esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Depositions  
#### 2.2.1 Powder and Substrate  
The powder used in the depositions consists of Bohler W722 (Bohler Edelstahl GMBH, Kapfenberg, Austria), a grade equivalent to 18 Ni300 Maraging steel, produced by inert gas atomisation [35]. Its morphology was analysed through scanning electron microscopy (SEM) with a FEI Quanta 400 FEG (FEI Company, Hillsboro, OR, USA), while its size distribution was analysed through dynamic light scattering (DLS) in a Coulter LS300 (Beckman Coulter, Brea, CA, USA), by first suspending a sample of powder in ethanol and subjecting the solution to ultrasonic vibrations for one minute; results are shown in Figure 2, respectively. The powder's calculated \(D_{10}\), \(D_{50}\) and \(D_{90}\) are 48.8, 89.8 and 143.7 um, respectively.  
The substrate was composed of Uddeholm Orvar 2M (FRamada, Aveiro, Portugal), an equivalent grade to AISI H13 tool steel, in its annealed state, at a temperature of 850 C. The chemical composition of the substrate was obtained through mass spectroscopy with a SPECTROMAXx (Spectro, Berwyn, PA, USA) machine and whose results are shown in Table 1. The nominal values for this alloy were obtained from the supplier's website [36].  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Chemical Composition [\%]**} \\ \hline
**H13** & **Cr** & **Mo** & **Mn** & **P** & **C** & **Si** & **V** & **Fe** \\ \hline Nominal & 4.80–5.50 & 1.20–1.50 & 0.25–0.50 & \(\leq\)0.03 & 0.35–0.42 & 0.80–1.20 & 0.85–1.15 & Bal. \\ Measured & 5.23 & 1.12 & 0.37 & 0.01 & 0.40 & 0.87 & 0.74 & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemicals composition of the substrate, an AISI H13 tool steel alloy.  
Figure 2: Powder morphology obtained through SEM and its size distribution calculated through DLS.
# Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
## 2 Materials and Methods
### Metrics  
An important aspect of this study is to assess the performance of the developed models when processing data associated with clinical pathology images. This evaluation will be carried out during both the training and testing phases. During training, only overall accuracy will be calculated, and its value will be distinguished in separate tables and bar charts within the results section, labeled as \"Accuracy Train\". This metric assesses how effectively the model has captured patterns from the training data. However, it may not fully reflect the model's performance on unseen data (test dataset). It is computed as the percentage of training examples correctly classified by the model relative to the total number of training examples, as depicted in Equation (1):  
\[\text{Training Accuracy}=\frac{\text{Correctly Classified}}{\text{Total Training Examples}}\times 100\% \tag{1}\]  
Regarding the metrics in model testing, the use of the confusion matrix was mentioned in the introduction of this work due to its ability to provide information on both overall predictions and class-specific predictions. Consequently, the overall accuracy in testing will be distinguished in the various tables and bar charts in the results section under the name \"Accuracy Test\". For class-specific predictions, sensitivity, specificity, and precision metrics will be calculated, and these will be distinguished in the tables and bar charts in the results section by appending each metric with the initial letter \"N\", \"V\", or \"B\", corresponding to the classes normal, virus, or bacteria, respectively. It should be noted that the overall accuracy in testing assesses how well the model generalizes unseen data during training. Sensitivity indicates the percentage of positive cases detected. Precision represents the percentage of correct positive predictions. Specificity indicates the percentage of negative cases detected. It is important to note that the calculation will be performed using the Caret package in \(R\) for the test datasets [50]. For further information on the confusion matrix, refer to [30; 51].
