esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The polymer backbones of AEM are usually based on polysulfone, poly(arylene ether), poly(phenylene), polystyrene, polypropylene, poly(ether sulfone), poly(phenylene oxide), polyolefin, poly(arylene piperidinium) and poly(biphenyl alkylene) [8; 12]. In comparison to PEM, the performance of AEM is admittedly lower due to the higher ion mobility in H\({}^{+}\) (4.76) compared to OH\({}^{-}\) (2.69) [13]. In an anion exchange membrane fuel cell (AEMFC), oxygen is reduced at the cathode side and to produce hydroxide (OH\({}^{-}\)) ions and electrons, where hydroxide ions transport through the AEM towards the anode side and subsequently react with hydrogen to produce water, whereas the electrons travel through the external circuit to produce electricity current. Equations (1)-(3) describe the electrochemical reactions at the cathode side, anode side and overall reaction, respectively. Cathode:  
\[\text{O}_{2}+2\;\text{H}_{2}\text{O}+4\;\text{e}^{-}\to 4\;\text{OH}^{-}, \tag{1}\]  
Anode:  
\[2\;\text{H}_{2}+4\;\text{OH}^{-}\;\rightarrow\;4\;\text{H}_{2}\text{O}\;+4\; \text{e}^{-}, \tag{2}\]  
Overall:  
\[2\;\text{H}_{2}+\text{O}_{2}\;\rightarrow\;2\;\text{H}_{2}\text{O}\;, \tag{3}\]  
On the other hand, the anion exchange membrane electrolysis cell (AEMEC) uses electricity to split the water molecules into hydrogen gas and hydroxide ions at the cathode side, and the hydroxide ions transport through the membrane and eventually oxidize to produce oxygen gas and water, as shown in Equations (4)-(6). Ideally, water should be fed and consumed at the cathode, but many reported systems still feed hydroxide-ion-rich alkaline solutions to the cathode. Cathode:  
\[4\;\text{H}_{2}\text{O}+4\;\text{e}^{-}\to 2\;\text{H}_{2}+4\; \text{OH}^{-}, \tag{4}\]
Anode:  
\[4\;\mathrm{OH}^{-}\rightarrow\mathrm{O}_{2}+2\mathrm{H}_{2}\mathrm{O}+4\;\mathrm{ e}^{-}, \tag{5}\]  
Overall:  
\[2\;\mathrm{H}_{2}\mathrm{O}\;\rightarrow\mathrm{2}\;\mathrm{H}_{2}+\mathrm{O}_{ 2}, \tag{6}\]  
While the efficiency of both electricity generation and water splitting depends highly on the performance of overall membrane electrode assembly (MEA) [14], which includes the catalyst, gas diffusion layers and electrolyte membrane, the mobility of OH\({}^{-}\) in the membrane plays the most essential role in dictating the overall fuel cell/electrolysis cell performance. In addition to high anion conductivity, high thermo-mechanical and chemical stability properties of AEM are also highly sought after. Selection of cationic head groups, morphology design of membrane, product scalability, cost of raw materials and fabrication methods are factors to be considered and optimized to meet the commercial viability criteria [15].
## 3 Data Collection  
Urdu is widely spoken in South Asia and is the national language of Pakistan. Predicting a digit from speech and making automatic speech digit classification efficient and robust is the basic reason for our motivation. Secondly, there is no big dataset available publicly. To the best of our knowledge, this is the largest publicly available dataset that can be used for many research purposes.  
In our data collection, different age groups of people participated. There was a total of 740 participants, the age ranging from 5 to 89. Most participants were 5 to 14 years old, as shown in Figure 1. Comparing male and female participants, male participants were slightly more numerous than female participants, as shown in Figure 2. We also checked the number of samples in each class; most classes sampled more than 2000 samples, overall samples were 25,518, and the class-wise distribution is shown in Figure 3. For diversity purposes, we recorded each sample in different environments, i.e., participants record samples in a normal environment, noisy environment, closed room environment, at different heights, and different distances. Furthermore, we asked the participant to record samples with different characteristics, such as slow voice, fast voice, low loudness, high loudness, etc.  
