esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Numerical Results
### Superstructure Accelerations  
In contrast with base-isolated tanks, the probability of failure for fixed-base liquid storage tanks presents high values even for medium intensity levels [26]. This can be attributed to the significantly reduced accelerations transmitted to the superstructure due to base isolation; thus, the isolated tank exhibits a linear response. In this manner, tank wall damages and liquid content leakages can be avoided for the usually expected range of PGA values, while any system failure (i.e., for the tank and the isolation) is mainly associated with the exceedance of the allowable SFPB and TFPB displacement capacity, as presented in the form of the fragility curves in Figure 9.  
To illustrate the beneficial role of the isolation, Figure 10 presents the base accelerations transmitted to the superstructure (i.e., measured just above the isolators) for all schemes. More specifically, the results for all the examined approaches and isolator types are depicted for the maximum imposed values for every accelerogram, i.e., at the last step of the fragility curve generation process. As expected, the accelerations transmitted to the superstructure are notably reduced for all isolation configurations. This is more pronounced in the case of triple bearings, which, due to their increased capacity, can withstand much higher PGA levels compared to the single bearings, as can be seen by comparing the PGA values used to obtain the fragility curves in the two plots of Figure 9. Analogously, triple bearings exhibit much higher maximum acceleration values in Figure 10, in which the presented results correspond to the ultimate scaled values for each excitation. It can also be observed that for both isolator types, the dual optimization approach (i.e., SFPB3 and TFPB3) provides slightly better results compared to the other two approaches, especially for extreme acceleration values for certain accelerograms, e.g., #9 (Kobe, 1995), #16 (Palos Verdes 1) and #19 (Palos Verdes 4).  
Figure 9: Fragility curves for the storage tank equipped with: (**a**) SFPB and (**b**) TFFB schemes.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Pollution Sampling with Adaptive Path Planning
#### 4.6.2 Black Carbon Measurement over Kongsvegen
Additionally, during helical ascent and descent near the map's edge, the simulated aircraft was blown off the map due to wind. The GPP-generated paths perform similarly to the human-designed reference path. Although these paths are, on average, 12.06% longer and 6.45% less energy-efficient, GPP's automatic generation of densely sampled waypoints results in paths that are more predictable and feasible. To include dynamic re-planning for both global and local paths, the experiment was extended as outlined in Table 10. Following the approach of the simplified scenario, 30 independent runs were performed, each with a unique random seed. Table 12 presents the results of the complete scenario. Figure 8: The expert-designed path (**top**) and the best path generated by the GPP (**bottom**). Paths 19, 25, 27, and 29 fail to meet the FT success criterion. Among them, Path 19 is the longest and most energy-demanding, with LEN = 443,148 m, EEE = 1042 Wh, and FT = 36,065 s. All paths satisfy the other criteria, including CT and NCOL, resulting in an overall success rate of 86.67% for this experiment. Path 23 stands out as the shortest and most energy-efficient, with LEN = 304,098 m, EEE = 601 Wh, and FT = 20,729 s. It is also the smoothest (SMOO = 0.836).
# Optimization of the Battery Pack Heat Dissipation Structure of a Battery-Type Loader
## 3 Numerical Simulations and Experiments
### Numerical Simulations and Experiments
#### 3.2.2 Simulation Model Validation  
As shown in Figure 5 below, the temperature trends of the simulation and experimental results were basically the same. The reason why the simulation value was slightly lower than the experimental value is that the heat generation rate of the battery increased during the actual test, while the heat generation rate in the simulation was constant throughout the charging and discharging process.  
The simulated values for the developed model and the real values obtained experimentally can be quantified using the mean and maximum values of the relative errors [24; 25; 26]:  
\[AARE(0/0)=\frac{1}{N}\sum_{i=1}^{N}\left|\frac{E_{i}-S_{i}}{E_{i}}\right|\times 1 000/0, \tag{1}\]  
\[\varepsilon_{\max}=\max\{\varepsilon_{1},\varepsilon_{2},\varepsilon_{3}\cdots \varepsilon_{i}\}, \tag{2}\]  
where \(\varepsilon_{i}=\left|\frac{E_{i}-S_{i}}{E_{i}}\right|\times 1000/0\), \(E\) denotes the experimental measurements, and \(S\) denotes the model simulation values.  
