esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Case Study--Arsenic Contamination of Groundwater in Bangladesh
### Mobilization of Arsenic in Groundwater in Bangladesh  
The two most widely discussed hypotheses for the sources of arsenic contamination of groundwater in Bangladesh are the pyrite oxidation hypothesis and the iron oxyhydroxide reduction hypothesis [6; 30].  
The \"Barind and Modhupur Tracts\" made up of up-faulted terraces of older (Pleistocene) sediments are the key characteristics of the surface geology in north-central Bangladesh. Unlike the surrounding alluvium deposits, these sediments have experienced significantly more weathering [27]. The younger alluvial sediments in these tracts are present at depths ranging or greater than 50-200 m. However, for the older sediments, the extent of its distribution is still relatively unknown [27]. In south-east Bangladesh, older (Tertiary) sediments dominate the geology of the Chitagong Hill Tracts and their main composition are sandstone, silt and limestone [27]. Within 20-80 m depth, the sediment was found to be very rich in arsenopyrite [31]. The granitic and metamorphic processes of the Himalayas was the original source of the sediment [31]. After the introduction of the sediment into the Ganges delta, it became part of the aquifers [31]. Additional arsenic is retained as an adsorbed coating with ferric oxy-hydroxoxide on the sediments [32]. It is understood that the transport and deposition of arsenic and ferric oxy-hydroxide in the Ganges delta along with abundant organic matter resulted from the oxidation of arsenopyrite [33; 34]. Figure 4 illustrates the arsenic contaminated areas in Bangladesh [24]. Obviously, the arsenic concentrations present in many areas are considerably above 50 \(\upmu\)g/L.
# Reliability of Social Networks on Activity-on-Node Binary-State with Uncertainty Environments
## 4 Proposed AON BAT-Based Algorithm
### AON Bat  
The proposed AON BAT is based on the traditional AOA BAT proposed in [48]. In the proposed AON BAT, the values of the first and last coordinates, that is, the source node and the sink node, are always 1 because the BN fails if any one of these two nodes fails.  
Unlike the traditional AOA BAT that is implemented backward from the last to the first coordinate, the proposed AON BAT is implemented forward; that is, from the first to the second last node. Note that the states of the first and last nodes are always one and do not need to be changed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Node \(i\)** & **EPS\({}_{\mathbf{L}}\) (\(i\))** & **FPS\({}_{\mathbf{R}}\) (\(i\))** & **FPS (\(i\))** & \(k\) & **FFR (\(i\))** & **R (\(i\))** \\ \hline
3 & 0.2 & 0.9375 & 0.86875 & 1.225514 & 0.059496 & 0.940504 \\
4 & 0.552239 & 0.55239 & 0.5 & 2.301 & 0.005 & 0.995000 \\
5 & 0.411764 & 0.696970 & 0.642603 & 1.892283 & 0.012815 & 0.987185 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Example of STEPs P3 and P4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input:** & An AON BN \(G(V,E,\mathbf{D})\) with uncertainty nodes. \\
**Output:** & Convert each uncertainty node into a crisp node. \\  & Each expert rates the uncertainty nodes using linguistic variables. \\  & Transfer each linguistic variable into a fuzzy number. \\  & Average experts’ fuzzy numbers and calculate their related \(\alpha\)-cuts for each \\  & uncertainty node to have an AFN [47]. \\  & Convert AFN to FPS with defuzzification [55]. \\  & Convert FPS to FFR [56]—which is the unreliability. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Example of STEPs P0, P1, and P2.  
Moreover, in the traditional AOA BAT, there is a variable \"SUM\" to count the number of coordinates--of which the values are one--to decide when to stop the BAT procedure. To improve the efficiency, the proposed AON BAT adopts another method by checking whether the current coordinate is the last coordinate; that is, the coordinate \(n\). If the answer is yes, then the algorithm is stopped.  
