esa-sceva/satcom-chunk-collection huggingface dataset:
# Near-Field-Based 5G Sub-6 GHz Array Antenna Diagnosis Using Transfer Learning
## 2 Research Method  
### Transfer Learning  
A CNN is a type of artificial neural network used to analyze visual characteristics such as edges and textures of images, and it is made deep by repeatedly placing convolution and pooling layers and finally implementing a fully connected layer. A convolution layer extracts the image features by the convolution operation, and a pooling layer reduces the dimension using the average and maximum values in the filter size. Moreover, the fully connected layer connects the extracted features to create a feature map and classifies the image using a function. However, because a CNN contains various variables, such as initial value, activation function, number of layers, learning rate, and batch size, numerous factors need to be considered. To compensate this shortcoming, transfer learning was devised. Briefly, the concept of transfer learning originated from the problem of slow learning when training from scratch a very large deep learning model. In this case, the learning can be accelerated by introducing and reusing the lower layer of a similar pre-trained model [10]. Figure 1 shows the concept diagram of the transfer learning method employed in this study. For CNN models trained on ImageNet, the convolution kernels are set suitably for object classification. In the pre-trained model trained on ImageNet, the output of the last stage is set to classify 1000 classes. The transfer learning can be performed if only the classification layer, i.e., the last stage, is supplemented and modified to enable the classification of the data desired by the user. In this study, Alexnet [14] and Resnet-50 [15] were used as the pre-trained networks; the classification layers of these networks were changed for the purpose of the array antenna diagnosis.
# Study on Multi-Objective Optimization-Based Climate Responsive Design of Residential Building
## 4 Discussion of Optimization Results
### Comparison of Optimization Results with Reference Buildings  
Finally, the proposed optimal solution is compared with the climate-related reference design defined in the previous Tables 11 and 15 shows the differences between BED, GC, IC, and DH. In Houston, compared to the reference design, the BED of the nZEB optimal solution is reduced by approximately 20.55 kWh/m\({}^{2}\), the global cost GC is reduced by approximately 36.55 $/m\({}^{2}\), the initial investment cost is reduced by 25.22 $/m\({}^{2}\), and the DH is reduced by 29.16%. In the CO optimal solution, although the value of GC decreased significantly, it was 67.26 $/m\({}^{2}\), which was due to the low initial investment, which reduced to 65.4 $/m\({}^{2}\); but the reduction in BED was very small, only 3.3 kWh/m\({}^{2}\), and the reduction of DH is not as effective as the nZEB program, which is 16.66% less than the reference building, making the uncomfortable hours of the year still 41.67%, so the applicability of this program in actual practice is not as good as nZEB optimal solution. In Los Angeles, the energy demand of the reference building is very low, which is only 7.28 kWh/m\({}^{2}\), thus the nZEB optimal solution cannot improve it a lot. From the Table 15, it can be seen that the BED in nZEB optimal solution is only improved by 5.24 kWh/m\({}^{2}\), but DH is increased by 45.84%, meanwhile the IC is also increased by 13.7 $/m\({}^{2}\). Compared with nZEB optimal solution, the C-O optimal solution decreases the GC by 92.98 $/m\({}^{2}\) and IC by 91.71 $/m\({}^{2}\), while only increasing DH by 8.34%. Therefore, in this climate zone, C-O optimal solution is more practical than nZEB optimal solution.  
Compared with the reference design, the climate zone 5 represented by Chicago has reduced the BED in the nZEB optimal solution by approximately 20.45 kWh/m\({}^{2}\) and DH by 13.17%, but increased the global cost by a small amount, the GC increased by approximately 14.71 $/m\({}^{2}\), which is due to an increase in IC of approximately 25.94 $/m\({}^{2}\). Compared with nZEB optimal, CO optimal greatly reduced the GC about 79.68 $/m\({}^{2}\) and the IC about 82.54 $/m\({}^{2}\), but at the expense of building energy demand and thermal environment comfort. Specifically, BED increased by 5.21 kWh/m\({}^{2}\), DH remains the same as the reference building in 59.17%, thus compared to the C-O optimal solution, the nZEB optimal solution is more suitable for facilitating the actual design.  
