esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture
### Downlink Data Path  
The downlink (DL) data goes first from the DU to an RRU via an optical link and then from an RRU antenna [18] to a user as shown in the upper half of Figure 4. The signals for transmission are provided to the DU FPGA by the PC via the PCIe interface; to collect the received signals for performance monitoring, the user is connected to the PC with Ethernet.  
On the DU FPGA, sixteen parallel low-pass SDMs are implemented to serve two RRUs as four I-Q pairs are required by each RRU; the SDMs modulate the baseband signals at 3.6864 Gbps. Our previous work [19] has described the implementation of the real-time SDMs in detail. As illustrated in Figure 1, every four bi-level sigma-delta modulated signals, i.e., two pairs of sigma-delta modulated I and Q signals, are time-interleaved together with one bi-level control sequence into one NRZ signal, which is converted to the optical domain and transmitted over one fiber by a QSFP.  
At each RRU, a QSFP converts the received 18.432 Gbps bitstreams from the optical domain to the electrical domain. On the FPGA, each bitstream is first de-interleaved back to two pairs of 3.6864 Gbps sigma-delta modulated I and Q signals; the signals are then up-sampled and digitally up-converted to the carrier frequency. Afterwards, band-pass filters on the AAU filter out the quantization noise. The radio-frequency (RF) signals are amplified and sent to antennas.  
The DL data passes only simple and non-blocking modules; the signals are in fact streamed directly from the DU to all antennas. As a result, the transmit timing offsets between them, which may come from hardware mismatches or the locked phases of PLLs, are inherently fixed, although there is no absolute time shared between all transmitters.  
Figure 5: Photo of **(a)** the distributed unit (DU), **(b)** a remote radio unit (RRU), and **(c)** a user. The phase lock loop (PLL) outputs are used for debugging and performance monitoring.  
Such offsets can either be calibrated or contained in the estimated channel information. The transmit timings can then be considered synchronous.  
At the user, the received signals are amplified by the LNA, down-converted using a zero intermediate frequency (zero-IF) receiver, and sampled. The FPGA collects the sampled data whose quality is later checked by the PC.
## 3 Results and Discussion
### Evaluation of the Self-Healing Ability of the Coatings
#### 3.7.2 Evaluation of the Self-Healing Ability by EIS Tests in NaCl and HCl Solutions
The healing reaction efficiency was measured to be 99% and 95% for the samples C (in NaCl solution) and B (in HCl solution), respectively, according to Equation 5) [46; 47]:  
\[\%\text{HE}=(1-\text{Rcorr}_{0}/\text{Rcorr})\times 100 \tag{5}\]  
where Rcorr\({}_{0}\) and Rcorr are the charge transfer resistances of the control and self-healing coatings, respectively.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 6 Discussion
### Audio and Text Information  
Audio guidance reported mostly positive results as supplementary information and text guidance was deemed to be unnecessary by half of the participants.  
A total of 70% of respondents reported the audio instructions to be helpful, and the ones that described the audio instructions to be distracting reported that the short time between audio loops made it difficult to follow the speech (possible language comprehension limitations). Given that the application audio is adjusted to give more concise information as well as less repetitive, it can be concluded that audio can be a suitable supplement to MR guidance. The speed of audio loops can be adjusted in the future, and, with more development effort, translation of the instructions in the most common languages for any country could be introduced.  
Only a few subjects found the text information on the UI panels to be the most helpful information medium excluding graphic guidance. Therefore, text information can be a good supplement, yet not as good of a supplement as audio guidance. That is why the application included other available choices of transferring information, which required more development effort. On the other hand, including familiar information formats such as text and audio improves the user experience. Although they might seem sometimes unnecessary, few users found them distracting or otherwise negatively impacting the MR experience, and they can be helpful for developers to avoid unwanted complications. The audio system indicated what screwdriver to use, as well as to not place labels upside-down. Such information is challenging to convey through graphic guidance. It should be noted that the trade-off of including additional information formats can result in more computational needs, additional development effort, and programming complexity. Their inclusion should be subject to cost-benefit analysis.
