esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Meteorological, Soil and Vegetation Measurements  
The auxiliary meteorological and soil measurements include photosynthetically active radiation (PAR), air temperature at the height of 2 m (Ta), precipitation (PPT), air relative humidity (RHa), vapor pressure deficit (VPD), soil temperature at depths of 5 cm (Ts), and soil water content (SWC). All measurements were averaged and recorded in a data-logger once per 30 min. We also measured the vegetation leaf area index (LAIg) with a harvest method every two weeks during the growing season. From 2009 to 2011, the frozen period (Ts \(<\) 0\({}^{\circ}\)C) is from November to March. Vegetation begins to green-up at the beginning of May, thus, in this study, we coarsely defined the growing season from 1 May (the 121 st day of year, DOY 121) to mid-October (DOY 281).  
Figure 2: Site descriptions of this typical alpine steppe—_Korthexia_ meadow at the Damxung grassland station: (**a**) Vegetation type of the Qinghai-Tibet Plateau and the geographical location of this study. (**b**) The position of flux-tower (red arrow), chamber-based observation field (red dotted box), and the corresponding MODIS 0.5 km \(\times\) 0.5 km pixel (red solid box). The black arrow represents the direction of prevailing wind (SSW, south-southwest) in this alpine meadow. (**c**) Real photo of this observational field.
# Three-Dimensional Imaging of Metallic Grain by Stacking the Microscopic Images
## 1 Introduction
However, it is difficult to determine the presence or size of a defect due to the edge effect that occurs at the end of the object to be measured [30]. This means that even when the grain is needle-shaped or the shape changes rapidly, the eddy current signal can be affected. It is important to provide a means of quantitatively measuring and expressing the three-dimensional shape of a grain in order to grasp the effects of such effects, i.e., the formation of magnetic domains, residual magnetization, and the effects of edge according to the shape and size of the grain. According to nano-radiography and tomography, the metal grain can be imaged in three dimensions [31; 32]. Nevertheless, this requires very high accuracy and expensive equipment, including X-ray shielding. Furthermore, it is difficult to measure the distribution of magnetic domains within the grain in three dimensions. In addition, under the heat treatment influence, the application of external field and mechanical stress can rearrange the martensite variant and can change the magnetic domains. It is important to understand their effect not only at the macroscopic level but also at the microscopic level [33; 34; 35; 36]. In this context, the use of an alternative method to overcome the limitations of non-destructive testing (NDT) techniques can make a valuable contribution to the characterization of materials and the evaluation of microstructure performance through rapid and efficient experiments and simulations. In this paper, we present a method to reproduce the real shape and size of the grains of the tested sample SA106 by pilling up two-dimensional images obtained by a metal microscope. To achieve this target, we introduce fixtures, procedures, and software algorithms based on mathematical formulas. Several studies have previously developed 2D and 3D techniques to construct a way to study microstructure using different experimental and simulation techniques. The results of these studies lead to a good outcome that provides access to crucial structural information of the investigated materials, which facilitates and enhances the understanding of physical and chemical concepts at the microscopic level [37; 38; 39; 40; 41; 42; 43]. However, we have not seen any work similar to our work, which allows the reconstruction, extraction, and selection of one or more grains with the exact shape and size of the inspected material, without using extensive software and hardware, but just simple equipment and some algorithms. It should be emphasized that the method presented here permits: (1) 3D grain shape obtaining; (2) using conventional research laboratory environment; (3) easy preparing specimens; (4) multi-parallel data acquisition; (5) easy 3D image processing. This is particularly important for the industry and research laboratories. The finding is encouraging for the near future application of the method for the analysis of 3D stress, strain deformation at grain level, and the analytical study of non-destructive evaluations using electromagnetic and ultrasound testing.
## 2 Design of Self-Inhaling Internal Cooling Structured Grinding Wheel  
During the grinding process, it is challenging for externally sprayed grinding fluid to enter the grinding zone. Because the grinding wheel rotates at high speed, the surrounding air also spins rapidly, forming a layer of air barrier around the wheel. This barrier prevents the grinding fluid from flowing into the working area, a phenomenon known as the \"air barrier effect\" in the mechanical industry [23]. The presence of this phenomenon reduces the effectiveness of the grinding fluid in the working zone, resulting in poor cooling performance. Moreover, the strength of the air barrier layer is related to the rotational speed of the grinding wheel. The higher the speed, the harder it is for the cooling fluid to reach the working zone.  