The number of all obtained vectors from the proposed AON BAT is \(2^{n-2}\) because the 1 st and \(n\)th coordinates are unchanged. In addition, the time complexity of the traditional AOA BAT is \(O(2^{m+1})\) with the space complexity being \(O(m)\)[52], whereas the time complexity and space complexity of the AON BAT are \(O(2^{n-2+1})=O(2^{n-1})\) and \(O(n-1)\), respectively. Moreover, to calculate the AON BN reliability, an additional step is needed.  
The pseudocode of the proposed AON BAT is presented in the following Algorithm 4:  
```
Input: A \(n\)-tuple vector \(X\). Output: All solutions in vector form are found in the related AON problems. \(X=0\), except that both the first and last are 1s, and \(k=2\).  while \(k<n\) if \(X(a_{k})=0\) \(X(a_{k})=1\) \(k=1\) if \(X\) is connected \(R=R+\Pr(X)\)  endif else \(X(a_{k})=0\) \(k=k+1\)  end end
```  
**Algorithm 4** AON BAT
## 5 Frequency Hopping Techniques
### Channel Aware, Probabilistic-Channel-Usage Techniques
#### 5.3.3 Smooth Adaptive Frequency Hopping  
Smooth Adaptive Frequency Hopping (SAFH) assigns usage probabilities to all channels based on an exponential smoothing filter for the quality metric to estimate and predict the channel conditions [32]. The algorithm uses exponential smoothing to predict the quality metric of the next time step. The predicted power metrics are referred as \(Q_{k}^{\prime}\), while the measured power metric is referred to as \(Q_{k}\). The predicted power metric at time \((t+1)\) is calculated by using the predicted metric at time \(t\) plus an adjustment for the error that occurred in the last forecast (measured  
This condition results in robustness of the link between the nodes.  
The following mapping function fulfils the above conditions:  
\[\left\{\begin{array}{l}P_{k}=(\beta+c\cdot d_{k})/\delta\quad if\quad d_{k} \geq 0\\ P_{k}=(\beta+s\cdot d_{k})/\delta\quad if\quad d_{k}\leq 0\end{array}\right. \tag{22}\]  
where the term \(d_{k}\) is the difference between the predicted metric and the threshold \(\xi\):  
\[d_{k}=Q_{k}^{\prime}-\xi \tag{23}\]  
A positive \(d_{k}\) value indicates a good channel and a negative value indicates a bad channel. The term \(c\) determines how good channels are rewarded, while the term \(s\) determines how bad channels are punished. The larger the \(c\) parameter, the more often good channels are used, and the larger the \(s\) parameter, the less often bad channels are used. The term \(\delta\) is a normalizing factor that ensures \(\sum P_{i}=1\):  
\[\delta=\sum_{i=1}^{K_{g}}(\beta+c\cdot d_{i})+\sum_{i=1}^{K_{b}}(\beta+s\cdot d _{i}) \tag{24}\]  
where \(K_{g}\) and \(K_{b}\) are the number of good and bad channels respectively. The term \(\beta\) is chosen so that the condition \(\sum_{i=1}^{K}P_{i}(t+1)\cdot Q_{k}^{\prime}(t+1)=\xi\) is fulfilled. The case when \(c=s=1\) and \(\beta=\xi\) results in  
\[P_{k}=\frac{Q_{k}^{\prime}}{\delta}=\frac{Q_{k}^{\prime}}{\sum_{i=1}^{K}Q_{i}^ {\prime}} \tag{25}\]  
which represents the WRFH probability function.  
