esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Factors Screening in a Full 2\({}^{5}\) Factorial Design for the Initial Regression Model
Figure 5: Pareto chart for (**a**) \(Y_{1}\) and (**b**) \(Y_{2}\). is less than 0.05 (bold). It was found that among the variables and their interaction, \(A\), \(B\), \(C\), \(D\), \(E\), \(AE\), \(BE\), \(CE\), and \(DE\) for \(Y_{1}\) and \(A\), \(B\), \(C\), \(D\), \(E\), \(AE\), \(BE\), and \(DE\) for \(Y_{2}\) response were statistically significant. \[ \begin{array}{ll}Y_{1}=&8.74-0.002A-39.7B-74.48C-19.41D+7.49E+0.02 AB+0.084 AC \\ &+0.03 AD+0.001 AE+247.6 BC+65.85 BD-30.93 BE\\ &+133.44 CD-30.32 CE-6.78 DE-0.2 ABC-0.09 ABD+0.03 ABE\\ &-0.21 ACD+0.05 ACE+0.01 ADE-402.2 BCD+139.9 BCE\\ &+27.72 BE+26 CDE+0.56 ABCD-0.173 ABCE-0.03 ABDE\\ &-0.03 ACDE-145.23 BCDE+0.16 ABCDE\end{array}  \tag{6}\]  
\[ \begin{array}{ll}Y_{2}=&31.3-0.01A-37.7B-43.37C-10.87D+1.3E+0.03 AB+0.04 AC+0. 01 AD\\ &+0.003 AE+128.3 BC+30.1528 BD-11.5 BE+95.75 CD\\ &-10.6 CE-5.4 DE-0.1 ABC-0.03 ABD+0.01 ABE-0.13 ACD\\ &+0.02 ACE+0.007 ADE-272.9 BCD+74.17 BCE+23.01 BDE\\ &+12.8 CDE+0.3 ABCD-0.11 ABCE-0.04 ABDE-0.02 ACDE\\ &-83.82 BCDE+0.12 ABCDE\end{array}  \tag{7}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Response** & **Model Terms** & **Effect** & **Coefficient** & \(p\)**-Value** \\ \hline \multirow{8}{*}{Phenol conversion (\%)} & Model Equation (6) & - & 8.74014 & **0.000** \\  & \(A\) & 17.704 & \(-0.00216\) & **0.008** \\  & \(B\) & \(-12.201\) & \(-39.6608\) & **0.016** \\  & \(C\) & 8.478 & \(-74.4782\) & **0.026** \\  & \(D\) & \(-11.43\) & \(-19.4077\) & **0.018** \\  & \(E\) & 41.859 & 7.49008 & **0.002** \\  & \(AB\) & \(-1.491\) & 0.023912 & 0.495 \\  & \(AC\) & 0.672 & 0.083992 & 0.727 \\  & \(AD\) & \(-0.518\) & 0.030147 & 0.784 \\  & \(AE\) & 10.552 & 0.000846 & **0.021** \\  & \(BC\) & 3.915 & 247.554 & 0.229 \\  & \(BD\) & \(-0.632\) & 65.8527 & 0.742 \\  & \(BE\) & \(-7.508\) & \(-30.9289\) & **0.036** \\  & \(CD\) & \(-3.939\) & 133.44 & 0.227 \\  & \(CE\) & 4.986 & \(-30.316\) & **0.049** \\  & \(DE\) & \(-7.779\) & \(-6.77565\) & **0.031** \\  & \(ABC\) & \(-2.213\) & \(-0.22352\) & 0.373 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of the regression coefficient of the initial model for phenol conversion (\(Y_{1}\)). Figure 6: Parity charts of predicted versus observed responses for the initial models of (**a**) phenol conversion (\(Y_{1}\)) and (**b**) hydrogen yield (\(Y_{2}\)), including all main and interaction independent variables.
