esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Precipitation Indices
#### 3.2.2 Number of Days with Precipitation  
The largest change in the number of days with precipitation in the RCP4.5 scenario for the 2030 horizon is expected in the northern and eastern parts of the country (Figure 7). The modeling results show an increase of 2% to 3% (2.2 days) in cities located in Pomerania and 3% to 4% (2 mm) in the eastern part of the Mazowieckie Voivodship. A 2% to 3% (2.2 days) increase is also predicted for cities in the Malopolska region. In the rest of the country, the changes are minor and do not exceed 2% (1.5 days) compared to the base year. In some cities in central Poland, a slight decrease in the number of days with precipitation is expected. The situation for the 2050 horizon is similar, with the highest increase in cities located in the northeastern part of the country, where the increase in the value relative to 2010 reaches 5% (4 days). An increase of up to 4% (2.9 days) is observed in the central part of the country, which indicates the value of changes in relation to the 2030 horizon. As for the 2030 horizon, the smallest changes are expected in the southern and western parts of the country.  
In the RCP8.5 scenario, these changes are more substantial. In the 2030 horizon, the largest changes are observed in the southern part of Poland, where the increase in the indicator value reaches 4% (3.6 days) and, in the case of Malopolska (Krakow), as much as 10% is expected (5.3 days). It is not predicted to exceed 3% (2.5 days) in the rest of the country. In the 2050 horizon, the largest growth is forecast in the central and southern parts of the country. The increase in value in these regions is up to 10% (5.6 days) compared to the base year. The smallest changes are expected to occur in the north and northwest of Poland.
# Wind Dispersal of Natural and Biomimetic Maple Samaras
## 4 Experimental Results
### Samara Morphology  
The natural Norway map samaras have a center of mass that occurs between 24% and 30% of its length measured from the heavy tip of the seed. The average center of mass was 28.5 \(\pm\) 4.0%, as illustrated for a seed of _Acer platanoides_ (Norway map) in Figure 2b.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Samara** & **Natural Norway Maple** & **Artificial Norway Maple** & **Artificial Silver Maple** \\ \hline Mass, \(m\) & \(102.4\pm 30.8\) mg & \(183.6\pm 2.3\) mg & — \\ Length, \(L\) & \(5.65\pm 0.76\) cm & \(5.00\pm 0.00\) cm & — \\ Center of mass & \(28.5\pm 4.0\%\)\(L\) & \(27.7\pm 1.2\%\)\(L\) & — \\ Wing loading, \(W/S\) & \(2.09\pm 0.50\) N/m\({}^{2}\) & \(3.18\pm 0.13\) N/m\({}^{2}\) & — \\ Rotational velocity, \(\Omega\) & \(81.4\pm 27.6\) rad/s & \(93.7\pm 14.4\) rad/s & \(138.6\pm 17.7\) rad/s \\ Wing tip speed, \(v_{t}=R\Omega\) & \(3.20\pm 0.94\) m/s & \(3.36\pm 0.33\) m/s & — \\ Lab descent speed, \(v_{d}\) & \(1.10\pm 0.24\) m/s & \(1.28\pm 0.17\) m/s & \(1.36\pm 0.22\) m/s \\ Field descent speed, \(v_{d}\) & \(1.03\pm 0.32\) m/s & \(1.29\pm 0.36\) m/s & \(1.32\pm 0.36\) m/s \\ Windage coefficient, \(C_{w}\) & \(0.87\pm 0.39\) & \(0.78\pm 0.33\) & \(0.81\pm 0.38\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphological and dynamical properties of natural Norway maple, 3D-printed Norway maple, and 3D-printed silver maple samaras across all three sets of experiments: morphological, laboratory, and field testing. The mean and standard deviation are reported for each characteristic. For the 3D-printed silver maple, the mass, center of mass, and wing loading were not measured.  
The wing loading was also calculated for each of the 30 samaras, with the average wing loading found to be 2.1 N/m\({}^{2}\). Similarly, the artificial Norway mples were sectioned and weighed to analyze their mass distribution characteristics. Their average wing loading was 3.2 N/m\({}^{2}\). As their natural counterparts were not available for testing in large enough numbers, the artificial silver mples did not undergo morphology characterization. The artificial Norway mple seeds were substantially heavier (\(183.6\pm 2.3\) mg), but with far less variability, than their natural counterparts (\(102.4\pm 30.8\) mg), leading to a larger, but more consistent wing loading (artificial: \(3.18\pm 0.13\) N/m\({}^{2}\), natural: \(2.09\pm 0.50\) N/m\({}^{2}\)). However, the center of mass of artificial Norway mple seeds (\(27.7\pm 1.2\%\)\(L\)) was found to be very consistent with the natural seeds (\(28.5\pm 4.0\%\)\(L\)).
