esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement Model of Full-Width Roughness Considering Longitudinal Profile Weighting
## 3 Equivalent Longitudinal Profile and Equivalent Elevation
It is worth emphasizing that the choice of sampling frequency in the lane width direction has a crucial impact on the distribution of elevation data on the cross-section. Specifically, when a higher sampling frequency is adopted, a denser point cloud data set can be captured, thereby significantly increasing the sample size of elevation data; conversely, if the sampling frequency is lower, the obtained point cloud data will exhibit a sparser distribution, leading to a corresponding decrease in the amount of elevation data. This characteristic directly determines that different sampling frequencies will result in significant differences in the calculated accumulated elevation values \(P_{3}(n)\) when collecting elevation data for the same road. Furthermore, these differences in elevation data will significantly affect the calculation results of the IRI, causing the IRI values obtained under different sampling frequencies to exhibit significant variation. Therefore, it is inappropriate to directly use \(P_{3}(n)\) for IRI calculations. Based on this, the present study proposes a scaling strategy for the accumulated elevation \(P_{3}(n)\), aiming to map it within a scientifically reasonable numerical range. Let \(S\) represent the cumulative sum of the weighting model function \(f_{\text{norm}}(x)\). It is evident that the cumulative sum \(S\) is significantly influenced by the cross-sectional sampling frequency \(Fs\). To further analyze this relationship, we define the ratio of \(S\) to \(Fs\) as the coefficient \(K\), where \(K=S/Fs\). Figure 3 systematically presents the trend of the coefficient \(K\) curve under different sampling intervals \(\Delta x\). Herein, \(Fs\) is indirectly represented by the sampling interval \(\Delta x\). As observed from the figure, despite variations in the sampling interval \(\Delta x\), the ratio tends to stabilize at a value of 0.3226. Additionally, the standard deviation of this ratio is 0.00455, with a coefficient of variation of only 1.41%, indicating a high consistency in results across different sampling intervals. Based on this significant and stable phenomenon, this study further proposes a scaling approach for equivalent elevation based on observed data. Specifically, Equation (10) is employed to scale the accumulated elevations \(P_{3}(n)\). This scaling process not only helps to eliminate the impact of sampling interval variations on model results but also enhances the model's generalization ability under different conditions. Furthermore, rounding off the values improves the acceptability of the parameters in practical applications.
## 4 Emerging Lightweight Security Approaches
### Digital Fingerprinting  
Spoofing is an IoT system's security vulnerability, which means malicious devices masquerading as authentic end nodes [101]. If attackers can discover the device's physical address (such as a serial number or a media access control (MAC) address) or its end point identification number, attackers can assume the identity of the device, to intercept messages or inject false data into the IoT system.  
One way to detect these attacks is to use digital fingerprinting, which authenticates communication devices. Similar to identifying humans by their fingerprints, devices can be identified by the network's server. This server must be able to ascertain the identity of the device that originated the message so that it can be trusted by the user application.  
The three LPWA communication networks discussed in Section 3 generate a message integrity code (MIC), which they attach to the data that travel over the air interface, from the device to the core network. This MIC is their way of fingerprinting and authenticating their devices, as they use the node's identification number and a secret key to generate the MIC. The MIC also uses information such as payload data and a frame counter to help the server to check that the data are not corrupted.  
Beyond authentication and integrity, fingerprinting is also useful for tasks such as device tracking, resource allocation, and performance tuning, according to [101], which proposed one method of fingerprinting that passively collects data from the physical and data link layers to authenticate the device's identity.  
Passive data collection means obtaining information without introducing additional traffic to the network. For example, a server can profile Bluetooth Low Energy (BLE) devices at the hardware level by collecting advertisement packets. Because the speed at which a node sends advertisement packets is specific to the hardware device, the server analyzes some properties of the incoming data, such as the delay interval between packets and the channel switching sequence, thus providing a fingerprint for the device [102]. As an alternate method to encryption algorithms, this type of fingerprinting can be used to authenticate low-resource IoT devices as software platforms. It was also added to our lightweight primitives in Table 7.