As can be seen from Figure 5, the average relative error between the highest temperature simulation and experimental results was 4.6%, while the maximum relative error was 5.1%; the average relative error between the average temperature simulation and experimental results was 5.4%, while the maximum relative error was 5.9%; finally, the average value of the relative error between the simulation and experimental results was 7.3%, while the maximum value of the relative error was 9.2%.From the above error analysis, it can be seen that although there were some individual cases with large errors, the overall temperature simulation results for the battery pack with five sets of structural parameters  
Figure 4: Battery pack heat dissipation test bench: (**a**) the experimental devices of the air-cooling system; (**b**) the positions of the temperature sensors.  
Figure 5: Battery pack temperature change patterns: (**a**) max. temperature (\({}^{\circ}\)C); (**b**) average temperature (\({}^{\circ}\)C); and (**c**) temperature difference (\({}^{\circ}\)C).  
were in good agreement with the experimental results and were within the acceptable range. Therefore, the simulation results obtained with the numerical model described in this paper can describe the temperature variation of the modeled battery pack well.
## 2 Related Works
### Previous Deep Voice Detection Techniques
#### 2.4.1 Previous Works Using the ASVspoof2019 Dataset  
Previous works have focused on detecting real or fake voices and classifying spoofing algorithms generating fake voices. In other words, these are works that verify whether one user is real or fake.  
In [15], the authors adapted a light convolutional gated RNN model to improve the long-term dependency for spoofing attack detection.  
In [16], the authors used the deep residual network (ResNet [17]) model tho alleviate the gradient loss problem for deep voice detection. Audio preprocessing was performed through a 60-dimension linear filter bank (LFB). To improve generalization performance in deep voice detection, they used a dataset that considered the actual situation and added a FreqAugment layer that randomly masked adjacent data frequencies. Through this work, the model could learn features well even if there were outliers or noise in the dataset.  
In [18], the authors proposed a method referred to as feature genuinization, which learns a transformer with a CNN using the characteristics of only a real voice. They then used this genuinization transformer with a light CNN classifier. Their system outperformed other single systems for the detection of synthetic attacks.  
In [19], the authors proposed a continuous learning technique called Defining Fake Without Forgetting (DFWF) to detect new spoofing attacks incrementally. Through continuous learning, it was possible to focus more on the characteristics of the real voice while remembering the previously learned information. The problem of forgetting information about the existing data when integrating additional data was addressed with two limitations: Learning without Forgetting (LwF) and Positive Sample Alignment (PSA). They showed that DFWF performed much better than fine-tuning, and the model generated 80-dimension high-level embeddings using a light convolution neural network (LCNN).  
In [20], the authors used two types of new acoustic features to improve deep voice detection performance. The first feature consisted of two cepstral coefficients and one LogSpec feature. Cepstral coefficients and LogSpec are extracted from the linear prediction residual signals. The second characteristic is the harmonic and noise subband ratio function.
# Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification
## 3 Materials and Methods
### Pre-Trained CNNs  
Considering the state-of-the-art methods for CNNs, we have chosen the following four architectures:  
* AlexNet [10]: the AlexNet network is one of the first convolutional neural networks that has achieved great classification successes. The winner of the 2012 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition, this network was the first to obtain more than good results on a very complex dataset such as ImageNet. This network consists of 25 layers, the part relating to convolutional layers sees 5 levels of convolution with the use of ReLU and (only for the first two levels of convolution and for the fifth) the max pooling technique. The second part of the CNN is composed of fully connected layers with the use of ReLU and Dropout techniques and finally by softmax for a 1000-d output.
* SqueezeNet [39]: in 2016 the architecture of this CNN was designed to have performances comparable to AlexNet, but with fewer parameters, so as to have an advantage in distributed training, in exporting new models from the cloud, and in deployment on a FPGA with limited memory. Specifically, this network consists of 68 layers with the aim of producing large activation maps. The filters used instead of being \(3\times 3\) are \(1\times 1\) precisely to reduce the computation by 1/9. CNN is made up of blocks called \"fire modules\", which contain a squeeze convolution layer with 1x1 filters and a expand layer with a mix of \(1\times 1\) and \(3\times 3\) convolution filters. This CNN has an initial and a final convolution layer, while the central part of the CNN is composed of 8 fire module blocks. No fully connected layers are used but an average pooling before the final softmax.
