esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 5 Performance Evaluation
The D2 method succeeds in fulfilling the latency threshold with an average latency of at most 66 ms for the case of 20 consumers. As far as the maximum experimented latency is concerned, in C2 it is constant and equal to 2.8 s, determined by the consumer furthest from the CNC. By contrast, the distributed solutions achieve maximum latency of 0.62 s for D1 and 0.46 s for D2, respectively (both with 20 consumers), showing their adaptability to the positions of both producers and consumers. It is worth noting that D2, while able to guarantee an average latency comparable to the configured threshold, provides no guarantee for the performance observed by the worst consumer; hence, the maximum is largely above the threshold. The maximum latency of 0.46 s is obtained with 20 consumers, while in the first scenario the maximum latency was 0.40 s for a network of 180 nodes. Figure 6: \(Cont.\)
# Study on Relay Contact Bounce Based on the Adaptive Weight Rotation Template Matching Algorithm
###### Abstract
Subsequently, by quantifying the bounce phenomenon based on the separation distance between the contacts, the paper seeks to analyze the relay's contact bounce phenomenon and its related parameters to optimize the structure of the relay. To achieve this goal, this paper proposes an adaptive weight rotation template matching algorithm. This algorithm can better handle changes in target angles and identify targets at different angles after rotation. Furthermore, applying this algorithm to the study of bounce phenomena in cylindrical contact relays differs from existing methods, which can only indirectly calculate the contact bounce time through voltage data. This method allows for a more direct observation of the entire bounce process of relay contacts and the acquisition of the distance curve between the moving and stationary contacts. Parameters such as the bounce time, bounce height, and time required to reach the maximum rebound distance can be calculated. Additionally, by combining image and data analyses to understand the causes of bounce, this paper provides a reference for guiding improvements in relay products and suppressing contact bounce.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 4 Chipless RFID Array Design
### Unit Cell Tag
The resonance frequency of the novel resonator is derived using a non-linear regression using the model equation in [27], and it can be calculated using the following:  
\[f_{0}=\frac{c}{1.25L}\left(\sqrt{\frac{2}{e_{\text{eff}}+1}}\right) \tag{7}\]  
where \(c\) is the speed of light in vacuum, \(e_{\text{eff}}\) is the effective dielectric constant, and the effective length of the Pi-shaped resonator \(L\),  
\[L=[a+2(b+d)]+2e \tag{8}\]  
where \(a\), \(b\), \(d\), and \(e\) are the dimensions of the resonator. For a better Q-factor, keep \(a\), \(b\), and \(d\) constant and only vary the dimension \(e\) to adjust the resonance frequency \(f_{0}\) of the resonator. \begin{table}
\begin{tabular}{l l l l} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Resonator Type**} & **Resonance** & **Simulated** \\  & & **Frequency** & **Q-Factor** \\ \hline  & & without Ground Plane & \\ \hline
[25] & U-shaped Slot Resonator & 4.372 & 329 \\
[28] & Asymmetric Circular Split Ring Resonator & 5.760 & 182 \\
[29] & Slot Ring Resonator & 4.352 & 125 \\
[30] & Cobweb Resonator & 4.504 & 111 \\
[31] & ELC Resonator & 9.249 & 178 \\ \hline  & & with Ground Plane & \\ \hline
[23] & Square Resonator & 4.597 & 159 \\
[32] & Tip Loaded Dipole Resonator & 5.461 & 229 \\
[33] & Triangular Patch Resonator & 5.308 & 128 \\ This work & Pi-shaped Resonator & 4.780 & 4515 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Q-factor comparison of common chipless RFID resonators. Figure 7: The spectral dip’s amplitude level variation of the Pi-shaped resonator arm length \(d\) changes from 0.7 to 1.4 mm when \(b\) is equal to 1.4 mm.
## 2 Materials and Methods
### First Part: Automated System Overview  
The objective of the automated FHC/DAE measurement system was to measure the distance between the FHC and the DAE from VDHE radiographic images. To achieve this, the development of the system was structured into three stages: FHC detection, DAE segmentation, and FHC to DAE distance calculation.
