esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Efficacy Studies for Water
### Heavy Metal Remediation--A Case Study of Nickel  
Heavy metal pollution is a serious environmental issue around the world. Globally, more than five million soil sites are heavily contaminated with HMs [72]. Soil serves as a major sink of HMs released into the environment. Unlike organic contaminants, HMs are biologically or chemically non-degradable. However, their chemical forms and bioavailability are changeable [73]. Therefore, they can accumulate and remain persistent for long periods of time, posing serious environmental threats [73; 74]. Hence, we conducted investigations to study the effect of contact time in order to estimate the equilibrium time for nickel adsorption onto barley nano-silica (NS-B) and wheat nano-silica (NS-W). The adsorption was quick at first, but it gradually slowed over time until it reached equilibrium after 65 min This might be owing to the fact that there are more vacant sites available at the beginning of the process than at the end. Nickel (II) ion adsorption was measured at nickel concentrations of 10, 25, 50, 100, and 200 mg/L. With a remarkable 95% absorption efficiency, adsorption capabilities rise with nickel content. Furthermore, when the nickel starting concentration increased the effectiveness of NS-B removal dropped. A comparable drop in NS-W was also found. At 10 mg/L, the maximum adsorption efficiency was 95% for NS-B and 82% for NS-W. At pH 8.0, the largest proportion of adsorption was observed, with adsorption decreasing steadily as alkalinity increased (-pH 10). Due to the greater number of positively charged sites in the NS, contaminant absorption was reduced at a lower pH. The number of negatively charged sites grows as the pH rises, making nickel adsorption easier. At a nickel concentration of 10 mg/L, the adsorbent dose was investigated by altering the adsorbent amount from 1 to 50 mg. These results demonstrate that the maximal adsorption efficiency began at 10 mg, after which the adsorption capacity of both barley and wheat NS remained relatively constant. The adsorptive potential of accessible NS-B was not completely explored at higher adsorbent doses compared to lower adsorbent dosages, resulting in a small decrease in Ni adsorption on NS-B with 35 mg addition. Table 3 shows results obtained from the application of NSPs prepared using different sources. Similar investigations are in progress for other metals and results will be presented in the future.
## 4 Engineering Example Application
### Technology Roadmap  
Figure 4 shows that the historical displacement data were input first, followed by decomposing the data into several different signal IMFs using the EEMD method and classifying the IMFs. Next, the support least squares vector machine optimization algorithm with an irrational escape strategy sparrow search algorithm was applied for simulation, and rainfall and water level data were input to fuse the predicted data. Finally, the results of slope displacement prediction were derived, which demonstrated the accuracy of IESSA in slope prediction.  
In this study, two factors affecting slope stability, namely rainfall and water level, were selected as input variables, while slope displacement was considered as the output variable. The monitoring points on the Xin Tan slope are depicted in Figure 5. The rainfall and water level data of the monitoring points are shown in Figure 6. Two monitoring points, namely xtGXT1 and xtGXT3 on the Xin Tan landslide of the Yangtze River, were selected as examples, and the monthly displacements of the two slopes were summarized as shown in Figure 7.  
The EEMD method was used to decompose the data for the two side slopes [21], as shown in Figures 8 and 9.  
Figure 8: EEMD decomposition xtGXT1 side slope map: (**a**) is the value before the decomposition of the EEMD method, (**b**–**g**) is the variable decomposed using the EEMD method.  
Figure 7: Monthly displacement accumulation chart of the slope.  
Figure 9: EEMD decomposition xtGXT3 side slope map: (**a**) is the value before the decomposition of the EEMD method, (**b**–**g**) is the variable decomposed using the EEMD method.
## 3 Artificial Neural Network
### Feedforward Neural Network for Portfolio Semivariance Prediction  
This section details the architecture of a feedforward neural network designed to predict a portfolio's semivariance.  
