esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparison on an Annual Scale
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Region** & **Name** & **Regression Coefficient** & **RMSD (mm)** & **R** & **STD** & **Bias (mm)** \\ \hline \multirow{4}{*}{\(1\)} & MSWEP & 1.45 & 17.38 & 0.42 & 0.29 & \(-\)36.48 \\  & CHIRPS & 0.65 & **5.51** & **0.55** & **0.85** & \(-\)24 \\  & PERSIANN-CDR & **0.83** & 9.41 & 0.44 & 0.53 & \(-\)32.5 \\  & TMPA & 0.62 & 6.73 & 0.46 & 0.75 & \(-\)**9.0** \\ \hline \multirow{4}{*}{\(2\)} & MSWEP & 1.32 & 10.06 & 0.83 & 0.62 & 12.0 \\  & CHIRPS & 0.67 & 9.34 & 0.77 & 1.15 & 9.0 \\  & PERSIANN-CDR & 0.97 & 6.21 & 0.87 & 0.89 & \(-\)30.0 \\  & TMPA & **1.01** & **3.22** & **0.96** & **0.95** & \(-\)**3.0** \\ \hline \multirow{4}{*}{\(3\)} & MSWEP & 0.68 & 4.42 & 0.75 & 1.11 & 2.0 \\  & CHIRPS & 0.44 & **2.39** & 0.81 & 1.83 & **1.0** \\  & PERSIANN-CDR & 0.59 & 6.28 & 0.57 & **0.97** & \(-\)10.0 \\  & TMPA & **0.78** & 4.03 & **0.82** & 1.04 & \(-\)2.0 \\ \hline \multirow{4}{*}{\(4\)} & MSWEP & 0.88 & 3.2 & 0.95 & 1.07 & \(-\)**1.0** \\  & CHIRPS & 0.67 & **2.72** & 0.94 & 1.45 & 3.0 \\  & PERSIANN-CDR & 1.24 & 5.18 & 0.94 & 0.75 & \(-\)21.0 \\  & TMPA & **1.05** & 3.71 & **0.96** & **0.96** & \(-\)3.2 \\ \hline \end{tabular}
\end{table} 
Table 3: Descriptive statistics of GPPs versus in-situ precipitation at annual time scale.
# An Investigation into Audio-Visual Speech Recognition under a Realistic Home-TV Scenario
## 4 Experiments and Analysis
### Experimental Results
#### 4.2.4 Results of System Fusion  
Finally, we performed the system fusion on the posterior probabilities, and the results are shown in Table 7. We can see that the system fusion has made significant improvements, and the performance of our final system is better than that of the champion system in the MISP2021 [37] challenge. Note that, in order to improve the diversity of the systems, we also use the Transformer [39] as the encoder in the AVSR-E2E system, as shown in 'ID 3' of Table 7. For better comparison with other methods, we also list the MISP baseline  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Task** & **Model** & **Pre-Training** & **Fusion** & \multicolumn{2}{c}{**Data**} & \multicolumn{2}{c}{**Eval**} \\ \cline{6-9}  & & & & & & & **Processing** & & \\ \hline \multirow{3}{*}{AVSR} & \multirow{3}{*}{E2E} & \multirow{3}{*}{HS (CAT)} & \multicolumn{2}{c}{Splicing} & \multicolumn{2}{c}{CAT} & \multicolumn{1}{c}{33.78} \\ \cline{3-9}  & & & & SA & & CAT & & 38.16 \\ \cline{3-9}  & & & & SA & & CAT + SP & 37.43 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results (CER %) of audio–visual fusion (self-attention in the encoder part) methods. HS means the hybrid system. CAT and SP mean splicing the continuous segments and speed perturbation, respectively. ‘Splicing’ means directly splicing the two modal features in audio–visual fusion. SA means self-attention.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Task** & **Model** & **Pre-Training** & \multicolumn{2}{c}{**A/V Extractor**} & \multicolumn{2}{c}{**Eval**} \\ \hline \multirow{3}{*}{AVSR} & \multirow{3}{*}{E2E} & ES & \multicolumn{2}{c}{VGG/Resnet} & \multicolumn{1}{c}{32.21} \\ \cline{3-9}  & & & ES (SP + CAT) & \multicolumn{2}{c}{Gate CNN/Resnet} & \multicolumn{1}{c}{29.88} \\ \cline{3-9}  & & & & VGG/Swin Transformer & \multicolumn{1}{c}{36.13} \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results (CER %) of the different model architectures. ES means the end-to-end system. SP means speed perturbation. CAT means splicing the continuous segments.