esa-sceva/satcom-chunk-collection huggingface dataset:
# Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization
## 2 Related Work
### Domain Adaptation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Proposed Techniques** & **Datasets** & **Advantages** & **Limitation** \\ \hline [PERSON] et al., & Learns representations of & & Provides a methodical & Strictly depending on CFG and \\  & program items and extracts & & approach by utilizing & DDG as graph characteristics \\ [PERSON] et al., & features from control flow & PROMISE & graph elements from CFG & would not be able to collect all \\  & graphs (CFGs) and data & datasets & and DDG to create a & the context-relevant \\  & dependency graphs & & predictive model & information required for \\  & (DDGs) using NetworkX. & & using LSTM. & defect prediction. \\ \hline [PERSON] et al., & Built and assessed a & & & The study was an attempt to provide a process for choosing \\ [PERSON] et al., & two-phase CPDP transfer & PROMISE & Discovered that the model & quality source projects. There \\  & learning model (TPTL). & datasets & effectively lessened the & are no suggestions for feature \\  & & & TCA+ instability issue. & engineering, preprocessing \\  & & & & techniques, or random datasets \\ \hline [PERSON] et al., & Extensive empirical & Open-source & models varied significantly, & The feature engineering \\  & analysis on forty & dataset with & and the clustering-based & improvements and time/cost \\  & unsupervised models. & 27 project & unsupervised systems did & chosen DP unsupervised \\  & & versions & on defect data when the & models were not included in \\  & & & three types of features & the study. \\  & & & were combined. & \\ \hline [PERSON] et al., (2020) & Training data & Relink and & Better outcomes compared & Limited emphasis was placed \\
[33] & selection for CPDP & AEEEM & to WPDP, conventional & on feature selection in favor of \\  & & & CPDP, and TCA+ in terms & minimize the distribution \\  & & of _AUC_ and F-measure. & divergence between the target & and reference data. \\ \hline [PERSON] et al., & Undersampling to & & We offer four approaches & \\  & eliminate any overlapped & Simulated and & based on neighborhood & Processing times are \\  & data points in order to & real-world & searching with various & lengthened when the \\  & address class imbalance in & datasets & criteria to find and remove & application is limited to one \\  & binary datasets. & & instances of the & minority class at a time. \\ \hline  & Domain adaptation & & & \\  & (DA)was implemented & & & \\  & with kernel twin support & & & \\  & vector machines (KTSVMs). & Open-source & & \\  & KTSVMs with DA & datasets & in terms of prediction & data already in existence be \\  & functions, or DA-KTSVM, & & accuracy as well as & made, with consideration \\  & were also employed as the & & outperform other & given to the reuse of data that \\  & CPDP model in this study. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of Related Work.
## 3 Simulation and Experimental Setup
### Image Quality Metrics  
This paper evaluated the imaging performances of all beamforming methods in terms of the lateral resolution, contrast, and quality of speckle pattern. These performances are quantitatively represented by performance parameters of the full width at half maximum (FWHM, \(-6\) dB beamwidth of the mainlobe), contrast ratio (CR), contrast-to-noise ratio (CNR), and speckle signal-to-noise ratio (sSNR) [38; 39; 40]. FWHM corresponds to the lateral resolution, while CR and CNR quantify the contrast performance. Speckle pattern represents the speckle statistics in ultrasound images. The speckle pattern in the DAS image closely obeys the theoretical Rayleigh distribution, while adaptive weighting methods usually alter speckle pattern and lead to a distribution far from the theoretical Rayleigh [27]. Using the DAS as a reference, the quality of speckle pattern for different beamformers means the degree of similarity as compared to the speckle pattern of DAS. The sSNR is used to quantitatively assess the quality of speckle pattern [18; 19]. CR, CNR, and sSNR are defined as:  
\[CR=|20 log_{10}(\frac{\mu_{i}}{\mu_{b}})| \tag{19}\]  
\[CNR=\frac{|\mu_{i}-\mu_{b}|}{\sqrt{\sigma_{i}^{2}+\sigma_{b}^{2}}} \tag{20}\]  
\[sSNR=\frac{\mu_{b}}{\sigma_{b}}. \tag{21}\]  
The above three metrics are calculated using envelope detected signals before log compression, in which \(\mu_{i}\) and \(\mu_{b}\) denote the mean values in the cyst targets and in the background speckle, respectively. \(\sigma_{i}\) and \(\sigma_{b}\) are the corresponding standard deviation values.  