**1. Inputs and Outputs:**  
* **Network Input:** The input layer receives the weights of the assets in the portfolio.
* **Network Output:** The output layer, consisting of a single neuron, predicts the portfolio's semivariance, which is a continuous value.  
**2. Architecture and Neurons**  
The network features three hidden layers with 30, 20, and 10 neurons, respectively.  
* **Weights (\(\theta_{ji}\)) and Biases (\(b_{j}\)):** These are randomly initialized and then adjusted during the learning process using the backpropagation algorithm to minimize prediction error.  
* **Activation Functions:*
* **Hidden Layers:*
* We employ the hyperbolic tangent sigmoid (tansig) function, defined as \(\psi(d)=\frac{1}{1+\epsilon^{-d}}\). This function compresses the output into the range \((0,1)\).
* **Output Layer:*
* For this regression problem, a linear function (purelin) is used.
* **Summation Function:** The weighted sum of inputs for each neuron is automatically calculated using \[c_{j}=\sum_{i=1}^{n}\phi_{ji}d_{i}+b_{j}.\]  
**3. Learning Process: Backpropagation**  
The learning and training process begins by feeding the network the asset weights (input) and the corresponding semivariance (output).  
* **Cost Function (\(J(\Theta)\)):** The training function implicitly uses a cost function, typically the mean squared error (MSE), to quantify the difference between the network's predicted semivariance and the actual semivariance. The primary goal of training is to minimize this cost function.
* **Backpropagation Algorithm:*
* This algorithm is implemented within the training function and involves two main passes:
* **Forward Pass:*
* The asset weights are propagated through the network to generate the predicted semivariance.
* **Backward Pass:*
* The error at the output layer is calculated. Subsequently, the gradients of the cost function with respect to all weights and biases across all layers are computed in reverse (from the output layer towards the input layer).
* **Weight and Bias Updates:** These calculated gradients are used to update the weights and biases (e.g., \(\phi_{ji}^{(l)}=\theta_{ji}^{(l)}-\kappa\frac{\partial l(\Theta)}{\partial \theta_{ji}^{(l)}}\)) using an optimization algorithm such as Levenberg-Marquardt. The learning rate \(\kappa\) is an internal parameter of these optimization algorithms that governs the size of each update step.
## 4 Performance Analysis
### Tracking and Economic Analysis

Table 9: Capacity loss value and the SOC of the battery under each controller after UDDS cycle (SOHini = 1). Figure 17 shows the battery capacity degradation curve and SOC reduction curve when the vehicle is driving under 10 UDDS cycles, showing that the AMPC controller performs better in terms of energy savings and life optimization. Finally, the NEDC working conditions were simulated and compared. The battery parameter changes are shown in Figure 18. Figure 18a shows that under the NEDC working conditions, the MPC2 controller has advantages in life optimization and energy consumption as most of the NEDC working conditions are medium- and low-speed conditions, and the economic coefficient of the fixed weight controller is higher than the variable weight coefficient, which can effectively improve the battery energy utilization rate after ensuring the safety and comfort of driving. Moreover, controllers that consider the decay of battery capacity have significantly improved energy consumption and life optimization compared to other controllers, and the specific values are shown in Table 10.  
Figure 19 shows the battery capacity degradation curve and the SOC degradation curve when the vehicle is driven under 10 NEDCs. The MPC2 controller outperforms the AMPC controller with the same results as a single NEDC, which can effectively mitigate the degradation of battery capacity. Based on the above result analysis, when the vehicle is driving under high-speed complex working conditions, such as WLTC working conditions, and with a new battery (SOH = 1), when optimizing the battery life is the goal, the fixed weight controller MPC2 is more effective. When the battery declines, the variable weight controller AMPC performs better. When the vehicle is driven in medium or low speed conditions, such as NEDC and UDDS standard working conditions, the fixed weight controller MPC2 performs better. Figure 17: Under 10 UDDS cycles, the battery parameters under the action of each controller: when SOH = 1, (a) battery capacity loss (c) SOC. When SOH = 0.9, (b) battery capacity loss (d) SOC.
