esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 2 Related Works
### Localization Approaches Jointly Using the RSSI and the Mobile Anchor Node  
Localization schemes [13; 14; 15] not only jointly use the RSSI and the mobile anchor node to reduce location cost, but also use a method of contrasting RSSI values to avoid the inaccurate ranging caused by directly mapping the RSSI value to the physical distance. In [13], a mobile anchor node moves along a linear trajectory and sends a message at each broadcast point on the trajectory. The sensor node measures the RSSI value of each received message. The broadcast point with the maximum measured RSSI value is approximately determined as the projection of the sensor node on the trajectory. Using two non-parallel trajectories, the location of the sensor node can be determined by calculating the intersection of perpendiculars passing through the determined projection on each trajectory. The location accuracy depends on the error between the determined projection and the actual projection. To improve location accuracy, different schemes are proposed to make the determined projection closer to the actual projection. [PERSON] et al. [14] tried to find an increasing as well as decreasing trends of measured RSSI values for each trajectory, and then used polynomial curve fitting for each trend, and determined the intersection of the increasing trend polynomial as the projection of the node on the trajectory. [PERSON] et al. [15] applied the polynomial curve fitting method to measured RSSI values, used the derivative method to calculate the point with the maximum value of the polynomial, and determined the point as the projection of the nodes on the trajectory. Compared with the scheme in [13], the schemes in [14; 15] reduce the error between the determined projection and the actual projection, and improve the location accuracy. However, the error of the determined projection of existing schemes can be further reduced, and the location accuracy of the sensor node can be further improved. In addition, existing schemes require at least two trajectories; the number of trajectories can be reduced to one and the location time can be further shortened. In this paper, we mainly research these two points, and the details of our proposed localization scheme are given in Section 3.
# A Robust Scheduling Framework for Re-Manufacturing Activities of Turbine Blades
## 4 Robust Scheduling Framework
### Modeling Uncertainty
#### 4.2.1 Uncertainty Affecting Processing Times  
The uncertainty affecting processing times can be modeled in different ways, according to the available amount of knowledge and data. The simpler approach is the definition of an interval so that the processing time of an operation \(p_{i}\) can assume values within an interval \([p^{L_{i}},p^{U_{i}}]\), i.e., \(p_{i}\in[p^{L_{i}},p^{U_{i}}]\). The probability distribution associated to the interval can be the uniform one [24].  
Pursuing this approach, only the possible minimum and maximum values of the processing times should be determined, grounding on the available knowledge or on historical production data [25]. In the re-manufacturing of turbines, the blades are repaired in batches and the processing time uncertainty associated to a batch depends on two main factors as anticipated in Section 3.2:  
1. The number of blades in a batch depends on their level of damage. Extremely worn blades cannot be refurbished and must be discarded. A rejection rate table based on the damage level of each type of blade is defined. Thus, each batch of blades to be processed is assigned a level of damage (high, medium, low) sampled from a discrete distribution. Hence, the rejection rate is modeled as an uniform distribution defined by a lowest and highest rejection rates for that class of blades.
2. The processing time of an operation on a single blade is modeled as an interval whose boundaries can be determined grounding on historical data. Thus, the distribution of the processing time of a batch of blades can be obtained through a convolution  
Figure 6: RCPSP model for re-manufacturing process of turbine blades.  
operation between the distribution of the number of blades in the batch to be re-manufactured and the distribution of the processing time of a single blade.  
If more detailed information and knowledge are available, uncertainty related to processing times can be modeled by independent random variables with an associated general probability distribution, either discrete or continuous [26]. Differently from the approach described above, additional hypotheses are required for the definition of the probability distributions, namely, their type and the associated parameters. The main rule here is trying to limit hypotheses that cannot be fully supported by the available data. Thus, simple distributions (e.g., triangular) are preferred [27].
## 3 Signalized Intersection Traffic Flow Model for Intelligent Connected Environment
### HDV Passing Model  
1. Free-Driving Area Evolution Rules: When the leading vehicle makes an emergency stop, the following vehicle updates its speed based on a minimum safe distance to avoid collision.