In addition, the generalized contrast-to-noise ratio (gCNR) [41] was also assessed to evaluate the imaging performances of different methods. The gCNR compares the overlap between the intensity distributions of background region and cyst region, and it is defined as  
\[\text{gCNR}=1-\int min\{p_{B}(x),p_{ROI}(x)\}\,dx, \tag{22}\]  
where \(p_{B}(x)\) and \(p_{ROI}(x)\) are the probability distributions of the background and cyst, respectively, and \(x\) is the pixel intensity. If the two distributions overlap less, the gCNR value will be higher, and this indicates enhanced lesion detectability.
## 4 Simulation and Test Verification
### Actual Experimental Verification  
In the above simulation experiments, the improved jamming suppression performance of the SCCI algorithm compared to the SCCME algorithm was verified. To further evaluate this algorithm's effectiveness, actual signal anti-jamming experiments were conducted on the rooftop of a new main building at Beihang University using a hardware platform, as shown in Figure 10. The system uses a dedicated Iridium antenna to collect signals. Gaussian jamming generated by the signal source was combined with the Iridium signal through a hybrid coupler. The combined signal was then frequency-shifted to an intermediate frequency (IF) using down-conversion devices. Finally, the signal was processed through a signal reception and processing platform using different anti-jamming processing algorithms for performance comparisons. As shown in Figure 11, the top curve indicates that during the test period, a total of four Iridium satellites were visible. The constellation map corresponding to the visible epochs of the Iridium satellites is shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Scene One** & **Scene Two** & **Scene Three** \\ \hline Jamming signal bandwidth & 10 kHz & 20 kHz & 40 kHz \\ JSR & & 5 dB–30 dB, the step was 2 dB & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters for anti-jamming performance verification.  
Figure 10: Hardware connection diagram.  
Figure 9: Verification of anti-jamming performance. (**a**) Scene One; (**b**) Scene Two; (**c**) Scene Three.  
The experiment involved setting different levels of jamming signal using a signal source and designing scenarios with strong jamming (JSR 35 dB) and weak jamming (JSR 15 dB). The jamming signal bandwidth was set to 40 kHz. Figures 12 and 13 show the results of the anti-jamming algorithm under different jamming scenarios.  
Figure 11: Constellation map during the satellite visibility period.  
Figure 12: The anti-jamming results of the strong jamming scenario for the SCCI algorithm and SCCME algorithm (JSR 35 dB). (**a**) Before anti-jamming; (**b**) after anti-jamming (SCCI); (**c**) after anti-jamming (SCCME).  
Figure 13: The anti-jamming results of the weak jamming scenario for the SCCI algorithm and SCCME algorithm (JSR 15 dB). (**a**) Before anti-jamming; (**b**) after anti-jamming (SCCI); (**c**) after anti-jamming (SCCME).  
The experimental results further validate the effectiveness and jamming suppression performance of the SCCI algorithm in the LEO satellites signal scenario, whereas the SCCME algorithm consistently exhibited poor anti-jamming effects with incomplete suppression of interference spectral lines.
# Constrained Optimization of FPGA Design for Spaceborne InSAR Processing
## 5 Experimental Results and Discussion
### Test of Different Threads in Coarse Registration  
It has been pointed out in Section 4.3 of Section 4 that when the thread of interferogram generation was 4, the scheme of the thread of coarse registration as 1 was the balanced design. In practice, for interferogram generation, the actual time of the scheme with 8 threads was 164.7 ms, which was longer than the theoretical value of 4 threads (i.e., 160 ms); when the thread of interferogram generation was 8, the thread 1 in coarse registration was still the balanced design of the system in theory. So, as shown in Table 8, we only needed to compare the scheme with 1 thread and the one with 2 threads in the coarse registration to find the balanced design.  
As shown in Table 9, \(\
abla G\) of scheme \(f\) and \(e^{\prime}\) was around 3, indicating that the scheme with the thread of coarse registration as 1 was the balanced design. The experiment result confirmed our previous analysis. From Table 4, \(\
abla G\) in theory between thread 1 and thread 2, that is the scheme \(d\) and \(e\), could be calculated according to the Formula (7). \(\
abla G_{B}\) was about 3.50 while \(\
abla G_{T}\) was around 3.00. Therefore, the relative error \(\varepsilon\) of \(\
abla G\) was at 14.21%.  