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Task** & **Model** & **ALL** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** \\ \hline ASR & \multirow{3}{*}{E2E} & 41.15 & 46.99 & 45.45 & 48.02 & 37.84 & 41.80 & 47.43 & 32.23 & 33.14 \\ \cline{1-1} \cline{3-9} AVSR & & 38.62 & 42.44 & 39.80 & 45.61 & 35.13 & 37.42 & 49.60 & 30.71 & 31.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison of ASR-E2E and AVSR-E2E under the same pre-training strategy and training data. For the pre-training strategy, we use the ‘HS (CAT)’ method. For the training data, we used the ‘CAT’ method.  
results [35] and the SJTU system results in the first MISP challenge [48] at the bottom of Table 7, which uses the same dataset as ours.
# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 4 Experiments and Results
### Implementation Details
#### 4.2.3 Metrics  
To evaluate the quality of super-resolution images after reconstruction, we will introduce several commonly used metrics to assess the effectiveness of image reconstruction.  
The most widely used evaluation metrics to measure the quality of super-resolution images are peak signal-to-noise ratio (PSNR) [22] and structural similarity (SSIM) [23].  
The formulas are as follows:  
\[PSNR=10 lg\left(\frac{MaxValue_{I}}{MSE}\right) \tag{10}\]  
where \(MaxValue_{I}\) is the maximum pixel value of the image and \(MSE\) is the mean square error.  
\[\text{SSIM}= l\left(\mathbf{I^{SR}},\mathbf{H^{HR}}\right)\cdot c\left(\mathbf{I^{SR}},\mathbf{ H^{HR}}\right)\cdot s\left(\mathbf{I^{SR}},\mathbf{H^{HR}}\right) \tag{11}\]where \(I\left(\mathbf{f^{SR}},\mathbf{H^{HR}}\right)\) is the brightness comparison of the image, \(c\left(\mathbf{I^{SR}},\mathbf{I^{HR}}\right)\) is the contrast comparison of the image, and \(s\left(\mathbf{I^{SR}},\mathbf{H^{HR}}\right)\) is the structure comparison of the image.  
PSNR and SSIM are based on a comparison between pixels and do not fully reflect the quality of the generated image. For example, even though a generated image has a high PSNR and high SSIM, it may suffer from high-frequency texture information loss. To evaluate the generated image texture details more accurately, we introduce the evaluation index of the perceptual index (PI) [24]. Its formula is as follows:  
\[\text{Perceptual Index}=\frac{1}{2}((10-Ma)+\text{NIQE}) \tag{12}\]  
where \(Ma\) is Ma's score [25] and NIQE is the naturalness image quality evaluator [26].  
Furthermore, we also need to consider the validity of iris recognition. We use equal error rate (EER) as a metric to evaluate the performance of iris recognition. Specifically, the images in the training set, ground truth images and super-resolution images are subjected to normalization using Osiris. Subsequently, we conduct fine-tuning of the VGG16, ResNet101 [27], and DenseNet121 [28], which were originally pre-trained on ImageNet, utilizing the images from the normalized training dataset. These classification networks are used as backbone extraction networks for feature extraction from super-resolution normalized images and ground truth normalized images. Finally, the EER is calculated separately for each method.