## 2 Materials and Methods  
_Inclusion and Exclusion Criteria_  
An open and time-unrestricted literature search was conducted using major electronic databases, including PubMed, Scopus, Google Scholar, and Web of Science. This narrative review was developed based on articles meeting the following inclusion criteria: (1) written in English and (2) study designs including in vivo and in vitro studies, prospective and retrospective studies, cross-sectional studies, narrative reviews, systematic reviews, and meta-analyses. Articles were excluded if they (1) were not available in English; (2) were not related to _P. gingivalis_; or (3) consisted of opinion pieces or conference abstracts.
# Identifying Key Characteristics of Business Rules That Affect Software Project Success
## 5 Discussion
#### Threats to Validity and Limitations of the Study  
Threats to construct validity were addressed by a rigorous literature review. Threats to external and internal validity were addressed by sending the survey to the 1000 biggest enterprises in Slovenia [39], thus we had no impact on group composition and the group composition is representative for software implementation projects in Slovenia. We addressed threats to statistical conclusion validity by using appropriate statistical techniques as follows. An independent sample \(t\)-test was used, as it is an appropriate statistical technique for normally distributed data. Additionally, we computed effect sizes for our research questions to ensure that our research questions do not lack sufficient power and represent non-trivial effects.  
Nevertheless, some limitations of these findings need to be considered. First, the fact that respondents already knew the outcome of their project when answering the survey may have caused response bias. Following the approach of similar studies [47], we surveyed mainly objective information related to project characteristics. Second, the limitation is also a relatively low response rate. Such response rates are typical for mail surveys conducted in enterprises and still allow researchers to treat the sample as a random sample [41; 42]. Third, the study was conducted in a specific cultural and business environment. Fourth, we did not conduct exploratory research to identify key BR characteristics, but included only the ones already present in the literature. Fifth, the research was conducted on software implementation projects. Sixth, the survey was conducted in IT departments of 1000 biggest enterprises in Slovenia, which generally conduct software development and implementation with professional IT partners, thus our sample is not representative of development approaches conducted by people without thorough knowledge in computer science and scarce skills in programming.
## 2 Materials and Methods  
### 2-LED-\(\mu\)Spectrophotometer Set-Up  
The whole instrument is located in an enclosure with a lid (Figure 1a), allowing easy access to the cuvettes. During measurements, the lid was closed to avoid ambient light inside. The central element was developed and optimized for production on consumer-level 3D printers with polylactic acid (PLA, Ultimaker B.V., Utrecht, The Netherlands) filaments (Figure 1c). It enables the mounting of two disposable UV/vis micro cuvettes (sample and blank) (Carl Roth GmbH + Co. KG, Karlsruhe, Germany). Below this central unit, there is a base plate with a mounted two-layer printed circuit board (PCB), which contains the microcontroller, a USB interface, the LED control, and a 16-bit analog-to-digital converter (Figure 1b). This system is equipped with two replaceable LEDs, each for illumination of both cuvettes, respectively, via a beam splitter. Two standard 5 mm LEDs of the desired wavelength were utilized. For the detection, two smaller PCBs with a photodiode (TEMD5080X1) and a self-developed low-noise, each a highly sensitive two-stage-programmable amplifier (IC AD8228, Analog Devices Inc., Norwood, MA, US), are located behind the cuvettes (Figure 1d). The software for device control is implemented in the popular and widely applied open programming platform Java.  
After optimizing the operating conditions, the effectiveness of the developed system for determining the color change/absorption over time as a function of DNA specificity was evaluated. Conventional spectrophotometers used in comparative study were Jasco UV/vis benchtop spectrometer V-670 (JASCO Deutschland GmbH, Pfungstadt, Germany) and NanoDrop(tm)One (Thermo Fisher Scientific Inc., Schwerte, Germany).
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
## 3 Evaluation Results and Discussion
### Evaluation Results
#### 3.3.3 Robustness against the Signal-to-Accompaniment Ratio  
In order to verify the robustness of different methods with respect to the signal-to-accompaniment ratio (SAR), all excerpts of Bach10 are mixed with different SARs, i.e., \(-5\) dB, 0 dB, 5 dB and 10 dB. Soprano is the signal (melody), and the other three parts (alto, tenor and bass) constitute accompaniment. Alto, tenor, and bass are mixed together as the accompaniment with the originally recorded samples, and soprano is mixed with the accompaniment again to obtain polyphonic music at different SARs.  