The value \(\varepsilon\) was relatively large for the following two reasons. First, the theoretical analysis did not take into account the efficiency loss during the storage process with the continuous address. In addition, after the 512 \(\times\) 512 points in the center were found from the 16,384 \(\times\) 4096 image, the addresses would be changed every 512 points taken, which also added to the efficiency loss.
# Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control
## 5 Experimental Application Results
### Evaluation of the QR Code  
Figure 15 shows a user calling the elevator by reading the QR code on the second floor of the building. The QR code automatically calls the elevator and opens its web interface to select the destination when entering the car. The QR codes displayed in Figure 11 were printed and evaluated using different sizes between 1 \(\times\) 1 cm and 7 \(\times\) 7 cm increasing in 1 cm steps. Figure 16 summarizes the sizes of the QR evaluated and the minimum, maximum, and mean scanning distances at which each QR code was successfully read with the front camera (16 Mpx) of a smartphone. The results show that a QR code of 7 \(\times\) 7 cm (Figure 15) can be read with a 100% success at a distance range from 0.22 m to 2.5 m. Therefore, there is no need to get closer to the elevator to use the QR code feature to call it through the IoT device.  
Figure 14: Evolution of the latency of the IoT device.  
Figure 15: User with a mobile phone reading the QR code (at 0.3 m) to call the elevator.
## 3 Case Study Results
### Case Study 2  
For this case study, we will analyze two sets of results depending on the system adopted (wind turbine or photovoltaics).  
For the wind turbine case, the gross annual GHG emission reduction is 93%, comparable to 55 cars (traditionally fueled) no longer used.  
Financial analysis shows, in Figure 10, that equity payback for the wind turbine power plant is reached after 3.5 years of the 25 years of the project's lifetime, but it should be taken into consideration that this analysis does not involve any incentive due to the rapid evolution of regulations, that it is progressively converting the initiatives for funding clean energy generation with initiatives for help in designing sustainable systems.  
Figure 10: Financial analysis chart (screenshot by author using RETScreen).  
For photovoltaic installation, the gross annual GHG emission reduction is 96%, comparable to 22 cars (traditionally fueled) no longer used. Even though the percentage is greater than the wind turbine case, we should consider that the electricity produced with photovoltaics is around one third of that produced by the other system, so effectively the CO\({}_{2}\) reduction is minor.  
Financial analysis shows, in Figure 11, that equity payback for the photovoltaic case is reached after 16.7 years of the 25 years of project's lifetime. For incentives and grants, the same consideration made for wind turbine system has been evaluated.
## 3 Results and Discussions
### Transformed Divergence Separability Index (TDSI)  
Separability analysis showed that water was the most separable from other LCZs (Transformed Divergence Separability Index (TDSI) = 2) while compact midrise and compact lowrise had similar spectral characteristics (TDSI = 1.071) (Table 2). Spectral similarity between compact lowrise and compact midrise could be due to the high density of buildings, which was common to both LCZ categories [17]. However, TDSI values were greater than 1.000, mostly between 1.071 and 2.000 for any two paired classes, which indicated that the LCZs were largely easily separable from each other using the ground truth and imagery data.  
\begin{table}
\begin{tabular}{l c c} \hline
**LCZ Category** & **Producer Accuracy** & **User Accuracy** \\ \hline Compact lowrise & 89.45 & 76.45 \\ Compact midrise & 63.47 & 81.99 \\ Dense forest & 93.02 & 93.18 \\ Lightweight lowrise & 96.81 & 94.85 \\ Low plants & 90.98 & 98.43 \\ Open lowrise & 98.08 & 91.74 \\ Open midrise & 79.17 & 85.93 \\ Water & 100.00 & 100.00 \\ \hline \end{tabular}
\end{table}
Table 1: Accuracy of LCZ classification.  
Figure 3: The spatial structure of Aspect (**a**), DEM (**b**), NDBaI (**c**), NDBI (**d**), NDVI (**e**), MNDWI (**f**), and UI (**g**) in Harare.
