esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison Experiments with Jamming Targets  
Figure 5 shows that the noiseless BTR diagram's bearing space starts from \(-180^{\circ}\) and ends at \(+180^{\circ}\), and the resolution step is \(0.5^{\circ}\). Brighter trajectories indicate more potent energy, and it is assumed that five acoustic targets were collected. The first and fourth targets have lower normalized power levels than others. In order to emphasise the performance of the proposed algorithm, the two weak targets are chosen as the interested targets with ISR = \(-15\) db. Then we conduct three implementations: traditional maximum energy tracking (ME), linear template matching (LTM) as reported in [21], and the proposed technique.  
The tracking results are depicted in Figure 6. For target motion analysis, 90 snapshots (equal to a 90 s data set) are employed. To begin, we do not apply Gaussian white noise to the simulated BTR. When using ME, as shown in Figure 6a,d, tracking bias and tracking failure are induced when the interested and interfering target trajectories intersect, and the proposed method or LTM can suppress this interference to a certain extent. However, Figure 6b,e show that the trajectory is broken, whereas the tracking results using the proposed method are smooth and continuous near the jamming part, as shown in Figure 6c,f. As a result, it can be inferred that the approach described in this study is capable of jamming-target suppression when compared to previous methods.  
Figure 4: Gabor template library.  
Figure 5: Original Bearing-Time Record.  
We extract a single snapshot at the jamming point on the BTR to display and explain the processing impact of the method more clearly, and the bearing spectrum before and after processing is presented in Figure 7. The noisy target is clearly muted, while the interesting one is amplified to a greater energy level. As a result, when the search window searches for the desired target on the BTR, the tracking success rate is considerably boosted.
## 1 Introduction
In [15], this problem is addressed by discussing section control technology for plowing operations while a first commercial application has been introduced by KUHN [16]. In general, it seems that there is not yet a way to enable a detailed quantification and differentiated assessment of the overlapping. The increasing utilization of controller area network (CAN) data on agricultural machinery and the ISO 11783 (commonly designated as ISOBUS) compliant machine communication networks, as well as affordable positioning systems using global navigation satellite systems (GNSS), are promising data sources for information-driven crop production. In recent years, researchers have been using CAN-Bus data for various purposes and it is becoming clear that these data will be used in the future for optimizing agricultural processes. Infield tractor load states are defined in [10] by determining tractor's engine performance from CAN-Bus data. The results indicate that information acquired from a tractor's CAN-Bus is reliable enough for an assessment of different machine-related parameters. CAN-Bus data are acquired from axle housing loads and the driver's operation signals for three different applications, namely plowing, subsoiling and implement transportation, in [17]. Georeferenced CAN-Bus data for different analysis purposes have also been used [3; 6; 7; 8; 9]. According to [PERSON] et al. [18], the functionality of ISOBUS compliant agricultural machines is increasing. Thus, the potential to gain sophisticated information from these data should also increase. However, there is still a need for algorithms and methods to handle the increasing amount of data that are collected [6]. The main aim of this study was the development of algorithms that allow detailed area-related analysis of a plowing operation, which involves the in-work status of the plow as well as the specific step-by-step way the area is covered, by using low-cost and embedded sensor data from ISOBUS. From this information, the cultivated area and the field boundary were determined. Furthermore, a differentiated analysis of the overlapping was performed by developing different indicators to quantify the lateral overlapping between single cultivated passes and the overlapping between the headlands and the main part of a field. To fulfill these aims, ISOBUS messages, as well as D-GNSSposition data from a plowing operation, were post-processed. This encompassed the determination of the in-work sequences, a distinction of the different field parts, the modeling of single cultivated tracks, and the calculation of different area-related parameters based on these tracks. The main contribution of this paper is providing methods for an automated derivation of accurate and sophisticated, area-related information from continuous data records.
## 3 Application to Cultural Heritage Science
### Coinage Debasement
The results from the muon analysis are in remarkable agreement with that of XRF and no surface enrichment was found on these coins (Table 2). Results show that Roman gold coins were not surface-enriched, different to the silver coins. With this work, muon analysis provided a new set of information for the study of Roman coinage, showing that for gold coins, the surface is representative of the bulk.
## 4 Results
### Time Series Analysis
This trend is illustrated in Figure 5.  