## 3 The Proposed Methodology
### Transfer from \(DNN_{s}\) to \(DNN_{t}\)
#### 3.2.2 Domain Adaptation  
The main focus of deep network adaptation is the specific number of specific layers and measurement standards for adaptive adaptation. Figure 4 shows the process of the adaptive approach. In this method, the initial model mainly consists of the following layers. Convolutional, standard and maximally pooled functions are run three times, followed by the addition of LSTM networks, flat layers with dropout processing, several fully connected layers and the addition of a current number of target sets. The model parameters are initialised randomly, and the target model is trained with the target dataset. The feature transfer capability decreases dramatically in the higher layers as the domain discrepancy increases when deep features eventually transition from universal to particular through the network. The network adaptation technique used in this work is MMD. The dense1, dense2 and dense3 layers modify the distribution of the learned features. The bottleneck layer of the transfer model is the layer where the features are extracted. The first three layers of the classifier are complemented by a layer that uses an adaptive measurement criterion. One measurement criterion is the loss function. The loss function consists of the multi-class cross entropy loss and the MMD. Between the source and target domains, the MMD with multiple kernels is used. The loss of the DTLCL model after optimisation is as follows:  
\[loss=-\sum_{i=1}^{n}y_{i}.logp_{i}+\lambda D \tag{14}\]  
\[D=\alpha D\big{(}f_{1}^{s},f_{1}^{t}\big{)}+\beta D\big{(}f_{2}^{s},f_{2}^{t} \big{)}+\mu D\big{(}f_{3}^{s},f_{3}^{t}\big{)} \tag{15}\]  
\[\alpha+\beta+\mu=1 \tag{16}\]  
where \(\lambda=\frac{\text{Fmax}}{\text{Fmax}+\text{Fmax}}\), \(\alpha,\beta,\mu\) are coefficients, \(f_{1}^{s},f_{1}^{t},f_{2}^{t},f_{3}^{t},f_{3}^{t}\) are the output of the layer dense1, dense2 and dense3 for both source and target domain, D is a multilayer MMD which is the linear combination of Gaussian kernels.  
3.2.3. Weighted Voting for Kernel MMD Selection  
The conclusion is that kernel MMD selection for transfer learning is crucial to improve the accuracy and generalisation ability of transfer learning. Weighted voting is a widely used combination strategy in which the weight of each model is determined based on its performance [44]. Suppose there are two base kernels, including Gaussian kernels. The weights of the two base kernels are w = {W1, W2}. WV takes into account the performance differences between the base kernels and gives a higher weight to the kernel with a higher accuracy. The weights of the kernels calculated with WV can be expressed as follows:  
\[w_{i}=\frac{Model\_Accuracy_{i}}{\sum_{i=1}^{2}Model\_Accuracy_{i}} \tag{17}\]  
where  
\[\sum_{i=1}^{2}Model\_Accuracy_{i}=1,w_{i}\geq 0 \tag{18}\]  
and \(Model\_Accuracy_{i}\) reflects the overall accuracy of the validation of ith kernel.