## 3 Methodology and Experiments
### Study 1: Orthogonal Gradients  
EEG recordings were carried out individually from the spherical phantom, the head-shaped phantom, and six human subjects during the execution of a modified echo-planar imaging (EPI) sequence. The purpose of this work was to investigate how the shape of the phantom affects the magnitude of the GA generated by each of the three orthogonal gradients (AP, RL, and FH). Prior to each slice acquisition, three additional gradient pulses, shown in Figure 6, were applied sequentially  
Figure 4: Schematic diagram of the experimental setup of the study.  
Figure 5: Arrangement of the amplifier (**A**) and cables attachment to a cantilever beam (**B**), utilized to separate it from the scanner’s vibrations.  
along the AP, RL, and FH directions. This study was approved by the local Ethics Committee of the University of Nottingham, and each subject provided written informed consent.  
The trapezoidal gradient pulse for 30 ms duration consisted of two 10 ms ramps, during which the gradient was changed at a rate of 2 Tm\({}^{-1}\)s\({}^{-1}\) and was kept constant between the consecutive ramps (Figure 6). Successive gradient pulses had a gap of 20 ms. The modified pulse sequence provided clearly defined intervals during which just a single gradient varied in time, allowing the easy separation of the effects of the three different gradients. In a standard gradient echo (GE) EPI acquisition, time-varying gradients are simultaneously applied along multiple axes at high frequencies, and the typical EEG system's low-pass filtering significantly attenuates and affects the induced voltages, which in turn makes it difficult to differentiate the voltages produced by individual gradient pulses. This introduces a challenge to compute the outcome of the applied time-varying gradient pulse along one specific direction. Therefore, the EEG data were recorded with a frequency range of 0.016-1000 Hz, which ensured the reliability of the acquisition of the EEG data (assuring a higher bandwidth) during the usage of gradient pulses.
## 3 Cellular Network Modeling  
Dealing with cellular networks for positioning and other related tasks requires managing two different kinds of data: information on cellular networks and their spatio-temporal arrangement. In this section, we account for both of them by developing a conceptual model using a spatio-temporal extension of the classical entity-relationship notation called ChronoGeoGraph (CGG) [33; 34].  
Then, following consolidated database design methodologies, we translate the conceptual model into a relational logical schema, which has been implemented as a database within a PostgreSQL (PostgreSQL website: [[https://www.postgresql.org/](https://www.postgresql.org/)]([https://www.postgresql.org/](https://www.postgresql.org/)), (accessed on 14 February 2022)) DBMS for evaluation purposes. As we will see, the design takes into account both spatial and temporal characteristics of the considered domain, in order to cope with the issues pointed out in Section 2.2.  
The section is structured as follows. In Section 3.1, we consider the modeling of the network from a high-level perspective. Then, Section 3.2, Section 3.3, and Section 3.4 deal with the aspects that are specific to GSM, UMTS, and LTE technologies, respectively (although here we focus on GSM, UMTS, and LTE, the proposed model is highly flexible and modular with respect to network technologies). Section 3.5 combines all the previous local sub-models into a general, comprehensive one, which also includes the temporal aspects (detailed in Section 3.6). Next, in Section 3.7, the translation of the conceptual model into a logical relational schema that goes through a restructuring phase is illustrated. We conclude some remarks about the physical implementation of the database.
# Investigations of the Energy Transfer
## 3 Results
### Excitation Energy Transfer in the Phycobilisome Antenna
#### 3.3.2 Transient Absorption
Global fitting of the time-resolved fluorescence spectra uncovered three kinetic components in the ps time scale assigned to intra-rod, rod to core, and whole PBS equilibration. Additionally, ns time-scale kinetic components were reported for the final decay of the PBS core (1.68 ns), as well as for the unconnected rods in the short rod PBS (1.2 ns). In our TCSPC experiments, the fast lifetimes could not be resolved due to the limited temporal resolution of our system; however, the final decay presented similar value to the wild type PBS. The two longest lived kinetic components obtained from the transient absorption experiments (622 nm pump pulses) reported for the wild-type PBS in the above-mentioned paper are comparable to ours: 132 ps and 1.2 ns in [42] versus 122 ps and 1.15 ns obtained here. The equilibration of the wild-type PBS was completed in 132 ps, after equilibration of PC pigments in 5 ps and rod to core energy transfer in 59 ps. Moreover, a similar decrease of the EADS amplitude was observed (bleach and stimulated emission area), which was assigned to annihilation processes taking place in the complexes absorbing two or more photons. A more similar rod to core equilibration time to ours was reported for the PBS with short rods (19 ps versus 14 ps obtained here). The results reported in [42], as well as those reported by [PERSON] et al. [41], and the ones reported here show that apart from minor dynamic differences, the kinetics obtained for PBSs extracted from different strains of cyanobacteria can be compared, regardless if their structures are similar or differing. The weak dissimilarities can in some cases be attributed to the conditions used for investigating the PBSs, such as excitation intensities and wavelengths, and of course, the temporal resolution of the analyzing systems.
