esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The integration of PV solar installations connected to the grid into the agriculture sector is proposed and evaluated under net-metering and self-consumption scenarios. This solar resource allows us to decrease emissions and fossil fuel dependence and improve economic benefits from a surplus energy sale standpoint. This multifocused analysis is an exportable and scalable solution that can be applied in different locations depending on different parameters, such as crop water need, aquifer depth, and grouped crop areas. A Spanish aquifer highly overexploited over the decades is used to evaluate the proposed methodology. Different surplus energy sale scenarios are analyzed according to the typical crops in this location and the corresponding annual water requirements and common grouping areas. In this way, relevant annual benefits are estimated in grouped areas of 800 ha, accounting for 50,000 to 140,000 euros/year in a net-metering situation excluding taxes and fees; and 28,000 to 90,000 euros under current Spanish regulations. Regardless of the level of grouped areas, PV power plants interconnected with the grid for the use of surplus energy could generate nonnegligible global revenues: between 10 and 18 million euros/year with a legislation prone to net-metering and between 5 and 10 million euros/year under the current Spanish legislation framework. Therefore, global policies focused on water management and efficient agricultural objectives should be promoted for massive integration of such renewables into the agriculture sector. More specifically, energy policies in terms of net-metering and/or self-consumption schemes that provide regulatory stability to this energy model in agriculture are required by the sector.  
Conceptualization, A. M.-G.; Formal analysis, J.M.S.-L.; Funding acquisition, M.S.G.-C.; Investigation, A.R.-A.; Project administration, A.R.-A.; Validation, M.S.G.-C.; Writing--original draft, A.M.-G. and J.M.S.-L.; Writing--review and editing, A.M.-G.  
This research was funded by the research project TIN2017-86647-P from the Spanish Ministry of Science and Innovation (including FEDER funds), and the Seneca Foundation 19882-GERM-15.  
The authors declare no conflict of interest.
## 4 Experimental Results
### Accuracy Evaluation of TFTP  
To quantify the accuracy of this TFTP system, a standard plane was measured. The reconstructed phase difference is shown in Figure 12a, at the moment \(t_{0}\) = 8, the root mean squared error (RMSE) of the phase distribution is 0.0380 rad, and that of height reconstruction is 0.0794 mm.  
Figure 11: System calibration process: (**a**) standard block. (**b**) deformed fringe pattern on block captured by the camera, (**c**) wrapped phase distribution calculated by TFTP, (**d**) unwrapped phase distribution.  
Figure 12: Reconstructed phase difference distribution of the tested standard plane by (**a**) TFTP and (**b**) traditional FTTP.  
As shown in Figure 12b, when the same plane is measured by the traditional FTP it is impossible to realize integer period sampling because the measurement error of the edge parts is extremely large. The RMSE of the phase distribution is 1.1792 rad and that of height distribution is 6.3815 mm.
## 2 Isolation and Characterization of the Sub-6 GHz Multi-Port MIMO Antenna  
The proposed antenna system is designed for sub-6 GHz 5G mobile applications, specifically operation in the n77 (3.3-4.2 GHz), n78 (3.3-3.8 GHz), and n79 (4.4-5.0 GHz) bands. The antenna is fabricated on a thin flexible substrate that can be integrated into smartphones and is optimized to ensure broadband impedance matching and stable gain across the target frequency range.  
The antenna features four ports (ANTs #1 to #4) symmetrically integrated along the smartphone metal frame. The design exploits capacitive coupling and shared current paths within the frame. This enhances impedance matching and minimizes radiation loss. Figure 1 illustrates two mechanical integration strategies commonly used in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency Band** & **Range (MHz)** & **Common Usage** \\ \hline n77 & 3300–4200 & Global 5G mid-band deployment \\ n78 & 3300–3800 & Widely used in Europe, China, and Korea \\ n79 & 4400–5000 & Used in Japan and China \\ n41 & 2496–2690 & Time division duplex spectrum in the U.S. and China \\ n1 & 1920–2170 & Existing long-term evolution (LTE) spectrum \\ n3 & 1710–1880 & LTE re-farming for 5G in some regions \\ \hline \hline \end{tabular}
\end{table}
Table 1: The sub-6 GHz 5G NR frequency bands.  
smartphone platforms. Each can accommodate the new antenna employing a flexible coupling configuration.
