esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study focuses on the Benggang of Tongluo Mountain in Longmen Town, Anxi County, Fujian Province, to explore the application of control-free-image-based nap-of-the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**With or Without** & \multirow{2}{*}{**Control Point Marker**} & **Aerial Triangulation** & \multirow{2}{*}{**DOM Image**} & \multirow{2}{*}{**DSM**} & \multirow{2}{*}{**Processing**} & \multirow{2}{*}{**Processing**} \\
**Image Control** & & & & & \\ \hline Image control & 58.00 & 13.91 & 21.00 & 190.93 & 283.84 \\ Control-free images & / & 15.88 & 20.65 & 180.05 & 216.58 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Internal image data processing with and without image control points (min).  
Figure 17: Image processing flow chart with and without image control points.  
object photogrammetry for dynamic erosion monitoring. The aim is to develop an innovative technique for advancing Benggang erosion monitoring processes. The key findings can be summarized as follows:  
1. The resolution of DSMs gradually increases with a robust linear correlation (R\({}^{2}\) = 0.914) over the course of the analysis. Considering positioning accuracy, point cloud data, and DSM analysis, the flight proximity distance of 20 m emerges as the optimal scheme for nap-of-the-object photogrammetry to obtain data in the Benggang study area.
2. The average reprojection errors for nap-of-the-object photogrammetry with and without image control do not differ significantly, both approximating 0.012 pixels. There is no significant difference between the two photogrammetry methods in the positioning accuracy of the DSMs in the planar and vertical directions, which resulted to be about 0.01 m and 0.03 m, respectively. The error analysis of the DSMs indicates a consistent trend in the profiles obtained with and without image control points before and after rainfall. Moreover, the elevation errors along the X- and Y-axes are less than 6.94 cm and 11.78 cm, respectively. Notably, both elevation and X-axis errors fall within the subcentimeter range.  
In conclusion, control-free-image-based nap-of-the-object photogrammetry technology proves capable of meeting the monitoring requirements of Benggang erosional areas. It can provide valuable technical support for large-scale, safe, and efficient erosion monitoring efforts.  
Conceptualization, L.Z.; Methodology, L.Z. and Y.Z.; Software, L.Z. and G.L.; Validation, J.L. ([PERSON]); Formal analysis, X.J.; Investigation, L.Z.; Data curation, L.Z. and J.L. ([PERSON]); Writing--original draft, L.Z.; Writing--review & editing, J.L. ([PERSON]); Supervision, J.L. ([PERSON]); Project administration, F.J. and Y.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Water Conservancy Science and Technology Project of Fujian Province (KJG21009A) and the Significant Science And Technology Projects of the Ministry of Water Resources (SKS-2022073).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Quantitative Characterization of Hydration Degree of Cement Paste  
Commonly used methods for determining hydration degree of cement paste include quantitative X-ray diffraction analysis (QXRD), magic angle spinning nuclear magnetic resonance spectra (MAS NMR), and the non-evaporable water method. In our previous research, all three methods were tried, and the results suggest that the non-evaporable water method is the most appropriate way to determine hydration degree in discussions of hydration process monitored by NMR.  
As the transverse magnetization of LF-NMR of cement paste is proportional to the content of evaporable water [8; 17], the amount of evaporable water at the time \(t\) can be derived from the sample mass, water-to-cement ratio, and transverse magnetization. The hydration degree of the cement paste at the corresponding time can be obtained. The specific calculation is as follows:  
\[m(t)=\frac{M_{0}(t)}{M_{0i}}\times m_{0} \tag{2}\]  
\[m_{0}=\frac{w}{c}\times m_{C} \tag{3}\]  
\[\alpha_{C}(t)=\frac{m_{0}-m(t)}{\gamma\times m_{C}} \tag{4}\]  
where \(m(t)\) represents the amount of evaporable water at hydration time \(t\); \(M_{0}(t)\) represents the transverse magnetization at hydration time \(t\); \(M_{0i}\) represents the transverse magnetization at the first time after mixing; \(m_{0}\) is the initial water content of the sample, \(w/c\) is water-to-cement ratio; \(\alpha_{C}(t)\) is the reaction degree of cement paste at the moment \(t\); \(m_{C}\) is the amount of cement; \(\gamma\) is the minimum water-to-cement ratio required for complete hydration of the cement. According to the Powers model [18], \(\gamma\) is 0.42. The non-evaporated water, also known as chemically bound water, can be obtained by subtracting the initial amount of water \(m_{0}\) from the amount of evaporable water \(m(t)\).  
