esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
HCl is better suited if hydride generation atomic spectrometric techniques are used, while for ion chromatography/high-performance liquid chromatography with inductively coupled plasma mass spectrometry (IC/HPLC-ICP-MS), EDTA-acetic acid preservation is preferred [21]. For arsenic speciation in water, HPLC-ICP-MS [22], capillary electrophoresis coupled to UV [23] or MS [24] and surface-enhanced Raman spectroscopy [25; 26] are routinely used. Electrochemical methods were also found to be convenient and reliable for arsenic detection [27; 28; 29; 30; 31]. Total inorganic arsenic content analysis is normally based on converting all arsenic species into the trivalent arsenic form, which is then determined by electrochemical techniques, while As(V) is electro inactive [7]. The anodic stripping voltammetry (ASV) is very sensitive and suitable for analysis of inorganic arsenic ions in water at ppb levels [31]. Differential pulse anodic stripping voltammetry (DPASV) speciation of trace-level inorganic arsenic compounds, using the scTRACE Gold electrode, offers a simple, highly affordable alternative to the spectroscopic determination of arsenic. The electrode allows straightforward and reliable monitoring of the WHO-permitted value for arsenic in drinking water of 10 \(\upmu\)g L\({}^{-1}\). Using an scTRACE Gold is very easy, because its sensor combines all three of the electrodes needed for the measurement. The gold microwire working electrode eliminates the need for the laborious preparation and conditioning of the electrode, and the screen-printed Ag/AgCl reference electrode is completely maintenance-free. As with any electrode, the performance of the scTRACE Gold deteriorates as more determinations are performed. Another advantage of voltammetric determination with the scTRACE Gold is the ability to differentiate between As(III) and As(tot) simply through the choice of measuring parameters, so this technique was used for this work [30; 31]. The aim of this research was to investigate the influence of different preservatives on the stability of inorganic arsenic species in model and natural groundwater samples by DPASV. The problem to be solved was the stabilisation of the state of arsenic species distribution from the moment of sampling until analysis. Here, the effects on the stability of As(III)/As(V) ratio of four different complexing agents. including citric acid (CA), sodium citrate (NaC), potassium oxalate (OX, OKS) and potassium sodium tartrate (TAR), were investigated, as well as the combination of these agents with acetic acid (HAc).
###### Abstract  
In the evolving landscape of green shipping, the accurate estimation of shaft power is critical for reducing fuel consumption and greenhouse gas emissions. This study presents an explainable machine learning framework for shaft power prediction, utilising real-world Internet of Things (IoT) sensor data collected from nine (9) Very Large Crude Carriers (VLCCs) over a 36-month period. A diverse set of models--ranging from traditional algorithms such as Decision Trees and Support Vector Machines to advanced ensemble methods like XGBoost and LightGBM--were developed and evaluated. Model performance was assessed using the coefficient of determination (\(R^{2}\)) and RMSE, with XGBoost achieving the highest accuracy (\(R^{2}~{}=~{}0.9490\), RMSE 888) and LightGBM being close behind (\(R^{2}~{}=~{}0.9474\), RMSE 902), with both substantially exceeding the industry baseline model (\(R^{2}~{}=~{}0.9028\), RMSE 1500). Explainability was integrated through SHapley Additive exPlanations (SHAP), offering detailed insights into the influence of each input variable. Features such as draft, GPS speed, and time since last dry dock consistently emerged as key predictors. The results demonstrate the robustness and interpretability of tree-based methods, offering a data-driven alternative to traditional performance estimation techniques and supporting the maritime industry's transition toward more efficient and sustainable operations.  