## 5 Performance Evaluation
### Real Dataset
#### 5.1.3 The Correctness of Cluster Head and Active Nodes Selection  
The preceding discussion describes that our data collection framework benefits the large scale dense WSNs in term of SNR. To exploiting the potential of SFDC, we use all 52 sensor nodes as a single cluster to perform the following simulations.  
For the purpose of comparison, we introduce a variant of our \(Min_{-}MSD\) approach-_random_ approach, where the cluster head node is randomly selected in the cluster. When the fidelity threshold is set to \(90\%\), we compare the SNR for \(Min_{-}MSD\) and _random_ approaches through 20 learning periods. As shown in Figure 4, the \(Min_{-}MSD\) presents better SNR than using the _random_ approach throughout all learning periods. Based on the design in Algorithm 1, the result can be easily explained by the fact that the smaller deviation produces the smaller distortion in our data transmission model.  
Figure 3: Fidelity curves without the dynamical adjustment of data collection period.  
In previous evaluation, both of \(Min_{-}MSD\) and random approaches used the same active nodes selection scheme as described in Algorithm 2 to derive the SNR. For simplicity, we also name the random active node selection the _random_ approach. Figure 5 shows the effect of active nodes selection using the \(Max_{-}MSD\) and _random_ approaches on SNR. It is obvious that the greater SNR is achieved by our \(Max_{-}MSD\) approach since each selected active node can eliminate the most difference between the current and original active nodes set. Another result viewed from the Figures 4 and 5 is that the performance degradation of _random_ approach shows a distinct advantage provided by \(Max_{-}MSD\) in terms of SNR, compared to that of the \(Min_{-}MSD\). The reason is that more active nodes with maximal MSD is selected instead of completely randomized nodes.  
Figure 4: Effect of cluster head selection using the \(Min_{-}MSD\) and _random_ approaches on SNR.  
Figure 5: Effect of active nodes selection using the \(Max_{-}MSD\) and _random_ approaches on SNR.
## 3 Problem Statement and the Dimension Reduction with Iovp
### Stochastic Gradient Adaptation  
In this subsection, we present a low-complexity SG [2] adaptive reduced-rank algorithm for efficient implementation of the IOVP algorithm. By computing the instantaneous gradient terms of (19) with respect to \(\mathbf{\omega}[i]^{*}\) and \(\mathbf{s}_{d}[i]^{*}\), we obtain:  
\[\bar{\mathbf{\omega}}[i+1]=\mathbf{\omega}[i]-\mu_{w}\mathbf{P}_{w}[i]\mathbf{S}_ {D}^{H}[i]\mathbf{x}[i]z^{*}[i] \tag{20}\] \[\mathbf{s}_{d}[i+1]=\mathbf{s}_{d}[i]-\mu_{s}\mathbf{P}_{s}[i]\mathbf{x}[i]z^{*}[ i]w_{d}^{*}[i],d=1,\dots,D \tag{21}\]  
where \(w_{d}\) is the \(d\)-th element of the reduced-rank beamformer \(\mathbf{\omega}[i]\) and the projection matrices that enforce the constraints are:  
\[\mathbf{P}_{w}[i]=\mathbf{I}_{D}-(\mathbf{a}_{D}^{H}[i]\mathbf{a}_{D}[i])^{-1}\mathbf{ a}_{D}[i]\mathbf{a}_{D}^{H}[i] \tag{22}\] \[\mathbf{P}_{s}[i]=\mathbf{I}_{M}-(\mathbf{a}^{H}[i]\mathbf{a}[i])^{-1}\mathbf{a}[i] \mathbf{a}^{H}[i] \tag{23}\]  
the scalar \(z^{*}[i]=\mathbf{x}^{H}[i]\mathbf{S}_{D}[i]\bar{\mathbf{\omega}}[i]=\tilde{\mathbf{x}}^{H}[i] \bar{\mathbf{\omega}}\), and:  
\[\mathbf{a}_{D}[i]=\sum_{d=1}^{D}\mathbf{q}_{d}\mathbf{s}_{d}[i]^{H}\mathbf{a}[i]\in\mathbb{C} ^{D\times 1} \tag{24}\]  
is the estimated steering vector in reduced dimension. The calculation of \(\mathbf{P}_{\bar{\mathbf{\omega}}}[i]\) requires a number of \(D^{2}+D+1\) complex multiplications; the computation of \(\mathbf{P}_{s}[i]\) and \(z[i]\) requires \(D^{2}+DM+M+1\) and \(DM+D\) complex multiplications, respectively. Therefore, we can conclude that for each iteration, the SG adaptation requires \(4 MD+4D^{2}+3D+M+6\) complex multiplications.