It can be seen from Figure 2 that the hydration degree of the cement paste grows slowly from the beginning to 2 h. During the hydration process from 2 to 10 h, the degree of hydration increased sharply. After 10 h, the growth rate of the hydration degree slowed down. Although the growth of hydration degree for different pastes in Figure 2 presents a similar trend, the values of hydration degree are still different from each other at a given time. The samples with larger water-to-cement ratio show a higher degree of hydration.  
Figure 1: The evolution of transverse magnetization during the hydration process.
## 2 Introduction
Intelligent jamming consists of hardware and software that work together to block any radio event in the specific band on the DL connection, which is enabled by an amplitude above a threshold set based on radio measurements detected on UL channels. Monitoring the DL connection provides knowledge of the availability of the network operators at a specific monitoring radio point. The list of available bands with their central frequencies is provided to Hack-RF. Python code developed from scratch is powered with the list of available DL frequencies. Based on this, it calculates the corresponding UL frequencies and starts monitoring them. An amplitude above \(-60\) dBm on a UL-monitored frequency enables an emission on the corresponding DL frequency for 15 s. After all the input parameters are set, the jamming system is autonomous in its decisions. The intelligence component of the process consists of the following:  
* Analysis of the spectral use by mobile communication operators. * The selection of the mobile communications service that has the most significant network parameters and that would cause a mobile device to register in that band/technology. * Jamming only on the DL frequency of the selected service for a limited period set by the operator (a few seconds). The originality here lies in the fact that broadband jamming is not performed for all DL frequencies in the range including 800, 900, 1800, 2100 and 2600 MHz, but only uses targeted jamming on the DL channel of the service with significant parameters selected by the operator and in a limited period. This reduces the exposure to electromagnetic radiation of people who are in the secure area; reduces the electricity consumption of the jamming system; avoids electromagnetic interference with other electronic communication systems; and, of course, obtains results from jamming the radio connections of the mobile devices in areas where their usage is prohibited. This article is structured as follows: Section 2 presents an overview of the radio downlink connections of the mobile network operators in Bucharest, Romania. Section 3 describes some theoretical aspects of jamming systems, conditions for jamming and simulations of the theoretical scenarios. Subsequently, Section 4 showcases the test results, confirming both the validity and sustainability of the proposed methods for analysing jamming possibilities, each with its environmental conditions and limitations. This paper concludes in Section 5, with discussions, remarks and future works.
# Self-Supervised Keypoint Learning for the Geometric Analysis of Road-Marking Templates
## 2 Related Work
### Visual-Element Analysis and Condition/State Assessment  
Identifying and localizing visual elements are often necessary precursors for automated analysis tasks in industrial and infrastructure monitoring. In the context of road markings, these involve detecting markings despite challenging conditions experienced by vehicle-mounted cameras [6; 7; 8]. Such tasks often necessitate aligning the observed real-world instance to a standardized template or a digital model for consistent measurement, verification against standards, or condition assessment (e.g., quantifying wear or damage) [9; 10]. A critical challenge here is that appearance changes due to genuine degradation or state change must be disentangled from those caused by variations in viewpoint, scale, rotation, or illumination. This highlights the fundamental need for robust geometric normalization, typically achieved through aligning the real-world instance with its corresponding template.  
However, achieving reliable and accurate template-to-real-world instance alignment remains a significant challenge. Traditional methods often struggle with the large appearance gap between clean templates and degraded, noisy instances. There is currently no widely accepted solid solution for this specific alignment problem in challenging real-world conditions. The ability to robustly align a template to a real-world instance is crucial for reliable downstream analysis. Still, it first requires a method capable of understanding the underlying geometric structure of the template itself in a transformation-robust manner.