## Abbreviations  
**OA** osteoarthritis, **VAG** vibroarthrography, **CT** computed tomography, **MRI** magnetic resonance imaging, **UT** ultrasonic testing, **MCI** muscle contraction interference, **EEMD** ensemble empirical mode decomposition, **DFA** detrended fluctuation analysis, **IMF** intrinsic mode function, **PDF** probability density function, **FF** form factor, **TC** turn count, **FT** Fourier transform, **TD** time domain, **FD** frequency domain, **TFD** time-frequency domain, **STFT** short-time Fourier transform, **WT** wavelet transform, **ESP** energy spread parameter, **EP** energy parameter, **FP** frequency parameter, **FSP** frequency spread parameter, **FFNN** feedforward neural network, **SVM** support vector machine, **LS-SVM** least squares support vector machine, **RF** random forest, **MLP** multi-layer perceptron, **SL** simple logistic, **XGBoost** extreme gradient boosting, **LR** logistic regression, **KNN** K-nearest neighbours, **NB** Gaussian Naive Bayes, **GRNN** general regression neural network, **ANN** artificial neural networks, **BDR** Bayesian decision rule, **ACC** accuracy, **SEN** sensitivity, **SPE** specificity, **K** kurtosis, **H** entropy, **S** skewness, **SF** statistical features, **FSI** fractal scaling index, **AEA** averaged envelope amplitude, **TDF** time domain features, **FDF** frequency domain features, **CF** complexity features, **SHE** Shannon entropy, **MSE** multiscale sample entropy, **RQA** recurrence quantification analysis, **RR** recurrence rate, **DET** determinism, **LAM** laminarity, **TT** trapping time, **LMAX** maxline, **STF** spatiotemporal features, **CWT** continuous wavelet transform, **SF** shape factor, **II** impulse index,  
**MF** margin factor, **CF** crest factor, **SDF** spectral domain features, **SA** straightened average, **RMS** root-mean-square, **PV** peak value, **PPV** peak-to-peak value, **IF** impact factor, **VAR** variance, **VMS** mean squared value, **MoA** sixth central moment normalised by the variance raised to the third power, **MSA** eighth central moment, normalised by the variance to the fourth power, **M** mean, **SM** straightened mean, **StDev** standard deviation, **FM4** difference signal kurtosis, **ApEn** approximate entropy, **SampEn** sample entropy, **R4** signal amplitude.
## 3 Results and Discussion
### Transient Engine Performance Assessment
In this case, the turbine efficiency differences are almost negligible, barely reaching a difference of 0.28%. In summary, Table 7 shows the peak BMEP achieved, and the time taken to reach 90% of the final BMEP at VGO open configuration, which is the most restrictive. Table 7 shows, for 1500 RPM, that the open VGO takes 3.36 s to reach 90% of its steady state torque, while  
Figure 11: Initial conditions for transient engine response: Air to Fuel Ratio (**A**), and Rack Position (**B**). the closed VGO takes 3.24 s to achieve the exact value of BMEP. An improvement of 0.12 s is achieved with the closed configuration at 1500 RPM and 0.3 s at 1000 RPM. Figure 12: Transient engine performance assessment at 1000 RPM: BMEP (**A**), Boost Pressure (**B**), Compressor Efficiency (**C**), Turbine Efficiency (**D**), Percentage difference in BMEP (**E**), Boost Pressure (**F**), Compressor Efficiency (**G**), and Turbine Efficiency (**H**). Figure 13: Transient engine performance assessment at 1500 RPM: BMEP (**A**), Boost Pressure (**B**), Compressor Efficiency (**C**), Turbine Efficiency (**D**), Percentage difference in BMEP (**E**), Boost Pressure (**F**), Compressor Efficiency (**G**), and Turbine Efficiency (**H**). Computing the overall developed work according to Equation (2), there is an improvement of 1.19 kJ and 6.2 kJ at 1000 RPM and 1500 RPM, respectively. In relative terms, using Equation (3), 1.52 % and 3.21 % improvements in the energy produced can be obtained thanks to the impact of the VGO technology.
## 3 Results and Discussion
### Influence of Incident Laser Power on the Cutting Effect
**(b)** Diagram of fracture strength variation with laser incident power when the processing point spacing is 1 \(\upmu\)m and the burst mode is 2. **(c)** Diagram of fracture strength variation with laser incident power when the processing point spacing is 1 \(\upmu\)m and the burst mode is 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Incident Laser Power (W)** & \
\begin{tabular}{c} **Mean Roughness of** \\ **Section (nm)** \\ \end{tabular} &
\begin{tabular}{c} **Regional Maximum** \\ **Difference (nm)** \\ \end{tabular} \\ \hline
7.5 & 234.35 & 439.6 \\
10 & 239.75 & 63 \\
12.5 & 219.25 & 18 \\
15 & 225.75 & 115 \\
17.5 & 248.5 & 168 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average roughness of the cross section and maximum difference in each region under different incident powers. In summary, when the laser power is 12.5 W, the best processing effect can be obtained.