In this study, a structured grinding wheel is designed to utilize an internal cooling method. A cavity is formed inside the end face grinding wheel to store cooling fluid. During grinding, the cooling fluid can flow into the working area through the cavity and internal channels, bypassing the air barrier layer around the grinding wheel and thus avoiding the \"air barrier effect\". This design allows the cooling fluid to directly enter the grinding zone, reducing the amount of cooling fluid required.  
A 3D model of the internally cooled grinding wheel is shown in Figure 1. The structure of this grinding wheel primarily consists of a base and a pressure cap with circular holes. The base serves multiple functions: facilitating the cladding of CBN abrasive grains on the end face region, connecting to the machine tool spindle, and delivering grinding fluid from the interior of the wheel to the grinding zone through cooling holes. The pressure cap with circular holes maintains the pressure of the grinding fluid and prevents leakage [12]. The grinding fluid is introduced into the internal cavity of the wheel through the circular holes in the pressure cap, via a pressurized pipe. Under the combined effects of pressure difference, gravity, and centrifugal force generated by the wheel's rotation, the fluid flows from the 12 cooling holes in the cavity of the base into the grinding zone, carrying away grinding heat and debris.  
Figure 1: _Cont._Simulation of Flow Field Characteristics of Self-Inhaling Internal Cooling Structured Grinding Wheel
## 3 Results and Discussion
### Determination of Antioxidant Capacity
In the present study, viscera was the side stream with the highest antioxidant capacity, with values of ABTS and ORAC up to 516.02 uM TE and 5794.64 uM TE, respectively. Moreover, our results are in close agreement to those obtained by [PERSON] et al. [33], who reported an antioxidant activity after using aqueous media, thus suggesting that substances with higher polarity can have more antioxidant capacity. In other study, [PERSON] et al. [34] investigated the effect of low-frequency ultrasound treatment at different times (6-14 min) on the properties of silver carp myofibrillar protein and observed a significant effect on antioxidant activity (DPPH inhibition from 16.07 to 36.51% and ABTS inhibition from 14.17 to 22.58%), obtaining the highest antioxidant activity after the UAE treatment at 12 min. On the other hand, other treatments such as mechanical separation resulted in lower antioxidant capacity (<50 ug Trolox/g sample) in sea bass, gilthead sea bream, and rainbow trout samples [35]. For instance, ultrasound could improve the extraction of antioxidant compounds by two mechanisms: i) the release of antioxidant compounds from inside of cells and ii) the induction of proteolysis, producing antioxidant peptides [33; 36]. The higher antioxidant activity observed in the present study for sea bass viscera compared with the other side streams (head, bone, and skin) could be due to its high content of peptides with low molecular weight. In this sense, the antioxidant activity of peptides increases as their molecular weight decreases [37].
## 4 The Dataset Usage
### Data Extraction and Feature Selection
#### 4.1.1 Feature Representation: Word2 vec and Principal Component Analysis (PCA)  
Extensive preprocessing was conducted, including feature extraction from source code, token frequency analysis, and word embeddings using Word2 Vec. To enhance feature representation, we applied dimensionality reduction techniques such as (PCA) and the most relevant features based on correlation analysis, as shown in Figure 3. We also introduced composite features, such as the ratio of conditional statements (if-else structures) and a combined try_catch metric to capture exception-handling behavior. These transformations ensured the dataset was computationally efficient and representative of vulnerability-related code characteristics.
## 3 Results and Discussion
### Comparison between the Proposed Method and Several Artificial Intelligence Algorithms
As shown in Figure 16, the confusion matrix of the testing dataset clearly illustrates the prediction accuracy of the different fault types, and the total prediction accuracy of the 10 fault types was 99.3%. The prediction accuracy of the proposed method for most of the fault types was 100%, and the worst prediction accuracy was 97% for condition 3 (inner race fault with a 0.021 mils fault diameter). Although the prediction accuracy of CNN can be improved by optimizing the structure; this is usually at the expense of a longer computation time. In addition, the training process of a complex DL structure needs more training data. Therefore, the proposed method can accurately identify the fault types of rolling bearings with less computation time and a smaller sized dataset. Figure 16: The confusion matrix of the fault diagnosis accuracy results for the proposed method. Figure 15: The performance of the proposed method compared with other artificial intelligence methods.
# Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
## 2 Materials and Methods
### Classifier's Performance Evaluations  
This study uses the cross-validation ten times to evaluate the performance of the proposed multilayer CNN-based classifier. Each time, the dataset is divided into the two groups of normal and abnormal feature patterns. The dataset is then randomly divided into two halves: 50% training dataset and 50% testing dataset. Repeat the procedure ten folds to confirm the performances of the proposed classifier, such as the evaluation indicators shown in the formulas for precision (%), recall (%), F1 score, and accuracy (%) [35, 36, 47] in Table 2. For each fold cross-validation, the multilayer CNN-based classifier will produce a confusion matrix comprising four parameters, including TP (True Positive), FP (False Positive), TN (True Negative), and FN (False Negative). These parameters help to determine the indexes for evaluating the performances of the proposed classifier. The precision (%) indicator represents the rate that a TP can be correctly identified among all TPs (positive samples). Generally, a model must be larger than 80% to be recognized as a good classifier. The recall (%) indicator is defined as TP/(TP + FN). The F1 Score (\(\in\) [0,1]) is the harmonic mean of precision (%) and recall (%) indexes. Its index combines the two in a single evaluation index. The higher the value of the F1 score and the closer it is to 1, the better the classifier is at prediction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Function** & **Manner** & **Feature Pattern** \\ \hline Input Feature Pattern & ROI Extraction with 100 \(\times\) 100 Bounding Box & 2D, 100 pixels \(\times\) 100 pixels \\
1 st Convolutional Layer & 3 \(\times\) 3 Fractional-Order Convolutional Window, Stride = 1 & 2D, 100 pixels \(\times\) 100 pixels \\
2 nd Convolutional Layer & 3 \(\times\) 3 Kernel Convolution Window, Stride = 1 & 2D, 100 pixels \(\times\) 100 pixels \\
2 nd Pooling Layer & 2 \(\times\) 2 Maximum Pooling Layer, Stride = 2 & 2D, 50 pixels \(\times\) 50 pixels \\
3 rd Convolutional Layer & 3 \(\times\) 3 Kernel Convolution Window, Stride = 1 & 2D, 50 pixels \(\times\) 50 pixels \\
3 rd Pooling Layer & 2 \(\times\) 2 Maximum Pooling Layer, Stride = 2 & 2D, 25 pixels \(\times\) 25 pixels \\ Flattening Layer & Flattening Process & 1D, 1 \(\times\) 625 feature vector \\  & Multilayer Classifier: 625 input nodes, 168 hidden nodes & \\ Classification Layer & 64 hidden nodes, 2 output nodes (for normality and abnormality) & 1 \(\times\) 625 Feature Vector Feeding \\  & Algorithm: ADAM Algorithm & into Multi-Layer Classifier \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant information about the proposed multilayer CNN.
# Movement Time for Pointing Tasks in Real and Augmented Reality Environments
## 3 Results  
### ANOVA Results on MT  
The ANOVA results showed that the MT of virtual pointing tasks was significantly (\(p<0.0001\)) higher than that of physical pointing tasks. The effects of inclined angle were also significant (\(p<0.0001\)) on the MT. Both the gender and handedness significantly (\(p<0.0001\)) affected the MT of the physical and virtual pointing tasks.  
The LSD test results indicated that the MT of the virtual pointing task (1533.5 ms) was significantly (\(p<0.05\)) higher than that (628.6 ms) of the physical one. The MT of the 90\({}^{\circ}\) inclined angle condition (1118.1 ms) was significantly (\(p<0.05\)) higher than that (1049.0 ms) of the 30\({}^{\circ}\) condition. For physical pointing tasks, the MT of female participants (639.1 ms) was significantly (\(p<0.05\)) higher than that (618.1 ms) of the male participants. The MT of the nondominant hand (642.8 ms) was significantly (\(p<0.05\)) higher than that (614.4 ms) of the dominant hand. For virtual pointing tasks, the MT of female participants (1630.2 ms) was significantly (\(p<0.05\)) higher than that (1446.7 ms) of the male participants. The MT of the nondominant hand (1581.1 ms) was significantly (\(p<0.05\)) higher than that (1495.9 ms) of the dominant hand.
