esa-sceva/satcom-chunk-collection huggingface dataset:
# A Watermarking Method for 3D Printing Based on Menger Curvature and K-Mean Clustering
## 5 Conclusions  
In this paper, we proposed a novel watermarking method for 3D printing. It is based on the Menger facet curvature and the K-mean clustering algorithm. We experiment on the proposed method using the XYZ Printing Pro 3D printer and XYZ 3D scanner. Experimental results showed that the proposed method is invisible and robust to geometric attacks, such as rotation, translation and scaling. Experimental results with the XYZ Printing Pro 3D printer and 3D scanner also verified that the accuracy of the proposed method is moderate and higher than the accuracy of the two previous methods in the 3D printing domain. Compared to the conventional works for 3D model watermarking, the accuracy of the proposed method is also higher. Therefore, the proposed method can be applied to both 3D printing watermarking and 3D model watermarking. As future works, we will consider some correction methods in the 3D scanning process or after the 3D scanning process to increase the accuracy of the proposed method. Moreover, we will improve the proposed method and experiment on it with other 3D printers and 3D scanners. We will consider applying the proposed method to some contexts or real applications.  
This research is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. 2016R1D1A3B03931003, No. 2017R1A2B2012456), the MSIP (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2017-2016-0-00318) supervised by the IITP (Institute for Information & communications Technology Promotion), the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2015-0-00225) and the Brain Busan 21 (BB21) project in 2017.  
Figure 10: Performance of the proposed method compared to the conventional works.  
**Author Contributions:** In this research activity, all of the authors participated in the data analysis and preprocessing phases, the simulation, the results' analysis and discussion, as well as the manuscript's preparation. All of the authors have approved the submitted manuscript. All of the authors equally contributed to the writing of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Genericity: Qualitative Assessment of Unique Features
### Mesos-Based Frameworks  
Mesos-based frameworks have various unique features that logically ensue from the two-level scheduler architecture with a central Mesos master and multiple framework schedulers:  
* Mesos is better suited for managing datacenter-scale deployments given the _inherent improved scalability properties_ of its two-level scheduler architecture with multiple distributed scheduler frameworks [41].
* Mesos offers a _resource provider abstraction_[372] for customizing Mesos worker agents to framework-specific needs.
* DC/OS offers _integrated support for load-balancing Marathon-based services as well as load-balancing of workloads that are managed by other, non-container-based frameworks_[373].
* _Local volumes can be shared by tasks from different frameworks_[374].
* _Framework rate limiting_[375] aims to ensure performance isolation between frameworks with respect to request rate quota towards the Mesos Master. Mesos has also various unique features to support high-performance computing:
* DC/OS has made the conscious design choice to enable _database-as-a-service offerings without using containers for running database instances_[376], presumably because of the non-negligible performance overhead of container orchestration frameworks for managing database clusters [49].  
With respect to container QoS management, Mesos contributes to improved network performance isolation between containers for both routing mesh networking [377] and virtual networks [378]. Unfortunately, little of these features are currently used by the CO frameworks Aurora and Marathon. Only container port ranges in routing mesh networks can be isolated in Marathon.
## 3 Methodology
### Image Preprocessing
#### 3.2.2 Intensity Normalization  
The intensities of the images are normalized to ensure that the model is not influenced by large variations in brightness or contrast. This step is important because the images are scanned at different times under different conditions which can result in brightness and contrast variations. Intensity normalization is a common preprocessing step in image analysis to correct for variations in intensity levels between different images. One common approach for intensity normalization is to stretch or shrink the intensity range of the image so that it covers a specified range, such as [0, 255] for 8-bit images. The equation for intensity normalization is given below:  
\[f^{\prime}(i,j)=(f(i,j)-\min)*\frac{(new_{\text{max}}-new_{\text{min}})}{(\text {max}-\min)+new_{\text{min}}} \tag{4}\]  
where \(f(i,j)\) is the pixel intensity at \((i,j)\) position in the original image, \(f^{\prime}(i,j)\) is the same pixel intensity in the normalized image, \([\min,\max]\) is the intensity range for the input image, and \([new_{\text{min}},new_{\text{max}}]\) is the intensity range required in the output for the normalized image. For example, if an image of intensity distribution [0, 200] is required to be intensity-normalized for [0, 255], then the following mathematical relation is applicable for this transformation:  
\[f^{\prime}(i,j)=(f(i,j)-0)*\frac{(255-0)}{(200-0)}+0 \tag{5}\]  
This equation maps the original intensity values to the desired intensity range in a linear manner. Figure 4 shows the sample image of the dataset with its color histograms. The pixel intensity distribution in the form of RGB channels is displayed. The colors in the histogram represent pixel intensities for three channels (red, green, and blue) in color images of the breast cancer dataset.  
