esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Human-Technology Comparison
### Performance Comparison
#### 5.1.1 Piece-Part Inspection  
The achieved inspection accuracy of the human operator in the piece-part inspection was 76.2% and was significantly higher than the software with 48.8%, U = 179,252.0, \(p<0.001\). The operators agreed, on average, 82.5% of the time with themselves when inspecting the same blade twice. The technological systems in contrast made the same serviceability determination each time, thus showing higher repeatability than the human operator, Z = \(-2.347\), \(p<0.05\). A similar observation was made for the inspection time that differed significantly between the two inspection agents, Z = \(-4.950\), \(p<0.001\). The software took 203 ms which was only a fraction of the time of the human operator, who needed, on average, 14.972 s. In other words, the software was able to process 74 blade images in the same amount of time the operator needed for a single blade.  
Figure 6 shows the mean plot of the inspection accuracies achieved by each inspection agent for each type of defect. The operator consistently outperformed the software on all defects except tip curl and non-defective blades. The biggest difference in detection accuracy occurred for airfoil dents. This was because the software was programmed to detect edge damage, not airfoil defects. Furthermore, it was noticed that the software performance was proportional to the amount of deformation of the blade, i.e., smaller defects such as nicks, dents and minor tip rub were more difficult to detect than more advanced and salient detects including bends, tears and tip curls (airfoil dents \(<\) dents \(<\) tip rub \(<\) nicks \(<\) bends \(<\) tears \(<\) tip curl). The human operator had a similar but not identical order of successful defect detection (dents \(<\) airfoil dents \(<\) bends \(<\) tip rub \(<\) tip curl \(<\) nicks \(<\) tears). This order represents the criticality of the different defect types rather than solely their appearance, although the two were somewhat correlated. There is a chance that the operators consciously or unconsciously applied their underlying mental model [9], which might have influenced their decision making, i.e., they know what defect types are more critical than others. The software does not have that contextual knowledge and relies solely on the visual appearance of the defect.  
Figure 6: Mean plot of inspection accuracy in piece-part inspection by defect type and for different inspection agents.  
All tip curls were successfully detected by the software, while the operator missed 11% on average. The second category for which the inspection accuracy was higher for the software than the operator was non-defective blades. This indicates that the software is not as sensitive to anomalies as the human operator is. While it is generally desirable to reduce the false-positive rate, this should not be at the expenses of high false-negative rates, i.e., missing critical defects. A high detection rate (even if incorrect) might be acceptable if the software is used as an inspector-assisting tool that highlights any anomalies but leaves the judgement to the human operator.
## 2 Materials and Data
### Winter Wheat Yield and Planting Distribution  
The historical county-level winter wheat yield data record of Hengshui City came from the 2005-2019 statistical yearbook sharing platform, recording yield data (unit: kg/ha). In Hengshui City, there are two municipal districts and one county-level city; there are 11 counties, since we all consider these to be county-level levels. Spatial distribution data of winter wheat in Hengshui at 250 m spatial resolution from 2005-2019 were provided by Chinese Academy of Agricultural Sciences [34]. The extraction results were evaluated in terms of area quantity and spatial location, and the average relative error of area quantity compared with the statistical yearbook for 20 years was 16.1%. In terms of spatial location, the extraction results of 2015 were verified by selecting sample points in Google Earth high-resolution historical images, and the overall accuracy was 86.8% with a kappa coefficient of 0.69. The results indicated that the winter wheat distribution data had a high extraction accuracy.
## 3 Results and Discussion  
### Data Preparation  
The dataset in this study is single time series data for the hourly power consumption of Turkey obtained in Kaggle [19]. This data represents the overall consumption of electrical power in the country. We took the dataset from November 2019 until April 2020 to conduct the experiment. The hourly power consumption in this study was taken for 24 h each day for 6 months. We divided the dataset into 80% for the training process and 20% for the testing process. Before we conducted the experiments, we set the configurations of the RobustSTL and TCN models. We set the length of the seasonality period in the RobustSTL to 24 h, because the hourly power demand for 24 h repeats somewhat in a similar pattern. For the parameter setting in the TCN model, we take only one residual block for the TCN model. Since the input sequence does not have high dimensionality, we set dilation factor \(d=1\), 2, 3, and 4, kernel size of \(k=3\), and the filter number of 64. In our proposed model, the power consumption \(y_{t}\) at hour \(t\) can be forecast by referencing the data sequence of previous data such \(y_{t-1}\), \(y_{t-2}\), and \(y_{t-3}\). Furthermore, we compare the proposed model  
Figure 4: Confusion matrix table.  
and counterpart models. MAPE, MAE, RMSE, and the confusion matrix are utilized as evaluation metrics.