# Experimental Development of Composite Bicycle Frame
## 2 Experimental Methods
### Composite Bicycle Frame Experimental Configuration  
A composite bike frame was made using composite tubes, manufactured by a filament winding technology, as the main structural parts. The technological advantage of this solution is the possibility of winding layers of composite with longitudinally oriented fibers. This includes the application of hybrid compositions of materials; in this case, high-strength (Toray T700 fiber) and high-modulus (Tenax(r) UMS40 fiber) carbon fibers. In combination with a two-component epoxy resin, a fiber volume fraction in the range of 51-55% was achieved. The tube-to-tube manual lamination method was then used to form the connections of these particular frame tubes, using carbon fabric and unidirectional (UD) tape and epoxy resin matrix. Micro-balloon-filled, two-component epoxy resin was used to complete the shape transitions. The strength of such joints with respect to the hand composite lay-up process may represent a weak point of the frame's strength. This should be experimentally verified. The composite frame in question is optimized for rigidity and weighs 800 g with a size of 54 cm.  
Two bicycle frame specimen types were manufactured. The first one was a complete bicycle frame ready for laboratory tests, and the full-scale test of the complete assembled frame that follows (frame specimen 0, Figure 1). The second specimen type was designed for extended laboratory testing, with the focus on the most loaded joint, which is the connection of the head tube to the down tube and the top tube. For this purpose, simplified frames containing the main frame triangle were made and tested (frame specimens 1, 2, 4, 5, 6 and 7), as shown in Figure 2. Frame specimen 7 differs from the others by the reinforced composite lay-up in the head tube joint area. Additional carbon fabrics were added so that the number of layers was doubled. A bicycle frame of the first type was instrumented with surface-mounted strain gauges and structural FBG sensors. Optical fibers were embedded into the joint connecting the top tube (FBG TT) with the head tube, and the joint connecting the down tube (FBG DT) with the head tube (Figure 3a), directly between the unidirectional carbon filament layers (UD tapes, see Figure 3b). From a safety point of view, these places are critical for the transfer of horizontal forces. The positions and markings of individual electrical and optical strain sensors are marked in the scheme in Figure 4a. Details of thelocations of the FBG sensors, FBG TT (Top Tube) and the FBG DT (Down Tube), in the head tube area are shown in Figure 4b. The head tube connection area is then covered with carbon fabric in the last step of the hand lay-up lamination process.  
Figure 4: (**a**) Position and marking of surface-mounted strain gauges; (**b**) Location of FBG sensors (blue marking).  
Figure 3: (**a**) Detail of FBG TT sensor placement during the hand lamination process; (**b**) Detail of FBG DT sensor placement during the hand lamination process.  
Figure 2: Simplified bicycle frame geometry (specimens 1, 2, 4, 5, 6 and 7).
## 5 Key 6G Technologies
### New Spectrum
#### 5.1.4 Dynamic Spectrum Management (DSM)  
In tackling the scarcity of spectrum resources, addressing both unused higher frequency spectrums and optimizing the utilization of limited frequencies is crucial. Recognizing the dynamic nature of traffic demand, it is imperative to urgently address the underutilization of frequency bands. In the 6G realm, two strategies for bandwidth enhancement stand out: exploration of higher frequency spectra and the use of DSM. DSM, originating from the listen-before-talk protocol, is crucial for efficient radio resource utilization. The ubiquity of ISM band access in mainstream cellular terminals underscores its importance. However, challenges, including broad-spectrum hardware design and context awareness, particularly in the face of dynamic environmental factors, highlight the imperative for the 6G DSM [40; 76; 77]. Efficient spectrum management strategies, including symbiotic radio (SR) [78], dynamic spectrum sharing techniques [79], and cognitive radio (CR) [80] emerge as vital methods to enhance the spectral and energy efficiency of 6G [25].  
The cognitive radio (CR), introduced by [PERSON] in 1999 [81], emphasizes adaptive spectrum awareness. Expanding upon this, [PERSON] [82]'s brain-empowered CR technology describes a smart wireless system that can sense its surroundings and adapt. CR-based vehicular networks, as suggested by [PERSON] et al. [83], leverage deep Q-learning to navigate dynamic topology changes and available spectrum in response to shifts in vehicle distribution.  
