esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanofiltration of Simulated Acid Mine Drainage: Effect of pH and Membrane Charge
## 1 Introduction
Concurrently, hydrogen sulfide generated by the bacteria raises the wastewater pH [8; 9]. Separation by ion exchange (IEX) technology is based on selective binding and elution interaction between the ions and appropriate IEX resins. It is a simple technology, which is efficient in removing trace impurities, and especially suitable if large volumes of dilute ionic solutions are to be treated. It is possible to remove copper (II) using a chelating ion exchanger under acidic conditions [10]. Recovery of copper, nickel, and cobalt from AMD using IEX has been proven successful by [PERSON] and [PERSON] [11]. IEX becomes less attractive if concentrated AMD is to be treated because of the need for more frequent IEX resin regeneration. Membrane technology is an attractive alternative to conventional AMD treatment options. Both reverse osmosis (RO) and nanofiltration (NF) are well-established methods for water desalination. They are highly efficient, cost-effective, and easy to scale-up processes. In the face of an ever-increasing stringent environmental criteria, applications of RO and NF are gaining acceptance from the mining industry. It is considered that NF offers a good opportunity compared to conventional methods to treat AMD, such as the preferential passage of mono-charged ions (e.g., hydrogen sulfate, protons) allowing for further recovery of sulphuric acid and high rejection of multi-charged ions, such as transition metals and rare earth elements [12]. NF is preferred over RO when treating metal pollutants as it provides a higher permeate flux at a lower pressure while yielding acceptable rejection performance [13]. This leads to reduced capital investment, and lower system operating and maintenance costs. Moreover,NF can be used to concentrate and recover target metals from AMD, which can be exploited as an invaluable resource commodity [12; 14]. [PERSON] et al. [14] investigated the impact of feed pH and membrane charge on NF performance for AMD treatment for two NF membranes (NF 270 and TS 80). They concluded that the retention of metal cations was maximized when the feed solution pH was below the membrane isoelectric point (IEP). In another study, the performance of three commercial membranes (NF99, DK, GE) at different pressures and concentrations on treating a Chilean copper mine AMD sample was investigated [8]. All membranes tested showed a promising high rejection of heavy metals (>98%). It can be concluded that membrane performance for single and mixed salt solutions can provide a clue to the performance of NF treating AMD streams.
## 2 Related Work  
To the best of our knowledge, there is little published work on the performance or channel analysis of IEEE 802.15.3 MAC with respect to different ACK policies, under error channel conditions. However, a large amount of literature is available on IEEE 802.15.3 MAC scheduling, optimization of superframe size, and various traffic analyses. Some of the important related works are as follows.  
In [3] the authors presented the implementation of IEEE 802.15.3 module in ns-2 and discussed various experimental scenario results, including various scheduling techniques, specifically, to investigate the performance of real-time and best-effort traffic with various super frame lengths and different ACK policies. In [4] the authors presented an adaptive Dly-ACK scheme for both TCP and UDP traffic with two main contributions. The first one is to request the Dly-ACK frame adaptively or change the burst size of Dly-ACK according to the transmitter queue status. The second is a retransmission counter to enable the destination DEV to deliver the MAC data frames to an upper layer in a timely and orderly fashion. In [5], its authors mainly focused on optimization of channel capacity. Both [4,5] lay a good foundation for simulation and analytical works on the IEEE 802.15.3 MAC protocol. Similarly, in [6] the authors formulated a throughput optimization problem under error channel conditions and derive a closed form solution for the optimal throughput. In [7] the authors presented a detailed study on performance analysis of Dly-ACK policy and proposed a dynamic Dly-ACK policy for WPAN MAC. Furthermore, they showed that the optimal burst size of Dly-ACK is heavily dependent on the input traffic load and is insensitive to the channel error rate within a normal error-rate change. The authors in [9] presented the throughput analysis of mm-wave WPAN and introduced a private channel release time mechanism to increase the throughput of mm-wave WPAN.  
The work presented in [6] is close to our work, but their analysis scope is limited only in terms of throughput analysis, while our work span covers the delay, throughput, and channel utilization with different ACK policies under error channel conditions. Furthermore, we propose K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC.  
The rest of the paper is organized as follows. In Section 3 we present the improvements in WPAN MAC and the design and analysis of the WPAN MAC in Section 4. Finally, conclusions are drawn in Section 5.