Figure 3 shows box plots of the RPA comparisons of the proposed method and other three reference methods on Bach10. There are four groups corresponding to different SARs, i.e., \(-5\) dB, 0 dB, 5 dB, and 10 dB from left to right. It can be seen that all of the RPAs rise with the increase of the SAR for these four compared methods. The proposed method performs best at all SARs. To determine whether the superiority of the proposed method is statistically significant, a paired-sample t-test is performed between the proposed and each of the other three compared methods in terms of RPA with respect to different SARs. The significance level is set as 0.05. If the \(p\)-value is smaller than the significance level, an asterisk is marked at the name of the reference method. This sign is used for the same purpose on the other figures in this paper. It can be seen that the proposed method outperforms the HCT and BHWM methods significantly in terms of RPA for all SARs, and it performs better than MEA (though not significantly) for \(-5\) dB, 5 dB, and 10 dB mixing. Additionally, the proposed method outperforms MEA significantly in terms of RPA at 0 dB SAR.  
Figure 2: Frame-wise raw chroma accuracy vs. penalty factor for \(\lambda\) different melody types.
## 1 Introduction
combination of solar irradiance forecasting algorithms with imitation model of the PVPP under consideration. Moreover, step-by-step calculation of PVPP generation provides extensive opportunities for the interpretation of forecasting results, unlike \"black box\" models, establishing correlation links between inputs (for instance, weather data) and output (PVPP generation). The PVPP short-term forecasting system was implemented in the industrial software package and piloted at the real PVPP allocated in one of the southern regions of the Russian Federation. In general, in the course of piloting, the PVPP forecasting system demonstrated a satisfactory accuracy level for sunny and cloudy days [39; 41]. However, the performance of the model significantly decreased during partly cloudy days and days with precipitation, which put the study on the model accuracy and robustness at the top of the priority list. The present study focuses on the measures to be introduced to improve the PVPP forecasting system performance in terms of accuracy and robustness. The authors scrutinize the weather data analysis and highlight the actions to be taken in terms of source dataset processing to improve PVPP forecasting accuracy. It should be noted that since the source data processing is aimed at eliminating the noise (outliers, produced by extreme weather conditions), the proposed actions are applicable regardless of the initially applied forecasting methodology. The second part of the study demonstrates the multiple time-domain hybrid approach, establishing the link between short-term PVPP generation forecast for the day-ahead perspective and operational forecast for intra-hour/intra-day planning of PVPP energy output. The latter one is implemented based on the supplementary function, characterizing the short-term forecast mismatch, giving the opportunity to evaluate the PVPP output for one hour-ahead time horizon, which resulted in integrated PVPP short-term and operational forecasting model. The remainder of the article is organized as follows. In Section 2, the PVPP forecasting errors are analyzed and major error sources are highlighted. Section 3 presents the results of the studies on source data filtration to improve the forecasting accuracy and the model performance for unstable weather conditions. Section 4 introduces the concept of PVPP short-term forecast operational correction and implements PVPP operational forecast based on the short-term forecasting errors analysis. Section 6 describes the hardware required for the presented forecasting system implementation, focusing on computational performance and scalability. Finally, the Conclusion Section provides brief study outcomes.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 4 Simulations and Experiment
### Experiments  
In the experiment, the proposed method tested in a real indoor environment which is a classroom with size of 8 m \(\times\) 6 m \(\times\) 3 m, approximately. The speech is acquired by the xCORE Array Microphone, conducted by XMOS, which features 7 MEMS microphones with PDM output. There are two persons around the microphone array. Their positions are about S1 and S2 in Figure 5. The usage ratio of ICA in proposed method is 30%. Figure 15 is the spectrum of mixed signal received by the center microphone.  
The localization result is 97\({}^{\circ}\) and 273\({}^{\circ}\). Figures 16 and 17 are the spectrum of the separated speech, respectively. In Figure 16, there is an obvious sparse band near 2.8 kHz, due to the use of the DSB with a mask in the proposed method. The method determines that these frequency components belong to the second sound source, and does not use the FDICA algorithm for separation. The separated signals have only a small loss in the time domain expression. Compared with using 100% FDICA, the time of separation decreases from 3.31 s to 1.13 s. The time of localization is 1.46 s.  