All voice samples were collected using these mobile devices: (i) VIVO mobile, model V204321 with 64 GB storage, 2.3 GHz Octacore processor, and 4 GB RAM. (ii) VIVO mobile, model 190,719 with 128 GB storage, 2.0 GHz Octacore, and 4 GB RAM. (iii) Galaxy J5, model SM-G570F. (iv) XIAOMI mobile, model Redmi 6 Pro with 64 GB storage, Octacore Max 2.0 GHz, and 4 GB RAM. All these devices were used for recording voice samples with different voice-recording applications, i.e., Easy Voice Recorder Pro, Smart Recorder--High-quality voice recorder. Each sample was collected at a sample rate of 48 kHz and was a mono channel with a minimum length of 1 s and a maximum length of 2 s. All samples were stored in opus format.  
Figure 1: Gender and Age of participants.
## 6 Conclusions  
A foundation pit group consisting of four foundation pits was systematically investigated in this paper. The lateral deflections of the enclosure pile, the ground settlement, and the vertical column movement were analyzed via field measurements, and the following conclusions were drawn:  
1. Excavation of foundation pit B reduced the width of soil outside foundation pit A, C, and D, resulting in a reduction in the active earth pressure acting on the retaining pile of foundation pits A, C, and D. Thus, the lateral deformation of the retaining pile decreased due to the stress release of the lateral earth pressure;
2. For foundation pits A, C, and D, \(\delta_{\mathrm{hm}}\) ranged from \(\delta_{\mathrm{hm}}\) = 0.14% \(H_{\mathrm{e}}\) to \(\delta_{\mathrm{hm}}\) = 0.48% \(H_{\mathrm{e}}\), which is in line with those empirical relationships. For foundation pit B, \(\delta_{\mathrm{hm}}\) ranged from \(\delta_{\mathrm{hm}}\) = 0.26% \(H_{\mathrm{e}}\) to \(\delta_{\mathrm{hm}}\) = 0.63% \(H_{\mathrm{e}}\), which is higher than those empirical relationships;
3. The excavation effect in the foundation pit group was the interaction of the foundation pits with the different excavation sequences. Excavation of the adjacent foundation pit had little effect on the unexcavated foundation pit. However, the post-excavation foundation pit had a significant impact on the excavated foundation pit;
4. The superposition effect of group excavation can be effectively diminished by controlling the deformation of a single foundation pit and blocking the superposition and transmission path of foundation pit deformation. Reasonable excavation sequences, such as excavation of adjacent foundation pits at different times, may reduce the superposition effect.  
Conceptualization, S.C. and F.L.; methodology, J.C.; software, S.C.; formal analysis, F.L.; investigation, J.C.; resources, S.C.; data curation, S.C.; writing--original draft preparation, J.C., S.C.; writing--review and editing, F.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are contained within the article.  
The authors wish to express their gratitude to the anonymous reviewers whose constructive comments helped to improve the overall quality of this paper.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Field Irrigation Experiments  
Three different macropores depths (5, 10 and 15 cm) were introduced in these experiments. The treatment without artificial macropore (0 cm depth treatment) was adopted as the control group. Three replicate plots were carried out for two salinization levels and four macropore treatments (twenty-four plots totally).  
The vegetation was removed, and the surface was leveled prior to the experiment. Plots were 40 cm apart. Each plot was 2.5 m long and 1.8 m wide, around which 0.6 m depth plastic film was buried, and 15 cm high ridge was built to avoid lateral flow. Then total treatments were randomly separated, different deep artificial macropores were installed with 10 cm apart in a row, and the row distance was 30 cm (Figure 1). After that, freshwater was applied to reduce the salt interference from irrigation water, and the chemical properties are shown in Table 2. According to local irrigation practice, we introduced flood irrigation for each plot under 1200 m\({}^{3}\) ha\({}^{-1}\), i.e., 0.54 m\({}^{3}\) per plot.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Soil Properties**} & \multicolumn{3}{c}{**Salinity Levels of Experimental Soil**} \\ \cline{2-3}  & **Mild Salinization** & **Severe Salinization** \\ \hline Soil texture & & & \\ Sand (0.02–2 mm, \%) & 14.9 & 10.6 \\ Silt (0.002–0.02 mm, \%) & 64.8 & 61.0 \\ Clay (\textless{}0.002 mm, \%) & 20.3 & 28.4 \\ Soil physical properties & & & \\ Bulk density (g cm\({}^{-3}\)) & 1.31 & 1.39 \\ Total porosity (\%) & 48.6 & 46.5 \\ Field capacity (\%, by weight) & 24.7 & 28.4 \\ Soil chemical properties & & & \\ Electrical conductivity (1:5 soil to water, dS m\({}^{-1}\)) & 0.56 & 2.04 \\ Salt content (g kg\({}^{-1}\)) & 1.95 & 7.08 \\ pH & 8.11 & 8.34 \\ Organic matter (g kg\({}^{-1}\)) & 17.9 & 10.4 \\ Available nitrogen (mg kg\({}^{-1}\)) & 55.6 & 64.5 \\ Available phosphorus (mg kg\({}^{-1}\)) & 12.1 & 31.4 \\ Available potassium (mg kg\({}^{-1}\)) & 97.8 & 63.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic soil characteristics prior to the initiation of field experiments.  