Keywords:explainable artificial intelligence; shaft power estimation; machine learning; vessel performance monitoring; maritime data analytics; fuel efficiency +
Footnote †: journal:  
Article
# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
## 2 System Model  
### Network Model  
As illustrated in Figure 1, this paper considers a satellite-ground collaborative network model based on an LEO constellation. Satellites in this model allocate corresponding computational resources based on the terminal's business requirements and provide services to the terminals. The satellite-ground collaborative system under consideration comprises \(N\) LEO satellites and \(K\) users, where \(\mathcal{N}\) and \(\mathcal{K}\) represent the sets of LEO satellites and users, respectively. In this integrated system, in addition to providing traditional satellite communication (SATCOM) radio access services to UEs, the LEO satellites also provide edge computing services to users for processing edge data. For convenience, we assume that each satellite has a beam capable of serving users within its beam coverage area and that each user can only connect to one LEO satellite.  
We employ the homogeneous binomial point process (BPP) satellite distribution model as provided in [35] to configure the spatial distribution of the LEO satellites. As depicted in Figure 2, the BPP is considered a more suitable alternative to satellite networks compared to the Poisson point process (PPP) since it is better suited for modeling a finite number of points within a finite area. The subsequent proposition outlines the distribution of the homogeneous BPP on a sphere.  
Figure 1: Network model.  
**Proposition 1**.: _For a point in homogeneous BPP, the azimuth angle is uniformly distributed between \(0\) and \(2\pi\), i.e., \(\phi_{BPP}\sim\mathcal{U}[0,2\pi]\) and the cumulative distribution function (CDF) of each point's polar angle(of the spherical coordinate) \(\theta_{BPP}\) as follows:_  
\[F_{\theta_{BPP}}(\theta)=\frac{1-\cos\theta}{2},0\leq\theta_{BPP}\leq\pi, \tag{1}\]  
\(\theta_{BPP}\) _can be generated as follows:_  
\[\theta_{BPP}=\arccos(1-2U[0,1]),0\leq\theta_{BPP}\leq\pi. \tag{2}\]  
Note that the BPP given in subsequent parts of this paper refers to homogeneous BPP unless otherwise stated. The distribution of user terminals is modeled randomly.  
In this work, we consider that each user can only run one service at the same time and that each service can only be provided by a single satellite. Therefore, each user can only access one satellite at the same time while the same satellite can serve multiple users simultaneously.
## 4 Results  
DOP outcomes for the A, B, C, and D diagnostic systems equipped with the three US probes tested in two configuration settings are reported in Table 4 and shown in Figures 1 and 2. Both the AdSTM \begin{table}
\begin{tabular}{c c c c c c}  & & & & & \\ \hline
**US System** & **Probe** & **Symbol** & **Distribution** & **Mean Value** & **Standard Deviation** \\ \hline \multirow{2}{*}{All} & \multirow{2}{*}{All} & \multirow{2}{*}{\(l\)} & \multirow{2}{*}{Uniform} & 30 px & 1 px \\ \cline{5-6}  & & & \(s\) & Uniform & 0 px & 2 px \\ \hline \multirow{4}{*}{A} & \multirow{2}{*}{L} & set 1 & Uniform & 1.91 & 0.04 \\ \cline{3-6}  & & set 2 & Triangular & 2.13 & 0.01 \\ \cline{3-6}  & & set 1 & Uniform & 1.52 & 0.04 \\ \cline{3-6}  & & set 2 & Uniform & 0.90 & 0.04 \\ \cline{3-6}  & & set 1 & Triangular & 1.10 & 0.02 \\ \cline{3-6}  & & set 2 & Triangular & 1.63 & 0.03 \\ \hline \multirow{4}{*}{B} & \multirow{2}{*}{C} & set 1 & Uniform & 1.86 & 0.06 \\ \cline{3-6}  & & set 1 & Triangular & 0.49 & 0.04 \\ \cline{3-6}  & & set 2 & Triangular & 0.48 & 0.02 \\ \cline{3-6}  & & set 1 & Triangular & 1.76 & 0.06 \\ \cline{3-6}  & & set 2 & Triangular & 1.73 & 0.04 \\ \hline \end{tabular}
\begin{tabular}{c c c c} L = linear array probe; C = convex array probe, V = vector/phased array probe. Mean and SD \(th_{\text{SNR}}\) values are different for each probe because of the Signal to Noise Ratio (SNR) threshold probe dependency.  
