esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface-Modified Compounds Improve the Detection Sensitivity of Terahertz Metasurface Biosensors
## 3 Results
### Detection of Cancer Markers after HNT Modification  
The second compound used for modification is Halloysite nanotubes (HNT), which is hollow tubular aluminum silicate clays. Its Molecular formula is Al\({}_{2}\) (OH) \({}_{4}\)Si\({}_{2}\)O\({}_{5}\cdot\) nH\({}_{2}\)O. The inner diameter of a tubular structure is usually between 10 nm to 40 nm, and the outer diameter is usually between 40-70 nm, with a total length is between 0.2 \(\upmu\)m to 2 \(\upmu\)m [30].  
The concentration of HNT dispersions at 10 mg/mL were prepared and fully dispersed by sonication. After that, 10 \(\upmu\)L of HNT dispersion was added to the sensor surface for 30 min. The microscope image (20\(\times\)) of the sensor has modified by HNT is shown in Figure 11a, from which, it can be seen that many black pins of HNT cover the surface of sensor. The Figure 11b shown the frequency shift caused by HNT, after calculated, the Q-factor of HNT-modified sensor is 13.57. It still less than the blank sensor, but more than WS\({}_{2}\). And then, the experimental procedure was similar to that in Section 3.1. The experimental results are shown in Figure 12.  
Figure 10: Results of three cancer markers detected by THz metasurface sensors after modification with WS\({}_{2}\). (**a**,**b**) detection SCC, (**c**,**d**) detection CEA, (**e**,**f**)detection NSE.  
Figure 11: (**a**)Microscope image of the sensor has modified by HNT. (**b**) The frequency shift caused by HNT.  
Figure 12: Results of three cancer markers detected by THz metasurface sensors after modification with HNT. (**a**,**b**) detection SCC, (**c**,**d**) detection CEA, (**e**,**f**)detection NSE.  
Figure 12a,b show the experiment results of the HNT modified sensor to detection SCC. The relationship between the concentration of SCC and the frequency shift is F = 1.25 \(\times\) C\({}_{\text{SCC}}\) + 2.6429, R\({}^{2}\) = 0.9804, and the sensitivity S\({}_{\text{SCC}}\) = 1.25 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)). Figure 12c,d show the experiment results of detecting CEA by HNT modified sensor. The relationship between CEA concentration and frequency shift is: F = 1.1786 \(\times\) C\({}_{\text{CEA}}\) + 2.6429, R\({}^{2}\) = 0.9840, and the sensitivity S\({}_{\text{CEA}}\) = 1.1786 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)). Figure 12e,f show the experiment results of NSE detection by HNT modified sensor. The relationship between the concentration of NSE and the frequency shift is F = 1.2643 \(\times\) C\({}_{\text{NSE}}\) + 2.3, R\({}^{2}\) = 0.9804, and the sensitivity S\({}_{\text{NSE}}\) = 1.2643 GHz/lg(pg\(\cdot\)mL\({}^{-1}\)).  
The experiment results show that compared to the unmodified sensor and the sensor modified with WS\({}_{2}\), the sensor modified with HNT has significantly improved sensitivity to these three markers. Meanwhile, the relationship between the change in resonance frequency and the concentration of the marker is more pronounced than that of sensors modified with WS\({}_{2}\).
## 2 Calculation Method of seepage Velocity in the Rock Total Stress-Strain Process  
The key task in rock seepage-stress coupling is to study the variation in the permeability coefficient during deformation and failure. Experimental studies play an irreplaceable role in examining the seepage characteristics of rock fractures. Limestone is dense in texture and it is permeable mainly in cracks [20]. Permeability changes in limestone specimens during deformation are directly caused by the initiation, opening, and connection of cracks [21].  
Many studies have shown that for a fractured rock mass, the coupling of stress and seepage fields is based on the fluid-solid coupling problem of a single fracture. The cubic law is the basis for studying the seepage of a single fracture [22]. When a fractured surface exists in a completely impervious rock mass, it is completely parallel and smooth. [PERSON] derived the theoretical formula of fluid motion in a smooth parallel plate gap under the condition of homogeneous constant motion for a viscous incompressible fluid, as follows [23]:  
\[u=\frac{g{b}^{2}}{12v}Jorq=\frac{g{b}^{3}}{12v}J \tag{1}\]  
where \(u\) is the average velocity (m/s) of the fluid in a fracture with opening \(b,g\) is the gravitational acceleration (m/s\({}^{2}\)), \(v\) is the viscous coefficient of fluid motion, \(J\) is the hydraulic gradient, and \(q\) is the flow rate (m\({}^{2}\)/s).  
