esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Two important operating regions were analyzed for the 1.5 [MW] WTs found near Oravita, Romania, based on a detailed, realistic mathematical model including an induction generator and two power converters for stator and rotor, which is a unique feature. The first operating region OR1 is intended for small wind speed variations in the range of 3\(\sim\)10 [m/s] and aims for MPP control achieved by MAS control through the stator frequency under a corresponding nominal current in the stator ensured by indirect control through an additional resistance actuated by the voltage of the rotor converter. Linear PI controllers were validated by pole placement and by the VRFT tuning method, and good tracking and disturbance rejection capability were shown in spite of the nonlinear time-varying nature of the analyzed process model. We motivate why a double integrator within the controller is not used, and this requires a performance tradeoff in terms of a small but non-zero steady-state tracking error. In the second operating region OR2, the focus changes to ensuring reliable operation by saturating the MAS and the generator power. In consequence, the WT power has to be regulated using the blade pitch angle. A linear double integrator controller, a sliding-mode controller and an MPAC were validated and compared, again showing acceptable tracking and disturbance rejection capacities, with favorable and fast transients. This operating region requiring reliable operation allows for a double integral controller.  
We conclude that the proposed controllers ensure the operation specifications in a wide wind speed range, characteristic of continental wind energy harvesting locations, delivering both maximum power point tracking when possible or reliable operation at higher wind speeds. There are several future work streams related to more accurate model identification of the WT characteristics, inclusion of the converter dynamics into the controlled object model and the development of a global control logic that unifies the control operation dedicated to the two individual regions presented in this work.  
Conceptualization, M.-B.R.; methodology, M.-B.R. and V.-D.M.; software, M.-B.R. and S.C.; validation, M.-B.R., V.-D.M. and S.C.; formal analysis, M.-B.R.; investigation, M.-B.R., V.-D.M. and S.C.; resources, V.-D.M. and S.C.; data curation, V.-D.M. and S.C.; writing--original draft preparation, M.-B.R., V.-D.M. and S.C.; writing--review and editing, M.-B.R., V.-D.M. and S.C.; visualization, M.-B.R., V.-D.M. and S.C.; supervision, M.-B.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement:Data is contained within the article.  
The authors declare no conflicts of interest.
## 4 Taillight Detection and Light Signal Identification Based on Image Processing
### Experiment Results and Analysis  
By using the methods described in this paper, break light detection results and direction light result are shown in Figure 21. The time spent on vehicle detection and taillight detection takes about 200 ms.  
If the taillight is turned on but not detected, it is called a false alarm. If the taillight is off but is identified to be on, it is termed a missing alarm. In some cloudy and rainy weather conditions, the taillights can be detected, but in severe weather conditions, such as fog, heavy rain, and bright light, missed detections will occur. Some test results are shown in Figure 22. The experimental results are shown in Table 8.  
The longer the distance that can be identified, the more the system can give the driver an earlier warning and improve the safety of driving. In the experiment, under the normal driving condition of the urban road, the taillight of the preceding vehicle can be accurately recognized when the distance from the preceding vehicle is approximately within the range of 30 m. When the distance increases, the area of the taillight in the image is too small and consists of only a few pixels. In this case, the front taillight cannot be detected, as shown in Figure 23.  
Figure 21: Taillight detection results. (**a**) Brake light detection results; (**b**) Direction light detection results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{-} & **Experiment** & **Accuracy** & **False** & **Missing** \\  & **Frames** & **Rate/(\%)** & **Alarm/(\%)** & **Alarm/(\%)** & **Average Time** \\ \hline Brake light & 200 & 92.5 & 6.0 & 1.5 & 107 \\ Direction light & 200 & 87.5 & 8.5 & 4.0 & 131 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental result list.  
Figure 22: Taillight detection result in different weather conditions. (**a**) The result of correct detection under the normal weather; (**b**) The result of missed detection under the intense illumination; (**c**) The result of the missed detection under the rainy weather.
# Role of Irisin in Endocrine and Metabolic Disorders--Possible New Therapeutic Agent?
## 1 Introduction  
Irisin was discovered by [PERSON] et al. in 2012. During physical exercise, the expression of fibronectin type III domain-containing protein 5 (FDNC5) encoded by FNDC5 gene, increases and undergoes cleavage, resulting in irisin secretion. It is possible that the positive effects of exercise could be mediated by this hormone [1, 2, 3, 4, 5].  