## 3 Modeling and Linearization of the System
### Stator Algebraic Equations
#### 3.3.3 Zero Sequence  
From Figure 3 and considering that \(R_{i}^{cer}\) and \(X_{i}^{cer}\) represent the zero sequence impedance parameters of the machine, the resulting equations to be used are the following:  
When posing [PERSON]'s voltage law:  
\[(V_{aR_{i}}^{cer}+jV_{aIm_{i}}^{cer})+(I_{aR_{i}}^{cer}+jI_{aIm_{i}}^{cer})(R_ {i}^{cer}+jX_{i}^{cer})+(I_{aR_{i}}^{cer}+jI_{aIm_{i}}^{cer})(3R_{ni}+j\,3X_{ni} )=0 \tag{30}\]  
When separating the real and imaginary parts, the resulting equations of the machine are:  
\[V_{aR_{i}}^{cer} + I_{aR_{i}}^{cer}(R_{i}^{cer}+3R_{ni_{i}})-I_{aIm_{i}}^{cer}(X_{i} ^{cer}+3X_{n_{i}})=0 \tag{31}\] \[V_{aIm_{i}}^{cer} + I_{aR_{i}}^{cer}(X_{i}^{cer}+3X_{n_{i}})+I_{aIm_{i}}^{cer}(R_{i} ^{cer}+3R_{n_{i}})=0 \tag{32}\]  
where:  
* \(R_{i}^{cer}\) the zero sequence resistance (pu);
* \(X_{i}^{cer}\) the zero sequence reactance (pu);
* \(R_{n_{i}}^{cer}\) the neutral resistance (pu);
* \(X_{ni}^{cer}\) the neutral reactance (pu);
* \(V_{aR_{i}}^{cer}\) the real part of the zero sequence voltage (pu);
* \(V_{aIm_{i}}^{cer}\) the imaginary part of the zero sequence voltage (pu);
## 3 System Architecture
### Soil Moisture Sensor  
Sixteen units DS 200 soil moisture sensor (Bejing Dingtek Technology Corporation Ltd., Beijing, China), were used to measure the amount of water in the soil. The Ht-1 type sensor has some advantages such as high stability, easy operation, and maintenance, and it can measure soil moisture and temperature at the same time. Also, it can be calibrated. Moreover, it has Tr-3100 type of tightness and protection rank ip68. It operates with 5-24 Vdc source. The output signal is 0-1 Vdc, 0-2.5 Vdc, 0-5 Vdc, 0-10 Vdc, 4-20 ma and rs485.  
Figure 4: (**a**) Sensors placement in the land; (**b**) Sensors map in the irrigation system.  
The DS200 humidity sensor is preferred because it is economical, leak-proof, long-term use, corrosion protection, and high transmission distance. The reason for our discussion of calibration is the presence of trimpot on some sensors and sensitivity adjustment here. However, the DS200 humidity sensor has an accuracy of \(\pm 2\%\) which is a very good value. The reason we discuss this here is that we would advise to be careful when choosing a sensor for such work.
## 3 Challenges for Designing MAC Protocols in UWSNs
### Doppler Spread  
The change in the position of the transmitter or receiver is one of several causes of the doppler spread, which is also referred to as doppler diffusion in some studies [61; 62]. In contrast to TWSNs, where RF is utilized as a medium of communication, this doppler shift is more evident in UWSNs due to the low velocity of acoustic signals used for communication between nodes [12; 63]. It has been discovered that the doppler spread can be modified when the given bandwidth is limited [64]. The data rate of the entire communication channel will be reduced due to doppler shift, resulting in a decrease in the performance of acoustic communication system in UWSNs. As a result, when building an active MAC protocol for UWSN networks, it is critical to understand the fundamental properties of signal imbalances caused by doppler dispersion, which lowers the MAC protocols performance [38; 65; 66].
## 7 Conclusions  
This paper explores different use cases of smart farming, the advantages and application of implementing IoT and UAVs in agriculture, the various communication technologies and identifies the issues and limitations of connectivity of IoT and UAVs in remote areas. We have outlined the connectivity limitations in terms of communication technology and transmission distance, and have discussed the special case of implementing IoT for smart farming in remote areas. We have also discussed how satellite communication can be used to offer connectivity of IoT and UAVs in rural parts of Australia. The paper provides an overview of applications of smart farming, as well as helps the researchers to identify the challenges and open research issues of smart farming, hence will be helpful in identifying the prospects and gaps of sustainable smart farming.  