[PERSON] et al. [24]. applied the cubic criterion to rock fracture seepage and found that it is effective, as shown in Figure 1. In the implementation process of this test scheme, when the water pressure difference between the two ends of the rock sample is large, the fissure water pressure causes additional deformation of the rock surface on both sides of the fissure runoff, which increases the width of the fissure. Hence, the cubic criterion is still valid for open rough joints. The following formula is used to calculate seepage velocity:  
\[u=\frac{Q}{A} \tag{2}\]  
where \(u\) is the seepage velocity, \(Q\) is the flow, and \(A\) is the cross-sectional area of the specimen.
# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
###### Abstract  
Under different degradation conditions, the complexity of natural oscillation of the piston pump will change. Given the difference of the characteristic values of the vibration signal under different degradation states, this paper presents a degradation state recognition method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and eXtreme gradient boosting (XGBoost) to improve the accuracy of state recognition. Firstly, ICEEMDAN is proposed to alleviate the mode mixing phenomenon, which decomposes the vibration signal and obtain the intrinsic mode functions (IMFs) with less noise and more physical meaning, and subsequently the optimal IMF is found by using the correlation coefficient method. Then, the time domain, frequency domain, and entropy of the effective IMF are calculated, and the new characteristic values which can represent the degradation state are selected by principal component analysis (PCA) that it realizes dimension reduction. Finally, the above-mentioned characteristic indexes are used as the input of the XGBoost algorithm to achieve the recognition of the degradation state. In this paper, the vibration signals of four different degradation states are generated and analyzed through the piston pump slipper degradation experiment. By comparing the proposed method with different state recognition algorithms, it can be seen that the method based on ICEEMDAN and XGBoost is accurate and efficient, the average accuracy rate can reach more than 99%. Therefore, this method can more accurately describe the degradation state of the piston pump and has a highly practical application value.  
piston pump; degraded state recognition; slipper; improved complete ensemble empirical mode decomposition with adaptive noise; principal component analysis; eXtreme gradient boosting 2020
# Path Planning under Force Control in Robotic Polishing of the Complex Curved Surfaces
## 2 The Proposed Method
### Tool Path Planning
#### 2.2.1 Surface Division  
It is known that the robot reachability at a point with a particular orientation of the end effector or tool frame \(\{T\}\) depends on its joint configuration \(q=\{q_{j},j\in[1\dots N]\}\in\delta\), where \(q_{j}\) the joint configuration, \(N\) is the number of joints, and \(\delta\) is configuration space. In three-dimensional space, minimum six joints are needed to position the \(\{T\}\) because the object in space requires six parameters, three from \(\mathbb{R}^{3}\) (coordinates of the origin of the attached frame), and three from SO(3) (rotation matrices for frame orientation) such robots are said to have six degrees of freedom [27]. The articulated robot with end effector poses \(\xi_{E}\)-\((x,y,z,\theta_{r},\theta_{p},\theta_{y})\) fulfills this demand.  
However, maintaining the required position and orientation continuously normal to the surface may not be feasible due to the joints limit. To achieve the non-stop smooth trajectory for the entire polishing surface, it is often necessary to divide the polishing surface into several regions. It converts a complex surface into several simple curved surfaces, which is comparatively easier to polish. It also helps to provide better tool contact with the workpiece surface, which improves the polishing quality. Moreover, it offers a quick change in the tool path of the particular area when needed without disturbing the other part surfaces.  
In this study, the symmetrical eyeglass frame is first divided virtually into two symmetrical parts A and B by the plane of symmetry as shown in Figure 9a. The surface division in one part shall be followed in the other part by mirroring. Secondly, the surface is further divided into the top and side face by following the edges of the faces. Additionally, other multiple factors are considered including the variation in the orientation of surface normal vector (\(n\)), the robot joints limit, the curvature of the part surface, the size, and complexity of the face. By taking these factors into account, the eyeglass frame is divided into 18 plain and simple curve part surfaces as in Figure 9b. Most of the parts are simple in shape with less \(n\) variation. Part surface 1-8 and 11-18 are symmetrical, therefore, the polishing tool will follow a similar tool path.  
Figure 8: The illustration of the freeform surface using loose cotton wheel (LCW).