An advancement over cognitive radio (CR), symbiotic radio (SR) combines AmBC (ambient backscatter communication) with CR. Through this integration, information may be embedded into the background radio frequency stream, enabling advantageous spectrum sharing. SR technology is promising for energy-efficient and spectrum-efficient communication architecture, especially in passive Internet of Things applications [84; 85].  
Recent research emphasizes intelligent and dynamic spectrum sharing. Full-duplex wireless technology, proposed by [PERSON] et al. [86] enables concurrent sensing and transmission, enhancing spectrum utilization effectiveness through dynamic spectrum sharing(DPS). A deep Q-learning-based distributed dynamic spectrum access method is presented by [PERSON] and [PERSON] [87]. Dynamic spectrum sharing enabled by blockchain is emphasized as having the ability to increase automation, security, and distribution. Furthermore, the importance of AI is emphasized, as it is anticipated to improve pattern recognition and judgment in situations involving dynamic spectrum sharing [88; 89].
## 3 TBM (Tunnel Boring Machine) Data Processing  
### Study Area  
The data analyzed in this study originates from a water supply project in Xinjiang, with a total length of approximately 285 km and tunnel diameters ranging from 6.8 to 8.9 m. The construction employed a combination of drill-and-blast methods and open TBM excavation. The TBM used in the project had a cutterhead diameter of 7.8 m. The excavated section primarily consisted of Variscan metamorphic granite, metamorphic biotite granite, and biotite quartz schist, characterized by high integrity, significant rock strength, and minimal joint and fracture development.  
The overall tunnel section was intact, with some localized fragmentation. The predominant rock mass classifications were Class II and Class IIIb, with Class IIIa and IIIb accounting for 98.97% of the total rock mass. A 2.5 km long faulted and fractured zone, constituting 0.93% of the total length, was present along the tunnel route. Groundwater in the excavated section was mainly bedrock fissure water, and the tunnel area was located in a region with low groundwater recharge, resulting in minimal seepage. The data acquisition segment spanned terrain with mild undulations, characterized by exposed bedrock and residual aeolian hills.
## 2 Biochemistry and Genetic Regulation of Aroma and Flavor Compounds in _C. sativa_ L.
### Flavonoids and Other Aroma and Flavor Compounds  
Flavonoids are synthesized via the phenylpropanol pathway [39]. This pathway begins with the conversion of phenylalanine to cinnamic acid, which is catalyzed by phenylalanine ammonia-lyase. Chalcone synthase converts cinnamic acid into chalcones, which are precursors of flavonoids. Flavonoid synthase and other enzymes modify these chalcones into diverse flavonoid structures. Cannabis-specific flavonoids, such as cannflavins A, B, and C, contribute to aroma and flavor, with the latter primarily through taste, while exhibiting notable bioactivity, including anti-inflammatory, antioxidant, and neuroprotective properties [39]. Common flavonoids like quercetin, kaempfer, and apigenin further enhance the sensory complexity of cannabis by contributing a bitter taste.  
In addition to terpenes and flavonoids, volatile organic compounds such as esters, aldehydes, and ketones play crucial roles in defining the nuanced aroma and flavor profiles of cannabis. Recent research has also demonstrated the significant role of minor volatile sulfur compounds (VSCs) and other nonterpene compounds in influencing the intricate aroma profile of _C. sativa_ L. [25]. Prenylated VSCs, such as 3-methyl-2-butene-1-thiol, are mainly responsible for the skunky and pungent odor of cannabis, with their levels peaking during the late flowering and curing stages. These compounds, which are structurally analogous to bioactive sulfur molecules found in garlic, exhibit potency even at low concentrations and substantially affect the sensory characteristics of certain cultivars. Furthermore, other nonterpene volatiles, including skatole (3-methylindole) and 3-mercaptoethanol derivatives, contribute distinctive sweet, savory, or citrus-like aromas to exotic cannabis varieties [26]. These findings challenge the conventional focus on terpenes as the primary determinants of aroma, underscoring the importance of minor volatiles in shaping the aromatic complexity of cannabis.