In the climate zone 6 represented by Helena, although the nZEB optimal solution has reduced BED by 22.32 kWh/m\({}^{2}\) and DH by 12.5%, the cost has increased by 61.99 $/m\({}^{2}\), which is due to the increase in IC (74.64 $/m\({}^{2}\)). The CO optimal solution reduces the building energy demand and improves the indoor thermal environment comfort, meanwhile, it also reduces the global investment cost. Specifically,
# Exploring Gait Recognition in Wild Nighttime Scenes
## 5 Results and Discussion
### Analysis of Generalization Performance  
To further investigate nighttime gait recognition and explore the model's generalization performance, we conducted more extensive cross-domain experiments. These experiments involved training on a source dataset and subsequently testing on a target dataset. We selected current mainstream public datasets as source datasets for training and tested separately on daytime data (GaitDN_Day) and nighttime data (GaitDN_Night) from the GaitDN dataset. This approach was chosen to assess the model's adaptability to complex scenarios, particularly nighttime scenarios, that were not included in the training process. The experimental results are presented in Table 8:  
1. When the target dataset is the daytime data from GaitDN, GaitSAT achieved the current best performance when using GREW and Gait3D as source datasets. The Rank-1 accuracies for GREW\(\rightarrow\)GaitDN_Day (where \"\(\rightarrow\)\" indicates the cross-domain direction from source to target dataset) and Gait3D \(\rightarrow\)GaitDN_Day were 88.89% and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**CASIA-B**} & \multirow{2}{*}{**OUMVLP**} & \multirow{2}{*}{**Gait3D**} & \multirow{2}{*}{**GREW**} & \multirow{2}{*}{**GaitDN**} \\ \cline{2-2} \cline{6-9}  & **NM** & & & & & & & \\ \hline GaitGraph & 86.37 & 76.5 & 65.24 & 76.04 & 4.24 & 8.60 & 10.18 & 69.38 \\ \hline GaitTR & **94.72** & **89.29** & **86.65** & **90.22** & 39.77 & 7.20 & 48.58 & 60.66 \\ \hline GPGait & 93.60 & 80.15 & 69.29 & 81.01 & 59.11 & 22.40 & 57.04 & 85.71 \\ \hline GaitSAT & 90.37 & 73.84 & 64.04 & 76.32 & **60.77** & **22.90** & **57.37** & **86.24** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of Rank-1 accuracy (%) of GaitSAT and recent state-of-the-art pose-based gait recognition methods on GaitDN and four popular datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Param.** & **Inference Time** \\ \hline GaitGraph & 0.320 & 0.0029 \\ \hline GaitTR & 0.512 & 0.0051 \\ \hline GPGait & 3.655 & 0.0428 \\ \hline GaitSAT & 2.961 & 0.0273 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison with SOTA methods on CASIA-B datasets in parameter number (million) and inference time (second/(sequences* GPU)).  
86.78%, respectively. However, when CASIA-B, OUMVLP, and GaitDN were used as source datasets, the cross-domain performance was slightly inferior to GPGait.
2. When the target dataset is the nighttime data from GaitDN, GaitSAT demonstrated the strongest cross-domain performance across all source datasets. Notably, the Rank-1 accuracies for GREW \(\rightarrow\) GaitDN_Night and Gait3D \(\rightarrow\)GaitDN_Night reached 71.67% and 70.03%, respectively. These results comprehensively showcase GaitSAT's excellent transferability on nighttime data. Regardless of the source dataset, the trained model maintains outstanding performance on nighttime data, indicating GaitSAT's robust domain adaptation capability, particularly suitable for nighttime gait recognition.
# A Linear Fitting Algorithm Based on Modified Random Sample Consensus
## 4 Algorithm Verification and Discussion
### Analysis  
When processing simulated data, the parameters fitted by the WLS, RANSAC, MLE, and the LSH-RANSAC algorithm exhibit Euclidean distances to the standard line parameters of 0.589, 0.893, 0.587, and 0.089, respectively. The Euclidean distance obtained by the LSH-RANSAC algorithm is significantly lower than that of the other three fitting algorithms, indicating its superior fitting performance. This improvement is attributed to the introduction of locality-sensitive hash functions, which reduces the influence of outliers on the fitting results.  
Figure 6: Comparison of the results of real data line fitting. (**a**) Data point sets. (**b**) Weighted least squares method. (**c**) Random sample consensus method. (**d**) Maximum likelihood estimation. (**e**) LSH-RANSAC algorithm proposed in this paper.  
In the case of real data, the LSH-RANSAC algorithm achieved a reduction of 29%, 16%, and 8% in the sum of squared residuals compared to the other three algorithms, while the coefficient of determination increased by 24%, 10%, and 5%, respectively. Additionally, the mean absolute error decreased by 42%, 30%, and 26%, demonstrating substantial improvements across all metrics.  
