esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analysis
### Impact of Using Existing 4G Infrastructure
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**5G Greenfield**} & \multicolumn{2}{c}{**5G with 4G Sites**} \\ \hline Components & Unit Cost & Quantity & Total Cost & Quantity & Total Cost \\ \hline Urban & & & & \\ \hline Civil work (m) & 20 & 14,800 & 296,000 & 9800 & 196,000 \\ \hline Fiber length (m) & 1 & 16,280 & 16,280 & 10,780 & 10,780 \\ \hline Sub Total & & & 312,280 & & 206,780 \\ \hline Microwave sites & 4000 & 74 & 296,000 & 49 & 196,000 \\ \hline Total & & & 608,280 & & 402,780 \\ \hline Final requirement (After CA) & & & & \\ \hline Civil work (m) & 20 & 13,200 & 264,000 & 8200 & 164,000 \\ \hline Fiber length (m) & 1 & 14,520 & 14,520 & 9020 & 9020 \\ \hline Sub Total & & & 278,520 & & 173,020 \\ \hline Microwave sites & 4000 & 66 & 264,000 & 41 & 164,000 \\ \hline Total & & & 542,520 & & 337,020 \\ \hline Sub-Urban & & & & \\ \hline Civil work (m) & 20 & 17,500 & 350,000 & 8000 & 160,000 \\ \hline Fiber length (m) & 1 & 18,550 & 18,550 & 8480 & 8480 \\ \hline Sub Total & & & 368,550 & & 168,440 \\ \hline Microwave sites & 4000 & 35 & 140,000 & 16 & 64,000 \\ \hline Total & & & 508,550 & & 232,480 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cost Estimation (All prices are in US dollars).
## 3 Discussion
### Antibacterial Activity  
The honey samples were also tested against various microorganisms, and two Gram-positive bacterial strains, _E. faecalis_ and _B. subtilis_, were susceptible. (Table 3). The antibacterial activity of honey produced by _Melipona_ species has previously been reported in the literature, with activity against _E. faecalis_, the CIM observed in honey from _M. grandis_ (62.50 \(\mathrm{\SIUnitSymbolMicro mL}\)), _M. flavolinenta_ (31.20 \(\mathrm{\SIUnitSymbolMicro g}\)/mL), and _M. seminigra_ (25 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) [32].  
This same bacterial strain (_E. faecalis_) was also found to be susceptible to honey produced by _M. baccaerri_[55], as well as by _M. seminigra_ (55 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) and _M. compressipes_ (75 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) [56]. Strains of _B. subtilis_ have also shown susceptibility to honey produced by other stingless bee species from the genera _Hetetrotrigona_, _Tetrigona_, and _Lepidorigona_[57].  
Honey presents a complex combination of physical, chemical, and biological factors that, acting synergistically, confer its natural antimicrobial properties. Among these factors are low water activity, high osmotic pressure, acidic pH, a glucose oxidase system with consequent hydrogen peroxide production, a high carbon-to-nitrogen ratio, low redox potential associated with elevated levels of reducing sugars, low protein content, as well as the presence of phytochemical components and volatile substances [58; 59].  
These variables, collectively, may be responsible for the antibacterial effects observed in the analyzed honey samples. Previous studies have demonstrated that secondary metabolites, such as phenols, flavonoids, and organic acids, along with the physicochemical factors of honey, play a relevant role in its antimicrobial activity [60].  
Therefore, it is suggested that the antibacterial action of the evaluated honey results from the complex interaction of various bioactive compounds.
## 6 Conclusions  
This paper proposes a classification-driven differentiated jamming suppression method to address the diverse interference challenges encountered in high-precision GNSS positioning. By dynamically selecting the optimal suppression strategy--either through parameterized suppressors or an open-set conservative mode--based on the matching between interference type and suppression approach, the proposed method effectively overcomes the performance limitations of traditional generic suppression techniques in multi-interference scenarios. The experimental results demonstrate that the proposed approach achieves significant performance gains under various interference types, including  
Figure 13: PLI and FLI after processing by various interference suppression methods.  
narrowband, chirp, and pulse jamming. Moreover, it successfully balances suppression effectiveness and signal fidelity in high-JSR environments, achieving a comprehensive optimization of both interference mitigation and signal quality. Future research may further focus on improving the precision of spoofing interference detection and enhancing the adaptability of suppression modules in order to strengthen the system's robustness in increasingly complex environments.  
