esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 7 ANN Results with Simulated Degradation on _I-V_ Curves
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Applied Degradation:**\(\boldsymbol{\eta^{deg}=0.96\eta(-4\%)}\)} \\ \hline \(G\left[\frac{\boldsymbol{W}}{\text{m}^{2}}\right]\) & \(T\left[\text{${}^{\circ}$C}\right]\) & & \(\boldsymbol{I_{ph}[A]}\) & \(\boldsymbol{I_{sat}[\mu A]}\) & \(\boldsymbol{\eta[\text{-}]}\) & \(\boldsymbol{R_{s}[\Omega]}\) & \(\boldsymbol{R_{sh}[\Omega]}\) \\ \hline  & & \(\mathfrak{ph}\) & \(2.194\) & \(0.204\) & \(1.331\) & \(0.219\) & \(496\) \\
445 & \(31\) & \(\mathfrak{P}_{\text{deg}}\) & \(2.192\) & \(0.222\) & \(1.290\) & \(0.252\) & \(538\) \\  & & \(\Delta\mathfrak{p}_{\%}\) & \(-0.07\) & \(8.8\) & \(-3.06\) & \(15.0\) & \(8.4\) \\ \cline{3-8}  & & \(\Delta\mathfrak{P}_{\text{mpp},\%}\) & \(0.9\) & \(-0.1\) & \(-0.63\) & \(-3.1\) & \(-0.37\) & \(0.14\) \\ \hline  & & \(\mathfrak{ph}\) & \(4.311\) & \(0.445\) & \(1.296\) & \(0.240\) & \(449\) \\
865 & \(41\) & \(\mathfrak{P}_{\text{deg}}\) & \(4.283\) & \(0.432\) & \(1.239\) & \(0.243\) & \(446\) \\  & & \(\Delta\mathfrak{p}_{\%}\) & \(-0.67\) & \(-2.8\) & \(-4.41\) & \(0.85\) & \(-0.6\) \\ \cline{3-8}  & & \(\Delta\mathfrak{P}_{\text{mpp},\%}\) & \(0.9\) & \(-0.67\) & \(0.23\) & \(-4.61\) & \(-0.07\) & \(-0.01\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SDM parameters estimated with ANN for _1-V_ curves of Figure 15.  
Figure 15: ANN identification of degraded curve with \(-3\%\) of variation on \(\eta\) for two different irradiance and temperature conditions. By analyzing the results shown in Tables 5-9, in particular \(\mathbf{\Omega}\mathbf{\mathrm{p}}_{\%}\), it is evident that the ANN allows to associate, with a good approximation, the degradation effect introduced on the \(I\)-\(V\) curve to the corresponding SDM parameter. Nevertheless, in some cases, the results of ANN parameters identification are not completely satisfactory. For instance, in the first example of Table 5, the ANN estimates a \(-43.5\%\) reduction in \(R_{sh}\), which does not correspond to a real degradation of such a parameter. The wrong estimation of \(R_{sh}\), which may occur also for the other SDM parameters, is due to the intrinsic nature of the ANN to provide generalized results when the input data change. Moreover, since the \(I\)-\(V\) curve sensitivity with respect to some parameters is very low, errors on the estimation of these parameters are more likely and more frequent. The results can be improved if the proposed procedure is repeated and the parameters degradation is detected by considering their average values. Some examples are reported in the following section. Figure 16: ANN identification of degraded curve with 70% of variation on \(R_{s}\) for two different irradiance and temperature conditions.