\end{table} 
Table 3: (**a**) Variable settings in Monte Carlo simulations (MCSs) for measurement uncertainty estimation of the AdSTM outcomes for A and B US systems. (**b**) Variable settings in MCSs for measurement uncertainty estimation of the AdSTM outcomes for C and D US systems. and the TTM repeatability uncertainties were evaluated through 2.5 and 97.5 percentiles of the data distributions from the second series of the aforementioned MCSs. Then, the total uncertainties reported in Table 4 were obtained by combining the above repeatability uncertainties with the probe-dependent uncertainty due to the influence of probe placement on the phantom scanning area, according to [28]. On the other hand, the one-way ANOVA statistical test (\(p<0.001\)) was used to test intra- and inter-observer variability in the naked eye test. Globally, by considering the same probe model, independently from the US scanner (except for D scanner), the measurement outcomes are compatible for both the SNR threshold and tangent method and the observers' judgment (Table 4). Nevertheless, the fact that DOP values provided by the AdSTM were generally higher than the TTM ones seems to suggest that the former may be the most sensitive. This may have been due to the different reference profile used in the two algorithms, SNR and gray level, respectively. Such issues seems to be confirmed by the outcomes retrieved for the D scanner. In fact for the latter, the TTM provided incompatible results with the mean observers' judgment, whose DOP values, in turn, almost agreed with the AdSTM outcomes.
## 2 Materials and Methods
### Measurement of Boundary Layer Profile  
#### 2.3.1 Reynolds Number Independence Study  
In order to obtain a fully developed turbulent flow in the wind tunnel, a Reynolds number independence study for the approaching flow was carried out. The wind profile was measured at 20 positions along a vertical line ranging from 0.003 m to 0.6 m above the wind tunnel floor at the upstream edge of the scaled model (i.e., at Line 1 in Figure 3). The wind profile measurements were performed without the presence of the scaled model at free stream wind speeds (\(U_{inlet}\)) of 4, 6, 8, 10, and 12 m\(\cdot\)s\({}^{-1}\), respectively. The streamwise (\(U\)) and vertical (\(W\)) velocity components were measured using the 2D LDA. The measurements at each measurement position were taken continuously until the sampling number reached 40,000 readings or the maximum sampling time reached 820 s before moving the LDA probe to another position. The average sampling rate during experiments was around 300 s\({}^{-1}\). A 10s pause between each measurement position was set in order to minimise the disturbance of the movement of the LDA probe to the flow field. The above LDA setup was chosen according to a preliminary experiment for the reproducibility of statistic results, in which air velocities at heights of 0.01 m, 0.03 m, 0.06 m, 0.1 m, and 0.2 m along Line 1 were measured and repeated three times at \(U_{inlet}\) of 8 m\(\cdot\)s\({}^{-1}\). The results showed that with this setup the measurement uncertainty for the time-averaged \(U\) and \(W\) velocities was 0.3%, and 5.5%, respectively (Figure 4). The above mentioned LDA setup was used for all air velocity measurements in this study.  
Figure 2: Photographs of the boundary layer wind tunnel (BLWT) at the Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Germany. (**a**) Exterior view of the BLWT viewed from the air inlet; (**b**) interior view of the test section of the BLWT with the 1:50 scaled pig barn model.
## 4 Optimization Design Process and Results  
The surrogate-based optimization strategy consists of design of experiments, surrogate models and optimization algorithm [30]. The high-precision CAA/CFD platform coupled with surrogate model can effectively reduce the computational cost and improve the optimization efficiency [31]. The overall framework of the optimization process is shown in Figure 12.
