esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks' machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology.  
1  
Institute of Civil Engineering, Southwest Forestry University, Kunming 650224, China  
*Correspondence: [EMAIL_ADDRESS]  
intelligent sensor; Internet of Things application; machine learning algorithm; LE-LPCCA model; engineering test optimization
## 4 Experimental Results
### Compressive Strength of Concrete Determined by Its Surface Hardness (Figure 4)  
To determine \(\mathrm{f_{c,Schmidt}}\) based on concrete surface hardness, a [PERSON] hammer, type N, from the company Proceq (Figure 4), was used. Studies were conducted on 27 cubes at concrete ages of 28, 244, 280, 293, 342 and 1126 days. For the first five curing periods, three cubes were tested, and for the 1126 th day, 12 cubes were tested. The test specimens were aged in an indoor space at a temperature of 20 \({}^{\circ}\)C \(\pm\) 2 \({}^{\circ}\)C and a humidity of 50% \(\pm\) 5% in an unloaded state. At a concrete age of 1926 days, measurements were performed on the reinforced concrete beams at the locations where the cores would be taken.  
The tests were performed according to the requirements of EN 12504-2 [25].  
In the experiment, cubes were tested on two opposite sides with the hammer while they were firmly pressed in the plates of the machine. Before the non-destructive testing of the specimens, they were loaded on compression with a force that induced normal stresses in them, from 7 N/mm\({}^{2}\) to 10 N/mm\({}^{2}\). The surface layer of the concrete at the experimental points was dry and had an undamaged surface. A correction was made to the compressive strength (\(\mathrm{R_{s}}\)), reported depending on the rebound number R, with a coefficient of concordance of the device and a coefficient for the age of the concrete. Calibration of the Schmidt hammer used was performed before starting each series of tests, measurements were made with the original [PERSON] calibration anvil and the results were within the range specified by the manufacturer.  
For each specimen on two opposite sides, 10 readings were made to determine the rebound number, R; the median of the values of R was calculated; and the compressive strength, \(\mathrm{f_{c,Schmidt}}\), was determined. For reinforced concrete beams, the rebound number for each test point was determined as the median of 10 results. At the concrete ages of 28, 244, 280, 293, 342 and 1126 days, the direction of the device was horizontal. At the age of 1926 days, the direction was vertical. The distances between the centers of the hits and from the edge of the cube (beam) were not less than 25 mm.  
The results regarding the compressive strength of the concrete, determined based on its surface hardness, for different ages are given in Table 1.  
Figure 4: Determination of the probabilistic concrete compressive strength based on its surface hardness with Digi Schmidt, type N, with the testing of (**a**) cubes and (**b**) reinforced concrete beams (source: author [PERSON]).
## 3 Joint Resource Allocation and Trajectory Planning under Known Users' Location
### Computation Offloading Optimization  
For given UAV trajectory, we can obtain the computation offloading variables optimization problem \(\mathbf{P3}\):  
\[\mathbf{P3}:\min_{P_{o,n}f_{u},f_{u}}\sum_{t=1}^{T}\theta_{n}f_{ n}[t]^{3}+\sum_{t=1}^{T-1}P_{o,n}[t], \tag{12}\] \[s.t. C2,C4.\]  
Obviously, \(\mathbf{P3}\) is a convex problem, which can be easily solved by Lagrange duality method. Then, we can obtain Theorem 1 by solving the Lagrangian function.  
**Theorem 1**: _For given UAV trajectory \(\mathbf{q}_{u}(t)\), the optimal offloading power and CPU frequencies of usern can be respectively expressed as_  
\[P_{o,n}^{opt}[t] =\left[\frac{\lambda B}{ln2}\frac{\sigma^{2}}{g_{n}[t]}\right]^{+}, \tag{13}\] \[f_{n}^{opt}[t] =\sqrt{\frac{\lambda}{3\theta_{n}C_{n}}}, \tag{14}\]  
_where \(\lambda\geq 0\) denotes the dual variable associated with the constraint C2._  
Proof: See the Appendix A.  
