esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Uptake of Cadmium and Chromium from Wastewater Using Carbon-Based _Capsicum annuum_
## 3 Experiments  
### Batch Experiments  
A solution (200 mg/L) was prepared from cadmium acetate [Cd(CH\({}_{3}\)COO)\({}_{2}\)] and potassium dichromate (K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\)). The effect of time was studied at different intervals from 1 to 80 min with a standard solution concentration of 100 mg/L at a pH of 5.5. A working solution of 20 mL was added to 10 mg of the adsorbent in sealed bottles. The concentration effect using 20-100 mg/L at a time of 60 min was investigated with 20 mL of the indicatedconcentrations added to sealed bottles containing 10 mg of activated carbon. The pH effect was investigated at pH values of 1-9 with 20 mL of the standard solution, which was then placed in bottles with 10 mg of the carbon. The temperature effect was investigated at 303, 308, 318, 328, and 338 K under similar experimental conditions. For each parameter, a shaker was used to shake the working standards and adsorbents at 200 rpm. Afterwards, the bottles were taken out and the adsorbents were allowed to settle overnight before being filtered. For the validity and correctness of the results, the adsorption experiments were conducted in triplicate.  
The calculation of adsorption capacity was achieved by using Equation (1), where \(q_{e}\) is capacity in mg/g of Cd(II) and Cr(VI) ions.  
\[q_{e}=\frac{\left(C_{o}-C_{e}\right)V}{W} \tag{1}\]  
The starting and ultimate concentrations are denoted by \(C_{o}\) and \(C_{e}\), respectively, in mg/L. The volume of the solution and the mass of the adsorbent are indicated by \(V\) (in mL) and \(W\) (in mg), respectively. The non-linear graphs for the fitting of adsorption can be viewed in the Supplementary Materials.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.12. Arrowhead  
A project that deals with many smart grid topics and their interconnection is the \"Arrowhead\" project, which involves partners from 15 countries. In particular, it addresses issues relating to collaborative automation for five categories: production, smart buildings and infrastructures, electro-mobility, energy production and end-user services, virtual market of energy. Embedded devices play an important role in the whole project, while interoperability among services by almost any device is a topic of investigation [110]. With respect to smart buildings, Multi-resources smart meters (MSM) have been applied. These meters have not been installed at the consumers' homes, but in a lab environment. The consumption of the electrical devices has been monitored, whereas WiFi or Ethernet cables have been used for communication purposes [111].
## 4 Results
### Results of the Structural Equation Model  
The structural equations software Lisrel 8.80 was used to test the research model with an estimation method of maximum likelihood [97]. Lisrel fuses factor analysis modeling from psychometric theory with structural equations modeling associated with econometrics [98]. The aim was to evaluate the model based on the significance and effect sizes (\(\beta\)) for each hypothesized path and to explain the variance (R2) for each dependent variable [99; 100].  
Table 5 shows an abridgment of the findings, evidencing the statistical significance of the defined hypotheses. Specifically, AUTO and REL were significant predictors of PEU (0.32, \(p<0.001\); and 0.415,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fit Indexes** & **Values** & **Recommended Value** \\ \hline \(\chi^{2}\)/grade of freedom & 0.0206 & \(\leq\)3.00 \\ Normed Fit Index (NFI) & 0.966 & \(\geq\)0.90 \\ Non-normed Fit Index (NNFI) & 0.981 & \(\geq\)0.90 \\ Comparative Fit Index (CFI) & 0.984 & \(\geq\)0.90 \\ Adjusted Goodness-of-Fit Index (AGFI) & 0.894 & \(\geq\)0.80 \\ Root Mean Square Error of Approximation (RMSEA) & 0.0489 & \(\leq\)0.05 \\ Goodness-of-Fit Index (GFI) & 0.918 & \(\geq\)0.90 \\ Incremental Fit Index (IFI) & 0.984 & \(\geq\)0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Goodness of fit statistics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **AUTO** & **COMP** & **REL** & **PEU** & **PU** & **BI** \\ \hline Autonomy (AUTO) & 0.970 & & & & & \\ Competence (COMP) & 0.660 & 0.930 & & & & \\ Relatedness (REL) & 0.668 & 0.675 & 0.920 & & & \\ Perceived Ease to Use (PEU) & 0.662 & 0.588 & 0.686 & 0.980 & & \\ Perceived Usefulness (PU) & 0.742 & 0.616 & 0.753 & 0.770 & 0.910 & \\ Behavioral Intention to Use (BI) & 0.630 & 0.531 & 0.642 & 0.714 & 0.827 & 0.980 \\ \hline \hline \end{tabular}  
* Diagonal elements are the square root of the shared variance between the constructs and their measures.  
