esa-sceva/satcom-chunk-collection huggingface dataset:
# Quasi-Isotropic Hybrid Dielectric Resonator Antenna--Bow-Tie Patch with Harmonic Suppression
## 1 Introduction
### Design of the Filter for Harmonic Suppression  
Figure 3 shows the configuration of the fifth- and seventh-order Chebyshev low-pass filters designed to suppress harmonics generated by the proposed antenna and the location where the filter is to be integrated with the feeding part of the proposed antenna. Therefore, the low-pass filter was designed in consideration of the feed line of the proposed antenna. Chebyshev coefficients must be derived to design a Chebyshev filter. We derived the fifth- and seventh-order Chebyshev low-pass filters response coefficients by referring to the literature [20], and the Chebyshev response coefficients are presented in Table 2.  
Figure 4 shows the equivalent circuit of the Chebyshev low-pass filter, and the response coefficient of Chebyshev can be equivalent to a resistor, an inductor, or a capacitor. To convert the inductor and capacitor into a distributed component, an electrical line length that satisfies the Chebyshev response coefficients of the inductor and capacitor is derived by using Equations (1) and (2) [21]. Based on the induced electrical line length, a physical transmission line length corresponding to the Chebyshev response coefficient of each lumped component is derived. Here, \(\beta\) is the wavenumber (\(2\pi/\lambda_{g}\)), and \(L\) and \(C\) are Chebyshev response coefficients corresponding to inductors and capacitors, respectively. \(R_{0}\) was defined as 50 \(\Omega\) as a characteristic impedance, and \(Z_{h}\) and \(Z_{l}\) were defined as 120 and 20 \(\Omega\), respectively. The \(\lambda_{g}\) was set so that the cutoff frequency was 4 GHz, and the filter design parameters were derived by using Equations (1) and (2). Table 3 shows the design parameters of the fifth- and seventh-order Chebyshev low-pass filters with the cutoff frequency set to 4 GHz.  
\[\beta l=\frac{LR_{0}}{Z_{h}}\quad(inductor) \tag{1}\]  
\[\beta l=\frac{CZ_{l}}{R_{0}}\quad(capacitor). \tag{2}\]  
Figure 5 shows the simulated S parameters (S11 and S21) of the fifth- and seventh-order Chebyshev low-pass filters. As a result of comprehensively examining the filter characteristics presented in Figure 5, the seventh-order Chebyshev low-pass filter is larger than the fifth-order Chebyshev low-pass filter. However, the filter has excellent skirt characteristics and high isolation between bands. In this paper, harmonics are suppressed using a seventh-order Chebyshev low-pass filter. A high-order filter is required to suppress  
Figure 2: Concept of the quasi-isotropic pattern: (**a**) direction of field; (**b**) three-dimensional radiation pattern.
# CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
## 4 Simulation and Experiment
### Path Planning Simulation  
In the path planning simulations, we tested the performance of the RRT, RRV, Fast-RRT and CERRT algorithms in four 2D environments. The objective of using a simple environment was to assess if the new algorithm's performance was significantly impacted by additional computation. On the other hand, the maze environment, narrow environment, and Bug Trap environment were utilized to assess the algorithms' performance in complex scenarios. The red dots in the map represent the starting points, the green dots represent the target points. The entire exploration process is represented by the green lines, and the generated path is represented by the red lines. The sampling method was consistent across all experiments.
## 3 Results
### Proportions of LOC Fractions Under Different Land Use Patterns  
Table 2 illustrates the impacts of different land use patterns on the percentage of labile components of SOC within the humus layers of mollusols, specifically in relation  
Figure 6: Particulate organic carbon (POC) concentration in soil humus layers of mollusols under different land use patterns. Note: The experiment treatments included continuous soybean cultivation (SC), continuous maize cultivation (MC), maize–soybean rotation (MSR), grass belt (GB), and forest belt (FB). Different letters mean significant differences among different land use patterns in the same soil layer at 0.05 level. \(n\) = 3. The same is below.  
