esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)
#### 2.5.2 OPLS-DA Plots  
The OPLS-DA method was applied to discern the separation between the different encapsulation treatments and to identify the variables responsible for the separation. The score plots provide a visual representation of the sample distribution and the ability of the encapsulating agents to maintain the aroma profile of the jasmine instant tea. The OPLS-DA score plot for the jasmine instant tea, depicted in Figure S4 in the Supplementary Materials, demonstrates the variation in the samples' aroma profiles post-encapsulation. Each sample is colored according to the encapsulating agent used, with the variation explained by the first predictive and orthogonal components (R2X [1] and R2X [2]). Similar to the PCA results, the plot reveals that the samples encapsulated with gelatin are more closely aligned with the fresh jasmine instant tea, suggesting that gelatin is more effective in preserving the aroma profile compared to acacia, CMC, and maltodextrin.
# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 4 Remote Sensing Image Data Processing
### Point-Cloud Data Processing
#### 4.2.1 Point-Cloud Data Pre-Processing
The results of the ground point classification are shown in Figure 5, where orange represents ground points and white represents non-ground points.
# An Effective Directional Residual Interpolation Algorithm for Color Image Demosaicking
## 2 Related Work  
### The Outline of MLRI  
The proposed algorithm is based on MLRI. First, we introduce the outline of MLRI.  
The interpolation process of the G pixel can be divided into three steps in MLRI.  
1. The G pixel values are calculated through residuals at the location of R and B pixels from horizontal and vertical directions. R and B pixel values also are calculated at the location of G pixels. The calculation of the residuals replaces the calculation of the color difference in [PERSON] and [PERSON]'s interpolation equation [15].  
* 2. MLRI calculates both horizontal and vertical color difference estimations based on Step 1 at each pixel, then MLRI combines and smooths the color difference estimations.
* 3. The color difference estimations are added to the observed R or B pixel values. It aims to interpolate G pixel values.  
After interpolating the G pixel, MLRI interpolates the R and B pixel values, and the method simply replaces the traditional method of using color difference as shown in Figure 2.  
In the past decades, color difference interpolation has been used gradually in image demosaicking. However, residual interpolation has been used recently in MLRI. MLRI uses a novel filter called guided filter. We compare the process of MLRI with that of the color difference interpolation in calculating R pixel. The process of calculating B pixel is similar.  
We introduce the process of color difference interpolation to help us understand the process of MLRI. Firstly, the missing pixel in G image is the average of four observed G pixels around it. By this way, it can get G pixel values at each pixel to combine the interpolated G image. Secondly, the color differences between R and G are calculated at the locations of R pixel by subtracting the interpolated G pixel values from the observed R pixel values (\(R-G\)). Then color differences are interpolated. Finally, an interpolated R image can be calculated by adding G pixel values to the interpolated color image.  
Figure 2 shows the interpolation of the R pixel values by using MLRI in detail according to [21]. First, it can get the interpolated G image as the same method as the color difference interpolation. To minimize the Laplacian energies of the residuals, the guided image and the input image are first passed through a sparse Laplacian filter. The sparse Laplacian filter with MLRI is shown in Table 1. Then MLRI uses the guided filter to generate the tentative estimation of the R (\(\widetilde{R}\)) image. The interpolated G image is used as the guided image, and the observed R image is used as the input image. After that, tentative estimation of the R image uses a mask to obtain the value at the location of R image. Then minimized Laplacian residuals are calculated at the location of R pixel by subtracting the tentative estimation \(\widetilde{R}^{\text{H}}\) values from the observed R pixel values (\(R-\widetilde{R}\)). Then MLRI interpolates the residuals. Finally, an interpolated R image can be calculated by
## 3 Results and Discussions
### Improved Load Ratio  
The load ratio (LR) parameter can be used to analyse and estimate the efficiency of stone columns in terms of ultimate bearing capacity. It is the ratio of the ultimate load sustained by a stone-column-reinforced soil to the ultimate load carried by soft soil without a stone column.Figures 6a-c and 7a-c show the LR variation with settlement for stone columns with diameters of 5,7.5, 10 cm, and various reinforcing forms. As can be seen from the graphs, the LR for stone columns with diameters of 5, 7.5, and 10 cm for VESCs varies between 1.07 and 1.41, 1.20 and 1.64, as well as 1.41 and 1.8. The maximum LR is found for a full-length encasement (L\({}_{\tau}\) = L) of VESCs with stronger geotextiles with a value of 1.8, and the minimum LR is for OSCs with a value of 1.07 in the present experimental study. The value of LR for various reinforcement lengths, along with the increasing diameter, progressively increases in the case of VESCs.  