## 2 Materials and Methods
### Data Collection
#### 2.1.2 Selection Process  
The search yielded 527 articles; only studies focusing specifically on lettuce, basil, or spinach were advanced to further stages of analysis. Of the 527 articles, 281 were deleted due to being duplicates. After applying these criteria, 246 papers were selected for an in-depth screening process. The in-depth screening process involved a thorough review of all titles and abstracts. For Google Scholar, this step was already completed during the initial search. To ensure relevance to sensing technologies, only studies utilizing spectral ranges between 200 nm and 2500 nm were included, while papers based solely on RGB imaging were excluded. Articles that did not detect or estimate biochemical traits were also excluded. For papers where the relevance could not be determined solely from the title and abstract, the full content of the paper was examined to determine whether it should be included or excluded. This systematic approach ensured that only the most relevant studies were included for detailed analysis. In the final exclusion, 60 papers were removed because they were review papers, not relevant, or not written in English. The PRISMA 2020 flow diagram of the screening process is shown in Figure 1. The plant type, spectral wavelengths, analytical methods, achieved accuracies, and the number of samples utilized across all accepted studies were meticulously gathered and analyzed. This information was organized into tables, categorized by the specific chemical compounds or elements of interest. Tables were deliberately chosen for their clarity and comparative utility due to significant methodological heterogeneity. Heterogeneity among studies was qualitatively described through structured tabular comparison rather than quantitatively via meta-analysis. The table contents are described in the following subsection.
## 3 Proposed Method
### Attention-Guided Transformer Module (AGTM)
#### 3.2.2 Attention Guiding Block--Bottleneck (AGB-B)  
Different from the above AGB-T, AGB-B in the bottleneck stage is designed to target and guide the low-level encoded features. The channel number of feature maps in the bottleneck stage is relatively large, but the size of each feature is relatively small compared to those in the encoding stage. Therefore, it is crucial to implement a dynamic selection mechanism that adaptively enables each neuron to adjust its receptive field size. Here, we introduce the selective kernel (SK) network [55] to the AGB-B, which is shown in Figure 3b. In the SK network, the input feature maps first pass through two convolution layers with different respective fields, followed by a batch normalization layer and a ReLU layer. The upper and lower outputs here are noted as **U** and **V**, respectively. Then, these output feature maps are elementwise summed and traverse a global average pool (GAP) to generate channel-wise statistics with different respective fields. After that, the inner feature maps are sent through two full connection layers to enable the guidance for the adaptive selections. Lastly, a soft attention layer is applied across different channels to extract information from different respective fields selectively. Here, we use the notations \(F(\textbf{U})\) and \(G(\textbf{V})\in\mathbb{R}^{C\times 1}\) to represent the upper and lower input of the Select layer in Figure 3b, where \(F(\cdot)\) and \(G(\cdot)\) denote the previous inner feature map process, and \(C\) denote the number of channels of the inner feature map, the output weight is:  
\[\textbf{w}_{c}^{upper}=\frac{e^{F_{c}(\textbf{U})}}{e^{F_{c}(\textbf{U})}+e^{ G_{c}(\textbf{V})}},\quad\textbf{w}_{c}^{lower}=\frac{e^{G_{c}(\textbf{V})}}{e^{F_{c} (\textbf{U})}+e^{G_{c}(\textbf{V})}} \tag{2}\]  
Figure 3: Architectures of the Attention Guiding Block (AGB); (**a**) is the Attention Guiding Block—Top (AGB-T); (**b**) is the Attention Guiding Block—Bottleneck (AGB-B); (**c**) is the Feature Distillation Network (FDN). Here, $ denotes the sigmoid function.  
