esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Aggregation Stereo Matching Network Based on Dense Grouping Atrous Convolution
## 2 Related Work and Methods
### Methods
#### 2.2.2 DenseGASPP for Dense Feature Extraction  
Feature extraction is the premise of correctly estimating the disparity in the stereo matching task. Making full use of global context information in feature extraction is helpful for capturing different proportions of objects and improving the accuracy of object segmentation, which is essential for stereo matching.  
DenseGASPP was utilized for feature extraction. In the DenseGASPP module, we designed three atrous convolution layers with a dense connection, allocated two convolution layers with a continuous small dilation rate in each layer, connected the two convolution  
Figure 2: The architecture of the multi-scale aggregation network based on dense grouping atrous convolution.  
layers with a continuous dilation rate in each layer, and lastly connected the feature maps of three resolutions obtained from the three layers of atrous convolution layers to fully fuse the feature information. This method can balance the influence of the hole part on the segmentation task. The model is shown in Figure 3. The fusion process is shown in Equation (3).  
DenseGASPP can be written as:  
\[\left\{\begin{array}{c}X_{i}=G(X_{i-1})\,\ i=1\\ X_{i}=\left[U_{X_{i-1}},G(X_{i-1})\right]\,\ i=2\,\ 3\end{array}\right. \tag{3}\]  
where \(i\) represents the number of network layers, \(X_{i}\) represents the output of dense grouping atrous convolution at layer \(i\), \(X_{i-1}\) represents the network input, \(G\) represents the grouping atrous convolution function (which is a combined operation), \(U\) represents the up-sampling operation, and \(\left[U_{X_{i-1}},G(X_{i-1})\right]\) represents the concatenation, that is, the combination between different network layers.  
In general, DenseGASPP is divided into two steps for feature extraction. First, feature maps of different scales containing rich context information are obtained using grouping atrous convolution at each layer. Then, different scale feature maps are concatenated to maximize the use of object detail information to improve the matching accuracy of the network for all objects, especially small objects.
## 5 Expanding the Transfer Scenarios of the TL-Based Diagnostic Algorithm
### Transfer between Four Operating Conditions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**TCA:**} & \multicolumn{4}{c}{**JDA:**} \\  & & \multicolumn{2}{c}{**Prediction Accuracy (\%)**} & \multicolumn{4}{c}{**Prediction Accuracy (\%)**} \\ \cline{2-10}  & & \multicolumn{4}{c}{**SOURCE**} & \multicolumn{4}{c}{**SOURCE**} \\ \hline  & Ground & 28,000 ft & 35,000 ft & 41,000 ft & Ground & 28,000 ft & 35,000 ft & 41,000 ft \\ \hline  & Ground & - & **88.95** & **90.06** & **90.06** & - & 38.12 & 41.99 & **90.06** \\ \cline{2-10}  & 28,000 ft & **100.00** & - & **94.48** & **95.58** & 93.37 & - & 91.71 & 64.64 \\ \cline{2-10}  & 35,000 ft & 92.27 & **93.37** & - & **100.00** & **92.82** & 47.51 & - & **100.00** \\ \cline{2-10}  & 41,000 ft & **96.13** & **97.79** & **100.00** & **-** & 93.37 & 92.82 & **100.00** & - \\ \hline  & & \multicolumn{4}{c}{Average: 94.89} & \multicolumn{4}{c}{Average: 78.87} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Predictive accuracy of TCA and JDA for the transfer across four operating conditions. Therefore, the predictive accuracy of JDA at convergence could be considerably lower than that of the TCA loop. Figure 14 shows the process of increasing incorrect pseudo labels for this transfer scenario from 28k ft to Ground as a result of the process described above. From the confusion matrix on Figure 14 (top), the reduction in accuracy after the TCA loop is due to more and more cases being misclassified as RI blockage. To illustrate the distribution of these misclassified RI blockage cases, the distribution of the first principal component value is plotted on Figure 14 (bottom) as a representation of the conditional distribution of cases with pseudo label or true label of RI blockage. Source domain cases with real RI blockage labels are shown by the blue distribution. Target domain cases with real RI blockage labels are shown by the orange distribution, and target domain cases with pseudo RI blockage labels are shown by the green distribution. Thus, an accurate prediction of all target domain RI blockage cases would be when the orange and green distributions  
Figure 14: Confusion matrix plot (**top**) and plot of distribution of cases with RI blockage label (**bottom**) for the transfer from 28,000 ft to Ground conditions at each key iteration. Figure 13: Predictive accuracy at each iteration in the transfer scenario: (**a**) from 28,000 ft to Ground, (**b**) from 35,000 ft to 28,000 ft.  