Figure 1: Schematic representation of artificial macropores with sandy fillings (AMSF) method.
## 3 Results
### Validation of the Low-Cost Radiometric Calibration through the Manufacturer Information  
One of the ways to validate the low-cost radiometric calibration of the thermal camera is by comparing the calibration results with those from the existing manufacturer calibration.  
The radiometric calibration model obtained through the low-cost calibration is the one represented in Equation (14), with T being the surface temperature values in Celsius, while the manufacturer's calibration model responds to Equation (15).  
\[\text{T}=-11.620+0.0036\times\text{DL}-4\times 10^{-8}\times\text{DL}^{2} \tag{14}\]  
\[\text{T}=-17.049+0.0036\times\text{DL}-3\times 10^{-8}\times\text{DL}^{2} \tag{15}\]  
Figure 8 shows that the low-cost calibration results in a homogeneous absolute error, with a maximum value of 3.46 \({}^{\circ}\)C. However, the manufacturer's calibration results in an increase of the absolute error for lower temperatures, with a maximum error value of 8.34 \({}^{\circ}\)C.  
Figure 7: (**a**) The borehole heat exchanger with the geothermal installation and (**b**) a thermal image of the experiment at the stationary regime.  
For both calibrations, the greatest uncertainty occurs in the temperature range between 16 and 18 \({}^{\circ}\)C, probably due to the interference of the temperature of the environment of the camera in this temperature range. However, (i) the greatest error in the low-cost calibration is below the nominal precision of the camera (\(\pm\)2 \({}^{\circ}\)C), and (ii) the thermal conductivity test is performed for temperatures between 28 and 40 \({}^{\circ}\)C, where the error in the low-cost calibration is stable.  
With respect to the calibration validation in the temperature range evaluated (3.5-53 \({}^{\circ}\)C), it can be concluded that: (i) less uncertainty is obtained when using the low-cost radiometric calibration model, and that (ii) the largest discrepancy between both models (the provided by the manufacturer and the one from the low-cost calibration), was \(\pm\)5.25 \({}^{\circ}\)C in terms of absolute error.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 4 Proposed Method
### AIDA Method
#### 4.4.2 Step 2--MST Edges LRP Calculation  
When the SM-based MST is established, the LRP calculation is performed for all MST edges. Based on technical information about the devices' reception sensitivity, three categories of LRP are established: (1) blue edges indicate high-quality links; (2) orange edges indicate medium-quality links with uncertain connection possibility; (3) edges are classified as red, indicating low-quality links, as they do not present conditions for connection. Thresholds for high, medium and low-quality links are specified in Section 5. As shown in Figure 3, the coloring of the edges allows the creation of a heatmap that helps to identify the areas where the connection is feasible and where it is uncertain.  
Equation (12) is considered for the LRP calculation. See Section 4.3 for more details.