## 3 The ILDACP Algorithm
### Total Power Consumption Clustering  
Fuzzy C-means algorithm (FCM) [39] is a clustering algorithm that is based on squared error and criterion. In this phase, FCM clusters the aggregation power consumption to detect the operating states of the largest power contribution appliance over the validation time horizon H. First of all, if the total power consumption values that are extracted from total power trajectory \(P\) include more than one appliance, it is clustered into optimal number clusters. Figure 3 shows a clustering result. We can see from the figure, the power consumption values of the highest power appliance in turned-on state are clustered shown in cluster 3. Then, the total consumption trajectory \(A^{j}\), only the power value in cluster 3 left (others are set to 0), is generated. Thus, the appliances power values with larger power contribution can be estimated first, as they give largest impact on the trajectory of total power consumption and weaken the traces of less contribution appliances.  
Figure 3: The example of clustering result.  
Figure 2: The flowchart of the algorithm proposed in this paper. DTW: Dynamic Time Warping.
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 4 Results  
### Denoised Night-Light Images  
After the images were pre-processed using various techniques such as denoising, annual night-light images of Cambodia were obtained. It is interesting to note that the denoising method produced better annual images when both the annual cloud-free averaging and month-specific substitution methods were applied. The annual images obtained by the annual averaging method were affected by cloud cover, causing the final image element DN values to be greatly distorted. Examples of the final images corrected by the three methods are shown in Figures 3-5.  
In this work, the total night-light values of the images obtained using the three different processing methods were counted. The different methods produced similar total night-light values in some years. The overall night-light value for Cambodia is increasing, suggesting that the country's electricity supply and national access rate are increasing. The specific data are shown in Table 4.
###### Abstract
We believe that by placing the CoT module behind the convolutional network, we can learn more fine-grained local region feature correlations as a way to reduce the interference of lighting changes on feature extraction, improve the robustness of facial expression recognition, and at the same time better serve the inputs of the ViT. Therefore, using the CoT module to connect the CNN and ViT structures can achieve more comprehensive and flexible feature extraction, thus enhancing the model's comprehensive understanding of facial expressions. On the other hand, to remove the interference of occluded regions and noise, we utilize an adaptive learning method in the Transformer encoder to dynamically retain the feature information most relevant to facial recognition, which not only removes extraneous information but also reduces the amount of computation and improves the performance of the model. The above two methods are used to ensure recognition of facial expressions even in complex scenes. Overall, our main contributions are as follows:  
1. The CoT module is used to enhance the model's correlation learning between local regions, capture more fine-grained spatial relationships, improve the ability to perceive subtle differences, make the model more adaptable to complex lighting conditions, and improve the robustness of facial expression recognition. 2. In the Transformer encoder, an adaptive learning method is used to dynamically adjust the parameters of the self-attention weight matrix, enabling the model to retain the top K tokens that are most relevant to the classification of expressions, removing noise and occlusion interferences, improving computational efficiency, and saving computational time. 3. Our proposed model achieves satisfactory results on several datasets. Excellent recognition performance is obtained on the Oulu-CASIA dataset (NIR scene and natural light under three different lighting conditions), CK+, RAF-DB, and FERPlus datasets, which validates the accuracy and robustness of our proposed model.
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
## 6 Conclusions  
In this paper, a novel MR damper configuration with multistage fold gaps and bending magnetic circuits was established. A multiobjective optimization of the structural parameters was proposed to improve the damping performance of the MR damper. The main conclusions can be drawn as follows:  
(1) The effects of the first shear effective length \(L_{1}\), the second shear effective length \(L_{2}\), and the nonmagnetic length \(t_{a}\) on the damping force and dynamic range of the MR damper are more significant compared to that of the other parameters.  
(2) When predicting the damping force and dynamic range, the EBFNN prediction model is more accurate than the RSM, orthogonal polynomial, Kriging method, and radial basis function neural network models.  
(3) The simulation results show that structural optimization improved the damping force and dynamic range significantly. The damping force of the optimized structure increased to 6917 N, which was improved by 50.8%; the dynamic range increased to 13.2, which was improved by 6.4% compared to that of the initial damper. Additionally, the optimization can be verified by the experimental correction result, where the output damping force and dynamic range were 7247 N and 13.8, respectively.  
