esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Measurement Results and Discussion  
Conventional the diagnostic tool to investigate the THz beam's spatial profile is typical beam imaging, where radiation intensity is scanned in the plane perpendicular to the direction of propagation. To measure the beam profile, focusing optics is usually used to concentrate THz radiation aiming to increase the detected signal. However, any additional focusing element in beam path can distort the beam profile and in many cases cause unwanted interferences. In the THz range, it is of particular importance to use off-axis parabolic mirrors, but then small misalignment can strongly affect the beam profile. As an alternative, one can use plastic lenses; however, they display feature to change focus distance depending on the frequency of incident radiation and they are not so attractive in broadband THz TDS systems, where frequency range, as a rule, extends by two orders of magnitude. To measure the beam profile correctly, one needs to scan unfocused beam that requires high responsivity THz sensor to have sufficient signal-to-noise ratio (SNR). The use of a coherent detector in such a case is limited because the Si lens coupled detector yields limited imaging resolution. Usually, one can employ conventional variable size aperture scanning and Hartmann test techniques to measure far field beam profile [16]. One can note that such approaches are easy to apply and do not require reconfiguration of set-up, nonetheless, reaching high spatial resolution, especially for high THz frequencies, can be rather  
Figure 1: The photo of the designed terahertz titanium-based microbolometer with antenna and filters (**a**). The enlarged view of the bridge active part of the microbolometer: the antenna area is approximately 500 \(\upmu\)m \(\times\) 500 \(\upmu\)m, the geometry of visible titanium bridge amounts to 12 \(\upmu\)m length and 2 \(\upmu\)m width (**b**). The cross-section of microbolometric detector. Note that the selectivity was enhanced by adjustment of the dipole antenna geometry and the resonant-cavity design: back side reflection mirror was positioned at the quarter wavelength distance (**c**). Adapted from Refs. [23; 24].  
Figure 2: Experimental set-up for imaging of terahertz beam profiles using different THz emitters. Source 1—electronic multiplier chain emitting radiation of 0.15 THz, 0.3 THz and 0.6 THz frequencies in continuous wave mode. Source 2—low temperature grown galium arsenide (LTG GaAs) photo-mixer coupled with silicon lens in frequency domain spectrometer delivering radiation from 0.1–2 THz. Source 3—photoconductive antenna on LTG GaAs from time domain spectrometer generating broadband radiation up to 2.5 THz. Source 4—optically pumped continuous wave molecular THz laser emitting the discrete spectrum at 0.76 THz, 1.63 THz and 2.52 THz. Center panel displays photo of Ti-based microbolometer.  
complicated because it is determined by the aperture size, and its reduction, consequently, induces the decrease in SNR.
## 4 Results and Discussion
### Potential Limitations and Future Study
In addition, malfunctions and repair works in some of the rain gauges or other unknown circumstances may have resulted in underestimations in the rainfall records. It should be noted that SEM-LEWS is inherently most suited for capturing rapid shallow landslide and debris flow typologies. This is because \(CR_{Critical}\), a physically based rainfall threshold in SEM-LEWS, was modelled considering the kinematics of shallow landslides primarily governed by short and intense rainfalls in Korean geological conditions, where poorly or well graded silty sand represents weathered residual soils in hillslope areas. In other words, the concept of \(CR_{Critical}\) may not be applicable to regions with fine-grained soils or rainfall conditions that exhibit progressive increases. In fact, landslides insuch geological/climatic settings exhibit completely different kinematics from shallow landslides and are primarily governed by long-term drainage effects of prolonged antecedent rainfalls. The quality of rainfall recordings and predictions are paramount factors that determine the quality of warning information provided by SEM-LEWS. This is because rapid shallow landslides and debris flows are initiated by intense and spatially localized rainfalls over short time scales, as mentioned in previous sections. Rainfall patterns can dramatically differ within 2- 3 km distances under such climate characteristics. Future solutions could include increasing the number of rainfall observatories to create denser arrangements or further advancing rainfall forecasting models.