2. Passing Decision Area Evolution Rules: Based on the vehicle's status and traffic environment, we determined the maximum travel distance and classified the following scenarios: green light with long remaining time, green light with short remaining time, red light with long remaining time, and red light with short remaining time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Style of Car-Following**} & \multirow{2}{*}{**Maximum Acceleration Sensitivity Factor \(\lambda_{n}\)**} & \multicolumn{2}{c}{**Safety Locomotive Time Distance Sensitivity**} \\  & & **Factor \(\omega_{n}\)** \\ \hline Hesitant & 0.97 & 1.91 \\ Steady & 1.31 & 1.30 \\ Trust & 1.70 & 0.65 \\ Normal & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of sensitive coefficients \(\lambda_{n}\) and \(\omega_{n}\) with values and following styles.  
HDV as Lead Vehicle  
Scenario a: The vehicle enters the intersection when the traffic light is green, and the remaining time is relatively long. During this green light period, it can pass the stop line, and a certain probability is given for accelerating or maintaining speed to pass the stop line.  
Scenario b: The vehicle enters the intersection when the traffic light is green, and the remaining time is relatively short. During this green light period, it cannot pass the stop line. The vehicle accelerates or maintains speed until the next green light period to pass the stop line, usually choosing to accelerate.  
Scenario c: The vehicle enters the intersection when the traffic light is red, and the remaining time is relatively long. It can reach the stop line when the red light ends and pass the stop line during the green light period.  
Scenario d: The vehicle enters the intersection when the traffic light is red, and the remaining time is relatively short. It cannot reach the stop line when the red light ends and accelerates or maintains speed until the stop line.  
2 HDV as the following vehicle  
Introduce the HDV as the following vehicle's driving style, and the driver follows the same pattern as the leading vehicle for decision making. The evolution rules of the HDV decision zones are illustrated in Figure 3.
# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 2 Materials and Methods
### Data Processing
#### 2.3.2 Dimension Reduction of Hyperspectral Imagery  
Hyperspectral data involves a large amount of data with rich information and high dimensions, but it also brings data processing problems such as information redundancy, low processing efficiency, and storage difficulties, so it needs to be processed for dimension reduction [35]. In this context, principal component analysis (PCA), minimum noise fraction rotation (MNF), and independent component analysis (ICA), which are commonly used in dimension reduction, were used in this study to reduce the redundant hyperspectral data. The band eigenvalue transformation results of each method are shown in Figure 7.  
Figure 5: Spectral curves of five typical ground objects before and after spectral curves stitching.  
Figure 6: Correlation analysis about spectral curves of typical ground objects before and after imagery mosaicking: (**a**) Roof; (**b**) Tree; (**c**) Earth; (**d**) Winter wheat; (**e**) Road.  
There is an inflection point for each method. The eigenvalue is large before the inflection point; in contrast, the eigenvalue is nearly zero after the inflection point. In other words, the eigenvalue transformation in the first few bands is faster and contains more useful information, whereas the eigenvalue transformation in the latter bands is slower and contains more redundant information. Thus, the inflection points can be used to select useful band information and reduce redundant information. Based on the dimension reduction results, the first 10 eigenvalues of PCA, the first 20 eigenvalues of MNF, and the first 10 eigenvalues of ICA were selected as the spectral features after the data dimensionality reduction.