Figure 6: _Cont._Figure 6: Load-Settlement variation of HRSC for various arrangements on single stone columns with diameters (a) 50 mm, (b) 75 mm, and (c) 100 mm.  
Figure 7: _Cont._  
In the case of HRSC, the value of LR for stone columns with diameters 5, 7.5 and 10 cm varies between 1.07 and 1.33, 1.20 and 1.54 as well as 1.41 and 1.72, respectively. As explained earlier, the maximum LR was found for the full length of reinforcement at equal spacing, which was found to be 1.72 compared to the other two methods of HRSC installation. The LR value increases with the tensile strength of the reinforcing material in VESCs and HRSCs, as shown in Figures 6a-c and 7a-c. Because the reinforcement material offered lateral confinement on the columns, the degree of bulging was minimised.
# Underwater Drone Architecture for Marine Digital Twin: Lessons Learned from SUSHI DROP Project +
## 2 Materials and Methods
### Mission Survey
#### 2.4.2 Close Seabed Survey  
As above mentioned, starting from the MBES 3D reconstructions, hotspots can be identified that can be inspected by optical sensor to obtain deeper morphological data. During this operation it is necessary to use Blucy in ROV mode because it navigates in hostile environment with low altitude from 5.0 m to 1.0 m depending on turbidity found in surveyed area. Furthermore, the data are transferred in real time with fiber optical cable with constant supervision by an operator who can perform corrective navigation maneuvers as needed. Moreover, the acquisition of appropriate optical images is possible only in situations of suitable seabed with a reduced suspension of sediment in water, at close distance from the seabed itself and in particular periods of the year in view of the fixed marine currents [49]. In spite of the previous precautions, it may also be necessary to improve the images during post-processing through the use of specific algorithms [50] and color correction [51].  
In this operation the survey lines are designed taking into account the following parameters:  
1. Constant Blucy navigation altitude from the seabed based on water turbidity;
2. FOV at seabed;
3. Speed of navigation;
4. Interval between images;5 Appropriate overlap for underwater surveys [52].  
Using the 24 mm focal length of the BottomCAM lens at a reference altitude of 5.0 m, it is possible to cover a seabed swath width of 7.5 m with a GSD of 1 mm, thanks to the 24 mpixel sensor.
## 4 Simulation Results and Discussions
### Many-Objective OPF Problem
#### 4.3.1 IEEE 30-Bus System  
For the IEEE 30-bus system, when all four objectives are considered as the objective functions, case 19, the best-compromised solutions between each objective for the MaOPF problem of the MaMPA are compared with those of PSO, PESA-II, SPEA2, and SSA, as presented in Table 13. The calculated hypervolume values for each algorithm are depicted in Table 14.  
For solving the MaOPF problem in the IEEE 30-bus system, the best-compromised solution provided by MaMPA is competitive with the compared algorithms as in Table 13. The provided cost is the third best one, and the provided loss and VSI are the best values among the compared algorithms while the emission value is almost the worst one. However, based on the hypervolume indicator in Table 14, the hypervolume values of MaMPA are better than those of PSO, PESA-II, SPEA2, and SSA at around 0.59%, 18.18%, 4.05%, and 17.07%, respectively. So, MaMPA generated the best hypervolume value indicating that the Pareto fronts of MaMPA are the best for this case.
# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
## 4 Case Study and Analysis
### Result Analysis and Discussion  
#### 4.3.1 Results Analysis  
Figure 18 shows that the model trained by adding CLAHE-applied data to model scaling applied model exhibits the best performance. However, it is a numerically very weak performance improvement result. Therefore, we tried to compare the results by extracting inference results using the model. Figure 19A shows the results extracted using the applied model scaling model that was found to be suitable in Figure 18. It verifies that only two classes ('NG' and 'T-NG') were extracted from four classes in the real results. Its mAP value was also approximately 61% for the 'NG' class and approximately 59% for the 'T-NG' class.  
