esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Dereverberation with a Lower Complexity at Presence of the Noise
## 3 Proposed Dereverberation Method
### Algorithm Architecture  
In the upper path of Figure 1, by using Equations (2), (3) and (15), the output \(x_{\text{b}}\) (\(l\)) of beamforming is obtained as follows:  
\[x_{\text{b}}(l)=\mathbf{w}_{\text{b}}^{H}\mathbf{y}(l)=x_{\text{e}}(l)+\mathbf{ w}_{\text{b}}^{H}[\mathbf{r}(l)+\mathbf{v}(l)] \tag{17}\]  
Although most of the noise can be suppressed by the MVDR beamformer, the effect of dereverberation is very limited [27]. The scalar \(x_{\text{e}}\) (\(l\)) denotes the inner product \(\mathbf{w}_{\text{b}}{}^{H}\mathbf{x}_{\text{e}}\) (\(l\)).  
In the lower path of Figure 1, the past (\(L-D\)) frames are used as the input signal of the linear predictor. By combining the vectors from \(\mathbf{y}\) (\(l-D\)) to \(\mathbf{y}\) (\(l-L+1\)), the input matrix of the linear predictor at the \(l^{th}\) frame is given as follows:  
\[\mathbf{t}(l)=\left[\mathbf{y}^{T}(l-D),\cdots,\mathbf{y}^{T}(l-L+1)\right]^{T} \tag{18}\]  
and the prediction signal \(z(l)\) can be expressed as follows:  
\[z(l)=\mathbf{w}_{\text{r}}^{H}(l)\mathbf{t}(l) \tag{19}\]  
where \(\mathbf{w}_{\text{r}}(l)\) is the linear prediction filter. An appropriate \(D\) should be selected according to the frame shift in the STFT operation in order to make the correlation between the prediction error \(x_{\text{e}}(l)\) and the prediction signal \(z(l)\) zero, i.e.,  
\[\langle z,x_{\text{e}}\rangle=\sum_{l}z(l)x_{\text{e}}(l)=0 \tag{20}\]  
The choice of \(D\) determines the amount of early reverberation preserved in the desired signal, which affects the performance of the algorithm [15].  
According to the architecture of the proposed method, the final enhanced speech \(s(l)\) is obtained as follows:  
\[s(l)=x_{\text{b}}(l)-\mathbf{w}_{\text{r}}^{H}(l)\mathbf{t}(l) \tag{21}\]  
where the output \(s\) (\(l\)) is regarded as an estimation of \(x_{\text{e}}\) (\(l\)). If \(\mathbf{w}_{\text{r}}\) (\(l\)) is available, we can estimate the final enhanced signal \(s\) (\(l\)) from Equation (21). In the ISCLP method [12], the Kalman filter was used to jointly estimate the parameters of the sidelobe canceller (SC) and the linear predictor. Since the linear predictor needs information from the past frames, the canceller and predictor cannot work on the same frame simultaneously. It does not mean they are mutually independent due to the common inclusion of the late reverberation components. This implies that changes in the canceller parameters cause changes in the predictor parameters, and vice versa. To avoid this problem, in this paper, the coefficients of the beamformer and the coefficients of the linear predictor are estimated separately. Thus, the result of the beamforming will improve the result of the linear prediction as well.  
Figure 1: Block diagram of the proposed dereverberation method.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Smart-Grid-Based Cybersecurity Standards
#### 2.5.4 ISO/IEC 27001  
ISO/IEC 27001 [52] establishes a general framework for an Information Security Management System (ISMS) as its standard helps bridge the security concerns of Information Technology (IT) and Operational Technology (OT), which is critical for controlling hybrid smart grid environments. It can also be applied in various parts of the energy sector, including power generation systems, control and automation technologies, and distributed field operations [53].
## 5 Experiments and Analysis
### Case II: IEEE 24 Bus Test System  
This section verifies the effectiveness of the proposed scheme in a relatively large system--IEEE 24 bus test system. The simulation time lasts 10 s. Both reinforcement learning control and DoS attacks are applied. The optimal attack strategy and the optimized control structure are obtained by solving  
Figure 4: Frequency deviation of IEEE 14 bus system under control.  