## 4 Results Analysis and Discussion
### Complete Trajectory Prediction
The Gradient Boosting Regressor and Decision Tree models show relatively lower prediction accuracy, with the Gradient Boosting Regressor particularly exhibiting larger deviations in COG and SOG predictions. This suggests that the Random Forest model possesses superior stability and generalization ability, making it well-suited for precise ship behavior prediction. In conclusion, the Random Forest model demonstrates outstanding performance in predicting the trajectories, COG, and SOG for ships of various sizes, drafts, and types, showing high prediction accuracy and stability. Therefore, in practical applications, the Random Forest model is recommended for use to obtain more accurate and reliable prediction results. These findings provide robust technical support for maritime traffic planning and management, contributing to enhanced safety and efficiency in maritime traffic. Figure 14: Predictions for the Bulk Carrier (Length 199 m, Beam 32 m, Draft 13.1 m): (**a**) Position Prediction; (**b**) COG Prediction; (**c**) SOG Prediction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Position** & **COG** & **SOG** \\ \hline Random Forest & 743.8657 & 0.5998 & 0.0199 \\ Gradient Boosting & 8899.3125 & 15.169 & 0.0178 \\ Regression & 193,224.89 & 5405.0668 & 1.5231 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A Bulk Carrier’s behavior prediction Error Summary(MSE).
# LO and Calibration Signal Distribution in a Multi-Antenna Satellite Navigation Receiver +
## 3 Distributed GNSS Receiver Concept with Two 2 \(\times\) 1 Antenna Sub-Arrays
### System Calibration
#### 3.2.1 LO Signal Distribution
A number of additional criteria have been weighted against each other to decide between the two options for the LO signal generation, including hardware and software  
Figure 4: Options for LO signal distribution: (**a**) distribution of a common REF signal and separate LO generation at each sub-array; (**b**) common LO generation in a single PLL module and distribution of the LO signals to both sub-arrays. efforts and costs; the low frequency of the REF signal allows twisted-pair cables to be employed instead of coaxial cables, which leads to a reduction in costs and weight. Another advantage of the REF signal distribution is that only one cable is required in contrast to the LO signal distribution, which needs to cover two frequency bands and thus two PLL modules, which have to be set up and monitored by additional software. Table 1 summarizes the pros and cons of both concepts. Eventually, the lower hardware and software efforts at similar RF characteristics led us to the decision to use the REF signal distribution for the generation of the LO signal at each sub-array.
# Simulation Tests of the Passing Distance of Ships on a Two-Way Fairway
## 3 Results  
The results of the simulation studies were statistically processed by determining the minimum distances between the sides of passing ships for each series of maneuvers. The minimum distances between the sides of passing vessels determined at the confidence level (\(1-\alpha\)) = 0.95 for the four series of tests are respectively:  
1. Kanal Piastowski: d\({}_{\text{min1}}\) = 26.4 m;
2. Zalew Szczecinski between the Brama Torowa I and II: d\({}_{\text{min2}}\) = 20.8 m;
3. Zalew Szczecinski between the Brama Torowa II and III: d\({}_{\text{min3}}\) = 37.9 m;
4. Swinoujscie Port--Kanal Zbiorczyk: d\({}_{\text{min4}}\) = 41.9 m.  
The simulation results obtained clearly show that for the minimum passing distances of ships on the fairways, chosen/judged by navigators to be safe, direct ship-to-ship interaction is insignificant. The minimum passing distances determined by the simulation method are larger than the breadth of the passing ships. At such lateral distances and ship speeds (8 knots), the magnitude of the mutual hydrodynamic interaction between ships is small and does not affect the movement of passing ships. This low interaction, in terms of a kinematic effect--linear/angular displacements produced by suction and repulsion phenomena, is basically due to brief time (high relative speed) as stated in the previous section. Considering the assumptions and conditions of the study and analyzing the results, it can be concluded that the minimum passing distances of identical loaded vessels with the block coefficient CB = 0.67-0.8 (bulk carriers, tankers, sea ferries) under good hydrometeorological conditions depend solely on:  
* Parameters of passing ships (i.e., their breadth);  
Figure 6: Research area, sections 1, 2, 3 (**a**) and section 4 (**b**).  
* Parameters of the navigation systems used (accuracy of position determination).  
The parameters of the navigation systems are considered by the deterministic-probabilistic MTE method developed at the Maritime University of Szczecin [6]. In this method, the maneuvering component of the width of the safe maneuvering area (lane) \(d_{m}\) is determined deterministically, while the navigational component of the width of the safe maneuvering area \(d_{n(1-\alpha)}\) is probabilistic, and determined at a certain confidence level. The passing distance determined by the simulation method is also probabilistic and its inclusion modifies the MTE method for two-way traffic on fairways.
