esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study employed stable isotopes of \(\delta^{18}\)O and \(\delta^{2}\)H in conjunction with other hydrological parameters to understand the origin, inferred residence time, and seasonal effect of groundwater in the shallow aquifers of the eastern Dahomey Basin. A total of 230 groundwater samples (97 in the wet season and 133 in the dry season) were collected from the borehole and shallow aquifer between May 2017 and April 2018. Groundwater analysis included major ions and \(\delta^{18}\)O and \(\delta^{2}\)H, isotopes data in precipitation from three selected Global Network of Isotope in Precipitation (GNIP) stations across West Africa, Douala in Cameroon, Cotonou in Republic of Benin, and Kano in Nigeria were used in comparative analysis. Results of the hydrochemical model revealed Ca-HCO\({}_{3}\) and Na-Cl as dominant water types with other mixing water types such as Ca-SO\({}_{4}\), Ca-Cl, Na-SO\({}_{4}\), and K-Mg-HCO\({}_{3}\), which characterised early stage of groundwater transformation as it infiltrates through vadose zone into the aquifer. \(\delta^{18}\)O and \(\delta^{2}\)H precipitation data from the three stations plotted along with the groundwater samples indicate recent meteoric water origin, with little effect of evaporation during the dry season. The plot of Total Dissolved Solids (TDS) against \(\delta^{18}\)O showed clustering of the water samples between the recharge and the evaporation zone with dry season samples trending towards increased TDS, which is an indication of the subtle effect of evaporation during this period. Tracing groundwater types along the flow paths within the basin is problematic and attributed to the heterogeneity of the aquifer with anthropogenic influences. Moreover, a comparison of the \(\delta^{18}\)O and \(\delta^{2}\)H isotopic compositions of groundwater and precipitation in the three selected stations, with their respective deuterium excess (D-excess) values established low evapotranspiration induced isotope enrichment, which could be due to higher precipitation and humidity in the region resulting in low isotope fractionation; hence, little effect of seasonal variations. The study, therefore, suggested groundwater recharge in the shallow aquifer in the eastern Dahomey Basin is of meteoric origin with a short residence time of water flows from soils through the vadose zone to the aquifers.  
hydrogen and oxygen isotopes; groundwater sources; residence time +
Footnote †: journal: Applied Sciences
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design
### Ecc  
Elliptic Curve Cryptography (ECC) is an asymmetric cryptosystem that exploits the complexity of algebraic operations on elliptic curves over finite fields. The calculation difficulty of finding the discrete logarithm of a random elliptic curve element makes ECC security very high even with relatively short keys. The ECC core of our chip operates on the 233-bit binary field and is inspired by the work presented in [25; 52]. The operation of elliptic curve point multiplication (ECPM) is implemented in accordance to the Lopez-Dahab algorithm [53]. The multiplier itself is an Iterative Karatsuba multiplier [54]. For each 233-bit operation the multiplier takes nine clock cycles. The core is optimized for low power but limited to the 233-bit binary field.  
The integrated ECC design is an implementation of the Montgomery kP-algorithm that performs bitwise processing of each secret key bit. The key bits are processed with the same type, amount and sequence of operations, independently of the bit's value. For this reason, each bit processing takes same amount of the time and requires a similar amount of power consumption. Thus, the core is protected against side channel attacks based on simple power analysis (SPA). However, a complex differential power analysis (DPA) attack could potentially crack the encryption. The vulnerability of the implemented ECC to DPA attacks as well as the countermeasures is thoroughly discussed in [55]. Other possible ways of increasing the resistance of our implementation against DPA attacks could be by randomizing its processed data. For example, randomizing the private key, randomizing the projective coordinates of the elliptic curve points or blinding the elliptic curve point can improve the robustness of the ECC [56]. Implementing such countermeasures implies additional costs regarding power consumption and area.  
Several ECC hardware-based solutions have been proposed in literature [57]. In [58], the authors proposed a low cost ECC processor to be used in sensor node systems. This solution had a small area footprint, but relatively weak performance. It requires 57,720 clock cycles for an ECPM over a 133-bit binary field and 95,159 clock cycles for an ECPM over a 163-bit binary field. For comparison, our solution takes 13,164 clock cycles for an ECPM over a 233-bit binary field. In [59], the authors compare three alternative ECC-163 implementations for MSP430-based sensor nodes: (1) a software solution; (2) a dedicated hardware; and (3) a drop-in module solution. The drop-in module, based on a modification of the MSP430 processor, is introduced to compromise between the software solution and relatively costly dedicated hardware. The comparison of performance and power figures (estimated after the synthesis) of the proposed ECC implementations is given in Table 2.