The mimic simulation validation may lose the ability to reveal potential bias problems when Gaussian noise was added. Although minor deviations did not affect the overall results which were observed at different noise parameters, the bias in real environment might not be displaced as balanced noise. There are inherent differences between simulated data and real data; the added noise is randomly distributed on both sides of the standard line, while the distribution of outliers in real data points may vary. Our algorithm thus perform a better solution for actual situations.  
Since different parameters, such as the number of hash functions, similarity threshold, and the number of iterations, may lead to varying computation times. In applications that require real-time processing, it is essential to adjust the algorithm parameters to better suit the data being processed. This paper does not conclude the time consumption of the algorithm, due to the varying parameters may cause huge difference. Furthermore, recent studies integrating deep learning with RANSAC algorithms may require more processing time than our classical algorithm.
## 2 Methodologies
### Quadcopter Control Logic  
To control the position of the quadcopter with respect to the inertial frame, the translational motion of the quadcopter expressed in the inertial frame is derived as [19]  
\[m^{\ N}\dot{\mathbf{r}}={}^{N}\mathbf{g}+C_{BN}^{\mathrm{T}}\dot{\mathbf{f}}_ {b}, \tag{10}\]  
and \({}^{N}\dot{\mathbf{r}}\) in Equation (10) is rewritten as  
\[{}^{N}\dot{\mathbf{r}}=\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{z}\\ \end{bmatrix}=\begin{bmatrix}\frac{f_{th}}{m}(\sin\phi\sin\psi+\cos\phi\cos \psi\sin\theta)\\ \frac{f_{th}}{m}(-\sin\phi\cos\psi+\cos\phi\sin\psi\sin\theta)\\ -g+\frac{f_{th}}{m}(\cos\phi\cos\theta)\\ \end{bmatrix}. \tag{11}\]  
Since positions \(x\) and \(y\) are coupled with the attitude of the quadcopter, they cannot be directly controlled using thrust force. Therefore, Equation (10) is rewritten as  
\[\dot{x}_{d} =\frac{f_{th}}{m}(\sin\phi_{d}\sin\psi+\cos\phi_{d}\cos\psi\sin \theta_{d}), \tag{12}\] \[\dot{y}_{d} =\frac{f_{th}}{m}(-\sin\phi_{d}\cos\psi+\cos\phi_{d}\sin\psi\sin \theta_{d}), \tag{13}\]  
where \(\dot{x}_{d}\) and \(\dot{y}_{d}\) are the desired accelerations with regard to \(x\) and \(y\), and \(\phi_{d}\), \(\theta_{d}\), and \(\psi_{d}\) are the desired roll, pitch, and yaw angles, respectively. To compute the desired attitude angles, small angle assumptions were considered in this work (\(\sin\phi_{d}\approx\phi_{d}\), \(\sin\theta_{d}\approx\theta_{d}\), \(\cos\phi_{d}\approx 1\), \(\cos\theta_{d}\approx 1\)). This assumption is generally valid except for the large changes of the roll and pitch angles duirng a short time period. Hence, Equations (12) and (13) are rewritten as  
\[\dot{x}_{d} =\frac{f_{th}}{m}(\phi_{d}\sin\psi+\theta_{d}\cos\psi), \tag{14}\] \[\dot{y}_{d} =\frac{f_{th}}{m}(-\phi_{d}\cos\psi+\theta_{d}\sin\psi). \tag{15}\]  
The desired \(x\) and \(y\) accelerations are obtained by the PID control as follows:  
\[\dot{x}_{d} =k_{p,pos}(x_{d}-x)+k_{l,pos}\int(x_{d}-x)\;\mathrm{d}t+k_{D,pos} (\dot{x}_{d}-\dot{x}), \tag{16}\] \[\dot{y}_{d} =k_{p,pos}(y_{d}-y)+k_{l,pos}\int(y_{d}-y)\;\mathrm{d}t+k_{D,pos} (\dot{y}_{d}-\dot{y}), \tag{17}\]  
where \(k_{p,pos}\), \(k_{l,pos}\), and \(k_{D,pos}\) are the proportional, integral, and derivative control gains for position control, respectively. Moreover, for the altitude control of the quadcopter, the thrust force is obtained by the PID control as follows:  
\[f_{th}=k_{p,pos}(z_{d}-z)+k_{l,pos}\int(z_{d}-z)\;\mathrm{d}t+k_{D,pos}(\dot{ z}_{d}-\dot{z}). \tag{18}\]After obtaining the desired accelerations, the desired attitude is obtained using Equations (14) and (15). Then, a PD controller is developed to control the attitude of the quadcopter. The control torque for attitude control is derived as  
\[\tau_{1} =k_{P,att}(\phi_{d}-\phi)+k_{D,att}(\dot{\phi}_{d}-\dot{\phi}), \tag{19}\] \[\tau_{2} =k_{P,att}(\theta_{d}-\theta)+k_{D,att}(\dot{\theta}_{d}-\dot{ \theta}),\] (20) \[\tau_{3} =k_{P,att}(\psi_{d}-\psi)+k_{D,att}(\psi_{d}-\dot{\psi}), \tag{21}\]  
where \(k_{P,att}\) and \(k_{D,att}\) are the proportional and derivative control gains for attitude control, respectively.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis
### Bit Increase Rate Experiment  
Because the watermark will modify the data in the video during the embedding process, the size of the video will change and affect the transmission of the video in the network. Therefore, this paper uses the bit increase rate index to measure video network affinity. The smaller the bit increase rate, the better video affinity can be obtained. The bit increase rate is calculated as follows:  
\[BIR=((BIR_{water}-BIR_{org})/BIR_{org})\times 100\% \tag{14}\]  
where, \(BIR_{water}\) is the bit rate after the video is embedded with the watermark, and \(BIR_{org}\) is the bit rate before the video is embedded with the watermark.  