## 3 Results and Discussion
### Effects of \(\mu_{0}\)  
Equation (29) suggests that \(\mu_{0}\) is a \"multiplier\" of \(\mu(y)\), the dimensionless viscosity, across the channel. At \(y=1\) or the upper channel wall, \(\mu(1)=\mu_{0}\); therefore, increasing \(\mu_{0}\) results in increasing \(\mu(1)\). At the lower channel wall, \(y=0\); values of \(\mu(0)\) are also dependent on \(\mu_{0}\), as calculated using (29) and (32), for a given value of \(\vartheta\).  
The results for logarithmically evenly spaced values of \(\mu_{0}\) can be found in Figure 4, where \(\mu_{0}=10^{-1},10^{-0.5},\ 1,10^{0.5},\ \text{and}\ 10^{1}\), while all other parameters take on the values identified in the Reference Case given in Table 1. Note that the velocity profiles are shown on a semi-log scale due to the significant reduction caused by the high viscosity flows. In terms of \(\mu\) being the inverse of the Reynolds number, an increase in \(\mu\) implies a decrease in Reynolds number, resulting in a slower flow. As seen in Figure 4, the variation of \(\mu_{0}\) produces a scaling of the velocity profile (and therefore \(\left(\frac{du}{dy}\right)\) and \(\left(\frac{d^{2}u}{dy^{2}}\right)\)) by a factor of \(\frac{1}{\mu_{0}}\). This is a result of each of these quantities being multiplied by \(\mu_{0}\) in Equation (27) or, as seen more directly in Equation (33), the division of the RHS by \(\mu_{0}\). These effects combine to produce identical Darcy friction and viscous forces profiles for all values of \(\mu_{0}\). As a result of the velocity profiles scaling by the factor of \(\frac{1}{\mu_{0}}\), the dimensionless flowrate, Q, becomes a straight line when plotted on a log-log axis. Since the profile of \(\left(\frac{du}{dy}\right)\left(\frac{du}{dy}\right)\) is not symmetric, the Darcy friction and \(\mu\left(\frac{d^{2}u}{dy^{2}}\right)\) also contain some slight asymmetry.
## 5 Analysis of Fault Isolation Methods for Pulsing Load Mutation  
### Fault Isolation Scheme for Pulsing Load Mutation  
From the above analysis, a pulsing fault produces a large load deviation, which causes a larger regulation of the CCPP, exacerbates the wear and tear of its component, and seriously affects its service life. Therefore, it is important to reduce the regulating frequency of the CCPP to extend its service life when a pulsing fault occurs.  
The load signal cannot represent the power output of the unit when a pulsing fault occurs. Therefore, the load was not a feedback signal for the load control system. Otherwise, the electrical interference fault is transmitted through the load control system, which affects the regulation of fuel quantity. Because pulsing faults caused by electrical interference are characterized by rapid changes and recovery to a normal state, a variable load quantity is used to determine whether a pulsing fault has occurred. A pulsing fault is considered to have occurred when the load variable quantity per second exceeds 0.5% Pe.  
Considering that the pulse width of a pulsing fault generally does not exceed 2 s, a fault isolation scheme for an electrical interference fault in a single-shaft CCPP is proposed, as shown in Figure 10. After detecting a pulsing load mutation, a pulse with a pulse width of 2 s was triggered. The load command temporarily replaced the unit load as a feedback signal to the unit load during these 2 s. After the pulse signal disappears and the unit load recovers to a normal value, the unit load is reintroduced as a feedback signal directly into the unit load control system to ensure normal regulation of the unit. In other words, we establish a one-out-of-two system using the two values of the load command and actual load. When load mutation occurs, the actual load is temporarily chosen as the feedback signal to the load control system during the regulation process. When this fault was eliminated, load commands were used in the control system during the regulation process.
