esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 4 Experiments  
### Dataset  
The LUNA16 dataset [30], which served as the foundation for training our classification model, consists of 888 computed tomography (CT) scans that have been meticulously annotated by four experienced radiologists via a detailed two-phase process, in which the radiologists evaluated and annotated CT scans to identify and confirm the presence of lung nodules. In the first phase, each of the CT scans in the dataset was independently reviewed by the radiologists, to detect potential lung nodules. The second phase involved a review process where the findings from the initial phase were verified. This dataset is primarily designed for studies focusing on the reduction of FPs in lung nodule detection. It includes a comprehensive collection of 495,958 nodule candidates, generated through the application of three sophisticated detection algorithms, out of which 1215 have been confirmed as positive samples. This equates to a positive-to-negative ratio of approximately 0.0025, indicating a pronounced imbalance that presents substantial challenges for effective model training. To address the reviewer's query, each nodule candidate within the dataset is annotated with additional attributes that provide deeper insights into their clinical characteristics. The nodules vary in size, typically ranging from 3 mm to 30 mm in diameter, and they are described with shapes that may be spherical, lobulated, spiculated, or irregular, reflecting their diverse physical manifestations in CT scans. Furthermore, the dataset categorizes these nodules into benign or malignant, based on their histopathological evaluation, where available from follow-up procedures or prior diagnostic data. However, it is important to note that not all nodules in the dataset have confirmed malignancy status, due to the limitations in available clinical follow-up. Moreover, the LUNA16 dataset includes information regarding the nature of the nodules, distinguishing between primary lung cancers and metastatic lesions. This distinction is crucial for the model, as it aids in understanding the different patterns associated with primary and secondary lung cancer manifestations in imaging studies. By integrating these detailed attributes into our analysis, the dataset not only facilitated the training of more accurate models for nodule detection but also enhanced the models' ability to generalize across a more diverse set of pathological features, thereby improving diagnostic precision in clinical settings.  
The LUNA16 dataset, which forms the basis of our experimental analysis, primarily includes nodules that are both benign and malignant in nature. However, it is important to clarify that while the dataset provides a robust platform for detecting and classifying lung nodules, it does not specifically differentiate all nodule types by the exact disease or condition they represent.
# Autonomous Pointing Control of a Large Satellite Antenna Subject to Parametric Uncertainty
## 4 Numerical Results  
In this section, numerical results showing the performance of the proposed autonomous control algorithms are presented. The satellite parameters are given in Table 1, and the first four-order modal parameters are chosen. The periodic error is firstly given as \(0.3^{\circ}\) sin(\(0.01t\)). The controllers are given by Equations (13), (23) and (30). The expected antenna pitch angle is \(6^{\circ}\), the initial pointing angle and angular velocity are both \(0\), and the installation error and the deployment error of pitch angle are both set at \(0.001^{\circ}\). Figures 6 and 7 present the outcome of the performance of the controller in Equation (13) subject to the first-order modal parameter uncertainty. As can be seen, the pointing angle error and the angular velocity error can converge to \(\pm 0.1^{\circ}\) and \(\pm 0.01^{\circ}\)/s, while there obviously exists periodic oscillation along the pitch axis. This demonstrates that a particular SF is only effective for the particular bending vibration signal. Once the modal parameters change, the performance of the closed-loop control system becomes worse.  
Figure 6: Antenna pitch angle error—controller (13).  
Figure 11: Antenna pitch angle error—controller, Equation (30).  
Figure 10: Control torque—controller, Equation (23).  
Figure 9: Antenna pitch angular velocity error—controller, Equation (23).  
Figure 14: Antenna pitch angle error—controller, Equation (30).  
Figure 12: Antenna pitch angular velocity error—controller, Equation (30).  
Figure 13: Control torque—controller, Equation (30).