Figure 5: Optimal DoS attacks and control structure 3D Graph in the IEEE 14 bus system.  
From the optimal attack strategy results shown in Figure 7, we know the attacker should focus on the communications from buses 2, 3, 4, 14, 19, 21, and 22 with the attack possibility of 0.2-0.35. The optimal control structure under the worst case of DoS attacks is shown in Figure 7. The sensor should be installed in buses 2, 3, 4, 6, 14, 15, 19, 22, 23, and 24. The weight of bus 19 is high, which means the information obtained from bus 19 has high importance. Thus, DoS attacks have such an attack strategy that attacks the communication lines of bus 19 with a relatively high attack rate.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Equalization
#### 3.2.6 Post-Equalization PCVNN  
To improve the SNR in Underwater Visible Light Communication (UVLC) system, high LED power must be encouraged due to the LED's incoherent characteristic and the water medium's considerable attenuation. The nonlinearity grows more severe as the signal amplitude increases. Consequently, symbols on the outside of the constellation sustain a more nonlinear distortion than those on the inside. Based on complex-valued neural network (CVNN) [59], an adaptive partition equalizer (PCVNN) [60] has been presented, which reduces the complexity and has superior performance.  
In PCVNN, the constellation is segmented into two areas by a proper threshold to distinguish between large-amplitude signals and small-amplitude signals. Then, the large- and small-amplitude signals are fed into two complex-valued neural networks. Finally, a fully connected neural network is then used to combine the signals into a complete one. Since large and small signals experience different nonlinear impairments, such a network structure can recover the signal more accurately and can greatly reduce the complexity of the model for small signals. The final experimental results also verified this conjecture [60]. PCVNN achieves up to 56.1% computational complexity reduction compared with the standard CVNN at the same performance.
## 4 Results
### VOS-Net  
Finally, a video-based real-time oil sheet detection algorithm (VOS-Net) was developed, providing users with a visual image with oil sheet detection result. Figure 6 demonstrates the VOS-Net functions. Given a video input, the VOS-Net will show the image extracted on the top window with time, detected frame number, and prediction result of the current frame. Above the video image, a record of the prediction result is shown.  
The image extraction frequency can be adjusted manually, based on the performance of the users' computer, to offset the latency effect of the VOS-Net. In the test, the model inference time to predict one image is 0.03-0.04 s (2.4 frames on average). The shortest time that oil sheet appears on the water surface in OS-SS is 3 s, based on our lab experiment. In this case, setting the image extraction frequency anywhere from 3 frames to 180 frames, the VOS-Net can achieve real-time prediction on each image. To achieve higher accuracy and avoid response latency, we suggest selecting the image extraction frequency parameter in a range from 30 to 60 (0.5 s to 1 s).  
When we applied a filter (k) larger than two, the filter could efficiently suppress false-positive predictions to increase the accuracy of VOS-Net.  
Figure 5: Example oil sheen image ground truth and OS-Net prediction results.  
Figure 6: VOS-Net prediction window and result; a demo of VOS-Net result is available at [[https://youtu.be/kCZnMAYBByk](https://youtu.be/kCZnMAYBByk)]([https://youtu.be/kCZnMAYBByk](https://youtu.be/kCZnMAYBByk)) (accessed on 15 July 2022).
## 4 Cellular Network Reconstruction and Validation
### Continuous and Periodic Validation  
The previously mentioned filtering operations applied during the database population phase can be referred to as \"continuous validity checks\", to underline the fact that they were launched for each new measurement, in contrast with \"periodic validity checks\", which are instead run only at regular intervals, due to their semantic nature and computational complexity. An example of a periodic check is as follows: if a cell has enough associated observations, it can be determined whether the (possibly estimated) locations of its latest observations (that is, those entered after the last launch of this periodic check) are consistent with the coverage of the cell--i.e., if the geometric distances of the locations with respect to the previously known cell extension are plausible (e.g., not too large). Finally, the detection of cell renaming phenomena has also been implemented as a periodic routine: briefly, on the basis of [15], we verified whether several (spatio-temporal) conditions characterizing the renaming phenomenon are satisfied. Ideally, periodic checks should complement and be run in parallel with continuous ones.