As an illustrative example, let us consider a four-channel problem with \(Q\): {0.84, 0.8, 0.82, 0.86}. Table 1 shows the channel usage probabilities for the WRFH, UBAFH and SAFH techniques for the specified parameter values. Same results are represented in Figure 12. It can be observed how increasing the temperature parameter in the UBAFH technique penalizes the usage of the worst channels in favour of the best ones. The same happens when increasing the \(c\) parameter in the SAFH technique.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **WRFH** & **UBAFH** & & **SAFH** \\ \cline{2-7}  & & & & \(P_{k}=\frac{Q_{k}^{\prime\prime}}{\sum_{i=1}^{K}Q_{i}^{\prime\prime}}\) & \(\xi=0.85\) & \(\xi=0.85\) \\  & & & & \(\mathbf{ff}=\mathbf{10}\) & \(\mathbf{ff}=\mathbf{100}\) & \(c=\mathbf{10}\) & \(c=\mathbf{100}\) \\ \hline \(Q_{1}\) & 0.84 & \(P_{1}\) & 0.253 & 0
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 6 Simulation and Results Analysis
### Comparison of Localization Errors in Non-Uniform Network Topologies
(**a**) C-type network; (**b**) O-type network; (**c**) W-type network. (3) Impact of different number of anchor nodes on localization performance  
In a network with 100 nodes and a communication radius of 20 m, the proportion of anchor nodes was progressively increased. The performance of each localization algorithm in three network topologies is presented in Figure 6. The figure shows that the original DV-Hop algorithm exhibits the highest localization error, with significant fluctuations in the O-type network, distinguishing it from the other two algorithms. The proposed algorithm and GWODV-Hop show slight fluctuations in localization errors, but the proposed algorithm consistently achieves superior accuracy. Table 3 shows the average normalized localization errors for the original DV-Hop, GWODV-Hop, and the proposed algorithm as the number of anchor nodes increases from 15 to 45 in the three network topologies. The average data indicate that all three algorithms perform best in the O-type network, while their performance is comparatively poorer in the W-type network. The simulation results indicate that as the proportion of anchor nodes increases, the proposed algorithm's anchor node selection mechanism significantly enhances the localization accuracy of  
(4) The impact of different communication radius on localization performance  
Figure 7 illustrates the variation in localization errors for each localization algorithm as the communication radius increases, with 100 nodes and 30% anchor nodes across three network topologies. Overall, both the GWODV-Hop and proposed algorithms exhibit significantly lower localization errors than the original DV-Hop, with their errors being similar. In the C-type network, the localization errors of all three algorithms gradually decrease with increasing communication radius. In the O-type networks, the error trends for all three algorithms are similar, initially decreasing rapidly and then stabilizing. In the W-shaped network, the proposed algorithm slightly outperforms GWODV-Hop. The average localization errors of the three algorithms within the communication radius range are shown in Table 4. The experimental results suggest that as the communication radius increases, the proposed algorithm outperforms others in localization across the three network topologies.
# Estimation of Litho-Fluid Facies Distribution from Zero-Offset Acoustic and Shear Impedances
## 4 Methodology
### Zero-Offset Impedance Modeling
#### 4.3.2 Multilayer Feed-Forward Neural Network (MLFN)  
Due to the availability of vast amounts of data in recent years, ANNs have become the most common prediction methods [100]. One of the widely used ANNs is the multilayer feed-forward neural network (MLFN) [101; 102]. The MLFN consists of an input layer, one or more hidden layers, and an output layer [103]. An activation function is used to map the summation of the weighted inputs into the output layer [104]. The number of hidden layers and number of neurons in each of them should be carefully determined because this step controls the model's accuracy [105].  
The selection of appropriate activation functions has a high impact on a model's performance. There are various types of activation functions such as the sigmoid, or logistic, function, which is commonly used in classification models. The current study  
Figure 4: The graphical representation of the statistical \(Z_{P}\) or \(Z_{S}\) models.  
considers a linear activation function to solve for the zero-offset impedance in a regression model. The choice of linear function is based on the moderate-to-strong correlation between the input and output variables as discussed earlier.  
The connection between any two neurons is controlled by a random weight. A model's random weights are adjusted until reaching the least mean square error and the best match between the predicted and actual output values. The backpropagation method is commonly used to obtain the optimal set of weights and calculate the error in a repetitive way until reaching the best training accuracy [106; 107]. The backpropagation process involves an optimization algorithm such as stochastic gradient descent (SGD) [108] and adaptive moment estimation (Adam) [109]. The algorithm used in our model is the Levenberg-Marquardt (LMA) [110] due to its fast performance and ability to train MLFN models [111].  