# Biomechanical Analysis of Two Types of Oseointegrated Transfemoral Prosthesis
## Materials and Methods
### Data Post Processing: Failure Analysis  
To evaluate possible mechanical failures of the bone or the prosthesis, the equivalent stress values have been calculated and verified for all fall scenarios. Peak stresses, \(\sigma_{\text{max}}\), have been compared with the corresponding material strengths (S). According to [51], the strength of the bone (Sbone) has been calculated as Sbone = 114 x \(\rho_{\text{ash}}\)[1; 72], where the ash density has been assumed to be \(\rho_{\text{ash}}\) = 1.22 g/cm\({}^{3}\), thereby obtaining Sbone = 160.5 MPa. Considering that plastic deformations could invalidate the prosthesis functionality, the yield tensile stress of the titanium alloy (\(\sigma_{\text{y}}\)) has been used to calculate the strength of the prosthesis (Synthesis = \(\sigma_{\text{y}}\) = 890 MPa). Bone or prosthesis failure risk was identified when \(\frac{\sigma_{\text{max, bone}}}{\text{S}_{\text{None}}}>1\) or \(\frac{\sigma_{\text{max, synthesis}}}{\text{S}_{\text{P prosthesis}}}>1\).  
To evaluate also possible failures at the bone/implant interface, the Hoffman criterion [55] has been used. This criterion transforms the local interface stress state to a value, termed Hoffman number or H, representing the probability of interface disruption [56]. A failure is expected to occur when H \(>\) 1; for H \(<\) 1 no interface failure is expected. The Hoffman number (H) over the bone/implant interface has been calculated as [25]:  
\[\begin{split}\text{H}=\frac{1}{S_{\text{c}}S_{\text{t}}}\sigma_{ \text{n}}^{2}+\left(\frac{1}{S_{\text{t}}}-\frac{1}{S_{\text{c}}}\right) \sigma_{\text{n}}+\frac{\sigma_{\text{s}}^{2}}{S_{\text{s}}^{2}}\\ \text{S}_{\text{c}}=32.4\times\rho_{\text{app}}^{1.85}\\ \text{S}_{\text{t}}=14.5\times\rho_{\text{app}}^{1.71}\\ \text{S}_{\text{s}}=21.6\times\rho_{\text{app}}^{1.65}\\ \rho_{\text{app}}=1.79\times\rho_{\text{ash}}+0.0119\end{split} \tag{1}\]  
\(\sigma_{\text{n}}\) and \(\sigma_{\text{s}}\) are the normal (tensile) and shear stress respectively.
## 2 Materials and Methods
### Methodology
#### 2.3.4 Extreme Value Model  
The generalized extreme value (GEV) distribution is a continuous probability distribution used for simulating and analyzing extreme value data [43]. The GEV distribution integrates three types of extreme value distributions, namely Gumbel, Frechet, and the inverted Weibull, which is also known as Type III extreme value distribution (used for minima but often transformed to represent maxima), providing a more general framework for extreme value modeling. The cumulative distribution function (CDF) of the GEV distribution is calculated using Equation (8):  
\[F(x;\mu,\sigma,\xi)=exp\left(-\left[1+\xi\left(\frac{x-\mu}{\sigma}\right) \right]^{-1/\xi}\right),\text{ for }1+\xi(x-\mu)/\sigma>0 \tag{8}\]  
where \(x\) is the random variable, \(\mu\) is the location parameter, \(\sigma\) is the scale parameter, and \(\xi\) is the shape parameter. When \(\xi\) equals 0, the GEV distribution simplifies to the Gumbel distribution. When \(\xi\) is greater than 0, the GEV distribution becomes the Frechet distribution. When \(\xi\) is less than 0, the GEV distribution corresponds to the inverted Weibull distribution.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 4 Experimental Setup and Performance Evaluation
### Bandwidth Measurements  
Another important aspect of this study focused on the comparison of bandwidth consumption requirements of an edge computing device and a cloud computing device. To perform the same processing steps on the cloud as was performed on the edge device, raw and uncompressed data were required to be sent to the cloud. For this study, the uncompressed and unfiltered data, which were Robot Operating System (ROS) bag format files, were uploaded to Google Drive via its application programming interface (API) accessed through Python. On the other hand, the final processed depth readings were uploaded onto the ThingSpeak Analytics dashboard via the representational state transfer (REST) API. The consumption of bandwidth was measured accordingly as shown in the block diagram in Figure 8. The ROS file contained data required for the cloud server to process the depth readings, without which the depth extraction pipeline would not work on the cloud.  