## 5 Experimental Results and Discussions
### Performing RPCA-SPC on Gammatone-Filterbank Features  
In the previous subsections, we have revealed that the proposed RPCA-SPC can enhance noise robustness of FBANK and MFCC features and thus improve the respective recognition accuracy. Here, we would like to further investigate whether RPCA-SPC can be also beneficial to the speech features that are derived from the well-known gammatone filter-bank [3], a widely used model of auditory filters in the auditory system. Briefly speaking, here the creation process of FBANK and MFCC is modified by replacing mel-filters with gammatone filters so as to build the logarithmic gammatone-filterbank coefficients and gammatone cepstral coefficients, denoted by GFBANK and GFCC, respectively. The experiments are conducted almost the same as those described in the previous subsections except replacing FBANK and MFCC with GFBANK and GFCC, respectively. As for the used gammatone filter-bank, the center frequency of the \(\mathrm{m}^{\mathrm{th}}\) filter is determined by the Greenwood function  
\[\mathrm{f}_{\mathrm{c}}[\mathrm{m}]=\mathrm{A}\left(10^{\mathrm{am/N}}-\mathrm{ k}\right)\,\mathrm{Hz} \tag{4}\]  
with \(\mathrm{A}=165.4\), \(\mathrm{a}=2.1\), \(\mathrm{N}=40\) (the number of filters) and \(\mathrm{k}=1\), and the frequency response of the \(\mathrm{m}^{\mathrm{th}}\) filter is the magnitude part of the Fourier transform of the impulse response  
\[\mathrm{h}_{\mathrm{m}}(\mathrm{t})=\mathrm{t}^{\mathrm{n}-1}\mathrm{e}^{-2 \pi\mathrm{Bt}}\cos(2\pi\mathrm{f}_{\mathrm{c}}[\mathrm{m}]\mathrm{t}+ \varnothing) \tag{5}\]  
with \(\mathrm{n}=4\), \(\mathrm{B}=0.025\), and \(\varnothing=0\).  
For simplicity, here only the proposed RPCA-SPC is tested, for which results are listed in Figures 7 and 8 for GFCC (with GMM-HMM) and GFBANK (with DNN-HMM), respectively. From these two figures, we reveal that RPCA-SPC significantly improves the recognition accuracy of GFCC and GFBANK features in all mismatched Test Sets (Sets 2-14), while it causes accuracy degradation for the clean noise-free set (Set 1). These results agree well with what we have observed previously in the cases of MFCC and FBANK. Therefore, the effectiveness of RPCA-SPC in enhancing noise robustness of speech features under mismatched conditions is reconfirmed.
## 5 Results
### Temperature Correction  
To evaluate the measurement and to make measurements with different environmental conditions comparable, two temperature corrections are compared. The correction curves, as shown in Figure 5, are similar in the area of 10 \({}^{\circ}\)C to 30 \({}^{\circ}\)C but differ greatly below that range.  
The curve labelled as _Norm_ is the one that is usually applied on standardized measurements according to UN/ECE Regulation No. 51 [2] and UN/ECE Regulation No. 117 [19]. The alternative curve is one that was constructed out of the experience that measurement engineers at Mercedes-Benz gained; therefore, it is referred to as _MB-Correction_.  
To analyze the impact of the temperature correction on measurements, the accelerated runs for 255P mounted on LSBEV are presented in Figure 6. Figure 6a shows the original recorded data, Figure 6b the norm corrected data and Figure 6c the MB-Correction corrected data. To apply the corrections, the air temperature is used. Each point indicates one accelerated run. The rolling coefficients are calculated once for tire 255P and through Equations (1) and (9) the accelerated noise is calculated. Since the rolling and accelerated measurements are conducted one after another, the air temperature changed from rolling at 4.3 \({}^{\circ}\)C, to AWD at 6.5 \({}^{\circ}\)C to RWD at 5.6 \({}^{\circ}\)C to FWD at 5.1 \({}^{\circ}\)C, making them viable to investigate the impact of temperature correction.  
Figure 4: Impact of different regression algorithms for the measurement of 255P mounted on LSBEV driven with FWD and MB-Correction (Section 5.1) applied.  
Under constant environmental conditions, the three curves are assumed to reach a delta of 0 dB(A) at an acceleration of 0 m s\({}^{-2}\). The constant offset is therefore mostly explained through the impact of the temperature since no data is processed for runs with higher wind speed than 5 m s\({}^{-1}\), which means no impact from wind needs to be assumed.  
Applying the norm temperature correction as well as the MB-Correction curve on the data set leads to different results. The MB-Correction leads to the lowest difference of all data points in the lower acceleration areas, as indicated by the arrows with corresponding values of 0.83 dB(A), 0.76 dB(A), and 0.69 dB(A). In theory, for an acceleration of 0 m s\({}^{-2}\), the data points should coincide. This is due to the assumption that no acceleration is almost equivalent to idle runs, and for idle runs no impact of the drive axle is assumed. Because of the observation, that the data point best coincides in the low acceleration range with MB-Correction. From this point in the paper, this correction is applied to all presented data.  
Figure 5: Two different temperature correction curves for absolute SPL, reference temperature is 20 °C.  