# On Connectivity and Energy Efficiency for Sleeping-Schedule-Based Wireless Sensor Networks
## 4 System Simulation
### The Simulation Results of the Connectivity on the Connectivity Probability  
According to Figure 3, we know that when the connectivity \(K\) is about 8, communication radius \(R\) is about 16, the wireless sensor network is a strongly connected network. Thus, when the connectivity \(K=8\), and the node communication radius \(R\) takes different values, the theoretical values obtained from Equation (2) are compared with the experimental results. The node communication radius \(R\) is  
Figure 3: The relationship between the network node connectivity \(K\) and the connected probability \(P_{r}\).  
\(9\sim 18\), and both \(a\) and \(b\) are equal to 1. When \(K=8\), the relationship between the node communication radius \(R\) and connectivity probability \(P_{r}\) is shown in Figure 4.  
As can be seen from the Figure 4, the simulation curve is very approximate with the theoretical curve, so in the actual situation can use Equation (2) to compute the maximum of the connectivity probability. When and only when the communication radius \(R=16\), the network connectivity \(K=8\), the wireless sensor network achieve the highest connectivity probability. This proves the conclusion from the fact that Figure 3 reflects, when the network connectivity \(K=8\) and node communication radius \(R=16\), the wireless sensor network can perform robust connectivity. Moreover, if the node communication radius \(R\) continues to increase, such as \(R=18\), the network connectivity probability \(P_{r}\) fell to 0.23. It cannot guarantee the network connectivity to be maximized and will increase the network energy consumption as well [27], therefore, reducing the service life of the network.
###### Abstract  
With its high water potential, the Ziz basin is one of the most important basins in Morocco. This paper aims to develop a methodology for spatiotemporal monitoring of the water quality of the Hassan Addakhil dam using remote sensing techniques combined with a modeling approach. Firstly, several models were established for the different water quality parameters (nitrate, dissolved oxygen and chlorophyll a) by combining field and satellite data. In a second step, the calibration and validation of the selected models were performed based on the following statistical parameters: compliance index R\({}^{2}\), the root mean square error and \(p\)-value. Finally, the satellite data were used to carry out spatiotemporal monitoring of the water quality. The field results show excellent quality for most of the samples. In terms of the modeling approach, the selected models for the three parameters (nitrate, dissolved oxygen and chlorophyll a) have shown a good correlation between the measured and estimated values with compliance index values of 0.62, 0.56 and 0.58 and root mean square error values of 0.16 mg/L, 0.65 mg/L and 0.07 ug/L for nitrate, dissolved oxygen and chlorophyll a, respectively. After the calibration, the validation and the selection of the models, the spatiotemporal variation of water quality was determined thanks to the multitemporal satellite data. The results show that this approach is an effective and valid methodology for the modeling and spatiotemporal mapping of water quality in the reservoir of the Hassan Addakhil dam. It can also provide valuable support for decision-makers in water quality monitoring as it can be applied to other regions with similar conditions.  
Ziz basin; water quality; satellite image analysis; modeling approach; nitrate; dissolved oxygen; chlorophyll a; climate change; time series analysis; environmental monitoring
## 3 Designing Virtualization of Wireless Sensor Network
### Challenges behind Designing VSNs
#### 3.2.12 Dynamism and Mobility Management  
The sensor network virtualization environment is highly dynamic. At a macro level, virtual sensor networks with shared interests can be dynamically aggregated together to create federations of virtual sensor networks. Multiple federations and virtual sensor networks can also come together to form virtual sensor network hierarchies. Aggregation and dissolution of control and data planes for macro level dynamism is an unresolved issue. At a micro level, mobility of end users from one physical location to another and migration of virtual sensor routers for operation and management purposes pose the biggest challenge. Finding the exact location of any resource or end user at a particular moment and routing packets accordingly is a complex research challenge that needs efficient solutions. In addition, sensor network virtualization allows end users to move logically from one virtual sensor network to another, which further complicates the problem.