Then, we adopt the subgradient method to obtain the value of dual variables.  
\[\lambda(i+1)=[\lambda(i)-\theta(i)\Delta\lambda(i)]^{+}, \tag{15}\]  
where \(i\) represents the iteration index, \(\theta(i)\) denotes the iterative step, and the corresponding subgradient \(\Delta\lambda(i)\) can be obtained by  
\[\Delta\lambda(i)=\sum_{t=1}^{T}\frac{f_{n}^{i,opt}[t]}{C_{n}}+\sum_{t=1}^{T-1 }Blog_{2}(1+\frac{P_{o,n}^{i,opt}[t]g_{n}[t]}{\sigma^{2}})-R_{n}, \tag{16}\]  
where \(f_{n}^{i,opt}[n]\), \(P_{o,n}^{i,opt}[t]\) represent the optimal solutions at the \(i\)th iteration.  
Then, according to optimal offloading power \(P_{o,n}^{i,opt}[t]\) and constraint C3, we can obtain the optimal CPU frequencies of UAV  
\[f_{u}^{i,opt}[t]=\frac{B}{\delta}\sum_{n=1}^{N}log_{2}(1+\frac{P_{o,n}^{i,opt} [t]g_{n}[t]}{\sigma^{2}}), \tag{17}\]  
since the lowest computation energy consumption can be obtained only when the computation frequency is a constant.
## 5 Conclusions  
(1) An identification methodology on a nonlinear system with central and offset freeplay was proposed and demonstrated in this paper. With the utilization of test data under random excitation force, both the underlying linear structure and overlying linear structure were estimated, with the company of estimated coefficients of the freeplay by exploiting the TNSI method. Furthermore, the identified state-space model can naturally be used in the control field.  
(2) A series of numerical experiments were implemented. With comparison to the theoretical system, the estimated regions of either central or offset freeplay were accurate using the TNSI method. The capability of the present approach to identifying the offset freeplay indicates a more widely potential application to real-life structures. Combined with the modal synthesis technique, the TNSI method can also identify complicated MDOF structures.  
(3) However, one limitation is that the outer stiffness of freeplay may shift depending on the division of response range. In the future, an experimental implementation of the TNSI method is in reach. However, a more robust integrated procedure based on TNSI needs to be developed on practical applications.  
Conceptualization, S.Y. and W.Z.; Data curation, W.Z.; Formal analysis, B.L.; Funding acquisition, Y.C. and W.Z.; Investigation, S.Y.; Methodology, S.Y.; Project administration, W.Z.; Resources, B.L.; Software, S.Y.; Supervision, Y.C.; Validation, Y.C. and W.Z.; Visualization, S.Y. and B.L.; Writing--original draft, S.Y.; Writing--review & editing, S.Y. and W.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (grant number 11372023). The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**Natural Frequency (Hz)**} & \multicolumn{2}{c}{**Identified Natural Frequency (Hz)**} & \multirow{2}{*}{**Errors (\%)**} \\ \cline{2-2} \cline{4-5}  & **FE Model** & **Central Freeplay** & & **Offset Freeplay** \\ \hline
1 & 90.6 & 90.8 & 90.8 & 0.22 \\
2 & 102.1 & 102.5 & 102.5 & 0.39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The natural frequencies identified by TNSI method.