## 2 Related Work
In the future, data from trucks will be incorporated into the proposed forecasting model to improve the accuracy of carbon monoxide (CO) pollution prediction. One work by [23] suggested the use of wireless sensors equipped with mini low-power artificial neural networks (ANNs), which are trained from their local environment, rather than from a base station. This work also considered the impact of microclimates on prediction accuracy and developed a prototype chip to reduce the computational complexity of the ANNs. Another work by [24] used ML models to predict air quality in Barcelona, gathering weather and pollutant concentration data using networks. The ML tool exhibited better predictive capabilities than the CALIOPE Urban v1.0 platform and showed that time of the year, daytime, and intensity of road traffic are the most significant factors impacting pollutant-level variability. In [25], the authors aimed to reduce CO2 emissions from the crane of the Casablanca Port of Morocco, studying the daily energy of eleven RTG cranes collected for two years. The energy consumption was analyzed using regression analysis to discover the factors that impact production, and the authors introduced inexpensive strategies for clean air. Another work by [26] provided a comprehensive review of the state-of-the-art ML models and their applications to international freight transportation management, including demand forecasting, vehicle trajectory, procedure and asset maintenance, and on-time performance prediction. Finally, Ref. [27] described two kernel-based supervised ML models for daily truck traffic in port terminals, which were compared with a multilayer feedforward neural network model. The Gaussian processes and \(\epsilon\)-support vector machine models performed well and required less effort in model fitting compared to the MLFNN model. These models are good candidates to substitute for the MLFNN in port-generated truck traffic.
## 3 Fuzzy Evaluation of Surrounding Rock Stability of Ioess Tunnel with Rich Water
### Determine Weights Based on FAHP  
Based on the analogical analysis of the aforementioned engineering data, research is conducted on the identified influencing factor indicators. Pairwise comparisons of the indicators are made, and a pairwise comparison matrix is obtained according to the scale judgment Table 2, resulting in a fuzzy complementary matrix \(A=(a_{ij})_{mn}\)Subsequently, the sum of the row elements of the fuzzy complementary matrix is calculated as \(r_{i}\), and the original fuzzy complementary matrix is adjusted using a formula to obtain the fuzzy consistent matrix for the criterion layer and decision-making layer, as shown below:  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & \multirow{2}{*}{**Decision Makers**} & \multicolumn{5}{c}{**Target Layer (Stability Evaluation Level of Tunnel**} \\
\[A=\begin{bmatrix}0.50&0.35&0.55\\ 0.65&0.50&0.70\\ 0.45&0.30&0.50\end{bmatrix}\to A=\begin{bmatrix}0.50&0.39&0.54\\ 0.61&0.50&0.65\\ 0.46&0.35&0.50\end{bmatrix}\]  
\[A_{1}=\begin{bmatrix}0.50&0.70&0.60&0.40\\ 0.30&0.50&0.40&0.20\\ 0.40&0.60&0.50&0.30\\ 0.60&0.80&0.70&0.50\end{bmatrix}\to A_{1}=\begin{bmatrix}0.50&0.63&0.57&0.43\\ 0.37&0.50&0.43&0.30\\ 0.43&0.57&0.50&0.37\\ 0.57&0.70&0.63&0.50\end{bmatrix}\]  
\[A_{2}=\begin{bmatrix}0.50&0.30&0.40\\ 0.70&0.50&0.60\\ 0.60&0.40&0.50\end{bmatrix}\]  
\[A_{3}=\begin{bmatrix}0.50&0.60&0.40\\ 0.40&0.50&0.30\\ 0.60&0.70&0.50\end{bmatrix}\to A_{3}=\begin{bmatrix}0.50&0.55&0.45\\ 0.45&0.50&0.40\\ 0.55&0.60&0.50\end{bmatrix}\]  
Based on the fuzzy consistent matrix, using the formula method and normalization principle, the weights of the various influencing factor indicators corresponding to the criterion layer and indicator layer are calculated and presented in Table 4.  
From the results of the influencing factor indicator weights in Table 3, it can be seen that among the criterion layer, the stratum factor has the greatest impact on the stability of the surrounding rock of the tunnel, followed by soil parameters, with construction factors being the weakest. In the decision-making layer, the water-rich conditions of the surrounding rock, support measures, and excavation methods have the greatest impact on the stability of the surrounding rock. Therefore, during the construction of water-rich loess tunnels, comprehensive evaluations should be conducted focusing on the water-rich conditions of the surrounding rock, support parameters and excavation methods, and targeted engineering disaster response measures should be implemented.