# Field Evaluation and Application of Intelligent Quality Control Systems
## 1 Introduction
These guidelines were integrated into the European technical standard CEN/TS 17006 \"Earthworks-Continuous Compaction Control (CCC)\" in 2018 [12]. The U.S. FHWA released generic IC specifications for soil applications, titled \"Intelligent Compaction Technology for Soil Applications\", in 2014, and State Departments of Transportation (DOTs) have been progressively approving IC standards that incorporate local regulations. In South Korea, the Intelligent Compaction Specification [13] has been established, and the introduction of intelligent quality management in road construction is in its initial stages. Currently, road construction sites use location information to monitor the number of passes made by compaction rollers. Additionally, there is growing demand for intelligent quality management utilizing Compaction Meter Value (CMV) technology. Although IC for soil applications is being adopted slower compared to IC for asphalt, its application is steadily increasing. Our study verified its field applicability by comparing and analyzing an intelligent QC system's test results against traditional quality inspection tests.
# Cloning and Characterization of Two Iridoid Synthase Homologs from _Swertia Mussotii_
## 4 Materials and Methods
### Sequence Alignment, Phylogenetic Analysis and Homology Modelling  
A multiple sequence alignment of the amino acid sequences of the ISs was performed using DNAMAN software (version 7.0.2, Lynnon Corp., Pointe-Claire, QC, Canada) (www.lynnnon.com). Phylogenetic analyses were performed using Molecular Evolutionary Genetics Analysis, version 7.0 (Biodesign Institute, Tempe, AZ, USA, [[http://www.megasoftware.net](http://www.megasoftware.net)]([http://www.megasoftware.net](http://www.megasoftware.net))) [26] with the Neighbour-Joining method [27]. The robustness of the trees was tested by running 1000 bootstrap replications. Evolutionary distances were computed using the Jones-Taylor-Thornton model [28], and all positions that contained gaps and missing data were eliminated from the data set. Three-dimensional models of SmIS1 and SmIS2 were generated using the Swiss-model server (swissmodel.expasy.org) [29]. C. _roseus_ CrIS (PDB: 5 COB) was used as the template.
## 4 Experiment and Results
### Validation Accuracy of the Models  
Figure S2 displays the validation accuracy of the OLSTM that starts from 96.25% to 98.5% after eight epochs. The accuracy of validation drops drastically in epoch three but raises again in epoch four. For OCNN and OANN, the accuracy increases from 97.5% to 98.75% and from 98.75% to 96.25%, respectively. The highest accuracy of CNN, ANN, and LSTM remained comparatively low, at 92.71%, 90.43%, and 88.51%, respectively.
## 4 Discussion
As demonstrated in the sensitivity analysis, even small variations in parameter values can significantly impact the quality of the solutions obtained. This highlights the need to incorporate systematic methods for automatic parameter calibration, such as adaptive strategies, Bayesian optimization, or self-tuning mechanisms based on meta-optimization techniques. Additionally, the initial modeling and parametrization process requires detailed technical knowledge of the robotic system and its workspace, which may hinder the adoption of the methodology in industries lacking a digital infrastructure or prior experience in advanced simulation and optimization. This underscores the importance of developing complementary tools, such as graphical interfaces and automated model validation, to facilitate its use by non-expert users. For future research, it is proposed to extend the model to account for uncertainty in workstation availability or variability in process times, as well as to incorporate real-time sensor data to enable dynamic adaptation of the layout. Furthermore, other metaheuristic approaches, such as PSO, differential evolution, or hybrid algorithms integrating reinforcement learning, should be explored to compare performance, convergence behavior, and robustness. Finally, considering the growing implementation of human-robot collaborative cells, the methodology should be adapted to environments where safety, human interaction, and ergonomic constraints introduce additional restrictions on layout design and robot motion. This will be a key component in the development of more flexible, adaptive, and human-centered manufacturing systems.