## 4 Experiments
### Evaluation on KITTI Dataset
#### 4.2.2 Qualitative Results  
Figure 2 shows the depth maps predicted by the various models for several images. Our model outperforms the other models. In particular, it shows better results than Monodepth [6], which has a network structure similar to ours. In depth-map images, areas are brighter when closer to the camera position and darker when further away. The depth-map images of our model do not have as sharp edges as Monodepth2 [1,7]. The boundaries between objects are relatively blurry. However, the overall predicted depth values appear more accurate. Areas inside objects are likely to have similar depth values, which means that the color values of the areas inside the object are uniform. In addition, the nearest and farthest areas should be represented with the lightest and darkest colors (excluding the sky), respectively. In the first image, our model accurately identifies the pixel area of the left truck. In addition, the depth values within the truck contour are constant. In the third image, the top right depth values of the ready-mixed concrete truck are well predicted. In the fourth image, the car on the right is the brightest. The depth values of the upper view of the sign located on the right-hand side of the sixth image are well measured. The absolute distance between the building in the seventh picture and the parked vehicle is not significant. Except for our model and Monodepth [6], the other models predict large differences in brightness, such as large differences in the distance between two objects. Our model correctly predicts the sign on the left and the building on the right in the last image. Although we do not use depth data for training, our model shows good results by using a powerful loss function.
## 2 Background and Related Work
### Traffic Categories  
Especially within the industrial context, there are different traffic types, which can be grouped into four categories based on three main traffic characteristics (see Table 2[10]):  
* Traffic regularity: defines if the traffic sending is periodic (cyclic) or irregular (acyclic);
* Timeliness: defines if Ethernet packets are sent at predefined or scheduled times (time-triggered) or not;
* Delivery mode: defines the requirements for receiving the packets in terms of maximum latency or if the packets should obey a given deadline.  
For the convenience of the analysis throughout this paper, these categories can be organized according to their periodicity, and in that case, we refer to them as streams and nonstreams, or according to their time sensitivity, namely, HTS (high time-sensitive) for Time-Aware Streams (i.e., that are cyclic, time-triggered, and have tight timing constraints on their delivery), LTS (low time-sensitive) for the streams that require higher QoS than the BE traffic but not to the level of the HTS streams, and BE (Best-Effort). A similar grouping, by sensitivity, was introduced in [11], from which we adopted the naming.  
Figure 1: An overview of the paper structure and how it fits into the TSN landscape.
## 4 Results and Discussion
### Ablation Study  
Experiments were conducted on the NSL-KDD dataset using consistent parameter configurations. Specific modules were removed to evaluate their contributions to the overall model performance, with comparisons made across accuracy, F1-Score, and mean squared error. The accuracy and the F1-Score decreased by 2.29% and 1.17%, respectively, when the ResNet module was removed, while the accuracy and the F1-Score dropped by 2.25% and 1.47%, respectively, when the RA module was omitted, as shown in Table 5.  
Compared with the loss curves from the ablation experiment, in the 10 th round, the loss of the proposed model is lower than that of the other models, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**InceptionNet** & **ResNet** & **RA** & **Accuracy** & **F1-Score** & **MSE** \\ \hline \(\surd\) & & & 0.91999 & 0.86064 & 0.05704 \\ \(\surd\) & \(\surd\) & & 0.92075 & 0.86711 & 0.05616 \\ \(\surd\) & & \(\surd\) & 0.92042 & 0.86974 & 0.05404 \\ \(\surd\) & \(\surd\) & \(\surd\) & 0.94195 & 0.88007 & 0.04749 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the performance of ablation experimental models.  
Through four sets of ablation experiments, we can find that the InceptionNet, ResNet, and SA modules in the proposed model played a crucial role in classification. The accuracy of the proposed model reached 0.94195, the F1-Score was 0.88007, and the mean squared error was 0.04749, representing the optimal values among the four sets of experiments. The ablation experiment verifies the effectiveness of combining multiple modules. Additionally, the loss was lower than that of the other models, indicating that the model effectively captured the data characteristics during the training process.
# Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings
## 5 Optimization Studies
### Parameter Selection  
Through a thorough literature review, the top four important parameters are identified and mentioned in Table 3. Although the number of tips is an important parameter, a decision was made to not include it in the parametric study due to dependency on the cant angle. Therefore, the number of tips is kept constant beforehand. It is observed that a three-tip design performs better than others (two- and four-tip), as it is able to reduce induced drag while not increasing the wetted area much, i.e., parasite drag [39].  
