esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Bioactive Compound in Mango By-Product Extracts  
A total of 30 phenolic compounds were identified in a previous work [13] from nine mango by-product extracts obtained from the peels and seeds of the three cultivars (Keitt, Sensation and Gomera-3) (Figure 2). Among them, five main groups of bioactive compounds were found: flavonoids (10 compounds), xanthones (1), gallates and gallotannins (10), ellagic acid and derivatives (2) and benzophenone derivatives (7).  
Figure 2: Phenolic compounds identified and quantified in mango peel and seed extracts from three mango cultivars produced in the Canary Islands. (**A**) Bioactive compounds identified in peel and seed extracts, analyzed by HPLC-ESI-QTOF-MS. Each color represents a specific class of compounds. Colored circles indicate the presence of the corresponding compound in each extract, while the absence of a circle indicates only trace amounts detected. (**B**) Quantitative content of principal phenolic compound classes (g/100 g dry weight) in peel and seed extracts, determined by HPLC. Values are expressed as means (n = 3). Different lowercase letters indicate significant differences (\(p\) < 0.05) among cultivars for the same extraction method, while uppercase letters indicate significant differences between extraction methods for the same cultivar.  
Flavonoid concentrations (g/100 g dw) varied significantly (_p_ < 0.05) among mango cultivars and extraction methods. Extracts from process A had, on average, 1.4 times more flavonoids than those from process B. Among the cultivars, Gomera-3 had the highest flavonoid content, with 2.5 and 6.5 times more than Sensation and Keitt, respectively. Flavonoid levels in seeds were significantly lower than in peel, ranging from 4.5 to 40 times less, regardless of the extraction method. Only four of the ten flavonoids identified in the peel were present in the seeds.  
Gallates and gallotannins in mango peel showed no significant differences (_p_ < 0.05) among cultivars within the same extraction method. However, Sensation and Gomera-3 peels had higher yields with extraction process B (31.5 \(\pm\) 1.75 g/100 g dw) than with process A (25.0 \(\pm\) 4.06 g/100 g dw). Seed extracts had significantly lower yields--on average, 13.8 times less than peel extracts--though the number of identified compounds was comparable.  
In extract A, ellagic acid derivatives showed no significant differences among cultivars. However, in extract B, the Keitt cultivar had significantly lower levels (_p_ < 0.05) than Sensation and Gomera-3 (1.7 and 1.3 times lower, respectively). In seed extracts, Sensation showed the highest ellagic acid concentration in extract B (24.9 \(\pm\) 4.9 g/100 g dw), significantly higher than the other cultivars (16.7 \(\pm\) 3.4 g/100 g dw), and 1.65 times higher than in its peel. The highest xanthone concentration was found in Keitt seed extracts (4.15 \(\pm\) 0.18 g/100 g dw), followed by Gomera-3 peel extracts A and B. Mangiferin was the only xanthone identified and quantified.
## 3 Results and Discussion
### Optical Experimental Results
Once \(A\) has been found, we performed the calculation based on Equation (8) to find the complex object wave. Subsequently, we numerically propagate the object wave to obtain the phase distribution of the object. The final results are presented in Figure 8, which confirm the effectiveness of the method proposed by comparing with the results shown in Figure 7. Strictly speaking, the reconstructions shown in Figure 8 are twin-image-free and reference intensity-free. Only when \(\beta\ll 1\) do we have an approximate twin-image free and zeroth-order-beam free reconstruction.