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 4 Experimental Results  
To assess the proposed scheme, we tested its robustness against collusion attacks. Experiments were carried out on six triangular mesh models from 3D mesh watermarking benchmark project [27]: Bunny, Rabbit, Venus, Horse, Cow, and Ramesses. We used the fingerprint code set constructed by [9] with parameters (72, 81, 9, 8, 0, 1), which could protect the model against \(n=8\) attackers. The watermarking algorithm was implemented in MATLAB 2012b using the mesh toolbox [28]. The geometric distortion was measured using maximum root mean square error (MRMS) [29]. Figure 4b shows the watermark with MRMS = 0.02% (\(\alpha=0.05\)), and no visual difference can be perceived between Figure 4a,b. In addition, we embedded the watermark with high strength factor (\(\alpha=0.5\)) to show the visual shape of the watermark (MRMS = 2%). The embedded watermark appears as noise strongly represented on certain areas of the model's surface. In addition, Figure 5 illustrates the distortion maps which represents the HSV maps of the geometric objective distortions between the original and the watermarked meshes based on the hausdorff distance [29].
# Recognizing and Recovering Ball Motion Based on Low-Frame-Rate Monocular Camera
## 2 Related Work  
### Projection Extraction  
Projection extraction extracts images of motion trajectories from photographs. A photograph contains three parts: (1) ball streak, (2) background, and (3) noises. Among them, ball streaks and noises constitute the foreground. In other words, projection extraction eliminates background and noises and leaves the ball streak alone.  
In the methods proposed in [3; 4], the foreground and background were distinguished based on the P-tile and Otsu algorithms to obtain gray scale thresholds. However, P-tile and Otsu rely on Region of Interest (RIO) to reduce the detection range, forcing these algorithms to exploit multiple frames.  
To extract ball streaks from foregrounds, a projection extraction method needs to filter noises, including underexposure noises, variations of ambient light, and human body movements. In [3], [PERSON] et al. processed images with the Gaussian filter, which removed underexposure noises and light variations. However, the Gaussian filter can displace several pixels towards the center of the streak in the smoothing phase, and thus the ball radius in that image will be smaller than its actual value. In [4], [PERSON] et al. used P-tile to address the underexposure noise issue and filtered ball projections from noises based on the contour shape characteristics. In the method proposed in [7; 14], [PERSON] et al. and [PERSON] et al. filtered ball candidates using sizes, width-height ratios of circumscribed rectangles, and color features. These methods focused on the external characteristic of streaks and worked well in uncomplicated environments. However, these methods fail to extract ball streaks when noises and projections have similar shapes and colors.  
To obtain precise contours in blur images, in [11] [PERSON] et al. extracted two sets of contour points using the pixel intensity values along the streak directions. Each set of points circled the ball image by conic fitting. However, such an approach only works well when the streak is short. Then, in [12] to deal with longer streaks, the authors proposed using semi ellipses to fit the pixel intensities along the streak profile to obtain precise lateral contours. However, this method requires an extra further candidate point matching process, which is unstable when fitting errors cannot be ignored.
# A Compact Monopole Wideband Antenna Based on DGS
## 2 Antenna Design Methodology and Optimization Process
### Parametric Analysis
#### 2.5.1 Effect of Length of L-Slot Vertical (Lvs)  
Varying Lvs at 18, 20, and 24 mm revealed a maximum bandwidth of 4.08-18.92 GHz and optimal impedance matching at Lvs = 24 mm. The simulated S\({}_{11}\) for various Lvs values is shown in Figure 7a. The lower the length of Lvs, the shorter the bandwidth coverage of the antenna, and this resulted in generating a dual-band response.
## 4 Conclusions  
A wideband microstrip-to-waveguide transition is designed in W-band. The proposed transition is based on the E-plane probe to excite the TE\({}_{10}\) mode of the waveguide. The working principle has been elaborated. In the design, a parasitic patch is introduced to the E-plane probe to obtain an additional resonance at a low frequency. The introduction of the parasitic patch can improve impedance matching and increase working bandwidth. The simulated results exhibit the excellent performance of the proposed transition. Moreover, for verification, a back-to-back transition was fabricated and measured. The measured results show that the proposed transition has a reflection coefficient below \(-13\) dB from \(70\) to \(110\) GHz and an average insert loss of \(1.1\) dB. With good performance and a simple structure, the proposed transition is desirable for W-band application.  