Figure 7: Microbial biomass carbon (MBC) concentration in soil humus layer of mollusols under different land use patterns. Note: The experiment treatments included continuous soybean cultivation (SC), continuous maize cultivation (MC), maize–soybean rotation (MSR), grass belt (GB), and forest belt (FB). Different letters mean significant differences among different land use patterns in the same soil layer at 0.05 level. \(n\) = 3. The same is below.  
to SOC. The ratios expressed in terms of LOC, which aggregates EOC, DOC, MBC, and POC, are presented below. Generally, the ratios LOC/SOC, EOC/LOC, and MBC/LOC for each treatment predominantly decreased with increasing soil depth, whereas DOC/LOC and POC/LOC exhibited a tendency to increase with soil depth. The ratio of LOC to SOC ranged from 20.71% to 37.76%. The ratios of EOC, DOC, MBC, and POC to LOC were 17.34% to 39.47%, 1.25% to 1.86%, 2.38% to 5.94%, and 55.03% to 78.84%, respectively, indicating that LOC constituted a relatively minor proportion of the soil in this study area. Notably, POC and EOC comprised a larger fraction of LOC compared to DOC and MBC. At different soil depths, the LOC/SOC and POC/LOC ratios within the 0-60 cm range were significantly higher in MSR, GB, and FB compared to SC and MC (EOC/LOC > LOC/SOC > MBC/LOC > DOC/LOC); for MSR, GB, and FB, the order was POC/LOC > LOC/SOC > EOC/LOC > MBC/LOC > DOC/LOC.
## 3 Results
### Chemical Compound Classes Based on Percentages of Sum of Areas  
The results of the four groups of variables representing percentages of different compound classes based on different total areas of the chromatograms were similar. The reason is that MQ values increased when deconvolution was used, and so the percentages proposed with MQ are not altered much by this limitation.  
Essentially, one outlier was detected in the VOCs of fruit 15A (S1) by using PCA and PLS-DA, due to the high proportion of alcohols, while no outliers were detected in S2 (Figure 2). Additionally, fruit 4B (S1) could be an outlier as judged by PLS-DA, due to the greater proportion of terpenes and/or acids than in other fruit (Supplementary Materials Figure S2). Certain differences between seasons were detected by PCA or the PLS-DA of percentages (e.g., in the graph of the first two axes; Figure 2 and Supplementary Materials Figure S3). The fruit classified as outliers in the previous section by reference to the area in absolute values were not classified as outliers using PCA, but using variables of compound classes based on percentages of the sum of the areas.  
In the last of the percentage variables (without MQ criteria and including total area), fruit 5A (S1) was also considered as outlier (only by PLS-DA) due to a slightly higher proportion of acids, acetate esters and/or alkanes (data not shown). This datum was not finally considered as a strong outlier.  
Figure 2: Correlation (**left**) and score (**right**) plots of the first three components of the PCA (with 50.27% of explained variance) applied to compound classes of aroma variable of percentages of different compound classes based on total area counts of such compounds with match quality >50, and without exogenous compounds, obtained in two seasons. ACE, acetate ester; NAE, non-acetate ester; ALD, aldehyde; ALC, alcohols, KET, ketones; SDC, sulfur derived-compounds; TER, terpenes; AHA, alkanes and aliphatic compounds; OTH, other compounds. Circles represent \(r^{2}\) = 50% and 100% variability explained by the components. Potential outliers are marked by dashed black line.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 2 Background
### Spatio-Temporal Data and the Automatic Identification System (AIS)
#### 2.1.2 Incomplete Trajectories
The authors mention that this potentially limits the method's applicability since it may be sensitive to the training dataset, in particular the number of ship trajectories available and domain representativeness.