Meanwhile, Figure 19B verifies that all four classes were extracted. Except for the 'T-NG' class, all other classes showed more than 90% of mAP. Compared with the aforementioned results, the mAP of the 'NG' class increased by approximately 36% and that of the 'T-NG' class increased by approximately 19%.  
Table 9 shows YOLOv5's large and medium as analysis comparison targets for additional model analysis. The performance of the large and medium models of YOLOv5 was 95.6% and 95.1, respectively. This is an improved performance compared with the small model of YOLOv5, the basic model of this study, but it is lower than the performance of the proposed model, 96.9%. In addition, even when training was conducted by adding data applied with CLAHE from the basic model, the results were about 1.6% lower than the proposed model.  
Table 10 presents the comparison results of the number of inspections per second using human inspectors and the proposed model. Existing inspectors can inspect one side  
Figure 19: Inference results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Precision**} & **Recall** & **mAP** \\ \hline \multirow{3}{*}{Epoch} & 100 & 0.917 & 0.890 & 0.944 \\ \cline{2-5}  & 150 & 0.915 & 0.897 & 0.951 \\ \cline{2-5}  & 200 & 0.926 & 0.883 & 0.950 \\ \cline{2-5}  & 400 & 0.907 & 0.901 & 0.948 \\ \hline \multirow{2}{*}{Learning rate} & 0.001 & 0.924 & 0.897 & 0.953 \\ \cline{2-5}  & 0.1 & 0.887 & 0.909 & 0.939 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance resolution based on hyperparameters.  
of the coil at a rate of around 10 coils per second. By contrast, the proposed model in this study takes approximately 0.9 ms to inspect one side of the coil, which gives a rate of around 1111 coils per second. However, this is a numerical result. When on-site tests were conducted with 1000 coils of data for the field applicability check, it took around 50-70 s. In summary, if this amount is inspected by a human inspector, it takes around 100-150 s, but the proposed YOLOv5 model takes nearly half the time.
## 2 Proposed Scheme
### On Content Owner Side
#### 2.1.4 3D Mesh Model Encryption and Uploading  
The last procedure on the content owner side is to encrypt the 3D mesh model. First, the three-dimensional coordinate of each vertex needs to be converted into the binary system, and the length of the binary bits is judged by:  
\[\delta=\begin{cases}8,&1\leq\sigma\leq 2,\\ 16,&3\leq\sigma\leq 4.\\ 32,&5\leq\sigma\leq 9\\ 64,&10\leq\sigma\leq 32\end{cases} \tag{9}\]  
As each coordinate of the OFF format 3D mesh model only has 6 valid bits, uniformly, we convert coordinates into the 32 bits binary system and preserve 5 valid bits in the experimental results. Then, for each reference vertex and unusable vertex, the model encryption key \(K_{m}\) with random binary bits is used to encrypt three-dimensional coordinates by excursive or operation. According to the descriptions in Sections 2.1.2 and 2.1.3, reference vertices and unusable vertices are predefined, and no additional records are necessary. Next, all binary bits of usable vertices can be vacated for embedding recovery information \(\mathbb{R}\). Before embedding, a recovery information encryption key \(K_{r}\) is generated to encrypt \(\mathbb{R}\) by excursive or operation. When embedding the encrypted recovery information \(\overline{\mathbb{R}}\), the first usable vertex is used to record the length of \(\overline{\mathbb{R}}\), and then replace all bits of following usable vertices' three-dimensional coordinates. As the distance between closest pair vertices is short, \(\beta\) is far less than 32 bits, so the usable vertex can be efficiently compressed. Therefore, after embedding \(\overline{\mathbb{R}}\), the rest bits of the usable vertices' coordinates can be fully vacated for additional data embedding on the cloud server side, but these bits should be replaced by random bits to ensure the security. Finally, the encrypted 3D mesh model \(\mathbb{M}\) can be obtained and uploaded to the cloud.