To measure the time taken and bytes uploaded, respectively, the program initialised a performance counter and recorded it, immediately followed by measuring the network input/output data prior to uploading the ROS file. As the data were successfully uploaded, the network input/output counter was recorded, and the second performance counter was recorded. The time taken to upload the data was the difference between the two performance counters and the total bytes uploaded were the difference between the two network input/output counters. The same methodology was applied to the data uploaded to the IoT analytics dashboard, which was processed on the Jetson Nano edge device.  
Figure 6: System setup of our proposed computer vision and edge-computing-based IoT real-time flood monitoring architecture.
## 2 2-D Inhomogeneous Cracked Bodies
### Modified Interaction Integral \(\overline{M}^{(1,2)}\)
 \tag{13}\]  
Here,  
\[\overline{J}^{(1)}=\int_{A}\Biggl{(}\sigma_{ij}^{(1)}\frac{\partial u_{i}^{(1 )}}{\partial x^{\prime}{}_{1}}-W^{(1)}\delta_{1j}\Biggr{)}\frac{\partial q}{ \partial x^{\prime}{}_{j}}dA-\frac{1}{2}\int_{A}\varepsilon_{ij}^{(1)}\frac{ \partial D_{ijkl}}{\partial x^{\prime}{}_{1}}\varepsilon_{kl}^{(1)}qdA \tag{14}\]  
\[\overline{J}^{(2)}=\int_{A}\Biggl{(}\sigma_{ij}^{(2)}\frac{\partial u_{i}^{(2 )}}{\partial x^{\prime}{}_{1}}-W^{(2)}\delta_{1j}\[PERSON]{)}\frac{\partial q}{ \partial x^{\prime}{}_{j}}dA \tag{15}\]  
denote the \(\overline{J}\)-integrals for state 1 and state 2 respectively, and  
\[\widetilde{M}^{(1,2)}=\int_{A}\Biggl{(}\sigma_{ij}^{(1)}\frac{\partial u_{i}^ {(2)}}{\partial x^{\prime}{}_{1}}+\sigma_{ij}^{(2)}\frac{\partial u_{i}^{(1)} }{\partial x^{\prime}{}_{1}}-W^{(1,2)}\delta_{1j}\Biggr{)}\frac{\partial q}{ \partial x^{\prime}{}_{j}}dA+\int_{A}\frac{1}{2}\Biggl{(}\sigma_{ij}^{(1)} \frac{\partial\varepsilon_{ij}^{(2)}}{\partial x^{\prime}{}_{1}}-\frac{ \partial\sigma_{ij}^{(2)}}{\partial x^{\prime}{}_{1}}\varepsilon_{ij}^{(1)}+ \sigma_{ij}^{(2)}\frac{\partial\varepsilon_{ij}^{(1)}}{\partial x^{\prime}{}_ {1}}-\frac{\partial\sigma_{ij}^{(1)}}{\partial x^{\prime}{}_{1}}\varepsilon_{ ij}^{(2)}\Biggr{)}\Biggr{\}}dA \tag{16}\]  
indicates the modified interaction integral. All the quantities in Equation (16) are evaluated based on a coordinate system aligned to the crack tip, and the identification of domain of integration \(A\) and the weighting function \(q(\mathbf{x})\) will be described in details in Section 3.2. Since two \(\overline{J}\)-integrals for inhomogeneous cracked bodies which are subject to mixed-mode loading also represent the energy release rates, one can obtain  
\[\overline{J}^{(1)}=\frac{1}{\overline{E}_{tip}}\Bigl{(}K_{l}^{(1)^{2}}+K_{ll}^ {(1)^{2}}\Bigr{)} \tag{17}\]  
\[\overline{J}^{(2)}=\frac{1}{\overline{E}_{tip}}\Bigl{(}K_{l}^{(2)^{2}}+K_{ll}^ {(2)^{2}}\Bigr{)} \tag{18}\]  
And  
\[\overline{J}^{(5)}=\overline{J}^{(1)}+\overline{J}^{(2)}+\frac{2}{\overline{E} _{tip}}\Bigl{(}K_{l}^{(1)}K_{l}^{(2)}+K_{ll}^{(1)}K_{ll}^{(2)}\Bigr{)} \tag{19}\]  