Conceptualization, Z.D. and Z.Z.; methodology, Z.Z.; software, Z.Z.; validation, P.L., Z.Z. and X.G.; formal analysis, Z.Z.; investigation, X.G.; resources, Z.Z.; data curation, Z.Z.; writing--original draft preparation, P.L.; writing--review and editing, Z.D.; visualization, X.G.; supervision, P.L.; project administration, Z.Z.; funding acquisition, Z.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (grant number: 2022 YFB3904702).  
Not applicable.  
The data which support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 1 Introduction
### Investigation of the Generalized Nonlinear Kinetic Equation of Ion Relaxation
 \tag{14}\]  
Applying the finite difference method to (14) yields  
\[\frac{n_{\mathrm{i}-1}\mathrm{E}_{\mathrm{i}-1}^{2l}+n_{\mathrm{i}+1}\mathrm{E} _{\mathrm{i}+1}^{2l}-2n_{\mathrm{i}}\mathrm{E}_{\mathrm{i}}^{2l}}{a^{2}}\to \frac{\partial^{2}}{\partial\mathrm{x}^{2}}\left(n_{\mathrm{i}}\mathrm{E}_{ \mathrm{i}}^{2l}\right),\frac{n_{\mathrm{i}-1}\mathrm{E}_{\mathrm{i}-1}^{2l+1}-n _{\mathrm{i}+1}\mathrm{E}_{\mathrm{i}+1}^{2l+1}}{a}\to-2\frac{\partial}{ \partial\mathrm{x}}\left(n_{\mathrm{i}}\mathrm{E}_{\mathrm{i}}^{2l+1}\right) \tag{15}\]  
Thus, we have \[\frac{\partial n_{\rm i}}{\partial t}=a^{2}\frac{\partial^{2}}{\partial x^{2}} \left[\sum_{l=0}^{\infty}\frac{1}{(2l)!}\cdot\left(\frac{\mathrm{q}a}{2\mathrm{k _{B}T}}\right)^{2l}W^{(2l)}\left(n_{\rm i}\mathrm{E}_{\rm i}^{2l}\right)\right] -\frac{\partial}{\partial x}\left[\sum_{l=0}^{\infty}\frac{2}{(2l{+}1)!}\cdot \left(\frac{\mathrm{q}a}{2\mathrm{k_{B}T}}\right)^{2l+1}W^{(2l+1)}\left(n_{ \rm i}\mathrm{E}_{\rm i}^{2l+1}\right)\right]\]  
Using the identities  
\[\Omega_{\rm i}=\frac{W_{{\rm i}j+1}^{(-)}+W_{{\rm i}j-1}^{(+)}}{2}=\sum_{l=0}^ {\infty}\frac{1}{(2l)!}\cdot\left(\frac{\mathrm{q}a\mathrm{E}_{\rm i}}{2 \mathrm{k_{B}T}}\right)^{2l}\cdot W^{(2l)},\]  
\[\Xi_{\rm i}=W_{{\rm i}j+1}^{(-)}-W_{{\rm i}j-1}^{(+)}=2{\sum_{l=0}^{\infty}} \frac{1}{(2l{+}1)!}\cdot\left(\frac{\mathrm{q}a\mathrm{E}_{\rm i}}{2\mathrm{ k_{B}T}}\right)^{2l+1}\cdot W^{(2l+1)}\]  
Yields  
\[\frac{\partial n_{\rm i}}{\partial t}=a^{2}\frac{\partial^{2}}{\partial x^{2 }}\left(\frac{W_{{\rm i}j+1}^{(-)}+W_{{\rm i}j-1}^{(+)}}{2}.n_{\rm i}\right)- a\frac{\partial}{\partial x}\left(\left(W_{{\rm i}j+1}^{(-)}-W_{{\rm i}j-1}^{(+)} \right)\cdot n_{\rm i}\right). \tag{16}\]  
Omitting the index \"\(i\)\" in (16), we move on to the generalized nonlinear by field \(\mathrm{E}(x;t)\) kinetic equation:  
\[\frac{\partial n}{\partial t}=\frac{\partial^{2}}{\partial x^{2}}\left( \mathrm{D}_{diff}(x;t)\cdot n(x;t)\right)-\frac{\partial}{\partial x}(\mathrm{ v}_{\rm mob}(x;t)\cdot n(x;t)). \tag{17}\]  
In (17), the following designations are accepted:  
\[\mathrm{D}_{diff}(x;t)=a^{2}\Omega(x;t),\;\mathrm{v}_{\rm mob}(x;t)=a\Xi(x;t), \tag{18}\]  