Conceptualization, L.L. and Y.X.; methodology, L.L. and G.H.; validation, Y.X. and F.Z.; formal analysis, L.L. and Y.X.; investigation, Y.X. and L.Y.; resources, L.L. and G.H.; data curation, Y.X.; writing--original draft preparation, L.L. and Y.X.; writing--review and editing, G.H., F.Z. and C.H.; visualization, Y.X.; supervision, L.L. and G.H.; project administration, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52165004), Key Project of Natural Science Foundation of Jiangxi Province of China (No. 20212 ACB204002).  
Not applicable.  
The authors declare no conflict of interest.
# A Review on Local Failure Probability Sensitivity Analysis
## 4 Local RSA in the Standard Normal Space
### Sensitivity through an Approximation of the Failure Domain Indicator Function
#### 4.3.3 Weak Approach with Advanced Sampling Techniques  
The Weak approach has been associated with the sequential importance sampling (SIS) framework in [52], to obtain a sensitivity estimate more efficient than the MC one. The selected value of \(\widetilde{\sigma}\) is the smallest value of \(\sigma\) such as the coefficient of variation of the sensitivity estimate is close to a CV target.  
\[\widetilde{\sigma}=\underset{\sigma\in(0,\sigma_{L})}{\text{argmin}}\left( \text{CV}\left(\frac{\widehat{\partial P_{f}(\mathbf{s},\sigma)}^{\text{SIS} }}{\partial s_{\ell}}\right)-\text{CV}_{\text{target}}\right)^{2},\]  
where \(\sigma_{L}\) is the final value of the decreasing sequence \(\sigma_{i}\) of the SIS framework (see [53]). This selection does not require any additional evaluation of the lsf. The resulting sensitivity estimate has a controlled CV; however, it is not possible to measure its bias, other than with the approximation mentioned above \(e\propto\widetilde{\sigma}^{2}\). Nevertheless, this method is efficient for  
Figure 2: Illustration of the relation between \(\widetilde{E}_{\text{mse}}^{\text{MC}}\) and \(\sigma\) in log–log scale, for a fixed sample size \(N\), with the approximations derived in [50] in a one-dimensional case.  
multiple failure regions [52] and the SIS framework is also quite robust in high-dimensional spaces [53].  
The Weak approach has been combined with the subset sampling framework in [51], for sensitivities with respect to design parameters. Equation (10), presented in Section 3.3.1, is still valid (with \(\delta\) instead of \(\mathbf{\theta}\)); however, the dependence on \(\delta\) in each \(P_{i}(\delta)\) is now contained in the intermediate failure domain indicator function, as well as in \(\mathbb{P}(F_{i-1})\). Each intermediate sensitivity \(\partial P_{i}(\delta)/\partial\delta_{\ell}\) is then estimated with the Weak approach. The selected value of \(\widetilde{\sigma}\) is obtained with a formula that depends on the number of failing observations in the sampling procedure, on \(P_{f}(\mathbf{s})\), and on the order of magnitude of the \(\mathrm{lsf}\)\(g\). More precisely, \(\widetilde{\sigma}\) is chosen as a quantile of the \(\left\|\mathbf{\chi}^{(j)}\right\|=\left\|\mathbf{\delta}\left(\mathbf{s},\mathbf{X} ^{(j)}\right)\right\|\) ordered values, for \(j=1,\ldots,N\). This choice results in a number \(N_{r}\) of response values within \(\pm\widetilde{\sigma}\), where \(N_{r}\) depends on \(N\) and \(P_{f}(\mathbf{s})\) and is set a priori. As previously mentioned, the bias of this estimate cannot be controlled other than with the approximation \(e\propto\widetilde{\sigma}^{2}\).