Figure 4: Visualization of the pixel intensity of the sample image in the form of RGB channels.
## 1 Introduction
The two types of analysis were conducted on a set of EEG recordings obtained from patients suffering from AD and from their respective healthy controls (HC), in an attempt to address the following research question:  
**RQ1. **_How does a functional connectivity analysis perform against a spectral analysis in finding differences between patients diagnosed with Alzheimer's disease (AD) and healthy controls (HC)?_  
Moreover, this study attempted to answer a secondary research question:  
**RQ2. **_Can a functional connectivity analysis localize the differences identified in the brain activity of AD subjects when compared to that of the HCs?_  
To answer this question, a series of statistical tests were performed using the results provided by the connectivity analysis.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Risk Parameters for the Environment Outside the Cableway
#### 2.3.6 Urban Climatic Conditions  
Climatic factors such as peaks, tides, and seasons directly affect the cooling and waterproofing performance of cables, especially in extreme weather.
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Asynchronous Duty-Cycled MAC Protocols  
#### 5.1.1 B-Mac  
In order to adapt asynchronous duty-cycling with WSNs, in 2004, [PERSON] et al. proposed a MAC protocol named B-MAC [28]. This protocol was specially designed for low-power sensor nodes with asynchronous wakeup-sleep behavior. This MAC protocol was built using a broadcasting mechanism; hence RTS (Request To Send)/CTS (Clear To Send)/ACK was absent here. This is why it is best suited for CSMA (Carrier Sense Multiple Access) behavior. The sensors using the B-MAC mechanism continuously send preambles until the receiver wakes up and starts receiving data, as the sender does not know about the receiver wakeup time.  
In Figure 5, node A sends a data packet to node B by first performing a CCA (Clear Channel Assessment), then sending preamble messages, and finally sending the data packet. Node B stays awake when it receives one or more beacon messages until the data transfer is completed. Node C will ignore the preamble and follow its sleep schedule even though it overhears the preamble, since that message is not destined for it.  
However, this protocol has several drawbacks, even though it can reduce synchronization overhead. The sender needs to send a longer preamble, that is longer than the sleep interval of the  
Figure 5: Packet transmission in B-MAC [28].  
node. By sending a longer preamble, it can ensure the successful transmission of data. However, for a small amount of data, this mechanism is costly and energy consuming. On the other hand, because it uses a broadcasting mechanism, there is a high probability of collision given the multiple packet problem. Because of the collision and absence of ACK, the sender will not be aware of transmission failure; hence the packet loss rate will be increased and, in the meantime, more energy will be drained. Moreover, the hidden node problem is not handled in this MAC protocol.
## 6 Conclusions and Outlook  
To benefit from more available navigation satellites due to the emergence of new GNSSs such as BDS, this work proposed a GPS/BDS single frequency combined high precision processing model. Kalman filtering is used to jointly process the carrier phase and pseudorange measurements from both constellations to estimate float ambiguities. The RTK algorithm for the dual constellations is discussed and the detailed work process and its implementation are presented by developing and integrating multiple functional modules. Readers can hopefully implement this algorithm without difficulty based on this contribution. The proposed method is validated via processing the field-collected single frequency carrier phase and pseudorange measurements of both GPS and BDS from a zero and two short baseline experiments. The experiment results show that the precision of the proposed method is comparable to the single constellation algorithm; the proportion of fixed solution based on the ratio test is improved and shorter time to first fix is obtained by using this combined method which extends the availability and reliability of the RTK algorithm.  