## 3 Results
### VIF Analysis  
Of the five metrics in the BMP summary group, none were found to be collinear (_i.e._, BMPs/farm, BMP % flow accumulation, BMP mean flow accumulation, BMP flow distance to sampling point, and BMP flow distance to river). In the BMP type grouping 19 of 28 metrics were found to be collinear, leaving only nine remaining metrics for further analysis, which included MS/farm, MS median flow accumulation, MS % flow accumulation, MS flow distance to sampling point, MS flow distance to river, LAR/farm, LAR median flow accumulation, LAR % flow accumulation, and % RBA. Among the water quality parameters, all phosphorus species were collinear leaving six of eight independent variables for further analysis including TN, NH\({}_{4}\), NO\({}_{2}\)-NO\({}_{3}\), TP, turbidity, and TSS.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site Characteristic** & **Mean** & **Min.** & **Max.** & **Med.** & **St. Dev.** & **CV** \\ \hline Average stream depth (m) & 0.14 & 0.06 & 0.27 & 0.12 & 0.06 & 0.42 \\ Average stream width (m) & 1.65 & 0.85 & 2.86 & 1.59 & 0.56 & 0.34 \\ Flow velocity (m/s) & 0.05 & \textless{}0.01 & 0.13 & 0.03 & 0.06 & 1.07 \\ Discharge (m\({}^{3}\)/s) & 0.02 & \textless{}0.01 & 0.05 & 0.01 & 0.02 & 1.04 \\ Temperature (\({}^{\circ}\)C) & 17.96 & 15.80 & 19.88 & 17.99 & 1.42 & 0.08 \\ Canopy cover (\%) & 29.27 & 6.92 & 85.75 & 25.08 & 22.50 & 0.77 \\ pH & 7.87 & 7.52 & 8.37 & 7.86 & 0.27 & 0.03 \\ Conductivity (\(\mu\)S/cm) & 575.84 & 472.00 & 753.00 & 551.00 & 87.74 & 0.15 \\ TN (N mg/L) & 1.99 & 0.75 & 4.68 & 1.87 & 1.11 & 0.56 \\  & 3.39 & 1.56 & 5.80 & 3.38 & 1.12 & 0.33 \\ NH\({}_{4}\)\({}^{+}\) (N mg/L) & 0.07 & 0.01 & 0.46 & 0.03 & 0.12 & 1.73 \\  & 0.30 & \textless{}0.01 & 2.62 & 0.04 & 0.73 & 2.41 \\ NO\({}_{2}\)-NO\({}_{3}\) (N mg/L) & 1.12 & 0.09 & 4.00 & 0.52 & 1.20 & 1.07 \\  & 2.20 & 0.06 & 4.08 & 2.33 & 1.36 & 0.62 \\ TP (P mg/L) & 0.22 & 0.02 & 0.92 & 0.07 & 0.30 & 1.37 \\  & 0.32 & 0.02 & 1.70 & 0.08 & 0.46 & 1.47 \\ SRP (P mg/L) & 0.15 & \textless{}0.01 & 0.71 & 0.02 & 0.25 & 1.62 \\  & 0.21 & 0.01 & 1.24 & 0.05 & 0.35 & 1.63 \\ TDP (P mg/L) & 0.16 & 0.01 & 0.68 & 0.03 & 0.23 & 1.50 \\  & 0.22 & 0.01 & 1.13 & 0.06 & 0.32 & 1.49 \\ Turbidity (NTU) & 7.75 & 1.53 & 27.46 & 3.97 & 7.70 & 0.99 \\  & 7.57 & 1.10 & 24.27 & 4.93 & 7.68 & 1.02 \\ TSS (mg/L) & 7.53 & 2.40 & 25.60 & 4.40 & 6.60 & 0.88 \\  & 10.16 & 1.60 & 28.53 & 5.20 & 9.38 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics for physico-chemical parameters collected from 13 study streams. Water quality parameters were measured during logger deployment (shaded rows) and after the sampling period during logger retrieval (unshaded rows). Values of <0.01 indicate concentrations or velocities below detection limits.
## 2 Architecture
### M2M Gateway Resource Control and Management
#### 2.2.2 Resource Mapping Mechanism
If there is no matching term, return an error message. 7. Encapsulate the data packet according to the specific protocol format.