\[W^{(\pm)}(x;t)=W^{(0)}+\sum_{l=1}^{\infty}\frac{(\pm 1)^{l}}{l!}\cdot\left( \frac{|\Delta\mathrm{U}(x;t)|}{\mathrm{k_{B}T}}\right)^{l}\cdot W^{(l)} \tag{19}\]  
In (19), \(|\Delta\mathrm{U}(x;t)|=\frac{a\Xi(x;t)a}{2}\) denotes an increment in the potential energy of the proton due to the electric field \(\mathrm{E}(x;t)\) when it passes through the potential barrier under the condition  
\[|\zeta(x;t)|=\frac{|\Delta\mathrm{U}(x;t)|}{\mathrm{k_{B}T}}{<1}.\]  
In (18), \(\Omega(x;t)=\frac{W^{(-)}(x;t)+W^{(+)}(x;t)}{2}\), \(\Xi(x;t)=W^{(-)}(x;t)-W^{(+)}(x;t)\).
# A Mathematical Model for Reduction of Trim Loss in Cutting Reels at a Make-to-Order Paper Mill
## 4 Solution Procedure
### Integer Solution  
The optimal solution of the linear programming model contains four non-integer decision variables. By considering its pre-decimal parts (12, 07, 15, and 12), the shortages in each order are calculated and are given in Table 5. The objective function is simply minimization of the summation of non-integer decision variables. To make the decision variables integer, a separate algorithm has also been explained in Figure 1 of Section 3.  
The model is solved for binary values of the decision variables. The resulting values are then added with the pre-decimal parts of the corresponding decision variables to get the final integer solution, as can be seen in Table 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Decision Variable** & **Non-Integer** & **Pre-Decimal** & **Binary** & **Integer** \\ \hline \(X_{60,035}\) & 12.50 & 12 & 1 & 13 \\ \(X_{68,071}\) & 07.25 & 07 & 0 & 07 \\ \(X_{67,079}\) & 16.00 \({}^{\star}\) & — & — & 16 \\ \(X_{61,202}\) & 07.00 \({}^{\star}\) & — & — & 07 \\ \(X_{61,214}\) & 15.50 & 15 & 1 & 16 \\ \(X_{60,229}\) & 12.50 & 12 & 1 & 13 \\ \(X_{30,243}\) & 10.00 \({}^{\star}\) & — & — & 10 \\ \(X_{25,246}\) & 08.00 \({}^{\star}\) & — & — & 08 \\ \hline \hline \end{tabular}  
* already integer.  
\end{table}
Table 6: Final optimal solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pattern Number** & **Width (in)** & **Cutting Pattern** & **Number of Reels** \\ \hline
035 & 69 & \(0\times 17+2\times 21+0\times 25+1\times 27+0\times 30+0\times 34\) & 12.50 \\
071 & 68 & \(0\times 17+0\times 21+0\times 25+0\times 27+0\times 30+2\times 34\) & 07.25 \\
079 & 67 & \(1\times 17+0\times 21+2\times 25+0\times 27+0\times 30+0\times 34\) & 16.00 \\
202 & 61 & \(2\times 17+0\times 21+0\times 25+1\times 27+0\times 30+0\times 34\) & 07.00 \\
214 & 71 & \(0\times 17+0\times 21+0\times 25+1\times 27+0\times 30+1\times 34\) & 15.50 \\
229 & 60 & \(0\times 17+0\times 21+0\times 25+0\times 27+2\times 30+0\times 34\) & 12.50 \\
243 & 30 & \(0\times 17+0\times 21+0\times 25+0\times 27+1\times 30+0\times 34\) & 10.00 \\
246 & 25 & \(0\times 17+0\times 21+1\times 25+0\times 27+0\times 30+0\times 34\) & 08.00 \\ \hline \hline \end{tabular}  
* already integer.  