## 2 Materials and Methods
### Motion Trajectory Compensation
\right] \tag{22}\]The noise covariance matrix \(R\) and \(Q\) in original Kalman filter are:  
\[R=\left[ \begin{array}{cc}1&0\\ 0&1\end{array} \right],\ \ \ Q=\left[ \begin{array}{cccc}0.01&0&0&0\\ 0&0.01&0&0\\ 0&0&0.01&0\\ 0&0&0&0.01\end{array} \right] \tag{23}\]  
when the _SDM_ value exceeds \(OT\), the object may be occluded. The process noise covariance matrix \(R_{update}\) and the observation noise covariance matrix \(Q_{update}\) can be written as:  
\[R_{update}=\left[ \begin{array}{cc}2&0\\ 0&2\end{array} \right],\ \ \ Q_{update}=\left[ \begin{array}{cccc}0.001&0&0&0\\ 0&0.001&0&0\\ 0&0&0.001&0\\ 0&0&0&0.001\end{array} \right] \tag{24}\]  
This means that the object is occluded and the _SDM_ value is greater than \(OT\), so the noise covariance \(R_{update}\) should be reduced and \(Q_{update}\) should be increased to afford the adaptive Kalman filter predicting the position of the occluded object more accurately. If the _SDM_ value is less than or equal to \(OT\), \(R_{update}\), \(Q_{update}\) can be expressed as:  
\[R_{update}=\left[ \begin{array}{cc}SDM&0\\ 0&SDM\end{array} \right]\] \[Q_{update}=\left[ \begin{array}{cccc}(1-SDM)^{2}&0&0&0\\ 0&(1-SDM)^{2}&0&0\\ 0&0&(1-SDM)^{2}&0\\ 0&0&0&(1-SDM)^{2}\end{array} \right] \tag{25}\]  
The object position is predicted from the second frame onwards, and the position of the target is calculated by Equation (24), which is then input into the tracker to continue tracking the target until the end of the tracking process. It should be noted that KF suffers from a convergence problem. If KF does not converge once occlusion appears, the object is lost, and the tracking fails. Table 1 highlights that KF achieves convergence at 32 frames on average, while AKF at 15 frames on average, compensating and correcting the object trajectory as soon as possible, thus reducing the tracking error and improving the performance and robustness of the object tracking process.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
## 4 Comprehensive Comparison
### Comparison of Methods 1 and 3  
To satisfy the accuracy requirement above, the bandwidth of the signal for method 3 can be obtained through Equation (7). The \(\sigma\) calculated in chips is 0.005. Therefore, the code rate should be larger than \(\sigma/33\) ns, which is 0.1523 MHz; it is ~7.3 times that of the unit band. Therefore, the bandwidth is set as 323.19 KHz, which is equal to eight unit bands. The capacity loss of the navigation signal is 3.17%. The highest CN0 of the navigation signal is 64.84 dBHz, calculated by Equation (10).  
From Figure 4, it can be seen that the accuracy threshold can be satisfied by the signal of method 1.  
According to Sections 2.3 and 3.1, \(CNSR\) is set as 15 dB for method 1. The CN0 margin of methods 1 and 3 is shown below, and the corresponding pseudorange accuracy is also shown below.  
From Figure 8, it can be seen that the CN0 margin of method 3 does not change with the increment of \(CN0_{total}\), but the CN0 margin of method 1 decreases significantly. The highest CN0 margin of the navigation signal is about 14 dB for method 1. The CN0 margin of method 3 can be 13.04 dB higher than method 1 when \(CN0_{total}\) is 80 dBHz.  
It can also be seen that the pseudorange accuracy of method 1 is still better than method 3, even though the CN0 margin is lower.  
Considering that \(CN0_{total}\) can be increased, the CN0 margin can be improved further. For method 3, when the CN0 of the navigation signal is increased to 80 dBHz and the power of the communication signal is kept the same, \(CN0_{total}\) increases to 82.94 dBHz. The CN0 margin becomes 30 dB for method 3. At this time, the CN0 margin increment for method 1 is ~2.08 dB, as calculated by Equation (12).  
It can be concluded that when the accuracy satisfies the requirement, method 3 keeps the advantage in CN0 margin and the increment of CN0 margin is easy to realize. This can solve the problem of signal weakness in urban valley or indoor environment. Hence it can be a supplement to GNSS.
## 3 Circuit Implementation
### Time Exponential Rate Power Modulator  
According to the time exponential rate P&O algorithm in Section 2, a proposed power modulator observes the power change by comparing TP<8:0> and provides the control signal V\({}_{\text{PMO}}\) to change the length of time \(t_{\text{MN}}\), as shown in Figure 9. The input control signals CLK_TP, CLK_TN, and CLK_SAV come from OSC and are used to execute the power modulator sequentially, as shown in Figure 9. Generally, an MPPT cycle, during which the circuit changes \(t_{\text{MN}}\) once, includes dozens of DC-DC converter cycles to ensure that the circuit reaches a stable state again.  
