esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 4 Dynamic-CAPIC: Cache Based on Popularity and Class with Dynamic Cache Proportion  
In the Static-CAPIC described earlier, the proportions are statically and easily pre-determined for known and fixed traffic patterns. However, this is not optimal because the demand characteristic fluctuates at intervals. Although Static-CAPIC has succeeded in increasing the network cache hit ratio, it has not delivered the expected cache hit ratio for the first class of content. For this reason, Static-CAPIC is developed into Dynamic-CAPIC that aims to optimize the cache hit ratio, accommodate the real-time changing request, and give each content class the appropriate cache hit ratio. Figure 5 shows that each content class's cache proportion is the same as all the intervals for Static-CAPIC. Meanwhile, in Dynamic-CAPIC, the cache size is changed dynamically based on the actual demand pattern of consumers.
## 5 Conclusions  
In summary, PS microsphere thin-film photonic crystals were first utilized as an SA in fiber lasers for generating Q-switched pulse output. While PS typically cannot achieve Q-switched pulse lasers, converting PS into PS microsphere photonic crystals unveils new possibilities. By harnessing the nonlinear characteristics of PSM-SA and integrating the PSM-SA into a self-constructed cavity system, it has successfully produced a Q-switched pulse laser with a duration of about 2 us and a repetition rate of 40 kHz at a wavelength of 1562 nm. These results emphasize the substantial potential of PS microsphere photonic crystals as effective and versatile nonlinear optical components in a range of optical devices. This innovative approach showcases the viability of utilizing organic and inorganic materials for Q-switched pulse lasers, furthering the field of photonic crystals and promoting advancements in nonlinear optics technology for innovative optical applications.  
Conceptualization, C.J. and Z.Z.; methodology, Y.H. and H.W.; validation, Y.H., H.C. and H.D.; formal analysis, B.L. and H.W.; investigation, Y.H., H.C. and H.W.; resources, C.J. and Z.Z.; data curation, Y.H. and H.D.; writing--original draft preparation, Y.H.; writing--review and editing, C.J. and Z.Z.; supervision, C.J. and Z.Z.; project administration, C.J. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Foundation of Shandong Natural Science Foundation of China, grant number ZR2023 QE216.  
Not applicable.  
Figure 6: Output characteristics of Q-switched laser based on PSM-SA: (**a**) Average output power and pulse repetition frequency; (**b**) Pulse width and output peak power.  
Data Availability Statement: The data presented in this study are available on request from the corresponding author.
## 5 Simulations and Results
### Normal Production Monitoring  
At first, normal production monitoring simulation was performed. In this case, only the static sensors are analyzed in the simulation. The initial amount of sensor nodes was 100 in total, and the parameters of CWCA were chosen to be \(\alpha_{1}\) = 0.2, \(\alpha_{2}\) = 0.2, \(\alpha_{3}\) = 0.3 and \(\alpha_{4}\) = 0.1, respectively. Figure 4 shows the overall structure of pipeline inspection and oil leakage monitoring system based on a wireless sensor network. The monitoring system mainly consists of three parts: monitoring nodes, sink node and control valve, which is responsible for data collection and control management of the wireless sensor network.  
Figure 5 shows a comparison of lifetimes using CWCA, _versus_ LEACH and CSIP [22], where the cluster heads and associated clusters are chosen initially and remain fixed and data fusion is performed at the cluster heads.  
We examine the possibility of exploiting the collaborative diversity among the nodes to achieve a large transmission range. When using a LEACH protocol, the cluster-head nodes would die quickly due to the heavy transmission tasks they undertake. Although the non-cluster-head nodes are still alive at that time, the cluster was in fact dead since there is no way to getting their data to the base station. As soon as the last cluster head died, the lifetime of the whole network would come to an end. Meanwhile, due to the fact the cluster heads are configured to be distributed evenly in the network, they die almost at the same time. The difference between the round that the first node dies and the round that the last node dies is no more than 10, which results in the sharp downward curve of static clustering shown in Figure 5.  
Figure 4: The structure of a pipeline inspection and oil leakage monitoring system.  