## 5 Performance Analysis
### Numerical Simulation
#### 5.2.2 Probability of False Locking on ACF Side Peaks  
Low ACF side peak ambiguity is an important advantage of ACE-FHBOC compared to ACE-BOC. To explore the ACF peak ambiguity for the example signals during signal acquisition, we conduct a Monte Carlo simulation. For an early-late spacing of 10.2 ns for ACE-BOC(12,1), 8.15 ns for ACE-BOC(15,10) and ACE-FHBOC(24:1:6,1), 9.8 ns for ACE-FHBOC(24:1:1,1), and a coherent integration time of 1 ms, Figure 13 shows the probabilities of false locking on ACF side peaks versus carrier-to-noise density ratio (\(C/N_{0}\)) for the example signals. ACE-FHBOC(24:1:1,1) has the smallest false locking probability among  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Signal Power Ratio & [1,1,3,3] \\ \hline Data Rate & 50 bit/s \\ Length of Spreading Code & 10230 \\ Length of Frequency-hopping Pattern & 10230 \\ Sampling Frequency & 286.44 MHz \\ Intermediate Frequency & 71.61 MHz \\ Filter Bandwidth & 50 MHz \\ Simulated Signal Duration & 100 ms \\ Data Type of Sampled Signal & Eight bit signed char values \\ \hline \hline \end{tabular}
\end{table}
Table 4: Common parameters of the simulated signal.  
Figure 12: Multiplexing efficiency of Type-I, -II, and -III ACE-FHBOC and ACE-BOC.  
the example signals, whereas ACE-BOC(12,1) has the largest. ACE-FHBOC(24:1:6,1) has a smaller false locking probability than ACE-BOC(15,10).
# Green IoT: A Review and Future Research Directions
## 5 Energy-Efficient and Eco-Sustainable Wireless Sensor Networks
### Energy Harvesting, Batteries, and Wireless Charging Techniques  
#### 5.3.1 Energy Harvesting Sources  
Advances in renewable energy technology can help to achieve the key features of wireless sensor energy sources, such as sustainability, reliability and reduction of greenhouse gas emissions [57]. In addition, the utilization of renewable energy technology holds great potential for enhancing the energy efficiency of wireless sensor networks situated in rural and remote regions where battery replacement may be problematic due to geographical barriers, such as challenging terrain that restricts access to these areas [58]. Solar cells are known for their high reliability and low maintenance requirements, and they can last for up to 20-30 years. Moreover, novel technologies have been introduced that enable sensors to collect energy from their environment, including wind and kinetic energy [59], and convert it into electrical energy that can be used directly or stored for later use. The characteristics and performance of renewable energy sources can vary significantly depending on the environment in which they are used. For example, outdoor renewable energy sources differ from those used indoors in industrial and commercial settings. A summary of indoor and outdoor energy sources and their properties is presented in Table 4. Nevertheless, it is essential to acknowledge that sensor nodes may exhibit an unequal distribution of remaining energy owing to variations in the amount of energy collected, and this must be taken into account when creating protocols [60].  
It is difficult to determine whether a specific IoT device would be a good candidate for energy harvesting without knowing more about its intended application and how it will operate in the system. However, there are certain categories of devices that are generally more or less suitable for energy harvesting, based on technical considerations, if not necessarily cost-effectiveness.  
Overall, to efficiently manage the available power, renewable energy technology often relies on energy prediction schemes. However, to achieve high reliability, energy-saving mechanisms, in addition to renewable energy technology, may still be necessary. Sensors need to estimate the evolution of energy in order to adjust their behavior dynamically and survive until the next recharge cycle. As a result, they can optimize key parameters such as sampling rate, transmit power, and duty cycling to adapt their power consumption to the periodicity and magnitude of the harvestable energy source.
# Effect Mechanism of Material Ratio on Ultrasonic P-wave Velocity in Coal Based Paste Fill Materials
## 4 The Correlation between the Strength and Ultrasonic P-wave Velocity
### The Correlation between the Shear Strength and Ultrasonic P-wave Velocity  
Similarly, Figure 9 shows that the ultrasonic p-wave velocity trends in harmony with the shear strength of the fill bodies. In the initial groups, an increase in shear strength was mirrored by an upsurge in ultrasonic p-wave velocity. Both metrics reached their zenith in the fifth group before simultaneously tapering off. A subsequent rise was noted following a minor dip in the seventh group of tests.  
The correlation coefficient between ultrasonic p-wave velocity and shear strength, as showcased in Figure 10, is 0.69. This coefficient indicates a positive correlation, signifying that samples with higher ultrasonic p-wave velocities tend to display greater shear strengths, while those with lower velocities tend to have reduced shear strengths.