## 1 Introduction
However, the network still has problems in image color restoration. To solve this problem, a deraining network based on multi-scale progressive fusion strategy was designed in [19], which gradually fused the complementary information of similar patterns of rain patterns in different scales. Therefore, it more comprehensively retained the color contrast of the image while removing rain streaks. In addition, the attention mechanism also plays an important role in image rain removal, which can make the network adaptively pay more attention to those important features. For example, both [20; 21] add an attention mechanism to the network to improve network performance. At the same time, MATNet [22] proposed a kind of asymmetric attention block called motor attention shift (MAT), which can produce accurate segmentation with clear boundaries, which will be helpful in image rain removal. The performance of the full supervision method has the greatest dependence on datasets. However, it is difficult to obtain a large-scale real dataset for network training. Thus, the semi supervised deraining method adds real data for joint training on the basis of full supervision, such as the most commonly used semi-supervised migration learning algorithm SIRR [23]. Weakly supervised networks such as those in [24; 25] use real rainy data sets to remove rain, and the corresponding rain-free image is acquired by picture software. The real rainy images are introduced to train the network model, which significantly alleviates the problem of insufficient training samples. However, the quality of the dataset is still a key factor in the performance of the model. Fortunately, transfer learning is commonly used to solve small sample learning problems. Considering the consistency of the final images acquired by the dehazing and deraining algorithms, it is a very interesting work on how to effectively implement deraining with a dehazing network based on the idea of transfer learning. Many scholars have used image dehazing networks to study image deraining algorithms [26, 27, 28]. Based on the above discussion, a two-channel and two-stage image deraining network is proposed in this paper. The remainder of this paper is organized as follows. Section 2 introduces the overall framework of our network and its details. In Section 3, the experimental results show the superiority of our method to state-of-the-art methods. The ship detection performance with deraining method is analyzed in Section 4. Finally, conclusions are presented in Section 4.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 5 Experimental Results and Analysis
### The End-to-End Latency  
As the average delay of packets from source to destination, the end-to-end latency (E2 EL) reflects the network's access rate and transmission quality. Smaller E2 EL indicates better performance of the routing protocol. We compared the E2 EL with different numbers of DNs, as shown in Figure 11. It is clear that the E2 ELs of the five algorithms had an increasing trend with increasing DNs. Among them, [PERSON]'s algorithm had the largest E2 EL compared to the EEHC and LEACH algorithms. However, FCO-MST had the most negligible end-to-end latency with a small number of nodes (50-100). Furthermore, FPO-MST had the smallest E2 EL when the number of nodes was 100-200. We know that the E2 EL of FPO-MST is more advantageous in a dense node environment.  
To demonstrate the benefits based on SDN architecture, we also compared the E2 EL of the five algorithms under different buffer utilization, as shown in Figure 12. It can be seen that with the increase in buffer utilization, the end-to-end latency of the five algorithms increased. Specifically, LEACH still had the highest latency, while EEHC and [PERSON]'s algorithms had the essential reaching delay. Both algorithms were higher than the two algorithms proposed in this paper. It is because routing decisions are executed in the control plane, whereas underwater nodes only forward data in the data plane. The main reason for the latency difference between FCO-MST and FPO-MST is that they have differences on network scales.  
Figure 11: The comparison of E2 EL under a different number of nodes.  
Figure 10: PDR vs. the number of nodes.
## 5 Simulations  
### Simulation Setup  
Assume that there are 100 clients that are randomly distributed over a \(2\) km \(\times\) 2 km area to participate in the FL training via a wireless network, where a BS is located at the center of the area to forward global/local models between the FL server and clients. In addition, the path loss between a client and the BS is estimated based on \(\phi=128.1+37.6d_{i}\)[39], where \(\phi\) and \(d_{i}\) are the path loss and distance between the BS and client \(i\). If fast fading is not considered, the channel gain \(g_{i}\) between the BS and client \(i\) is mainly determined by the path loss, i.e., \(g_{i}=10^{-\frac{\phi_{i}}{10}}\). Moreover, the transmission power of each client is 0.1 watt, i.e., \(\forall i\in\mathcal{I},p_{i}=0.1\). The amount of bandwidth available for the BS is \(B=1\) MHz. Also, the CPU frequency of a client \(f_{i}\) and the number of CPU cycles to process a single sample \(C_{i}\) are randomly selected, following based on two uniform distributions, i.e., \(f_{i}\in U(0.1,1)\times 10^{9}\) Hz and \(C_{i}\in U(1,5)\times 10^{7}\) CPU cycles. Other simulation parameters are listed in Table 1.