## 4 Results and Discussion
### Analysis of the Results of Testing Mortar in the Fresh State  
The mortar of series A-I has the highest value of Vebe degrees (16.33), which represents the measure of consistency according to this method. According to SRPS EN standards, it belongs to class V2.  
During the design of mortar-mixture compositions, it was assumed that in micro-reinforced composites of series (A-II) prepared with a small amount of fibers, there would be no change in the workability of fresh mixtures [22].  
During the preparation of the mortar mixture of series A-II, it was observed that the mixture became more inert and stiff, leading to the use of additional water compared to the projected amount. As a result, the Vebe degrees value for the mortar mixture of series A-II is lower than the reference value and amounts to 11.87, which falls into the same class of stiff consistency V2.  
Mortar mixtures for cement exceeds of series B exhibit expectedly lower Vebe degree values (11.06; 10.02), as confirmed by research from numerous authors. Electrofilter fly ash in the cement paste contributes to increased plasticity and better cohesion [23], alters the behavior of cement-paste dispersion [24], reduces segregation and bleeding, and influences the reduction of hydration heat.  
The plasticizer used in preparing mortar mixtures of series B, creates a thin coating around the cement particles and electrofilter ash, resulting in a notable reduction of friction within the mixture. As a result, the viscosity of series B mortar mixtures is lower than that of series A.  
The values of densities of fresh mortar mixtures are a reliable indicator of the characteristics of the hardened composite. Increasing density results in more compact mortars with better physical and mechanical properties.  
The results of testing densities in the fresh state indicate that the addition of polypropylene fibers in the mixtures does not significantly affect the change in values. Similarly, it can be observed that mortar-mixture samples containing 20% electrofilter ash have densities lower by 5 to 10%, which is expected considering that the specific gravity of electrofilter ash (2190 kg/m\({}^{3}\)) is significantly lower than that of cement (3100 kg/m\({}^{3}\)).
## 2 Materials and Methods
### HPLC Analysis of Juglone  
Chromotagraphic measurements were performed using Varian ProStar model 210/215 with Pro Star 325 UV/VIS detector (Varian, Walnut Creek, CA, USA) and ODS columns (Microsorb MV100C18, Varian, Walnut Creek, CA, USA). All measurements were carried out using the 150 \(\times\) 4.6 mm column. Samples were injected with the Rheodyne 7725 sample injector (Sigma-Aldrich, Poznar, Poland) equipped with a 20 \(\upmu\)L loop. The HPLC measurements were carried out using the following conditions: gradient elution B (25-40 %) from 0 to 30 min. The solvents A and B were water (with phosphoric acid, 0.5% \(v/v\)) and acetonitrile, respectively. Detection was carried out at 420 nm. The calibration curve for juglone was constructed for the concentration range 0.001-0.05 mg/mL. Its characteristics are as follows: calibration curve equation: y = 8.95 \(\times\) 10\({}^{8}\)x \(-\) 5.77 \(\times\) 10\({}^{4}\), standard error of the curve: S\({}_{\text{y}}\) = 1.6 \(\times\) 10\({}^{5}\), standard deviation of the slope coefficient: S\({}_{\text{a}}\) = 2.1 \(\times\) 10\({}^{5}\), standard deviation of the intercept: S\({}_{\text{b}}\) = 1.1 \(\times\) 10\({}^{5}\), limit of quantification: LOQ = 0.001 mg/mL, coefficient of determination: R\({}^{2}\) = 0.9989. The LOQ was assumed to be 10 \(\times\) S\({}_{\text{y}}\)/slope.  
Analyses were performed for the extracts obtained without and after 40-fold concentration. For this purpose, 20 mL of each extract was evaporated to dryness using CentriVap Cold Trap Laboconc (Laboconc Corp., Kansas City, MO, USA) and the dry residue was dissolved in 0.5 mL of the mobile phase.
# Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing
## 4 Proposed Technique
### CQI-Based Offloading to Fog Nodes  
The key idea of the first part of the proposed technique is to offload the task to the nearest fog node, depending on the channel quality between the task node and the fog node. In a vehicular network, RSUs regularly send pilot signals to vehicles for control and synchronization purposes. The received signal strength of these pilot signals is mapped to a 4 bit value (varying from 0 to 15), known as the channel quality indicator (CQI), which is a measure of channel conditions between RSUs and vehicles. These CQI values are periodically reported by each vehicle to RSUs.  
Let \(v_{s}\) be the source vehicle and \(r_{s}\) be the connected fog RSU. We take \(\alpha(v_{s},r_{s})\) as the offloading ratio of the task transmitted from \(v_{s}\) to \(r_{s}\). The proposed technique uses the mapped CQI value obtained at \(v_{s}\) to find \(\alpha(v_{s},r_{s})\), as follows:  
\[\alpha(v_{s},r_{s})=\frac{1}{1+e^{-c_{1}((CQI_{max}-CQI_{v_{s},r_{s}})-c_{2})}} \tag{4}\]  
Equation (4) is a logistic function, known as sigmoid. Here, \(CQI_{max}\) is the maximum CQI value of 15. Based on the values of \(c_{1}\) and \(c_{2}\), the sigmoid function returns a different value for the offloading ratio for different \(CQI_{v_{s},r_{s}}\) values. A key feature of the sigmoid function used in Equation (4) is that it returns a higher offloading ratio if the channel conditions are good, i.e., \((CQI_{max}-CQI_{v_{s},r_{s}})\) is less. As channel conditions deteriorate, the offloading ratio significantly reduces.
## 4 Implementation of Smart Cyber-Physical System
### Automation Level of the Testbed in the Case of Normal Status  
In order to activate the testbed, the PLC programs were used. Figure 6 shows the operation mechanism of the system in the case of normal status. The control program was generated according to the operation sequence, and the I/O diagram of PLCs. A scenario of machining process was applied for testing the functionality of the CPS on the testbed. Machining tasks including turning, drilling, and milling were carried out on three machines with the processing time of 4, 2, and 5 min, respectively.  
Figure 5: Analyzing communication protocols of devices.  
Figure 6: Generating programmable logic controller (PLC) program of the system in the case of normal status.  
The PLC programs for executing the testbed were generated using Simatic Step 7 TM software. The algorithm 1 for generating PLC program in case of normal status of the system is given as follows:  
```
1:Turning process for work-piece #1;  Setup time to start: 10; Ifts[1]=t0 then  Starting the turning process for work-piece #1 at PLC #1: turn on (green light); iftf[1]=t0+4 then  Finishing the turning process for work-piece #1 at PLC #1: turn off (green light); endif;
2:Turning process for work-piece [i]; ForW[i] (i=2 to n) ifts[i]=tf[i - 1]+1 then  Starting the turning process for work-piece[i] at PLC #1: turn on (green light); iftf[i]=ts[i]+4 then  Finishing the turning process for work-piece[i] at PLC #1: turn off (green light); endif; endif;
3:Drilling process; Forj=1 to n ifts[j]=tf[i]+1 then  Starting the drilling process for work-piece[i] at PLC #2: turn on (green light); iftf[j]=ts[j]+2 then  Finishing the drilling process for work-piece[i] at PLC #2: turn off (green light); endif; endif;
4:Milling process; Fork=1 to n ifts[k]=tf[j]+1 then  Starting the milling process for work-piece[i] at PLC #3: turn on (green light); iftf[k]=ts[k]+5 then  Finishing the milling process for work-piece[i] at PLC #3: turn off (green light); endif; endif; Else  The alarm is turned on: turn on (red light); EndIf.