A model's error can be represented by various loss functions such as cross-entropy [112], weighted binary cross-entropy (WCE) [113], and dice loss [114]. The loss function used in our study is the mean squared error (MSE), which is one of the best unbiased estimators used in regression models [115]. The MSE can be defined by the following equation:  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y}_{i})^{2} \tag{51}\]  
where \(N\) is the number of samples and \(y_{i}\) is the output variable at an observation (\(i\)).  
The structure of the MLFN, in this study, consists of three layers: an input, a hidden, and an output layer. Figure 5 shows the graphical representation of the MLFN models for both \(Z_{P}\) and \(Z_{S}\), where the input layer consists of three neurons, which are the near-, mid-, and far-angle impedances, while the output layer consists of one neuron, which is the zero-offset impedance. Several trials were performed in MATLAB to determine the best number of neurons for the hidden layer. The best performance was observed at 20 neurons for both the \(Z_{P}\) and \(Z_{S}\) models.
###### Abstract  
Modern agriculture requires technology to give precise measures of relevant parameters, such as those associated with pest control. Here, we developed an algorithm model as the basis for a bait spray intervention by monitoring the olive fruit fly _Bactrocera oleae_ (Rossi) with conventional traps covering 24.3 hectares of non-irrigated Baladi olive cultivars in the Hasbaya region. We installed 49 yellow sticky traps with ammonium bicarbonate. The adults, both males and females, were monitored on a weekly basis. The traps and trees were georeferenced, and parameters such as the temperature, relative humidity, tree phenology (BBCH), and fruit load rate were compiled. The results show that the infested fruits were correlated equally with the fruit load rate and the number of adults captured, which in turn were correlated more with the temperature than the relative humidity. The number of males captured was higher than that of females throughout the cultivation period. The first symptoms of the fruits were observed on 22 September, when the BBCH was equal to 85, with an average number of adult captures of less than five when using traps over 7 days.  
olive fruit fly; conventional traps; pest monitoring; algorithm for intervention 2023  
L.: [PERSON] [PERSON]: [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]: [PERSON] [PERSON]: [PERSON] [PERSON]: [PERSON] [PERSON]: [PERSON] [PERSON]: Evaluation of the Critical Parameters Involved in Decisions to Control _Bactrocera oleae_ in Olive Orchards in the Southern Region of Lebanon. _Appl. Sci._**2023**, 13, 12326. [[https://doi.org/10.3390/app132212326](https://doi.org/10.3390/app132212326)]([https://doi.org/10.3390/app132212326](https://doi.org/10.3390/app132212326))
# Biomass-Based Chemical Looping Gasification: Overview and Recent Developments
## 2 Process of Chemical Looping Gasification of Biomass
### Fouling and Corrosion  
Tars are produced during the pyrolysis stage of a gasification process. Certain high molecular weight tar compounds, which start to condense at lower than 450 \({}^{\circ}\)C and subsequent polymerization, can cause clogging and blockages in gas lines and filters and reduce process efficiency [52]. Furthermore, the removal of the condensed tars consumes intensive energy and cost. Ash is the remained inorganic residue and exists as particulate after biomass chemical looping processes at temperatures of 800-1200 \({}^{\circ}\)C. The particulate of the fuel reactor can deposit in downstream equipment and gas lines resulting in blockages and attrition; however, these particles can be removed by using filters and cyclone separators [5]. Another concern is that biomass fuels have a high content of minerals, including sodium,potassium, phosphorous, and chloride which can cause potential corrosion for equipment, particularly heat transfer surfaces. Several researchers investigated that chloride has a catalytic effect that promotes the dissociation of the steel material in the heat exchangers, even at low temperatures (100 to 150 \({}^{\circ}\)C) [53]. Corrosion mechanisms coexist and occur simultaneously, including several chemical reactions between the metal and metal oxides with gaseous substances of Cl and O, the solid phase of alkali metal salts (K and Na), and ash in the liquid phase and during phase changes [54]. Furthermore, the high content of silica from many types of biomass sources [48] and the formation of molten material in the ash can also cause significant abrasion and erosion on mechanical components and pneumatic ash handling systems [53]. Additionally, biomass-derived tars also have a highly acidic nature (typical pH < 2) and high water content; thus, its presence can cause considerably corrosion-related problems in downstream pipelines and equipment of the system [52].