## 3 Results and Discussion
### Optimisation of Extraction Performance  
The evidence emerged from recent examinations on polyphenol extraction with DES suggested that the three process (independent) variables considered, namely \(C_{\text{DES}}\), \(\text{R}_{\text{L/S}}\), and \(\text{S}_{\text{S}}\), are highly influential in solid/liquid extraction processes [19; 25]. For this reason, these three parameters were used among several others to build a predictive extraction model. Two cases were examined, one with a 30-min ultrasonication regime prior to every extraction and one without ultrasonication. The purpose of such investigation was to clarify whether an ultrasonication step before batch stirred-tank extraction could affect the optimization settings and assist in achieving significantly higher extraction yield. Appraisal of the fitted models and response surface suitability were based on the ANOVA test and lack-of-fit test. The \(p\)-value for each equation term was calculated to examine the contribution of linear, interaction, and quadratic effects in the independent variables (Table 2). Statistically non-significant terms were omitted from the mathematical models, which are given as polynomial equations in Table 3. The predicted response values calculated by the models, along with the measured (actual) values for each design point, are shown in Table 4. \(\text{R}^{2}\) provides indication of the amount of total variability around the mean explained by the regression model. \(\text{R}^{2}\) terms for both models were \(\geq\)0.95, so it would be argued that the estimation of the regression equations exhibited a good adjustment to the experimental data. The \(p\) value for lack-of-fit (assuming a confidence interval of 95%) was \(>\)0.05 for both models (Table 4), suggesting that the fitted models may be reliable predictors. By setting the values of all three process variables at their optima, using the desirability function (Figure 3), it was made possible to calculate the maximum predicted responses (Table 5). The response surface plots (Figure 4) were helpful in visualizing which variables were more influential to the response. Figure 2: The effect of ultrasonication time (\(t_{\text{US}}\)) on the extraction yield of total polyphenols. Extractions were performed with the 70% (_uvv_) aqueous GL-Nic (5:1), at 23 \({}^{\circ}\)C and \(\text{R}_{\text{L/S}}\) = 35 mL g\({}^{-1}\) dm. Assignment with “*” denotes statistically different value.
# Study on Composite Fracture Characteristics and Hydraulic Fracturing Behavior of Hard Rock
## 4 Characteristics of Composite Hydraulic Fracturing in Hard Rock  
### Composite Stress Intensity Factors in Hydraulic Fractures  
To effectively reveal the initiation pattern of hydraulic fractures under the influence of in situ stress and hydraulic pressure, combined with laboratory experiments on hydraulic fracturing, it is possible to establish a hydraulic fracturing plane model with pre-existing fractures. This model represents an infinite strip with a circular hole plate and symmetric cracks on both sides of the hole, as shown in Figure 6a. The mechanical analysis of the pre-existing fracture hydraulic fracturing model can be controlled by horizontal principal stresses (maximum horizontal principal stress \(\sigma_{\rm H}\), minimum horizontal principal stress \(\sigma_{\rm h}\)), as shown in Figure 6b, and is superimposed with the stress field controlled by the injection pressure, as depicted in Figure 6c.  
Using the conformal mapping method, the region of the rock matrix outside the borehole and pre-existing fractures in the rock specimen are mapped from the \(z\)-plane with coordinates \(x\) and \(y\) to the \(\xi\)-plane with coordinates \(\zeta\) and \(\eta\), centered within the interior of the unit circle. The boundaries of the borehole and pre-existing fractures on the \(z\)-plane are  
Figure 5: Effect of T stress on the fracture toughness of the CSTBD splitting test crack.  
mapped onto the circumference of the unit circle. This transforms the problem of boreholes and pre-existing fractures into a study of the unit circle, as shown in Figure 7.  
When the horizontal principal stresses \(\sigma_{\rm H}\) and \(\sigma_{\rm h}\), along with the injection pressure \(p_{\rm w}\), jointly control, the Mode I and II stress intensity factors at the crack tip are given by [32]:  
\[K_{\rm I}^{\rm HF}=\left\{p_{w}-\frac{1}{2}[(\sigma_{H}+\sigma_{h})-(\sigma_{H} -\sigma_{h})\cos(2\beta)]\right\}\sqrt{\pi a\!\left(1-\frac{r_{w}{}^{4}}{a^{4} }\right)} \tag{19}\]  
\[K_{\rm II}^{\rm HF}=\frac{1}{2}(\sigma_{H}-\sigma_{h})\sin(2\beta)\sqrt{\pi a \!\left(1-\frac{r_{w}{}^{4}}{a^{4}}\right)} \tag{20}\]  
where \(K_{\rm I}^{\rm HF}\) and \(K_{\rm II}^{\rm HF}\) represent Mode I and II stress intensity factors in hydraulic fractures, respectively.  
According to Equations (19) and (20), hydraulic pressure determines the type I stress intensity factor of hydraulic fracture in the rock, and the larger the hydraulic pressure, the higher the type I stress intensity factor. In hydraulic fracturing, a higher water pressure is usually required, exceeding the ground stress, resulting in a higher type I stress intensity factor than the type II stress intensity factor for hydraulic fractures.
