esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
This study aimed to combine plants with photovoltaic glass curtain walls. Through the physiological activities of plants, the temperature of the micro-environment on the inner side of the photovoltaic glass curtain wall and its surface temperature can be lowered, addressing the decline in power generation performance of traditional photovoltaic glass curtain walls caused by excessive temperatures.  
Previous studies primarily focused on photovoltaic agriculture, with limited research on the integration of plant cultivation with photovoltaic glass curtain walls. In terms of combining photovoltaic power generation with plant cultivation, plants were typically grown beneath photovoltaic modules. Moreover, research has predominantly emphasized the impact of photovoltaic power generation on plants to achieve agricultural production. In contrast, the influence of plants on the power generation performance of photovoltaic modules has been rarely mentioned. This study combined plants with photovoltaic glass curtain walls, focusing on researching the temperature and power generation performance aspects of the photovoltaic glass curtain walls.  
In this experiment, the impact of the plant cultivation devices on the experimental results was not considered in detail when comparing the two groups of A\({}_{1}\) and A\({}_{2}\) photovoltaic glass curtain walls. This oversight may introduce systematic errors, causing the temperature of the micro-environment on the inner side of the A\({}_{2}\) photovoltaic glass curtain wall with plants and its surface temperature to be higher than the actual values. Consequently, the ability of plant transpiration to regulate temperature may be better than the experimental results suggest, potentially underestimating its actual advantages.  
Finally, since this paper primarily focuses on addressing the problems encountered by traditional photovoltaic glass curtain walls, the selection of plant species was made to achieve a favorable combination. Future research could explore whether other shade-loving plants or adjustments to the distance between plants and the curtain wall would yield further optimization effects. Additionally, replacing _Epipremum aureus_ with shade-loving crops suitable for growth, such as lettuce, Chinese chives, and crown daisy, could achieve agricultural production alongside addressing the issues caused by the photovoltaic glass curtain wall, resulting in a dual benefit of \"one plus one is greater than two\".
## 1 Introduction and Background
In addition, it can be integrated with smart surveillance cameras that are available on roadways and controlled by the Central Traffic Department. Additionally, the U2-net model is responsible for saving the captured images/video for the distracted driver, especially those who use their phone while driving, and send captured images to the main database for manual confirmation to take the correct action. The main contribution of our work can be summarized as follows:  
1. We develop a new model called U2-net that automatically detects distracted drivers, especially those who are using mobile phones;
2. We modify the original U-net model by adding more layers to both contracting and expanding paths, which can capture more intricate features and patterns in the data. Additionally, we add a flattened layer along a dense layer, which helps in the detection and classification process;
3. We update the regularization technique, optimization technique, and the loss function to improve the performance and avoid model overfitting;
4. We apply the U2-net into the image classification domain and evaluate it using two datasets: MI-AUC and State Farm datasets;
5. The evaluation results show that the U2-net model achieves higher accuracy, recall, precision, IoU, and F-score than what is achieved by many other state-of-the-art models.
## 5 Results and Discussion
### Implications Regarding Data Privacy  
With respect to data privacy, it can further be discussed to which extent the transparent data tunnel can be used to gain access to sensitive consumer data. Depending on the implementation of the CLS endpoint in the HAN and the contract with the customer, an aEMP may get access to plentiful sensitive data. In the case of DERs being controlled and monitored, the aEMP knows exactly when and how the DERs have been used. This information can be exploited to derive more sensitive data; e.g., detecting occupancy from consumption patterns. Furthermore, consumers can grant access to the SMGW's consumer interface to CLSs, e.g., to make high resolution meter measurements available to the CLS. If an aEMP asks for access to the consumer interface and the consumer grants it, the aEMP's CLS can access sensitive data and transmit it to the associated aEMP via the data tunnel. This would bypass the restrictions dictated by the tariff use cases. However, it is worth noting that this can be done only intentionally, as the consumer interface is secured and the data are only accessible after authentication. Hence, consumers need to actively allow the data access by providing their credentials. When drafting a contract between the aEMP and the CLS customer, it may therefore also be necessary to regulate the accessibility to the consumer interface. Further, if access to the SMGW's consumer interface is granted to an aEMP, it may be sensible to protect the customer by prohibiting to share the received data with third-party companies.  