where \(\overline{E}_{tip}\) at the crack tip is \(E_{tip}\) for plane stress, while it is \(E_{tip}/\Bigl{(}1-v^{2}\Bigr{)}\) for plane strain. By equating Equation (13) with Equation (19), one can obtain  
\[\widetilde{M}^{(1,2)}=\frac{2}{\overline{E}_{tip}}\Bigl{(}K_{l}^{(1)}K_{l}^{(2) }+K_{ll}^{(1)}K_{ll}^{(2)}\Bigr{)} \tag{20}\]  
In 2-D linear fracture mechanics, the closed-form near-tip displacement fields are available for mode I and mode II [35]. The stress intensity factor \(K_{l}^{(1)}\) for mode I can be obtained by letting state 2 be the pure mode-I asymptotic field (i.e., \(K_{l}^{(2)}=1\) and \(K_{ll}^{(2)}=0\)):  
\[M^{(1,\text{ Model})}=\frac{2}{\overline{E}_{tip}}K_{l}^{(1)} \tag{21}\]  
In a similar manner, the stress intensity factor \(K_{ll}\) for mode-II can be also determined.
## 3 Experimental Results and Discussion
### Evaluation on the Single Unobserved Video Subsequence with Random Length Scenarios  
In this section, we evaluate the performance of the proposed approach in recognizing the four fall-related activities from partially-observed video sequences. In particular, we have constructed partially-observed video sequences by generating temporal gaps with random lengths at the beginning, end and middle of the testing video sequences. Next, we discuss the evaluation results of our proposed approach for each temporal gap configuration.
## 2 Materials and Methods
### Self-Attention Mechanism  
The self-attention module through \(1\times 1\) convolutions calculates the correlation matrix with the C channel feature map, which represents the spatial correlation between any two positions in the input characteristic graph, as shown in Figure 1. Each location is calculated and updated by the weighted sum of all other locations. Among them, the weight value is determined by the learning dependency between the two positions. The formula for calculating the extent of different locations in each area of the reconstructed image is as follows:  
\[\beta_{\mathrm{j,i}}=\exp\left(\mathrm{s_{ij}}\right)/\sum_{i=1}^{N}\exp\left( \mathrm{s_{ij}}\right),\mathrm{where\ s_{ij}=f(x_{i})^{T}g(x_{j})} \tag{1}\]  
where \(\beta_{\mathrm{j,i}}\) indicates the extent to which the model attends to the ith location when synthesizing the jth region. N is the number of feature locations of features. The output of the attention layer o is defined as  
\[\mathrm{o_{j}=v\left(\sum_{i=1}^{N}\beta_{\mathrm{j,i}}\mathrm{h(x_{i})} \right),h(x_{i})=W_{h}x_{i},v(x_{i})=W_{v}x_{i}} \tag{2}\]  
In the above formulation, \(W_{h}\in\mathrm{R}^{\overline{C}\times C}\) and \(W_{v}\in\mathrm{R}^{C\times\overline{C}}\) are the learned weight matrices, which are implemented as \(1\times 1\) convolutions. Among them, \(\overline{C}=C/8\). In addition, we further multiply the output of the attention layer by a scale parameter and add back the input feature map. Therefore, the final output is given by  
\[\mathrm{y_{i}=\gamma o_{i}+x_{i}} \tag{3}\]where \(\gamma\) is a learnable scalar and it is initialized as 0, which allows the network to first rely on the information in the local neighborhood, and then gradually learn to assign more weight to the non-local evidence.