where \(c\) in \(\mathbf{w}_{c}^{upper}\) denotes the \(c\)-th element of the \(\mathbf{w}^{upper}\), likewise \(\mathbf{w}_{c}^{lower}\), \(F_{c}(\mathbf{U})\) and \(G_{c}(\mathbf{V})\). The final attention maps of the SK network are obtained through attention weights on inner feature maps from various respective fields:  
\[\mathbf{A}_{c}=\mathbf{w}_{c}^{upper}\times\mathbf{U}_{c}+\mathbf{w}_{c}^{lower }\times\mathbf{V}_{c},\quad\mathbf{w}_{c}^{upper}+\mathbf{w}_{c}^{lower}=1 \tag{3}\]  
where \(\mathbf{A}=[\begin{array}{cccc}\mathbf{A}_{1},&\mathbf{A}_{2},&\ldots,& \mathbf{A}_{C}\end{array}]\) denotes the output attention maps, \(\mathbf{A}_{c}\in\mathbb{R}^{H\times W}\). \(H\) and \(W\) denote the height and width of the feature maps, respectively.
## 3 Experimental Setup for Wire Rope Vibration Measurement under Tension
### Wire Rope Dynamic Characteristics Analysis under Tension
The bending stiffness of the wire rope increased from 2.20 to 5.49 Nm\({}^{2}\). The loss factor of the wire rope decreased from 0.062 to 0.038. Furthermore, the regression model developed for the wire rope facilitates predictions of bending stiffness and loss factor dependent on tension. Figure 9: Measured dynamic characteristics of the steel beam and wire rope with tensions (kN); (**a**) bending stiffness and (**b**) loss factor of the steel beam. (**c**) Bending stiffness and (**d**) loss factor of the wire rope. Figure 11: Average dynamic characteristic values of the steel beam and wire rope with tensions; (**a**) average bending stiffness and (**b**) loss factor of the steel beam. (**c**) Average bending stiffness and (**d**) loss factor of the wire rope. In the steel beam, the R\({}^{2}\) and \(p\)-value were 0.643 and 0.055 for the bending stiffness, and they were 0.012 and 0.837 for the loss factor. In the wire rope, the R\({}^{2}\) and \(p\)-value were 0.995 and \(9.75\times 10^{-6}\) for the bending stiffness, and they were 0.990 and \(3.66\times 10^{-5}\) for the loss factor. Figure 10: Hysteresis curves of the steel beam and wire rope. The area of the hysteresis curve expresses the energy dissipated by internal friction.
# A Review of Blockchain Technology Applications in Ambient Assisted Living
## 3 Results
### Decentralized Data Management
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ambient Assisted** & **Blockchain Usage** & **References** \\
**Living Use Case** & Safe, decentralized storage of data & [42; 59; 77; 82; 91] \\ \hline \multirow{3}{*}{Decentralized patient data management} & Data sharing and smart contracts & [25; 38; 39; 87; 88] \\  & Data ownership & [12; 14; 25; 26; 49; 83] \\ \cline{1-1}  & Health and care processes integration & [89; 90] \\ \cline{1-1}  & Data analytics & [40; 42; 43; 46] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Blockchain usage benefits data management in AAL systems. IoT-driven care systems and tackle different difficulties. It offers a two-stage architectural solution for integrating IoT with blockchain using dew and cloudlet computing. In [46], a blockchain framework is proposed that allows data owners to create preferred access controls for electronic patient records. A two-chain architecture is used to store access controls as well as data transactions and employs a clustering strategy to handle the real growth difficulties associated with distributed ledgers. Even though, as listed above, blockchain technology brings potential benefits for data management applications to ambient assisted living systems, there are challenges that limit its adoption [30; 37]. Services built on fuzzy systems and blockchain technology are proposed in [49] to provide a behavior-driven intuitive security measure for healthcare IoT environments and networks based on blockchain. In [85], the authors explore different methods for assisting in medication usage. Blockchain can be used to store and disseminate information concerning adverse responses to prescription pharmaceuticals [97]. In [26], various scenarios are presented in the form of an analysis that verifies the key aspects of establishing, verifying, and changing people's identities. It presents various blockchain identity verification solutions available on the market built on top of public or private blockchains. Finally, a significant amount of time might be saved if patient characteristics are disseminated among all relevant stakeholders across the care continuum [91], illustrating how distributed ledgers and blockchain technology might be used for AAL systems to support decentralized data management [82].