\end{table}
Table 3: Correlation Matrix 1.  
\(p<0.001\), respectively), although the relationship between COMP and PEU has not been demonstrated. In a similar way, AUTO (0.276, \(p<0.001\)) and REL (0.306, \(p<0.001\)) were significantly related to PU. Nevertheless, COMP is not a predictor of PU.  
Furthermore, PEU had a significant positive impact on PU (0.35, \(p<0.001\)). Finally, PU (0.742, \(p<0.001\)) and PEU (0.197, \(p<0.001\)) had a significant positive impact on BI. Considering these results, the percentages of variance explained by the research model for the dependent constructs were as follows: (1) 69.8% for BI, (2) 55% for PEU, and (3) 73.3% for PU.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 3 Methods
### Structure of the WFCNN Model
#### 3.1.2 Decoder  
The decoder is composed of five decoding units (D5, D4, D3, D2, and D1) that correspond to and decode the encoding units with the same number (Figure 4). The fusion units F4, F3, F2, and F1 are used to fuse the feature values obtained by the decoding unit. Other than F1, which only contains one pooling layer, the structure of the other units is the same. Each unit includes one up-sampling layer, several convolution layers, and one weight layer.  
The up-sampling layer is a deconvolution layer, which is used to restore the size of the feature graph. WFCNN adopts a gradual recovery strategy, in which the number of rows and columns are respectively doubled during each adjustment; the number of rows and columns of the feature graph are eventually restored to be consistent with the original image.  
The convolutional layer adjusts the feature values after up-sampling to ensure the consistency of the structure of the feature graphs involved in fusion. The adjustment strategy adopted by WFCNN reduces the depth of the feature graph (Table 3). The decoder finally generates a feature vector consisting of 64 elements for each pixel.  
Each weight layer contains only one convolution kernel of type \(1\times 1\times\mathrm{h}\), which is used to unify the feature values for stretching or narrowing transformation before fusion. The essence of the weight layer is to uniformly multiply a certain coefficient for a certain feature layer and convert it into a convolutional operation, to ensure that the model can be trained end-to-end.
# Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants
## 2 Related Work
### Machine-Learning-Based Plant Disease Detection  
Some experts confirmed the labeling to identify unhealthy from healthy guava fruits. Handcrafted features named local binary patterns (LBPs) are extracted and further reduced using principal component analysis (PCA). The multiple types of machine-learning (ML) classifiers are used, where a cubic support vector machine performed the best among the various methods in [24]. Edge- and threshold-based segmentation is performed for plant disease detection using images of leaves. Multiple features of color, texture, and shape are extracted, which are fed into a neural network classifier that classifies different plant diseases [25].  
Shadows are removed from the background by enhancing, resizing, and isolating the region of interest (ROI), with clustering performed by using K-means for pomergnate fruit disease detection in [26]. Guava disease detection was performed using basic color transformation functions in image processing to detect the actual diseased parts of plant leaves. Classification was performed using support vector machine (SVM) and K-nearest neighbor (KNN) in [27]. Apple disease detection was performed using the spot segmentation method, where feature extraction and fusion were also performed. The decorrelation method was used for the fusion of extracted features in [28]. A soil-based analysis to recognize the soil indicator that plays an important role in plant yield was also used in [29]. Similarly, the weather forecasting history could play an important role in plant monitoring systems to avoid any natural hazards, as in [30]. The hue-saturation-intensity (HSI) color space was initially used, where unhealthy areas were detected using textures. Multiple features were extracted after the color conversion of the data. Features such as homogeneity, energy, and other cluster-based features were extracted. Lastly, SVM was used for classification in [31].
## 4 Motor-Driven Pump Flowrate Control
### Mechanism  
Today, fuel metering system (MEE-FMS) in the more electric engine (MEE) has attracted great attention in the aeroengine. The MEE-FMS adopts motor-based FMS and reduces engine fuel consumption and CO\({}_{2}\) emissions. The MEE-FMS is an advanced FCS, which employs the latest innovations of motor and power electronic technology to replace the traditional engine accessories with electric motor driven pumps and electro-mechanical actuators.  