coincide. However, this was not observed and instead, from the 3 rd iteration, cases in the green distribution exceeds those in orange distribution, because there were healthy cases mistaken as RI blockage cases. Then, applying JDA to the healthy cases misclassified into RI blockage cases would continue to cause deterioration until the final loop. By the 20 th loop, all healthy cases were misclassified.
# An Efficient Library for Reliability Block Diagram Evaluation
## 2 Reliability Block Diagrams  
An RBD decomposes a system into its independent components and shows the logical connections needed for the successful operation of the system [3; 44; 45; 4; 5]. The basic assumptions of the RBD methodology are the following ones:  
1. The modeled system, as well as each component, has only two states, i.e., _success_ and _failure_.
2. The RBD represents the _success_ state of the modeled system through the usage of _success paths_, i.e., the connections of the success states of its components.
3. The system components are statistically independent. Under this assumption, the probability of failure of the block \(A\), \(P(A)\), is not related with the probability of failure \(P(B)\) of the block \(B\)\(\forall\)\(A\), \(B\) such that \(A\
eq B\). \[P(A|B)=P(A)\)\(\forall\)\(A\), \(B\)\(|\)\(A\
eq B\)
## 2 Methodology
### Analyzing Literature Sample  
The selected literature sample, which included 21 papers, was analyzed using quantitative and qualitative methods. Quantitative analysis was performed to gain insight into the characteristics of the collected sample related to the publication trends and main research topics. The former was achieved using the bibliometric analysis method, which is an applied mathematical analysis of bibliographical units [41]. By analyzing the publication data, citations, and keywords, this method systematically represents citation patterns and publication trends within a specific field or across several disciplines [42].  
Authors of this study performed the described three-step research process. Two researchers were involved in the literature search and collection steps, while the third author was engaged in overseeing and reviewing the collected sample to ensure the quality of the research. The search was performed independently by two researchers to avoid bias.  
Figure 3: PRISMA flow diagram of literature review.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Inclusion Criteria** & **Value** \\ \hline Papers belonging to the research categories unrelated to the construction industry & Exclude \\ Papers written in the English language & Include \\ The title includes at least one searched keyword & Include \\ The abstract includes at least one searched keyword from each topic & Include \\ An abstract is relevant to the research question & Include \\ Papers that are not accessible in full text & Exclude \\ Full text is relevant to the research question & Include \\ \hline \hline \end{tabular}
\end{table}
Table 2: Papers inclusion criteria.  
Later, combining search parameters and the screening process was performed in two steps, with one of the researchers performing the title and abstract screening phase and the other author performing full-text screening. Similarly, the data analysis phase was performed by two authors, with the third author independently verifying the results.
## 3 Matching the Images of Objects  
Matching the images of objects cannot be done unless both the segmentation and detection of SIFT feature points has been completed. At present, there are a number of methods of segmentation. The LBF (Local Binary Fitting Energy) method [21; 22] is employed in this paper, because it has a better segmentation result, especially for images with intensity inhomogeneity. The SIFT feature points are invariant to image scale and rotation, with robust matching across a substantial range of affine distortion, addition of noise, and change in illumination [23; 24].  