Our future work will include adapting this method to multi-GNSS and multi-frequency applications with the consideration of its computational efficiency and testing its performance under dynamic environments. Other endeavors will lie in the measurement filtering and rejection strategy and optimization of fixed solution criterion that can help improve the solution precision, reliability and proportion of fixed solutions.
## 2 Methods
### Optimum Combination of Initial Polarization  
Figure 4 clearly illustrates the grating stripe direction. In previous research [13; 16; 17; 18], effects are paid on elimination/reduction of Stripe 3 by using polarization modulation and experimental results verified that these proposals are effective in the reduction of Stripe 3 despite downsizing of the grating surface. But after deep investigation and careful calculation of these grating stripes with consideration of the exposure system configuration, it can be found that the influence of the Stripe 3 varies when not only the polarization but also the incident angle \(\theta\) are different. As that proposed in [16], denoting the degree of orthogonality between beams 2 and 3 \(\gamma_{23}\), and Stripe 3 might be neglected when \(\gamma_{23}\) is lower than a threshold or an experimentally obtained tolerance.  
We employed the combination of three angles (\(\alpha_{1}\), \(\alpha_{2}\), and \(\alpha_{3}\)) to represent the combination of the initial orientation of polarization. Counter-clockwise and clockwise rotation were recorded as positive and negative angle, respectively. With the strict method to modulate the initial polarization status based on the theory of three-dimensional polarization ray-tracing calculus [16; 19], the lowest \(\gamma_{23}\) of approximately 0.048 can be achieved when the incidence angle \(\theta\) = 71.79\({}^{\circ}\) corresponding to a grating period of 1 \(\upmu\)m under a polarization status of polarization modulated three beams (direct beam, X-beam, and Y-beam: s, \(-\)33.5\({}^{\circ}\), 33.5\({}^{\circ}\), respectively). On the other hand, under such an incident angle, without polarization modulation, i.e., three beams polarization status (s, s, and s), the values \(\gamma_{23}\) of are 0.06 and 0.08 for metal-type mirror and dielectric-type mirror, respectively. It can be seen that this difference is not significant. It can be expected that, even without polarization, the Stripe 3 will not influence the grating uniformity so much. These findings are fundamental to this research.  
Figure 4: Interference fringe patterns generated by two of beam 1, 2, and 3. (**a**) beams 1 and 2; (**b**) beams 1 and 3; and (**c**) beams 2 and 3.  
Unfortunately, however, it should be noted that on the premise of the incident beam with linear polarization states, there does not exist a combination of \(\alpha_{2}\) and \(\alpha_{3}\) that could completely prevent the interference of beam 2 and beam 3, i.e., \(\gamma_{23}=0\). For a near zero \(\gamma_{23}\), polarization modulation of the incident beam might be required, this will be discussed in the future.
# High-Throughput Bit-Pattern Matching under Heavy Interference on FPGA
## 4 Pattern-Matching Circuit and Implementation
The proposed design, however, provides four times the throughput. In another study [6],  
Figure 5: Pattern-matching circuit. only illustrations are provided for the hardware consumption; however, the throughput provided is still half of the proposed design (for one core). The designs reported in [2; 3; 4] are not specialized and the throughput they present is much lower (<30 Gbps). Table 3 presents the aggregated results described above. Since the circuit is a kernel implementation, it does not take into account the I/O circuitry. This is left as a choice for the user, and it is heavily dependent upon the type of application that the circuit will realize. The throughput comparison, however, is valid. One previous study [7] uses rules with a much shorter length so that all the I/O pins can be directly mapped on the FPGA, with a very restrained impact on the hardware consumption and throughput. Another study [6] presents only illustrations. The throughput in [2; 3; 4] is much lower. In the current case, the board of implementation (ZCU106 Ultrascale+) has multiple I/O devices (multiple I/O pins and gigabit transceivers), which can be used in unison to provide the inputs. The choice of what I/Os are used and how often this happens will determine the end-to-end performance and hardware consumption of the system. Generally, a value of 800.4 Gigabits/s is the highest possible throughput that the synthesizer allows for correct implementation. The design can very easily be run at lower speeds, to provide some leniency for the I/O circuitry. Finally, the output can simply be given as the number of the entry and not the entire pattern, while the input does not necessarily have to be 1200 bits per cycle. With data compression techniques, fewer bits can enter the board, and the larger sequence can be created and fed to the circuit afterward.