## 5 Conclusions  
This paper develops an AM machine selection approach based on ontology-supported multi-attribute three-way decisions. This approach consists of two parts. The first part is a DL ontology for AM machine selection, which automatically generates the values of attributes according to the AM machines to be selected and the attributes to be considered. The second part is a decision-making method based on multi-attribute three-way decisions, which prioritizes the alternative AM machines based on the generated attribute values. The paper also illustrates the application of the approach via four AM machine selection examples and demonstrates the effectiveness and advantages of the approach via two experiments. The demonstration results show that that the developed approach is as effective as the existing approaches and the smaller the \(\sigma\) value (i.e., the more insufficient the information obtained or the cost for undesirable decision results is high), the larger the dominant value and dominant range (\(x_{ij}\) interval corresponding to the dominant value) of decision \(a_{B}\), so it is more flexible and reasonable to make three decisions (i.e., \(POS(C)\), \(NEG(C)\), \(BND(C)\)) than to make two decisions (i.e., \(POS(C)\), \(NEG(C)\)).  
A main limitation of the proposed method is that inappropriate risk coefficients \(\sigma_{j}\) may cause no or many alternatives to be classified into the acceptance region, thus making further decisions difficult. In the proposed method, the risk coefficients \(\sigma_{j}\) were manually assigned according to experience. It would be of importance to develop an automatic approach to determine proper risk coefficients \(\sigma_{j}\). Another limitation is that distortions of the input data may have negative influence the final decision-making results. It would be of necessity to study the reduction of such influence. Last, future work will also focus on applications of the developed approach to other MADM problems in the field of AM, such as material selection, build direction determination, and process parameter optimization. More criteria relevant to part performance would be considered in these applications.  
Conceptualization, M.H., B.F. and Y.Q.; methodology, M.H., B.F. and Y.Q.; software, Y.P.; validation, B.F.; formal analysis, L.C.; investigation, B.F.; resources, B.F.; data curation, B.F.; writing--original draft preparation, M.H., B.F. and Y.Q.; writing--review and editing, M.H., B.F. and Y.Q.; visualization, L.C.; supervision, project administration, funding acquisition, M.H. and Y.Q. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 52165064 and No. 52105511).  
**Institutional Review Board Statement:** Not applicable.  
Figure 9: The dominant value curves of decision \(a_{B}\) under different \(\sigma\).
## 3 Interference Cancellation Method  
Figure 2 illustrates the interference cancellation method used in this paper. To cancel out the interference, the CP is first removed, and \(N\)-point FFT is performed. This process corresponds to (4). If the interference frequency is known, interference cancellation can be simply performed by nulling the corresponding frequency. For example, if the frequency is \(f_{l}=K^{\prime}/NT(Hz)\), the interference cancellation can be simply performed by nulling the corresponding FFT grid. In other words, the corresponding spectrum is set to \(0^{\prime}\) or \(Y(k^{\prime})=0\). However, the interference frequency is unlikely to exactly match one of the FFT grid points \(0/NT,\ 1/NT,\cdots,\ (N-1)/NT\) If the NBI frequency does not align with one of these grid points, the best approach is to select the grid closest to the interference frequency and perform nulling [22]. In this case, however, the NBI will not be completely removed.  
Let us consider an example for this issue. Assume \(N=256\). Figure 3 shows \(\left|Y(k)\right|^{2}\) before and after interference cancellation when \(f_{l}=50/256T\). The SIR is 10 dB. Interference cancellation is simply performed by nulling, setting \(Y(50)=0\). When the interference frequency perfectly aligns with the grid frequency, it is confirmed that the interference is completely removed.  