## 4 Discussion
### Research Limitations and Future Directions  
Despite the systematic analysis of VPD effects on grassland productivity in Xinjiang's arid region presented in this study, several key scientific issues require further exploration. Firstly, productivity estimates based on MODIS GPP data (at a 500 m resolution NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science Center, Sioux Falls, SD, USA) may not adequately capture the small-scale patchy distribution char acteristics of certain grasslands (less than 100 m). Additionally, due to spatial limitations and the relatively short temporal scale of the study period, the conclusions drawn may not be universally applicable to all specific sub-regions or capture longer-term dynamics. Furthermore, this study has not explored the potential of using machine learning approaches, such as explainable analysis based on Shapley values, for identifying key factors and their thresholds.  
This study focused on atmospheric drought (characterized via VPD), which helps reveal its independent effects. Nevertheless, in arid regions, the dominant role of soil moisture stress in regulating productivity is well-established. Consequently, concurrent soil moisture data were not incorporated. This omission precludes disentangling the relative and interactive contributions of atmospheric drying (VPD) and soil drought to GPP suppression [37]. Therefore, future research must prioritize investigating the compound effects of these co-occurring stressors, as their interaction likely defines critical thresholds for ecosystem function.
## 3 Application to Secure Communications
### Bit Error Rate Performance  
To study the bit-error-rate (BER) performance of the system under different noise intensities, we considered several cases of chaotic signals \(S\) used in the transmission. The signals considered were \(S=100\cdot x\), \(S=100\cdot\sin(x+y+z)\), \(S=100\cdot\sin 3(x+y+z)\), \(S=100\cdot\sin 5(x+y+z)\). For each case, the simulation results for 100 s are shown in Figures 10-13 for \(Eb/No=-10\) dB. From the simulations, it can be seen that the combination of all the states with a sine function yields a more complex signal, especially when the gain term inside the sine function increases. In addition, it is seen that, in all cases, the information signal is successfully retrieved at the receiver end.  
The BER performance results are shown in Figure 14. The noise intensity is considered in the range of \(-10\) dB to \(3\) dB, and the simulation time is 40,000 s. It is verified that more complex chaotic signals yield a consistently better BER performance compared to simply using a state of the chaotic system.  
Figure 11: Simulation results of the SCSK secure communications design, for chaotic signal \(100\cdot\sin(x+y+z)\).  
Figure 10: Simulation results of the SCSK secure communications design, for chaotic signal \(100\cdot x\).  
Figure 14: BER simulation results.  
Figure 12: Simulation results of the SCSK secure communications design, for chaotic signal \(100\cdot\sin 3(x+y+z)\).  
Figure 13: Simulation results of the SCSK secure communications design, for chaotic signal \(100\cdot\sin 5(x+y+z)\).
# Communication Technologies for Smart Grid: A Comprehensive Survey
## Ethernet  
Suited for communication in WAN between substations and control centers. Advantages with this form of communications is its high availability and high reliability. Ethernet is also used in HAN for the communication between smart meters and home central.
## 3 Results and Discussion
### CMM Measurements
Upon evaluating the entire dataset, it is evident that, for applications not requiring extremely tight tolerance ranges, 3D printing can yield results that closely resemble those produced by injection molding. The comparison of CMM device measurements with the solid model measurements in the computer environment revealed significant negative discrepancies at numerous points. The primary cause of this discrepancy is believed to be the material shrinkage that occurs during the solidification process, which is inherent in production methods involving temperature application.
# Addition of Industrial Hemp (_Cannabis sativa_ L.) Dry Inflorescence in Beer Production
## 3 Results and Discussion  
### Hemp Inflorescence Material Analysis  
Due to legal requirements, industrial hemp growers must determine the THC levels in their crops. Since this paper dealt with the addition of IHF to beer, a product that contains alcohol, it was essential to verify the levels of THC as well as other cannabinoids in the starting raw material, industrial hemp infloresence. The industrial hemp infloresence was subjected to HPLC analysis, and the results can be seen in Table 1.  
The results in Table 1 verify that the TCH level was under the legally prescribed limit of 0.2% and was quantified as 0.09%. CBDA (cannabidiolic acid) was the dominant compound in industrial hemp (9.95%), while the share of CBD (cannabidiol), its product, was only 0.78%. The dominant presence of CBDA compared to CBD is expected since decarboxylation had not been previously carried out. CBDA, as the main precursor of cannabinoid acids, degrades under high temperatures to its decarboxylated analog, CBD. The content of CBD and its acidic precursor (CBDA) is particularly important for industrial hemp, as they are attributed to analgesic, antibacterial, antidiabetic, antiepileptic, antiinflammatory, antiproliferative, and antipsychotic properties [21].  