The chosen winglet has a three-tip configuration. Hence, three cant angles are required to define the design. However, it increases the number of parameters, which further complicates the analysis. Hence, an equation is developed to ensure all tip cant angles are dependent on the first tip's cant angle. It has been noted that the tips are aligned with the same increment in cant angle with each other. Moreover, the optimal increment angle is found to be 10 degrees [21; 22]. Thus, equations are developed as shown below.  
\[\text{If }x\text{ is the }\Phi_{First\ tip}\] \[\Phi_{Second\ tip}=(x-10) \tag{1}\] \[\Phi_{Third\ tip}=(x-20) \tag{2}\]
###### Abstract  
Anchor systems are widely used to stabilize soil slope and suppress slope failure. Thus, monitoring conditions of an anchor system is important to prevent disasters due to slope failure. The slope condition can be indirectly monitored by sensing the tensile force applied to the anchor because the slope deformation directly affects the anchor force. Previously, we propose a way to monitor the tensile force of the anchor by measuring the strain field on a bearing plate using a distributed fiber optic sensor (OFDR) and experimentally demonstrate that the anchor force has a large correlation with the strain distribution on the bearing plate. However, it was found that a spatial variation of the strain and thermal strain due to temperature change makes it difficult to get a reliable correlation coefficient. In this study, we newly propose a way to get a reliable correlation coefficient between the anchor force and the strain field on the bearing plate. We install a distributed optical fiber sensor in two concentric circles on the bearing plate and measure circumferential strain distribution. We take average values of the strain field in each circle as representative strain values minimizing the spatial variation and takes a difference of the two strains to exclude the temperature effect. We experimentally demonstrate that the proposed method gives a reliable correlation coefficient between the anchor force and the strain field on the bearing plate. This technique can be applied to various anchor systems to monitor the anchor force and manage the anchor systems safely.  
anchor force; ground anchor; bearing plate; distributed strain; OFDR (optical frequency domain reflectometry); temperature effect +
Footnote †: journal: Applied Sciences
# DCAGS-IoT: Dynamic Cross-Domain Authentication Scheme Using Group Signature in IoT
## 4 Analysis of Proposed Protocol
### Security Attribute Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & \begin{tabular}{c} **Privacy** \\ **Protection** \\ \end{tabular} & \
\begin{tabular}{c} **Efficiency is Independent of the** \\ **Number of Members** \\ \end{tabular} & **Anonymity** & **Traceable** &
\begin{tabular}{c} **Dynamic User** \\ **Addition** \\ \end{tabular} \\ \hline Ref [8] & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) \\ Ref [14] & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\ Ref [20] & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\surd\) \\ Ref [24] & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) \\ Ref [26] & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) \\ Ours & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison of the security attribute analysis with other schemes.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 3 Methodology
### Building Corner Extraction Step 1 to Step 3
#### 3.3.1 Route over the Top (Step 4 to Step 9)
Figure 7: (**a**) (i1, i2), (i3, i4), (i5, i6) are the intersecting points found after the intersection of plane XZ (between S and D that has shown in the previous figure) with Building B, Building E, Building F denoted by (). (Values in x and \(y\)-axis are in meter). (**b**) This line j1 to i1 denotes the height of the Building B edge denoted by O1. Similarly, for other edges (j2–i2), (j3–i3), (j4–i4), (j5–i5), (j6–i6) the heights are O2, O3, O4, O5, O6. The source is denoted by “S” and Destination is denoted by “D”. Step 8: For the upward travel, apply \"For loop\" for choosing a source, for loop in _range_ (0, _len_ (_upward array_) \(-\) 2). Initially, \(S\) is chosen as the primary source. From \(S\) to (i3-j3), a line equation is formed, and checks which line equation in the upward array intersects with the _S_-(i3-j3) line. While checking for the upward travel line, _S_-(i3-j3) intersects with i1-j1 and creates an array that is:  