# Energy-Efficient On-Off Power Control of Femto-Cell Base Stations for Cooperative Cellular Networks
## 4 Performance Evaluation
As we can see from the simulation results, the relative power consumption and spectral efficiency decrease as \(\sigma\) decreases because more FBSs are likely to be turned off. Moreover, from Figure 7, we can find that the spectral efficiency of Prop and ES schemes is higher compared to that of Thr, which indicates that the constraint on \(R_{thr}\) is satisfied. It should be noted that the performance of our proposed scheme and that of ES are almost identical, which validates the optimality of our proposed scheme, especially when \(N\) is low. In practice, \(N\) is unlikely to be more than 10 such that our proposed scheme works well in practical cellular networks. In addition, we can observe that the relative power consumption is lower when \(N\) is high because only few FBSs have major influence on the performance of cooperative transmission. Figure 5: Spectral efficiency vs. \(\epsilon\) by varying \(N\). Figure 6: Relative power consumption of FBSs vs. \(\sigma\) by varying \(N\). Finally, we compare the computation times of our proposed scheme and that of the ES scheme in Figure 8. We can observe that the computation time of ES scheme increases exponentially as \(N\) increases because all the combinations of FBSs have to be examined to determine the optimal set \(\mathbb{A}\). However, the computation time of our proposed scheme does not increase abruptly as \(N\) increases, which validates the merit of our proposed scheme.
## 2 The Proposed Theory
### Classification Network Structure Design
#### 2.3.2 The Proposed Self-Adaptive Residual Shrinkage Network (SARSN)
Its calculation is shown in Formula (12):  
\[GMean=\sqrt{TPR\times FPR}. \tag{12}\]  
where _TPR_ is the true positive rate and _FPR_ is the false positive rate. The expression of the weight factor \(\phi\) for each category of adaptive update loss distribution is shown in Formula (13):  
\[\Phi=K\times exp(-\frac{GMean}{2})\times exp(-\frac{1-accuracy}{2}). \tag{13}\]  
where \(\phi\) is the weight factor of each category, and \(k\) is the most significant proportion of a few sample datasets, that is the sample imbalance ratio. Accuracy is the classification accuracy; then, (\(1-accuracy\)) is the error rate, its calculation is shown in Formula (14):  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN}. \tag{14}\]  
where _TP_ denotes the true positive, which is the number of positive classes with correct classification, and FN denotes the false negative which is the number of negative classes with incorrect classification. _FP_ and _FN_ can be deduced in the same way. To avoid the phenomenon of gradient explosion caused by the weight of a few samples being too large in the dataset, this paper uses the tanh activation function to map the value of \(\phi\) between \(-1\) and \(1\), and its expression is shown in Formula (15):  
\[T_{h}=\frac{exp(1-\Phi)-exp(\Phi-1)}{exp(1-\Phi)+exp(1+\Phi)}. \tag{15}\]  
According to Formula (15), the weight of a few samples is between \(-1\) and \(1\), while the weight of most samples is close to \(0\). This operation not only avoids the gradient explosion phenomenon, but also does not reduce the sensitivity of the loss function when dealing with an unbalanced data distribution [30]. The accuracy between categories can be measured by the Euclidean distance between the real sample value and the predicted value [31]. The square of the Euclidean distance is usually used in research, as shown in Formula (16):  
\[\rho^{2}=\sum_{i=1}^{n}{(y_{i}-y^{\prime}_{i}+\beta)}^{2}. \tag{16}\]  
where \(\rho\) is the square of the Euclidean distance, \(y_{i}\) represents the true value, \(y^{\prime}_{i}\) represents the predicted value, and \(n\) represents the sample size of each mini-batch, and \(\beta\) is the parameter to adjust the sensitivity of the loss function. The expression of the loss function is shown in Formula (17):  
\[Loss(\theta)=-\frac{\rho^{2}}{n}\sum_{i=1}^{n}{T_{h}log({y_{i}}^{\prime})}. \tag{17}\]  
where \(\theta\) is a parameter that can emphasize a few samples in the training process, increase the classification accuracy of a few samples, and cooperate with the optimizer to realize the global optimization of the neural network.
# Time-Dependence of the Mechanical Behavior of Loess after Dry-Wet Cycles
## 4 Discussion  
### The Long-Term Strength of Loess Samples with Different Dry-Wet Cycles  
The long-term strength of rock and soil is an important parameter for assessing the stability of landslides [49; 50]. At present, the main methods to obtain long-term strength through creep test curves include the isochronous curve method [3; 51], which uses the creep test curve and the Boltzmann superposition principle [52] and states that the stress response of a system to a time dependent shearing deformation may be written as the sum of responses to a sequence of step strain perturbations in the past [53], to obtain the stress-strain isochronous curves corresponding to the different deviatoric stress at the same time [54]. Figure 10a-e show the isochronous curves corresponding to the creep curves of the samples after five kinds of dry-wet cycles (\(n=0\), \(5\), \(10\), \(15\), \(20\)). Based on the isochronous curves, the long-term strength (\(q_{\rm L}\)) was obtained.  