## 3 Controller Design
### Yaw Rate Control  
The yaw rate controller adopts a PI control algorithm based on state feedback. The first-order Nomoto model can be expressed as  
\[T\dot{r}+r=K\delta \tag{33}\]  
where \(K\) and \(T\) are, respectively, the gain and time constant. The commanded angular acceleration adopts PI control with reference feedforward, and its formula is as follows:  
\[a_{\sigma}=\dot{r}_{\rm d}-K_{\rm pr}(r-r_{\rm d})-K_{\rm ir}\int_{0}^{t}(r-r_ {\rm d}){\rm d}\tau \tag{34}\]  
Therefore, the commanded azimuth angle is  
\[\delta_{\rm c}=T/K\cdot[\dot{r}_{\rm d}-K_{\rm pr}(r-r_{\rm d})-K_{\rm ir}\int _{0}^{t}(r-r_{\rm d}){\rm d}\tau]+r/K \tag{35}\]  
The PI controller gain selection is  
\[K_{\rm pr}=2\lambda_{r} \tag{36}\]  
\[K_{\rm ir}=\lambda_{r}^{2} \tag{37}\]  
At this point, the equilibrium point \(u-u_{\rm d}=0\) is globally exponentially stable [15], where \(\lambda_{r}>0\) is the designed parameter.  
The reference model adopts a second-order oscillation element, and it can be expressed as follows:  
\[\frac{r_{\rm d}}{r_{\rm c}}=\frac{\omega_{\rm nr}^{2}}{s^{2}+2\zeta_{r}\omega _{\rm nr}+\omega_{\rm nr}^{2}} \tag{38}\]  
where \(r_{\rm d}\) is the reference yaw rate, \(\zeta_{r}\) is the damping ratio, and \(\omega_{\rm nr}\) is the natural frequency.
## 3 Results and Discussion
### Concentrations of Heavy Metals and Arsenicals in Seaweeds
Therefore, in most countries, the standard regulatory As intake limit is determined on the basis of inorganic As levels rather than total As amounts, due to these differences in toxicity. are bound in organic forms, which are not acutely toxic like the inorganic species, but interestingly, _Sargassum_ spp. in brown algae had the highest amount of arsenate As(V) among six arsenicals. The _Sargassum_ spp. group contained high levels of inorganic As (summation of As[V] and As[III]), accounting for approximately 69% to 85% of the total As concentration, but others can be seen to have less than 1.5%. On the other hand, other groups of seaweeds contained high levels of unidentified As (approximately 45% to 72%) (Table 4). [PERSON] [47] reported that the main As species in seaweed, excluding the _Sargassum_ spp. group, were asenosugars. Despite the fact that, because of the absence of asenosugar group standards, the unidentified species is unclear, it is likely an arsenosugar. Therefore, these results show that the _Sargassum_ spp. group contains high concentrations of inorganic As when compared to other seaweeds. [PERSON] [47] reported that the major As species in seaweed belonged to the arsenosugar group, except that in the case of _Sargassum_ spp., the asenosugar group was not certain as there were no standards; therefore, the unidentified species are likely to belong to the group of arsenosugars. This is because _Sargassum_ spp. has a higher mineral content than other seaweeds. Reasons for the large amount of inorganic As in most _Sargassum_ seaweeds have been proposed in several related studies. [PERSON] et al. [48] demonstrated that two main reasons were (i) lack of metabolic conversion ability from inorganic forms to organic forms, caused by the efficient use of energy or genetic differences; and (ii) the accumulation of inorganic As as a specific defense mechanism against predation. In addition, oxy-anions such as As(V) and As(III), which are taken up in the marine environment efficiently induce the biosynthesis of phytochemicalins, which helps store inorganic As in _Sargassum_ seaweeds [49; 50]. When the concentrations of As compounds in seaweed obtained from this study were compared with those in previous studies, most of the As values demonstrated a tendency consistent with that of inorganic As being relatively higher in _Sargassum_ spp., including _Hijiki_, than in other seaweeds [51; 52; 53]. Although the levels of arsenicals in various seaweeds from different countries were compared with those in this study, it is difficult to thoroughly compare data from several authors due to differences in sample treatments and analytical methods. Therefore, it is important to establish international best practices for analyzing arsenicals as soon as possible.