# Efficient Optimization-Based Trajectory Planning for Truck-Trailer Systems
## 2 Methods
### Proposed Solution
#### 2.3.5 Phase 5: Adding Trailers  
This phase presents the solution to the trajectory planning for the entire truck-trailer system described by Equation (2). It uses the output from the previous Phase 4 as an initial estimate. We now require the desired rotation of the entire truck-trailer system at both the initial and target locations. The initial conditions reflect the initial state of the entire system, provided by Equation (26).  
\[\psi=\begin{bmatrix}x_{1N}\\ x_{2N}\\ x_{3N}\\ x_{4N}\\ x_{5N}\\ x_{6N}\end{bmatrix}-\begin{bmatrix}x_{0_{1}}\\ x_{0_{2}}\\ x_{0_{3}}\\ x_{0_{4}}\\ x_{0_{5}}\\ x_{0_{6}}\end{bmatrix} \tag{26}\]  
New constraints on the angles between individual trailers and between the first trailer and the truck at the final time point are introduced in Equation (27).  
\[\begin{split} x_{tf_{4}}-\frac{pi}{2}\leq& x_{4N}\leq x_{tf_{4}}+ \frac{pi}{2}\\ x_{tf_{5}}-\frac{pi}{2}\leq& x_{5N}\leq x_{tf_{5}}+ \frac{pi}{2}\\ x_{tf_{6}}-\frac{pi}{2}\leq& x_{6N}\leq x_{tf_{6}}+ \frac{pi}{2}\end{split} \tag{27}\]  
In addition to penalizing the location of the final truck's endpoints as defined by Equation (28), it is possible to add constraints on the locations of the endpoints of all trailers within the target area, as expressed by Equation (29).  
\[\begin{split} x_{tf_{1}}-6\leq& K_{x_{tf}}\leq x_{tf_{1 }}+6\\ x_{tf_{1}}-6\leq& L_{x_{tf}}\leq x_{tf_{1}}+6\\ x_{tf_{1}}-6\leq& M_{x_{tf}}\leq x_{tf_{1}}+6\\ x_{tf_{2}}-1.5\leq& K_{y_{tf}}\leq x_{tf_{2}}+ 1.5\\ x_{tf_{2}}-1.5\leq& L_{y_{tf}}\leq x_{tf_{2}}+ 1.5\\ x_{tf_{2}}-1.5\leq& M_{y_{tf}}\leq x_{tf_{2}}+ 1.5\\ x_{tf_{2}}-1.5\leq& N_{y_{tf}}\leq x_{tf_{2}}+ 1.5\\ \end{split} \tag{28}\]  
The endpoints are referred to as \(K1,L1,M1,N1\) for the first trailer and \(K2,L2,M2,N2\) for the second trailer. Due to the mutual connection of all the vehicles in the truck-trailer system, it is possible to omit the constraints on the endpoints of the first trailer. This reduces the number of constraints and speeds up the computation.  