## 4 Discussion
### Uncertainty Analysis  
Uncertainties in wind speed and wind power forecasting result in an imbalance between projected and actual time series, and this influences the operation reliability of wind farms [58]. Based on the forecasting results of a combined model for wind speed and wind power, interval forecasting was employed to analyze the level of uncertainty in wind speed and wind power forecasting and to assess possible threats for decision planners in terms of power dispatching. More specifically, the uncertainty analysis used interval forecasting to analyze wind speed and wind power data sets from four seasons, each with a span of 10 min. By comparing the forecasting performances of different combined models and sub-models, the forecasting error of each model was analyzed in four seasons, and the differences in the prediction error were compared with the level of error in the proposed models, namely, the sub-model based on the ANN and the MODA-based combined model with different numbers of sub-models. Sub-models and combined models were used as contrasting models to verify the superiority of the proposed combined model in terms of interval forecasting. If the evaluation results of the proposed combined model were better than those of all comparison models under the same conditions, this would verify that the proposed model is better than combined models with less than five sub-models or sub-models alone in terms of uncertainty forecasting and stability forecasting [59]. To better identify the characteristics of the wind speed and wind power forecasting values from each model, a diverse probability density function including the \(t\) location-scale, stable distribution, logistic, and normal distribution functions was employed to fit the forecasting wind speed series and wind power series via maximum likelihood estimation (MLE) [60]. Referring to the results of the assessment index \(R^{2}\) shown in Table 9 and Figure 6, the \(t\)-location scale and stable function was selected as the most suitable probabilistic distribution for further interval forecasting. The evaluation indices FICP, FINAW, and AWD were adopted to assess the interval forecasting results and to analyze the forecasting uncertainty of the four selected models [61]. Notably, the larger FICP values gave better analysis results, whereas larger FINAW and AWD values gave worse analysis results. The above three metrics and the lower and upper confines of the wind prediction values [62] are defined in Table 9, and the results of the uncertainty analysis are presented in Table 10. Furthermore, the expectation probability, defined as \((1-\alpha)\times 100\%\), was set at 95%, 90%, and 85% to assess the forecasting uncertainty of the four selected models in the uncertainty analysis.
# Regression Equations for Estimating Landslide-Triggering Factors Using Soil Characteristics
## 3 Results
### Landslide Scales  
Landslide patterns in the areas of study mostly changed from translational-type to flow-type. Translational-type landslides were largest in the granite area at about 98% compared to 75% in the gneiss area. In particular, most of the translational-type landslides in the granite area showed a shallow collapse that formed along the interface of the bedrock. Subsequently, the collapsed soil material developed into a flow.  
Figure 4 shows the length, width, and depth of landslides depending on the geology of the study area. The scale of landslides in the gneiss area ranged from 20 m to 290 m based on the length. On a 20 m criteria, 41-60 m accounted for the highest occupancy rate of about 24% with 18 sites recorded among  
Figure 3: Geological status map including soil sampling points for landslides and non-landslides regions in the areas of study. (**A**) Landslides in Yangju, Gyeonggi-do. (**B**) Landslides in Sangju, Gyeongsangbuk-do.  
77 outbreaks, followed by 61-80 m and 21-40 m, which occupied about 13%, in 10 sites, respectively. On the other hand, landslides of over 100 m in length accounted for 31 out of 77 sites, which was about 42%, revealing that several long-length landslides occurred.  
Moreover, the length of landslides in the genesis region ranged from 8 m to 22 m, with 16-20 m and 11-15 m accounting for about 42% and about 40%, respectively, taking up the most impact. In contrast, about 58% of landslides in the granite region came within the 6-10 m length interval, on account of the gentle V-shaped water sump type topography of the landslide site.  
More than about 88% of landslides in these areas were found to have occurred within a depth of 1 m as landslides often occur in areas with low slopes, and outcrops are well developed on slopes at higher elevations, resulting in relatively low rates of landslides. In addition, the depth of landslides was considered shallow because destruction occurred at the boundary between the shallow top layer, which consists of residual and colluvial soils, and the bedrock.