In recent years, research has brought to attention a multitude of metabolic and non-metabolic effects of irisin; for example, it has been shown to improve muscle-bone connectivity [6], the browning of white adipose tissue (WAT) [7], and the reduction in insulin resistance and body mass index (BMI) [8, 9, 10, 11, 12].  
Despite available treatments, there are serious questions related to various diseases which are yet to be answered. Obesity and diabetes, which affect millions of people, as well as polycystic ovary syndrome (PCOS), metabolic syndrome, cardiovascular disease, osteoporosis, and hypothalamic disorders, do not yet benefit from curative treatment.  
Taking into account the existing data about the actions of irisin, could this myokine become a novel therapy for all of these diseases? The capability of irisin to regulate adipose tissue, glycemic homeostasis, and the musculoskeletal system may be used in clinical practice in order to manage important chronic modiThe negative association with BMI and percentage of fat mass might suggest an important role of irisin in the control of obesity [8; 9; 10; 11; 12]. Furthermore, the metabolic syndrome and some PCOS phenotypes can be considered as pathologies in which irisin might represent a therapeutic option. Even if the results are somewhat contradictory, regarding the role of irisin in regulating fat tissue and BMI, research in progress is expected to improve upon current knowledge.  
Thyroid dysfunctions and hypothalamic disorders may also benefit from the action of irisin, due to its important involvement in weight control. Osteoporosis and sarcopenia could also become new targets for the therapeutic use of irisin [6].  
In this narrative review, we present the profile of irisin in several endocrine and metabolic disorders, along with its potential pharmacological uses.
# Effects and Mechanisms of Microbial Remediation of Heavy Metals in Soil: A Critical Review
## 5 Microbial Remediation of Heavy Metal Pollution in Soil and Water
### Microbial Living Environments
#### 5.1.2 Ambient Temperature  
Ambient temperature mainly affects the rate of heavy metal absorption by affecting the growth and proliferation of microorganisms [75]. The optimum temperature for different microorganisms is often different [76]; _Thibobacillus ferrooxidans_, _Thibocillus acidophilus_ and _Thibocillus tepidarius_, are medium-temperature bacteria; _Sulfolous solfa-tataricus_ and _Acidianus brierleyi_ are highly thermophilic bacteria. The absorption of Cd\({}^{2+}\), Cr\({}^{2+}\) and Zn\({}^{2+}\) by _Bacillus licheniformis_ and _Bacillus jeotgali_ U3 were studied by [PERSON] et al. [67] and [PERSON] et al. [73], respectively, and their results showed that the optimum temperature for the same microbes on different heavy metals is different (Table 6). However, the most suitable temperatures generally range from 25 to 35 \({}^{\circ}\)C (i.e., the range is not significant) [26; 48; 74]. The absorption of Cd\({}^{2+}\) by _Bacillus jeotgali_ U3 is highest when the temperature is 35 \({}^{\circ}\)C; however, for Zn\({}^{2+}\), the optimum temperature is 30 \({}^{\circ}\)C. _Bacillus licheniformis_ shows similar results, but the influence of temperature on it is less significant [45].
# A Framework of Wearable Sensor-System Development for Urban 3D Modeling
## 3 Calibration Framework for Wearable Sensor Systems
### Point-Plane Correspondences Based System Calibration for Sensor Bundles
#### 3.2.3 Advantages of the Proposed System Calibration Method  
The proposed point-plane correspondence-based approach offers four advantages as follows:  
1. First, the proposed methodology only requires information regarding the target plane from the point cloud. It does not require an additional complicated process to accurately extract the edge points or lines of the target plane and is less affected by the density of the point cloud.
2. Second, arbitrarily determined initial GCPs employing the BA process are used instead of the coordinates of previously measured GCPs. Therefore, the need for prior work measuring the coordinates of GCPs using a total station, etc., is eliminated and the time required to perform system calibration can be reduced.
3. Third, because the proposed method is based on BA using the collinearity condition, implementing it utilizing various existing open-source codes is easy.
4. Fourth, the proposed method offers the advantage that when sensor data for part of the target plane is acquired, information on the entire target can be used for calibration.  
Figure 7 shows further specific details for the fourth advantage of the proposed system calibration method. In the proposed method, the coordinates of the initial GCPs corresponding to the control points (red and blue points in Figure 7a) were set to random points on the plane (white points in Figure 7a), which were then adjusted to their actual positions through BA. Therefore, control points outside the plane patch without corresponding point clouds (red dots in Figure 7a) can also be used for calibration. In particular, the above advantage becomes more prominent when the FOV overlap ratio between the LiDAR and the camera is low, as shown in Figure 7b. The proposed method utilizes all the control points shown in Figure 7b (red and blue dots), thereby allowing more stable calibration than when only GCPs inside the planar patch (blue dots) are used.  