Figure 6 shows a schematic of one kind of MEE-FMS with metering valve, where the fuel flowrate is regulated by the opening and differential pressure of FMV, and the FADEC can collect \(\Delta p\) from the pressure-difference sensor and control \(\Delta p\) to track the desired pressure-difference by regulating the speed of the motor. The fuel flow feedback can compensate the performance degradation of fuel pump by increasing the pump speed.  
The schematic of a higher efficiency novel MEE-FMS without the metering valve is shown in Figure 7. The expression among outlet flowrate of fuel pump, outlet pressure of fuel pump, fuel pump speed, fuel temperature, and other physical quantities can be acquired through the off-line calibration method. Then, by comparing the flowrate by online calculation with desired flowrate, the output flowrate of fuel pump can track the desired value by regulating the motor speed. Due to the elimination of the throttling loss, the novel FCS is superior to other fuel control schemes in the ability to absorb external heat loads [22; 23]. Motor-based FMS, which consists of a motor-based pressure difference compensator and distributed metering valve, is the development tendency of FMS.
## 4 The Proposed DSB Workflow Scheduling Algorithm
### Proposed Algorithm
#### 4.4.3 Deadline Distribution  
The third step of the proposed algorithm is called deadline distribution. First, the cheapest VM type (\(m_{sel}\)) is determined among all the available VM instances such that, if assigned to all the workflow tasks, their estimated makespan (calculated by using Equation (10)) would be lower than the user-defined deadline (\(DL_{W}\)). If the estimated makespan exceeds deadline, then the next cheapest VM type (\(m_{sel}\)) is selected until the estimated execution time of the workflow on \(m_{sel}\) is lower than \(DL_{W}\). The elapsed time between the deadline and the estimated makespan, denoted as the available spare time \(AS_{W}\) is calculated according to Equation (11). \(AS_{W}\) is distributed proportionally over all levels of the workflow on the basis of runtime of tasks according to Equation (12). Then, \(AS_{lp}\) is distributed among all tasks of each level proportional to the number of tasks in the corresponding level according to Equation (13). Afterwards, an estimated sub-deadline \(SD_{i}\) is computed for each task of the workflow under the determined VM type \(m_{sel}\), by using Equation (14). This sub-deadline will be a guiding factor for taking decisions at runtime about whether to reuse existing VM or rent a new one. It is larger for the BoTs with longer running tasks. The sub-deadline of each BoT is assumed to be the maximum finish time of its tasks corresponding to \(m_{sel}\) (Equation (15)). In other words, the tasks within a BoT are assigned the same sub-deadline which would be equal to the estimated start time of the successor task. The algorithm for distributing deadlines to the BoTs is given in Algorithm 1.  
```
procedureDD(Workflow \(W\), Deadline \(DL_{W}\), Runtime of tasks on VMs \(RT_{i}^{r}\))
1:\(m_{sel}\leftarrow\) Find cheapest VM type such that \(MP_{W}<DL_{W}\)
2:if \(m_{sel}=null\)then
3:while\(m_{sel}\
eq null\)do
4:\(m_{sel}\leftarrow\) Next cheapest VM type such that \(MP_{W}<DL_{W}\)
5:endwhile
6:endif
7: Calculate available spare time of workflow \(AS_{W}\)//according to Equation (11)
8: Distribute \(AS_{W}\) proportionally over all levels //according to Equation (12)
9: Distribute \(AS_{W}\) proportionally over all tasks in each level \(lp\)//according to Equation (13)
10: Calculate estimated sub-deadline \(SD_{i}\) for each task //according to Equation (14)
11: Update sub-deadline of each BoT as the maximum finish time of its tasks
```  
**Algorithm 1** Deadline Distribution
# Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps
## 2 Materials and Methods
### Region Proposals  
The study recreates the Region Proposal Network (RPN) introduced in Faster R-CNN [15] to estimate the regions that are likely to contain polyps within an image. Feature extraction and RPN together act as a first stage that scans an image and generate proposals. Proposals are small bounding box regions that are likely to contain an object (polyp). The RPN scans the features obtained from FPN in a convolution fashion with a small sliding window.  