## 4 Experimental Results and Discussion  
### Comparison with HMM-DNN Hybrid Baseline System and Related Work  
The first set of experiments compares the end-to-end method with the traditional HMM-DNN hybrid method and the CNN-RNN-CTC method [22] in terms of vowel reduction detection and phoneme recognition. The CNN-RNN-CTC method was originally  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subset** & **Sentences** & _ah_ & _ah_ & _ih_ & _ih_ \\  & & _Schwa_ & _Normal_ & _Schwa_ & _Normal_ \\ \hline Train & 3696 & 3892 & 2266 & 7370 & 4248 \\ Dev & 400 & 403 & 248 & 757 & 444 \\ Test & 192 & 186 & 135 & 377 & 203 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of sentences and phonemes in each subset.  
designed for the mispronunciation detection and diagnosis (MDD) task. Recently, it has been cited by some CAPT studies for comparison [18; 29; 30; 31]. Since it is an E2E ASR-based method, as is ours, we implement it on the vowel reduction detection task as a related work for comparison. Table 3 lists the results of HMM-DNN hybrid method, the CNN-RNN-CTC method, the general E2E method, and our proposed E2E method with pronunciation prior knowledge assistance. It shows that the general E2E method has a slight advantage in \(ih_{schwa}\) phoneme detection and overall phoneme recognition performance compared with the HMM-DNN method. Furthermore, our proposed E2E method has more improvements in various indicators compared with the traditional HMM-DNN method. Since the vowel reduction phenomenon will be affected by the context, E2E methods may benefit from directly modeling the whole sequence. The CNN-RNN-CTC method is superior to the HMM-DNN method and the general E2E method in the vowel reduction detection performance, but it has obvious defects in phoneme recognition performance. Furthermore, it is inferior to our proposed method in both performances. The reason the CNN-RNN-CTC method exceeds the general E2E method in the detection of vowel reduction may be that the convolutional layers can obtain the frequency characteristics related to this phenomenon.
# A Survey on Dynamic Corrective Control of Asynchronous Sequential Machines
## 5 Fault-Tolerant Control
### Transient Faults
In this case, a stricter existence condition is needed to ensure a proper corrective controller. Fault tolerance against transient faults has been much investigated not only in input/state ASMs but also in input/output ASMs. Unlike input/state ASMs, transient faults occurring to input/output ASMs raise the necessity of indirect fault diagnosis, since the exact faulty state reached by the machine cannot be observed by the controller. A study exists that presents a synthesis procedure for a state observer serving as a diagnoser. The prior work also addresses a corrective controller which eliminates any adverse effect of transient faults to input/output ASMs, while solving the problem of model matching. The authors of [71] apply the previous result to controlling an asynchronous 5-clock divider. On the other hand, another report proposes a so-called simple fault-tolerant control system for input/output ASMs in which feedback paths for recovering the normal input/output behavior can be built without using the state observer, provided that some reachability condition on the machine is satisfied. Based on the controller addressed in [71], some approaches conduct experimental studies by applying the proposed scheme to acquiring a robust configuration controller for field programmable gate arrays (FPGAs). Since they do not employ the state observer, fault tolerance in their results implies that the controlled machine reaches a state that is output equivalent with the original state at which the fault occurs. On the other hand, a refined result exists that improves the previous result by elucidating the existence condition for a corrective controller that drives the input/output ASM toward the exact original state without using the state observer. The problem of tolerating transient faults is also tackled in the framework of composite ASMs. First, a diagnosis scheme is presented for identifying transient faults happening to submachines of a composite ASM in serial connection. Another approach further proposes the fault diagnosis and fault-tolerant control configuration for composite ASMs in serial connection subject to transient faults. As with parallel interconnected ASMs and switched ASMs, some interesting reports are found in which a single corrective controller achieves fault tolerance for every submachine in a composite ASM vulnerable to transient faults, and in which model matching and fault-tolerant control are simultaneously realized for switched ASMs.
