esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 4 Proposed Hardware Architecture for the Digital QSM Detector
### Top View of the QSM Detector Architecture  
Using the description of the QSM communication system in Section 2.1 and the architecture proposed in Figure 3, a top view of the detector is presented considering the following parameters: \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Scheme/\(\eta\)**} & **QSM** & **QSM** \\  & **ML** & **Near ML** \\ \hline
4 bpcu 2 \(\times\) 2 & 256 & 282 \\ QPSK & (100\%) & (73\%) \\ \hline
8 bpcu 8 \(\times\) 8 & 12,288 & 1475 \\ QPSK & (100\%) & (12\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of complexity (\(\eta\)). Figure 3: QSM detector architecture. * _NT_: number of Tx antennas. * _NR_: number of Rx antennas. * _WL_: word length used for fixed point representation. * IP: amount of bits used to represent the integer part of data. * FP: amount of bits used to represent the fractional part of data. Result of subtracting _WL_ and _IP_. * _HXQC_: number of columns of an auxiliary matrix needed for calculations in det1. * _HXQIS_: size of an auxiliary vector needed for calculations in det2. Considering Table 3, which summarizes the inputs and outputs of the whole system, the detection process started when the received signal \(y\) was mapped into ports _yt_real_ and _yt_imag_. This input signal was then processed by det1, which executed Algorithm 1 in hardware. After that, det2 received data in its _yt1_real_ and _yt1_imag_ (equivalent to the modified received vector \(\mathbf{y}\)) ports to continue with Algorithm 2. The sort operation was utilized by both detection processes; therefore, sort can be used by det1 and det2. At the final step of the detection process, the fd module compared the current detection results with predefined parameters (\(V_{th1}\), \(V_{th2}\), \(d_{min}\), in Algorithm 3); if the process matched these parameters, the current results became the final results; otherwise, the detection process was restarted until the conditions were met. The ctrl module controlled and synchronized the whole interoperability of the architecture. The outputs _detected_index1_ and _detected_index2_ represent the pair _[transmit antenna, symbol sent]_ for QSM. In what follows, the modules of the detector are described in detail.
## 5 Results and Discussion
### AOp Prediction Model Comparison  
As a further means of illustrating the overall performance of the optimum SMA-ANN model and to make comparisons with other developed soft computing, MLR, and empirical models, a Taylor diagram (Figure 9) was made using the overall datasets instead of the training and test datasets in Table 3 and Figure 8, respectively. The Taylor diagram combines three error metrics--namely, the coefficients of correlation (R), standard deviation (Std), and root mean squared difference (RMSD)--to evaluate and compare model performance [48]. As shown in Figure 9, the SMA-ANN model had the lowest RMSD and highest R values compared with the MVO-ANN, stand-alone ANN, MLR, and USBM models. This shows that the SMA-ANN model is the most robust of all developed models for predicting AOp during rock excavation blasting at construction sites. The performance of the MVO-ANN model was close to that of the SMA-ANN model, with little observed difference in RMSD and R values compared to the SMA-ANN model against the reference or observed point (blue color). Compared to the MLR and USBM models, the three soft computing models (SMA-ANN, MVO-ANN, and ANN) were superior in predicting AOp from construction rock excavation blasting, judging from their lower RMSD and higher R values. The poor performance of both the MLR and USBM models could be due to their inability to capture the nonlinear relationships that exist between the AOp and the nine input parameters.  
Figure 8: Developed AOp prediction model performances. (**a**) ANN model; (**b**) SMA-ANN model; (**c**) MVO-ANN model; (**d**) MLR model; (**e**) USBM model.  
In addition, it can be concluded that the use of two metaheuristic algorithms to tune the ANN hyperparameters improves the accuracy and predictive performance of the hybrid ANN models.  
To further compare the developed AOp models, all six error metrics were compared for the test datasets, as shown in Figure 8(b). The comparison results show that the optimum SMA-ANN model when using a Taylor diagram is also the most accurate and robust of all the developed models. This is closely followed by the MVO-ANN, stand-alone ANN, MLR, and USBM models. Thus, the SMA-ANN and MVO-ANN models are excellent and are proposed for the prediction of AOp at construction sites.  
Figure 9: Comparison of the developed AOp models by means of (**a**) a Taylor diagram; (**b**) six error metrics.