## 8 Results
### Insulated Roof
The maximum heat flux traveling through the roofs occurred between 17:30 and 18:10 h. Insulated traditional roofs (ITR and IGR) had an average peak heat flux of 27 and 25.3 W/m\({}^{2}\), while insulated white reflective roofs, IWR1 and IWR2, had a peak heat flux of 15.4 and 14.6 W/m\({}^{2}\). As mentioned above, the total heat gain of the roof over a day was determined by calculating the area under the heat flux curve of each day. The ITR had an average heat gain of 304 W\(\cdot\)h/(m\({}^{2}\)-day), the IGR of 273 W\(\cdot\)h/(m\({}^{2}\)-day), IWR2 of 140 W\(\cdot\)h/(m\({}^{2}\)-day), and IWR2 of 128 W\(\cdot\)h/(m\({}^{2}\)-day). The ITR located in Hermosillo had a 6% higher heat gain than the IGR, while IWR1 and IWR2 had a 37 and 40% lower heat gain than the IGR. Finally, the insulated roofs' thermal behavior was simulated for the remaining cities following the same procedure used for Hermosillo. Table 4 presents a summary of the  
Figure 5: Thermal behavior of the insulated roof with traditional and reflective coatings in Hermosillo: (**a**) solar irradiance and wind speed; (**b**) temperature of exterior surface and outdoor air; (**c**) temperature of interior surface; (**d**) heat flux of the roofs. results obtained from the evaluation of the insulated roofs in the four cities of Mexico. The table shows the values for the average peak temperature of the exterior (\(T_{es}\)) and the interior surface (\(T_{is}\)), the average peak heat flux of the roofs (\(Q_{R}\)), and the average daily heat gain (\(HG\)). Taking the IGR as the control case, the differences between the peak temperatures (\(T_{es}\) and \(T_{is}\)) were calculated, along with the peak heat flux (\(Q_{R}\)) and the heat gain (\(HG\)). The white reflective roofs were able to reduce \(T_{es}\) between 17 and 21\({}^{\circ}\)C compared to the IGR. Therefore, they could reduce the \(Q_{R}\) that crossed the roofs by a factor ranging between 39 and 54%. Further, these roofs had an \(HG\) between 37 and 56% smaller than the ISG. On the other hand, the ITR reached a maximum temperature of 3 \({}^{\circ}\)C above the IGR. This temperature increment caused an increase of the \(Q_{R}\) between 7% and 15%. Moreover, the ITR increased the \(HG\) by about 11 and 33%.
## 4 Thermographic Analysis on Frescoed Masonry Walls and Results  
### Experimental Campaign  
New technologies make it possible to intensify the experimental investigations without harming the architectural quality of buildings, such innovation is exactly fitting with the discipline inherent the heritage architectures [14; 15; 16; 17]. Among the important factors influencing the seismic response of Heritage buildings, the masonry quality and the knowledge of its processes of transformation and stratification are certainly included. Therefore, the acquisition of information about the non-visible masonry texture is the subject of such contribution, claiming that the masonry quality is the basic condition to analyse and eventually improve, with reference to the desirable monolithic behaviour, before to insert any reinforcement interventions (e.g., metal tie-rods).  
During the present experimental campaign, the measurements were focused mainly on the left side (north) frescoed wall of the naive and the thermographic \"shots\" were taken under passive conditions: the inner wall's surface was analysed as it appeared at the moment of the investigation, so without heating it, by exploiting direct sunlight and observing the thermal transient that allowed to obtain different responses from the elements with different thermal capacity, in particular by identifying with black pixels the mortar and with the white ones the stone elements.  
With regard to the frescoes that decorate the examined wall, rows of overlapping ashlars are reproduced to imitate an austere wall face made of stone, a typical decoration of the Templar churches to recall the poverty of the holy land and of the holy sepulchre. Moreover, the series of the 12 apostles stand out, distributed also on the other side wall, on the facade and the back walls of the apse; such pictorial cycle was superimposed on the original decoration during a second phase between 1283 and 1285. In particular, the first tranche of thermographic investigation covered the mural painting composed by an apostle proceeded by three Saints [PERSON], [PERSON] e [PERSON], positioned according to the voitre sites required by the ancient order, Figure 8.  
Figure 7: Identified texture of random masonry sample: (**a**) thermographic image, (**b**) surface temperature as greyscale values \(T_{i}(x,y)\), (**c**) black and white image after thresholding \(b_{i}(x,y)\), (**d**) identified texture after erosion and dilation, \(b_{d}(x,y)\).