Figure 6: Application of the proposed system calibration methodology for sensor bundles.
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 4 Conclusions  
In this paper, an on-board parameter optimization method based on ADS-B data is proposed. Parameters such as detection band, integration time and integration capacitance are adjusted in real-time to improve the target detection and recognition performance. Numerical simulation results show that the system performance after on-board parameter optimization using ADS-B data had a significant improvement compared with a conventional fixed-parameter detection system. The final optimized spectral band of marine cloud background was 2.8-3.2 \(\upmu\)m during daytime, and 2.45-2.95 \(\upmu\)m in the nighttime. The proposed method increased \(JSNCR\) by 1.16 to 1.31 times and reduced \(NET\) from 2.4 W/Sr to 1.2 W/Sr, which better meets the robust detection need of an air vehicle in complex cloud clutter. This study lays a solid theoretical foundation for the spectral band analysis of space-based AVD system design. Meanwhile, the parameter optimization can be used as a standard procedure to improve on-board performance.  
In future work, the use cases of the algorithm for the downstream modules in terms of object detection and tracking using simulation infrared image sequences with the AVD scenario should be further carried out.  
Conceptualization, Y.L. and J.A.; Investigation, Y.L. and Z.L.; Methodology, Y.L. and P.R.; Simulation, Y.L. and Z.L.; Data curation, Z.L.; Funding acquisition, P.R.; Project administration, P.R. and J.A.; Supervision, J.A.; Writing--Original draft preparation, Y.L.; Writing--Review and editing, P.R. and J.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62175251. This research work was supported by the open project of the Key Laboratory of Intelligent Infrared Perception, project ID: CAS-11 RP-02.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Verification
### Practicality Validation in the Strain Measurement of the CFRP Wing Skin
However, the maximum relative errors between measured strain and simulated strain are 21.4%, 19.1%, and 16.4% for FBG sensors 1, 2, and 3. By compensating the measured strain with corresponding strain transfer ratios, the modified strain is more consistent with the simulation results. The maximum relative errors reduce to 1.9%, 1.7%, and 1.9% for FBG sensors 1, 2, and 3. The comparison results indicate that the measurement errors decrease by 11.6% to 19.8% based on the compensation with corresponding transfer ratios. Therefore, the proposed strain transfer model is practical and suitable for compensating the strain measurement errors of FBG sensors bonded to CFRP laminates. Figure 13: Aircraft wing finite element simulation. (**a**) Aircraft wing 3D model; (**b**) Strain field of the wing skin.
# Algorithms for Various Trigonometric Power Sums
## 2 Background  
Before more intricate trigonometric power sums as discussed in [21] can be considered, one needs to study the untwisted Dowker and Gardner-Fisher sums, which can be represented in a general form as  
\[S_{v,l}(m)\doteq\sum_{k=1}^{m-1}\csc^{2v}\Bigl{(}\frac{k\pi}{lm}\Bigr{)}. \tag{10}\]  
The untwisted Dowker sum is given by \(l\!=\!1\) in the above definition, while the Gardner-Fisher sum corresponds to the \(l\!=\!2\) case. Later in this work, we shall study more general alternating forms of the sums as given in (9), where \(r\) is an integer ranging from \(1\) to \(m-1\). It should also be mentioned here that the Gardner-Fisher sum studied in [4; 5] was defined with an extra normalization factor of \((\pi/2m)^{2v}\). Therefore, to make contact with the results presented in [21], the results presented here need to be multiplied by the appropriate normalization factor for each value of \(v\). General expressions for the two sums were obtained by employing a novel integral technique in Theorems 3.1 and 4.1 of [21]. In the case of the Gardner-Fisher sum, it was found to be given by  