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 2 Study Area and Data
### Remote Sensing Imagery  
We used 21 GF-2 images covering Feicheng County, including six obtained on 20 February 2017, seven on 8 December 2017, three on 15 February 2018, and five on 16 March 2018. Each GF-2 image comprised a multispectral image and a panchromatic image. Each GF-2 image is divided into a multispectral and panchromatic image. The former is composed of four spectral bands (blue, green, red, and near-infrared), and the spatial resolution of each multispectral image is 4 m, whereas that of the panchromatic image is 1 m.  
We designed a program using Python to perform geometric correction. The control points used during this stage were obtained from worldview-4 images that had undergone correction and which had a spatial resolution of 0.3 m. We used the Environment for Visualizing Images (ENVI) software  
Figure 1: The spatial location of Feicheng county and remote sensing images of the study area.  
(Harris Geospatial Solutions, Broomfield, CO, USA) to perform radiometric calibration, atmospheric correction, and image fusion. The parameter used in radiometric calibration stage is published in CRESDA [76]. The Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model in ENVI was used to conduct atmospheric correction with the Interactive Data Language, which can co-optation better with ENVI. Finally, the pan-sharpening method was used to fuse the multispectral and the panchromatic images. After preprocessing, the spatial resolution of the fused images was 1 m. The fused images were comprised of four bands, namely the blue, green, red, and near-infrared bands.
## 3 Review of Coefficient of Variation for Resistance and for Loads
### Resistance-Compressive Strength of Concrete  
Reference property of concrete is the compressive strength f\({}_{\text{co}}\) of the standard test specimen tested, according to standard conditions and at a standard age of 28 days. The in situ compressive strength, f\({}_{\text{c}}\), can be related to f\({}_{\text{co}}\) as [8]:  
\[f_{c}=\alpha(t,\tau)f_{co}^{\lambda}\qquad[MPa] \tag{5}\]  
where \(\lambda\) is a factor, taking into account the difference between the compressive strength of the concrete as measured in situ, and the strength according to standard tests on concrete cylinders; \(\alpha\)(t,\(\tau\)) is a deterministic function, which takes into account the concrete age at the loading time, t [days] and the duration of loading, \(\tau\) [days]. The concrete compressive strength can be assumed to be lognormal distributed. As it has been found that \(\lambda\) varies only insignificantly, the in situ concrete compressive strength is proposed in the literature to be lognormal, distributed with a COV equal to 0.15 [9].  
[PERSON] et al. [10] tested 6845 samples of concrete cubes for concrete compressive strength. The COV that was calculated for all of these samples was between 0.06 and 0.19, depending on the concrete class--the higher the class, the smaller the COV.  
One of the basis for US design codes provides the value of COV for concrete compressive strength from 0.15-0.18, again depending on the concrete class [11]. The values of the coefficient of variation, based on many test results for cast \"on-site\" concrete test cylinders and cubes varies between 0.1 and 0.2 depending on the quality control, according to [12]. These values are appropriate for between batch variation (i.e., considering concrete from all sources). The coefficients of variation are roughly halved for within batch variation (i.e., for concrete from one source).  
Although the statistical distribution of concrete compressive strength has been of interest for a long time, it often has a much smaller influence on structural strength and behavior than do reinforcement properties. This is entirely due to the conventional design philosophy of attempting to achieve ductility in the structure. Nevertheless, it is important for estimating reliability of reinforced columns and for serviceability investigations [12].