Figure 14: Computation time of separation with different number of sound sources.  
Figure 16: Spectrum of the separated speech by a male.  
Figure 17: Spectrum of the separated speech by a female.  
Figure 15: Spectrum of the mixed speech received by center microphone.
## 2 System Model of NR-ODBR-DCSK
### Receiver Structure of NR-ODBR-DCSK  
Shown in Figure 5 is the receiver structure of NR-ODBR-DCSK.  
The receiving end receives the signal from the transmitting end and generates the initial received signal. Next, the initial received signal is fed into a sliding average filter, and the signal is smoothed using a sliding average filter with a window size of \(P\) to generate a three-way transmission-averaged received signal. Subsequently, two signals are transmitted to two multipliers, and the other signal is transmitted to a delay with a delay length of \(\beta/P\). The delayed signal is divided into two transmissions, one of which is transmitted to a Walsh orthogonalization function, which performs orthogonality processing, and is subsequently multiplied with a multiplier to generate a delayed received signal, and the other transmission is transmitted to the other multiplier, which generates another delayed received signal. Next, the separately generated delayed signals are correlated with the initial received signal of one way, and the correlation value is obtained and summed to generate an output signal. At the same time, the delayed received signal is correlated with the initial received signal of the other way, the correlation value is obtained and summed, and another output signal is generated.  
In order to recover the information bits \(a_{k}\) and \(b_{k}\), the correlator at the receiving end correlates the received signal \(r_{i}\) with the delayed received signal \(r_{i-\beta}\). The correlator is a bit time correlator. At the end of the \(i\)th bit time, the output of the correlator is  
\[r_{i}=s_{i}+n_{i} \tag{10}\]  
Substituting Equation (9) into (10), we get  
\[\left\{\begin{array}{l}Z_{i,1}=\sum\limits_{k=1}^{\beta/P}(x_{i,\lceil\frac {k}{P}\rceil}+\frac{1}{P}\sum\limits_{p=1}^{P}n_{i,P+k})(a_{k}x_{i,\lceil\frac {k}{P}\rceil}+b_{k}x^{\prime}_{i,\lceil\frac{k}{P}\rceil-\beta}+\frac{1}{P} \sum\limits_{p=1}^{P}n_{i,P+k+\frac{\beta}{P}})\\ Z_{i,2}=\sum\limits_{k=1}^{\beta/P}(x_{i,\lceil\frac{k}{P}\rceil}+\frac{1}{P} \sum\limits_{p=1}^{P}n_{i,P+k})(b_{k}x_{i,\lceil\frac{k}{P}\rceil}+a_{k}x^{ \prime}_{i,\lceil\frac{k}{P}\rceil-\beta}+\frac{1}{P}\sum\limits_{p=1}^{P}n_{ i,P+k+\frac{\beta}{P}})\end{array}\right. \tag{11}\]  
Notice that the input to the detector consists of three components, namely, the required signal, inter-user interference and noise.  
Figure 5: NR-ODBR-DCSK receiver.  
The \(k\)th decoded symbol for the \(l\)th user, denoted by \(\hat{a}_{k}\) and \(\hat{b}_{k}\), is determined according to the following rule:  
\[\hat{a}_{k}=\left\{\begin{array}{ll}+1,&Z_{i,1}>0\\ -1,&Z_{i,1}\leq 0\\ \end{array}\right. \tag{12}\] \[\hat{b}_{k}=\left\{\begin{array}{ll}+1,&Z_{i,2}>0\\ -1,&Z_{i,2}\leq 0\\ \end{array}\right.\]
## 3 Materials and Methods
### RSR Methodology  
RSR is an analytical method designed for evaluating the comprehensive level of multiple indicators [41]. Its core principle involves utilizing the RSR value for various mathematical calculations, with the RSR value ranging continuously between 0 and 1. Generally, higher RSR values indicate a better evaluation outcome [42]. This method is widely applied in multi-indicator evaluations due to its simplicity and convenience. Key advantages of the RSR algorithm include its strong comprehensiveness, ease of understanding and operation, and its capacity to rank evaluation objects hierarchically.  