We assume that the image \(A\) is partitioned into \(m\) subregions by the LBF image segmentation approach. The \(m\) subregions are \(A_{1}\), \(A_{2}\),..., \(A_{m}\), and \(X_{i}\) is the SIFT feature points set of the \(A_{i}\) subregion. where \(i\) = 1, 2,..., \(m\). Similarly, the image \(B\) is segmented into \(n\) subregions: \(B_{1}\), \(B_{2}\),..., \(B_{n}\), and \(Y_{j}\) is the SIFT feature points set of \(B_{j}\). where \(j\) = 1, 2,..., \(n\). \(Z_{i}\) is the matching feature points set of \(X_{i}\). Therefore, \(Z_{i}\subseteq(Y_{1}\cup Y_{2}\cup\ldots\cup Y_{n})\). The \(card(S)\) means the number of elements in the set \(S\).  
\[Y_{k}=\underset{Y_{j}}{\operatorname{argmax}}(card(Z_{i}\cap Y_{j})) \tag{4}\]  
\[\frac{card(Y_{k})}{card(X_{i})}>T_{p} \tag{5}\]  
where \(T_{p}\) is a threshold. The object \(A_{i}\) is matched with \(B_{k}\) if both the Formulas (4) and (5) are satisfied, namely, the subregion \(A_{i}\) in image \(A\) and the subregion \(B_{k}\) in image \(B\) are the images of the same object.
# A 100 Gb/s Quad-Lane SerDes Receiver with a PI-Based Quarter-Rate All-Digital CDR
## 2 Proposed Multi-Lane SerDes Receiver Architecture
### Proposed PI Controller
As the update rate of the CDR increases, the input jitter tolerance improves, whereas the dithering jitter of the recovered clock deteriorates. The jitter tolerance refers to the ability of the CDR to maintain the targeted bit error rate (BER) when a low-frequency input sinusoidal jitter (usually from a few kHz to tens of MHz) is added and injected into the input data of the CDR. In applications that do not require spread-spectrum clocking, the BER can be minimized by paying more attention to jitter generation than jitter tolerance. Therefore, it is necessary to have the ability to adjust the update rate of the CDR depending on the application field of the CDR. In this paper, to reduce the dithering jitter of the recovered clock and prevent the BER increase, the FSM of the proposed DLF performs a decimation filter [6] function. When the decimation factor (DF) control (DF\({}_{\mathrm{CONT}}\)) signal is 0, the FSM operates as a four-state machine and outputs once when four consecutive Comp impulses occur. Thus, the update rate of the CDR is \(f_{\mathrm{CLK}_{\mathrm{CONT}}}\times\mathrm{DF}\), where DF = 1/4. When the DF is 1, the FSM operates in eight states, and the update rate of the CDR becomes \(f_{\mathrm{CLK}_{\mathrm{CONT}}}\)/8 with DF = 1/8, which has the effect that the dithering jitter is further reduced. Figure 6 shows the Early/Late (E/L) detector, which consists of four bang-bang phase detectors (PD), four de-multiplexers (DEMUX), and one 1/2 divider. When the sequential tracking mode starts after the initial tracking mode ends, the four bang-bang PDs using the Alexander equation [34] generate  
the L0-L3 and E0-E3 signals. After going through the four DEMUX blocks, the Early\(<\)7:0\(>\) and Late\(<\)7:0\(>\) signals are generated.
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## Methods
### GFDM Predistorter  
This research focused on the HPA linearization technique with predistorter on the transmitter side in the BS for the 5G downlink system. The predistorter was chosen because it is a simple HPA linearization method, limiting the regrowth spectrum and eliminating the effect of amplitude distortion. It was designed to model the inverse of a nonlinear HPA response. The device was then placed before the non-linear HPA to obtain a linear output signal, as shown in Figure 2.  
With a HPA and predistorter, the output of the GFDM modulator may be written as in the following equation [37]:  
\[x(n)=\rho(n)e^{i\Phi(n)}, \tag{7}\]  
where \(\rho(n)\) is the amplitude of the GFDM modulator output signal and \(\phi(t)\) is the signal phase of the GFDM modulator output. The output of the predistorter is formulated as follows [38]:  
\[u(n)=r(n)e^{i\rho(n)}, \tag{8}\]  
where \(r(n)\) is the amplitude of the HPA input and \(\theta(n)\) is the phase of the HPA input.  