Finally, regarding the introduced CLS management systems in Section 3.6, further security concerns arise. As shown in Figure 3, the communication between the CLS management system provider and the DSMgr in the WAN is not regulated by the TR-03109. Therefore, this part of the communication chain is not subject to the security requirements, resulting in a possible unsecured communication between EMS and DSMgr. Furthermore, all other requirements are not binding either, including the certification according to ISO 27001. As a consequence, a false sense of security and privacy may be provided to consumers.
# Seismic Performance Evaluation of Highway Bridges under Scour and Chloride Ion Corrosion
## 2 Earthquake and Scour Combination in the Failure Probability of Bridge
### Calculation of Seismic Failure Probability of Bridges at Different Scour Depths
#### 2.1.2 Solution of Structural Failure Probability  
The distribution of structural resistance can be defined by performing an analysis of bending moment curvature on critical sections through the calculation of the fiber unit ([PERSON] et al., 2013). When the distribution function of the seismic effect is certain, the state equation of the structure can be established as  
\[Z=S-R \tag{3}\]  
where \(S\) is the seismic effect value of the structure; and \(R\) is the value of structural resistance.  
The absolute failure probability can be calculated by means of direct integration when the distribution of constituent variables in state equation is certain. The solution process is shown in Figure 1. The failure probability \(P_{\text{f}}\) of the structure is  
\[P_{\text{f}}=P(Z>0)=P(S>R)=\int_{-\infty}^{+\infty}F_{\text{R}}(x)f_{\text{S} }(x)dx \tag{4}\]  
where \(P_{\text{f}}\) is the failure probability of the structure; \(F_{\text{R}}(x)\) is the function of resistance distribution; and \(f_{\text{S}}(x)\) is the probability density function of the effect.  
The reliability index can be obtained as  
\[\beta=\Phi^{-1}(1-P_{\text{f}}) \tag{5}\]  
where \(\beta\) is the reliability index; and \(\Phi^{-1}(*)\) is the inverse function of normal distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Average Value** & **Coefficient of Variation** & **Probability Distribution** \\ \hline \(\lambda_{\text{s}}\) & 0.57 & 0.6 & \\ \(K_{2}\) & 1 & 0.05 & Normal distribution \\ \(K_{3}\) & 1.1 & 0.05 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Probability distribution of scour depth parameters.  
Figure 1: Schematic diagram of solving the failure probability by means of direct integration.
## 5 Use Cases
### Example 3: Binary Transfers to the End Device  
We developed a new service definition (serviceDef.js) for LDAF based on the bsdiff-nodejs library [47] to enable the following.  
Application adaptation. The LDAF server caches a version of a previously downloaded file. Upon request from the device or a push from the cloud server for an update, the LDAF service  
Figure 7: Traffic ratio with packet filtering: communication between the LDAF client and LDAF server vs. LDAF server and the Ethereum node.  
calculates the binary difference between the new and previous content and returns it to the device. The device patches the old version of the file.  
Despite binary content, binary LDAF message content serialization still takes place. It is a generic part of LDAF message encoding and not a part of the service. However, it does not additionally reduce the size of the already compressed binary content. If LDAF was applied exclusively for transferring binary files, the binary difference could be implemented as the default LDAF message content serialization. However, being a service allows for the broader use of the framework.  
The effect of this service predominantly depends on the binary difference of the two files transferred. To test the operation and estimate the impact of message and communication overhead, we used two binary installation files, named v62 and v63, each ~1.8 MB. The size of the binary difference patch to upgrade from v62 to v63 (as reported by bsdiff) was 1531 kB. The size of the patch when the v62 file was compared to itself was 145 B, to allow for the mandatory bsdiff patch header. Transferring the v63 file from the file server to the LDAF server resulted in 1942 kB in download. The corresponding patch from the LDAF server to the end device resulted in 1627 kB download. The difference between the original file v63 and the patch was 292 kB. The difference in download traffic was even a bit bigger, 315 kB, as a consequence of more efficient WS download compared to HTTPS. The void patch for v62 to v62 resulted in only 619 B download, and that was mostly due to WebSocket establishment. The exact communication traffic loads might vary a little bit, if the experiment was repeated, due to changed network conditions (e.g., more or less frequent retransmissions of lost packets).  