* ResNet18 [40]: this CNN, introduced in 2015, and inspired by the connection between neurons in the cerebral cortex, uses a residual connection or skip connections, which jump over some layers. With this method, it is possible to counteract the problem of degradation of performance as the depth of the net increases, in particular the \"vanishing gradient\". This CNN is made up of 72 layers, the various convolutions are followed by batch normalization and ReLU, while the residual connection exploits an additional layer of two inputs. The last layers consist of an average pooling, a fully connected layer and softmax.
* GoogLeNet [41]: this architecture is based on the use of \"inception modules\", each of which includes different convolutional sub-networks subsequently chained at the end of the module. This network is made up of 144 layers, the inception blocks are made up of four branches, the first three with \(1\times 1\), \(3\times 3\), and \(5\times 5\) convolutions, and the fourth with \(3\times 3\) max pooling. After that,all feature maps at different paths are concatenated together as the input of the next module. The last layers are composed of an average pooling and a fully connected layers and the softmax for the final output.
# A Small-Sample Scenario Optimization Scheduling Method Based on Multidimensional Data Expansion
## 1 Introduction
Let there be an existing subset of intra-week data \(D_{\text{weekday}}\), a subset of weekend data \(D_{\text{weekend}}\), the number of generation days \(K,D_{\tau}\) is a sampling subset, \(\tau\) denotes the data type \(\tau\in\{\text{weekday},\text{ weekend}\}\), and let the data to be generated be \(\hat{D}\), which can be expressed by the following equation:  
\[\hat{D}=[\hat{d}_{k,0},\hat{d}_{k,1},\dots,\hat{d}_{k,23}]\in\mathbb{R}^{K \times 24},k\in[1,K] \tag{7}\]  
\[D_{\tau}=\begin{cases}D_{\text{weekday}},&\tau=\text{weekday}\\ D_{\text{weekend}},&\tau=\text{weekend} \tag{8}\]  
For the target moment \(t\in\{0,1,\dots,23\}\), sample independently from the corresponding type of dataset:  
\[\hat{d}_{t}= \begin{cases}D_{t}^{(k_{t})},&k_{t}\sim\mathcal{U}\{1,n\},\text{ If weekly data is generated}\\ D_{t}^{(s_{t})},&s_{t}\sim\mathcal{U}\{1,m\},\text{ If weekend data is generated}\end{cases}  \tag{9}\]  
where \(\mathcal{U}\{\cdot\}\) denotes a uniform discrete distribution. The specific process of data augmentation is as follows: First, the corresponding original dataset (\(D_{\text{weekday}}\) or \(D_{\text{weekend}}\)) is automatically selected based on the type of date (weekday/weekend), and then, the synthetic data are generated by a double-loop structure: the outer loop controls the generation of \(K\) time series, and the inner loop randomly samples the loading values from the selected dataset on an hour-by-hour basis (0-23 h). A uniform distribution is used for each sampling to ensure data diversity, and the constructed complete 24-h sequence is finally added to the augmented dataset \(\hat{D}\). This method effectively addresses the lack of historical data for new energy systems by preserving the temporal characteristics of the original data (e.g., the difference in load patterns between weekdays and weekends). Algorithm 1 is the pseudo-code for this process.
## 2 Methods
### Data Preprocessing and Normalization  
#### 2.3.1 Data Correlation Analysis  
Data correlation analysis can be used to determine the correlation between input and output variables, guiding further operations during modeling. This paper adopted the Pearson correlation coefficient method to analyze the correlation of variables such as weight on bit (WOB), torque (TOR), pump flow rate (Q), rotational speed (RPM), rate of penetration (ROP), bit wear, and bit cutting depth. Highly correlated parameters were selected as features. The calculation method is shown in Equation (5).  