At each sliding window position, multiple region proposals (sets of boxes) were generated, which are called anchors; these have different sizes and different aspect ratios. Anchors help bind features to their raw location in the image. The RPN contains two separate fully connected layers to extract box probability scores (object or background) and bounding box deltas (box refinement). The targets of the RPN are ground-truth classes and ground-truth bounding boxes. Each anchor is assigned to a corresponding target by evaluating anchors based on intersection over union (IoU) values. The IoU values of the anchors are computed against the ground truth objects in the image. Positive anchors are the anchors whose IoU \(\geq\)0.7, anchors with IoU \(<\)0.7 and IoU \(\geq\)0.4 are considered neutral anchors, and anchors with IoU \(<\)0.4 are negative anchors, which do not cover any object. These values are empirically chosen. Neutral anchors are discarded and not used for training. Often, positive anchors do not cover objects completely, so the RPN performs regression with the bounding box deltas to shift and resize anchors according to the object location. Based on the RPN predictions, anchors are filtered according to their probability scores. The anchors that have a majority of their area overlapping with adjacent anchors are trimmed down to one anchor by choosing the one with the highest foreground score (non-maximum suppression). Finally, filtered proposals (RoI) are sent to the second stage.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
###### Abstract  
The multipath effect is one of the main error sources in the Global Satellite Navigation Systems (GNSSs). The code correlation reference waveform (CCRW) technique is an effective multipath mitigation algorithm for the binary phase shift keying (BPSK) signal. However, it encounters the false lock problem in code tracking, when applied to the binary offset carrier (BOC) signals. A least-squares approximation method of the CCRW design scheme is proposed, utilizing the truncated singular value decomposition method. This algorithm was performed for the BPSK signal, BOC(1,1) signal, BOC(2,1) signal, BOC(6,1) and BOC(7,1) signal. The approximation results of CCRWs were presented. Furthermore, the performances of the approximation results are analyzed in terms of the multipath error envelope and the tracking jitter. The results show that the proposed method can realize coherent and non-coherent CCRW discriminators without false lock points. Generally, there is performance degradation in the tracking jitter, if compared to the CCRW discriminator. However, the performance promotions in the multipath error envelope for the BOC(1,1) and BPSK signals makes the discriminator attractive, and it can be applied to high-order BOC signals.  
GNSS; multipath; BOC; least-squares; SVD; CCRW Article
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 2 Background and Related Work  
This section first describes the hierarchical quadtree partitioning structure employed in the HEVC encoding architecture followed by an elaboration of the state-of-the-art methods available in the literature that focus on reducing the encoding complexity.
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 3 System Model
### Sparse Jamming Estimation
#### 3.4.2 Bayesian Spare Jamming Estimation Algorithm
The MAP estimation of \(\mathbf{\mathrm{w}}\) can be calculated within the same iteration for determining \(\hat{\mathbf{\mathrm{e}}}\) in Equation (16):  
\[\hat{\mathbf{\mathrm{w}}}=\sigma^{2}\mathbf{\mathrm{A}}^{H}(\mathbf{\mathrm{A}}\mathbf{\Gamma} \mathbf{\mathrm{A}}^{H}+\sigma^{2}\mathbf{\mathrm{A}}\mathbf{\mathrm{A}}^{H})^{-1}\mathbf{ \mathrm{b}}. \tag{21}\]  
Note that Equations (1 proposed BSJE algorithm is equivalent to the SBL algorithm for the general measurement noise model. The BSJE algorithm for the proposed system is described as Algorithm 2. To guarantee the completeness of the algorithm, two parameters, \(t_{\text{max}}\) and \(\delta\), can be specified. \(t_{\text{max}}\) is defined by the maximum number of iterations and \(\delta\) is the lower bound on the minimum required update, \(\text{tr}(\left|\boldsymbol{\Gamma}^{(t)}-\boldsymbol{\Gamma}^{(t-1)}\right|)\), where \(\text{tr}()\) is the trace operator. These two parameters can be set according to the time budget. The BSJE algorithm does not require prior knowledge about jamming. The BSJE algorithm only requires the channel, codebook, and noise variance as inputs, which can be easily obtained by a legitimate receiver. This mild requirement on prior information is a potential benefit of BSJE, making it more attractive than GSJE in practical implementation. Furthermore, the BSJE algorithm has no model-mismatch problem in the arbitrary JNR condition region, whereas GSJE suffers from estimation performance degradation in the moderate-JNR region. ```
