed and milled workflows [45; 46]. Generally speaking, 3D printing with the stereolithography technique seems to offer higher flexibility when dealing with complex geometries, improving the internal fit [47]. However, it is worth mentioning that all groups were equally able to achieve high marginal precision, falling within the clinically acceptable range. Moreover, an interesting aspect that must be highlighted is that 3D printing has still room for improvements at different levels. For example, several studies are working on the printing angle, the thickness of the printing layer, the curing procedures, and the biocompatibility of materials [48; 49; 50]. Among the limitations of the present study, we should consider the limited quantity of the machines tested, which does not allow a direct comparison between 3D printing and milling technologies. Further studies will include, with the same preparation design and sample preparation protocol, other 3D printers with variable technologies and industrial milling units. Moreover, biological, mechanical, and bonding-related properties will be evaluated to better assess if 3D printing technology can replace milling or must be improved in some aspects before in vivo application.