Other file pairs would result in larger or smaller binary differences, but the key is that the LDAF framework reduces the size of the communication traffic at least as much as the binary patch is smaller than the patching file.
## 4 Experiments and Results
### Accuracy (Q2)  
In this section, we focus on the accuracy of the model using different datasets to address the second question. The experimental results are presented in this section:  
(1) Figure 12 shows the performance results for dataset 1:  
(2) Figure 13 shows the performance results for dataset 2:  
(3) Figure 14 shows the performance results for dataset 3:  
Figure 12: Performance of Accuracy of each model on dataset 1.  
Figure 13: Performance of Accuracy of each model on dataset 2.  
(4) Conclusions:  
Through the comparison in this section, we found that within a reasonable fluctuation range, the accuracy of the ARFM model was excellent in all datasets, and all of them were at the maximum value in all models. It can be seen that the ARFM have a very high comprehensive quality, excellent robustness, and generalization ability.  
Data summary:  
In this section, we present the data results of ARFM and other models in the form of tables, and finally provide an increase in the accuracy of ARFM compared with other models.  
(1) Table 1 shows the loss:  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Dataset1** & **Dataset2** & **Dataset3** \\ \hline
**ARFM** & 0.3206 & 0.4177 & 0.4328 \\ \hline
**DeepCrossing** & 0.3169 & 0.4503 & 0.4835 \\ \hline
**Wide and Deep** & 0.2650 & 0.3894 & 0.4511 \\ \hline
**DeepFM** & 0.3295 & 0.4350 & 0.4570 \\ \hline
**AFM** & 0.3327 & 0.4633 & 0.4649 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Loss value of each model.  
Figure 14: Performance of Accuracy of each model on dataset 3.  
(3) Table 3 shows the performance growth:
## 2 System Design
### PANTHYR Data Acquisition Protocol  
The measurement protocol presented here is based on various precursors including References [6; 25; 26] and, as usual for above-water radiometry, gives a water-leaving radiance or reflectance for an off-nadir geometry, which can be matched to satellite viewing geometries by appropriate models of a bidirectional reflectance distribution function. Research to determine an optimal measurement protocol for PANTHYR operations within the WATERHYPERNET network is in progress and some aspects may change in the future, e.g., potential additional zenith and/or azimuth angles and/or a different number of replicate measurements, especially for water viewing. However, the current protocol is considered as already sufficient for demonstrating performance of the hardware.  
The PANTHYR system performs automated measurements every 20 min from sunrise until sunset. Each cycle consists of measurements with a 90\({}^{\circ}\), 135\({}^{\circ}\), 225\({}^{\circ}\), and/or 270\({}^{\circ}\) relative azimuth to sun. In general, and depending on the installation location, platform geometry, time of day (sun location), and associated platform shading of the water target, only one or two (or sometimes zero) of these azimuth angles are appropriate for measurement of water reflectance; other azimuth angles will be contaminated by platform shading or even direct obstruction of the water target as defined from the instrument field of view. A selection of acceptable azimuth angles is made a priori based on expert judgement (used here for prototype testing; see Section 3) or, better, a study of platform shading effects by modeling or experimentation [27]. The measurement of unacceptable azimuth angles, defined by a \"no-go zone\", can then be avoided to save power.  