To compensate for nonlinearity, it must satisfy the equation: \(x(n)=s(n)\), such that:  
\[r(n)=A^{-1}[\rho(n)], \tag{9}\] \[\theta(n)=\phi(n)-\Phi[r(n)],\]  
where \(A\) [.] and \(\Phi\) [.] are the AM/AM and AM/PM characteristic functions of HPA local linearization and should cancel the distortion in each HPA, so that the signal at the transmitter at each branch is in a linear area. The inverse \(A\) [.] function will exist if \(\rho(n)\geq\sqrt{\frac{\alpha_{a}^{2}}{4\beta_{a}}}\), so the predistorter input must be limited not to exceed \(\sqrt{\frac{\alpha_{a}^{2}}{4\beta_{a}}}\). The predistorter output can be written as follows [39]:  
\[r(n)=\left\{\begin{array}{l}\frac{\alpha_{a}-\sqrt{\alpha_{a}^{2}-4\beta_{a }\rho(n)}^{2}}{2\beta_{a}\rho(n)},\,0<\rho(n)<\sqrt{\frac{\alpha_{a}^{2}}{4 \beta_{a}}}\\ A^{-1}\Big{(}\sqrt{\frac{\alpha_{a}^{2}}{4\beta_{a}}}\Big{)}=\frac{1}{\sqrt{ \beta_{a}}},\,\,\rho(n)\geq\sqrt{\frac{\alpha_{a}^{2}}{4\beta_{a}}}\end{array} \right., \tag{10}\]  
\[\theta(n)=\phi(n)-\frac{\alpha_{a}\rho^{2}(n)}{1+\beta_{a}\rho^{2}(n)}.\]  
For ideal predistortion (assuming the Saleh model for HPA is exact), the input phase of the signal does not change, but the amplitude does. Therefore, the characteristic functions of AM/AM and AM/PM are  
\[A(\rho(n))=\left\{\begin{array}{l}\rho(n),\,0<\rho(n)<\sqrt{\frac{\alpha_{a }^{2}}{4\beta_{a}}}\\ \sqrt{\frac{\alpha_{a}^{2}}{4\beta_{a}}},\,\,\rho(n)>\sqrt{\frac{\alpha_{a}^{2 }}{4\beta_{a}}}\end{array}\right. \tag{11}\]  
Figure 2: GFDM system with predistorter.  
with HPA output as  
\[s(n)=R(n)e^{i\psi(n)}, \tag{12}\]  
where \(R(n)=A[r(n)]\) is the output amplitude and \(\psi(n)=\theta(n)+\Phi[r(n)]\) are phase output HPA.
## 2 Theory  
### Mathematical Theory  
When a high-frequency alternating current flows through the induction coil, an eddy current is induced in the surrounding metal (conductor). The penetration depth of the eddy current is closely related to the properties of the metal and the excitation frequency, and the penetration depth equation for this is as follows [27; 28; 29]:  
\[\delta=\frac{1}{\sqrt{\pi\sigma\mu}} \tag{1}\]  
where \(\delta\) is the penetration depth, \(\sigma\) is the electrical conductivity, \(\mu\) is permeability, and f is the excitation frequency. The penetration depth of eddy currents has a characteristic inversely proportional to the electrical conductivity, magnetic permeability, and excitation frequency.  
If the eddy current passes through the discontinuity after induced in the metal, the density of the current changes, as shown in Figure 1. In the region where the eddy current density is increased, a high level of Joule's heating occurs, and the equation for this is as follows:  
\[\mathrm{q}_{\mathrm{s}}=|\mathrm{J}|^{2}/\sigma \tag{2}\]  
where \(\mathrm{q}_{\mathrm{s}}\) is the intensity of the heat power and \(\mathrm{J}\) is the density of the current. In the case of induction thermography, the effect on \(\sigma\) is negligible because the change with respect to temperature is relatively small, so it can be regarded as a constant.  