# Dictionary Encoding Based on Tagged Sentential Decision Diagrams
## 3 Tagged Sentential Decision Diagrams
### Operations on TSDDs  
The main operations on TSDDs include conjunction (\(\wedge\)), disjunction (\(\vee\)), and negation (\(\overline{x}\)). The algorithm of the binary operations conjunction (\(\wedge\)) and disjunction (\(\vee\)) was shown in [8]. Here, we show the negation algorithm on TSDDs in Algorithm 1.  
```
Input:\(F\): a TSDD \((\mathbf{T}^{1},\mathbf{T}^{2},\alpha)\); Output:\(H\): The resulting TSDD \((\mathbf{T}^{3},\mathbf{T}^{4},\beta)\). if\(F=(0,0,0)(\textit{resp.}(0,0,1))\)thenreturn\(H=(0,0,1)\textit{resp.}(0,0,0)\) if\(F=(\mathbf{T}^{1},0,1)\textit{resp.}(\mathbf{T}^{1},\mathbf{T}^{1}, \overline{x})\) and \(\mathbf{T}^{1}\) is a leaf \(\textit{tree node}\)return\(H=(\mathbf{T}^{1},\mathbf{T}^{1},\overline{x})\textit{(resp.}(\mathbf{T}^{1},0,1))\) if\(F=(\mathbf{T}^{1},0,1)\) and \(\mathbf{T}^{1}\) is not a leaf \(\textit{tree node}\)then \(F\leftarrow(\mathbf{T}^{1},\mathbf{T}^{1},\{((\mathbf{T}^{1}_{r},0,1),( \mathbf{T}^{2}_{r},0,1)),(Negate((\mathbf{T}^{1}_{r},0,1)),(0,0,0))\})\) \(\gamma\leftarrow\emptyset\) foreach element \((p_{i},s_{i})\) of \(F\)do \(s\gets Negate(s_{i})\)  add element \((p_{i},s_{i})\) to \(\gamma\) \(H\leftarrow\texttt{Trin}(\texttt{Compress}(\mathbf{T}^{5},\mathbf{T}^{6}, \gamma)))\) return\(H\)
```  
**Algorithm 1**Negate(\(F\))  
For special terminal nodes, which are the two kinds of TSDDs mentioned in Section 3.1, we can directly compute the resulting TSDD (Lines 1-2). For the other terminal nodes, we need to transform them into another form, namely, decomposable nodes (Line 3). And then, we apply \(Negate(s_{i})\) to every element and construct the following elements: \(\gamma=\{(p_{1},Negate(s_{1})),\ldots,(p_{n},Negate(s_{n}))\}\); the resulting TSDD is \((\mathbf{T}^{1},\mathbf{T}^{2},\gamma)\) (Lines 4-7). Finally, we apply compression and trimming rules to \(H\) (Line 8).  
The binary operation is represented by \(Apply(F,G,\circ)\), where \(\circ\) represents \(\wedge\) or \(\vee\), as shown in [8]. With these three operations, we can construct TSDDs to represent any Boolean function based on existing TSDDs. Here, we give an example. Given the Boolean function \(f=(\overline{x_{1}\lor x_{2}})\wedge(x_{2}\vee\overline{x}_{3})\), we have the following initial TSDDs: \(F(\overline{x}_{1}),F(\overline{x}_{1}),F(x_{2})\), \(F(\overline{x}_{2})\), \(F(x_{3})\), and \(F(\overline{x}_{3})\), where \(\langle F(x_{1})\rangle=x_{1}\) and so on. The steps are as follows: (1) Let \(F=Apply(F(x_{1}),F(x_{2}),\vee))\). (2) Let \(F=Negate(F)\). (3) Let \(G=Apply(F(x_{2}),F(\overline{x}_{3}),\vee)\). (4) Let \(F=Apply(F,G,\wedge)\). Finally, we obtain TSDD \(F\), where \(\langle F\rangle=f\).
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 5 Robust Watermarking Algorithms Based on Compressed Videos  
In the Internet the amount of video data is huge, so it is usually stored and transmitted in compressed form. The conventional watermarking algorithm, based on original video, needs to decode the video completely in order to embed and detect the watermark, which is not suitable for compressed videos. With the successive promulgation of international video coding standards, some video watermarking algorithms based on compressed domain have emerged as the times require. They embed watermark information into compressed videos, so watermark embedding processes must be combined with corresponding video coding standards. In this section, we will focus on introducing robust video watermarking algorithms based on three coding standards, which include moving picture experts group (MPEG) [67], H.264/advanced video coding (H.264/AVC) [68], and H.265/high efficiency video coding (H.265/HEVC) [69].