## 2 Materials and Methods
### Results
#### 2.3.2 Water Absorption Tests  
The percentage of water absorption by the different specimens was calculated from the following Equation (1):  
\[\textit{Water absorption (\%)}=\textit{(Wi }-\textit{Ws)}/\textit{Ws}\cdot\textit{100} \tag{1}\]  
where:  
Wi = average weight of dry sample (g),  
Ws = the average weight of the wet sample (g).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Curing Days** & \begin{tabular}{c} **Specific Weight** \\ **(daN/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Compressive Failure** \\ **Stress (N/mm\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Flexural Failure Stress** \\ **(N/mm\({}^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Tensile Failure Stress** \\ **(N/mm\({}^{2}\))** \\ \end{tabular} \\ \hline  & & **Value** & \(\sigma\) & **Value** & \(\sigma\) & **Value** & \(\sigma\) \\ \hline
7 & 2140 & 31.2 & \(\pm\)3.10 & - & - & - \\
28 & 2140 & 42.6 & \(\pm\)5.724 & 3.3 & \(\pm\)3.74 & 2.2 & \(\pm\)1.73 \\
60 & 2115 & 45.3 & \(\pm\)4.32 & - & - & - \\
90 & 2113 & 47.1 & \(\pm\)1.34 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean values and standard deviations of the mechanical characteristics of slag and microsilica concrete.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Curing Days** & \begin{tabular}{c} **Ordinary Concrete** \\ **(N/mm\({}^{2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Slag and Fly Ashes Concrete** \\ **(N/mm\({}^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Slag and Microsilica Concrete** \\ **(N/mm\({}^{2}\))** \\ \end{tabular} \\ \hline  & **Value** & **Value** & \(\sigma\) & **Value** & \(\sigma\) \\ \hline
28 & 47 & 36.5 & \(\pm\)2.12 & 42.6 & \(\pm\)5.724 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Compressive strength for the different concrete types.  
For a clearer approach, Figures 5 and 6 show the curves and the histograms relative to the results of the water absorption tests. In Figure 5, h = 0 represents the time at which the immersion of the specimens in the water occurred.  
The material porosity decreases both with the addition of fly ashes and microsilica fume. In fact, in the first case the reduction of total pore volume is probably due to the outcome of the continuous generation of pozzolanic reaction products from the hydration of FA that fill the pores. In the second case for microsilica fume concrete, the reduction of total pore volume is caused by the high pozzolanic reactivity, but also by the very small dimensions of SF particles and their pore-filling effect (they subdivide the pore space by amassing themselves between the cement grains).  
Figure 6 shows the percentage of water abrsbed by the different concrete specimens. It is possible to notice that the higher amount of absorbed water is obtained for the ordinary concrete specimens. In fact, the percentage of absorbed water with respect to the total weight is equal to 4.87% for an ordinary concrete, and 4.20% and 4.03% for FA and SF concretes, respectively.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
###### Abstract  
To address the critical limitations of conventional Grey Wolf Optimization (GWO) in path planning scenarios--including insufficient exploration capability during the initial phase, proneness to local optima entrapment, and inherent deficiency in dynamic obstacle avoidance--this paper proposes a multi-strategy enhanced GWO algorithm. Firstly, the Piecewise chaotic mapping is applied to initialize the Grey Wolf population, enhancing the initial population quality. Secondly, the linear convergence factor is modified to a nonlinear one to balance the algorithm's global and local search capabilities. Thirdly, Evolutionary Population Dynamics (EPD) is incorporated to enhance the algorithm's ability to escape local optima, and dynamic weights are used to improve convergence speed and accuracy. Finally, the algorithm is integrated with the Improved Dynamic Window Approach (IDWA) to enhance path smoothness and perform dynamic obstacle avoidance. The proposed algorithm is named PAGWO-IDWA. The results demonstrate that, compared to traditional GWO, PAGWO-IDWA reduces the path length, number of turns, and running time by 9.58%, 33.16%, and 30.31%, respectively. PAGWO-IDWA not only overcomes the limitations of traditional GWO but also enables effective path planning in dynamic environments, generating paths that are both safe and smooth, thus validating the effectiveness of the algorithm.  