## 2 Three-Dimensional Object Segmentation and Labeling Algorithm  
The 3D object segmentation and labeling algorithm proposed in this paper can be divided into four processing steps, namely, the texture construction edge detection algorithm (TCEDA) [28], distance connected component algorithm, object extension and merge algorithm, and object segmentation. First, TCEDA is used to detect large amounts of edge contours in the images. Second, the distance connected component algorithm detects the distance of each edge pixel and uses the distance information to determine whether the pixels belong to the same line segment and records their label numbers, number of valid points, and coordinates. The above two processing steps may cause line segment fragmentation for two reasons. First, the change in images is not evident; therefore, the edge detection is not complete. Second, owing to the matching error generated during image matching, parts of a line segment are incorrectly detected as having different distances. The third processing step uses the line segment extension and merge algorithm to extend and construct the disconnected line segments. The line segments that satisfy the extension connection conditions are merged into the same line segment. Therefore, the third processing step can solve the fragmentation problems of object contours caused by edge detection and image matching methods. Finally, morphology and run-length smoothing algorithm are used to merge the line segments into different segmented objects, and the 3D information is estimated for each segmented object. Each processing step of the proposed algorithm will be subsequently described in detail in each subsection.
# Shape Modelling and Volume Optimisation of Salt Caverns for Energy Storage
## 2 Three-Dimensional Numerical Model and Assessment Criteria
### Numerical Model and Boundary Conditions  
The numerical simulations were performed for a set of individual caverns (cylindrical with a maximum initial diameter of 60 m and an enlarged bottom and top with maximum initial diameters of 75 m, 90 m and 120 m (Figure 3)). Boundary conditions were adopted in the form of stress conditions to obtain solutions that tended towards being safe. The model was a cylinder with a diameter of 250 m and a height of 320 m. The direct roof and the bottom of the rock salt beds consisted of anhydrite layers with a thickness of 35 m at the roof and 80 m at the bottom. The mesh contained about 1.65 million tetrahedral elements. To ensure that the cavern shapes were accurately represented, the element size at the cavern sidewalls was smaller than others (about 3 m). Outside the cavern, the sidewall elements were larger than others (about 20 m). The initial value of the hydrostatic stress changed within a depth from 22.32 MPa at the top to 30.00 MPa at the bottom of the 3D model.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 4 Software Profiling Analysis of the OSNMA Functions
### Analysis Results
\(I\) is defined as \(I=\frac{\left(a\varpi_{p2}-a\varpi_{px}\right)}{a\varpi_{p2}}*100\), where subscripts \(p2\) and \(px\) indicate platform 2 and platform \(x=\{3,\ 4\}\) respectively. It can be noticed how ODROID-C4 exhibits better performance (\(I\) ranging from 50% to 61%) than Raspberry Pi4 (\(I\) ranging from 24% to 41%). \begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Platform 2 [16]**} & \multicolumn{3}{c}{**Platform 3**} & \multicolumn{3}{c}{**Platform 4**} \\ \cline{3-10}  & & **Call Rate (Hz)** & \(a\varpi\) (\(\omega\)) & \(\sigma\) (\(\omega\)) & \(\varepsilon\) (\%) & \(a\varpi\) (\(\omega\)) & \(\sigma\) (\(\omega\)) & \(\varepsilon\) (\%) & \(a\varpi\) (\(\omega\)) & \(\sigma\) (\%) & \(\varepsilon\) (\%) \\ \hline \multirow{3}{*}{Calling function} & Digital Signature & & & & & & & & & \\  & verification & & & & & & & & & \\  & Asil encoding & & & & & & & & & \\  & EVP Verify Init & 0.03 & & 4.05 & 0.19 & 4.70 & 3.97 & 0.23 & 5.84 & 6.74 & 1.28 & 19.05 \\  & EVP Verify Update & & & & & & & & & \\  & EVP Verify Final & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Software profiling analysis results of the digital signature verification and its subroutines obtained on the three embedded platforms with the set of parameters S1 in Table 3. © [2020] IEEE. Extended, with permission, from [16].
## 3 Numerical Simulation  
After preliminary experimental research and theoretical analysis [18], the factors that affect fluid distribution in the compact parallel channels were mainly divided into two types. One set of factors was the inlet and outlet conditions: inlet velocity \((V)\), inlet pressure \((P_{1})\), outlet pressure \((P_{2}\), which is atmospheric pressure with a value of 0.1 MPa), and pressure difference \((\Delta P=P_{1}-P_{2})\). Another set of factors was the structural dimensions: inlet diameter \((D)\), channel diameter \((d)\), area ratio \((A)\), number of channels \((N)\), channel spacing \((S)\), and channel length \((L)\). The following experiments were designed for the above parameters.  
Defining the non-uniformity flow coefficient for analyzing flow distribution in the compact parallel channels, the uniformity of flow distribution in the structure can be intuitively reflected by:  
\[\Phi=\frac{m_{\max}-m_{\min}}{\overline{m}} \tag{1}\]  
The flow ratio is used for the analysis of flow distribution in each channel, which is defined as the ratio of flow rate in a single channel to the total flow rate, that is:  
\[\Gamma=m/M \tag{2}\]  
where \(m\) is the mass flow rate in channel (kg/s), \(M\) is the total mass flow rate (kg/s), \(m_{\max}\) is the maximum flow rate in channels, \(m_{\min}\) is the minimum flow rate in channels, and \(\overline{m}\) is the average flow rate of each channel. It can be seen that a smaller value of \(\Phi\) represents more uniform flow of each channel. In this paper, because the structure was symmetrical, only \(N/2\) channels were studied in the simulation, so the greater value of \(\Gamma\) tended to be \(2/N\), representing the more uniform flow of each channel.  