\[S_{\varphi,2}(m)=\frac{1}{(2v-1)!}\Big{(}\frac{2m}{\pi}\Big{)}^{2v}\sum_{n=0}^{v -1}\Bigl{(}\frac{\pi}{m}\Bigr{)}^{2n}\,s(v,n)\Gamma(2v-2n)\,\zeta(2v-2n)\biggl{(} 1-\frac{1}{m^{2v-2n}}\biggr{)}, \tag{11}\]  
where \(n\leq v-1\) and \(s(v,n)\) represents the \(n\)-th elementary symmetric polynomial obtained by summing over the entire sequence of quadratic powers or squares of integers from \(1^{2}\) to \((v-1)^{2}\). That is, the polynomials are given by  
\[s(v,n)=\sum_{1\leq i_{1}<i_{2}<\cdots<i_{n}<v-1}x_{i_{1}}x_{i_{2}}\cdots x_{i_{ n}}\, \tag{12}\]  
where \(x_{i_{1}}<x_{i_{2}}<\cdots<x_{i_{n}}\) and each \(x_{i_{j}}\) is equal to at least one value in the set \(\bigl{\{}1,2^{2},3^{2},\ldots,(v-1)^{2}\bigr{\}}\). It was also found in [21] that  
\[s(v,n)=2^{-2n}\,(2v-2n)_{2n}\,c_{2v,n}\ \, \tag{13}\]  
where \((x)_{k}\) denotes the Pochhammer notation for \(\Gamma(k+x)/\Gamma(x)\) and the \(c_{\rho,n}\), known as the generalized cosecant numbers, represent the polynomial coefficients in \(\rho\) of the generating function for \((x\csc x)^{\rho}\). The first ten orders are displayed in Table 1, while important applications and alternative methods of evaluating them are described extensively in [21; 22]. For the special case of \(\rho=1\), the generalized cosecant numbers reduce to the cosecant numbers, \(c_{k}\), which are given by  
\[c_{k}=2\Bigl{(}1-2^{1-2k}\Bigr{)}\frac{\zeta(2k)}{\pi^{2k}}=\frac{(-1)^{k+1}} {(2k)!}\,\Bigl{(}2^{2k}-2\Bigr{)}\,B_{2k}. \tag{14}\]  
In the above equation, \(B_{2k}\) represent the Bernoulli numbers. The \(c_{k}\) have the advantage of converging quickly to zero as \(k\to\infty\), whereas the Bernoulli numbers diverge.
## 3 Simulation Results and Discussions
### Impacts of LEF and SEF on States of LD  
The BDs of the output photon numbers from the back facet of the laser with OFB intensity at several LEF and SEF values are shown in Figure 1. In the calculations of the BDs, the [PERSON] noise sources in the rate Equations (1)-(3) are omitted to clarify the type of state induced by OFB. In these figures, we are interested in reporting the operation dynamic of the short EC LD through OFB wide range.  
Figure 1 shows the BDs of the laser when the LEF \(\alpha=1.0\), \(3.0\), and \(5.0\) and the SEF \(C=10^{-4}\), \(10^{-5}\), and \(10^{-6}\), respectively. In the weak OFB (\(K_{ex}<0.01\)) regime, the laser keeps in CW as no points are presented in the figures, which represents a stationary solution of the rate Equations (1)-(3). Figure 1 shows that increasing the LEF from \(1.0\) to \(5.0\) and the SEF from \(10^{-6}\) to \(10^{-4}\) led to decreases/increases in the width of the CW region. The same scenario occurs at \(K_{ex}>1.0\) at strong OFB. By enhancing the OFB \(K_{ex}\), the CW state bifurcates into a PO region. The width of that region and the photon number intensity (\(S(t)/<\)\(S>\)) depend strongly on the values of the LEF and SEF. As shown in Figure 1(b1-b3,c1-c3), the PO width becomes the narrowest, and the intensity decreases, with increasing SEF. Figure 1(b2,b3,c2,c3) shows the reverse situation while increasing the LEF PO region width is the widest, and the intensity decreases with an increase in the LEF. The point at which the state changes is called the HB point, which moves toward decreasing the OFB \(K_{ex}\) from \(0.079\) to \(0.036\) as the LEF increases from \(1.0\) to \(5.0\) and moves toward increasing the OFB \(K_{ex}\) from \(0.00495\) to \(0.079\) when the SEF increases from \(10^{-6}\) to \(10^{-4}\), respectively.  
By enhancing the OFB value \(K_{ex}\), the PO divaricates into two branches representing the PD dynamic state, as represented in Figure 1(b1-b3,c1,c2), which decreases the OFB \(K_{ex}\) from \(0.0154\) to \(0.0076\) and changes the LEF from \(1.0\) to \(5.0\) and the SEF from \(10^{-5}\) to \(10^{-6}\), respectively. Under the moderate region of the OFB, the LD state transmits to a chaotic state, as indicated in Figure 1.