## 2 FSO Communication System
### Transmitter Side
#### 2.1.1 Transmission Wavelength  
FSO systems are predominantly designed to function in the near-infrared (near-IR) spectrum, specifically within the 750 to 1600 nm wavelength range. While the clear atmosphere is generally highly transparent to near-IR wavelengths, certain atmospheric molecules can cause significant absorption at specific wavelengths within this range. Also, there are particular wavelength windows around 850 nm, 1060 nm, 1250 nm, and 1550 nm, where the atmospheric attenuation is notably low, measuring less than 0.2 dB/km. Notably, the 850 nm and 1550 nm wavelengths align with the standard transmission windows used in fiber optic communication systems [16]. This compatibility allows FSO systems to leverage existing fiber optic components, which are readily available on the market, thereby reducing costs and improving system integration [18; 1; 3; 19; 20].  
In addition to near-IR wavelengths, recent research has explored other spectral regions for FSO applications. Wavelengths around 10 \(\upmu\)m have been identified for their superior performance in foggy conditions, offering better transmission characteristics. Ultraviolet (UV) wavelengths have also been considered due to their robustness against pointing errors (PEs) and beam blockages. Furthermore, UV wavelengths exhibit lower sensitivity to solar and other background interferences, making them a promising alternative for certain FSO applications [1; 21; 22]. A detailed discussion of these promising wavelengths is provided in Section 5.2.
# Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
## 4 Big Data Technologies for Fish Farming Use Case
### Data Ingestion: Apache Sqop, Apache Flume  
Apache Sqoop is a tool designed for efficiently transferring bulk data between Apache Hadoop and relational database management systems such as MySQL and Oracle. It is an open-source command-line interface application ETL tool [32].  
Apache Flume is an open-source, powerful, and flexible system to collect, aggregate, and move large amounts of unstructured data from multiple data sources into Hadoop (HDFS or HBase). It is written in Java and has a flexible architecture based on streaming data flows. Flume has its own query processing engine to transform new batches before moving to the intended sink [32].
## 3 Methodology
### Image Feature Extraction  
In order to extract features of the driving environment more efficiently, this paper uses a convolutional neural network for feature extraction from image information. The extraction neural network architecture is shown in Figure 5. Unlike conventional convolutional neural networks, this paper incorporates a convolutional block attention module (CBAM) during the training process. The CBAM module calculates attention maps from two different dimensions: channel and space, and then multiplies the obtained attention maps with the input feature maps for adaptive feature refinement [27]. Since CBMA is a lightweight universal module, it can be seamlessly integrated into any CNN architecture without additional overhead and can be trained end-to-end with the basic CNN. The layout of its fused convolutional neural network architecture is shown in Figure 6. Among them, the channel attention module uses average pooling and maximum pooling to aggregate spatial information, which is then processed and input into a multilayer perceptron (MLP), and finally outputs feature \(F\prime\). Unlike channel attention, the spatial attention module first performs average pooling and maximum pooling operations to aggregate channel information, and finally outputs \(F^{\prime\prime}\) as a feature map.  
Figure 5: Image feature extraction network.  
Figure 6: Convolutional block attention module network structure.  
The output feature map shows that the pretrained convolutional neural network method can effectively classify different environmental images. The ability to automatically extract features through deep learning enables better understanding and utilization of image data [28]. The generated image features can provide reference, for the selection of environmental quantification indicators in the future.
## 3 Methodology
### Data Pre-Processing
#### 3.2.3 Data Segmentation  
Due to the extensive volume of signal data gathered by wearable sensors, it is unfeasible to enter all possible information into the HAR model concurrently. Consequently, sliding window segmentation is executed before inputting the data into the model. The sliding window technique is one of the most widely used data segmentation approaches in HAR. It is effective in capturing both dynamic activities (e.g., jogging, walking) and static activities (e.g., standing, sitting, lying down) [27]. The unprocessed sensor signals are segmented into fixed-length intervals, with an overlapping section between consecutive intervals. This overlap enhances the quantity of training data samples and is beneficial in preventing the omission of changeover between operations. The windowing procedure is depicted in Figure 3.  