The reason for such an improved precision in FFSAR with respect to UFSAR, despite the fact that FFSAR WL\({}_{i}\) estimates are obtained with a simple Gaussian retracker, is attributed essentially to the improved number of independent measurements provided by the FFSAR, thus to its increased along-track resolution. Though over ideal specular and quasi-specular surfaces only the nadir echoes should contribute to the measurement, for the reservoirs being considered the fact of sampling the backscattered energy with high resolution allows one to inherently remove small off-nadir scatterers that otherwise are mixed with the waveforms considered acceptable in the UFSAR algorithm. This is observed in the reduced FFSAR bias with respect to the UFSAR one. In addition, the increased number of measurement in the FFSAR case also allows one to operate with larger collections of independent measurements, which is appreciated from the statistical point of view. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Target** & **Method** & **Retracker** & **RMSE (cm)** & **Bias (cm)** & **MAD (cm)** & **SDE (cm)** & **Passes** \\ \hline \multirow{3}{*}{Yesa} & FFSAR & Gaussian & 6.8 & 4.5 & 3.9 & 5.1 & 55/70 \\  & UFSAR & OCOG & 49.8 & 35.4 & 29.4 & 35.0 & 55/70 \\  & UFSAR & Ocean & 47.4 & 35.9 & 32.2 & 30.9 & 55/70 \\ \hline \multirow{3}{*}{Itoiz} & FFSAR & Gaussian & 16.3 & 6.3 & 3.8 & 15.0 & 47/70 \\  & UFSAR & OCOG & 32.4 & 27.6 & 25.6 & 17.0 & 35/70 \\  & UFSAR & Ocean & 40.9 & 37.1 & 34.8 & 17.2 & 35/70 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Mequinensa-North \\ \end{tabular} } & FFSAR & Gaussian & 30.5 & -5.4 & 3.7 & 30.0 & 73/73 \\  & UFSAR & OCOG & 29.3 & 23.6 & 26.9 & 17.2 & 73/73 \\  & UFSAR & Ocean & 33.6 & 31.6 & 33.4 & 11.4 & 73/73 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Mequinensa-South \\ \end{tabular} } & FFSAR & Gaussian & 5.3 & 1.5 & 3.3 & 5.0 & 73/73 \\  & UFSAR & OCOG & 28.8 & 26.9 & 28.7 & 10.3 & 73/73 \\  & UFSAR & Ocean & 38.3 & 35.5 & 38.1 & 14.3 & 73/73 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Mequinensa-Cape \\ \end{tabular} } & FFSAR & Gaussian & 7.0 & 5.6 & 6.2 & 4.2 & 43/70 \\  & UFSAR & OCOG & 36.7 & 34.8 & 34.1 & 11.5 & 43/70 \\ \cline{1-1}  & UFSAR & Ocean & 46.6 & 44.9 & 42.4 & 12.9 & 43/70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical analysis of the water level time series obtained in the areas under study from 1 September 2021 to 1 September 2023. Data were validated with in situ measurements. The included methods are (a) an FFSAR processor with the Gaussian retracker, (b) an operational UFSAR processor with the OCOG retracker, and (c) an operational UFSAR processor with the ocean retracker. The statistical indicators include the root mean square error (RMSE), the bias, the median absolute deviation (MAD), and the standard deviation of the error (SDE). The last column refers to the number successful measurements with respect to the number of overflights.
## 3 Methodology  
### Displacement Retrieval by InSAR  
Our InSAR processing sequence follows the procedure by [PERSON] et al. [14] and includes the co-registration of the single-look complex Sentinel-1 and COSMO-Skymed images, the computation of interferograms in series with a multi-looking factor of 5 pixels in slant-range and of 1 pixel in azimuth for Sentinel-1 and of 5 \(\times\) 5 pixels for COSMO-Skymed, the removal of the topographic-related phase with use of an external DEM (TanDEM-X 30 m), adaptive phase filtering [49], phase unwrapping using a minimum cost-flow algorithm [50] as implemented in the Gamma Software [51], low-pass phase filter, computation of summer cumulative displacement maps and time series of movement via short-baseline InSAR [52], and terrain-corrected geocoding. For the precise co-registration of Sentinel-1 SAR data a refinement based on the spectral diversity within bursts and swaths was included [53]. Occasional ionospheric disturbances were mitigated using a low-pass filter but without applying a procedure based on split-beam [54] or split-spectrum [55] interferometry. Phase unwrapping errors could be not completely avoided and were observed in particular at the scale of a few tens of meters. They are also depending on the acquisition time interval and season (i.e., better for very short time intervals and at the end of the season when displacements are smaller). The InSAR reference point was selected on an airstrip at 70.317\({}^{\circ}\)N 68.324\({}^{\circ}\)E.  