## 2 Methodology
Therefore, the envelope is mathematically described by the coordinates \(X_{env}(k)\) and \(Y_{env}(k)\) of each vertex k of the polygon (\(1<k<N_{env}\), \(N_{env}\) being the total number of vertices of \(\Sigma\)). In Appendix B, the analytical obtaining of the enclosure for photovoltaic collectors is detailed in a generic way. According to Figure 4, the end of vector \(\overset{\rightarrow}{d}_{i}\) would be located inside the envelope \(\Sigma\) and, therefore, there would be shading between the panels \(\Pi_{0}\) y \(\Pi_{i}\) if there is some vertex \(k\) for which the mathematical condition given by Equation (26) is fulfilled. \[ \begin{split}[sign(d_{Xi})=sign\ (X_{env}(k))]\ and[sign(d_{Yi})=sign\ (Y_{env}(k))]and\\ [|d_{Xi}|<|X_{env}(k)|]\ and\ [|d_{Yi}|<|Y_{env}(k)|]\end{split}  \tag{26}\]  
Figure 3: Determination of \(\Sigma\), auxiliary curve of the intersection criterion and existence of shadow between panels. In accordance with the above, Figure 5 shows, by means of a flow diagram, the inter-shading \(\left(\overrightarrow{n}\right)\) function, especially designed to implement the method described in this section to determine the existence of shadow between modules in PV installations with dual-axis tracking and regular distribution of collectors. This Boolean function depends on the vector \(\overrightarrow{n}\), Equation (19), returning a value \"true\" if \(\overrightarrow{n}\) implies inter-shading and \"false\" otherwise. As can be seen in Figure 5, only possible shading is studied in the reference collector, considered representative of the set, which is totally surrounded by \(N_{t}\) trackers. Figure 4: Form. Generic shape of a solar collector (orange) and its envelope \(\Sigma\) (blue). Figure 5: Flow diagram of the inter-shading function \(\overrightarrow{n}\)  
In this way, in the case that the value of \(\overrightarrow{n}\), obtained from Equation (19), does not imply shadows between collectors, \(\mathit{intershadows}\left(\overrightarrow{n}\right)=False\), the optimal tracking strategy will propose the orientation of the collectors according to the address of \(\overrightarrow{n}\). On the contrary, if the value of \(\overrightarrow{n}\), obtained from Equation (19), implies shadows between collectors, \(\mathit{intershadows}\left(\overrightarrow{n}\right)=True\), the orientation that does not imply shadows and that maximizes the incident irradiance must be sought. In that case, back tracking is used following the process indicated in Figure 6.  
Figure 6: Flow diagram to determine the orientation \(\overrightarrow{n}\) of maximum irradiance uptake without any inter-shading between collectors.
# Industrial Internet Identity Resolution+5G Full Connection Digital Factory Research
## 2 Related Work
### Key Considerations for Full Connection Digital Factory
#### 2.2.1 Data Security  
This is a problem that both the owner and the user of data are very concerned about. It involves legal, ethical, and computer systems. These factors can be divided into two categories: the external conditions that are not directly related to the database system itself and the database system defense ability. As far as external conditions are concerned, it includes a series of appropriate management policies and security measures such as classification of data by secret level, control of who has access to the data, data testing, etc. It also includes computer physical damage, equipment security, and radiation protection. The defense ability of the database system is reflected by the measures it provides for data security:  
It provides an authorized means for administrators to control access to the database.  
It allows the users to be classified and granted different access rights.  
It sets a password and other methods when the user enters the system security check.  
It uses view and other methods to conceal and encrypt the data.  
The security of a database system can be measured in terms of its integrity, flexibility, extra cost of the security checks, and robustness of the security mechanism.
## 5 Model Verification with Experimental Results
### Model Verification Results  
Using the aforementioned identification algorithm with the experimental results, the key parameters of three subjects are identified in Table 4. The original range of each parameter is derived from reference [33]. All NMS parameters are within a reasonable range, which indicates the model identification results are practicable. In particular, higher stiffness and damping coefficients indicate greater muscle force applied by the driver.  
Figure 8: Muscle activation analysis.  
In order to prove the accuracy of the dynamic model and to observe the driver's personalized behavior, the estimated value with the UKF observer of the first subject and actual value based on the experiment are illustrated in Figures 9-11. As can be observed, the UKF estimated value has good consistency with the actual value.