The segmented sample data, utilizing a sliding window of length \(N\), has dimensions of \(K\times N\). The sample \(W_{t}\) is represented as:  
\[W_{t}\ =\ [a_{t}^{1}a_{t}^{2}\ldots a_{t}^{K}]\in\mathbb{R}^{K\times N} \tag{3}\]  
where the column vector \(a_{t}^{k}\ =\ (a_{t_{1}}^{k}a_{t_{2}}^{k}\ldots a_{t_{N}}^{k})^{\mathrm{T}}\) represents the signal data of sensor \(k\) at window time \(t\). Additionally, T denotes the transpose operator, \(K\) is the number of sensors, and \(N\) is the length of the sliding window. To utilize the correlations among windows and facilitate the training process, the window data is divided into sequences of windows.  
Figure 2: Sample accelerometer signal frames of walking activity, shown before and after applying filtering techniques.  
\[S\ =\ \{(W_{1},y_{2}),(W_{1},y_{2}),\ldots,(W_{T},y_{T})\} \tag{4}\]  
where \(T\) denotes the duration of the window sequence and \(y_{t}\) signifies the associated activity label for window \(W\). If a window comprises several activity classes, the predominant activity sample will be selected as the label for that window.
## 3 Analysis and Comparison of Machine Learning-Based Anomaly Detection
### Machine Learning Models
#### 3.1.2 Support Vector Machine (SVM)  
A Support Vector Machine (SVM) is a widely used machine learning classifier that divides various classes of data points by locating the best hyperplane in a feature space. The main goal of SVM is to locate the hyperplane that optimizes the distance between the classes while minimizing classification errors [45]. The margin, also known as the distance between the hyperplane and the closest data point of each class (support vector), is a crucial parameter in the SVM algorithm. The SVM algorithm looks for the hyperplane with the maximum margin (as illustrated in Equation (3)), as it effectively separates the data and increases the model's ability to generalize to unseen data. In the SVM classifier, a specific weight or coefficient is given to each feature in the dataset, and it then learns to build a decision boundary by optimizing these weights. The best hyperplane is discovered by resolving a mathematical optimization problem that aims to minimize a cost function while ensuring that all data points are accurately classified, and the margin is maximized. Suppose the hyperplane is defined by \(w\) and \(b\) is a set of points in which \(H=\{x\mid w^{T}.x+b=0\}\); the hyperplane is shown by \(\gamma\). The maximum margin would be:  
\[\arg\max_{\mathbf{w},b}\gamma(\mathbf{w},b)\quad\text{such that}\quad\forall i \,y_{i}(\mathbf{w}^{T}.\mathbf{x_{i}}+b)\geq 0 \tag{3}\]  
where \(y_{i}\) is the class label associated with the \(i\)-th data point \(\mathbf{x_{i}}\).  
This method makes SVMs efficient for both linearly separable data, where a straight line serves as the hyperplane, and non-linearly separable data, where SVMs can translate the data into a higher-dimensional space using a kernel function, allowing them to identify a more complex decision boundary.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 3 Trajectory Data Sparsity and Anomaly Detection
### Data Preprocessing
#### 3.1.2. Trajectory Segmentation  
After preprocessing the trajectory data, this study found that the time intervals between some of the neighboring trajectory points were too long. To avoid the influence of AIS data discontinuity on the subsequent analysis, this paper utilized the time threshold method to segment the trajectory. First, the data of a vessel were sorted into chronological order and the timestamp intervals between trajectory points were calculated. Secondly, a period of 1800 s was selected as the time threshold according to expert experience, all timestamp intervals were traversed, and the trajectory points with time intervals larger than the time threshold were taken as cutting points for segmenting the trajectory. Finally, track segments with less than 50 track points were deleted to improve data availability.
## 5 Evaluation
### Dataset  
To avoid bias, the set of 144 \"all time most popular tags\" as determined by Flickr were used as start tags. 400 photos with metadata were collected for each start tag. When locating photos on Flickr, the \"most interesting\" photos were retrieved first, as determined by Flickr itself. _Interestingness_ is a patented relevance function developed by Flickr to describe images \"that are beautiful, amazing, moving and striking\" [29]. Each photo was then processed to extract their CEDD feature descriptors.  