**Author Contributions:** conceptualization, N.I. and S.M.; investigation, N.I., M.M.R., F.P., B.R.; writing--original draft preparation, N.I., M.M.R., F.P., B.R.; writing--review and editing, R.K., M.M.R., S.M.; supervision, N.I., M.M.R., S.M., R.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Batch Studies
#### 3.2.2 Adsorption Temperature Effect for the Removal of MB and IBF  
Temperature is an important parameter in the adsorption process. The effect of adsorption temperature for MB and IBF with AC-MOP was carried out at three temperatures of 35, 45 and 55 \({}^{\circ}\)C with 100 mg/L concentrations. The temperature effect on the removal capacity with AC-MOP for MB dye and IBF is illustrated in Figure 6. The temperature plots show that MB and IBF had different adsorption trends. It was observed that a rise in temperature solution from 25 to 45 \({}^{\circ}\)C resulted in higher uptake for MB; after that, a decrease in capacity was observed. On the other hand, the adsorption for IBF increased from 25 to 45 \({}^{\circ}\)C, but at higher temperatures, the capacity remained constant. The high uptake of MB and IBF at 45 \({}^{\circ}\)C could be attributed to lowered activation energy required, increased kinetics between the adsorbate molecules and the surface of the adsorbent as well as change in the textural properties of the AC-MOP [38].  
The determination of entropies (\(\Delta\)S\({}^{\circ}\)) and enthalpies (\(\Delta\)H\({}^{\circ}\)) for the slope and intercept--respectively--including the Gibbs free energy (\(\Delta\)G\({}^{\circ}\)) of MB and IBF is shown in Table 3. \(\Delta\)S\({}^{\circ}\) values were positive, offering the freedom of randomness of MB and IBF in the solution during the process. The positive \(\Delta\)H\({}^{\circ}\) suggests that the reaction was endothermic. The positive \(\Delta\)H\({}^{\circ}\) values may have resulted from the dissociation of water [34]. Generally, thermodynamic parameters showed that enthalpy is the driving force for the deproto  
Figure 5: pH for AC-MOP at 25 \({}^{\circ}\)C under the initial concentration of 100 mg/L and 0.4 g dosage.  
nation of the AC-MOP surface. The \(\Delta\)H\({}^{\circ}\) data of this work were similar to those of [PERSON] et al. (2021) [38]. The negative \(\Delta\)G\({}^{\circ}\) values signify that the sorption process was spontaneous and exothermic.
## 2 Materials and Methods  
### Study Area and Plants Analyzed  
The waste heap of the \"Antigua Pilar\" mine is located in the municipality of Colmenarejo in the western part of the Community of Madrid (Spain) (Figure 1). The area is situated at an elevation between 780 and 815 m, with an average temperature of 13.4 \({}^{\circ}\)C and an annual precipitation average of 637 mm. The soils in the Colmenarejo area are classified as cambsiols formed from granites, graisses, mica schists, and quartz [21]. Summer temperatures can reach up to 35 \({}^{\circ}\)C, which combined with the absence of precipitation, gives the climate a semi-arid character.  