## 3 Testbed Vehicle Specifications and Loading Scenarios  
The proposed swingarm design is installed on the front suspension system of a three-wheel electric motorcycle (Daedalus). Using two wheels at front, two identical single-sided swingarms are used to hold the wheels. The top and side view of this assembly is shown in Figure 2a,b, respectively. In order to calculate the forces acting on the testbed vehicle, the technical specifications and corresponding dimensions of CoG related to Figure 1 are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Deceleration (g)**} \\ \hline
**Reference** & **Front Brake Only** & **Rear Brake** & **Front and Rear Combined** \\ \hline [PERSON] [31] & 0.88–0.89 & 0.38–0.46 & 0.96 \\ [PERSON] [32] & - & - & 0.51–0.75 \\ [PERSON] [33] & - & - & 0.67 \\ [PERSON], [PERSON], [PERSON] [34] & 0.44–0.76 & 0.31–0.43 & 0.59–0.89 \\ [PERSON], [PERSON], [PERSON] [35] & 0.65 & 0.42 & 0.71 \\ [PERSON], et al. [36] & 0.518–0.709 & 0.345–0.386 & 0.61–0.71 \\ [PERSON] et al. [26] & 0.5 & 0.35 & 0.48–0.83 \\ \hline
**Min-Max decelerations** & **0.44–0.89** & **0.31–0.46** & **0.51–0.96** \\
**Mean values of min and max** & **0.62–0.75** & **0.34–0.42** & **0.57–0.78** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Braking deceleration rates in g reported in the literature (dry and wet road).
## 3 ADRC Design and Parameterization
### NLSEF Design  
In the NLSEF proposed in this paper, the system's state error is defined as the difference between the estimated outputs of the ESO's load perturbation and speed state and the outputs of the TD:  
\[e_{1}=v_{1}-z_{1} \tag{34}\]  
where \(z_{1}\) represents the estimated rotational speed, \(z_{2}\) represents the estimated load disturbance, and \(v_{1}\) represents the estimated rotational speed tracking signal.  
The fractional linear PID combination is then transformed into a series controller form, which may be derived as the equivalent NLSEF form by differentiating the \(Nref\) tracking signal of the TD from the \(Nref\) observation signal in the observer:  
\[\left\{\begin{array}{l}e_{1}=v_{1}-z_{1}\\ iq_{0}=K\cdot fal(e_{1},\alpha_{1},\delta)\\ iq=iq_{0}-\frac{1}{b-z_{2}}\end{array}\right. \tag{35}\]  
where \(i_{q0}\) is the preset current value; \(i_{q}\) is the current value after adding the feedforward disturbance; \(K\) is the gain parameter for ADRC, and \(b\) is the disturbance compensation parameter. In this paper, \(\alpha_{1}\) and \(\delta\) are taken as 0.95 and 0.01, respectively, in the nonlinear controller.  
Precise observation and appropriate correction of the overall system disturbance form the foundation of ADRC. The compensation parameter \(b\) reflects the compensation strength of the controller. Increasing \(b\) can reduce the phenomenon of system oscillation, but it may also deteriorate the control performance of the system. In this paper, we first take \(b\) in Equation (25), which is calculated by the model, as the base value, and keep increasing it until finally taking \(b\) = 5000 as appropriate.  
The larger the system control gain \(K\), the shorter the system settling time. However, too large a value of \(K\) may lead to system overshoot or even oscillation. After the disturbance compensation factor \(b\) has been established, the control gain \(K\) can then be suitably modified in accordance with how the system is operating. The control gain used in this paper is \(K\) = 4 following system debugging.
# FFNN-TabNet: An Enhanced Stellar Age Determination Method Based on TabNet
## 5 Experiments and Results
### Experimental Results and Analysis  
In this study, the age of red giants is determined using an optimized TabNet model, wherein the model's efficacy is enhanced through strategic modifications to the network architecture and meticulous fine-tuning of Bayesian hyperparameters. The scatter plot depicting the determination results is illustrated in Figure 8, with a red solid line denoting the optimal linear fit for the data points.  