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 3 Multiple Target Tracking
### System Modeling  
It is assumed that the kinematic state of a target follows CA motion. The uncertainty of the process noise, which follows the Gaussian distribution, controls the kinematic state of the target. The discrete state equation of the CA model for the IMM estimator is as follows:  
\[\mathbf{x}^{t}(k+1)=F(\Delta)\mathbf{x}^{t}(k)+q(\Delta)\mathbf{v}_{j}(k),\ j=1,\ldots,M, \tag{2}\]  
\[F(\Delta)=\left[\begin{array}{c}1\Delta\ \frac{\Delta^{2}}{2}\ 000\\ 01\ \Delta 000\\ 001000\\ 0001\Delta\frac{\Delta^{2}}{2}\\ 00001\Delta\\ 00001\end{array}\right],\ q(\Delta)=\left[\begin{array}{c}\frac{\Delta^{2}}{2 }0\\ \Delta 0\\ 10\\ 0\ \Delta^{2}\\ 0\ 1\end{array}\right] \tag{3}\]  
where \(\mathbf{x}^{t}(k)=\left[x^{t}(k)\ v^{tx}(k)\ a^{tx}(k)\ y^{t}(k)\ v^{ty}(k)\ a^{ty}(k) \right]^{T}\) is a state vector of target \(t\) at frame \(k\), \(x^{t}(k)\) and \(y^{t}(k)\) are positions in the \(x\) and \(y\) directions, respectively, \(v^{tx}(k)\) and \(v^{ty}(k)\) are velocities in the \(x\) and \(y\) directions, respectively,\(\Delta\) is the sampling time, M is the number of modes, and \(\mathbf{v}_{j}(k)\) is a process noise vector, which is Gaussian white noise with the covariance matrix \(Q_{cyj}=diag\Big{(}\Big{[}\sigma_{jx}^{2}\ \sigma_{yy}^{2}\Big{]}\Big{)}\). The measurement vector for target \(t\) consists of the positions in the \(x\) and \(y\) directions. The measurement equation is as follows:  
\[\mathbf{z}^{t}(k)=\left[\begin{array}{c}z^{tx}(k)\\ z^{ty}(k)\end{array}\right]=H\mathbf{x}^{t}(k)+\mathbf{w}(k), \tag{4}\]  
\[H=\left[\begin{array}{c}100000\\ 000100\end{array}\right], \tag{5}\]  
where \(\mathbf{w}(k)\) is a measurement noise vector, which is Gaussian white noise with the covariance matrix \(R=diag\Big{(}\Big{[}r_{x}^{2}\ r_{y}^{2}\Big{]}\Big{)}\). The superscript \(t\) in Equations (2) and (4) indicates a target number. The target number may differ from the track number due to missing targets, broken tracks, and redundant tracks. From the next subsection, the superscript \(t\) will denote the track number.
### Motivation  
In PD-NOMA, decoding and SIC implementation complexity has always been a vital concern; SIC with increased complexity in ultra-dense networks also increases the chances of error and eventually affects the network's performance, resulting in throughput deprivation. Therefore, limiting the number of interference cancellations is essential, i.e., the number of users associated with each small cell base station (SBS) [15; 27].  
Clustering is a solution to the problem mentioned above, i.e., by forming users into clusters and reducing the number of messages decoding SIC at the user end, the SIC complexity and the chances of errors will be reduced [22; 28]. When there will be fewer users associated with SBS, the interference among users will also be reduced.  
Therefore, the issues can be managed when PD-NOMA is implemented on a dense network, with a reduced number of users associated with the SBS, within a densely deployed SBSs heterogeneous network to improve user association. The PD-NOMA limitations can be addressed by implementing a clustering technique. PD-NOMA can achieve efficient results by keeping the reasonably low number of associated users linked with the SBSs in every cluster, since, based on clustering, we can have more power controlling the supervision on the related users, and consequently, the system interference can be reduced, resulting in enhanced system capacity and user association.