The study area was the abandoned waste heap of the \"Antigua Pilar\" copper mine in the municipality of Colmenarejo in the Community of Madrid (Spain). The mine's waste heap mainly contains the following minerals: chalcopyrite (CuFeS\({}_{2}\)), arsenopyrite (FeAsS), malachite (Cu\({}_{2}\)CO\({}_{3}\)(OH)\({}_{2}\)), azurite (Cu\({}_{3}\)(CO\({}_{3}\))\({}_{2}\)(OH)\({}_{2}\)), pyrite (FeS\({}_{2}\)), siderite (FeO\({}_{3}\)), fluorite (CaF\({}_{2}\)), divenite (Cu\({}_{2}\)AsO\({}_{4}\)(OH)), and chalcanthite (CuSO\({}_{4}\)\(\cdot\)5H\({}_{2}\)O) [C1196]. In addition to these minerals, the mine's waste heap contains torberite (Cu(UO\({}_{2}\))\({}_{2}\)(PO\({}_{4}\))\(\cdot\)8-12H\({}_{2}\)O) and zeunerite (Cu(UO\({}_{2}\))\({}_{2}\)(AsO\({}_{4}\))\(\cdot\)12H\({}_{2}\)O), whose presence is caused by geological formation through hydrothermal processes [22]. Therefore, the samples from the waste heap and its surroundings have a ratio of the activity concentration of uranium and thorium radioactive series that is significantly greater than 1 [23]. Figure 2a-c depicts the mine tailings pile and a detailed view of the minerals present. The four plant species used in this study were selected based on their relevance to the following aspects: (i) _Scolymus hispuncius_ L. (9 plants) for its use in phytoremediation by absorbing numerous heavy metals [24]; (ii) _Errgium campestre_ L. (5 plants) for the uptake of radionuclides such as \({}^{137}\)Cs by mycorrhizal fungi associated with its roots [25,26]; (iii) and _Chenopodium vularia_ L. (7 plants) and (iv) _Chenopodium album_ L. (4 plants) due to their use as fodder in livestock feed as well as their medicinal applications as anti-inflammatory agents [27]. The 25 sampled plants were collected from the sampling points shown in Figure 1.  
Figure 1: The location of the sampling points of the 25 plants collected from the surroundings and in the tailings of the Antigua Pilar copper mine.
## 3 Results and Discussion
### Machine Learning Results and Discussion
#### 3.2.2 Models Results
While GBR's metrics were higher than RFR, its overall performance was considerably better. The reason lies in GBR achieving superior metrics for the training set compared to the RFR model. Metrics and visualization collectively demonstrate the RFR model's ability to match predictions with actual values. Despite a slight increase in prediction errors compared to the training set, the model showcased a notable capacity to generalize predictions on unseen data. In summary, GBR surpassed RFR in performance, showcasing enhanced predictive capabilities. However, deviations from the ideal line persisted, particularly for low stress values. GBR is highly recommended for dry unit weight predictions due to minimal deviations from the ideal line. XGBoost Model Results  
The analysis presented in Figure 21 delves into the _XGBoost_ model's predictive prowess concerning \(\sigma_{min}\) and \(\gamma_{max}\) across both training and test datasets. Notably, the training set showcased exceptional accuracy, boasting a coefficient of determination (R-squared) of 0.9999 for both \(\sigma_{min}\) and \(\gamma_{max}\). Additionally, the model exhibited low RMSE and MAE values (0.932 and 0.7035 for \(\sigma_{min}\), 0.01 and 0.0068 for \(\gamma_{max}\)), signifying its adeptness in capturing training data variance with minimal prediction errors. While comparable to GBR, the _XGBoost_ model slightly outperformed GBR in the training set. In the test set, XGBoost outshone GBR, evident in the R-squared values of 0.9856 and 0.9976 for \(\sigma_{min}\) and \(\gamma_{max}\), respectively. Correspondingly, the RMSE and MAE values (10.43 and 7.92 for \(\sigma_{min}\), 0.062 and 0.039 for \(\gamma_{max}\)) reinforced XGBoost's proficiency in delivering precise predictions on independent data. Although there was a marginal increase in prediction errors on unseen data compared to RFR, XGBoost's metrics and performance  
Figure 20: Comparing the actual and predicted values for the training and test datasets in the GBR model for (**A**) \(\sigma_{min}\) and (**B**) \(\gamma_{max}\). surpassed GBR. The key differentiator lies in XGBoost achieving higher metrics for the training set than the GBR model. In summary, XGBoost unequivocally outperformed the DTR, RFR, and GBR models in terms of predictive accuracy and closely aligned with the ideal line. Despite minor deviations from the ideal line among certain data points, these discrepancies remained within acceptable bounds. Crucially, these instances hold significance, particularly in predictions at low stress levels, where the predicted stress occasionally exceeds the actual value. This serves as a safety measure, preventing swelling and thereby enhancing the model's overall predictive utility and practical applicability.