## 2 Monitoring Principle  
### Piezoceramic Smart Aggregates  
The most typical feature based on piezoelectric materials is the piezoelectric effect, which has been widely used in SHM. PZT has become a popular piezoelectric material due to its strong piezoelectric effect. Due to the special piezoelectric properties, PZT with dual functions of stress wave transmission and detection can be utilized as both actuator and sensor [30]. Since the PZT is fragile, special treatment methods are usually adopted to protect the PZT from working properly. In the experimental study in this paper, the type of PZT-5A piezoceramic patch with both electrodes on the same side is required to be in the compressive vibration mode because the signal propagates in the structure in the form of longitudinal waveform. The size of PZT patch is 15 mm \(\times\) 10 mm \(\times\) 0.3 mm, as shown in Figure 1.  
Figure 1: (**a**) A photo of a smart aggregate (SA) and Piezoceramic Lead Zirconate Titanate (PZT) patch; (**b**) the schematic structure of the SA.  
The smart aggregate (SA) was made by sandwiching PZT patch with lead wires between two marble blocks using epoxy resin [27]. For the connection, one end of the lead wire is soldered to the PZT patch and the other end is connected to the Bayonet Neill-Concelman (BNC) connector. The data can be easily transmitted to the instrument through the BNC connector. The fabricated piezoelectric sensor is shown in Figure 1. The diameter of the SA is 25 mm and the height is 20 mm.
## 4 LiCSAR Applications for Measuring Tectonic and Volcanic Deformations
### LiCSAR for Tectonic Applications
#### 4.1.2 Value-Added LiCSAR-Based Products for Tectonic Studies  
An InSAR displacement time series serves as an example of products that can be generated using LiCSAR products. Interferometric products of LiCSAR over Turkey have been used to study the North Anatolian Fault (NAF), a major right-lateral, strike-slip fault accommodating the relative motion between the Anatolian and Eurasian tectonic plates at a rate of \(\sim\)25 mm/yr [66]. We have processed LiCSAR frames for Anatolia starting with the first Sentinel-1A acquisitions in October 2014 until October 2019 to obtain the average satellite LOS velocities using LiCSBAS [64]. An example of a LOS velocity map for a selected frame, overlain by the main faults, clearly shows the right-lateral interseismic motion across the NAF (Figure 11a). Figure 11b shows the time-series of cumulative displacement for a sample pixel north of the NAF w.r.t. a reference pixel located south of the fault. The best-fit LOS velocity of \(-\)15.2 mm/year for this pixel is representative of the westward movement of the Anatolian microplate. We also see a clear seasonality to the relative LOS motion.  
The global coverage of LiCSAR makes it possible to derive high-resolution, precise and global estimates of tectonic strain rate based on velocities from the LiCSBAS time series inversion [64]. The LOS velocities for the frame shown in Figure 11a have been used to derive strain rates for Anatolia after mosaicking with neighbouring frames using available GNSS data compilations in order to create seamless LOS velocity maps following the method outlined in [48]. [PERSON] et al. [48] also provide an estimate of LOS velocity uncertainties for the mosaicked frames, which are on the order of \(\sim\)2-3 mm/yr and largely depend on the uncertainties of the GNSS data [64].
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 6 Conclusions  
We believe positioning is important not only for IoT end applications, but also to support network self-management. Our paper addresses the lack of comprehensive studies comparing IoT solutions and their fit-for-positioning applications. The paper first covered three possible measurement domains from which IoT devices could derive their location. Afterwards, we focused on classification of the IoT solutions and we discussed several system parameters that should be considered when designing a positioning system. We concluded our study with a comparative table and discussion between multiple IoT and other wireless solutions. We also provided an overview of achievable system performance with unique results for three positioning systems built on top of IEEE 802.11 az, LoRa and Wirepas, respectively.  
Overall, based on our study, we conclude that power-domain positioning currently offers the best trade-off between implementation cost and positioning accuracy for low-power systems. Dedicated positioning signaling as well as space-based approaches are some of the feasible ways to push for higher accuracy and still offer low-power operation. Cooperation with other wireless technologies, such as WiFi and 5G, could allow for mobility support and ability to operate at large scales when low-power operation is not critical.  
Conceptualization, P.F.e.S. and V.K.; Funding acquisition, E.-S.L.; Investigation, P.F.e.S.; Software, P.F.e.S. and E.-S.L.; Supervision, V.K. and E.-S.L.; Writing--Review and Editing, P.F.e.S., V.K. and E.-S.L.  