Figure 5: Number of sensors still alive for different time steps in normal production monitoring.  
The advantage of using dynamic clustering (CWCA and CSIP) _versus_ using LEACH can be clearly seen in the simulation results. The useful lifetime of dynamic clustering is almost six-times longer compared with that of the static approach. Besides, the dropdown of the dynamic curves is much slower. The performance difference between CWCA and CSIP is not obvious enough in this simulation. We ran similar experiments with different weight parameters and the results remained almost the same.  
Figure 6 shows the energy consumption with respect to simulation time in normal production monitoring. Figure 7 shows the information gain with respect to simulation time in normal production monitoring. In CWCA, the sensor nodes communicate with a fusion center during a time frame. We can clearly see that the energy consumption of CWCA is lower than that of other protocols and the information gain is substantial and increases with time.
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 6 Simulation Results Discussion  
In this section, we use MATLAB software for the performance simulation. To prove the overall performance of the entire system, under the mechanism of our proposed transmission scheme, we perform PAPR and Bit-Error Ratio (BER) simulations, as well as independence tests, at the same time. In this section, the simulation sets the carrier spacing to \(\triangle f=15\) kHz and the carrier frequency to \(f_{c}=35\) GHz, and the channel fading follows the Rayleigh variables' distribution. The number of propagation paths is two, and a 16-QAM symbol modulation is used.
# Effect of Load Transfer Section to Toughness for Steel Fiber-Reinforced Concrete
###### Abstract  
This study analyzed the correlation between the load transfer properties and the overall toughness in the flexural behavior of steel fiber-reinforced concrete after concrete matrix cracking. Beam specimens with identical aspect ratios were made with three different types of steel fiber, each of which had different properties, and were used for the flexural test. The load displacement graph from the test was divided into sections by behavioral properties, and the regression model by mix was analyzed to extract the correlation between the load transfer section (concrete-fiber) and the overall toughness. The analysis results showed three types of load displacement curve based on the fiber type and fiber content, and confirmed that the load reduction section towards fiber after concrete cracking and the slope and area of the load reascension section had a huge impact on the overall toughness. The regression model of the whole toughness (\(T_{b}\)) was then acquired by proposing the resulting correlation as the load transfer factor (LTF).  
steel fiber; concrete; flexural behavior; toughness; load transfer; regression curve +
Footnote †: journal: Applied Sciences
## 2 Background Projects and Database Construction
### Categories of Factors Related to Primary Support Deformation
#### 2.2.1 Dataset for Deformation Prediction  
Based on the influencing factors of initial tunnel support deformation proposed in Section 2.1.2, considering the distribution characteristics of the influencing factors in the axial direction of the tunnel, a data coding structure based on the longitudinal direction of the tunnel is proposed to realize the feature extraction of the information factors among the longitudinal direction of the tunnel. Considering the construction characteristics of drill and blast tunnels, some of the parameters have already been determined in the tunnel planning stage, such as geometric parameters. Due to geological uncertainties, geological information, construction information, and support information are continuously updated as the tunnel is excavated during the construction of a bored and blasted tunnel.  
To facilitate data input, the tunnel is divided into n segments at fixed intervals, and the information within each segment can be categorized into four types: geometric information, geological information, support information, and construction information. Based on the order in which data of each type are generated during the construction process, a data organization and input method are proposed, as shown in Figure 5. Due to the variability of the geology, the geological information, support information, and construction information of the tunnel are continuously determined as construction progresses. The geometric parameters of the tunnel are fixed during the structural design phase of the tunnel. However, the geometric information of the tunnel is established during the construction phase. In the subsequent prediction of tunnel structural deformation, the four types of information are input within the range indicated by the blue area in Figure 5, and the ultimate deformation of the structure at the face position is used as the prediction value. After the screening of the data, 385 sets of monitoring data were used to build the database, and the ratio of training and test sets was divided according to the ratio of 8:2.