In applying the Rank Sum Ratio method, the n evaluation objectives are classified into m levels. An evaluation matrix is constructed, RSR values are calculated, and the weights are integrated to obtain the \(WRSR\) values, ensuring a robust and systematic ranking of the evaluation outcomes.  
\[WRSR_{i}=\frac{1}{n}{\sum_{j=1}^{m}{\omega_{i}R_{ij}}} \tag{13}\]  
Here, \(R_{ij}\) represents the rank of the element located in the \(i\)th row and \(j\)th column of the matrix.  
The sorted cumulative frequency was converted into Probit values, which were then used as the independent variable in a regression analysis, with WRSR serving as the dependent variable. This resulted in the formulation of the following regression equation:  
\[WRSR=a+b\times Probit \tag{14}\]  
The evaluation objects are ranked and categorized according to the WRSR values derived from the regression equation, and their validity is subsequently verified. This ranking and classification process, grounded in the WRSR values, ensures an accurate and systematic evaluation of the objects.  
We decided to use the combination of TOPSIS and RSR methods for several reasons. Firstly, both methods are well-suited for multi-criteria analyses, which is essential for our study as we need to consider multiple factors in assessing injection [43; 44; 45; 46]. TOPSIS provides a comprehensive way to measure alternatives against ideal solutions, while RSR offers a reliable ranking system. Secondly, their combination allows us to overcome the limitations of individual methods and obtain more accurate and robust results.
# SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics
## 4 Validation with Real Data
### Validation with the Interferogram over Hong Kong  
An interferogram over Hong Kong is used in this section to verify the proposed filter. The ERS pair was acquired on 18 and 19 March 1996. The baseline of the SAR ERS pair (Frame: 3159; Track: 404) is about 100 m. The SAR images are first processed by a multi-look operation (\(L\) = 5) in the azimuth direction to generate an interferogram with a resolution of about 20 \(\times\) 20 m. The size of the area is 600 \(\times\) 600 pixels. The interferogram and corresponding coherence map are shown in Figure 10.  
The interferogram is filtered with the Goldstein filter, the wavelet filters and the proposed filter, separately, and their results are shown in Figure 11. Clearly, these methods have removed most noises. But the Goldstein filter causes damage to the fringe information in the image center (Figure 11(a1-a3)). The magnified filtering results in Figure 11(b2,b3) show the fringe details as poor, and the results in Figure 11(c2,c3) possess speckle noise. Comparison between our filtering result and the other three filtering results shows the proposed method has advantages in noise suppression and fringe preservation.  
Figure 11: _Cont._  
Figure 10: (**a**) Original interferogram; and (**b**) corresponding coherence map over Hong Kong. A and B, labeled by white rectangles, indicates the enlarged areas.  
Estimated deformation phases obtained using the Goldstein, wavelet, Lee and the proposed methods are shown in Figure 12(a1-d1), respectively. The results of the proposed method are more similar to the real deformation phases than those of the other three methods.  
Different values along the transect (shown as the black segment in Figure 12(a1-d1)) of the four different images are extracted (Figure 12(a2-d2)). The blue line is the true deformation phase, and the red line is the estimated deformation phase. It is clear the proposed method performs better.  
Figure 11: Interferogram over Hong Kong filtered by **(a1)** the Goldstein filter; **(b1)** the wavelet filter; and **(c1)** Lee filter; **(d1)** the proposed filter; **(a2–d2)** Close-up of the results of area A in Figure 10; **(a3–d3)** Close-up of results of area B in Figure 10. (Unit: rad).  
For further analysis on denoising and details preservation, the number of residues, PSD, SPD and RMSE between the true and estimated deformation phases are listed in Table 3. About 81.56% residues have been reduced by the proposed filter. However, the equivalents for the Goldstein filter, wavelet filter and Lee filter are 64.08%, 77.34% and 73.04%, respectively. In addition, the proposed filter obtained the minimum PSD value and minimum SPD. As for the detail preservation, the deformation phase RMSE of the proposed method has the smallest value. The experimental results demonstrate the effectiveness of the proposed method in phase noise reduction and fringe detail preservation.