## 4 System Design
### Network Layer: Communication Protocol  
In this study of mine safety, BLE with a bandwidth of 2.4 GHz (which is a common feature of mobile phones) has been used. BLE takes less time compared to Bluetooth classic in discovering in-range devices. In addition, BLE consumes less energy for tracking when compared to Bluetooth classic and WiFi [32]. The receive signal strength index of Bluetooth has several advantages, including the fact that it can send data without any additional data transmission processes [33]. The main element of SN and RNs is Arduino, which is equipped with an ATmega32u4 BLE (shown in Figure 4b) (L-com, North Andover, MA, USA) chip connected in serial. Arduino runs at 8 MHz clock speed and 3.3 V logic for compatibility with 3.3 V. It has 28 KB of FLASH available for program storage, and 2 KB of RAM. The BLE can support 40 connections with a data throughput of 250 kbps. The efficiency was 98% when performing at 15.5 dBi, while the error drop-down transmission was \(\pm\)25. As the miners enter the mine, they are provided with a MN and a Bluetooth connection is maintained between the MN, SN, and RNs for sharing information.
## 5 Simulation Experiments and Analysis
### Path Planning Scenarios
#### 5.2.2 Simulation Scenarios That Contain Only Non-Convex Obstacles  
In order to verify the robustness of the OWS Q-learning algorithm, the experimental scenario is complicated on the basis of the experimental scenario setting in Section 5.2.1. The more complex scenario is equipped with eight irregularly shaped obstacles, and the environment size is still 38 \(\times\) 38. The coordinates of the start and target points of each UGV are shown in Table 2.  
In the complex scenario, the path planning simulation results of the SARSA algorithm, Q-learning algorithm, speed Q-learning algorithm, and the OWS Q-learning algorithm are shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Number** & **SARSA** & **Q-Learning** & **Speedy Q-Learning** & **OWS Q-Learning** \\ \hline \multirow{4}{*}{Shortest path steps} & UGV1 & 0 & 261 & 37 & 35 \\  & UGV2 & 106 & 178 & 34 & 34 \\  & UGV3 & 556 & 212 & 36 & 34 \\  & UGV1 & 0 & 279.98 & 46.36 & 45.60 \\ \multirow{2}{*}{Path length (unit)} & UGV2 & 114.47 & 181.41 & 41.70 & 43.18 \\  & UGV3 & 574.04 & 231.04 & 46.60 & 42.94 \\ \multirow{2}{*}{Calculation time (s)} & — & 10,191 & 8545 & 5007 & 3345 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of simulation results of the four algorithms containing only convex obstacles.  
Figure 8: Path planning that contains only non-convex obstacles. (**a**) SARSA algorithm path planning. (**b**) Q-learning algorithm path planning. (**c**) Speedy Q-learning algorithm path planning. (**d**) OWS Q-learning algorithm path planning.  
The path planning results in the complex scenario show that the SARSA algorithm does not plan the path for UGV1, the Q-learning algorithm does not plan the path for UGV3, and the speedy Q-learning algorithm and the OWS Q-learning algorithm have planned collision-free paths for the three UGVs. Compared with the path planning results in the simple scenario, it shows that the SARSA algorithm and Q-learning algorithm cannot solve the multi-UGV path planning problem well. The specific experimental results are shown in Table 4.  
The results in Table 4 in the complex experimental scenario show that compared with the other three algorithms, the OWS Q-learning algorithm has the shortest path, the shortest path steps, and the shortest calculation time for UGVs. In general, in the complex scenario, the OWS Q-learning algorithm still shows the efficiency of solving the multi-UGV path planning problem.
# Optical and SAR Image Registration Based on the Phase Congruency Framework
## 2 Proposed Method
### The Proposed Algorithm
#### 2.2.2 Feature Description
In Figure 6a,b, the yellow circles represent the local regions used for the descriptor construction, and the size is set to \(153\times 153\) pixels. Note that the dimension of the proposed descriptor is 48, which is smaller than the descriptor of OS-SIFT. Therefore, only the first 48-dimensional vectors are displayed in Figure 6c,d. It can be seen that the two curves of the optical and SAR images in Figure 6d have a higher fitting degree. Meanwhile, the dimension of the proposed descriptor is smaller than that of the OS-SIFT. It is more conducive to the subsequent calculation efficiency.