Conceptualization, M.H. and C.W.; methodology, M.H.; software, C.W.; formal analysis, C.L.; investigation, S.X.; resources, J.M.; data curation, H.S.; writing--original draft preparation, M.H.; writing--review and editing, C.W.; visualization, M.H. and C.W.; supervision, H.S.; project administration, H.S.; funding acquisition, C.W. and C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 62104259 and Grant 62105370.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Analysis
### Initial LSP Results  
#### 4.2.1 Initial Landslide Susceptibility Results  
The RF model was used for initial LSP modeling in the study area, with the data distribution shown in Table 3. The Natural Breaks method categorized the results into five levels: very high, high, medium, low, and very low. A landslide susceptibility map was generated, as shown in Figure 4. The proportions of different susceptibility levels are very low (38.28%), low (23.19%), medium (15.43%), high (14.04%), and very high (9.06%). Among these, the low-susceptibility areas (very low and low levels) account for most of the study area, comprising over 60%.  
Table 4 shows that the very high susceptibility level has an FR value of 8.37, much higher than the other levels, suggesting that the LSP results from the initial RF model closely  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**LCFs** & **Class** & **Area Count** & **Landslides** & **Percent of Area (E)** & **Percent of Landslide (F)** & **FR = F/E** & **RF (\%)** & **Max** \\ \hline \multirow{4}{*}{Distance} & 0-500 & 133,635 & 212 & 15\% & 36\% & 2.34 & 35\% & 2.34 \\  & 500–1000 & 100,139 & 103 & 12\% & 17\% & 1.52 & 23\% & \\  & 1000–1500 & 87,300 & 64 & 10\% & 11\% & 1.08 & 16\% & \\  & 1500–2000 & 76,050 & 63 & 9\% & 11\% & 1.22 & 18\% & \\  & \(>\)2000 & 472,747 & 147 & 54\% & 25\% & 0.46 & 7\% & \\ \hline \multirow{4}{*}{Fault density} & 0-0.037 & 443,076 & 343 & 51\% & 58\% & 1.14 & 26\% & 1.14 \\  & 0.037–0.105 & 129,035 & 84 & 15\% & 14\% & 0.96 & 22\% & \\ \cline{1-1}  & 0.105–0.170 & 201,592 & 115 & 23\% & 20\% & 0.84 & 19\% & \\ \cline{1-1}  & 0.170–0.25 & 68,716 & 37 & 8\% & 6\% & 0.79 & 18\% & \\ \cline{1-1}  & 0.25–0.401 & 23,009 & 10 & 3\% & 2\% & 0.64 & 15\% & \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 4: Initial LSP by RF.  
match the actual landslide occurrence patterns. The model achieved an ROC accuracy of 0.897, which meets the LSP accuracy requirements but could be further improved by adjusting non-landslide sampling ratios.
# A Road Crack Detection Method Based on Residual and Attention Mechanism
## 3 Experimental Results and Analysis
### Experimental Setup
[22] proposed a deep convolutional neural network based on attention mechanism and residual structure;
* [PERSON] et al. [23] proposed a refined crack detection method via LECS-Former for autonomous road inspection vehicles.
## 7 RF-EH Circuit
### Antenna Configurations
#### 7.1.3 Antennas with Multiple Bands
Varying techniques have been adopted to compress the antenna size, such as the meandered lines concept [134], fractal geometry [135], the multibending curves method [136], and the Sierpinski fractal technique [137].
# Health Care Accessibility Analysis Considering Behavioral Preferences for Hospital Choice
## 2 Method
### Combined 2 SFCA Method  
Isochronous and isometric circles centered at a certain point will not overlap due to the uneven spatial distribution of hospitals and residential areas and the inconsistent traffic conditions in each region. In this way, different residents will choose to visit specific hospitals based on the already formed preferences of distance priority or time priority, and eventually the actual accessibility of medical facility services in the real world. For example, with equal travel time, some residents will prefer the hospital with the closest spatial distance, which will result in more people choosing hospitals near their residence for medical care, thus causing the spatial accessibility of the area to deteriorate. Conversely, temporal accessibility will also deteriorate. This result is easily perceived by the local residents.  
We know that real-life residents' choice of hospitals for medical care is not uniform but highly subjective, especially when there are multiple hospitals around their place of residence and not too far away. However, the calculation results of existing methods are mostly ideal solutions based on simple influencing factors, such as only considering time or distance, without considering the impact of residents' subjective choices on the accessibility of medical facilities.  
This study proposes a new accessibility analysis method based on the two-step floating search method with the addition of subjective behavioral factors of residents' choice of medical care, making full use of increasingly accurate distances and travel times. In the new method, both travel time and distance are taken into account, and the weights of both are determined according to the residents' health care habits, such as transportation distance priority or time priority.