We use multiple interferograms acquired during the summer season, which show very good coherence under snow-free conditions. Yearly interferograms from the end of the summer do not show a sufficient level of coherence (>-0.2) in order to link data from the two years, what does not allow the reconstruction of longer time-series of deformation showing summer thaw subsidence followed by frost heave. We therefore limit our analyses to separate records acquired during the summer season under snow-free conditions for each year. The pixel spacing of the resulting datasets is 26 m \(\times\) 30 m for Sentinel-1. COSMO-Skymed has been sampled to the same spacing for this study.  
applied a conversion to all measurements from Sentinel-1 as well as COSMO-Skymed. We refer to the respective results as'scaled' in the following.
## 4 Discussion
### Amendments and Soil Properties
#### 4.1.2 Biogeochemistry in the Absence of Plants
Variations among biochars can also affect the N mineralization-immobilization process differently [83]. For instance, manure biochar is more likely than plant-based biochar to stimulate N mineralization due to a lower C:N ratio [84]. Additionally, in a study of biochar made from four feedstocks (wood pellets, soft wood bark, switchgrass (_Panicum vigatum_ L) straw, and anaerobically digested fiber) and amended at three rates, [PERSON] et al. (2011) showed that soil type has a direct influence on N mineralization, in addition to application rate and feedstock [38]. As mentioned above, native soils appeared to have INN concentrations comparable to COMP and INF at the beginning of the experiment, following the 22-week pre-incubation. This points to the potential of native OM N priming in UNAM during pre-incubation. Nitrogen priming is a change in the soil mineralization process set off by a specific \"trigger,\" like the addition of N, mechanical disruption, or rhizodeposition, that increases or decreases microbial mineralization [85]. Thus, disruption of native soil and the addition of water that occurred during pre-incubation could have resulted in priming, followed by declines in INN and DON over time, as seen in UNAM.
# Enhancing Wind Energy Harvesting Using Passive Turbulence Control Devices
## 3 Performance of the GEH-PTC  
In this section, we experimentally compare two different harvesters (their cylinders have the same mass). Figure 4a,b are respectively the experimental output voltage and vibration frequency along with the wind speed under the open circuit condition. From Figure 4a, the GEH-PTC and the vortex-induced vibration energy harvester (VIVEH) with an ordinary smooth cylinder respectively represent two completely different vibration forms, which are galloping vibration and vortex-induced vibration. Galloping vibration shows that the output voltage increases along with increases in wind speed, while vortex-induced vibration shows that the output voltage firstly increases and then decreases with an increase in wind speed. Figure 4b further analyzes the difference between them based on the vibration frequency.  
The natural frequency of both two harvesters is 9.2 Hz. When the wind speed is small, the vibration frequency of the GEH-PTC and the VIVEH differs greatly from the natural frequency, therefore, it is difficult to generate resonant vibration. With the increase in wind speed, the vibration frequency of the two energy harvesters is close to the natural frequency. Their effective speed ranges are \(U\) > 1.9 m/s and 1.67-1.83 m/s, respectively. Therefore, the GEH-PTC has a much wider effective speed range than the VIVEH. In other words, the GEH-PTC can efficiently harvest energy once the wind speed is larger than the critical wind speed. More importantly, the design of the GEH-PTC is very convenient for changing to different PTCs, which may change its dynamic characteristics and benefit wind energy harvesting. Figure 5 compares the output power of two harvesters with the optimal load \(R_{opt}\) = 1.1 M\(\Omega\). It can be found that the maximum power of the GEH-PTC and the VIVEH is 0.058 mW and 0.03 mW when \(U_{1}\) = 2.372 m/s and \(U_{2}\) = 1.687 m/s, respectively. The output power of the GEH-PTC is improved by 93% compared with the VIVEH.  
Figure 4: Experimental comparison of the GEH-PTC and the vortex-induced vibration energy harvester (VIVEH): (**a**) output voltage; (**b**) vibration frequency.