\[\begin{split} x_{tf_{1}}-6\leq& K2_{x_{tf}}\leq x_{ tf_{1}}+6\\ x_{tf_{1}}-6\leq& L2_{x_{tf}}\leq x_{tf_{1}}+6\\ x_{tf_{1}}-6\leq& M2_{x_{tf}}\leq x_{tf_{1}}+6\\ x_{tf_{1}}-6\leq& N2_{x_{tf}}\leq x_{tf_{1}}+6\\ x_{tf_{2}}-1.5\leq& K2_{y_{tf}}\leq x_{tf_{2}}+ 1.5\\ x_{tf_{2}}-1.5\leq& L2_{y_{tf}}\leq x_{tf_{2}}+ 1.5\\ x_{tf_{2}}-1.5\leq& M2_{y_{tf}}\leq x_{tf_{2}}+ 1.5\\ x_{tf_{2}}-1.5\leq& N2_{y_{tf}}\leq x_{tf_{2}}+ 1.5\\ \end{split} \tag{29}\]The cost function used in this phase does not change in this regards, and stays as described in Equation (25). However, a fundamental change is applied in the case of collision detection, which is now extended to detect both collisions between obstacles with the truck and collisions between obstacles and individual trailers.
# Development of a Maintenance and Spare Parts Distribution Model for Increasing Aircraft Efficiency
## 4 Simulation Model Results
### System Dynamics Model Definition
#### 4.1.5 Distribution Network Sub-Model  
To exemplify the goal of the model, the distribution network in Figure 8 was selected based on a literature review [32]. The production facility follows a make-to-stock strategy in order to satisfy production and maintenance orders from stock. Therefore, production relies on demand forecasts instead of real orders, that is, it followed the \"push\" principle. Therefore, inaccurate forecasts could lead to an excessive inventory or stock-outs. The distribution network consists of a two stages distribution for a single spare part. Every existing warehouse in a different level determines one new stage. The production facility (PF) has no warehouse and pushes the products to the central warehouse (CW). We hypothesize that the first distribution stage is the central warehouse that should take care of the deliveries for a certain spare parts distribution area. Moreover, every warehouse of the second stage (i.e., regional warehouse) is located in a different region of the distribution area. The central warehouse receives all components produced by the production facility and stores them, except for those spare part units that are sent to the final assembly line when producing the initial aircraft units. These products are standard and finished products at this stage, which means that there is no change in the products and that the only activities performed during distribution are those related with storage and transport. The regional warehouse (RW) or distributor is the second stage. In this warehouse the goods are stored and then delivered to the airports at the appropriate time, which is when the maintenance orders are placed. It is assumed that all maintenance activities, planned and unplanned, are performed at the airport locations available for maintenance purposes.  
The distribution network can have different strategies depending on its maintenance order decoupling point or postponement point. A push-strategy means that all spare parts are distributed and sent to the maintenance locations, i.e., the airports. A pull strategy entails the spare partsonly being sent if a maintenance order is placed. A hybrid push-pull strategy implies a push strategy until the maintenance order decoupling point is reached, with a pull strategy after that point.
###### Abstract  
Clashes between space debris and spacecraft in orbit may cause air leakages, which pose a substantial danger to the crew and the spacecraft. Lamb wave dispersion in spacecraft structures and the randomness of leak holes are the difficulties in leak location. To solve these problems, a frequency weighting matrix beamforming algorithm is proposed in this paper. The elastic Lamb waves that are caused by leakages are acquired by an 'L' shaped sensor array consisting of eight acoustic emission sensors. The angle of a leak can be obtained through the superposition of different time delays, and the intersection of two angles can be used to find the location of the leak. Traditional beamforming is improved by matching the wave speeds in different frequency bands and weightings according to the energy distribution. Narrowband filtering is used to delay overlay different signal speeds with different frequency bands via a dispersion curve. The weighting method is used to compensate the frequency band response of different leak holes. The detailed location algorithm process is introduced and verified by experiments. For 1.5 and 2 mm leak holes, location direction accuracies of 1.33\({}^{\circ}\) and 1.93\({}^{\circ}\) for one sensor array were obtained, respectively.  
leakage location; Lamb wave; beamforming; spacecraft in orbit +
Footnote †: journal: Applied Sciences
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 2 Technologies for LV Smart Metering--Background Information
### Wired Technologies
#### 2.1.1 Narrowband PLC (NB-PLC)
This technology is optimum for the transmission of very short messages, especially for PLC and wireless IP communications [15].