This work has been partly supported by EU FP7 Marie Curie Initial Training Network MULTI-POS (Multi-technology Positioning Professionals) under Grant No. 316528 and by the Academy of Finland, project numbers 303576 and 313039.  
Two of the authors are currently full time employees at Wirepas. Regardless of their relationship with Wirepas, the authors have kept the reporting objective and fair for all the technologies under examination. Furthermore the authors have not received any additional financial incentive to execute or complete this work. Wirepas contributed with hardware for the activities performed in this report which was part of the research visit arranged within the MULTI-POS project.
## 4 Simulation  
To evaluate the proposed method in comparison with the reference method, both are simulated; real data cannot be used since there is no way to obtain the geometry of a silicon wafer without any effect induced by gravity. To simulate under realistic measurement conditions, two sources of inaccuracies in the measurement setup are added: the placement error of the supports and the angle of the flip axis.  
As previously observed, each measurement, either in the normal or inverse orientations, is composed of the gravity and shape of the wafer.  
First, the gravity effect \(g(x,y)\) is simulated. For this purpose, a set of three spatial positions is defined. This set of positions creates an equilateral triangle with a distance in respect to the center of 75 mm; these points are given a value of 1 on the \(z\)-axis. The fourth point is the center of the mass of these three points and is given a value of 0. These four scattered points act as control points, and, finally, they are interpolated to a regular grid of a given sampling interval \(\Delta_{xy}\) in order to create a dense grid with the same extension as the wafer (300 mm \(\times\) 300 mm) (Figure 4). Many methods can be used for this purpose, but thin-plate spline interpolation [9] is used in this work; the mathematical model of this interpolation method is an analogy to sheet metal deformed under stress.  
Figure 3: MGR method flowchart.  
To simulate the placement error of the three-point support, a Gaussian distribution with zero mean and a variable standard deviation \(\big{(}\sigma_{x},\sigma_{y}\big{)}\) is added to the three first control points. The placement error is added in two ways: an independent error for each support \((\sigma_{1}\
eq\sigma_{2}\
eq\sigma_{3})\) and an equal error for every support \((\sigma_{1}=\sigma_{2}=\sigma_{3})\). The flip angle error is also modeled using Gaussian distribution with zero mean and a variable deviation \(\sigma_{\varphi}\).  
For the reference method, the normal direction measurement \(y_{f}(x,y)\) is obtained as in Equation (1). The inverse direction measurement is calculated with a certain error in the flipping axis:  
\[y_{b}(x,y)=g(x,y)-F_{\Phi}(s(x,y)),\ \Phi\ \sim N\Big{(}0,\sigma_{\varphi}^{2} \Big{)} \tag{16}\]  
Finally, the shape \(s(x,y)\) is obtained by applying Equation (4).  
In the proposed method, the normal orientation measurement is calculated in the same way as in the reference method. The inverse orientation measurement is obtained by applying Equation (17):  
\[y_{b}(x,y)=\frac{1}{N}\sum_{i=0}^{N-1}\big{(}g_{i}(x,y)-F_{\Psi_{i}}(s(x,y)) \big{)} \tag{17}\]  
\[\Psi_{i}=i\frac{360}{N}+N\Big{(}0,\sigma_{\varphi}^{2}\Big{)} \tag{18}\]  
Finally, the symmetric and non-symmetric components are calculated as in Equation (14), and operating with these components using this equation leads to obtaining the final value of \(s(x,y)\).
## 2 Materials and Methods
### Plant Growth Promoting Effect Assay  
The growth-promoting effect of the isolate G34 on selected durum wheat lines during the germination stage under varying salinity conditions was investigated following the work in [11]. For this purpose, two durum wheat lines with contrasting salinity tolerance were selected: Tamaroi, an Australian cultivar known for its sensitivity to salt, and a salt-tolerant BC4F2 homozygous line carrying the _TmHKT1;5-A_ gene (Line 5004, also known as _NAX2_) [22]. Seeds of both genotypes were sterilized by first washing with 70% ethanol for 1 min, followed by three rinses with sterile distilled water. Then, the seeds were soaked in a 1.5% sodium hypochlorite solution for 5 min, followed by six successive rinses with sterile water to ensure complete removal of the disinfectant. To verify the efficacy of the sterilization process, seeds from each genotype were plated on NA medium for 7 days, and the plates were carefully observed for any signs of microbial growth.  