The determination analysis indicates that the FFNN-TabNet model exhibits remarkable precision in estimating the ages of red giants, as evidenced by its MAE of 0.535, RMSE of 0.711, and R\({}^{2}\) of 0.946. These exceptional results underscore the superior performance of the FFNN-TabNet model. The model's high-accuracy age estimations significantly contribute to a more profound understanding of stellar evolution, offering substantial support for studies exploring the genesis and evolutionary trajectory of the Milky Way. Moreover, the findings of this research provide vital insights into the ongoing refinement and optimization of models for stellar age determination.
# Measurement of Super-Pressure Balloon Deformation with Simplified Digital Image Correlation
## 2 Simplified Digital Image Correlation Method
### The Simplified DIC and the Intersection Detection  
SiDIC is a simple method to measure deformations. Instead of reading the light intensity values of the image, SiDIC recognizes the movement of the intersections to measure the deformation. Figure 2 shows how SiDIC measures the deformation. Basically, SiDIC reads the intersections of the net covering the balloon by using the intersection detection technology. SiDIC calculates the amount of deformation by reading the coordinates of the intersections. In this research, we detected the intersections by visual inspections. First, we read the coordinates of the intersections before and after deformation. Since actual intersections of net elements are represented by lines, there is no clear intersection in the image captured. In this experiment, the center of the line is defined as the intersection of the net. After reading the coordinates of the intersections, Equations (2) and (3) are used to calculate the amount of displacement. As a result, SiDIC measures the displacement from the amount of movement in the x and y directions before and after the deformation of each intersection.  
\[\Delta\mathrm{x}_{1}=\big{(}\mathrm{x}_{1}^{{}^{\prime}}-\mathrm{x}_{1}\big{)}. \tag{2}\]  
\[\Delta\mathrm{y}_{1}=\big{(}\mathrm{y}_{1}^{{}^{\prime}}-\mathrm{y}_{1}\big{)} \tag{3}\]
## 3 Experimental Setup and Instrumentation  
### Bridge Model Description  
A large-scale prestressed concrete (PC) bridge model equipped with an adjustable post-tensioning system is shown in Figure 1.  
The bridge model is located at the BAM (Bundesanstalt fur Materialforschung und -prufung, Berlin, Germany) test site TTS, 50 km south of Berlin. It is exposed to ambient environmental conditions, including sunshine, wind, and day-night temperature cycles.  
The bridge model measures 24 m in length, 0.9 m in width, and 0.3 m in depth (Figure 2). It is a two-span structure, and each span is 12 m. This model is a monolithic beam with three supports and a rigid connection to the foundation slab. It features an inverted U-beam structure with a compression strength based on concrete type C45 and Young's modulus of 36,000 MPa. Tests on the compressive strength of the concrete were conducted prior to the construction of the bridge model in 2016. However, the compressive strength of the bridge was not monitored during the study.  
Figure 1: A large-scale post-tensioned concrete bridge model located at the BAM TTS.  
Figure 3 illustrates the post-tensioning system and the unbonded tendons. It shows two tendon ducts, each containing two steel tendons of type 32 WR, as specified in ETA-05/0123 (European Technical Assessment) [35]. These tendons can provide a maximum prestressing force of 676 kN. The prestressing force is applied using hydraulic cylinders, as shown in Figure 3. The force is controlled through the hydraulic oil flow, allowing precise regulation of the tension applied to the steel tendons. By varying the post-tensioning force, openings, and closures in the existing cracks can be induced, generating a range of damage phenomena.
## 4 Experiment
### Experimental Results Analysis  
#### 4.5.1 Comparative Test  
On the DuIE dataset, the experimental results of entity relation joint extraction for the proposed model and seven other benchmark models are presented in Table 4. Among the seven compared joint extraction methods, MultiR is a weakly supervised entity relation extraction method, CoType employs sequence annotation for joint extraction, and the pointer annotation model, FETI, CasRel, word mixture model, and BSCRE are joint extraction methods based on parameter sharing. From the data in the table, it is evident that the CJE-PCHF model proposed in this paper achieved accuracy, recall, and F1 scores of 82.1%, 82.2%, and 82.2%, respectively. All metrics are significantly higher than those of the compared benchmark models, with average improvements of 10.8%, 22.1%, and 17.1% in accuracy, recall, and F1 score, respectively. This confirms the effectiveness of the proposed model in the task of entity relation extraction.  