## 3 Results and Discussion
### Accuracy and Generalization Performance Analysis of Data Recovery Model  
Two sets of experimental data were selected for the experiment: 8:00 on 10 April in spring and 18:00 on 10 December in winter. Then, we analyzed the forecast accuracy for the future period at two-time points. Since these two sets of test sets did not appear in the process of model training and tuning, they could reflect the generalization performance of the model and could characterize the periodic change process of temperature. At the same time, the BP and SVM predictors were compared with the LSTM predictors to analyze the accuracy of the LSTM predictors.  
The experimental results are shown in Figures 7-10. The experimental time in Figure 7 was 8:00 in the morning. After this time, the load gradually increased, and the temperature of the transformer also increased in increments. The LSTM model always followed the temperature of the transformer. From Figure 8, it could be found that the relative error of the LSTM model was the smallest compared to the BP and SVM models and met the accuracy requirements of an error of less than 0.5 \({}^{\circ}\)C. The experimental time in Figure 9 was 18:00. After this time, the load gradually decreased, and the temperature of the transformer decreased accordingly. The LSTM could still follow the temperature change well. It can be seen in Figure 10 that the output value of BP had a certain deviation from the real temperature, and the error of the SVM model was too large, which seriously lost the recovery ability. LSTM met the accuracy requirement with an error of less than 0.5 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Impact Fault** & **Drift Fault** & **Power Failure** & **Constant Output** & **Deviation Fault** \\ \hline Diagnosis & & & & & \\ rate/\% & 100 & 100 & 100 & 98 & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The diagnostic accuracy of the different faults.  
Figure 8: A comparison of the relative errors of the spring data forecast values.  
Figure 7: A comparison of the forecast values for the spring data.  
Figure 9: A comparison of the forecast values for the winter data.  
Table 2 shows the mean absolute error (MAE), root mean square error (RMSE), and maximum relative error (MRE) of the prediction results of each model. It can be seen that the prediction effect of the LSTM model was significantly better than that of the other models.
## 2 Theoretical Mechanism Design and Model
And the coordinates of point \(A\), \(B\), \(C\), and \(D\) can be solved by the tangent equation of the two circles. To illustrate this, point \(A\) can be taken as an example, which can be express as Equation (2). \[\left(x_{l}-r_{1}\sqrt{1+\frac{1}{\left(k_{AB}\right)^{2}}},y_{l}+\frac{r_{1}} {k_{AB}}\sqrt{1+\frac{1}{\left(k_{AB}\right)^{2}}}\right) \tag{2}\]  
And \(k_{AB}\) means that the gradient of line \(AB\), where \(b\) means the intercept of line \(AB\), can be solved by Equation (3). \[\frac{\left|k_{AB}\cdot x_{1}-y_{1}+b\right|}{\sqrt{\left(k_{AB}\right)^{2}+1} }=r_{1},\frac{\left|k_{AB}\cdot x_{3}-y_{3}+b\right|}{\sqrt{\left(k_{AB} \right)^{2}+1}}=r_{3} \tag{3}\]  
Equation (3) has four sets of a solution, and they should be selected correctly. Furthermore, the coordinates of point \(ABCD\) can be solved, and the length of \(AB\), \(CD\), \(BC\), and \(AD\) can be calculated. Moreover, the length of arc \(AE\) can be expressed by Equation (4). \[AE=\frac{1}{2}(L-AB-CD-BC-AD) \tag{4}\]  
The coordinates of point \(E\) in the polar coordinates were established with the center of the left roller as the origin, which can be expressed as Equation (5). \[\left(r_{1},arctan\frac{y_{A}-y_{l}}{x_{A}-x_{l}}+\frac{AE}{r_{1}}\right) \tag{5}\]  
With the rotation of the double-layer bearings on the center gear, the polar coordinates of point \(E\) kept changing, and the flapping angle and speed of the flapping wing fixed on the roller were also found to be constantly changing. The calculated trajectory of point \(E\) was in an ideal state when the string was in a tight state. During the rotation of the double-layer bearings on the center gear, the center of the bearing and direction of rotation constantly changed. This caused the string to become either too loose or too tight, thus causing stuck or slack phenomena within the mechanism.