\end{table}
Table 4: Non-integer optimal solution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(X_{69,035}\) & \(X_{68,071}\) & \(X_{61,214}\) & \(X_{60,229}\) & **Shortages** & **Excesses** \\ \hline
0 & 2 & 1 & 0 & 1 & 0 \\ \hline \hline \end{tabular}  
* \end{table}
Table 5: Non-integer decision variables.  
The final solution leads to over-production of one reel of each width 21, 27, and 30 inches, which may be stocked for later usage or negotiated with the client. The optimal objective function value is 5128.  
While cutting the pattern 229 thirteen times, 26 new reels of width 30 inches are produced. Thus, only 9 stocked reels of width 30 inches are picked to fill the order. By doing this, the objective function value reduces to 5098.
## 4 Results
### Performance of Vital Parameters  
In order to assess the value of each distinct vital parameter (i.e., HRV, RF, SpO2, and PFI) for predicting LOS, we trained each model on each vital parameter separately. In Figure 8, we present the AUROC test scores for all of the training windows (i.e., \(t_{i}=-48\) to \(t_{i}=-6\)) for each feature set. As expected, and in line with the literature, the HRV features performed best. However, we also found that the ensemble models (i.e., RandF, ADA, and GB) yielded acceptable results for the feature sets of the parameters SpO2 and RF. In particular, using a training interval from around \(t_{i}=-48\), we found that the models using features from SpO2 outperformed the RF features. On the other hand, we found that while all separate feature sets performed worst around \(t_{i}=-6\), RF performed best around this time interval. Thus, this is an indication that the vital parameters of respiratory (RF) are best used shortly before \(t_{0}\).  
All in all, when training the model with HRV features and adding the feature sets from RF or SpO2, it was observed that most models improved most when adding RF (see Table 12 and Figure 13 in the Appendix A). Interestingly, however, it was also observed that the GB model was most improved by adding features from SpO2. Thus, except for the GB model, we found that the models improved most when adding features from RF. Nevertheless, it is also important to mention that our best performing model (RandF) performed very well with the HRV feature alone. Lastly, it was observed that features from PFI added the least value to the model (see Table 12).
# Spatio-Temporal Behavior Detection in Field Manual Labor Based on Improved SlowFast Architecture
## 3 Dataset and Methods
### Improvements to SlowFast Network Structure
#### 3.2.3 Loss Function  
This paper employs the Binary Cross-Entropy Loss (BCE Loss). Different categories of physical labor behaviors in the field are not mutually exclusive; instead, they can occur simultaneously. Therefore, normalizing the output using Softmax to probability values between [0, 1] (summing to 1) is not applicable. In the introduced multi-label classification task, the network's output data are fed into the Sigmoid function to scale the values between [0, 1]. Subsequently, they are treated as multiple binary classification problems. The Sigmoid function--as represented by Formula (21), where y denotes the output probability, \(z\) is the input, and BCE Loss, as per Formula (22)--calculates the loss for each category and obtains the total loss through summation. Here, \(t\) represents the true label values, \(w\) denotes the weight, and \(n\) is the sample count.  
\[y=\frac{1}{1+e^{-z}} \tag{21}\]  
\[L(y,t)=-\frac{1}{n}\sum w\times(t\log(y)+(1-t)\times\log(1-y)) \tag{22}\]
# RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression
## 3 The Proposed Methods
### Entropy Model  
Compression encoding requires the encodable information to be discrete, corresponding to a discrete density model. Most neural networks utilize continuous models for image modeling, wherein data are first quantized inversely to learn a continuous model. Encoding with such a model involves quantizing the variables first and discretizing the learned model, which is a complex process. However, we directly model discrete pixel values; each pixel is an 8-bit integer \(x\in\{0,\...,\ 255\}\), and the pixel density model becomes a 256-way categorical distribution. For example, when we input \(N\times N\) image data into the model and process it through a series of network layers, we obtain the parameters of pixel density distribution with a final size of \(N\times N\times 1\times 256\). The last dimension indicates  
Figure 4: Residual-causal-attention block.  