## 4 Experimentation and Discussion
### Visualization of Head Pose Estimation  
Due to the significant impact of head tilt on the measurement of interpupillary distance and pupil height, we need to determine whether the head is tilted in the images obtained by the user. For this purpose, we have adopted a head pose estimation algorithm based on the conversion of 2D facial landmark points to 3D. We selected 15 different angle photos of an individual, ran the trained head pose estimation algorithm, and then compared the algorithm's estimated head pose with the actual pose. Experimental results indicate that, in most cases, this algorithm can accurately determine whether the head is tilted in the image  
Figure 13: The results of glasses lens detection.  
and can estimate the tilt angle. The visualization of the head pose estimation process is shown in Figure 14.  
After obtaining the head pose information, we conducted tests to assess the influence of head tilt angles on the measurement results of interpupillary distance and pupil height. To ensure acceptable accuracy, we set the following thresholds.  
If \(-15^{\circ}\leq\alpha\leq 15^{\circ}\), \(-15^{\circ}\leq\beta\leq 15^{\circ}\), and \(-10^{\circ}\leq\gamma\leq 10^{\circ}\), then our measurement results are considered within an acceptable range of errors; otherwise, the measurement is not considered reliable. Here, \(\alpha\), \(\beta\), and \(\gamma\) represent the yaw angle, roll angle, and pitch angle of the head pose, respectively. With this judgment condition, we can obtain more accurate and reliable measurement results while ensuring that the head pose is suitable for measurement. This processing approach effectively avoids the interference of head pose on the measurement results, thereby enhancing the accuracy and reliability of the measurements.
###### Abstract  
Recent improvements in remote sensing technologies have boosted building detection techniques from rough classifications using moderate resolution imagery to precise extraction from high-resolution imagery. Shadows frequently emerge in high-resolution urban images. To exploit shadow information, we developed a novel building detection and classification algorithm for images of urban areas with large-size shadows, employing only the visible spectral bands to determine the height levels of buildings. The proposed method, building general-classified by height (BGCH), calculates shadow orientation, detects buildings using seed-blocks, and classifies the buildings into different height groups. Our proposed approach was tested on complex urban scenes from Toronto and Beijing. The experimental results illustrate that our proposed method accurately and efficiently detects and classifies buildings by their height levels; the building detection rate exceeded 95%. The precision of classification by height levels was over 90%. This novel building-height-level detection method provides rich information at low cost and is suitable for further city scene analysis, flood disaster risk assessment, population estimation, and building change detection applications.  
Building detection; building classification; building height estimation; shadow detection; urban analysis; morphology; high resolution image 2023
## 2 Methodology
### Life Cycle Input and Output Data for Each Biosolids Production Process
#### 2.2.1 Air Drying
For example, the Portage Lake Water & Sewage Authority WRRF in Houghton, Michigan, transports biosolids to a maximum of approximately 10 miles one way to a land application site. In comparison, utilities in California generally transport biosolids hundreds of miles to Arizona, Nevada, and Oregon [19] to be applied to land, become landfilled, or become composted. To examine the impact of these variations in transportation distances on the LCA results, a sensitivity analysis was conducted using 0, 20, 100, 200, and 400 miles as round-trip transport distances. The output emissions from the Air-Drying process are listed in Table 1. There is little available information on the biogenic emissions from Air Drying. Therefore, a sensitivity analysis was conducted to illustrate the impacts of the uncertainty in these key emissions data. For the sensitivity analysis, the percentage of the initial biosolids carbon emitted as methane and the percentage of initial biosolids nitrogen emitted as nitrous oxide were varied in three scenarios. For the baseline case (Scenario 1), the percentage of initial carbon emitted as methane (3.8%) and the percentage of initial nitrogen emitted as nitrous oxide (0.87%) were selected to simulate a relatively inefficient aerobic