Input: H, G, \(\sigma^{2}\), \(t_{\text{max}}\), \(\delta\) Initialize: A \(\leftarrow\)\(\text{svd}(\mathbf{G}^{H}\mathbf{H}^{H})\), \(\mathbf{b}\leftarrow\mathbf{A}\mathbf{y}\), \(\boldsymbol{\Gamma}^{(0)}\leftarrow\mathbf{I}\)
1:for\(t=0\) to \(t_{\text{max}}\)do:
2: Compute \(\mathbf{T}\) matrix by \(\mathbf{T}\leftarrow\mathbf{A}^{H}(\mathbf{A}\boldsymbol{\Gamma}^{(t)} \mathbf{A}^{H}+\sigma^{2}\mathbf{A}\mathbf{A}^{H})^{-1}\)
3: Update the mean vector \(\boldsymbol{\mu}\leftarrow\boldsymbol{\Gamma}^{(t)}\mathbf{b}\)
4: Update the noise vector \(\mathbf{w}\leftarrow\sigma^{2}\mathbf{T}\mathbf{b}\)
5: Update the covariance matrix \(\mathbf{X}\leftarrow\boldsymbol{\Gamma}^{(0)}-\boldsymbol{\Gamma}^{(t)} \mathbf{T}\mathbf{A}\boldsymbol{\Gamma}^{(t)}\)
6: Update the hyperparameter \(\boldsymbol{\Gamma}^{(t+1)}\gets diag(\left|\mu_{k}\middle|_{2}+\Sigma_{kk} )\right|_{k=1,2,\ldots,N}\)
7:while\(t\leq t_{\text{max}}\) and \(\left.tr(\left|\boldsymbol{\Gamma}^{(t)}-\boldsymbol{\Gamma}^{(t-1)}\right|) \geq\delta\)return:\(\widetilde{\boldsymbol{\epsilon}}\leftarrow\boldsymbol{\mu}+ \mathbf{w}\)
```  
**Algorithm 2** Bayesian sparse jamming estimation (BSJE) algorithm
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 4 Building a QKD Emulation System to Test Protocols
### System Setup
#### 4.4.3 Prototyping Setup (Test Bench)  
To facilitate debugging and analysis, we simplified our prototype for this initial phase, reducing the system to one computer that acts as both [PERSON] and [PERSON]. This single computer has both the image of the PUF and a physical connection to the PUF. It sends a stream into the quantum channel and receives the output on the other end. By configuring our testing environment in this manner, we effectively reduce the sources of error and simultaneously allow for direct comparison of input and outputs; thus permitting a clear breakdown of the error introduced into the system, and the parts of the system responsible. Changing code is better supported and components are more easily isolated for deeper analysis.
## 4 The Analysis of PTBO and a Comparative Study  
### The Analysis of Time Complexity  
For PTBO, the main operations include the operation of population initialization and the stochastic operator, shrinkage operator, and vibration operator. The time complexity of each operation in a single iteration can be computed as follows:  
1 Population initialization operation: \(O(N*D)\).  
2 Stochastic operator: \(O(N*D)\).  
3 Shrinkage operator: \(O(N*D)\).  
4 Vibration operator: \(O(N*D)\).  
Therefore, the total worst time complexity of PTBO in one iteration is \(3*O(N*D)+O(N)\). According to the operational rules of the symbol O, the worst time complexity of one iteration for PTBO can be simplified as \(O(N*D)\). It is worth noting that PTBO has the similar time complexity to some popular meta-heuristic algorithms such as PSO (\(O(N*D)\)).
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 3 Analysis of ATM Big Data Sharing
### Foundation of Aviation Big Data Sharing
#### 3.2.2 Aviation Big-Data-Sharing Trends and Characteristics  
Under the impetus of digital transformation in the aviation industry, ATM data sharing exhibits three major trends [30].  
1. Exponential growth in data exchange volume.  
The frequency of daily data interactions shows exponential growth, with ATM data evolving from \"limited data flows between fixed stakeholders\" to \"massive data-sharing exchanges reaching tens of millions within specific timeframes.\"
2. Normalization of cross-domain sharing.  
Data flow boundaries are breaking through traditional security perimeters, transitioning from \"ATM data being used only within fixed, limited security domains\" to \"ATM data being utilized across different security domains.\" This aligns with the \"Internet of Aviation\" concept proposed in ICAO's Global Air Navigation Plan, which emphasizes network interconnectivity among all aviation nodes (aircraft, ANSPs, ground facilities) and achieves cross-domain information integration through large-scale data sharing across Flight Information Region (FIR) boundaries [31].
3. Diversification of participants.  
With third-party analysis institutions and research organizations emerging as new data users, the sharing participants are evolving from \"data accessors being solely operational executors\" to \"including third-party analysts and researchers.\"