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 8 Conclusions  
We presented a new method to perform an adaptive joint Tx/Rx compensation, of stringent filtering effected caused by CMDs and ROADMs in fixed-grid optical networks while using typical HW blocks in a coherent transponder. When the Rx filtering is not matched to the Tx pulse shape and the optical channel, this can result in insufficient electrical bandwidth and noise enhancement. The proposed method leverages Tx and Rx static filters compensation capability, of the channel frequency response, such that they effectively constitute a matched filter pair hence maximizing SNR at the input of the Rx adaptive filters [54; 55]. With the filtering models representing real optical links and components, as well as the emulation of PDL, we showed an improvement in overall system performance. We showed for the first time, to the best of our knowledge, that splitting the compensation of heavy filtering in real deployment scenarios is better than doing only Tx pre-compensation or only Rx post-compensation. That is especially the case in presence of realistic implementation noise profiles at Tx, such as jitter at DAC, limited ENOB of DAC, clipping of filter coefficients and noise in the analog chain. With the 18 xWSSs scenario, the simulation showed gain of up to 0.6 dB in the linear operation region and 1.1 dB in the nonlinear operating region. Lab measurements confirmed the method advantage in linear operating region by up to 0.4 dB.  
[PERSON] and C.D. conceived the idea and performed simulations and measurements. As well, analyzed the data and wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Natural Sciences and Engineering Research Council of Canada (NSERC) Grant #211300.  
The authors declare no conflict of interest.
# City Architectural Color Recognition Based on Deep Learning and Pattern Recognition
## 4 Framework  
### Image Data Processing  
The initial step in building recognition involves gathering image data of building objects. This thesis explored two approaches to test the effectiveness of architectural feature recognition. The first approach involves using approximately 500 images of buildings and landscapes, without any specific categorization, in an attempt to identify general buildings. All 500 images used in this test experiment were captured by the author. In the second method, the same type of building is photographed from all angles, and around 30-50 images of the building are identified. In the first method, some identification of architectural features can be achieved. However, this approach yields relatively low accuracy in correctly identifying buildings in panoramic images and fails to classify different types of buildings effectively. Therefore, the second approach was adopted in this thesis to photograph individual buildings and classify them, which involves capturing images of each type of building, such as residential buildings, special library buildings, and office buildings, from various angles. By focusing on specific building types and conducting initial identification of building areas, the system was trained to study the building objects. First, for each type of building, 30-50 images were selected to train the deep learning model on building feature extraction. The image processing procedure primarily involved training the deep learning model on buildings and extracting features specific to each building type. Figure 4 illustrates the training process for different types of buildings, where rectangular bounding boxes were utilized to locate buildings in the images and collect their respective features for subsequent classification. Figure 4 provides a glimpse of some images used for training the feature recognition of administrative buildings.
## 3 Results and Discussion
### Adsorption of Ammonium Nitrogen on Biochar
#### 3.2.3 Effect of Adsorption pH on the Adsorption Capacity of Biochar  
The pH value of NH4+-N solution has a critical effect on the charge distribution of biochar and the electrostatic or molecular interactions between the biochar and NH4+-N, which accordingly determine the NH4+-N adsorption capacity of biochar. The effect of adsorption pH on the adsorption capacity of KOH-activated biochar is presented in Figure 2. It was found that the adsorption capacity increased with increasing pH value from 3.0 to 9.0, and further enhancement of the pH resulted in a sharp decrease in adsorption capacity. Similar results have been documented by other researchers [32; 33]. The low adsorption capacity at low pH may be caused by the low negative zeta potential of biochar at low pH as shown in Figure 2. Since ammonium nitrogen is a positive ion, biochar with a high negative zeta potential facilitates adsorption through electrostatic attraction. However, when the solution pH value is increased to 10.0, most of the NH4+ ion can be transformed into the NH3(aq) form as the NH4+ ion has a pKa of about 9.3 [33]. Therefore, at this pH value (10.0), the electrostatic attraction mechanism was no longer effective, which resulted in the observed reduction in the adsorption capacity [32]. It is notable that the pH value of biochar in water without adjustment was about 8.4 and the adsorption capacity of biochar at this pH was almost the same as the highest value at pH 9.0. In order to simplify the adsorption process, the following adsorption experiments were conducted without pH adjustment (pH about 8.4).