The heat generated by the eddy current propagates inside the metal specimen, and the equation for this is as follows [30]:  
\[\mathrm{pc}_{\mathrm{P}}\frac{\partial\mathrm{T}}{\partial\mathrm{t}}+\mathrm{ \rho}_{\mathrm{C}}\mathrm{\mu}\cdot\
abla\mathrm{T}=\
abla\cdot(\mathrm{k} \
abla\mathrm{T})+\mathrm{q}_{\mathrm{s}} \tag{3}\]  
where \(\mathrm{\rho}\) is the density, \(\mathrm{c}_{\mathrm{P}}\) is the specific heat, \(\mathrm{T}\) is the temperature, \(\mathrm{t}\) is the time, \(\mathrm{k}\) is the heat transfer coefficient, and \(\mathrm{u}\) is the dynamic speed of the metal.
# Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark
## 5 Models versus Datasets, a Comparison Study
They also reported 95.14% accuracy on CNN, whereas our CNN achieved 100% accuracy. The one notable difference where our models returned minimum accuracy is against the N_BaloT dataset, where Reference [53] reported 99.57% accuracy on CNN and 96.13% on MLP, our models achieving on average 90.80% accuracy. Lastly, Reference [85] reported only 54.43% accuracy on MLP compared to our MLP classifier achieving 100% accuracy. Although our results show an overall better performance than other researchers' work, we \begin{table}
\begin{tabular}{l l l l} \hline \hline  & & \multicolumn{2}{c}{**Other Researcher’s Model and Accuracy**} \\ \cline{3-4}
**Ref.** & **Dataset** & **Model** & **Accuracy** \\ \hline
[10] & NSL-KDD & CNN & 92.99\% \\ \hline  & & MLP & 86.34\% \\
[70] & CICIDS2017 & CNN & 95.14\% \\  & & LSTM & 96.24\% \\  & & CNN + LSTM & 97.16\% \\ \hline
[71] & CICIDS2017 & CNN + LSTM & 99.03\% \\ \hline
[58] & NSL-KDD & TCN & 90.50\% \\ \hline
[51] & UNSW-NB15 & DNN & 99.24\% \\  & & MLP & 98.96\% \\ \hline
[53] & N-BaIoT & CNN & 99.57\% \\  & & MLP & 96.13\% \\ \hline
[85] & BoT-IoT & CNN & 90.76\% \\  & & MLP & 54.43\% \\ \hline
[8] & KDD CUP 1999 & BRNN & 99.04\% \\ \hline  & & \multicolumn{2}{c}{**Accuracy of Our Models**} \\ \hline
**MLP** & **CNN** & **LSTM** & **AE +** & **AE +** & **AE +** & **CNN +** \\  & & **TCN** & **LSTM** & **BRNN** & **BLSTM** & **LSTM** \\ \hline
98.90\% & 98.60\% & 98.70\% & 99.00\% & 98.90\% & 51.90\% & 98.70\% & 98.90\% \\
99.90\% & 100.00\% & 100.00\% & 99.99\% & 99.40\% & 99.40\% & 99.90\% & 100.00\% \\
98.90\% & 98.60\% & 98.70\% & 99.00\% & 98.90\% & 51.90\% & 98.70\% & 98.90\% \\
97.80\% & 97.70\% & 98.00\% & 97.90\% & 97.90\% & 97.70\% & 97.70\% \\
90.80\% & 90.90\% & 90.80\% & 90.70\% & 80.50\% & 23.30\% & 90.80\% & 90.90\% \\
100.00\% & 100.00\% & 100.00\% & 100.00\% & 100.00\% & 100.00\% & 100.00\% \\
99.90\% & 99.90\% & 99.90\% & 99.90\% & 99.90\% & 79.20\% & 99.50\% & 99.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy comparison with previous work. believe that there is a need for baselines or benchmarks to reuse the same configurations to reproduce and provide true comparable results.