# Watermarking Tiny MLCommons Image Applications Without Extra Deployability Costs
## 9 Attacks and Results
### Results for the Black-Box Watermarking Method
The results for which the origin of the watermark can be recognized are highlighted in green. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Watermark Detected** & \multicolumn{3}{c}{**ST Edge AI**} & \multicolumn{3}{c}{**Original Framework**} \\ \cline{2-5}  & **VWW Dataset** & **Trigger Dataset** & **VWW Dataset** & **Trigger Dataset** \\  & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline \multicolumn{5}{l}{**Original models**} \\ UnWMd MobilenetV1 & 78.25 & 50.70 & 78.25 & 50.70 \\ WMd MobilenetV1 & 72.73 & 100 & 72.73 & 100 \\ \hline \multicolumn{5}{l}{**Gaussian attacks**} \\
0.001 & 72.86 & 95.07 & 72.86 & 95.07 \\
0.01 & 72.36 & 93.66 & 72.36 & 93.66 \\
0.1 & 67.92 & 73.94 & 67.92 & 73.94 \\
1 & 49.77 & 57.74 & 49.77 & 57.74 \\
10 & 50.00 & 52.81 & 50.00 & 52.81 \\ \hline \multicolumn{5}{l}{**Prune attacks**} \\
0.1 & 72.50 & **94.36** & 72.50 & 94.36 \\
0.2 & 71.06 & 77.46 & 71.06 & 77.46 \\
0.3 & 70.06 & 76.05 & 70.06 & 76.05 \\
0.4 & 64.86 & 63.38 & 64.86 & 63.38 \\
0.5 & 59.38 & 55.63 & 59.38 & 55.63 \\ \hline \multicolumn{5}{l}{**Quantization attacks**} \\
16 bit & 72.87 & 95.07 & 72.87 & 95.07 \\
8 bit & 72.56 & 96.47 & 72.56 & 96.47 \\
7 bit & 71.93 & 97.18 & 71.93 & 97.18 \\
6 bit & 72.08 & 88.02 & 72.08 & 88.02 \\
5 bit & 67.63 & 71.83 & 67.63 & 71.83 \\
4 bit & 51.73 & 48.59 & 5 The results of experiments discussed in Section 7.2.2 reported in Table 16 revealed varying degrees of effectiveness for each attack on the watermarked model that uses the dataset explained in Section 5.2 that uses a MobilenetV1 model with Section 6.2 approach and a Section 6.2.2 composed of random shapes and colors. The performances of the model subjected to the three types of attacks (Gaussian, prune, and quantization), in terms of accuracy, were also consistent with the level of attack. However, a strong drop was recorded for the last attack levels, especially in the case of the Gaussian attack and wuantization attack, where an accuracy value of 10% was reached. The trend of the Trigger dataset was very similar to Table 15; in fact, the WMd model remained robust in the first levels of attack, starting from a percentage of 100% or 97.27%. The further increase in the power of attack led to a sharp drop in the percentage, reaching values below 20%.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 5 Materials and Methods
### 2D Localization  
For a two-dimensional scene, the tag can be located by knowing the relative incident angle and distance between one antenna and tags. Figure 13a is a top view of the system (taking one tags array as an example), the absolute position of the antenna and tags array is fixed, and the distance between them \(L\) is known. We record the three-dimensional coordinates of the tag as \(X_{T}\), \(Y_{T}\), and \(Z_{T}\). According to the method in Section 5.3, the angle \(\theta\) in the figure can be calculated. \(X_{T}\), \(Y_{T}\) of the tag are (\(L\times\tan\,\theta\), L) and the distance from the antenna to the tag in the XOY plane is \(W=|L/\text{cos}\theta|\). Figure 13b is a sectional view of the antenna and the tag, wherein \(W\) is known, the distance d from the antenna to the tag in space is calculated through the method depicted in Section 5.4. According to the Pythagorean theorem, the vertical distance between the tag and the antenna can be obtained and the antenna is placed at height of \(H\), then \(Z_{T}=H-\sqrt{d^{2}-W^{2}}\), so \(X_{T}\), \(Y_{T}\), \(Z_{T}\) are :  
\[(X_{T},Y_{T},Z_{T})=\Big{(}L\times\tan\,\theta,L,H-\sqrt{d^{2}-W^{2}}\Big{)} \tag{15}\]
## 3 Methods
### Incorporation of External Influencing Factor Data  
From the semantic perspective, this study incorporates external factor data into the predictive model, which is represented as \(X^{F_{i}}_{t}=\left(X^{F_{i}}_{t},X^{F_{i}}_{t},\ldots,X^{F_{i}}_{t}\right)\), where \(l\) is the number of external factors. In this paper, the external factor data includes temperature and precipitation. The underlying reason is the substantial impact that weather conditions have on individuals' choices of transportation modes when traveling. For instance, higher temperatures or heavy rainfall can lead to changes in the usual patterns of subway usage, such as increased ridership during rainy days, as people avoid walking or cycling. By including these external factors in the predictive model, the accuracy and reliability of the prediction model is enhanced.