## 4 Experiments and Analysis
### Model Comparison Experiment
#### 4.4.1 Comparison of Detection Accuracy Across Models

Table 3: Performance comparison on the SfdDD dataset. The top results for each metric are shown in bold, with the next best results underlined.
## 3 Opportunities, Challenges and Suitability
### Challenges  
Although RDMA provides higher performance compared with traditional protocols, the dilemma between RDMA performance achievement and redesign cost of an existing application according to RDMA semantic can be a challenging task. Redesign can be as broad as the whole system or limited to the communication component with the RDMA send/receive counterpart. It should be noted that the performance achievement by the latter approach is restricted.  
One of the challenging problems in an RDMA-enabled distributed application is orchestrating local and remote memory accesses, since these are transparent to each other. Practically, synchronizing these concurrent accesses hinder the RDMA performance through the concurrency control mechanism. Typically, applying concurrency control incurs access amplification to guarantee the data consistency. However, there are some limited hardware-based solutions such as _Hardware Transactional Memory_ (_HTM_). Furthermore, this concurrency problem is not limited to remote and local memory accesses, and there is a race among the remote memory accesses as well. For such condition, RDMA supports atomic operations such as CAS and FAA. The performance of these atomic operations intrinsically depends on its implementation in hardware [14]. Furthermore, the size of an atomic operation is limited (e.g., 64 bit). RDMA atomic operations can be configured to global (_IBV_ATOMIC_GLOB_) or local (_IBV_ATOMIC_HCA_) granularity. The local granularity guarantees that all the atomic operations to a specific address within the same HCA will be handled atomically, the global one guarantees that all the atomic operations on a specific address within the system (i.e., multiple HCAs) will be handled atomically. However, until now, only local mode is implemented in the existing HCAs.
## 11 Study of Mesh Pattern for Optically Transparent Flexible Antenna with Feedline  
In this paper [10], a systematic parameter study is carried out on mesh pattern for optically transparent flexible antenna. When a transparent flexible antenna is implemented using a metal mesh, the opposite factors are the transparency and performance of antenna. To understand the relation between the mesh design parameters and antenna performance, the authors carried out the parametric study of diamond and square meshes. The authors also provided measured results that demonstrate the feasibility of the proposed antenna.  
A Dual-Band Dual-Polarized Antenna with Improved Isolation Characteristics for Polarimetric SAR Applications  
In this paper [11], a dual-band dual-polarized antenna is proposed with high isolation characteristics for polarimetric synthetic aperture radar (PolSAR) applications. The antenna is composed of 2 \(\times\) 2 patch antenna arrays and four dipole antennas. The dipole antennas and the patch antenna arrays require dual-linear polarization characteristics to achieve PolSAR data. Improvements of the isolation characteristics are obtained by inserting a metamaterial absorber with a fractal geometry between the transmitting (Tx) and receiving (Rx) dipole antennas. The isolation characteristics with the absorbers are higher than 23.44 dB over the target operated band. The results demonstrate that the proposed antenna can be a good candidate for PolSAR applications.
## 1 Introduction
### Motivation  
Nowadays equipment vendors are making an effort to supply easy-to-use HW (Hardware) and SW (Software) so that crop monitoring can be performed by individual farmers. The bundle of Parrot Sequoia(r) (Parrot S.A., Paris, France) and Pix4D(r) (Pix4D S.A., Prilly, Switzerland) is a clear and popular example of this approach. The present paper arises from one simple yet crucial question: what is the reliability of the radiometric information and of the related vegetation indices acquired by the Sequoia camera and processed with the bundled Pix4 DMapper software? Considering that UAV surveys for precision agriculture typically are multitemporal, the original question can be rephrased: what is the consistency between repeated surveys? In other words, when two datasets highlight differences for a certain part of a field, to what extent is this due to acquisition and processing errors, and to what extent does this point out a variation in the status of the crop? The importance of such questions is confirmed by the fact that only a few papers in the literature have explored them to date.  