# Effects of Time Content on Road Performance of Low Liquid Limit Clay
## 4 Discussions  
In view of the problem mentioned above that the original engineering soil does not meet the filling requirements, this paper improved the original soil by mixing ash. At the same time, the improvement effect is judged by comparing the basic properties of undisturbed soil and lime-doped soil before and after mixing.  
The addition of lime has little effect on the liquid plastic limit, and the plastic limit of the three soil samples is about 3%. This shows that the addition of lime does not change the plastic limit property of soil liquid considerably, but the addition of lime has a great effect on dry density and water content. As can be seen from Figures 5-7, the curve of water content and dry density presents the following rules with the increase in lime content: First, with the increase in lime content, the maximum dry density corresponding to the curve gradually decreases, while the optimal water content gradually increases. Moreover, the effect of lime improvement on the CBR value is also obvious. With the increase in compaction times, the CBR value of the soil sample will be increased to a certain extent. With the increase in lime content, the CBR value gradually increases, and basically stabilizes when the lime content reaches 6%. Following the addition of lime, the basic physical characteristics of the original soil, such as the liquid-plastic limit, CBR, and optimal water content, can be enhanced due to the aforementioned interaction between lime and bad soil, allowing the bad soil that did not initially meet the filling requirements to do so.  
Figure 10: CBR test results of soil sample C.  
Ion exchange, carbonation, and crystallization will occur throughout the process of enhancing low liquid limit clay with lime mixing, resulting in an increase in the plastic limit of each soil sample with an increase in lime concentration and a corresponding decrease in the plastic index. On the one hand, the interchange of Ca2+ in the lime with other cations on the surface of the soil particle is what causes the water loss of the improved soil. On the other hand, more water is required to produce the best compaction effect because the dissolution and hydration of lime in the soil causes soil to be compressed in real time [18].  
Compared with other treatment methods, such as replacement, lime improvement has the following advantages: first, lime improvement is convenient in construction, as it does not need to be transported back and forth to shorten the construction period and save costs; second, the price of lime is low, and the improvement of 1 m3 bad soil only needs about 60 rmb; finally, the technology of lime improved soil is mature, mixing is simple, and there is no complicated process.