In contrast, Figure 4 shows \(\left|Y(k)\right|^{2}\) before and after interference cancellation when \(f_{I}=50.4/256T\). In this case, nulling is also performed by setting \(Y(50)=0\). Observing the spectrum after interference cancellation, it is seen that the NBI is not completely removed, with some residual interference still present. For complete cancellation, it is possible to null multiple grid frequencies that exceed a certain threshold, rather than just one grid. However, the more grids are nulled, the more the desired signal is also damaged, resulting in a decrease in reception performance.
# Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
## 2 Materials and Methods
### Problem Formulation
#### 2.1.4 Assessment of Model Fit and Prediction  
To compare the fit of the above-mentioned models, the coefficient of determination was determined \(R^{2}\)  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y})^{2}}. \tag{12}\]  
where \(y_{i}\) denotes the observed value of the dependent variable; \(\hat{y}_{i}\) denotes the predicted value based on the model, \(i=1,2,\dots,n\); and \(\hat{y}\) denotes the mean value of the dependent variable. Additionally, the standard deviations for the actual values of the dependent variable were estimated, as follows:  
\[\sigma_{y}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y})^{2}}}. \tag{13}\]  
and the standard deviation of predicted values of the dependent variable, as follows:  
\[\sigma_{y}^{pred}=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}\big{(}\hat{y}_{i}-\hat{y}_{ pred}\big{)}^{2}}}. \tag{14}\]  
where \(\hat{y}_{pred}\) denotes the average for the predicted values of the dependent variable. Additionally, the correlation coefficient between the actual and predicted values of this variable was determined, as follows:  
\[r=\frac{\sum_{i=1}^{n}(y_{i}-\bar{y})\Big{(}\hat{y}_{i}-\bar{y}_{pred}\Big{)}} {\sqrt{\sum_{i=1}^{n}(y_{i}-\bar{y})^{2}\sum_{i=1}^{n}\Big{(}\hat{y}_{i}-\bar{y }_{pred}\Big{)}^{2}}}. \tag{15}\]For a visual comparison of the model fit, a Taylor diagram is presented, in a polar system, showing the correlation coefficient between the actual and predicted values of the dependent variable and the standard deviation of the predicted values of the dependent variable. According to the Taylor diagram, the model fits the data better if \(r\) is closer to 1 and the difference between \(\sigma_{y}\) and \(\sigma_{y}^{pred}\) is smaller.  
The following indicators were used to analyse forecast discrepancies:  
* Mean Squared Error \[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}.\] (16)
* Root Mean Squared Error \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}.\] (17)
* Mean Absolute Error \[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|.\] (18)
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 3 System Overview  
The proposed method is an upgrade that is based on our previous study. It is designed to share the same structure of management, containing a real-time predictor and a pattern matching-based anomaly detector, and be engined by a moving window in each step [5].To realize the real-time automatic model optimization, balance the prediction accuracy and calculation speed requirements, the moving window is designed to select the real-time load data (\(t\)) and the historical load data from the previous 24 h of data _data(24 h)_ as the inputs of the predictor and to update the prediction model [5]. Figure 1 illustrates the framework of this method. After obtaining the prediction result, the proposed pattern matching-based anomaly detector classifies the _data(\(t\))_ and the prediction result. The final output is composed of the prediction result for the next step and the classification result of _data(\(t\))_.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Integration Circuit Implementation
#### 6.2.2 Implications  
The experimental work extends beyond basic MATLAB simulation of PWM signals to demonstrate a novel derivative-based PPM generation technique. While VCO and PLL implementations are established technologies, their application in derivative-based carrierless PPM represents a specific contribution to position modulation techniques. A key innovation is the practical use of a derivative-based framework to generate position-modulated pulses with reduced circuit complexity.  
The comparative analysis between conventional and proposed methods provides quantitative evidence of system performance differences. The experimental validation using multiple demodulation approaches demonstrates the robustness and versatility of the proposed technique across different implementation scenarios.