## 3 Results and Analysis
### Just Noticeable Difference (JND)
In average values, JND of both parameters is less than unity, taking for C80 the value of 0.67 and for C50 the value of 0.56. Figure 8 shows the dispersion of the JND values of C80 and C50 with CATT-Acoustic simulation for each of the sound sources (A-I). Figure 9 shows a comparison between measurement and simulations with ODEON and CATT-Acoustic of C80 and C50 average values obtained with sound source A. In these representations it can be seen how both pieces of acoustic software adequately reproduce the behavior of C80 and C50 in the singular acoustic environment studied. Figure 7: (a) Just noticeable difference of C80 (ODEON simulation). (b) Just noticeable difference of C50 (ODEON simulation). In view of the results obtained, the calibration of the acoustic model in both software, ODEON and CATT-Acoustics, is considered valid, presenting the ODEON simulation best JND values.
## 3 Research Methodology
### Data Preparation  
Data preparation was conducted with the aim of changing or converting the descriptive values of the data questionnaire, represented by a five-point Likert scale scoring the degree of impact and its occurring probability, into numerical values to use these data as inputs for the PLS-SEM. The aim was achieved by replacing the five-point scale with its corresponding normalized values, as shown in Table 3. For each respondent and risk (the response to a question), the \(P\) and \(I\) were normalized using the maximum value of the Likert scale (5). Then, the risk score (\(RS\)) of the \(i\)th risk was computed by multiplying the degree of probability of the \(i\)th risk \(P_{i}\) with the degree impact of the \(i\)th risk \(I_{i}\), as shown in Equation (1).  
\[RS_{i}=P_{i}\times I_{i} \tag{1}\]  
The \(RS\) data represent the qualitative data. Therefore, the \(RS\) matrix included 12 participants \(\times\) 53 risks.  
On the other hand, the quantitative data consist of the time and cost of the installation of a bored pile with a diameter of 1 m and a length of 20.0 m in sand, rock. To obtain the appropriate values of the time and cost of constructing the pile, the following limitations were considered in this study: the diameter of the pile is one meter, and the depth of the pile is twenty meters. Then, the third stage was performed through phone calls and by providing explanations to each expert. The experts were asked: What is the type of ground condition (rock, clay, or sand)? What is the drilling rate in an hour? How much is the total cost of constructing the pile per meter? Based on the answers to the previous questions, the time and cost of constructing a single pile were determined. Therefore, the three ground conditions were assigned as input data beside the 53 risks. Therefore, each participant provided the degree of probability and impact of the 53 risks and estimated the average time and cost of constructing a pile in common terrain conditions (sand, rock, and clay). The time (\(T_{i}\)) and cost (\(C_{i}\)) data were normalized using Equations (2) and (3), respectively.  
\[\overline{T_{i}}=\frac{T_{i}-T_{min}}{T_{max}-T_{min}} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Option** & **Option Coding** & **Normalized Value** \\ \hline Very low & 1 & 0.1 \\ Low & 2 & 0.3 \\ Moderate & 3 & 0.5 \\ High & 4 & 0.7 \\ Very high & 5 & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Conversion of the Likert scale to normalized values [40].  
\[\overline{C_{i}}=\frac{C_{i}-C_{min}}{C_{max}-C_{min}} \tag{3}\]  
where \(T_{min}\) and \(C_{min}\) are the minimum values of time and cost, respectively, and \(T_{max}\) and \(C_{max}\) are the maximum time and cost, respectively.
## 3 Background  
### Threat Model  
We begin by showing our threat model in Figure 1. We assume that the opponent can passively observe user traffic, such as a network censor located at an ISP or with control of a large-scale gateway. Censored users, who have normal access to web content, also tend to evade censors through encryption, steganography, tunneling, anonymization, mutation, morphing [29], and other methods. In the supervised network, the censor monitors the packets transmitted in the network and obtains packets' information without decrypting them. They have certain computational and storage capacity to process traffic messages or flow-related information. Then, they use statistical classification, machine learning, deep learning and other methods to determine the user's behavior.