# A Novel Drone Design Based on a Reconfigurable Unmanned Aerial Vehicle for Wildfire Management
## 4 Electronics Design
### Battery [Tattu 22.8V 25C 6S 23,000 mAh Lipo HV Battery (Tattu, Shezhen, China)]  
Lithium polymer (LiPo) batteries are rechargeable batteries that use lithium-ion technology and a semisolid (gel) polymer electrolyte instead of an electrolyte in a liquid form. They provide very high specific energy (per their unit of mass) and therefore are widely used in mobile phones, multicopters, and other mobile equipment.  
There are three important parameters that should be taken into account before battery selection: cell configuration, C-rating, and battery capacity. First of all, the number of cells defines the voltage of the battery. Each cell has a nominal voltage of 3.7 V, so a 6S battery thus provides 6 \(\times\) 3.7 V = 22.2 V. C-rating is an indicator of the continuous discharge rate of a LiPo. It allows users to easily calculate the maximum constant current that can be drawn from the LiPo pack safely without harming the battery. For instance, a 6 s 23,000 mAh 25C LiPo pack has a maximum average draw of 25,000 mAh/1000 \(\times\) 25C = 625A. Lastly, the capacity determines how long a battery can run before it needs recharging. A larger capacity pack may give longer flight times but being heavier, it will adversely affect performance.  
The following Equation (2) determines the flight time, knowing the current drawn from the LiPo battery of the multicopter as well as the specifications of the battery, where the battery discharge indicates the effective capacity of the battery:  
\[\text{Flight Time (in min)}=\frac{\text{(Battery Capacity (Ah)}\times\text{ Battery Discharge)}}{\text{Average Amp Draw (A)}}\times 60 \tag{2}\]  
Using a discharge of 0.8, the selected battery provides almost \(\sim\)25 min of flight time. Note that, for the proposed UAV, two identical batteries are used in series configuration; hence, the total battery voltage is 45.6 V while the total battery capacity remains 23,000 mAh.  
It is important to note that the UAV has been designed with a relatively spacious interior to accommodate various pairs of batteries. Each pair of batteries can serve different purposes or needs, providing flexibility in the UAV's power supply and mission requirements.
# A Crypto-Steganography Approach for Hiding Ransomware within HEVC Streams in Android IoT Devices
## 4 Results and Comparisons  
This section validates the proposed crypto-steganography approach by testing its performance on different resolutions of HEVC frames (cover media) and different sizes of ransomware files (secret data) to assess its hiding and extracting efficacy. This demonstrates the superiority of the proposed approach in hiding and extraction competence in the case of using different ransomware sizes within cover frames while preserving the video frame imperceptibility without affecting its visual quality and without recognizing the presence of ransomware data. Tables 1 and 2 show the examined stream resolutions and ransomware sizes, respectively. We comprehensively utilized subjective and objective assessment metrics to evaluate the proposed crypto-steganography approach.  
For subjective evaluations, the visual results in terms of the cover/stego frames are presented, in addition to the difference between them as will be demonstrated in Section 4.1. Moreover, the visual outcomes of the histograms and Gaussian Laplacian edges of the cover and stego frames are offered in Section 4.2. Furthermore, for objective evaluations, more and different 19 quality assessment metrics that are listed in Table 3 with their optimal values were utilized to test the efficacy of the proposed approach.