## 5 Numerical Results
### Bit Error Rate  
In this sub-section, we study the effect of packet size and network size on the BER. The BER of each user is given by Equation (15). The total BER of the network is calculated for each packet size and network size and the results are summarized in Figure 6.  
The total BER is in general an increasing function of packet size, as seen from Figure 5(a),b. The reason for the increase is that an increase in packet size with all other system capabilities kept constant, leads to an increase in the possibility of data getting more corrupted due to increased collisions among the channel users. The general non-smoothness of the graphs of BER is attributed to the fact that BER is not constrained as a total channel BER but rather individual BER for each user on a channel. The total BER also increases with increasing network size. This is because the more the number of users on the network increases, the higher the chances of collisions and interference. This in turn leads to an increase in a higher possibility of corrupted packets.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 4 Methodology
### PLM-ICD-C  
In the present project, a model named PLM-ICD-C was used, in order to improve PLM-ICD results based on the Cosine similarity method. Combining these two methods, it is possible to discover some more useful ICD-10 codes to suggest to the medical doctors that perform the classification. The results show that PLM-ICD-C performs better than just PLM-ICD or Cosine similarity alone.
## 2 Development of the Positioning Procedure
### Custom Positioning Procedure  
The custom developed procedure for GNSS positioning under adverse conditions relies on the modified L\({}_{1}\)-norm robust estimation. A residual value of zero results in a deletion by zero, see Equation (1). To avoid such a result, each observation with a residual of less than \(10^{-4}\) m is given a weight of \(10^{4}\). The modified L\({}_{1}\)-norm weight function is therefore defined as:  
\[w_{i}=\begin{cases}\dfrac{1}{\left|v_{i}\right|}&\left|v_{i}\right|\geq 10^{-4}m\\ 10^{4}&\left|v_{i}\right|<10^{-4}m\end{cases} \tag{2}\]  
As mentioned above, Doppler measurements can be used to smooth code observations. In our experiments, however, this procedure has not produced promising results. Doppler values can also be used for so-called Doppler positioning. Positioning is realised using Doppler observations only, as suggested in [10], but practical realisation is particularly difficult to achieve in environments with a decreased number of available signals. Doppler measurements basically measure the range change between a receiver and a satellite between consecutive epochs. The following equation can be used to calculate the delta range [11]:  
\[\Delta D=\dfrac{1}{2}\left(D1_{i-1}+D1_{i}\right)\cdot\lambda\cdot dt \tag{3}\]  
where \(D1_{i-1}\) and \(D1_{i}\) are Doppler observables in the previous and the current epochs, respectively, \(\lambda\) is the wavelength of GPS L\({}_{1}\)frequency and \(dt\) is the time interval between consecutive epochs. However, this method of Doppler positioning requires reference range values. These can only be achieved using code pseudoranges. Therefore, the initial position can only be determined by code-based observations. This would ideally be determined in an environment with little or no multipath or attenuation. Differential GPS (DGPS) can also be used in such environments if the reference station data is available. DGPS increases the accuracy of the initial positions and consequentially all the following positions if determined relatively by Doppler positioning.  
The custom procedure for the positioning of the following epochs (after the initial epoch) determines the receiver clock offset and the position unknowns separately. The receiver clock offset is determined by Doppler observables only. The use of code-based observations in estimating the receiver clock offset can cause anomalies which often occur when the number of tracked satellites changes between epochs. After the receiver clock offset has been determined, the position unknowns are determined using code and Doppler observations. Doppler observations are included in the model as shown in Equation (3). Both types of observation are equally weighted.  
One additional feature of the procedure is the recalculation of the ranges after each epoch. The ranges between the receiver and the satellites are calculated using the adjusted current position. These ranges then become input data for the following epoch instead of the pseudoranges.