Figure 6: Impact of different temperature corrections on acceleration induced tire noise of one tire at point P-P on the left microphone. (**a**) Original 255P on LSBEV; (**b**) Norm Correction 255P on LSBEV; (**c**) MB-Correction 255P on LSBEV.
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 5 Discussion
### Current Deficiencies and Prospects for Future Improvement  
The SWAT model has been developed over a considerable amount of time--more than 20 years from 1990 to the present. It is also semi-distributed, making it simpler to learn and comprehend than completely distributed models. Simultaneously, the rate of calculation is accelerated, and the accuracy is improved, better reflecting the physical mechanism of the water cycle than the conceptual model [35]. However, the model's description of the water cycle mechanism--such as irrigation and submersible evaporation--is not as good as it may be when compared to other hydrological models. Furthermore, there is a low degree of modularity, a large number of input files that are difficult to handle, and a source program that is difficult to learn and alter (there are over 300 FORTRAN source programs). This paper's research focus was northeast China, where four distinct seasons cause notable climate variations. The groundwater recharge and discharge relationship simulation findings in the winter have a wide range and poor precision. To lessen the impact of temperature on the coupling model, the SWAT and MODFLOW models should be coupled [36; 37], the LU-SWAT-MODFLOW model should be created, and a range of data should be merged to calibrate and validate the coupling model. Additionally, dynamic land use input should be introduced to the model since dynamic updating of land use data is necessary to re-determine the threshold value during the simulation process, which is highly unfavorable in places with changeable soil.  
In general, the hydrologic data required for it to work cannot be directly gathered; instead, inverse observation is required. Remote sensing observation is a type of indirect observation.  
It is impossible to prevent errors in rededuction precision [38; 39]. Remote sensing rainfall, in particular, is only more accurate on a monthly scale, with significant error on a day-by-day and hour-by-hour basis. Second, there is a significant inaccuracy in the predicted groundwater recharge because remote sensing is unable to monitor the evapotranspiration of inland water bodies. Future scientific research projects should include the relative error coefficient in the final calculation formula.
## 3 Microdrilling Fused Silica  
Figure 3 shows the drilling force measured during the microdrilling of fused silica using a PCD microtool. The PCD tool was cylindrical and flat, as shown in Figure 1b. The tool's diameter was 300 \(\upmu\)m. The axial feedrate was 10 \(\upmu\)m/s and the tool's rotational speed was 50,000 rpm. At the beginning of drilling, the thrust force (F\({}_{z}\)) increased to its peak value (F\({}_{peak}\)), 8 N, and then decreased rapidly because of the contact stiffness between the tool and workpiece. In the grinding process, [PERSON] et al. [26] reported that the actual depth of cut is less than the applied depth of the cut because of the elastic deformation of the grinding wheel, which is associated with the contact stiffness. For this reason, the material was not removed until the thrust force reached F\({}_{peak}\), at which time a crack occurred on the surface of the workpiece, which then began to be removed with a lower thrust force. As the peak force increases, the radial force (F\({}_{x}\) and F\({}_{y}\)) also increases to 1 N, leading to more edge chipping and tool breakage caused by the tool's wobble. In this paper, therefore, effects of tool shape, tool surface roughness, and axial feedrate on peak force and hole quality were investigated.  
\begin{table}
\begin{tabular}{l l} \hline PCD tool & \(\phi\) 300 \(\upmu\)m \\ \hline Workpiece & Fused silica \\  & (0.5 mm thickness) \\ \hline Feedrate & 2\(\sim\)30 \(\upmu\)m/s \\ \hline Rotational speed & 50,000 rpm \\ \hline Coolant & Cutting oil \\ \hline \end{tabular}
\end{table}
Table 2: Microdrilling conditions.  
Figure 3: Drilling force of microdrilling of fused silica using a micro-PCD tool.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 3 BackCom: An Effective Solution
### Different Types of BackCom
#### 3.3.2 Ambient BackCom  
This type of BackCom uses ambient (surrounding) RF signals, such as television (TV), cell phone, and WiFi signals as the carrier, to power up the passive tags, respectively [37]. A passive tag can communicate with another passive tag by reflecting incident ambient signals after modulating its information [12].  