The experimental data listed in Table 3 was plotted in Figure 11. One can observe that there is a clear correlation between the long-term strength and the number of dry-wet cycles. The overall relationship between the long-term strength of Ioess and the number of dry-wet cycles can be described by the following equation:  
\[q_{\rm L}=123.50\mathrm{e}^{(-n/9.98)}+127.33,\ n\leq 20, \tag{1}\]  
where \(q_{\rm L}\) is the long-term strength measured in kPa and \(n\) is the number of the dry-wet cycles.  
Figure 10: _Cont._  
Figure 11: The fitting curve of the long-term strength after different dry-wet cycles.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(n\) & **0** & **5** & **10** & **15** & **20** \\ \hline \(q_{\rm L}\) & 250 & 206 & 172 & 150 & 148 \\ \hline \end{tabular}
\end{table}
Table 3: Long-term strength after different dry-wet cycles (qL).  
It is clear that the long-term strength of loess decreases when increasing the dry-wet cycles. However, the reducing magnitude of long-term strength gradually decreases with the increase of the number of dry-wet cycles, until it reaches a constant value. That is to say, the long-term strength of the loess and the number of dry-wet cycles (\(n\leq 20\)) shows an exponential decreasing relationship (see Equation (1)).
# Case Study: 3D Modelling and Printing of a Plastic Respirator in Laboratory Conditions
## 3 Description of the 3D Printing Procedure of the Selected Object
It is also possible to see transitions between the layers on the folds of the filter cover. Darker places are visible mainly on the main connections. When looking at the respirator from the top, the X-shaped layers can be seen on the filter cover. The shape of each gap is the same and there are no differences in the layers of the individual grids. As well as on the sides and at the top, there are darker places. With an unprinted filter inserted, the respirator provides full protection. It is only necessary to use the material to be tied around the head. However, the respirator is not certified and therefore cannot be used by paramedics and front-line people. The ZMorph [17] printer confirmed the expected print quality. Printing one complete respirator takes about 3 h and 40 min. The complete respirator and its separate parts its show in Figure 11. It is possible to adjust the size of the respirator according to the size of the shape--a man or a woman to fit exactly on the face. Only one size was printed in the case study. As the PLA material deforms, the ABS material was chosen because it does not deform outdoors or under the influence of sunlight. However, for indoor use, it does not matter. Models printed with the ZMorph VX printer are flawless and of high quality, even when printing much more demanding models. Although the time required for printing is  
Figure 11: Complete respirator and its separate parts. Figure 10: A more detailed shot of 3D printing on the ZMorph printer. longer than with the TriLab DeltiQ XL printer, the resulting product has a significantly better processing quality. The main advantage of the ZMorph VX printer is its multifunctionality. Whether hybrid printing (printing two different materials at once), the possibility of CNC machining or engraving. Specific parameters of respirator:  
* Washable and disinfectable respirator with replaceable filter;
* Different materials can be used as a filter--the recommendation for achieving high quality is colloidal silver;
* The inner space of the filter can be modified;
* The respirator has an ergonomic shape for most face types--resize by adjusting the STL model;
* The cord is not part of the 3D printing--it must be rubber, resp. another improvised means. Table 3, [18] shows the processed selected parameters in comparison to the different types of respirators to prove the validity of the implementation of the respirator's own design. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Respirator**} & \multicolumn{2}{c}{**Selected Parameters**} \\ \cline{2-3} \multicolumn{1}{c}{} & **Filter** & **Ergonomic Shape** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Selected parameters for respirator comparison.
# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 4 Remote Sensing Image Data Processing
### Construct Models
#### 4.1.2 3D Reconstruction  
In this study, after the completion of the Air-3 computation, DJI Terra software was used to process the aerial images from the first and second phases, enabling the three-dimensional reconstruction of the study area. This process generated a three-dimensional model of the area (Figure 3) and exported a point-cloud model in LAS format (Figure 4). The first reconstruction took 12 min and 32 s, while the second reconstruction took 13 min and 49 s.