process based on the findings of [PERSON] [20], who studied how CH\({}_{4}\) and N\({}_{2}\)O emissions can change in response to shifting C:N ratios in composted manure piles (Table 2). Compared to Compostig, the Air-Drying process has a lower C/N ratio and less aeration due to less mixing. The C/N ratio for digested solids is 15.7, and the percentage of nitrogen is 1.88% [21]. A small C/N ratio results in higher emissions of CH\({}_{4}\) and N\({}_{2}\)O [22]. At the other extreme, Scenario 3 assumed the same emissions as those generated by the Compostig process (discussed below) to simulate a case in which significant aerobic activity is occurring in the Air-Drying pile. In Scenario 3, 1.9% of initial carbon was emitted as methane, and 0.6% of initial N was emitted as N\({}_{2}\)O. Finally, Scenario 2 represents an intermediate case between Scenario 1 and Scenario 3, where 2.85% of the initial carbon was emitted as methane and 0.74% of the initial N was emitted as N\({}_{2}\)O. \begin{table}
\begin{tabular}{l l l} \hline
**Scenario** & **Methane Emissions** & **Nitrous Oxide Emissions** \\ \hline Scenario 1—Most conservative and highest emissions & 3.8\% of initial carbon & 0.87\% of initial nitrogen \\ \hline Scenario 2—Intermediate emissions & 2.85\% of initial carbon & 0.74\% of initial nitrogen \\ \hline Scenario 3—Least emissions (Composing-like conditions) & 1.9\% of initial carbon & 0.60\% of initial nitrogen \\ \hline \end{tabular}
\end{table} 
Table 2: Air-Drying emissions used in sensitivity analysis.
# A 112 Gb/s DAC-Based Duo-Binary PAM4 Transmitter in 28 nm CMOS
## 1 Introduction
Under the condition of limited bandwidth transmission, the duo-binary PAM4 modulation can keep the bit error rate at a lower level than the PAM4 modulation, reducing the bandwidth requirement [10; 11; 18; 20]. This paper describes the general circuit architecture of the transmitter, the critical module of the transmitter is analyzed in detail, and the simulation results are given.
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
## 2 Related Work
### Physical Layer RF Fingerprint Features  
Due to security threats such as counterfeiting, eavesdropping, and tampering faced by wireless communication networks, it is crucial to ensure their security performance. However, the security protection measures of wireless networks mostly cover the link layer and above, such as common authentication and authorization or encryption of key information. These security protection measures usually face problems such as complex calculations and key leakage. Therefore, the physical layer security protection has become a new security mechanism [24]. Specifically, physical layer security authentication can use wireless signals to reflect the RF characteristics of the source and analyze the wireless device's communication signals to obtain the device's RF fingerprint for individual identification. Due to the tolerance of the electronic components of the transmitter [25], even the hardware parameters of the wireless devices of the same model and the same batch will be different. This difference is directly reflected in the signal emitted by the device, i.e., the RF fingerprint. It followsthe four principles of universality, uniqueness, collectability, and stability [26]. Universality means that every device capable of wireless communication has corresponding characteristics. Uniqueness means that this feature has nothing to do with the signal modulation method, rate, modulation information, etc., and is determined by the unique physical characteristics of the device. Collectability means that RF fingerprint features can be quantitatively measured. Stability means that the features are not subject to drastic changes such as temperature and environmental changes, and remain stable for a period of time.  
Therefore, due to the uniqueness and nonclonability of RF fingerprints, they can become the unique identification of vehicle nodes.
## 3 Results  
### Tracking Data Analysis  
Considering the periodicity of POD, a week's (25 July 2022 to 31 July 2022) MGEX tracking data are adopted for analysis. A typical representative of BDS-3 MEOs (C21, C23, C29) and IGSOs (C38, C39, C40) detail tracking data analysis are displayed in Figures 3-6 and all the BDS-3 satellite statistical information is listed in Table 3.  
Figure 3: _Cont_.  
Figure 2: Flowchart of BDS-3 POD procedure based on B1 CB2a, B1 CB2b and B1 IB3I dual-frequency measurement combinations.  
Figure 4: The MP series of BDS-3 MEO satellites in different frequencies.  
Figure 3: Average C/N0 series of BDS-3 satellites in different frequency.  