grey wolf optimization; chaotic map; convergence factor; evolutionary population dynamics; dynamic weights; dynamic window approach Article
## 2 Methods
### PPP-B2b-Based Orbit and Clock Corrections  
According to the BDS B2b-PPP protocol, corrections of satellite orbit and clock offset for broadcast ephemeris are expressed in SSR format [30; 31]. By using these corrections, the precise satellite position \(\mathbf{X}_{orbit,prec}\) can be obtained by  
\[\mathbf{X}_{orbit,prec}=\begin{bmatrix}X_{brdc}\\ Y_{brdc}\\ Z_{brdc}\end{bmatrix}-\begin{bmatrix}\boldsymbol{e}_{radial}&\boldsymbol{e}_{ along}&\boldsymbol{e}_{cross}\end{bmatrix}\begin{bmatrix}\delta O_{radial}\\ \delta O_{along}\\ \delta O_{cross}\end{bmatrix} \tag{14}\]  
\[\left\{\begin{array}{l}\mathbf{e}_{radial}=\frac{\mathbf{r}}{|\mathbf{r}|} \\ \mathbf{e}_{cross}=\frac{\mathbf{r}\times\hat{\mathbf{r}}}{|\mathbf{r}|\times \hat{\mathbf{r}}|}\\ \mathbf{e}_{along}=\mathbf{e}_{cross}\times\mathbf{e}_{radial}\end{array}\right. \tag{15}\]  
and the precise satellite clock \(dt_{prec}\) can be expressed by  
\[dt_{prec}=dt_{brdc}-\frac{\delta r^{s}}{c} \tag{16}\]  
where \(\mathbf{r}\)(=\(\begin{bmatrix}X_{brdc}&Y_{brdc}&Z_{brdc}\end{bmatrix}^{T}\)) and \(\hat{\mathbf{r}}\) are the vectors of satellite position and velocity, which are calculated by using broadcast ephemeris; \(\delta O_{radial}\), \(\delta O_{along}\), and \(\delta O_{cross}\) are orbit corrections that are provided by the BDS PPP-B2b; \(c\) is the speed of light; \(dt_{prec}\) is the PPP-B2b-corrected precise satellite clock offset, which is calculated by broadcast ephemeris (\(dt_{brdc}\)) and the clock offset correction (\(\delta r^{s}\)). Here, the broadcast ephemeris is the civil navigation message (CNAV1) that is transmitted on the BDS B1C frequency and the navigation message (LNAV) that is transmitted on the GPS L1C/A frequency, respectively [30].
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
## 4 Calculation of Jacobian Matrix Elements Using Exact Zeoppritz and Biot-Gassmann Equations
### The Calculation of \(\frac{\partial A}{\partial m_{j}}\)
#### 4.1.3 Partial Derivative of Matrix A with Respect to \(\mu_{d1}\)  
Since only \(\alpha\) is independent of \(\mu_{d1}\), the partial derivatives of the trigonometric functions of angle \(\alpha\) with respect to \(\mu_{d1}\) are zero. Letting:  
\[\eta_{\mu d1}=\frac{v_{p1}}{v_{s1}}\frac{\partial}{\partial\mu_{d1}}\Bigg{(} \frac{v_{s1}}{v_{p1}}\Bigg{)}=\xi_{kd1}\frac{v_{p1}}{v_{s1}} \tag{22}\]  
\[\xi_{\mu d1}=\frac{\partial}{\partial\mu_{d1}}\Bigg{(}\frac{v_{s1}}{v_{p1}} \Bigg{)}=\frac{1}{v_{p1}^{2}}\Bigg{(}v_{p1}\frac{\partial v_{s1}}{\partial\mu _{d1}}-v_{s1}\frac{\partial v_{p1}}{\partial\mu_{d1}}\Bigg{)}=\frac{v_{s1}}{ v_{p1}}(\tau_{\mu d1}^{v1}-\tau_{\mu d1}^{vp1}) \tag{23}\]  
where \(\tau_{\mu d1}^{vp1}=\frac{1}{v_{p1}}\frac{\partial v_{p1}}{\partial\mu_{d1}}\) and \(\tau_{\mu d1}^{v1\approx 1}=\frac{1}{v_{s1}}\frac{\partial v_{s1}}{\partial\mu_{d1}}\). The partial derivatives of the trigonometric functions of angles \(\beta\), \(\alpha\prime\) and \(\beta\prime\) with respect to \(\mu_{d1}\) are shown in Appendix B.  