## 2 Materials and Methods
### Testing Method
The compacted \"pre-specimens\" were extruded and vertically cut or rotated 90\({}^{\circ}\) prior to sampling for oedometer vertical swelling (OVS) or horizontal swelling (OHS) tests, respectively. As depicted in Figure 4, the modified preparation approach orientates clay mineral flakes differently to reflect field conditions. For the OHS tests, the pre-specimens were initially rotated 90\({}^{\circ}\) prior to coring. This modification oriented the platy clay structure differently to induce intrinsic anisotropy. The extracted specimens were then carefully positioned within the retractable ring for confinement during testing. Trimming and greasing of sample surfaces were carried out to minimize the effects of side friction. A consistent test procedure was followed for all series under varying net normal stress conditions (Table 2). The initial matric suction of the specimens was measured as 500 kPa using the filter paper method before being gradually reduced in staged equilibrium steps. Vertical swelling displacements, lateral confining pressures, and equilibrium times were recorded, with pressure transducers monitoring net stresses. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Test ID**} & **Dry Unit Weight** & **Moisture Content** & **Net Normal Stress** & **Matric Suction** \\  & & **(kN/m\({}^{3}\))** & **(\%)** & **\(\sigma_{\textbf{v}}-u_{\textbf{a}}\) (kPa)** & **\(u_{\textbf{a}}-u_{\textbf{w}}\) (kPa)** \\ \hline \multirow{4}{*}{OVS} & OVS-0 & & & 0 & \\  & OVS-12.5 & & & 12.5 & \\  & OVS-25 & & & 25 & \\  & OVS-50 & & & 50 & \\  & OVS-100 & & & 100 & 200\(\rightarrow\)100\(\rightarrow\)50\(\rightarrow\)20\(\rightarrow\)10\(\rightarrow\)5\(\rightarrow\)0 \\  & OHS-0 & 16.0 & 18.0 & 0 & 200\(\rightarrow\)100\(\rightarrow\)50\(\rightarrow\)20\(\rightarrow\)10\(\rightarrow\)5\(\rightarrow\)0 \\  & OHS-12.5 & & & 12.5 & \\  & OHS-25 & & & 25 & \\  & OHS-50 & & & 50 & \\  & OHS-100 & & & 100 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of testing program conditions. Figure 4: Schematic view of specimen preparation. Each test series was conducted under constant net normal stress and followed the same decreasing matric suction path. For instance, OHS-12.5 denoted an OHS test series conducted with a net normal stress of 12.5 kPa, and 50\(\rightarrow\)20 indicated that the controlled matric suction decreased from 50 kPa to 20 kPa. Furthermore, the compressed vertical strain of the specimen manifested once a certain net normal stress was applied, leading to the complete alteration of the initial volume and dry unit weight of the tested specimen. Therefore, the matric suction level of 500 kPa was not included, and the matric suction was initiated at 200 kPa. Upon the completion of swelling under a given suction, the stresses were adjusted to maintain net stress while further lowering suction. The tests were terminated once specimens were fully saturated. This testing program generated a robust anisotropic stress-strain-suction dataset for characterizing soil behavior and developing constitutive models.
# Short-Term Wind Power Prediction Based on Feature-Weighted and Combined Models
## 3 Results and Discussion
### Prediction and Analysis  
3.2.1. Experiment I: The Impact of the Attention Mechanism  
It is obvious from Figure 8 that without adding an attention mechanism to the model, the model's predicted values fluctuate significantly compared to the true values. Additionally, the order of introducing attention mechanisms also leads to differences in prediction performance.  
Table 3 shows that the LSTM exhibits a significantly lower prediction error compared to the ANN, indicating that the LSTM has better non-linear fitting capability when dealing with wind power sequence data. The MAE, MSE, RMSE, and R\({}^{2}\) values of LSTM-AM are 7.072, 76.694, 8.758, and 0.954, respectively, which are 10.38%, 24.93%, 13.35%, 1.71% higher than the error values of LSTM. This indicates that the prediction accuracy can be improved by adding the attention mechanism to the model. The four error metric values of AM-LSTM are 6.260, 60.582, 7.784, and 0.963, which are 11.48%, 21.01%, 11.12%, and 0.94% higher than the error performance of LSTM-A. This suggests that using the attention mechanism before the LSTM model can better leverage its advantages compared to using the attention mechanism after LSTM. Through analysis, this order can assist the model in more effectively utilizing the information of the input data, reduce information loss, and learn feature representations more flexibly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline ANN & 11.282 & 196.680 & 14.024 & 0.881 \\ LSTM & 7.891 & 102.159 & 10.107 & 0.938 \\ LSTM-AM & 7.072 & 76.694 & 8.758 & 0.954 \\ AM-LSTM & **6.260** & **60.582** & **7.784** & **0.963** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Error metrics for prediction models. (The best value of the data has been bolded, the following tables are the same.)  
Figure 8: Prediction curves of different models.