# Current Status and Future Directions for Combined Tillage and Sowing Technology and Equipment
## 2 Status of Development of Combined Tillage and Sowing Machines
### Quality Inspection of Seeded  
Tillage depth and uniformity are key indexes affecting the quality of the seeded, and also key links in the combined tillage and sowing machine; its detection and control is the focus of research at this stage. [PERSON] et al. designed a tillage depth detection device based on ultrasonic and infrared sensors to solve the problem of the large error of existing tillage depth detection devices. The device filtered and fused the detection data of the two sensors using the Kalman filtering method, which effectively improved the accuracy of plowing depth detection [63]. [PERSON] et al. designed a nonlinear tillage depth monitoring system by taking the tillage depth of a suspended rotary filler as the detection object, and obtained high-precision tillage depth information in real time by constructing a three-parameter nonlinear tillage depth measurement model, which improved the detection efficiency [64]. [PERSON] et al. aimed at the problem of the plowing depth and width stability of a combined tillage machine, and obtained the optimal parameter combination to improve the plowing stability and optimize the quality of compound tillage from the three perspectives of traction, horizontal surface force, and machine vibration through the second orthogonal rotary combination test [65]. Depth control after tillage depth detection serves as an important part of ensuring the stability of tillage depth [66]. [PERSON] et al. proposed a plowing depth monitoring method based on the pitch angle of the tractor body and the lifting arm angle of the suspension device, which realizes the plowing depth monitoring and control by controlling the electromagnetic proportional reversing valve and adjusting the lifting and lowering of the three-point suspension device, solving problems such as the unstable plowing depth during operation [67]. [PERSON] et al. designed a system for the automatic measurement and adjustment of tillage depth based on integral separated PID control for rear suspension rotary tillage components, which narrowed the adjustment time and improved the stability of the tillage depth [68]. In terms of seeded quality detection, current research mainly focuses on detection accuracy, response speed, and operation stability. Many studies have achieved the precise detection of tillage depth and uniformity through a variety of means such as different devices and different forms. By means of mathematical analysis, model construction, and other methods, the interaction relationship between key components and soil, as well as the motion model, are obtained. Combined with intelligent tillage technology, new technologies and new devices are integrated to realize real-time tillage depth monitoring and control during operation, further improving the quality and intelligence level of seeded preparation.
## 3 Results and Analysis
### Geological Structure Characteristics of Zhongtang and Wentang Hotsprings  
Zhongtang hotspring outcrops in the middle porphyry medium grain biotite monzogranite intruded by Mesozoic, and the ground surface is covered by thin-layer deluvial deposit. It is 1420 m away from Checun-Xiatang fault (F1-1) in the north. Wentang hotspring outcrops in the gneissic mafic potash feldspar granite of Mesoproterozoic, and it is 2240 m away from the Checun-Xiatang fault in the north (Figure 6).  
F3-2 fault passes through 750 m east of Zhongtang and 250 m east of Wentang, with a strike of 45\({}^{\circ}\) and an inclination of 65\({}^{\circ}\). Fluorite vein, calcite vein, and quartz vein are filled along the fault. F3-3 fault passes through Zhongtang village, with a strike of 60\({}^{\circ}\), and inclines to northwest, with an inclination of 53\({}^{\circ}\). F3-4 fault passes through the south side of Zhongtang hot spring, with a strike of 45\({}^{\circ}\) and a kaolin alteration zone about 20 m wide along the fault zone. The F3-4 fault intersects F3-3 fault at Zhongtang village. There is F1-2 fault in the south of Wentang village. And some other faults or secondary faults are distributed in the area. For example, it is clear in satellite remote sensing images that there is a NW trending fault near Wentang hot spring. The strike of the fault is 355\({}^{\circ}\), the dip angle is 77\({}^{\circ}\), and the fracture width is 6.6 m. There are fault friction mirror surface, fault gouge, fault breccia, and silicified rock.  
According to the above geological situation, the fault structure of Zhongtang and Wentang field is extremely developed, with obvious hydrothermal activity. Zhongtang hotspring is obviously controlled by fault F3-4, and Wentang hotspring is obviously controlled by fault F1-2. The intersections of fault F3-3 and F3-4, fault F1-2 and other faults provides favorable structural conditions for the outcropping of Zhongtang and Wentang hotsprings.  
Geophysical Characteristics of Zhongtang and Wentang Filed  
In the south bank of Sha River, a magnetotelluric sounding (EH-4) line with a length of 1000 m and a point distance of 50-100 m was arranged, and a total of 16 points were measured. The measured profile is shown in Figure 7. There is a depression with a depth of about 500 m from the starting point of the profile to 700 m, and there is a belt with a low resistivity at a depth of about 800 m. The Cagniard resistivity is 350 \(\Omega\)-m. It is speculated that it is a water-bearing structural fracture zone, or the contact zone between the Mesoproterozoic intrusive rock and the metamorphic rock of Taihua Group. Because of the development of the original fracture and the influence of the later faults, the rock fracture contains hot water.  
Figure 6: Geological structure of Zhongtang and Wentang geothermal field.