Therefore, the partial derivative of matrix A with respect to \(\mu_{d1}\) is:  
\[\frac{\partial A}{\partial\mu_{d1}}=\tau_{\mu d1}^{vp1}\Bigg{[}\begin{array}[] {cccc}0&-\frac{\eta_{\mu d1}}{v_{p1}^{2}}\tan\beta\sin\beta&\sin\alpha^{\prime }&\tan\beta^{\prime}\sin\beta^{\prime}\\ 0&\frac{\eta_{\mu d1}}{v_{\mu\mu 1}^{2}}\sin\beta&\tan\alpha^{\prime}\sin\alpha^{ \prime}&-\sin\beta^{\prime}\\ 2\frac{\eta_{\mu d1}}{v_{\mu\mu 1}^{2}}(a_{31}-1)&\frac{\eta_{\mu d1}}{v_{ \mu\mu 1}^{2}}a_{32}(2-\tan^{2}\beta)&-\bigg{[}3a_{33}+\frac{2\rho_{2}}{\rho_{1}} \frac{v_{p2}}{v_{p1}}\bigg{]}&a_{34}(\tan^{2}\beta^{\prime}-2)\\ a_{41}\bigg{[}\frac{\eta_{\mu d1}}{v_{\mu\mu 1}^{2}}+1\bigg{)}&\frac{\tau_{\mu d1}^{ v1}}{v_{\mu\mu 1}^{2}}a_{42}+\frac{2\eta_{\mu d1}}{v_{\mu\mu 1}^{2}}(a_{42}-v_{s1})&a_{43} \Big{(}\tan^{2}\alpha^{\prime}-1\Big{)}&-2(a_{44}+\frac{\rho_{2}}{\rho_{1}}v_{ s2})\\ \end{array} \tag{24}\]
# Effect of Planting _Portulaca oleraca_ L. on Improvement of Salt-Affected Soils
## 2 Materials and Methods
### Measurement Methods
#### 2.3.3 Soil Physicochemical and Biological Analysis  
The salt content of the soil profile from 0 to 60 cm was determined, with sampling depths at 0-10, 10-20, 20-40, and 40-60 cm. The total soil salt content was determined by  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Blocks** & \begin{tabular}{c} **Soil Bulk** \\ **Density** \\ **(g/cm3)** \\ \end{tabular} & \begin{tabular}{c} **Soil Salinity** \\ **(g/kg)** \\ \end{tabular} & \begin{tabular}{c} **Soil Organic** \\ **Pattern** \\ **(g/kg)** \\ \end{tabular} & \
\begin{tabular}{c} **Available** \\ **Nitrogen** \\ **(mg/kg)** \\ \end{tabular} &
\begin{tabular}{c} **Available** \\ **Plossium** \\ **(mg/kg)** \\ \end{tabular} \\ \hline S1 & 1.47 \(\pm\) 0.03 & 2.16 \(\pm\) 0.08 & 8.01 \(\pm\) 0.01 & 8.34 \(\pm\) 0.03 & 28.59 \(\pm\) 0.80 & 10.42 \(\pm\) 0.27 & 195.53 \(\pm\) 4.92 \\ S2 & 1.51 \(\pm\) 0.02 & 4.08 \(\pm\) 0.07 & 7.95 \(\pm\) 0.02 & 6.93 \(\pm\) 0.04 & 26.45 \(\pm\) 0.81 & 9.23 \(\pm\) 0.29 & 187.67 \(\pm\) 9.51 \\ S3 & 1.48 \(\pm\) 0.03 & 5.43 \(\pm\) 0.09 & 8.05 \(\pm\) 0.02 & 6.64 \(\pm\) 0.06 & 24.72 \(\pm\) 0.75 & 8.92 \(\pm\) 0.26 & 176.6 \(\pm\) 7.41 \\ \hline \hline \end{tabular} Note: The data is presented as mean \(\pm\) SD (\(n\) = 3).  
\end{table}
Table 1: Basic properties of three blocks with different salt levels in 0–20 cm soil.  
the weight loss of a 5:1 (water-soil mass ratio) extract after being oven-dried at 105 \({}^{\circ}\)C to a constant weight [19].  
The measurements of soil bulk density, porosity, pH, organic matter content (SOM), and enzyme activity were conducted across three soil layers (0-10, 10-20, and 20-40 cm). The soil bulk density and porosity were measured by inserting steel cylinders. Soil pH was measured by drying and grinding the soil samples, followed by extraction with a 5:1 (water-soil mass ratio) solution and then determining the pH of the extract using a pH meter ([PERSON], PB-10, Gottingen, Germany). The soil organic matter content was determined using a potassium dichromatic oxidation technique combined with external heating [20].  