\[\textbf{upward intersection}=[\textbf{i1--j1}]\]  
The last index element is chosen from the (upward intersection array). It replaces i3j3, and again a line formed between (_S_ to i1-j1) (will be repeated till the upward intersection array becomes empty). After this, choose the secondary source (after _S_) to be the last remaining element in the upward intersection array. Repeat the steps until the route between the \(S\) and the tallest height is found. We found an upward route (_S_-j1-j3), shown in Figure 8a. Step 9: For the downward travel, apply \"For loop\" for choosing a source, for loop in _range_ (0, _len_ (_downward array_) \(-\) 2). Initially, \(O\)3 (i3-j3 tall height) is chosen as a source. From i3-j3 to \(D\), a line equation is formed, checking which line equation in the downward array intersects with the (i3-j3)-_D_ line. While checking for the downward travel line, (i3-j3) -_D_ intersects with i4-j4 and creates an array that stores the intersected value. \[\textit{downward intersection}=[\textbf{i4--j4}]\]  
The last index element is chosen from the (downward intersection array). It replaces the \(D\), and again a line formed between j3 to i4j4 (will be repeated till the downward intersection becomes empty). After this, it is required to choose the secondary source after O3, which is (i3-j3). _It_ is found to be the last remaining element in the (Downward intersection array). Repeat the steps (from step 8 to 11) until the route between the tallest height and \(D\) is found. The authors found a downward route (j3-_D_), shown in Figure 8b. The algorithm was also tested over complex terrain. The results for the complex buildings scenario and the route over the top for the project area of RGIPT are included in Supplementary Section S2.3.1.
# Punching Shear Strength of Reinforced Concrete Flat Plates with GFRP Vertical Grids
## 3 Experimental Program
### Specim Details  
A total of nine specimens were manufactured of which eight were shear reinforced with GFRP vertical grids and one did not have any reinforcement. Figure 3 shows a plane view of the specimen. The specimens had dimensions of 2000 \(\times\) 2000 mm; the slab was 180 mm thick. Column dimensions were 250 \(\times\) 250 mm; the height of the upper column was 300 mm and that of the lower column was 150 mm. The GFRP shear reinforcement was placed along the four sides of the column as described in Figure 3. Table 2 presents the specimen details. The specimen labeling method represented the variables of this study such as the amount and the spacing of the shear reinforcement. The control specimen was the plain slab without shear reinforcement. G represented the specimens that were shear reinforced with a GFRP plate. The amount of shear reinforcement (\(A_{f}\times f_{fu}\)) was the product of the cross-sectional areas of the vertical strips of the GFRP grid in the critical section needed for the shear strength calculation and the tensile strength. A, B and C represented the spacing of the vertical strips of the shear reinforcement and they were 40 mm (0.3d), 69 mm (0.5d) and 104 mm (0.75d), respectively. To evaluate the shear performance, this study was designed to induce a punching shear failure rather than a flexural failure. In this case, the flexural strength was computed by the yield line theory by [PERSON] [18]. [PERSON] suggested the calculation of the maximum flexural strength, \(P_{y}\), demonstrated by the plasticity bending moment of the slab with simple support from the four sides as shown in Equation (8).  
\[P_{y}=8m_{r}\bigg{(}\frac{1}{1-c/l}\bigg{)}. \tag{8}\]  
\[m_{r}=\rho f_{y}d^{2}\bigg{(}1-0.59\frac{\rho f_{y}}{f^{\prime}{}_{c}}\bigg{)}. \tag{9}\]  
\(P_{y}\): yield line theory flexural strength (ACI 318-19) (kN).  
\(m_{r}\): flexural capacity of slab per unit width.  
\(c\): side length of column.  
\(\rho\): ratio of tension reinforced bar.  
\(f_{c}\): compressive strength of concrete (MPa).  
Figure 3: Schematic view of the test specimen.  