## 3 Training Signal Optimization
### Suboptimal Power Allocation in Closed-Form
Then, we have  
\[g^{\star}(\mathbf{x}_{T},\mathbf{x}_{Q}) =\min_{\mathbf{d}_{P}\in\mathcal{D}_{P}}\ g(\mathbf{d}_{P},\mathbf{ x}_{T},\mathbf{x}_{Q}) \tag{25a}\] \[\leq\beta\sum_{i=1}^{M_{t}}\biggl{(}\frac{1}{\lambda_{T,i}+d_{T,i }}+\frac{\mathcal{P}_{T}/M_{t}}{\lambda_{Qi}+d_{Qi}}\biggr{)}^{-1}\] (25b) \[\leq\beta M_{t}\biggl{(}\frac{1}{\lambda_{T}+\delta_{T}}+\frac{ \mathcal{P}_{T}/M_{t}}{\bar{\lambda}_{Q}+\delta_{Q}}\biggr{)}^{-1} \tag{25c}\]  
where \(\bar{\lambda}_{T}=\frac{1}{M_{t}}\sum_{i=1}^{M_{t}}\lambda_{T,i}\) and \(\bar{\lambda}_{Q}=\frac{1}{M_{t}}\sum_{i=1}^{M_{t}}\lambda_{Q,i}\). The inequality (25b) follows from the fact that \(\mathbf{d}_{P}=(\mathcal{P}_{T}/M_{t})\mathbf{1}_{M_{t}}\) is a feasible solution to the problem in (25a). The inequality (25c) follows from the concavity. The upper bound (25) becomes tight for the following cases: (i) \(M_{t}=1\), (ii) \(\mathcal{P}_{T}=0\), and (iii) \(\mathcal{P}_{T}\rightarrow\infty\). Therefore, we deduce that the suboptimal scheme may provide near-optimal performance at low and high training powers when the number of transmit antennas is not large enough. Otherwise, the performance of the suboptimal scheme may degrade because the bound (25) becomes loose. To clarify our discussion, in Figure 2, we plot the value of \(g^{\star}\) and that of its upper bound versus the total training power \(\mathcal{P}_{T}\) for various numbers of transmit antennas. In the figure, we set \([\hat{\mathbf{T}}]_{m,n}=[\hat{\mathbf{Q}}]_{m,n}=\rho^{|m-n|}\), \(1\leq m,n\leq M_{t}\), \(\rho=0.5\), \(\beta=M_{t}\), \(\epsilon_{T}=0.3||\hat{\mathbf{T}}||_{F}^{2}\), and \(\epsilon_{Q}=0.3||\hat{\mathbf{Q}}||_{F}^{2}\). The real curve and dashed-dot curve indicate the value of \(g^{\star}\) and that of its upper bound, respectively. From the figure, we can observe that the upper bound is tight to the actual value when \(\mathcal{P}_{T}\) is sufficiently low or high. Also, for the range of \(\mathcal{P}_{T}=-5\) dB to \(\mathcal{P}_{T}=15\) dB, the gap between \(g^{\star}\) and its upper bound becomes larger as the number of transmit antennas increases. In the following section, the performance of the suboptimal training scheme is concretely demonstrated by numerical simulations. Figure 2: Minimum MSE \(\zeta\) and its upper bound versus \(\mathcal{P}_{T}\) for various values of \(M_{t}\). The actual values of \(\zeta\) are indicated by the real curves and the upper bounds are indicated by the dashed-dot curves.
# An Improved Artificial Bee Colony for Feature Selection in QSAR
## 3 Preliminaries  
### QSAR Modeling  
In QSAR study, the number of parameters describing the molecular structure of compounds is generally much larger than the number of samples, and there may be obvious chance correlations and multicollinearity between these parameters. By decomposing and screening the information in the data system, the Partial Least Squares (PLS) method can extract the variables with a strong explanation to overcome the adverse effects of chance correlations and multicollinearity in modeling. Therefore, the PLS method is often used as a prediction model for QSAR modeling.  
The PLS method is often utilized to predict the relationship between compounds and their corresponding biological activities or chemical properties. It models the relationship between two data matrices, the independent variables \(X\) and target variable \(Y\), by a linear multivariate model with factor analysis [61]. The basic principle of PLS regression depends on latent variables. Latent variables are extracted from a set of descriptors that include the basic information essential for modeling the target. QSAR is modeled by using a dependent (response variable) and several independent (molecular descriptors) variables.  
The value of \(Q^{2}\), a well-known metric which employs the cross-validation technique, measures the accuracy of QSAR modeling, and it is defined as follows:  
\[Q^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\bar{y}_{i})}{\sum_{i=1}^{n}(y_{i}-\bar{y}_ {i})} \tag{1}\]  
where \(y_{i}\), \(\hat{y}_{i}\), and \(\bar{y}_{i}\) are the observed value of activity of the compound, the predicted value by the PLS model via using cross-validation procedure, and the average observed value of all compounds, respectively. \(n\) is the total number of compounds.