the probability of the 256 possible values for each pixel. Therefore, a softmax operation is applied to the prediction features.  
\[soft\text{max}(z_{i})=\frac{\exp(z_{i})}{\sum\limits_{j=0}^{N}\exp(z_{j})} \tag{4}\]  
Normalizing the last dimension of the model's output ensures that the sum of the probability distribution is equal to 1, \(\sum_{i=0}^{255}p(z_{i})=1\), guaranteeing that the model's output in this dimension represents a valid probability distribution.  
The model used for data compression is denoted as \(q\), while the actual distribution of the data is represented by \(p\). The training objective is to minimize the distance between the model \(q\) and the data distribution \(p\), which can be expressed as  
\[\begin{array}{ll}D_{KL}(q(x)&||p(x))=p(x)(\log q(x)-\log p(x))\\ &=p(x)\log q(x)-p\log p(x)\\ &=H(q,p)-H(p)\end{array} \tag{5}\]  
So, the cross-entropy is given by  
\[H(q,p)=H(p)+D_{KL}(\text{q}||p) \tag{6}\]  
where \(H(p)\) represents the entropy of the data, and when the data are given, it remains constant. Therefore, minimizing the KL divergence is equivalent to minimizing the cross-entropy. Cross-entropy \(H(q,p)\) represents the amount of information required to encode data using the model and is equal to the codelength. Thus, we use cross-entropy as the training objective for the compression model. In the encoding process, the autoregressive model estimates data density, and this estimation can be computed in parallel all at once. In the decoding process, on the other hand, pixel probabilities are estimated step by step during decoding. As a result, autoregressive algorithms compress data quickly, and while decompression is slower, they offer good compression performance.
## 4 Privacy-Preserving Optimization Based on Advanced CH Selection (P2O-ACH)
### Data Routing in Clustering  
At the initial stage, the network is subdivided into several clusters that are headed by the CH. CH will collect the data packets from the CM nodes and transfer that to the SINK \(S_{node}\). To reduce the delay and energy consumption, the data are fused and the volume is reduced. The core concept of clustering is data routing and data collection, and then  
Figure 1: Architecture of the Proposed P2O-ACH Method.  
forwarding that to that \(S_{node}\). For the process of data routing, we are using a model called anchor-based routing. N number of anchors \(\gamma\) can be used for experimentation. In our research simulation, we used \(\gamma=3\). In the first segment, the \(S_{node}\) chooses the nearest node as an anchor and that is represented as \(AR_{1}\), Then, \(S_{node}\) transmits the _DiscoverNearestNode_ message to all the nodes in its coverage area. The nodes which are in that coverage area will receive the message and transmit an acknowledgement message with its ID and the residual energy at each instance of time. According to the signal strength, the best node is chosen by the \(S_{node}\). The \(S_{node}\) transmits the _SINKRequest_ message (\(S_{node}\), \(AR_{1},E,\delta_{th}\)); here, E is represented as the event and the \(\delta_{th}\) is represented as the threshold value of the event.  