# The Influence of Ionizing Radiation on Paclitaxel-Loaded Nanoparticles Based on PLGA
## 2 Materials and Methods  
### Materials  
L-Lactide (L-LA, (3S)-_cis_-3,6-Dimethyl-1,4-dioxane-2,5-dione, 98%), poly(vinyl alcohol) (PVA, \(M_{\mathrm{w}}\) = 13-23 kDa, 87-89% hydrolyzed), Cremophor(r) EL and poly(ethylene glycol) (PEG, \(M_{\mathrm{n}}\) = 200 Da) were purchased from Sigma-Aldrich Co. (Poznan, Poland). Glycolide (GA, 1,4-Dioxane-2,5-dione, 98%) was purchased from TCI Europe N.V. Co. (Zwijndrecht, Belgium), and bismuth 2-ethylhexanoate (BiOct3) was obtained from Alfa Aesar Co., which is part of Thermo Fisher Scientific (Kandel, Germany). Methanol (CH3 OH, analytical pure), chloroform (CHCl3, analytical pure), dichloromethane (DCM, CH2 Cl2, analytical pure) and hydrochloric acid (HCl, 35-38%) were obtained from POCH Co. (Gliwice, Poland). Acetonitrile (ACN, gradient grade for liquid chromatography) and trifluoroacetic acid (TFA, \(\geq\)99.0%) were purchased from Merck KGaA (Darmstadt, Germany), and phosphate-buffered saline (PBS, pH = 7.4) from Carl Roth GmbH + Co. KG (Karlsruhe, Germany). Paclitaxel (PTX, T45) was received from the Medical University of Gdansk (Gdansk, Poland).
# Marine Bioactives and Their Application in the Food Industry: A Review
## 3 Marine Bioactive Compounds and the Food Industry  
The marine environment is a valuable source of numerous bioactive compounds with unique biological functions suitable for use as functional food ingredients. Marine products are rich in different kinds of polysaccharides, including SPs and chitin/chitosan, proteins, including both essential and non-essential amino acids, PUFAs, vitamins, minerals, carotenoids, and other bioactive compounds [4; 5]. Various sources of marine organisms offer these bioactive compounds in varying quantities, and they can be incorporated into the food production process at different stages, from processing to storage [137]. Moreover, nowadays, consumers are becoming more conscious of the link between a wholesome diet and the prevention of diseases. Hence, marine-derived biologically active components have enormous potential to be the best ingredients for incorporation into foods as they enhance both the nutritional and economic value of the food. In the last decades, a wide range of marine-derived functional foods have been launched into the world market, which could be fortified with many functional ingredients, including antioxidants, vitamins, carotenoids, probiotics, minerals, fiber, PUFAs, polysaccharides, protein hydrolysates, and other dietary supplements. For instance, seaweeds have been incorporated in meat, dairy, confectionery, bakery, and pasta products due to their ability to reduce blood pressure, fight obesity, lower cholesterol content, and tackle free radicals. Most common commercially available seaweed-based supplements include seaweed-derived functional drinks and seaweed biscuits, among others [4,8]. Apart from this, marine-derived polysaccharides (e.g., chitosan, alginate, and carrageenan) and protein (e.g., gelatin) have gained extensive research attention as primary biopolymers for preparing edible films and coatings as they are available worldwide at relatively low costs with high performance [128,144].