# A Review of the Optimal Design of Neural Networks Based on FPGA
## 5 Design of the DNN Accelerator and Acceleration Framework Based on FPGA
### FPGA Accelerator Design for Different Neural Networks
#### 5.1.3 The GAN Accelerator Based on FPGA
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Platform** & **Freq (MHz)** & **Neural Network** & **Methodology** & **Performance** \\ \hline \multirow{2}{*}{[107]} & ZCU102 FPGA & 200 & GAN & Winograd, Pipeline, & 639.2 GOPS \\ \cline{2-5}  & VC706 FPGA & 167 & & Fixed-16 & 162.5 GOPS \\ \hline \multirow{2}{*}{[138]} & XCKU9P FPGA & 200 & LSTM & Pruning, Fixed-16 & 200 GOPS \\ \hline \multirow{4}{*}{[74]} & XC7Z020 FPGA & \multirow{4}{*}{100} & ResNet-18 & \multirow{4}{*}{Mixed quantization} & 77.0 GOPS \\ \cline{2-5}  & XC7Z045 FPGA & & ResNet-18 & & 359.2 GOPS \\ \cline{2-5}  & & MobileNet-v2 & & 326.9 GOPS \\ \hline \multirow{2}{*}{[139]} & Stratix10 GX2800 FPGA & 260 & LSTM & Optimize multiplication, & \\ \hline \multirow{2}{*}{[131]} & Xilinx ADM-PCIE-7V3 & \multirow{2}{*}{200} & Vanilla LSTM & Pruning, optimize & 379507 FPS \\ \cline{2-5}  & FPGA & GRU & multiplication & 378788 FPS \\ \hline \multirow{4}{*}{[117]} & XC7Z020 FPGA & \multirow{4}{*}{100} & ResNet-18 & \multirow{4}{*}{Mixed quantization} & 101.3 GOPS \\ \cline{2-5}  & XC7Z045 FPGA & & MobileNet-V2 & & 80.1 GOPS \\ \cline{2-5}  & XC7Z045 FPGA & & ResNet-18 & & 446.8 GOPS \\ \cline{2-5}  & & MobileNet-V2 & & 363.5 GOPS \\ \hline \multirow{4}{*}{[140]} & \multirow{4}{*}{ZC706 FPGA} & \multirow{4}{*}{200} & VGG-16 & \multirow{4}{*}{Pipeline} & 706 GOPS \\ \cline{2-5}  & & AlexNet & & 624 GOPS \\ \cline{2-5}  & & ZFNet & & 648 GOPS \\ \cline{2-5}  & & YOLO & & 702 GOPS \\ \hline \multirow{2}{*}{[135]} & Stratix 10 SX FPGA & 185 & GAN & Convolution optimization, & \\ \hline \multirow{2}{*}{[130]} & XCKU115 FPGA & 200 & LSTM & Pruning, Fixed-8, Fixed-12 & 1424.8 GOPS \\ \hline \multirow{4}{*}{[141]} & \multirow{4}{*}{Arria 10 GX1150 FPGA} & \multirow{4}{*}{220} & Bayes-VGG11 & \multirow{4}{*}{Optimize calculation} & 533.75 GOPS \\ \cline{2-5}  & & Bayes-ResNet18 & & 1590 GOPS \\ \cline{2-5}  & & Bayes-C3D & & 1449 GOPS \\ \hline \multirow{2}{*}{[64]} & Xeon 4116 CPU + Arria10 & \multirow{2}{*}{163} & VGG + BiLSTM & Winograd, Fixed-8, & 1223.53 GOP/s \\ \cline{2-5}  & CX1150 FPGA & & 640 & MNIST LSTM & Memory optimization, & 44.5 GOPS \\ \hline \multirow{2}{*}{[142]} & XCZU7 EV FPGA & \multirow{2}{*}{420} & Character LSTM & 16–27 fixed & 363.7 GOPS \\ \cline{2-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different networks deployed using different optimization methods on different FPGA platforms in recent years. Figure 16: The GAN acceleration framework.