Figure 8 depicts ambient configurations. The working principle of Amb-BackCom is described below:  
1. Passive tags receive RF signals from the surrounding RF sources.  
Figure 7: Dedicated BackCom.  
Tags harvest power from ambient signals to modulate their information, using the OOK scheme.
* Tags transmit binary bits due to change in the impedance. The \"0\" bit is transmitted when the antenna has high impedance and when major parts of the signals are reflected. On the other hand, for binary \"1\" the antenna impedance would be low, and the signals are considered least reflected.
* Since the TV or other nearby signals already have information on them, to avoid data overlapping between original and modulated signals, the passive tags transmit their data by reflecting the signals at a smaller bit rate than the surrounding radio waves rate. Afterwards, the receiver can differentiate among both signals by taking their mean value.  
This type of BackCom revived the use of RFID technology by utilizing nearby signals of different technologies [13]. Different types of communication channels such as the basic backscatter channel and the dyadic backscatter channel for Amb-BackCom have been explored with multiple antenna designs [13]. Furthermore, the idea of Amb-BackCom was utilized for the communication between two passive tags to calculate the associated bit error rate (BER) [65]. Unfortunately, the ambient signals have low signal power compared to dedicated source, and backscattering operation weakens these signals even more. In order to provide sufficient power to tags, another approach is suggested below.
## 5 Regulation of CRISPR/Cas Systems
In theory, it is possible that infection with other phages triggers amino acid starvation, leading to LeuO-dependent activation of the CRISPR/Cas system. The BaeR-S two-component regulatory system is activated in response to envelope stress (reviewed by [PERSON] _et al_. [104]). Phage infection potentially causes envelope stress by accumulation of viral proteins in the membrane. Upon detection of membrane stress, the histidine sensor kinase BaeS is activated by phosphorylation and in turn activates the BaeR protein. Activated BaeR gains the ability to bind DNA, modulating gene expression [104]. In _E. coli_, the BaeR binding site is located at the H-NS binding site near the _cse1_ (_casA_) promoter. Therefore, bound BaeR serves as an antagonist for H-NS binding, predicted to result in the release of the _cas_ promoters for RNA polymerase recognition and _cas_ expression [105, 106]. The high-temperature protein G (HtpG) chaperone affects the CRISPR/Cas system in _E. coli_ by stabilizing Cas3. If HtpG is absent, the CRISPR/Cas system is no longer able to efficiently prevent phage infection [107]. However, HtpG is not present in all bacteria containing CRISPR/Cas systems. Indeed, most archaeal strains that contain Cas3 homologues do not carry an HtpG homolog, suggesting that it is not universally required. Therefore, other bacteria may require other chaperones in order for CRISPR/Cas interference to occur [108]. Other systems that affect CRISPR/Cas regulation were identified in _Myxococcus xanthus_, _T. thermophilus_ and Sulfolobales. In _Myxococcus_, the _dev_ operon is co-transcribed with _cas_ genes, and this operon is negatively auto-regulated by DevS (a Cas5 protein) [109]. _T. thermophilus_ contains 12 CRISPR, and 3 different _cas_ operons, type I-E, type III-A and type III-B [10, 39]. During phage infection, the _cas_ genes for the type I-E and type III-A systems were up-regulated via CRP (**c**AMP receptor **p**rotein) [39, 110]. When CRP is bound to cAMP, it recognizes promoters and activates gene expression. Interestingly, inactivation of _crp_ did not abolish the _cas_ activation, suggesting both _crp_-dependent and -independent pathways. In addition, some _cas_ genes and CRISPR arrays were regulated by phage infection in a CRP-independent manner [39]. In _Sulfolobus islandicus_ and _S. solfataricus_, the expression of pre-crRNA is regulated by Cbp1 (CRISPR DNA repeat binding protein), which directly binds the DNA repeats and might influence the activity of promoters and terminators present within some spacers or repeats [111, 112]. In summary, CRISPR/Cas systems can be regulated at the level of transcription of the _cas_ genes and CRISPR arrays and post-translational level on the Cas proteins (Figure 4). The picture of how these systems are regulated is far from complete and diverse and complex regulatory strategies exist.