###### Abstract  
In the production process of cross-cutting boards, real-time measurement of dimensions online has been a long-standing technical problem in the production field. Currently, the detection of board dimensions in the production field relies on manual observation based on workers' operational experience or stopping the machine for measurement. This paper proposes a machine vision-based real-time online measurement system for dimensional measurements of cross-cutting units. A certain angle measurement model is established by using a face-array industrial camera, and a more accurate edge contour extraction is realized by deep learning. A novel edge intersection extraction algorithm based on line fitting and least squares method was proposed to accurately measure the length, width, diagonal lines of cross-cutting boards using four intersection coordinates. The measurement of 100 cross-cutting boards in the industrial production site shows that the proposed online measurement system for cross-cut board dimensions in this article has high accuracy, with a length perception error of \(\pm\)50 mm, width of \(\pm\)2 mm, and diagonal difference of \(\pm\)5 mm, meeting the production requirements in industrial settings. The on-site shutdown measurement work was reduced, thereby doubling the production efficiency and saving two staff members.  
cross-cutting board; machine vision; series-connected filter; line fitting; least squares method; real-time online detection Article
## 6 Conclusions  
In this study, the proposed TFR-based algorithm is proven to be effective in identifying time-varying cable tension, addressing the issue of insufficient efficiency and low resolution in the vibration-based method. Based on the results of the workshop tests and the dynamic experiment on the Sutong Bridge, the conclusions are remarked as follows:  
1. The proposed algorithm can identify the time-varying instantaneously from reassignment spectrograms of acceleration responses. The measured time interval for the identification of frequency is within 12.8 s. Compared with the STFT results, the time-frequency resolution of the proposed algorithm is clear and has a satisfactory accuracy.
2. The experimental results show that the identification accuracy had a controllable error from the actual loads. The average error is between 0.16% and 0.59%, while the maximum error is 5.70%. The error usually appears at the boundary of the signals or calculated results by the low-order frequencies.
3. The identified tension has a synchronous trend with the adjacent structural responses, such as the GPS displacement of the tower and the stress of the girder. The identified results are consistent with the applied moving loads.
4. The proposed method can assess the deterioration or abnormal conditions of cables with two important indices, the 40% ultimate bearing tension and the initial cable tension measured in the construction stage.  
Figure 18: Assessment of service performance of the cable. (**a**) Results of the field experiment and (**b**) identified cable tension from 2017 to 2019 and an abnormal condition during strong winds.  
For future research, the algorithm has potential application in the real-time structural health monitoring of the service performance of cables in extreme events, such as earthquakes, typhoons and accident predictions.  
Conceptualization, M.C. and X.Z.; methodology, S.H. and S.Z.; software, S.H.; validation, S.Z. and D.S.; writing--original draft preparation, S.H. and J.P.; writing--review and editing, M.C. and D.S.; project administration, X.Z.; funding acquisition, M.C. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Anhui International Joint Research Center of Data Diagnosis and Smart Maintenance on Bridge Structures (No. 2021 AHHGHYB04), the Changzhou Policy Guidance Plan-International Science and Technology Cooperation Project (No. CZ20200003), the Nanjing Science and Technology Project (No. 202002014), and the Nantong Science and Technology Project (No. MS22021016).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Microsatellite Diversity  
Informativeness of 12 SSR markers used in molecular analyses of eight wild hop populations sampled in Croatia is presented in Table 1. In our study, 12 microsatellites yielded a total of 152 alleles, ranging from 5 (GA4-K16-8, GA6-P20-14, and GA7-A6-14) to 25 (GA5-G3-10), with an average of 12.67 alleles per locus. This is higher than the values obtained in the study by [PERSON] et al. [9], in which an average of 10.8 alleles per locus was obtained in 67 accessions using 29 SSR markers, but lower than the values obtained by [PERSON] et al. [39], 15.7 in 124 accessions, or [PERSON] et al. [35], (14.7 in 133 accessions), but similar to the average number of alleles (12.5) obtained in the study of 12 wild hop populations from Italy [37]. In the analysis of individual loci, the polymorphism information content (_PIC_) ranged from 0.547 to 0.881 with an average value of 0.731, which is higher than in the studies mentioned above: 0.635 [39], 0.607 [9], 0.64 [38], and 0.697 [41]. However, comparison with the results of the other studies should be taken with caution, considering that different sets of microsatellite loci were used. In our study, values higher than 0.70 were observed for seven loci (Table 1), indicating their high informativeness in biodiversity studies. The probability of identity (_PI_) for single locus ranged from 0.021 (M03) to 0.216 (M06) with a combined value of 1.91 \(\times\) 10\({}^{-14}\), indicating their usefulness in discriminating among wild hop individuals.