This numerical simulation was conducted using the commercial software COMSOL Multiphysics(r) [27]. The peristaltic flow module (spf) under the single-phase flow module was applied in this study, the finite element method was employed to solve the CFD problem, and the CFD numerical model is shown in Appendix C. Grid independence was verified. As shown in Figure 3, the relative deviation of flow ratios between the grid with  
Figure 2: Physical models used for CFD and channel numbers.  
94,713 cells and the densest grid with 247,042 cells was less than 1%, so the grid with 94,713 cells was adopted as the calculation grid.
## 3 Experiments and Comparisons
### Experimental Dataset Introduction  
To validate the effectiveness of the proposed method for diagnosing faults in rotating equipment under variable working conditions, this study utilizes a publicly available gearbox bearing variable operating conditions fault dataset as the experimental data. This dataset contains the operating data of gearbox rotating equipment under variable working conditions, such as various speeds and loads. These conditions are analogous to the complex working conditions encountered in actual application scenarios. Experiments utilizing this data can accurately reflect the impact of variable working conditions on fault diagnosis methods [20]. As shown in Figure 4a, the gearbox test bench consists of a motor, a motor controller, a planetary gearbox, a reduction gearbox, a brake, and a brake controller. It uses vibration sensors to collect data in the x, y, and z directions of the planetary gearbox and the reduction gearbox, respectively. The sampling frequency is 5120 Hz, and the sensor collection position is shown in Figure 4b, where each fault type corresponds to two working conditions.  
The vibration signal in the x direction of the planetary gearbox is extracted as input, and 1024 sampling points are set as a sample. Given the sample imbalance characteristics of fault data under variable working conditions, an unbalanced configuration strategy for the number of samples is implemented. The detailed information is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
Figure 4: (a) Gearbox test platform. (b) Sensor collection location diagram.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 5 Experiments and Results  
### MATLAB Simulations  
#### 5.1.1 Syntheses of PWM and PPM Signals  
To validate the analysis presented in the previous section, this section utilizes computer simulation via MATLAB under an academic license. The synthesis incorporates components of the PWM signal (see Equation (5)) and the PPM signal (see Equation (9)).  
A. PWM Signal Generation and Analysis  
The PWM signal generation process utilizes a comparative approach where the input message signal is continuously compared against a reference sawtooth waveform. This fundamental technique produces a PWM output where the width of each individual pulse directly correlates with the instantaneous amplitude of the message signal at the samplingmoment. This relationship ensures that the analog information content is accurately preserved within the digital pulse train format, enabling reliable signal transmission and subsequent recovery. The implementation code for this section can be found in Code S1 of the Supplementary Materials.  
The synthesis results of the PWM signal components are demonstrated in Figure 13, which illustrates both the modulation process and the demodulation capability. The simulation reveals that the sampled-and-held information signal maintains a direct correspondence with the PWM pulse widths, while the demodulated output successfully reconstructs the original information signal with minimal distortion. This validates the effectiveness of PWM as a robust modulation technique for analog signal processing applications.
# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
## 3 Network Energy Models (Methods)  
This section presents the models employed in this study. Initially, the system model is presented, along with the necessary assumptions that have been made. Further, the energy consumption model based on the first order radio model for transmission and reception is presented. Subsequently, the proposed network model is also presented.
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 6 Discussion
### Future Research Directions  
In light of the findings from the present study, several avenues for future exploration become apparent. One crucial direction involves the conduct of more exhaustive literature reviews. While our study has encompassed numerous papers, an even broader sweep, incorporating a wider range of databases and publications, may unveil overlooked, yet pertinent works. Additionally, the practical implementation of the highlighted trajectory prediction methods in real-world warehouse settings is paramount. Such hands-on testing would pave the way for an empirical comparison of their efficacy and feasibility.  
Moreover, with models such as NSP-SFM showcasing potential yet facing challenges such as overfitting, there is an evident need for research into the adaptation or combination of these models, potentially birthing hybrid solutions that harness both the prowess of deep learning and the clarity of probabilistic methods. Lastly, given the inherent data anomalies and localization errors, especially with tools such as UWB sensors, the development or refinement of noise-resilient trajectory prediction models stands out as a vital research endeavour. Pursuing these directions will not only deepen our understanding of trajectory prediction but also bolster the safety and efficiency of warehouse operations.