\[\rho_{\text{ab}}=\frac{\text{cov}(\text{a},\text{b})}{\sigma_{a}\sigma_{b}}= \frac{\sum\limits_{i=1}^{n}{(a_{i}-\overline{a})(b_{i}-\overline{b})}}{\sqrt{ \sum\limits_{i=1}^{n}{(a_{i}-\overline{a})}^{2}}\sqrt{\sum\limits_{i=1}^{n}{( b_{i}-\overline{b})}^{2}}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data** & **WOB** & **TOR** & **Q** & **RMP** & **ROP** \\
**Number** & **(kN)** & **(N-m)** & **(L-min\({}^{-1}\))** & **(r-min\({}^{-1}\))** & **(m-h\({}^{-1}\))** \\ \hline
1 & \(-\)429 & 765 & 601 & 600 & 1.39 \\
2 & \(-\)579 & 747 & 600 & 600 & 1.39 \\
3 & \(-\)467 & 871 & 601 & 600 & 1.39 \\
4 & \(-\)629 & 877 & 601 & 600 & 1.39 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
10,000 & 464 & 1646 & 600 & 600 & 2.78 \\
10,001 & 415 & 1704 & 602 & 600 & 2.78 \\
10,002 & 945 & 1641 & 600 & 600 & 2.78 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
13,884 & 1852 & 2610 & 601 & 600 & 4.17 \\
13,885 & 2018 & 2566 & 602 & 600 & 4.17 \\
13,886 & 2243 & 2580 & 600 & 600 & 4.17 \\
13,887 & 2230 & 2547 & 601 & 600 & 4.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Drilling parameters.  
Figure 5: Drilling data for the different ROPs.  
Here, \(\rm cov(a,b)\) represents the covariance equation of variables a and b; \(\sigma_{a}\) and \(\sigma_{b}\) indicate the standard deviation of variables a and b respectively; \(\overline{a}\) and \(\overline{b}\) represent the average of variables a and b; and n indicates the size of the dataset.  
The results of the correlation analysis are shown in Figure 6. Due to the small number of experimental groups and minimal wear on the drill bit, the wear condition of the drill bit can be considered negligible; hence, the correlation between drill bit wear and the mechanical ROP is 0. Furthermore, because of the good drillability of the concrete blocks and the relatively shallow drilling depth, with each block drilled to a depth of 50 mm, the effect of the drill-bit cutting depth on the mechanical drilling rate is also minor, with a correlation of 0.08. Among these, the WOB, TOR, Q, and RPM have a high correlation with the mechanical ROP; therefore, these are used as feature values for modeling in this experiment.
# Model Test and Numerical Simulation of Single Pile Response under Combined Loading in Slope
## 6 Discussion
### Different Slope Gradients Affect the Internal Force and Displacement  
The pile displacement under the various lateral load was affected by the slope, and similarly, the internal force distribution of the pile was affected by different slopes [32].  
A gradually increased load caused an increment in the pile's horizontal displacement, and the slope of 45\({}^{\circ}\) increased faster than the slope of 15\({}^{\circ}\) (see Figure 8). When lateral load acted on the pile top with a gentle slope, the contact at the pile-soil interface was very small. The slope then became larger, and the interface between the soil and the pile cracked noticeably (see Figure 9). At this time, the earth pressure on the pile side was less than that on the other side, so that a specific horizontal thrust was generated on the pile. As the slope angle increased, the pile horizontal thrust increased accordingly.  
Under the lateral load, the bending moment gradually increased from the pile top downwards and reached the maximum at 0.3 m below the pile head. The depth at this time was six times the  
Figure 10: The bending moments of piles with different slope angles.  
pile diameter (see Figure 10), and then it gradually decreased to zero. In the structural design of the round pile, it is recommended to strengthen the steel bar at a position several times the diameter of the pile below the pile top so as to increase the horizontal bearing capacity. [PERSON] [33; 34] theoretically calculated that the maximum bending moment of the anti-slide pile was about 2 m under the sliding surface (1 times the pile width). This difference might be caused by the shape of the pile and stiffness of the rock under the sliding surface. When the pile head was subjected to a lateral load of 450 N, the maximum bending moments of the piles were 91.05 N-m, 104 N-m, and 111.7 N-m, corresponding to 15\({}^{\circ}\), 30\({}^{\circ}\), and 45\({}^{\circ}\) slopes, respectively. The maximum bending moments of the piles increased with an increment of the slope gradient.  