## 3 Results and Discussion
### Verification Experiment  
To validate the optimal process parameters, the threshing experiment was repeated under the optimal conditions. The valid test was repeated three times to obtain the result. The results were presented in Table 5. The average Lr reached 1.93%, and the average Dr reached 3.40%. The errors were within \(\pm\)5%. This result indicated that the applicability of the optimal process parameters and the optimization method of combining NSGA-II and RSM was effective to determine the optimal process parameters.
# Online Distributed User Association for Heterogeneous Radio Access Network
## 8 Conclusions  
In this paper, we considered a three-tier HetNet environment, proposing user association schemes and dynamic load-balancing through _Prob._ and _d-choices_ schemes. In the K-tier HetNet, the user association, conventionally connected to the BS with maximum SINR, results in less association with pico and femtocells. This ultimately leads to load imbalance in the whole network. Unlike the _max-SINR_, in the _Prob._ scheme, UE selects the BS in a probabilistic manner upon arrival into the network by considering both SINR and the load of a BS. This technique eventually offloads the UEs toward small cells. Inspired by the \"balls and bins\" load-balancing problem and the power of two choices, we proposed the _d-choices_ association scheme. The UE can choose \(d\) number of BSs and selects the BS which has the lightest load among the chosen BSs. _Prob. scheme 2_ showed better results in minimum user rate, while _d-choices 2_ showed good results for tier-wise load-balancing in both uniform and non-uniform kinds of UE deployment. Load-balancing naturally provides the possible inclusion of more numbers of UEs in the network, typically giving better fairness and improving UE experience. These distributed schemes show an impressive result in dynamic environments by reducing the signal overhead between UE and BS, increasing the throughput per UE and network load balance. Future work includes the optimization of small cells in k-tier HetNet, while considering both energy and spectral efficiency with high user mobility.  
Conceptualization, B.B.K.; Formal analysis, B.B.K.; Investigation, L.S.; Methodology, B.B.K.; Software, B.B.K.; Supervision, S.L.-W.; Validation, L.S.; Writing--original draft, B.B.K.; Writing--review & editing, L.S.  
Figure 12: Performance of different association schemes in different deployment scenarios against optimal values.  
**Funding:** This research was funded by the Ministry of Science and Technology (MOST), Taiwan, under Grant Numbers 106-2221-E-182-013-MY3.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Research on Optical System of Dim Target Simulator Based on Polarization Stray Light Suppression
## 2 Analysis and Design of Optical System of Dim Target Simulator
### Analysis and Design of Depolarization Optical Engine
#### 2.2.3 Analysis of Illumination Optical System Design Objectives
Illumination lens design  
The system spot diagrams and axial aberration curve are shown in Figure 7:0 According to the spot diagrams, the color difference correction of each field of view is good, the dispersion spot of the optimized image surface is not large, and the energy concentration is high. According to the axial aberration map, the color difference is corrected at the maximum aperture, although there is a certain secondary spectrum; but because this system is an illumination system, there is no need to correct the secondary spectrum, and the system aberration meets the requirements of use. (3) Analysis of simulation results of illumination optical system  
Tracepro 7.3 software was used to simulate the illumination system. The light tracing diagram and the outgoing light irradiance diagram of the illumination system are shown in Figures 12 and 13. Figure 7:1 Aberration curve. (**a**) Optical path diagram; (**b**) spot diagrams; (**c**) longitudinal aberration. Figure 7:2 Light tracing diagram of illumination system. Figure 7:3 Imaging and mapping diagram of the homogenizing rod. The uniformity of luminous intensity \(\Delta E\) over a range of \(\phi\)25 mm can be expressed by Equation (18):  
Figure 7:4  
where \(E_{\text{max}}\) is the maximum luminous intensity and \(E_{\text{min}}\) is the minimum luminous intensity. The uniformity was calculated to be 95%. Therefore, the design parameters of the illumination optical system are better than \(\pm\)9.2\({}^{\circ}\), the uniformity is better than 92%, and the irradiation surface is \(\phi\)25 mm, which meets the requirements of use.