###### Abstract  
The adjusting parasitic patch size technique for the broad-beam microstrip antenna array using the cavity-backed slot coupling is presented. The phase of each element of the microstrip array has been designed to emulate the reflection of waves on the surface of parabolic backscattering. In order to increase the efficiency of this array antenna, the back-slot cavity with an exciting probe will be employed for coupling the electromagnetic waves to the back of this array. The proper sizes and locations of patches and the optimized position of the cavity have been investigated by the Computer Science Technology (CST) Microwave Studio. The gain, the radiation pattern, the bandwidth, and the return loss are extensively analyzed. The fabricated antenna has the return loss of \(-22.39\) dB, the bandwidth of \(47\) MHz (\(4.975\)-\(5.022\) GHz), and the maximum directive gain of \(5.6\) dB at \(5\) GHz, and it can produce a wide beam width (half-power beam width around \(130^{\circ}\)). The antenna could be applied for wide applications in the wireless communications system. In particular, this realizing antenna covers the low earth orbit (LEO) satellite beam.  
microstrip; broad-beam; antenna; cavity-backed; slot-coupled; satellite +
Footnote †: journal: Applied system  
slot-coupled microstrip patch [20; 21; 22] is excited by a linear electric probe, which is mounted at the inner surface of the rectangular cavity [23]. In order to achieve the broad-beam antenna, the phase of each array element in the microstrip array antennas is specifically designed. This design emulates the function of curvature of the parabolic backscattering by modifying the parameters of the parasitic patch array around the radiation patches. These parameters are as follows: the patch sizes, the slot length, the slot width, the probe length, the cavity positions, and the gaps. Consequently, this proposed technique has been investigated and monitored by computer simulation technology software. Finally, the proposed antenna is fabricated and its performance is measured and then compared with that of the simulation. The proposed antenna has the potential to be applied to a LEO satellite for an earth coverage beam or used for other applications in a wireless communications system, particularly WLAN [24; 25; 26] and sub-6 GHz 5G communications [27; 28; 29].
## 4 Applications of FBFET  
### Logic Device Applications (FBFET-Based Inverter)  
An inverter design that functions properly requires efficient operation of the pullup and pulldown devices. As the device must be operated under a forward bias, the magnitude of V\({}_{\text{DS}}\) should be higher than the built-in potential of FBFET [81]. This can be optimized through various combinations of bias, which requires a significant amount of effort. Nevertheless, the advantages of an inverter with a  
Figure 7: I\({}_{\text{D}}\)-V\({}_{\text{G}}\) curve for 30–50 nm channel lengths when 1 V is applied to the drain (**left**), and for a 0.9–1.1 V drain bias with an L\({}_{\text{CH}}\) of 40 nm (**right**) [75].  
combined FBFET are as follows: FBFETs have a steep-switching feature and high current on/off-current ratio; moreover, the saturation region is clear, especially beyond \(\rm V_{TH}\). In other words, the amount of current supplied to the operating and non-operating regions can be distinguished by the gate voltage (\(\rm V_{g}\)), and thus the power consumption during operation and in the off-state can become minimal. The steep-switching characteristic of the FBFET is utilized in digital integrated circuits. A typical example is that the switching is controlled by an FBFET in the inverter. In a conventional inverter circuit, as the input voltage continues to increase, the charge stored in the capacitor of the circuit tends to run low over a long period of time [81; 82]. However, if the FBFET plays the role of controlling the input voltage supply for the operation of the inverter, the voltage stored in the capacitor changes rapidly at \(\rm V_{TH}\) of the FBFET. In addition, starting from \(\rm V_{TH}\), the voltage supply appears as a saturation region, showing stable on/off-states. The FBFET-based inverter is shown in Figure 8.