The pile material also affects the pile head displacement under the lateral load [35]. The material of the pile is generally reinforced concrete, and waste steel rails can also be used instead of steel bars. This material substitution can improve the shear resistance of the pile. The combined structural stresses of this approach need to be further studied. When the position of the pile and pile shape changes on the slope, the soil pressure around the pile will also change. The effect of the pile position change on the internal force of the pile and the pile top displacement needs further research.
# Some Aspects of the Modelling of Dried Red Beets Rehydration Process
###### Abstract  
Some dehydrated products must be rehydrated before consumption or further industry processing. Optimization of the rehydration process needs mathematical models of the process. Despite the widespread use of computers and their associated software, empirical equations are still widely used in view of their simplicity and ease of computation. The mathematical description of the kinetics of mass gain, volume increase, dry matter loss, and moisture content increase and changes of rehydration indices during the rehydration of dried red beets was investigated. The effects of drying air temperature (\(T_{d}\)), drying air velocity (\(v_{d}\)), characteristic dimension (\(L\)), and rehydration temperature (\(T_{r}\)) on model constants were also examined. Red beets cubes (10 mm) and slices (5 and 10 mm) were dried in natural convection (\(v_{d}\) = 0.01 m/s), forced convection (\(v_{d}\) = 2 m/s), and fluidization (\(v_{d}\) = 6 m/s) at \(T_{d}\) = 50, 60, and 70 \({}^{\circ}\)C. The rehydration was conducted in distilled water at \(T_{r}\) = 20, 45, and 70 \({}^{\circ}\)C. The kinetics of rehydrating dried red beets was modelled applying five empirical models: Peleg, [PERSON] (Newton), Henderson-Pabis, Page, and modified Page. Equations were developed to make the model constants dependent on \(T_{d}\), \(v_{d}\), \(L\), and \(T_{r}\). Artificial neural networks (ANNs) (feedforward multilayer perceptron) were adopted to condition the rehydration indices on \(T_{d}\), \(v_{d}\), \(L\), and \(T_{r}\). The following models can be recommended as the most acceptable: (1) the modified Page model for mass gain (RMSE = 0.0236-0.0897) and for volume increase (RMSE = 0.0213-0.0972), (2) the Peleg model for dry mass loss (RMSE = 0.0161-0.610), and (3) the Henderson-Pabis model for moisture content increase (RMSE = 0.0350-0.1062). The ANNs performed the rehydration indices in an acceptable way (RMSE = 0.0528-0.2285). Both the rehydration indices and model constants depended (but to a different degree) on the investigated drying and rehydration conditions.  
rehydration; red beet; quality; mathematical model; ANN Article
## 6 Security and Performance Analysis
### Implementation Result
#### 6.3.1 Implementation Environment  
The SP-MAACS scheme and the fully secure decentralized CP-ABE scheme [26] were implemented using the well-developed and robust JPBC library [51]. We used the Eclipse IDE to implement the simulation code and the code was written in Java. The tests were conducted on a laptop running Windows 10 (64-bit) and equipped with a 2.50 GHz Intel (R) i5-3210M processor and 4 gigabytes of RAM.
# A Wireless Monitoring System for Cracks on the Surface of Reactor Containment Buildings
## 3 Hardware Selection and Assembly  
Each wireless sensor node in a structural monitoring system is responsible for three primary functions: data acquisition, signal processing and wireless communication. Correspondingly, a sensor node usually consists of a microprocessor, radio transceiver, data storage, batteries and sensor boards. Up to now, many commercial wireless nodes have been used for structural monitoring. In addition, some researchers have also designed their own platforms to carry out monitoring tasks. Table 1 shows the features of typical commercial nodes [4; 5; 7; 8; 17; 18; 19; 20] and some customized ones [12; 21; 22] used in structural monitoring. Based on the experience of past researchers, we design our own nodes with off-the-shelf components. For sensors, crackmeters and thermometers are selected according to monitoring contents. Our monitoring system typically comprises multiple sensor nodes, several cluster nodes and a sink node that is connected to a laptop.