Soil invertase activity was determined using 3, 5-dinitrosalicylic acid colorimetry, soil urease activity was measured by the phenol-sodium colorimetric method, and soil alkaline phosphatase activity was calculated by colorimetry using p-nitrophenyl phosphate disodium as the substrate [21,22].  
We measured soil physicochemical and biologic properties across three soil layers (0-10, 10-20, and 20-40 cm), except for salt content, which was measured across four layers (0-10, 10-20, 20-40, and 40-60 cm). The objective of this study was mainly focused on phytoremediation of salt from soils. The salt in the soils is very easy to move up and down with water, but the other parameters are relatively stable and less changed at 40-60 cm soil depth, since _P. oleracea_ is a shallow-rooted plant.
## 2 Case Study--Arsenic Contamination of Groundwater in Bangladesh
### Mobilization of Arsenic in Groundwater in Bangladesh
#### 2.3.2 Iron Oxyhydroxide Reduction Hypothesis  
Another suggestion proposed as the source of groundwater contamination with arsenic in Bangladesh is based on the so-called iron oxyhydroxide reduction hypothesis. This hypothesis is based on the premise that the source of arsenic was from the Ganges region, upstream of Bangladesh, where arsenic sulfide minerals followed by abrasion were carried by water about 1.6-1.8 million to 10,000 years ago during the late Pleistocene age [43]. As these arsenic-containing minerals travelled down the Ganges, arsenic was adsorbed to iron oxyhydroxide (FeOOH). The arsenic-rich iron oxyhydroxides were deposited at the Gangetic delta, and formed an alluvial aquifer. Various processes, including burial of vegetation, agriculture and floods, led to the introduction of organic carbon into the aquifer [8]. This organic carbon provided food for bacteria and the presence of methane in the water indicates that organic matter is being utilized in the aquifer by anaerobic bacteria [30]. Consequently, the redox potential of the groundwater is lowered by this biological process. Due to this reducing environment, the iron oxyhydroxide is broken down, resulting in the introduction of adsorbed arsenic into the groundwater [36; 39; 43; 44; 45]. The adsorption of arsenic by hydrous iron oxyhydroxide is supported by the following reactions [8]:  
\[\text{Fe}\;(\text{OH})_{3}+\text{H}_{3}\text{AsO}_{4}\rightarrow\text{FeAsO} _{4}\cdot 2\text{H}_{2}\text{O}+\text{H}_{2}\text{O}\]  
\[\equiv \text{FeOH}^{0}+\text{AsO}_{4}{}^{3-}+3\text{H}^{+}\rightarrow\equiv \text{FeH}_{2}\text{AsO}_{4}+\text{H}_{2}\text{O}\]  
\[\equiv \text{FeOH}^{0}+\text{AsO}_{4}{}^{3-}+2\text{H}^{+}\rightarrow\equiv \text{FeHAsO}_{4}{}^{-}+\text{H}_{2}\text{O}\]  
The reduction of hydrous iron oxides can occur by the following reaction:  
\[\text{Fe}(\text{OH})_{3}+\text{e}^{-}\rightarrow\text{Fe}^{2+}+3\text{OH}^{-}\]  
Therefore, the presence of anoxic conditions is necessary for arsenic-rich iron oxyhydroxide to be reduced and, in turn, release arsenic. This process mobilizes iron and its adsorbed load into the groundwater. Some researchers have accepted the arsenic mobilization based on the iron oxyhydroxide reduction hypothesis as the main mechanism for groundwater contamination by arsenic in Bangladesh [27; 36; 42; 46]. However, a more comprehensive sampling and systematic analysis of ferric oxyhydroxide in the areas affected is needed to validate the reduction hypothesis [27; 42].  
Furthermore, it may also be possible for both of the mechanisms discussed above to contribute to arsenic release. This is in view of the fact that the pyrite oxidation occurs under an oxic condition, while iron oxyhydroxide reduction occurs under an anoxic condition.
## 4 Experimental Results and Dataset
### Performance Metric  
In order to evaluate the performance of the IQA methods, two performance metrics were adopted: SROCC and prediction accuracy.