The bacterial inoculum was prepared from log-phase cultures (OD\({}_{600}\) of 0.60) of isolate G34. The culture was centrifuged at 5000 rpm for 10 min, and the pellet was washed three times with sterile distilled water before being resuspended in 25 mL of sterile distilled water. For the growth-promoting effect assay, sterilized seeds were immersed in the bacterial suspension for 1 h, while control seeds were immersed in sterile water. After immersion, the seeds were air-dried under a laminar airflow cabinet for 2 h. The seeds were then placed in sterile plastic boxes lined with sterile filter papers soaked in 30 mL of varying NaCl concentrations (0, 80, 120, 160, and 200 mM). Each treatment had three replicates with 20 seeds per plate (10 seeds of Tamaroi and 10 seeds of Line 5004). The boxes were incubated in complete darkness at 22 \({}^{\circ}\)C for the first 3 days, followed by a photoperiod cycle of 14 h light and 10 h darkness for the remaining 7 days. The experiments were repeated twice. Germination percentage was assessed after 10 days, and the impact of bacterial inoculation on the root projected area, root length, and root volume (6 replicates) was evaluated using the WinRhizo root scanning software system (version 2009c; Regent Instruments, Inc., Quebec City, QC, Canada).
# Design of Novel Fiber Optical Flexible Routing System
###### Abstract  
In this paper, we propose a new versatile routing device that utilizes arrayed waveguide gratings (AWGs), optical switches, and optical circulators to implement reconfigurable optical add/drop multiplexers (ROADMs), optical interleavers, and optical cross-connect (OXC). With the development of dense wavelength division multiplexing (DWDM) technology, ROADM and OXC technologies have also been put into practical use. Thus, the optical signal can be routed directly in the optical network according to its wavelength without the need for optical-electrical-optical (OEO) conversion. Although different optical network units (ONUs) have different bandwidth requirements, the use of optical interleavers has successfully solved the connection problem between old and new systems. According to the numerical experiments of static characteristics, the proposed routing device can effectively implement three different functionalities, thereby providing greater flexibility for fiber optic network applications.  
optical router; arrayed waveguide grating (AWG); optical cross-connect (OXC); reconfigurable optical add/drop multiplexer (ROADM); optical interleaver +
Footnote †: journal: Applied Physics Letters  
19 September 2019; Accepted: 25 October 2019; Published: 7 November 2019
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 3 Description of the GeoHyTE Platform
### Computational Architecture
#### 3.2.2. Experimental Data  
GeoHyTE's first GUI is illustrated in Figure 3. It accommodates the information coming from the experiment, including the output signal, the mixture density of the material, and the travelling distance of the shear wave. This information, together with the tentative arrival time, is needed to compute the initial shear modulus.  
Figure 3: Layout of GUI 1.  
Figure 2: GUI structure in GeoHyTE.  
The experimental data are loaded and plotted from the _Experimental signal_ panel. The input file should contain the data in tab-separated columns, where the first column should be the time, measured in seconds. The file may contain as many columns and as many header lines as desired. The column containing the output signal that should be loaded and the number of header lines should be specified in the same panel. After the Plot button is pressed, the plot of the output signal appears.  
As shown in Section 3.1.3, the user needs to define the correlation window and the tentative arrival time. Left click on a point on the plot sets that point as the arrival time of the shear wave and marks it with a magenta circle. Right click on a point on the plot sets that point as the limit of the correlation window, which is marked in blue on the plot. Edit boxes _Arrival time estimate_ and _Correlation window limit_ are filled automatically with the instants selected by the user. However, these values can be edited by the user, if needed. The first shear modulus estimate is also filled automatically in the \(G\)0 edit box.
## 4 Case Study and Economic Analysis
### Multi-Angle Economic Analysis
#### 4.3.3 Environmental Efficiency in Peak Shaving  
Figure 13 indicates the environmental efficiency of UPI and UOI at different peak-shaving levels. It shows that both the environmental efficiencies of UPI and UOI grow monotonically with increased peak shaving levels during RPS and DPSW. For UOI, the environmental efficiency reaches the optimal point around at the peak-shaving level of 55%, and then in DPSC period, it drops significantly. That is because the oil injection of UOI has a negative effect on environmental efficiency and increases oil consumption cost. For UPI, the environmental efficiency is notably improved in DPSC period and reaches the optimal point at the peak-shaving level of 65%. Since UPI at that peak shaving level could effectively reduce wind curtailment with relatively low coal consumption, UPI is much more environmentally friendly in DPSC period. Comparing UPI with UOI at the peak-shaving level of 65%, the environmental efficiency of UPI improves by 3% and shows better environmental benefits than UOI. That means from an environmental perspective, UPI is more apt to operate at deeper peak-shaving levels than UOI.