For each measurement cycle, the system performs a sub-cycle for each of the configured relative azimuth angles as described in Table 1. Based on the AERONET-OC protocol [6], but with repetition of the \(E_{d}\) and \(L_{d}\) replicates, each azimuthal measurement sub-cycle consists of 2 \(\times\) 3 replicate scans each of downwelling irradiance, \(E_{d}\), and sky radiance, \(L_{d}\), and 11 replicate scans of upwelling radiance, \(L_{u}\), where \"(spectral) scan\" refers to acquisition of a single instantaneous spectrum. Firstly, the irradiance sensor is pointed upward, with the radiance sensor offset by 40\({}^{\circ}\), and three replicates of \(E_{d}\) followed by three replicates of \(L_{d}\) are measured. The radiance sensor is then moved to a 40\({}^{\circ}\) downward viewing angle to make 11 replicate \(L_{u}\) scans. The irradiance and radiance sensors are then repositioned to make  
Figure 3: Screenshot of SQLite database interface.  
three more replicate scans each of \(L_{d}\) and \(E_{d}\). Scans are stored as digital counts (DCs), including the embedded dark pixel counts, with instrument serial number and integration time in the metadata.
## 5 Results and Discussion
### Comparison and Evaluation of Optimization Algorithms on Benchmark Function  
Table 2 shows the result of running the SDSGA on 17 benchmark functions and comparing its performance with that of the GA, PSO and BBO algorithms. The light green color in the table was used to highlight the best mean values per function, while the light brown color highlighted the highest standard deviation values. Table 2 shows that the developed SDSGA performed better than the three compared algorithms in terms of accuracy in 65% of the benchmark functions. In some cases where the SDSGA did not perform as well as other algorithms used for comparison, the standard deviation shows that the result does not deviate too far from the global optimum and that its results are stable across runs.  
Figure 4: Effect of varying the parent size.  
Figure 5: Comparison of accuracy of the UCI datasets under varying parent population percentages.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**BBO**} & \multicolumn{4}{c}{**Algorithms**} \\ \hline
**S/N** & **Functions** & **Global Sol** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline  
akharov & 0 & \(-2.32\times 10^{+02}\) & \(3.53\times 10^{+01}\) & \(-2.52\times 10^{+02}\) & \(4.65\times 10^{+01}\) & \(-1.94\times 10^{+02}\) & \(4.50\times 10^{+01}\) & \(-1.91\times 10^{-02}\) & \(5.81\times 10^{+01}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result of comparison of metaheuristic algorithms on benchmark functions in 10D.  
This is evident in the Griewank function results in Table 2 where it is noted that, while the SDSGA is not the best performing algorithm, its standard deviation is the best. Furthermore, when it came to optimizing the KTablet function, the GA came out on top in terms of both mean best solution and standard deviation (=\(9.35\times 10^{+02}\), \(1.13\times 10^{+03}\)=). The SDSGA (=1.09 \(\times 10^{+03}\), \(1.40\times 10^{+03}\)=) is not far from the GA when compared to both BBO (\(4.96\times 10^{+04}\), \(1.18\times 10^{+04}\)) and PSO (\(1.39\times 10^{+04}\), \(7.81\times 10^{+03}\)).
## 3 Methodology
### Development of the ANN Model
The accuracy of the proposed ANN model was assessed using the average absolute relative error (AARE) (Equation (1)), root mean square error (RMSE) (Equation (2)), and mean absolute error (MAE) (Equation (3)) between measured and predicted settling velocity. Average absolute relative error (AARE):  
\[\text{AARE}=\frac{1}{N}\sum_{i=1}^{n}\left|\frac{\hat{U}_{p,i}-U_{p,i}}{U_{p,i} }\right| \tag{1}\]  
Root mean square error (RMSE):  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{n}\left(\hat{U}_{p,i}-U_{p,i}\right)^ {2}} \tag{2}\]  
Mean absolute error (MAE):  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{n}\left|\hat{U}_{p,i}-U_{p,i}\right| \tag{3}\]  
where \(\hat{U}_{p,i}\) is the particle settling velocity predicted by the ANN model, \(U_{p,i}\) is the measured particle settling **velocity**, and \(N\) is the total number of experimental data. Figure 3: The architecture of the ANN model.