As the existing tag set of each photo (obtained with the photo from _Flickr_) was used to evaluate the effectiveness of the auto-tagging algorithms, it was important that the majority of photos were associated with a predefined minimum number of tags. Photos in the dataset are assigned 26 tags in average with a standard deviation of 15 tags, with 99% of photos having five or more tags, 94% with 10 or more and 59% with 20 or more. Notably, [PERSON] _et al._ found in their evaluation that photos were typically annotated with 19.6 words [27], which is slightly less than in our data set.  
Figure 10: The 50 most frequently used tags in our data set.  
The 50 most frequent tags of the dataset are given together with their frequencies in Figure 10. A quick examination reveals that almost half of these tags could be regarded as tags not describing the actual content, like _abigfave_, _plusfoto_, _nikon_, _anawesomeshot_, _hdr_, _etc._
# Carbon Mineralization in a Soil Amended with Sewage Sludge-Derived Biochar
###### Abstract  
Biochar has been presented as a multifunctional material with short- and long-term agro-environmental benefits, including soil organic matter stabilization, improved nutrient cycling, and increased primary productivity. However, its turnover time, when applied to soil, varies greatly depending on feedstock and pyrolysis temperature. For sewage sludge-derived biochars, which have high N contents, there is still a major uncertainty regarding the influence of pyrolysis temperatures on soil carbon mineralization and its relationship to soil N availability. Sewage sludge and sewage sludge-derived biochars produced at 300 \({}^{\circ}\)C (BC300), 400 \({}^{\circ}\)C (BC400), and 500 \({}^{\circ}\)C (BC500) were added to an Osisol in a short-term incubation experiment. Carbon mineralization and nitrogen availability (N-NH4\({}^{+}\) and N-NO3\({}^{-}\)) were studied using a first-order model. BC300 and BC400 showed higher soil C mineralization rates and N-NH4\({}^{+}\) contents, demonstrating their potential to be used for plant nutrition. Compared to the control, the cumulative C-CO2 emissions increased by 60-64% when biochars BC300 and BC400 were applied to soil. On the other hand, C-CO2 emissions decreased by 6% after the addition of BC500, indicating the predominance of recalcitrant compounds, which results in a lower supply of soil N-NH4\({}^{+}\) (83.4 mg kg\({}^{-1}\)) in BC500, being 67% lower than BC300 (255.7 mg kg\({}^{-1}\)). Soil N availability was strongly influenced by total N, total C, C/N ratio, H, pore volume, and specific surface area in the biochars.  
biochar; soil organic matter; tropical soil; available nitrogen; pyrolysis Article
###### Abstract  
Pre-stressed high-strength concrete pipe piles (PHC pipe piles) have been widely used in actual soft foundation treatment projects due to their reliable quality, fast construction, assembly line production, and environmental friendliness. However, large-scale slip damage still occurs in construction projects. In order to reduce and avoid such accidents, a highway in Guangdong (section K31+100-K31+388) was taken as an example for this study. Plaxis 2D software (V22.01.00) was used to establish a PHC pipe pile composite roadbed model and investigate the effects of tie beam form, pile lengths, pile spacings, pile verticality, and embankment filling loading modes on the settlement and stability of the composite roadbed. The results show that the original treatment plan, which had the form of a PHC pipe pile with caps, had a low horizontal bearing capacity and a poor anti-disturbance ability, leading to the occurrence of a landslide accident. A comparison of different structural forms revealed that the longitudinal and transverse tie beam form was the most stable, followed by the transverse tie beam form, longitudinal tie beam form, PHC pipe pile form with caps, and PHC pipe pile form without caps. Compared to the structural form of PHC pipe piles with pile caps, the stabilities of the transverse tie beam form and the longitudinal tie beam form were improved by 42.4% and 38.61%, respectively, while that of the longitudinal and transverse tie beam form was improved by 50.87%. The application of longitudinal and transverse tie beams effectively reduced the settlement of the composite roadbed, as confirmed by both measured data and finite element analysis. This structure achieved the desired vertical settlement control and lateral anti-slip effects.  
PHC pipe pile; composite roadbed; roadbed slip; tie beams; finite element analysis; factor of safety; settlement