## 2 Q1: What Are the Recent Trends of UIoT System?
### Communication Technologies of UIoT
As stated, electromagnetic signals and optical signals have a limited transmission range. In addition, these signals are heavily affected by attenuation, scattering, and turbulence. This leads to a limit on the transmission distance. Therefore, acoustic communication technology is used for long-distance propagation in UIoT. The communication distance is up to 1 km at a speed of approximately 1500 m/second. Magnetic Induction (MI) based communication technology is most commonly used in the underground of the seabed. It can cover a maximum of 10 m. The MI signal propagation speed is the same as the speed of light inside water, 3 \(\times\) 10\({}^{8}\) m/s. Moreover, the data rate is in the order of kilobits per second (kbps).
## 3 Results and Discussion
### Effect of Pyrolysis Temperature on BCs toward MB Removal  
The adsorption capacities of BCs derived from CPP toward MB at different temperatures were discussed (Figure 6). Clearly, CPP200 possessed the highest adsorption capacity for MB. Although BET data showed that the SSA and TPV of CPP500 were larger than CPP200, the adsorption capacities of BCs gradually decreased with an increase in temperature, indicating that surface functional groups dominated the adsorption process, and that a decrease in surface functional groups would negatively affect adsorption capacities. The existence of more oxygen-containing groups on the surfaces of BCs could provide more negatively charged sites, which facilitate electrostatic attractions with positively charged MB; hence, higher adsorption capacities could be achieved with these groups [39; 40]. The elemental analyses also showed that an increase in pyrolysis temperature would lead to a loss of oxygen-containing groups. A decrease in the O/C ratio would cause a subsequent decrease in surface hydrophilicity and surface polar functional groups, and hence BCs prepared at higher temperatures would become less hydrophilic [41].  
Figure 5: XPS analysis: (**A**) survey XPS spectra of CPP200; and XPS-peak-differentiation-imitating analyses of C1s (**B**) and O1s (**C**).  
Figure 6: Adsorption of MB by various BCs (CPP200, CPP250, CPP300, CPP350, CPP400, CPP450 and CPP500; MB concentration of 50 mg/L, pH = 7.32, adsorbent dosage of 5.0 mg and contact time of 90 min, T = 25 \({}^{\circ}\)C).
## 2 Materials and Methods
### Gamma Ray and Penetrometry Surveys  
\(\gamma\)-rays and penetrometry prospections were carried out on a regular grid of points (20 \(\times\) 20 m\({}^{2}\)) for 81 total points [22]. The GF Instruments Gamma Surveyor is a \(\gamma\)-rays spectrometer designed to measure natural and artificial radioisotopes in the ground, and in this work, was used to measure \(\gamma\)-rays dose rate, U, Th and K% to identify any anomalous increase (or decrease) in background radiation. Three minutes were used to set up for each measurement of \(\gamma\)-rays dose rate (81 points) and five minutes were used for the measurement of U, Th and K on a selection of points (19 points). The instrument used in this study was delivered with a factory calibration set to high-volume uranium standard. The total energy window was set from 0.12 to 3.00 MeV. Field measurements were carried out in static mode and the radioactivity was measured at discrete points. The detector was placed directly on the earth surface, to minimize the effects of local variation in relief and radioelement distribution. Spectrum and Assay modes were selected in the GF \(\gamma\)-rays spectrometer menu using a \({}^{137}\)Cs \(\gamma\)-rays source, to stabilize the automatic gain control with an external GSSI (\(\pm\)4 m). Measurement time was taken (180 s on each of the 81 points).  
Then, an ultrasonic cone penetrometer (Rimik CP20) was used for the measurement and recording of cone index (CI) data on soils to the depth of 600 mm in field, with CI values up to 5 MPa. Penetrometers equipped with ultrasonic transducers calculated the depth of the probe and its sinking speed in real time, measuring the return time of a signal sent by the transducer, which bounced off a metal target placed near the ground and returned to the transducer. A data logger electronically recorded the force required to push the probe into the ground and the depth reading. For each point, 4-5 measurements in a radius of 0.5 m were acquired, to avoid erroneous interpretation due to fragments in soil. Collected data were informative of soil density and compaction.  
Both \(\gamma\)-rays and CI data were interpolated and spatialized using the inverse distance weighting (IDW), 30 m radius, with QGIS 3.10, an open source software, developed by a team of dedicated volunteers, companies and organisations (www.qgis.org).