## 4 Production System Case Study
### Design of the Generic Case Study
#### 4.1.3 Assumptions  
After the problem was defined, we generated assumptions and defined the standard values that informed the models. These provided the basis for the model behavior and how the research questions were determined. First, assumptions were defined to simplify the model with a focus on the simulation goal:  
* Time restrictions: first, we defined a time horizon and units of time. In this study, we simulated ten working years, or 500 weeks, to evaluate influences in the medium and long terms.
* Production times for the two products in the supply chain were not variable.
* Times for material transport and employee movements were not variable.
* Distribution of finished products as given.
* Procurement of raw material as given.
* Demand change depended on service level. Therefore, market demand was reduced in quantity of units to real demand.
* Each order had a production unit.
* Bills of materials were not considered.  
The following assumptions were made related to the points to be fulfilled in order to conduct a comparison between the simulation models for a defined simulation demand scenario:  
* The same demand using replication.
* Same initial situation with no backlog and the same conditions of WIP (products on their ongoing transportation to the customers), the same initial inventory in the different warehouses, and initial inventory ready to deliver to customers.
* Same number of employees with same initial distribution and same capacity to perform warehouse activities.
* Same supply chain distribution network (production facility, warehouses, etc.).
* The warehouses had no stock capacity limitations. It was assumed that outsourcing warehouses could be located with additional costs incurred.
* There were no transport limitations between the different production stages. It was assumed that additional third-party logistics could be found.
* One product unit was assumed to be in a mature stage with stable demand and provided USD 1000/unit of margin. The second product was in the process of being launched and provided USD 2000/unit. These values were used to calculate profits. It was assumed that the new model would have a loss in volume due to unknown future demand.
* The simulation model considered sales loss starting from a customer order lead time greater than 60 days.
* A product was considered a finished product after it left the production facility.
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## 5 Results
### High Reflectivity Threads Scenario  
For this test, Array A has been used (see Table 2), tuned as indicated in the table. In Figure 4, three ultrasound images are shown (a-c) resulting from standard DAS (a) and the proposed beamformer for two different \(\kappa\) values--(b) and (c). Figure 4b displays RiB 10%, RiB1% for Figure 4c. In this example, probability density function (pdf) fitting is closely related to speckle effects: on the one hand, \(\epsilon\)--Figure 4d--it is related to the structure reflectivity; and on the other, \(\sigma\) estimates--Figure 4e--to the interference zones caused by point sources. In this case it is shown how interferences contribute to lower lateral resolution on TFM-DAS -Figure 4a.  
Figure 4: Results obtained using Array A on a crossed high reflectivity Nylon structure in water medium. (**a**) DAS. (**b**) RiB 10%. (**c**) RiB 1%. (**d**) \(\epsilon\). (**e**) \(\sigma\). (**f**) DAS vs. RiB.  
Using the proposed method (RiB 10% and RiB 1%, Figure 4b,c), a more reliable image with respect to the analysed structure is obtained, with a considerable increase on image contrast. A point to point comparison of the values obtained using DAS vs RiB methods is depicted in Figure 4f. Theoretical 1:1 ratio is included in red. As illustrated in Figure 4a-c the more reflective the zone, e.g., upper part of graph in Figure 4f, all beamformers behave similarly. However, on regions corresponding to the transmission medium, these suffer a considerable attenuation, inversely proportional to \(\kappa\).
# Study of Load Calculation Models for Anti-Sliding Short Piles Using Finite Difference Method
## 2 Structural Calculation Model for Anti-Sliding Short Piles  
Traditional differential methods frequently partition anti-sliding piles into two segments: the load-bearing section and the embedded section. They rely on iterative techniques to address the problem, incorporating the continuity condition at the sliding surface, which can be a cumbersome process. To circumvent this computational methodology and account for the load characteristics of anti-sliding short piles, as well as principles from differential theory, this study embraces the Euler-Bernoulli beam theory. It conceptualizes anti-sliding short piles as vertically oriented elastic foundation beams and incorporates soil springs to simulate the soil in front of the pile, thus modeling its interaction with the anti-sliding short pile. Additionally, this study uniformly discretizes the support structure from top to bottom, bypassing the conventional differential algorithms that partition the anti-sliding short pile into two sections at the sliding surface. This streamlines the solving process, consequently augmenting both efficiency and precision in the computation of internal forces within anti-sliding short piles.