## 5 Analysis of Numerical Simulation Results
### Internal Deformation Law of Rock Formation in Coal Seam 3 Mining
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Rock Formation** & **Loose** & **Fine** & **Sandy** & **Fine** & **Sandy** & **Fine** & **Sandy** & **Fine** \\  & **Layer** & **Sandstone** & **Mudstone** & **Sandstone** & **Mudstone** & **Sandstone** & **Mudstone** & **Sandstone** \\ \hline Buried depth (m) & 0 & \(-180\) & \(-460\) & \(-516\) & \(-806\) & \(-884\) & \(-1092\) & \(-1202\) \\ Maximum influence range (m) & 3975 & 3949 & 3884 & 3867 & 3764 & 3710 & 3477 & 3164 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Influence range of rock formation mining. Figure 8: Fitting curve of the maximum influence range of the rock formation. (**a**) Sine function fitting curve. (**b**) Fourier function fitting curve. (**c**) Quadratic polynomial fitting curve. (**d**) Exponential function fitting curve. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Fitting Types** & **SSE** & **RMSE** & **R-Square** & **Adjusted R-Square** \\ \hline Sine function & \(5.415\times 10^{4}\) & 104.1 & 0.9576 & 0.9406 \\ Fourier function & \(3.407\times 10^{4}\) & 92.3 & 0.9733 & 0.9533 \\ Quadratic polynomial & \(3.407\times 10^{4}\) & 82.55 & 0.9733 & 0.9626 \\ Exponential function & 551 & 11.74 & 0.9996 & 0.9992 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Function fitting errors of the maximum influence range of the rock formation. According to the analysis of the function fitting error, the best fitting function model is an exponential function, which is consistent with the results of [PERSON] et al. [47]. The equations obtained in this paper are as follows. \[y=4293e^{-8}\exp(0.006013x)+2113\exp(-0.0001763x) \tag{9}\]  
The analysis of the equation for the exponential function is shown in Figure 9. The 95% confidence interval encompassed all of the data points. Since the function equation's first derivative was larger than 0, the function curve was continuous and monotonically growing. Both the concavity and convexity of the function were convex, and the second derivative was smaller than 0. The function curve lay on one side of the X axis, and the integral curve declined monotonically.
### Background  
#### 1.1.1 Normal Pressure Hydrocephalus  
NPH was initially described in 1965 by [PERSON] and [PERSON] [2]. They identified a triad of symptoms that are now recognized as characteristic of NPH: gait disturbance, urinary incontinence, and dementia. Individuals with NPH typically exhibit normal CSF opening pressure, lending to its name \"normal pressure hydrocephalus\" [10].  
The exact etiology of NPH remains elusive. NPH is largely categorized into two main types: idiopathic NPH (iNPH) and secondary NPH. iNPH is the most common type and occurs in individuals with no identified secondary cause. On the other hand, secondary NPH can develop in response to intracranial hemorrhage, infection, or other causes that are believed to damage the CSF drainage pathways via inflammatory response, leading to scarring and/or blockage with eventual CSF accumulation. Even though iNPH is the most common and extensively studied type, our review focuses on NPH as an overarching term encompassing all types for the purpose of generalization.  
NPH is a clinic-radiological diagnosis that relies on the presence of clinical findings supported by radiological markers of NPH [11]. Additional invasive testing, such as cisternogram or CSF infusion testing, has been utilized to aid in diagnosis. However, the confirmatory tests are invasive and include large volume lumbar puncture or lumbar drain; the tests look for transient improvement in clinical symptoms after the removal of fluid. If appropriately utilized, a positive response is usually associated with higher positive predictive value for improvement with CSF diversion surgery.
## 7 Applications and Future Research Directions of RIS
### Achieving High Beamforming Gain for IoT Networks  
In future wireless networks, the IoT is an important feature. Some IoT devices are, however, limited in scale as well as energy consumption. In the mmWave channels, where highly directional antenna gains are necessary for achieving efficient high-rate communications, future 5G and beyond cellular networks will run. The RISs can be used to provide significant beamforming gains for these devices, far greater than they can afford due to their limited size.
# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 5 Conclusions and Future Work  
In this paper, we proposed a novel lane detection method of parallel multi-scale feature aggregation based on key points, FPLane. Extensive experiments show that our method obtained a remarkable detection performance and that the proposed modules are effective. The method focuses on the precise location of the key points in the global lanes, ignoring the influence from ambiguous background pixels, which effectively simplifies the output of the model. It can also be applied to various scenes that include arbitrary numbers or any structures of lanes. More importantly, MFANet with the DAFU enables the model capture discriminative information to dope out lanes accurately in complex scenes such as night, shadow and occlusion. The proposed method achieves the state-of-the-art detection performance on Tusimple and CULane datasets: the accuracy and F1-score of the model are 96.82% and 75.6%, respectively, and the real-time detection efficiency can maintain 28 ms.  
Since challenging scenarios with a weak appearance do not involve rainy or muddy road conditions in this paper, we plan to build larger datasets with comprehensive challenging situations in the future to enhance the generalization of our proposed method. In addition, another future research direction might be to develop more powerful architectures incorporated into the FPLane framework, such as the ones with the self-attention mechanism or extensible self-supervised learning, to further improve the performance.  
Conceptualization, C.Z. and Y.Z.; methodology, C.Z.; software, C.Z.; validation, C.Z. and Y.Z.; writing--original draft preparation, C.Z.; writing--review and editing, Y.Z.; visualization, C.Z.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Materials  
Based on the sampling grids (8 samples each square kilometer) and the land use types, the surface soil samples were collected vertically from top to bottom along the pit wall within a depth of about 0-20 cm, removing the grass roots, gravel, and fertilizer clumps. Each soil sample was composed of 3-5 sub samples near the sampling point with a weight of about 1 kg. A total of 3312 surface soil samples were collected in the study area with an actual average sampling density of 8.95 samples in each square kilometer.  
Maize and rice as bulk grain crops and walnut as an economic crop were collected in the study area on harvest season and sunny days, while avoiding special plants with pests and diseases. Each crop sample was collected from multiple points and mixed evenly to form a composite sample with a weight of about 500 g. A total of 95 crop samples were collected in the study area including 40 maize, 40 walnut, and 15 rice samples.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 5 Key Cooperative and Cognitive Technologies
### Physical-Layer Security
#### 5.4.2 Physical-Layer Security of Cognitive HSTN
In this section, we have provided an overview of the key techniques of NOMA, beamforming, software-defined radio, and physical-layer security and their utility in HSTNs. The role of NOMA as an efficient access technique capable of improving spectral efficiency was discussed, as was the mitigation of co-channel interference through the use of multi-antenna beamforming and the use of software-defined radio in HSTNs for centralization and network control. Keeping in mind security risks and eavesdropping threats due to complex communication infrastructure, we have also provided an overview of the physical-layer security of HSTNs in this section.
# Nucleobase-Containing Polymers: Structure, Synthesis, and Applications
## 4 Applications of Nucleobase-Containing Polymers in Materials Science  
### Hydrogels  
Hydrogels have served as matrices for 3D cell culture with widespread applications in tissue engineering, biomolecule delivery, and regenerative medicine [100; 101]. Based on the crosslinking mechanism, hydrogels can be classified into physically crosslinked hydrogels and chemically crosslinked hydrogels. Although chemically crosslinked hydrogels usually provide a stable polymer network with slow degradation kinetics, physically crosslinked hydrogels have also attracted a lot of attention, especially as injectable biomaterials in translational medicine [102]. Among all the physical crosslinked mechanisms for hydrogel formation, hydrogen bonding remains one of the most frequently used methods, which makes nucleobase-containing polymers an ideal candidate for physically crosslinked hydrogels. In 2012, [PERSON] et al. developed a biological hydrogel that self-assembled via nucleobase interaction from adenine- and thymine-functionalized tetra-arm PEG polymers (Scheme 7) [103]. This hydrogel exhibited both thermal and mechanical responsiveness, such as gel-sol transition under high strain conditions or high temperature, which could break the physical linkage between adenine and thymine. Additionally, they demonstrated that this nucleobase-interaction-based hydrogel can serve as an injectable scaffold for growth factor delivery and cell encapsulation, which indicated the excellent biocompatibility of these materials and their potential for use in further biomedical applications.  
Figure 9: Structures of nucleobase-functionalized polythiophenes [44]. Reproduced with permission. Copyright John Wiley and Sons, 1998.