Figure 7: BER and NC values under requantization attack.  
Figure 8 shows the bit increase rate of the proposed algorithm, all of which are lower than 0.04, indicating that the proposed algorithm has good real-time performance.
## 2 One-Dimensional Consolidation Model  
The one-dimensional consolidation problem is the consolidation of an infinite saturated homogeneous clay layer, loaded by an infinite and uniform pressure. The infinite layer of soil undergoing consolidation can be drained on both sides (see Figure 1) or only on one side and rests on a non-deformable base (e.g., rock). Only excess pore water pressure, which arises after application of the external load, is considered.  
Despite the fact that one-dimensional formulation seems to be a simplistic approach it is often used for the prediction of the settlement of engineering structures, such as road embankments [17; 18; 19].  
The simplest model of one-dimensional consolidation was presented by [PERSON] [20]. The partial differential equation of flow can be expressed as:  
\[\frac{k}{\gamma_{w}}\frac{\partial^{2}\sigma^{\prime}(z,t)}{\partial z^{2}}= \frac{\partial\varepsilon(z,t)}{\partial t} \tag{1}\]  
where \(k\) is the coefficient of permeability, \(\gamma_{w}\) is the unit weight of water, \(\sigma^{\prime}\) is the effective stress function of depth and time, and \(\varepsilon\) is strain function of depth and time.  
Figure 1: One-dimensional consolidation problem.  
Assuming the linear constitutive law for the soil's skeleton, in which the strain function \(\varepsilon(z,t)\) is related to the effective stress \(\sigma^{\prime}(z,t)\) by the following equation  
\[\varepsilon(z,t)=\frac{\sigma^{\prime}(z,t)}{M} \tag{2}\]  
where \(M\) denotes the oedometer modulus, Equation (1) can be solved with appropriate initial and boundary conditions. For the two-side drainage (see Figure 1) the boundary conditions are as follows  
\[\sigma^{\prime}(0,t)=q(t)\qquad\sigma^{\prime}(H,t)=q(t) \tag{3}\]  
where \(q(t)\) is a function of the applied load and \(H\) is the thickness of the soil layer. The initial condition is \(\sigma^{\prime}(z,0)=0\). Equation (1) can be solved both analytically and numerically [20]. In the paper [10] numerical solutions of the Terzaghi problem were demonstrated. Moreover, some more advanced rheological schemes modelling the soil skeleton were also taken into consideration and the results of numerical solutions were compared with known analytical formulas [8].  
The simple linear relationship between the effective stress and strain (Equation (2)) does not take into account secondary consolidation to be observed in organic or soft clay soils. This can be shown by comparison of the model prediction with oedometer laboratory test data carried out on a remoulded sample of soil with about 7% organic matter content (see Figure 2). Taking different values of parameters \(M\) and \(k\), it was impossible to obtain a satisfying fit to the oedometer test results.