## Abbreviations  
This manuscript utilizes the following abbreviations:  
\begin{tabular}{l l} ANFIS & Adaptive-Network Fuzzy Inference System \\ AUVs & Autonomous Underwater Vehicles \\ BDS & BeiDou Navigation Satellite System \\ BN & Blind Node \\ DRKF & Dual-Rate Kalman Filter \\ DOF & Degrees of Freedom \\ EKF & Extended Kalman Filter \\ GAF & Gramian Angular Field \\ GLONASS & Global Navigation Satellite System \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ LS & Least Squares \\ RFID & Radio Frequency Identification \\ RMSE & Root Mean Square Error \\ SP & Static Person \\ SPL & Static Person Localization \\ TJU-1 & Tianjin University-1 \\ NARX & Nonlinear Autoregressive Exogenous model \\ NLOS & Non-Line-Of-Sight \\ PDF & Probability Density Function \\ UWB & Ultrawide Band \\ BS & Base Station \\ \end{tabular}
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 3 Methods
### Pre-Processing
3:procedureSAX(\(D\),\(r_{i}\) for all \(i\),\(w\))
4:for each subject \(h\) in \(D\)do
5:for each TS \(\{x_{i}^{h}[t]\}_{0\leq t\leq T}\), with \(1\leq i\leq n\)do
6:for each \(t\), with \(0\leq t\leq T\)do
7:\(Norm_{i}^{h}[t]\gets z\_Norm(x_{i}^{h}[t])\)\(\triangleright\) Normalization
8:functionPAA(\(Norm_{i}^{h},w\))\(\triangleright\) Dimensionality reduction
9:\(k\gets 0\)
10: Partition the \(Norm_{i}^{h}\) in contiguous blocks of size \(T/w\)
11:for each block \(BL_{k}\)do
12:\(\hat{x}_{i}^{h}[k]\leftarrow(w/T)\sum_{l\in\mathbb{H}_{k}}Norm_{i}^{h}[t]\)\(\triangleright\) Compressed slices
13:\(k\gets k+1\)
14:functionDiscretization(\(\hat{x}_{i}^{h}[k],r_{i}\))\(\triangleright\) Symbolic discretization
15:\(\beta\gets SegmentGaussianDistrib(r_{i})\)
16:for each value \(val\) in \(\hat{x}_{i}^{h}[k]\)do
17:\(Discrete_{i}^{h}[k]\gets ToSymbolic(val,\beta)\)
18:\(return(Discrete)\)\(\triangleright\) Return discretized MTS dataset ```
**Algorithm 1** Data Pre-Processing  
When choosing the most suitable value for the alphabet size \(r_{i}\), experiments conducted [32] demonstrate that a value in the range of 5 to 8 is optimal in most datasets. The latter means that the information loss during discretization is minimized. However, it is always advised to test different values when possible and consider the particularities of each domain.
## 2 Mobility data
### Accuracy Test Results  
Figure 11 shows the test results of the accuracy performance of our EMDI system for Top-N queries. Both correlation coefficients PCC and Spearman's rho, show a statistically significant relationship, with values for Spearman's rho ranging from roughly 0.9999 for data size that equals to 20k tuples, to around 1 for data size that equals to 500k tuples. Those are the figures for granular geohash precision that equals to 6. Similar results obtained for the PCC, despite being slightly lower than the Spearman's, however, remain statistically significant. Comparatively speaking, for a coarser geohash precision, specifically a value that is equal to 5, the figures are lower, with PCC ranging from roughly 0.84 for data size that equals to 20k tuples, to around 0.89 for data size that equals to 500k tuples, still statistically significant despite that we obtain better numbers for the Spearman's rho as shown in Figure 12.  
It is obvious that we obtain higher accuracy for the Top-N queries by applying geohash precision 6 instead of 5 as shown in Figure 12.  
Figure 11: Top-N query accuracy performance, EMDI versus baseline, geohash precision 6.  
Figure 12: Top-N query accuracy performance, EMDI versus baseline, geohash precision 5.  
This is very significant as we have noticed that increasing the geohash precision will increase the correlation coefficient values (Spearman's rho and PCC). This is so because by increasing the geohash precision we are working on a granular level, which means we are considering more geohash values for the same geographic areas, and subsequently means granular, and more accuracy is achieved because we depend on grouping by geohash values for the PM\({}_{10}\) observations and then joining the grouped result with the mobility data. This means a higher join cost should be involved; however, more accuracy is obtained.  
For the average queries, we show the results of testing using MAPE as discussed in Section 5.4.2 Specifically, we apply Equation (6). Again, we vary the data size and the geohash precisions. Results obtained are shown in Figure 13.  
We notice that the higher the geohash precision the lower the value of MAPE, which is desirable.