Compared with other joint extraction models, this model demonstrates superiority for two reasons: (1) The introduction of part-of-speech (POS) and pinyin information assists the model in learning prior features. This paper proposes an interactive fusion network that effectively merges semantic features with POS features and pinyin features, thereby enhancing the model's feature representation capabilities. (2) The proposed progressive contrastive learning constraint model optimizes the distribution of the fused features in the feature space through progressive contrastive learning constraints, ensuring the alignment of the distributions of the fused features. By introducing a hyperparameter  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1** \\ \hline MultiR & 0.577 & 0.356 & 0.440 \\ CoType & 0.661 & 0.605 & 0.632 \\ Pointer Annotation Model & 0.694 & 0.639 & 0.665 \\ FETI & 0.757 & 0.356 & 0.440 \\ CasRel & 0.772 & 0.764 & 0.768 \\ Word Mixture Model & 0.813 & 0.781 & 0.797 \\ BSCRE & 0.816 & 0.795 & 0.805 \\ CJE-PCHF (ours) & 0.821 & 0.822 & 0.822 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparative experimental results.  
for loss adaptation, the balance between contrastive constraint loss and the model's self-training loss is maintained, thereby improving the overall performance of the model.  
In summary, the proposed model outperforms all baseline models, confirming its effectiveness in entity relation extraction.
## 2 Critical Infrastructures  
### Critical Infrastructure Definition  
The term critical infrastructure refers to the physical and virtual assets, systems, and networks that are crucial for the functioning of a society. These infrastructures are considered vital due to their significant impact on national security, economic stability, and public health and safety. Their incapacitation, destruction, or disruption can have severe consequences, leading to societal vulnerabilities and adverse effects on citizens. Critical infrastructure is a fundamental component of modern society, encompassing assets, systems, and networks that are essential for the maintenance of vital societal functions. The protection and resilience of critical infrastructure is paramount to ensuring citizens' security and well-being [6; 7].
## 3 Results and Discussion
### Validation of MW-SPM and HPLC-FD Methods
#### 3.3.3 Robustness and Ruggedness  
Both methods' robustness, the impact of slight variations in the procedure variables on the method's effectiveness, was assessed. The method's analytical results were found to be unaffected by the method's little fluctuation; recovery values ranged from 97.8-102.6 (\(\pm\)1.86-2.31%) and 98.4-101.2 (\(\pm\)0.94-1.85%). This confirmed the convenience of both MW-SPM and HPLC-FD for the routine application for the analysis of DUV and SEL.  
By having two different analysts perform the MW-SPM and HPLC-FD procedures on three different days, ruggedness was also investigated. The greatest RSD values did not exceed 2.24%, hence the results of the day-to-day variations daily changes were reproducible.
# Reliability Analysis of Response-Controlled Buildings Using Fragility Curves
## 2 IDA Pre-Requirements (Target Building, Dampers, and Input Earthquakes)
### Passive Dampers  
The current study uses two types of dampers: hysteresis and oil dampers. A hysteresis damper is a deformation-dependent damper, such as the buckling restrained brace [22]. It consists of steel as a damping material to absorb vibration energy by means of its plastic deformation [23]. The hysteresis damper is modeled as a shear spring, as shown in Figure 2a, with a bi-linear force deformation relationship, as shown in Figure 2b [22]. An oil damper is a velocity-dependent damper that uses the orifice flow resistance mechanism. It consists of a low-viscosity oil in a cylindrical tube configuration. The force-velocity relationship of the oil damper normally appears as an ellipse hysteresis shape [24]. The bi-linear oil damper is modeled as a shear spring, including the Maxwell model with an elastic spring with stiffness, K\({}_{\text{D}}\), and a dashpot with bilinear type damping coefficient, C, connected in series [25]. The element model of the bi-linear oil damper is shown in Figure 3a. The force-velocity relationship of the dashpot is shown in Figure 3b. The technical parameters of both the hysteresis and oil dampers were calculated by Prof. [PERSON] [4] and are given in Table 4.  
Figure 3: Bi-linear type oil damper: (**a**) element model, (**b**) force–velocity relationship of oil damper.  
Figure 2: Hysteresis damper: (**a**) element model, (**b**) bi-linear force–displacement relationship.