## 3 Laser Tracker as a Measurement System
### Measurement Uncertainty  
As a result of the design, calibration, and accuracy of components used, the LT has a measurement uncertainty [24; 25; 26]. Therefore, manufacturers provide specifications of their LTs, and their accuracy is evaluated based on the standards ISO DIS 10360-10 and ASME B 89.4.19-2005. In order to reduce measurement uncertainty as a result of LT angular encoders, different multilateration techniques can be used, and the ones most commonly used in MT verification are studied in Section 4.  
Figure 3: Laser trackers from different manufactures.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 5 Discussion
### Limitations and Prospects  
Although the proposed Siamese-SAM performs well, it does have certain limitations. Firstly, the computational overhead of the current model remains relatively high, making it challenging to process high-resolution remote sensing images efficiently. Secondly, the current tasks focus on single-classification tasks, whereas many real-world applications (such as land-use transition monitoring) require multi-class change classification, which is an area not explored in our work. Thirdly, while SAM was trained on natural images, its inductive bias towards remote sensing imagery remains unavoidable. Although our method mitigates this to some extent, it remains a challenge.  
Therefore, we envision several future directions to address these limitations:  
1. **Hybrid Encoder Development:** We plan to develop a hybrid encoder that combines the segmentation prior of SAM with lightweight, remote-sensing-specific adapters. This approach not only strengthens the model's capability to leverage multispectral feature information but also minimizes computational expenses, thereby preserving robust detection performance under varying environmental conditions [53; 54; 55].
2. **Expanding Datasets and Multi-Image Change Detection:** We hope to create more specialized datasets for remote sensing image change detection and extend the analysis from two images to N images, which will provide more in-depth insights into urban change processes.
3. **Exploring Large Segmentation/Detection Models:** Future work could explore larger-scale segmentation [56] and detection models specifically tailored for remote sensing tasks [57].
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 4 Validation Using the International Parallel Test Benchmarks
### Experimental Campaign  
#### 4.1.1 Bender Element System  
The bender element apparatus involves two T-shape transmitter and receiver bender elements embedded in standard inserts. The bender elements are supported by a function generator (Huntingdon TG2511) and a digital oscilloscope with two channels with sampling rates of 80 Ms/s and two of 20 Ms/s used to store and display the signals. A spectrum analyser software averages several measurements to filter the noise out from the output signal. Figure 10 presents an overview of the experimental apparatus.  
Figure 10: Overview of the bender element apparatus [19].
## 6 Fuzzy Based Adaptive Resource Allocation
### Fuzzy Logic System Implementation in CNQF  
A Fuzzy Rule Based (FRB) system is developed within the CNQF RMS Java prototype to provide _decision support_ in the admission control process through adaptive (bandwidth) resource allocation  
Figure 6: Effect of increasing background traffic load on test flow without adaptive CNQF reconfiguration policy.  
between the existing service classes. The capability to handle uncertainty and imprecision as network and traffic conditions experience sudden, rapid and unexpected variation makes the fuzzy approach promising within dynamic operational environments. Thus fuzzy logic is proposed as a crucial component of the admission control system of CNQF RMS as shown in Figure 7.  
The FRB component is designed as a mechanism for dynamically controlling the global resource allocation parameters for the traffic classes with temporal changes in the network and traffic conditions. The FRB component fine-tunes the policy-based resource allocation process within the RB admission control entity thus making it _adaptive_ and _resource-aware_. The motivation is to combine high level policies with resource-aware rule-based control so as to exploit the flexibility of fuzzy logic and improve system performance. Furthermore, the incorporation of fuzzy logic allows for easier extension of functionality without rebuilding the system from scratch; whereas this is difficult or impossible with traditional measurement-based admission control. This leads to cost-effective, easier-to-modify admission control and resource allocation within the CNQF management framework. For example, the rules governing the allocation can be altered or the number of classes changed without re-implementing the RB algorithms or the underlying control and configuration mechanisms in the PEPs.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 4 Machine-Learning Algorithm  
The CatBoost algorithm was chosen due to its ability to effectively manage categorical variables [31; 35], which is essential for describing the diverse characteristics of cloud service instances. The algorithm is resistant to over-fitting and can deal with absent values [36; 37] in real-world datasets.  