The present work studies geometric and radiometric consistency of two overlapping datasets, acquired with a Sequoia camera and processed with the bundled software. We focus on geometry to avoid the influence of its inconsistencies on the quality of the radiometry. A distinctive feature of the paper is that the geometric consistency is not assessed by means of a (generally limited) number of check points (CPs), as is usually done. Instead, we assess it by exhaustively evaluating the distance between the whole generated point clouds. We investigate radiometry as well, because it is the main source for agronomic studies. Moreover, we compare datasets acquired almost at the same time. This is a strength, as the difference assessed in vegetation indices can only be attributable to sensor noise, and possibly to issues in the radiometric calibration procedure.
## 3 Results
### Colorimetry  
Based on Figure 11a-c, unirradiated (INT0) bare epoxy resin (C0) was transparent and exhibited high L*, since measurements were conducted over a white background. Colour parameter a* was on the green scale (a* = -2), possibly due to a reaction between molecular oxygen and unreacted amine groups of the hardener [50]. The same samples exhibited low values of colour parameter b* (yellowness) [51], due to the presence of chromophore groups (i.e., nitrogen groups, conjugated bonds, etc.) also affected by auxochromic groups of the epoxy resin (i.e., -OH), even at low light conditions [52]. Incorporation of ZnO filler into the epoxy matrix (INT0) caused lightness parameter L* to increase as the ZnOconcentration increased. In the same manner as in bare samples, ZnO-loaded ones exhibited greenness (a* \(<\) 0) due to the hardener, as stated above, and to possible defects in ZnO crystal structure [53]. Yellowness parameter b* did not undergo significant changes.  
Furthermore, it is evident that, with the onset of AWE treatment (INT576), oxidation was induced on the C0 sample surfaces, resulting in a more yellowish hue. Treatment also caused parameter a* to increase and L* to decrease. The yellow colour grew more intense (increasing b* values) as the irradiation period progressed up to INT2304. This can be designated as a border AWE treatment interval, after which reduction in b* value at INT2880 is observed, denoting the initiating decomposition of -OH groups, as observed in FTIR measurements.  
Moreover, colour parameter L* remained almost unaltered after INT576 for ZnO-loaded samples. Parameter a* continued to increase into the red region as AWE treatment progressed and as ZnO content increased. The latter may be attributed to ZnO crystal defects, namely oxygen vacancies (V\({}_{\text{O}}\)) and oxygen interstitials (O\({}_{\text{I}}\)), while -OH group defects contribute to yellowing [54]. Regarding parameter b*, by increasing the ZnO content, smoother increases in b* values were observed. C0.1 and C0.5 samples exhibited the lowest yellowing during AWE treatment. C0 samples sustained the most abrupt increases as AWE treatment progresses, exhibiting the highest values at INT2304 compared to all samples. In the case of C2 samples, colour parameter b* underwent the smoothest changes as aging continues, indicating stabilization of the yellowing discoloration and thus, the aging of the composite, due to the protecting properties of the increased ZnO content. Finally, as a whole, \(\Delta\)E of C0.1 and C0.5 samples was the lowest among all samples (Figure 11d).  
Figure 11: (a–d) CIELAB colour parameters (a) L*, (b) a*, (c) b* and (d) \(\Delta\)E of sample surfaces during AWE treatment.
# Phase Center Corrections for BDS IGSO and MEO Satellites in IGb14 and IGSR3 Frame
## 2 Basic Information
### Status of BDS Satellites and Tracking Network
Trimble NetR9 as well as deployment of Trimble Alloy, the number of stations tracking C23 increased gradually, and even exceeded that of C32 around DOY 180, 2019. Owing to the update of Sepentrito firmware, C35 was tracked by more stations, and beyond 100 around DOY 297, 2019. However, for the satellite beyond C37, no more than 30 tracking stations were available, and were mainly from iGMAS. Hence, their PCCs are not estimated in this study.