# Rough Estimator Based Asynchronous Distributed Super Points Detection on High Speed Network Edge
## 7 Discussion  
From the experimental results, it can be seen that for the network with only one observation node, the memory consumption and the estimation accuracy of READ are similar to that of the existing algorithms. This is because both READ and the existing algorithms estimate the cardinalities based on LE. However, in the distributed environment with multiple observation nodes, the communication overhead of READ is much lower than that of other algorithms. This is because READ does not need to transmit all the data structures used to estimate the cardinalities in the distributed environment, thus reducing the communication between observation nodes and the global server. In addition, READ processes each IP packet with the time complexity of O(1), and has no read-write conflict. Hence, READ can perform fast calculation on the parallel environment, so as to realize real-time super points detection in high-speed network.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment Traffic**} & \multirow{2}{*}{\begin{tabular}{c} **Statistic** \\ **Name** \\ \end{tabular} } & \begin{tabular}{c} **Number of** \\ **Candidate** \\ **Super Points** \\ \end{tabular} & \begin{tabular}{c} **Size of** \\ **Size of** \\ **REC (MB)** \\ \end{tabular} & \begin{tabular}{c} **Size of** \\ **Candidate** \\ **Super Super Points** \\ \end{tabular} & \
\begin{tabular}{c} **Total Trans-** \\ **Super Points** \\ **LE (MB)** \\ \end{tabular} &
\begin{tabular}{c} **Sum Size** \\ **of REC and** \\ **Linear (MB)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Caida 2015\_02\_19} & Average & 955.3333 & 3 & 0.00364 & 1.86589 & 4.86953 & 323 & 1.50759 \\  & Min & 801 & 3 & 0.00306 & 1.56445 & 4.56751 & 323 & 1.41409 \\  & Max & 1106 & 3 & 0.00422 &  
From the above discussion, the following conclusions can be drawn:
* The memory consumption and error rate of READ is similar to the existing algorithms.
* The running time of READ is small enough to handle 40 Gb/s networks in real time.
* In a distributed environment, READ only needs to transmit up to 10.4 MB of memory between each observation node and the global server, which accounts for less than 3.21% of the size of master data structure. It is obviously superior to other algorithms and has the advantage of low communication overhead.
## 2. Stray Current Corrosion Mechanism and Interference Characteristics
### Corrosion Mechanism and Interference Characteristics of AC Stray Current
#### 2.2.5 Passivation Damage and Pitting AC Corrosion Theory  
[PERSON] et al. [33] proposed the AC corrosion theory of passivation destruction and pitting corrosion. They believed that when cathodic protection and AC interference work together, the passivation film generated on the metal surface during the positive half cycle of AC interference is eliminated during the negative half AC cycle interference. It is reduced to Fe\({}^{2+}\) and accumulates to form a porous rust layer. In the next half cycle, a new passivation layer will be formed under rust layers, and then it will continue to be reduced to Fe\({}^{2+}\) in the negative half cycle. When the cathode protection current is large, the Fe\({}^{3+}\) passivation film generated in the anode half cycle will be continuously reduced, leading to continuous metal corrosion. The potential fluctuation of the alternating current causes periodic damage to the passivation film. When potential fluctuation exceeds a certain threshold, its corrosion rate increases significantly. The frequency and intensity of alternating current are important factors that influence the passivation film and corrosion rate. [PERSON] [34] studied the corrosion behavior of X65 steel under AC current density without cathodic protection. The study found that AC interference promoted uniform corrosion and pitting corrosion, and the anode dissolved in a weak passivation environment. When the AC current density increased, the corrosion rate and pitting depth also increased significantly.  
The above mechanisms were later tested by some scholars. Although they have not been unified, through summarizing the above mechanisms and models, it can be seen that most of these theories divide AC corrosion into two cycles: AC positive half cycle and AC negative half cycle. The corrosion in the two cycles affects each other and continues during AC current disturbances, resulting in continuous metal corrosion.
###### Abstract  
The present study investigates the prediction efficiency of nonlinear system-identification models, in assessing the behavior of a coupled structure-passive vibration controller. Two system-identification models, including Nonlinear AutoRegresive with eXogenous inputs (NARX) and adaptive neuro-fuzzy inference system (ANFIS), are used to model the behavior of an experimentally scaled three-story building incorporated with a tuned mass damper (TMD) subjected to seismic loads. The experimental study is performed to generate the input and output data sets for training and testing the designed models. The parameters of root-mean-squared error, mean absolute error and determination coefficient statistics are used to compare the performance of the aforementioned models. A TMD controller system works efficiently to mitigate the structural vibration. The results revealed that the NARX and ANFIS models could be used to identify the response of a controlled structure. The parameters of both two time-delays of the structure response and the seismic load were proven to be effective tools in identifying the performance of the models. A comparison based on the parametric evaluation of the two methods showed that the NARX model outperforms the ANFIS model in identifying structures response.  
vibration control; system identification; modeling +
Footnote †: journal: Applied Sciences