(1) C/N0 analysis  
As in Figure 3, the C/N0 of each frequency varies at the same level of 30 dB to 55 dB, and the C/N0 tends to be stable when the elevation angle reaches 60 degrees. The C/N0 of MEO satellite is 2-3 dB larger than that of IGSO at the same elevation angle. This may be caused by the higher orbital height and the lower satellite transmission power of the IGSO satellite. The correlation trend of C/N0 between each frequency point and the elevation angle is almost the same. Both the C/N0 of MEO and IGSO has the same sort of B3I > B2b > B2a > B1I > B1C.  
(2) Pseudo-range MP analysis  
As in Figures 4 and 5 and Table 3, the MP of the MEO satellite has systematic variation at each frequency. With the increase in elevation angle, the MP becomes smaller, and it tends to be stable while the elevation angle is greater than 50 deg. The MP of IGSOs shows no obvious systematic variation with elevation angle. The average MP of MEOs is obviously smaller than that of IGSOs, which may be due to the large number of low elevation angles during the IGSO satellite observation period. In general, the MP trend of MEOs and IGSOs have the same sort of B1I > B1C > B3I > B2a \(\approx\) B2b, and all the MPs fluctuate within \(\pm\)0.5 m.  
(3) Pseudo-range noise analysis  
As in Figure 6 and Table 3, the pseudo-range noise of B1C is much larger than that of B2a. Based on the statistical results, it can also be discovered that the noise of B1C is 0.3 m and 0.24 m for IGSOs and MEOs, which is larger than that of a B2a signal with 0.21 m and  
Figure 5: The MP series of BDS-3 IGSO satellites in different frequencies.  
0.18 m for IGSOs and MEOs, respectively. The pseudo-range noise of B3I is significantly better than B1I, and the noise of these two signals are in the range of 0.2-0.4 m. Meanwhile, the noise of B3I and B1I is slightly worse than B2a, while the noise of B2a is almost equal to B2b which is in the range of 0.18-0.22 m. For BDS-3, the pseudo-range noise accuracies of MEOs are better than those of IGSOs, which may be because IGSOs have a large proportion of low elevation angle observation periods, resulting in more pseudo-range noise. Overall, pseudo-range measurement accuracy is in the following order: B2b \(\approx\) B2a \(>\) B3I \(>\) B1C \(>\) B1I.
## 4 Results and Discussion
### Prediction Results of Dual-Layer LSTM  
Figure 7 shows the power prediction results of different predictors. Partial areas are zoomed in Figure 7 for a clear illustration. To assess the effectiveness of the proposed method, two more predictors, i.e., single-layer LSTM and DNN, are compared with the used dual-layer LSTM network predictor. The combination of power information and future planned paces generally leads to periodic predictions for the three prediction methods. It can be seen that the prediction deviations all become larger at high power demand  
\begin{table}
\begin{tabular}{l l} \hline \hline
\begin{tabular}{l} Calf Linkage length/m \\ \end{tabular} & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters of the single-leg servo system.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline \hline \
\begin{tabular}{l} Battery pack nominal capacity (mAh) \\ Battery pack nominal voltage (V) \\ Battery current boundaries (A) \\ SC pack capacitance (F) \\ Maximum voltage of SC pack (V) \\ SC current boundaries (A) \\ DC bus voltage (V) \\ \end{tabular} &
\begin{tabular}{l} 6000 \\ 18.5 \\ \(-1  
power. The three methods are tested in the same generated operating environment, and the resulting statistics are shown in Table 2.  
Table 4 summarizes the mean absolute error (MAE), root mean square error (RMSE), and \(R^{2}\) of all prediction methods. \(R^{2}\) indicates the coefficient of determination, and the high \(R^{2}\) values indicate that the prediction methods are effective. The single-layer LSTM has a lower \(R^{2}\) than 0.9, indicating that the single-layer LSTM is less accurate in predicting the power of the legged robot. As shown in Figure 7, the single-layer LSTM suffers from higher prediction error, especially under high power demands. The dual-layer LSTM has the highest \(R^{2}\). At the same time, the RMSE and MAE are also lower than the other two prediction methods, indicating that the dual-layer LSTM network is more accurate in predicting the future power of the legged robot compared to the other two algorithms.