# A Roadmap for Gigabit to Terabit Optical Wireless Communications Receivers
## 2 Materials and Methods
### Numerical Simulations  
Numerical simulations have been performed by using a Monte Carlo simulator, which has been described in detail previously [19]. This simulator does not include the effects of dark counts, afterpulsing, and optical cross-talk. Previously, it was shown that omitting these phenomena from the simulator did not impact the ability to predict the impact of ambient light on a SiPM [19]. These phenomena have not been added to the current simulation. In the case of the dark counts this is because, for data rates of 1 Gbps or higher, the dark count rates of the simulated SiPMs are negligible compared to the rate at which photons are detected [19]. Similarly, for the SiPMs that are simulated, both the afterpulsing probability and the probability of optical cross-talk are less than 10%. These phenomena might therefore increase the irradiance falling on a SIPM required to achieve a particular performance by up to 10%. However, this is a small increase compared to the impact of the non-linearity and finite pulse width that is the subject of the current study.  
The original implementation of the simulator was used to investigate the impact of ambient light. This meant that photons were detected when a zero was received. In contrast, the simulations in this paper assume that the SiPM has been protected from ambient light by using a filter. This means that, when a zero is received, the dominant noise source is the noise from the electronics, for example, the RF amplifier, in the receiver. After amplification, the peak-to-peak voltage for a single avalanche event in a J 30,020 SiPM was 15 mV\({}_{\text{PP}}\). When the beam from the transmitter is blocked a 5 mV\({}_{\text{PP}}\) (three standard deviations) white noise signal was observed. For the simulations whose results are reported in this paper, Gaussian white noise with a peak-to-peak amplitude of one-third of the peak-to-peak amplitude of an avalanche event was added to the output of the simulator before decoding.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 3 The Proposed Scheme
### Energy Efficient Grid-Based Geocasting Path Construction  
After the region head selection process and gateway selection process, the sink can construct the energy efficient grid-based geocasting path between the sink and the two target regions. The operations for route discovery are as follows. First, when the sink calculates the location of the region head and gateway, it floods a route request (RREQ) packet on the network with the time to live (TTL) value towards gateway \(G\). The RREQ packet carries the information <_Type_, _SourceGID_, _DestGID_, _PathTravel_, _TTL_> in its header. The _SourceGID_ and _DestGID_ refer to the GID of the source and destination, respectively. The _PathTraversed_ records the routing information. In this case, the sink broadcasts the RREQ packet, and its information is <_RREQ_, _Sink GID_, _Gateway GID_, _PathTravel_, _TTL_>.  
When any cell header receives a RREQ packet, it first checks the _DestGID_ in the RREQ to determine whether the entry is its own address. If not, this node appends its GID into the _PathTravel_ and decreases the value of _TTL_ by one. It then floods the RREQ packets on the network until the value of _TTL_ is zero. When the gateway \(G\) receives a RREQ packet, it floods two RREQ packets on the network to the region heads \(H_{1}\) and \(H_{2}\) and sends a route reply (RREP) packet back to the sink, respectively. The RREP packet includes the information <_Type_, _SourceGID_, _DestGID_, _ReversePath_, _TTL_>. Here the _Type_ is RREP and _ReversePath_ and includes the reverse path from _PathTravel_ of RREQ. Gateway \(G\) also tries to find the shortest path routes from the gateway to the two region heads, \(H_{1}\) and \(H_{2}\). When a region head (\(H_{1}\) or \(H_{2}\)) receives a RREQ packet, it then sends a RREP packet back to the gateway. Finally, an energy efficient grid-based geocasting path could be constructed, as shown in Figure 6.
## 2 Materials and Methods
### Gridded Precipitation Products
#### 2.3.1 Arc2  
ARC2 was developed by the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC), which offers daily rainfall data for Africa [1]. It uses inputs from two sources: (i) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organization for the Exploitation of Meteorological Satellites (EUMESTSAT), and (ii) quality-controlled GTS gauge observations reporting 24 h rainfall accumulations across Africa [45]. ARC2 has a spatial resolution of 0.1\({}^{\circ}\) over Africa (40\({}^{\circ}\)N-40\({}^{\circ}\)S, 20\({}^{\circ}\)W-55\({}^{\circ}\)E) with daily temporal resolution and can be downloaded at: [[https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.ARC2/.daily/](https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.ARC2/.daily/)]([https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.ARC2/.daily/](https://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP/.CPC/.FEWS/.Africa/.DAILY/.ARC2/.daily/)) (accessed on 3 June 2021).