When THC oxidizes due to exposure to air and light, it breaks down to form CBN. Therefore, the proportion of CBN increases as more THC degrades, and it is especially present in dried cannabis flowers that have been stored for a long time. Good storage conditions in airtight containers will prevent the formation of CBN, which can cause paranoia in large amounts. Table 1 shows that CBN was not detected in the samples, indicating that fresh samples were suitable for brewing.  
The results of the starting raw material analysis exhibited satisfactory levels, and thus, the DHI could be used in further study. Even though these levels of THC and CBD in beers produced with industrial hemp are marked as safe, it should be noted that the \(\Delta\)9-THC guidance level does not cover possible adverse effects caused by other cannabinoids, including CBD, as well as the potential combination of effects of the intake of \(\Delta\)9-THC- and CBD-containing products [22]. The Federal Institute for Health Protection of Consumers and Veterinary Medicine (BgVV) in Germany has recommended that the daily intake of \(\Delta\)9-THC in hemp-containing foods should not exceed 1-2 \(\mu\)g per kg body weight. For alcoholic and non-alcoholic beverages, this would mean that an amount of 5 \(\mu\)g/kg is allowed [22].
# Estimation of Resonance Frequency for Systems with Contact Using Linear Dynamics Methods
## 4 Formula Created for Resonance Frequency Estimation  
The estimation formula for the resonance frequency was prepared by the resonance frequencies extracted in the classical modal analysis simulation with two types of boundary conditions--neglecting the contact and fixing the edge of the contact. The formula includes a correction factor that is connected to the stiffening of the structure with the contact as \(f_{e}\) to \(f_{0}\) ratio.  
The preliminary form of the equation is based on observations of vibration with contact behavior. The part of the formula responsible for vibration without support was taken as the half period of the cycle, and the part responsible for the specimen in contact was taken as the half period of the cycle with additional correction factor. The preliminary Formula (1) is as follows:  
\[f_{i}=\left(\frac{1}{2\cdot f_{0}}+\frac{1}{2\cdot\left(f_{e}-\left(A\cdot f_ {e}+\left(\frac{f_{e}}{f_{0}}\right)^{B}\right)\right)}\right)^{-1} \tag{1}\]  
Based on the collected data for vibrations with 10 contact lengths and some additional cases, such as thicker specimens or different material orientation, nonlinear regression was applied to extract the values of the \(A\) and \(B\) correction parameters. The values of the parameters were equal to:  
* \(A=-0.00351\)
* \(B=1.97057\)  
For simplification of the formula, the correction factors were rounded to integer values. The final values of the correction factors were as follows:  
* \(A=0\)
* \(B=2\)  
The final shape of the formula is as follows (2):  
\[f_{i}=\left(\frac{1}{2\cdot f_{0}}+\frac{1}{2\cdot\left(f_{e}-\left(\frac{f_{ e}}{f_{0}}\right)^{2}\right)}\right)^{-1} \tag{2}\]  
Figure 4: Characteristics of resonance frequency by contact length.  
The equation was proposed according to the observation presented in Figure 1, that the full cycle of vibration can be split into two. The first part of the cycle, while contact is closed, may be described with modal analysis with the fixed edge of the contact, while the second cycle can be described with modal analysis omitting the contact. The formula presents the sum of times that it takes each of the half-cycles to occur, with some correction factors. The value of the correction factors (\(A\) and \(B\)) were defined with a regression method, in such a way that the error of the formula result was minimized according to non-linear simulation case for couple of iterations.  
The formula was back-tested on the entry data. The error of the formula-based estimation for the case that was verified with non-linear dynamics for each iteration of the contact length is shown in Figure 5.
# PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
## 4 Conclusions  
In this paper, we propose a crack detection model based on our proposed PSNet. In the PSNet, a Parallel Convolution Module (PCM) is designed to avoid feature information  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Blocks** & **Cracktree200** & **CRACK500** & **CFD** & **OAD\_CRACK** \\ \hline
2 & 0.906 & 0.792 & 0.978 & 0.751 \\
3 & 0.914 & 0.805 & 0.981 & 0.756 \\
4 & 0.926 & 0.812 & 0.985 & 0.762 \\
5 & 0.921 & 0.809 & 0.983 & 0.761 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effects of using different number of blocks in PCM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attention Mechanisms** & **Cracktree200** & **CRACK500** & **CFD** & **OAD\_CRACK** \\ \hline Only Channel Attention in SGAB & 0.893 & 0.799 & 0.973 & 0.743 \\ Only Spatial Attention in SGAB & 0.901 & 0.782 & 0.973 & 0.742 \\ Only Channel Attention with & & & & \\ Gate Convolution Filter in SGAB & 0.902 & 0.80 & 0.975 & 0.753 \\ Only Spatial Attention with Gate & & & & \\ Convolution Filter in SGAB & 0.906 & 0.791 & 0.976 & 0.759 \\ Both Channel Attention and & & & & \\ Spatial Attention with Gate & 0.926 & 0.812 & 0.985 & 0.762 \\ Convolution Filter in SGAB & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effects of using different attention mechanisms in the SGAB.  
loss caused by down-sampling. Therefore, more low-level features could be used for the final classification. In addition, a Self-Gated Attention Block (SGAB) is used to control the feature information flow. Thus, features related to cracks would be permitted to flow to the top layers and features unrelated to cracks would be suppressed. Experimental results show that our PSNet model achieved an accuracy of 92.6%, 81.2%, 98.5%, and 76.2% on the Cracktree200, CRACK500, CFD, and OAD_CRACK datasets, respectively, which is 2.6%, 4.2%, 1.2%, and 3.3% higher than that of the traditional attention model. Our work could effectively solve the problem of detecting small edge cracks in the field of crack detection. However, the model size of the PSNet is still very large. Our future research will focus on reducing the size of the PSNet.  
Conceptualization, X.Z. and H.H.; methodology, X.Z.; software, X.Z.; validation, X.Z. and H.H.; formal analysis, X.Z. and H.H.; investigation, X.Z. and H.H.; resources, H.H.; data curation, H.H.; writing--original draft preparation, X.Z. and H.H.; writing--review and editing, H.H.; visualization, H.H.; supervision, H.H.; project administration, H.H.; funding acquisition, H.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to business confidentiality needs.  
The authors declare no conflict of interest.
## 4 Optimizing EE- and SE-Based Beam Training
### A Low-Complexity AB-HP
\(V_{n}=[\varepsilon_{n-M},\ \varepsilon_{n-M+1},\ \ldots\ldots,\ \varepsilon_{n-1},\ \bar{ \varepsilon}_{n}]^{M}\) is the vector ut for EE, where the first \(M\) entries represent the EE attained in the previous \(M\) time steps, and the final item, \(\hat{\varepsilon}_{n}\), represents the EE assessed using the pre-assessment at the current time step \(n\). Concerning SE, the vector \(V_{n}\) is denoted by \(V_{n}=[\ r_{n-M},\ r_{n-M+1}\,\ \ldots\ldots,\ r_{n-1},\ \bar{r}_{n}]^{M}\), containing the appropriate \(M+1\) elements of SE. \[R_{n}^{ee}=\alpha\delta E_{n}-\alpha(1-\delta)r_{n}-\lambda\left(\frac{M_{n}} {M_{max}}+\frac{p_{n}}{p_{max}}\right)\,\ \ 0\leq\delta\leq 1,\ \ n=1,2,\ldots \tag{10}\]  
The agent learns a policy to maximize the long-term predicted reward through interactions with the environment [22]. Prioritized experience replay preserves all states for effective learning, but only high-impact states according to their temporal difference (TD) error. Based on available processing capability, the DRL agent dynamically modifies computational complexity to allow for real-time decision-making without overtaking the system. Furthermore, resource-constrained DNN training remains within the maximum power limit \(p_{max}\) thanks to an adjustable learning rate. Our goal in this work is to minimize the overhead related to beam training while still achieving good EE or SE performance for a mobile UE. In another way, to optimize the trade-off between the overhead of beam training and the achievable EE or SE. The Equations (9) or (10) offer the option to weigh the performance metric's significance while choosing a beam training method, thanks to the incentive system as shown in Algorithm 1. The agent can be trained to attain varying performance levels for distinct applications by adjusting the value of \(\delta\). A bigger trade-offfactor is desirable for applications that need high transmission rates, like high-definition video streaming, where a higher data rate matters more than beam training latency.