## 4 Semantic Interoperability Provision in Heterogeneous Healthcare Service Environments
### Semantic Mediation Model
The base ontology model has an interface to the standard ontology models of the healthcare domain and other external ontologies to express and provide interoperable features with existing systems. Moreover, the functionalities in the semantic mediation model have been designed through a microservices design pattern to support scalable interoperability provision in dynamically changing environments. A few of these services that have been designed are semantic annotation microservice and alignment microservice, semantic format registration, and query processing microservices. Figure 4: Semantic mediation model in heterogeneous healthcare service environments.
## 2 Literature Review  
### Urban Computational Modeling Context  
Urban computational modeling (UCM) approaches have been growing diversely since the 1960s, evolving from analytical to systematic modeling approaches [4]. In the last decade, spatio-temporal urban data streams (such as street view imagery, OpenStreetMap, and satellite imagery) are exponentially increasing, spawning many data-driven applications [5]. In the early modeling phase, urban models essentially simulate city functions which translate theory into a form that is testable and computable. Those modeling methods require the idealization of reality, so they only approximate reality to a certain extent. To address more aspects of urban problems, a multi-model idealization is often used to integrate heterogeneous simulation into a multidimensional computational model [6]. However, the increasing number of properties and their relationships make the model expand in an exponential manner [1]. In addition, it is hard to represent all the related conditions and implicit knowledge (preference, perception, etc.) by explicit properties. Consequently, the conventional approaches are not adequate to address the urban complexity and dimensionality.  
With the growing diversity of modeling approaches with machine learning and accessibility of digital data, it is apparent that we should use data streams to embrace a shift in the modeling paradigm in urban studies. Data streams allow for analyzing and modeling the complicated interactions between spatial, temporal, emotional, and social aspects in a dynamic urban system [7]. Therefore, it offers opportunities to integrate explicit urban indicators with implicit urban perceptions, to advance the knowledge and understanding of urban dynamics. With this in mind, we aim to construct models to deal with complex urban problems by proposing a flexible framework that helps integrate dimensions of different urban aspects.  
Achieving the aforementioned goals requires using proper techniques from the multidisciplinary field of UCM. The subset of UCM techniques discussed here relates to rule-based and data-driven computation approaches. The following sections discuss some existing techniques useful for urban form and urban perception studies from the perspective of representation and data streams.
## 4 Conclusions  
In this study, we presented a low-cost and efficient fire detection system that utilized UV radiation detection and Fourier spectrum analysis. By utilizing a parabolic mirror and digital sensors, we were able to detect the position of the fire accurately. Furthermore, the proposed classification models, based on the Fourier transform algorithm, successfully extracted features of a fire and a UV source, which enabled us to minimize the occurrence of false alarms. The experimental results showed that the proposed system achieved high accuracies of 95.15% and 91.25% using the SVM classifier, and approximately 95.45% and 93.65% using the Naive Bayes classifier for the flame and UVB lamp, respectively.  
Our proposed system offers several benefits compared to existing fire detection systems. It is cost-effective, making it a viable option for small and medium-sized buildings, homes, and vehicles. Moreover, the system's simple design and high accuracy make it attractive for use in areas where expensive systems are not feasible. Our system has been shown to be effective in detecting flames up to 50 m away, making it suitable for a wide range of applications.  
In conclusion, the proposed system has demonstrated the effectiveness of using UV radiation detection and Fourier spectrum analysis as an efficient method for fire detection. Future studies could explore ways to improve the system's performance in challenging environments and investigate its potential for integration with other fire detection systems. This study provides valuable insights into the development of low-cost and efficient fire detection systems that can contribute to the safety of individuals and properties.  
Conceptualization, C.T.T.; Methodology, V.Q.V.; Software, T.H.N., V.Q.V. and V.H.D.; Validation, V.Q.V. and D.T.N.; Formal analysis, C.T.T. and V.H.D.; Investigation, C.T.T., T.H.N. and D.T.N.; Resources, V.Q.V.; Data curation, C.T.T., T.H.N., V.Q.V. and V.H.D.; Writing--original draft, C.T.T.; Writing--review & editing, D.T.N.; Supervision, D.T.N.; Project administration, C.T.T.; Funding acquisition, C.T.T. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Vietnam Ministry of Education and Training, Project number B2020-BKA-16.  
Not applicable.  
The authors declare no conflict of interest.