## 3 Models  
The BTYD models, which rely on statistical distributions, do not identify patterns through learning. These models generate aggregate predictions at the individual level for the holdout periods, rather than offering predictions for a specific time. Consequently, to obtain weekly predictions, we derive cumulative forecasts and subsequently calculate  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Study** & **Model** & **Application Area** \\ \hline A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction [1] & Box-Jenkins ARIMA, RNN & System Failure Analysis \\ \hline “Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model [15] & Pareto/NBD & Customer Base Analysis, Repeat Buying \\ \hline Probability Models for Customer-Base Analysis [16] & RFM & Customer Base Analysis \\ \hline Customer Shopping Pattern Prediction: A Recurrent Neural Network Approach [17] & RFM, RNN & Customer Behaviour \\ \hline Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity [18] & Pareto/NBD & Customer Base Analysis, Purchase Regularity \\ \hline Forecast of individual customer’s demand from a large and noisy dataset [14] & Market Segmentation, ARIMA & Behavior Patterns, Market Segmentation \\ \hline Improving time series forecasting using LSTM and attention models [13] & LSTM, Attention & Time series forecasting \\ \hline Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning [12] & BTYD, Lasso Regression, ANN, LSTM, GRU & Customer Repurchase \\ \hline Customer base analysis with recurrent neural networks [19] & BTYD, LSTM & Customer Base Analysis \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of literature on behavior analytics.  
the differences for each time point. For instance, assuming a three-week scenario, when calculating predictions for the third week, we first compute forecasts for two and three-week periods and then, calculate the difference between the predictions for three and two-week periods. To overcome this limitation, we use GRU and LSTM.  
Figure 1 illustrates the daily login patterns of fifteen randomly selected mobile application users. The terms 'Past' and 'Future' refer to the calibration and holdout periods, respectively. Within this context, models are employed to predict the future behavior of individual users.
## 4 Simulation and Analysis
### System Performance Analysis
The effectiveness of the proposed ISI cancellation algorithm is verified at different acceleration factors in Figure 12, comparing the system performance with and without the proposed algorithm for four acceleration factors (0.5, 0.6, 0.7, and 0.8) under Gamma-Gamma medium turbulence conditions with a \(4\times 2\) antenna configuration at \(ipce=0.5\) and \(\lambda=1550\) nm. Firstly, in the absence of the ISI cancellation algorithm and for \(\tau<0.8\), a significant occurrence of ISI arises due to the close overlap of waveforms, resulting in a degradation of normal communication performance. However, upon application of the algorithm, there is a notable enhancement in the BER performance. Specifically, when \(\tau=0.8\), there is an improvement in system performance by 5.5 dB; and at \(\tau=0.7\) and \(\tau=0.6\), the ISI cancellation algorithm elevates the system SNR to 18 dB and 19.4 dB, respectively, at \(BER=10^{-5}\), thereby restoring normal communication capability. It is also noted that when \(\tau=0.5\), employing the ISI cancellation algorithm has minimal impact on system performance. The simulation results indicate that this algorithm significantly  
Figure 11: Relationship between BER and SNR under different laser wavelengths. Figure 10: Relationship between BER and SNR under different turbulence intensities. enhances system performance particularly within a range of \(\tau\) values between 0.6 and 0.8, with smaller values yielding better interference cancellation. To further validate the impact of the acceleration factor on the interference cancellation capability, Figure 13 shows the system BER with and without the ISI cancellation algorithm under Gamma-Gamma moderate turbulence conditions, using a 4 \(\times\) 2 antenna configuration at \(ipce=0.5\), \(\lambda=1550\) nm, and SNR of 15 dB. The results demonstrate that for \(\tau\geq 0.5\), there is an improvement in the cancellation effect of the algorithm as \(\tau\) decreases. However, when \(\tau<0.5\), it is observed that the algorithm becomes less effective in mitigating ISI introduced by the FTN, resulting in significant performance degradation. The acceleration factor below 0.5 significantly increases ISI due to increased symbol overlap. A critical threshold is reached at an acceleration factor of 0.5, where the wider pulse shape reduces the symbol overlap and weakens the effect of ISI on demodulation. Conversely, when the acceleration factor drops below 0.5, the narrower pulse width leads to abnormally high ISI with weakened effectiveness of the interference cancellation algorithm. On the other hand, within the range of 0.5 to 1, our proposed algorithm effectively eliminates ISI, and its effectiveness improves as ISI increases.