As shown in Table 2, shear failure was induced in all specimens by designing the flexural strength to be greater than the shear strength. The G-184B, G-246B, G-307B and G-369B specimens had equally spaced vertical strips and the amount of the shear reinforcement was set as a variable. G-246A, G-246B, G-246C, G-307A and G-307B had equal amounts of shear reinforcement but the spacing of the vertical strips was a variable. Figure 4 shows the position of the GFR vertical grid within the specimens.
# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 4 Results
### Maize Cropping Systems Mapping  
Visual interpretation of the RF capability to separate the two mapped classes (mono cropping and mixed cropping) using a multidimensional class separability proximity matrix indicate that the majority of the pixel are generally well separable as shown in Figure 5.  
The final thematic cropping systems map produced via the RF algorithm is shown in Figure 6. It shows that mixed-cropped fields are mostly present in the middle and towards the north-eastern part of the study area which is characterized by a lower altitude (around 1100 MAMSL), while most of the mono-cropped fields are found in the south-western part of the study area at a mean altitude of 2000 MAMSL Mono cropped fields in the higher lying areas in the range of 1400-2000 m above sea level appeared larger and less scattered (fragmented) than the mixed cropped fields in the lower areas with elevation below 1400 m (Figure 6).  
Figure 5: Random forest class separability proximity matrix using multidimensional scaling (MDS); Dim 1 refers to dimension 1 and Dim 2 to dimension 2.  
Figure 6: Maize cropping systems classification map obtained using the proposed classification scheme (Figure 2). The two inserts, as black and blue squares, illustrate two contrasting areas in terms of cropping systems.
## 4 Finite Element Numerical Calculation
### Model Building  
A 3D model was established according to the actual size of the slope engineering. The slope angle was \(60^{\circ}\); the actual dimension of the pile was 10 times that of the model piles in the laboratory model test; the sectional size of the crown beam was also expanded by 10 times. Due to the geometric axial symmetry of the composite structure, only P1-P6 were taken as research objectives for numerical simulation.  
The constitutive model serves as the foundation for mechanical calculations and analysis, and the ABAQUS software can provide many constitutive models. Many scholars have shown that the Mohr-Coulomb model could work well for an analysis of the failure and deformation characteristics of soil [32,33]. The soil was used as an ideal elastic-plastic material with the Mohr-Coulomb yield criterion. The elasticity model was adopted for both the pile and the crown beam made of C30 concrete, and the relevant material parameters in the numerical simulation are listed in Table 3.  
The contact property also determines the accuracy of results in the numerical simulation. Generally, contact pairs should be set at the pile-soil contact. The pile surface is set as the main surface; the soil surface is set as the slave surface, and the normal contact between the pile and the soil is set as hard contact. In this way, penetration between the pile  
Figure 11: Bending moment of pile along the downhill direction: **(a)** P6; **(b)** P8; **(c)** P10; **(d)** P11.  
and the soil can be avoided. The pile-soil interaction is adopted by the Coulomb friction model, whose feasibility and accuracy have been validated by many researchers [34,35]. The friction coefficient was set along the tangential direction, and the tangent value of the internal friction angle of the soil was taken as the friction coefficient [36,37]. The pile top and the crown beam were connected as a whole through the binding constraint, which ensured that the internal force of the pile top and crown beam were also equal at the node. Given both the geometric and mechanical characteristics of the 3D model, the mesh was divided more specifically in a relatively large number for the piles, the crown beam, and the soil mass near the piles and the crown beam. The mesh was divided more coarsely in a relatively small of the soil away from the piles and the crown beam, as shown in Figure 12.  
In order to prevent rigid displacement in the model during numerical simulation, the fixed constraint was applied onto the bottom surface of the model, and simultaneously, the normal constraint was applied around all sides of the model. After the model was established, the stress and the displacement of the model were calculated first under the gravity load, and the initial ground stress balance was conducted for the model through imported ODB [38]. Only the initial ground stress balance condition was met. The vertical uniform load was applied on the slope top, which was divided into five levels, with 30 kPa of each level.