\(AR_{1}\) transmits the _EVENTRequest_ message (\(AR_{1},E,\delta_{th},H=0\)) in a flooding manner. The node which receives the transmitted message is represented as \(N_{j}\) for the first time and then the hop count is incremented by 1. That node that transmits the message is denoted as the parent node P. Then, currently the packet format of the _EVENTRequest_ message becomes (\(N_{j},E,\delta_{th},H\)). Finally, the flow of the process becomes: the CM node transmits the event report to CH, from CH to \(AR_{1}\) along with P and \(AR_{1}\) to the \(S_{node}\) only in case the \(S_{node}\) is in the coverage area of the \(AR_{1}\). If the \(S_{node}\) is not in the coverage area, then the new anchor node \(AR_{2}\) is selected. Then, the new anchor request message will be transmitted by the \(S_{node}\)_LatestAnchorRequest_ (\(S_{node}\), \(AR_{1}\)). The node that receives the message becomes \(N_{j}\), and transmits the latest message to the next hop called (\(S_{node}\), \(AR_{1}\), \(N_{j}\)). If this process is successful, then the node \(N_{j}\) becomes the next anchor with the reply message _LatestAnchorAck_ (\(S_{node}\), \(AR_{1}\), \(N_{j}\)). Finally, the flow of the path becomes (\(node\to CH\to AR_{1}\to AR_{2}\to S_{node}\)). The same process will be continued to \(AR_{3}\), if required.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 3 Process Integration of New Transistor Architecture
### Additional Sources of Variation  
Variability control is more critical and becomes increasingly challenging for FinFETs [90; 91]. FinFET's electrical variation is very sensitive to fin width and verticality variations. Fin height variation comes from fin etching, STI deposition, STI CMP, and STI recessing.  
In general, gate etching profile and \(Lg\) variation over fin topography are difficult to control. S/D epitaxy is a sensitive process over fin topography [92] and the resistance and stress fluctuations may be resulted if the fin shape is affected. Furthermore, in \(I/I\) process, the defected layers would correspond to another source of variability [91]. In a fin with channel doping concentration of 6 \(\times\) 10\({}^{17}\) cm\({}^{-3}\), about a third of the variability comes from random dopant fluctuation (RDF). While channel doping can be avoided in FinFETs, RDF from S/D doping gradient appears when \(Lg\) < 10 nm [33; 93]. Channel interface and gate stack work function variations have remarkable negative effect on the transistor performance. Double patterning raises concerns about the way in which individual polygons are split across two masks. As overlay is not scaling as fast as minimum feature size, mask-alignment issues introduce a new source of variability in the spacing between polygons [27].
# Application of Fly Ash as an Adsorbent for Removal of Air and Water Pollutants
## 3 Physical and Chemical Properties of Fly Ash
### Chemical and Mineralogical Composition
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Heavy Metals** & **Unit** & **Fly ash 1** & **Fly ash 2** & **Fly ash 3** & **Fly ash 4** & **Fly ash 5** & **Fly ash 6** & **Fly ash 7** \\ \hline Fe & mg/kg & 4.2 & 3.2 & 4 & 2.8 & 4 & 4.6 & 3.4 \\ Ca & mg/kg & 67,040 & 55,360 & 58,400 & 48,320 & 78,720 & 85,120 & 74,880 \\ Zn & mg/kg & 2 & 1.6 & 1.8 & 1.6 & 2 & 2 & 2 \\ Cd & mg/kg & 2.8 & 2.2 & 2.4 & 2 & 2.8 & 2.8 & 3 \\ Co & mg/kg & 3.2 & 2.8 & 2.2 & 2 & 2 & 2.6 & 2.4 \\ Ni & mg/kg & 4 & 3.6 & 4.8 & 4 & 3.4 & 3.4 & 5.6 \\ Cu & mg/kg & 2.8 & 2.6 & 2.6 & 2.6 & 3.2 & 3.2 & 3.4 \\ Pb & mg/kg & 11 & 10 & 11 & 10.4 & 14.2 & 15 & 14.4 \\ Mn & mg/kg & 1.8 & 1.6 & 1.6 & 1.4 & 1.6 & 1.8 \\ \hline \end{tabular}
\begin{tabular}{c} Fly ash 1: >212 \(\mu\)m; Fly ash 2: 150–212 \(\mu\)m; Fly ash 3: 106–150 \(\mu\)m; Fly ash 4: 75–106 \(\mu\)m; Fly ash 5: 53–75 \(\mu\)m; Fly ash 6: 38–53 \(\mu\)m; Fly ash 7: <38 \(\mu\)m. \\ \end{tabular}
\end{table} 
Table 3: The concentration of heavy metals in Yenikoy Toxicity Characteristic Leaching Procedure (TCLP) leachates (adapted with permission from [71], Elsevier, 2018).