## 6 Summary of the Method
Perhaps the most elegant way is a logical approach based on the outranking credibility index of a solution with respect to the limiting boundary (see Equations (17) and (19))._  
Let us denote as \(z_{\text{G1}},\ldots,z_{\text{G1}}\) the points in the decision variable space, which are all pre-image of the best consensus (\(N_{\text{sat}}\), \(N_{\text{dis}}\))\". Let \(C_{\text{sg}}\) denote the agreeing coalition (the set of group members who are satisfied with the image of \(z_{\text{Gk}}\)). In the paragraph below, we use \"\(i\)\" (respectively \"\(j\)\") for the DMs who are compatible with the outranking (resp. weighted sum functional) model. If we denote as \(Z_{\text{Gk}}\) (resp. \(Z_{\text{Gk}}\)) the image of \(z_{\text{Gk}}\) in the original objective space \(O_{i}\) (resp. \(O_{j}\)), \(\sigma_{i}\) (\(Z_{\text{Gk}i}\), \(B_{i}\), \(\lambda_{i}\)) (resp. \(\sigma_{\text{P}j}(Z_{\text{Gk}j}\), \(B_{j}\))) can be interpreted as the degree of truth of the predicate \"the \(i\)-th DM (resp. the \(j\)-th DM) considers that \(Z_{\text{Gk}i}\) (resp. \(Z_{\text{Gk}j}\)) outranks the limiting boundary \(B_{i}\) (resp. \(B_{j}\))\". Then, the degree of truth of the predicate \"all the DMs belonging to the agreeing coalition consider that \(Z_{\text{Gk}i}\) (resp. \(Z_{\text{Gk}j}\)) outranks the related limiting boundary\" can be calculated as the conjunction of all the values \(\sigma_{i}(Z_{\text{Gk}i}\), \(B_{i}\), \(\lambda_{i}\)) and \(\sigma_{\text{P}j}(Z_{\text{Gk}j}\), \(B_{j})\), where \"\(i\)\" (resp. \"\(j\)\") is the index of the \(i\)-th DM (resp. the \(j\)-th DM) within the agreeing coalition. Such a truth value is calculated as:  
\[\mu_{\text{sat}}(z_{\text{Gk}})=min\{\sigma_{i}(Z_{\text{Gk}i},B_{i},\lambda _{i}),\sigma_{\text{P}j}(Z_{\text{Gk}j},B_{j})\} \tag{20}\] \[\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad i,j \in C_{\text{sg}}\]  
**Remark 8**. : _Although the proposal exhibits remarkable advantages in comparison with previous approaches to group multi-objective optimization, it can be still criticized from the following points:_  
* To set the limiting boundaries could be a hard cognitive task for DMs; it would be more complex in large scale problems. * The bi-objective measure of collective satisfaction/dissatisfaction does not contain information about which DMs are strongly (dis)satisfied. This information can be important to discriminate among non-dominated solutions of Problem 1. Perhaps the multi-criteria ordinal classification method should take into account more classes of satisfaction/dissatisfaction, but this would require much more cognitive effort from the DMs in defining more limiting boundaries. * The role of the moderator is crucial in choosing the final consensus decision among the non-dominated solutions of Problem 1. A model of consensus agreed by the group would be welcome. Such a model should aggregate the information about satisfaction/dissatisfaction, thus helping to make the final choice among non-dominated solutions to Problem 1.