```  
**Algorithm 1** Generating PLC program in case of normal status of the system
## 4 Discussion
### The Length and Diameter of the Pipes  
In addition to the load pipe, there are two long, slim pipes in the variable displacement mechanism affecting the oscillation of the swash plate. There are severe resonance points when the pipe's length increases in Figure 8a, which coincide with the pressure response of a typical pipe. Hence, resonance points of the swash plate oscillation are due to the length of the pipe when it equals the integral multiples of the pressure wavelength in the pipe. Increasing the load pipe diameter can significantly increase the amplitude of the oscillation, as shown in Figure 8b. The oscillation reaches its maximum at a diameter of 16 mm. Once the diameter of the load pipe exceeds 16 mm, the larger the diameter, the lower the oscillation amplitude. This is because the large diameter of the pipe can be considered as additional volume of the outlet chamber. The amplitude increases as the length of the pipe increases before the control valve in Figure 8c, and similar trends can be observed in Figure 8e. The changing amplitude range of the pipe behind the control valve is about 0.04, which is almost twice as large as the pipe before the control valve. The diameters of the two pipes have an almost converse influence on each other. The large diameter of the pipe before the control valve shows stronger capacitive characteristics than an additional volume of the outlet chamber. Meanwhile, the influence of the pipe diameter behind the control valve and the volume \(V_{3}\) of the actuator piston chamber show similar performance.  
Figure 8: \(Cont.\)  
A short length and large diameter pipe before the control valve, and a short length and small diameter pipe behind the control valve, are a reasonable design choice to reduce the amplitude of the swash plate oscillation.
# A Multi-Scale Rebar Detection Network with an Embedded Attention Mechanism
## 4 Experimental Results and Analysis
### Comparison of Test Results  
In order to demonstrate more intuitively the performance of the RebarNet, the trained model is tested on the test set (confidence parameter conf = 0.45, non-maximum suppression threshold IOU = 0.6), the detection results of both are compared, and results are shown in Figure 16. In Figure 16, the green box is the effect graph of the original YOLOv5s detection, the purple box is the detection effect graph of RebarNet, and the blue circles marked are missed by the original YOLOv5s and accurately detected by the corresponding RebarNet. In Figure 16a,b, the rebar arrangement is denser, and the original YOLOv5s missed rebar detection, while RebarNet detected all of the rebars. Figure 16c,d show the comparison of YOLOv5s and RebarNet detection in the case of uneven, adhering, small rebar cross-sections in the bundled rebar. RebarNet still performs well under non-uniform illumination conditions as shown in Figure 16e,f. When the shooting angle of the image deviates, the detection performance of the original YOLOv5s also has occurrences of missed detection, while RebarNet performs excellently, as shown in Figure 16g,h. On the images with smaller rebar diameters, the original YOLOv5s also showed missed detection, while RebarNet was able to correctly detect the location of the rebars, as shown in Figure 16i. In summary,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Precision** & **Recall** & **mAP0.5** & **Parameters** & **Weight** & **FPS** \\ \hline
**Faster** & 87.8\% & 70.3\% & 89.8\% & 91,050,000 & 182.1 MB & 45 \\
**R-CNN** & & & & & \\
**SSD** & 66.7\% & 50.9\% & 84.9\% & 45,300,000 & 90.6 MB & 37 \\
**RetinaNet** & 65.8\% & 52.4\% & 71.5\% & 59,000,000 & 118 MB & 49 \\
**CenterNet** & 56.6\% & 48.7\% & 72.1\% & 77,000,000 & 154 MB & 56 \\
**CornerNet** & 62.5\% & 50.8\% & 71.7\% & 81,500,000 & 163 MB & 63 \\
**YOLOv3** & 95.7\% & 84.5\% & 86.2\% & 9,500,000 & 19.0 MB & 81 \\
**YOLOv4** & 76.6\% & 68.4\% & 90.3\% & 244,200,000 & 488.4 MB & 85 \\
**YOLOv5s** & 95.2\% & 79.5\% & 88.9\% & 7,022,326 & 14.4 MB & 92 \\
**ReparNet** & 96.3\% & 96.6\% & 97.9\% & 7,862,352 & 17.0 MB & 89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental results of different detection algorithms.  
RebarNet can accurately detect all rebars in different detection environments, which can basically achieve the requirement of rebar detection in a smart detection environment.  
Figure 16: _Cont_.