At the beginning of each MPPT cycle, two groups of flip-flops (DFF1<8:0>, DFF2<8:0>) are triggered to save the present and previous time-length values TP<8:0>. After that, a digital comparator (D-COMP) compares these two values, and outputs a high level when the previous one is larger. Then, the D-COMP output (RVS) and the previous moving direction of \(t_{\text{MN}}\) (TND_PRE) perform the XOR operation. If RVS is low, the present moving direction (TND) is set to equal to the previous. If not, the TND will be reversed. According to the TND, signal TN<4:0> will be made plus or minus one by 5-bit BDC when the pulse CLK_TN arrives. An exponential voltage follower consists of an amplifier, a transistor, and a series of resistors, and outputs a series of voltages, which grows exponentially. According to TN<4:0>, the output of the power modulator (V\({}_{\text{PMO}}\)) increases or decreases exponentially in each MPPT cycle by the multiplexer (MUX) and the voltage follower. Finally, the DFF3 is triggered to save TND for calculation in the next MPPT cycle.
###### Abstract  
During the ongoing studies designed to examine the fungal diversity present within the abandoned and flooded Escadia Grande gold mine (Gois, Portugal), we repeatedly isolated several specimens belonging to a _Penicillium_ species. Molecular phylogenetic analysis, coupled with morphological observations, positioned this fungus within subgen. _Penicillium_ sect. _Paradoxa_, series _Aramentosa_, pinpointing its identity as _Penicillium mexicanum_ (the first record for mining soils and the country). Given the limited research conducted on _Penicillia_ isolated from similar environments, the species genome was sequenced utilizing the Oxford Nanopore(r) MinION(tm) methodology and studied through bioinformatic analysis. The obtained genome has a size of 29.62 Mb, containing a 47.72% GC content, 10,156 genes, with 44 rRNAs and 178 tRNAs/tmRNAs, providing the first genomic resource for this microorganism. Bioinformatic analysis allowed us to identify multiple genomic traits that can contribute towards this species survival in these extreme environments, including the presence of high levels of major facilitator transporters (MPS), Zn (2)-C6 fungal-type DNA-binding domains, P-loop containing nucleoside triphosphate hydrolases, specific fungal transcription factors and sugar transporters. Furthermore, putative advantageous metabolic traits, such as methylotropy, assimilatory nitrate and sulfate reduction abilities, were also detected. In addition, the results also highlighted a strong genomic and metabolic organization and investment towards arsenic detoxification (transport and oxidation). Lastly, thirty-two putative biosynthetic gene clusters were predicted, including some with high similarity values to monaconvibrin, nidulanin A, histidyltrytyptophanyldiketopiperazine/dehydrohistidyltrytyptophanyldiketopiperazine/roqueforine C/glandicoline A/glandicoline B/meleagrine, YWA1 and choline. Overall, this study expands the current _Penicillia_ knowledge from mining environments while also enhancing our understanding regarding fungal arsenic resistance.  
arsenic resistance; _Eurotionyvetes_; extreme environments; genomic analysis; gold mine mycobiome; _Penicillium mexicanum_
## 3 Results
### Quantitative Results
#### 3.3.2 Comparison of Loss Weighting Strategies  
We compared weighting loss methods with mask loss. Various methods have been used to efficiently combine multiple losses in multitask learning (MTL) [30]. The most straightforward method is uniform weighting: the losses are simply added together to produce a single scalar loss value. Dynamic optimization techniques are also important in MTL to optimize the set of possibly contrasting losses or gradients because conflicting gradient problems can degrade model performance. [PERSON] et al. [24] proposed an uncertainty-based weighting approach and applied it to a CNN. Ref. [31] adapted the regularization term in [PERSON]'s uncertainty-based method. A dynamic weight average  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sensitivity** & **Specificity** & **AUC** \\ \hline DenseNet & 56.25\% & 86.25\% & 0.8553 \\ DenseNet + MBN-V1 & 75.00\% & 92.50\% & 0.9252 \\ DenseNet + MBN-V2 & 87.75\% & 93.75\% & 0.9491 \\ DenseNet + MBN-V3 & 82.50\% & 92.50\% & 0.9472  
(DWA) was proposed by [32]. We adapted these methods to the proposed method with MBN-V1 and exactly the same experimental setting and compared the performance. The results are shown in Table 2, which shows that the uncertainty weighting and revised uncertainty weighting achieve higher performance compared to other settings. The results do not show many differences between uncertainty weight and revised uncertainty weight, but their performance is considerably better than the others. Considering this result, we applied uncertainty weighting to our method for the final result. The left of Figure 6 shows ROC curves depending on ways to weigh multiple losses.