The input data are preprocessed from the rows of the Amazon EC2 dataset. The target parameter for the algorithm is the _offered price per unit_, which is the predicted variable. In this work, we also seek the optimal historical period for the pricing forecast, taking into account factors such as the existence of sufficient historical data to identify significant patterns and trends, and accommodate multiple price cycles due to changing market conditions.  
Figure 2: Evolution of median price for each instance family.  
The model's performance will be evaluated using metrics such as the mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). If \(\hat{X}_{i}\) are the predicted variables and \(X_{i}\) the actual variable inside a data subset with \(n\) values (typically not included in the training), then these are given by [24]:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|X_{i}-\hat{X}_{i}\right| \tag{1}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(X_{i}-\hat{X}_{i}\right)^{2}} \tag{2}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{X_{i}-\hat{X}_{i}}{X_{i}}\right| \tag{3}\]  
Note that both the MAE and RMSE measure the absolute error. In the RMSE, differences are squared, and, hence, larger errors have a more significant impact. The MAPE, on the other hand, measures the relative error, which is often useful but is undermined in the case of small denominator values.
# DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models
## 5 Experiments  
### Evaluation Metrics  
In this study, parameters and FLOPs were used as evaluation metrics for model size and computational complexity. The model performance was evaluated using precision rate P (Precision), recall rate R (Recall), mean average precision (mAP), and FPS (Frames Per Second Transmitted).  
The precision rate represents the ratio of true positives (TP) to all samples predicted to be positive (TP + FP). That is, how many of the samples predicted to be defective are actually defective samples, with the following equation:  
\[\mathrm{P}=\frac{\mathrm{N}_{\mathrm{TP}}}{\mathrm{N}_{\mathrm{FP}}+\mathrm{N }_{\mathrm{TP}}}\times 100\% \tag{15}\]  
where \(\mathrm{N}_{\mathrm{TP}}\) indicates the number of samples correctly predicted as positive; \(\mathrm{N}_{\mathrm{FP}}\) indicates the number of samples incorrectly predicted as positive.  
Recall represents the ratio of true positives (TP) to all samples that are actually defective (TP + FN). That is, how many of the samples that are actually defective are predicted to be abnormal, with the following equation:  
\[\mathrm{R}=\frac{\mathrm{N}_{\mathrm{TP}}}{\mathrm{N}_{\mathrm{FN}}+\mathrm{ N}_{\mathrm{TP}}}\times 100\% \tag{16}\]  
where \(\mathrm{N}_{\mathrm{FN}}\) indicates the number of samples that were actually positive but mistakenly detected as negative.  
The mAP is the mean average precision average for a given threshold, with the following equation:  
\[\mathrm{mAP}_{\mathrm{IOU}}=\frac{1}{\mathrm{N}_{\mathrm{c}}}\sum\limits_{i=1 }^{\mathrm{N}_{\mathrm{c}}}\mathrm{AP}_{\mathrm{IOU}}^{(i)} \tag{17}\]  
where \(\mathrm{N}_{\mathrm{c}}\) indicates the number of defect categories.  
FPS indicates the detection speed of the model, which is the number of images that can be processed in a second. The formula is as follows:  
\[\mathrm{F}_{\mathrm{FPS}}=\frac{\mathrm{N}}{\mathrm{T}} \tag{18}\]  
where N represents the number of detected pictures; T represents the total detection time.  
\begin{table}
\begin{tabular}{c c} \hline
**Name** & **Configure** \\ \hline Operating System & Linux-Ubuntu 20.04 \\ CPU & Intel-i7-11700 \\ GPU & NVIDIA GeForce GTX 3090 \\ RAM & 24 GB \\ IDE & PyCharm 2020 \\ Deep Learning Frameworks & PyTorch 1.11 \\ Programming Language & Python 3.9 \\ CUDA & Version 11.3 \\ Image size & 640 \(\times\) 640 \\ Learning rate & 0.01 \\ Epoch & 100 \\ Batchsize & 16 \\ Learning rate & 0.01 \\ Optimizer & SGD \\ \hline \end{tabular}
\end{table}
Table 1: Configurations related to this experiment.