## 4 Conclusions  
In this paper, we propose a multidimensional fusion-based Atrous-CNN network structure and use the method to achieve disease detection and identification of potato hyperspectral images. The technique integrates hyperspectral data's spatial and spectral information for analysis, effectively reducing the network's computational cost compared with the traditional 3D-CNN. Since the network fuses multiple dimensional convolutions and uses null convolution to increase the perceptual field of the convolution kernel, it reduces the loss of hyperspectral data information. It makes the extracted spectral features more expressive, which in turn improves the performance of the classification of hyperspectral data. In this paper, Atrous-CNN is applied to potato leaf disease detection. The experimental results show that the proposed method has better classification results than the single 3D-CNN and the traditional method using convolution-pooling operation feature extraction and is an effective network structure for classification and feature extraction of hyperspectral data. Finally, combined with the use of the 1D-CNN network to classify and identify three types of diseases, anthracose, leaf blight, and early blight leaves, the recognition accuracy of this structure is up to 0.9987. Therefore, this study can  
Figure 12: Disease detection through multidimensional fusion of Atrous-CNN.  
be a heuristic method for researchers to design crop disease detection and identification models and provide new solutions for the field.  
The model proposed in this paper effectively solves the common problems in current agricultural disease image detection and has broad application prospects in precision agriculture and agricultural industry efficiency. Future work will expand this area of research to include more complex agrarian scenarios.  
Conceptualization, W.G.; methodology, Z.X.; software, W.G.; validation, T.B. and W.G.; formal analysis, Z.X.; Resources, Z.X.; writing--original draft preparation, W.G.; writing--review and editing, W.G. and T.B.; supervision, Z.X. and W.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China [China] (61661042), Natural Science Foundation of Inner Mongolia [China] (2021 GG0345), Natural Science Foundation of Inner Mongolia Autonomous Region [China] (2021 MS06020).  
Not applicable.  
Datasets can be accessed upon request to the corresponding author.  
The authors would like to appreciate all reviewers for their insightful comments and constructive suggestions to polish this paper's in high quality.  
The authors declare that there are no conflict of interest regarding the publication of this paper.
## 2 Materials and Methods
### (II) FGAGR Psychometric Testing
#### 2.2.2 Measures  
The adapted Greek Functional Gait Assessment scale (FGAGR) was performed to test its psychometric characteristics. The ten activities of walking that it contains in various conditions (in changing speed, with head turns, with eyes closed, in backwards direction, with a narrow base of support, with pivot turns, over obstacles, and on stairs), are scored in a 0-3 ordinal scale of a gradually increased performance from 0 (\"severe impairment\"), 1 (\"moderate impairment\"), 2 (\"mild impairment\"), to 3 (\"normal\"). The maximum score is 30 points [14].  
The Berg Balance Scale (BBS) is a well-established balance test with 14 items evaluating stability in various everyday conditions such as from sitting to standing, in standing position, in one leg stance, with eyes closed, during reaching forward and picking up an object from the floor, placing alternate foot on stool, looking over the shoulders, and turning 360\({}^{\circ}\)[29]. The items are scored in an ordinal scale (0-4) yielding a total score which ranges from 0 to 56 with higher scores indicating better performance and greater independence [30]. The scale was used for the concurrent validity evaluation of the pre-final FGAGR scale.  
The mini-Balance Evaluation Systems Test (mini-BESTest) comprises 14 items evaluating balance systems (anticipatory adjustments, reactive control, compensatory stepping corrections, sensory orientation, dynamic balance and gait) during functional tasks which take only 15 min to be delivered [31]. This balance scale has been recently developed, but its excellent reliability and its strong correlation with other balance measures make it one of the best and most common choices for assessing balance in older adults [32; 33].  
The Timed Up and Go (TUG) test is a very easy balance test which requires the participant to stand up from a chair, walk 3 m, turn, walk back, and sit down on the same chair [34]. The time to administer the test has been positively associated with a past history of falls, and therefore the TUG is commonly used to predict the risk of falling in elderly people. Although various values have been proposed as cutoff scores, the score of 11.1 min has been found not only to predict future falls, but also to be 80% sensitive and 56% specific in identifying actual falls reported in older adults (over 60 years) with vestibular dysfunction [35].  
The Falls Efficacy Scale-International (FES-I) questionnaire was used to explore the participants' \"concern\" of falling in everyday living activities [36]. It contains 16 questions concerning activities inside and outside the home, it is very easy and quick to complete, and has provided high certainty of evidence supporting its reliability, construct validity, and responsiveness [37]. Each item of the FES-I can be scored from 1 (not at all concerned) to 4 (very concerned), yielding a total score which ranges from 16 (absence of concern) to 64 (extreme concern) [38]. Score > 23 for the scale indicates a high concern about falling [39].