## 2 Materials and Methods
### Five-System Integrated PPP Time Transfer Model
Assuming that the observations from different satellites or on different frequencies are independent of each other, an elevation-dependent strategy is employed to determine the weights, which can be expressed as follows:  
\[\sigma^{2}(ele)=(a\cdot k_{1})^{2}+(a\cdot k_{2})^{2}/(\sin(ele))^{2} \tag{7}\]  
where \(\sigma^{2}(ele)\) is the variance of observations at the elevation angle \(ele\), \(k_{1}\) and \(k_{2}\) are constants, which can be set to 3 mm for phase observations and 3 dm for code observations, and \(a\) represents the weight factor. The weight factor does not remain consistent for all the GNSS and RNSS constellations due to different observation qualities. The weight factors of GPS, GLONASS, Galileo, BDS, and QZSS are set to 1.0, 1.5, 1.0, 1.0, and 2.0, respectively. The GLONASS and QZSS observations are down-weighted due to the complex influence of frequency division multiple access (FDMA) modulation scheme or the relatively lower accuracy of precise products. In addition, because of the inferior orbit accuracy of the BDS GEO and IGSO satellites, the corresponding factors are multiplied by 10 times and 2 times when determining the weights of their observations. As for the stochastic model for the pseudo-observation, it can be determined as:  
\[\sigma_{x}^{2}=\text{diag}(k_{3}^{2},k_{3}^{2},k_{3}^{2}) \tag{8}\]  
where \(\sigma_{x}^{2}\) is the variance of a priori 3D receiver coordinates, and \(k_{3}\) is also a constant. Although the receiver coordinates released by the IGS usually can reach an accuracy level of a few millimeters, the numerical value of \(k_{3}\) is taken as 2 cm here, so as to avoid the biased estimates.
## 4 Experimentation and Results
### Model Ensembling Strategy  
Training of these network was followed by ensembling in two ways i.e., simple averaging and weighted averaging. The experimentation settings for both the ensemble methods were kept same i.e., learning rate of 0.0001 with Adam optimizer and 100 Epochs. Images obtained through the cropping technique are used. Table 3 shows the performance of the two different ensemble methods. We select the best three models, i.e., VGG19, DenseNet201, and EfficientNetV2S according to Table 2 and denote their names as M1, M2, and M3 respectively. Next, we perform experiments by merging M1, M2, and M3 models in different combinations such as M1 + M2 + M3, M1 + M2, M2 + M3, and M1 + M3. According to Table 3, the best performance with an accuracy of 0.989 is achieved by combining models (M2 + M3) using simple averaging method. However, the lowest accuracy of 0.941 is achieved on M1 + M2 + M3 model that used weighted averaging method. In terms of the AUC, highest score is obtained by M1 + M2 with simple averaging method whereas lowest score is achieved by M1 + M2 + M3 model with weighted averaging method.
# Active Contours in the Complex Domain for Salient Object Detection
## 1 Introduction
* Finally, we combine salient object discovery and the complex force and implement the active contours in the complex domain for salient object detection. In the above, we discussed that previous research investigated different models to handle the limitation, such as improper initialization that influences on the final performance and expansive re-initialization. Second intensity inhomogeneity occurs in many real images of different modalities, which is worse for object detection in higher field imaging and etc. Our primarily aimed is introducing a model, which has the capability (1) to draw straight forward automatic active contour on the object boundary which in process leads to detect salient object. The model is also capable to handle (2) improper initialization, (3) eliminates reinitialization costly implementation, and (4) reducing the computational cost. Figure 1 concludes our aim and illustrates motivation results. Therefore, our proposed model avails the advantages of complex transformations. Complex transformation creates hints of saliency that serve as the initialization and overcomes the difficulty of improper initialization. Complex transformation gives smooth curve to handles the inhomogeneity problem. Our model eliminates the re-initialization process while using low pass filter by the discovered object of interest. The model also reduces the computational cost in terms of iteration. The rest of the paper is organized, as follows: In Section 2, we provide a brief overview of related works. Based on this, Section 3 illustrates our formulation of the active contour in the complex domain with its detailed implementation. In Section 4, we describe experiments conducted on both synthetic and natural images, which is followed by the conclusion in Section 5.