esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 3 The Proposed Model
### Tumor Detection  
Figure 1 illustrates the critical component of our research, the generator network, which employs a Mask R-CNN framework. Moving away from the traditional approach of GANs that typically use random noise to create synthetic outputs, our model adopts a more strategic method. Specifically, it utilizes detailed lung data obtained from an earlier phase as its input rather than relying on random noise. This strategic choice of input allows our generator to capitalize on the comprehensive information within the lung data to craft precise and relevant outputs. The generator's main objective is to deliver outputs that include detailed classification and location data. By processing the lung data, the generator can discern different regions within the lungs, assign appropriate class labels to these regions, and accurately determine their locations with bounding box coordinates. To steer the training of the generator and enhance its output accuracy, we introduce a specialized loss function, noted as Equation (4). This loss function evaluates the difference between the generator's outputs and the actual verified data. By employing this loss function, our framework methodically improves the generator's proficiency in producing precise classification and location details for the lung data it processes.  
\[L_{G}=L_{cls}+L_{box}+L_{adv}^{\cG_{b}} \tag{4}\]  
In this context, \(L_{cls}\) and \(L_{box}\) are the loss components used within the Mask R-CNN framework, targeting the accuracy of class predictions and the precision of bounding box placements, respectively. Equation (5) introduces the adversarial loss component \(L_{adv}^{\cG_{b}}\), which is instrumental in refining the performance of the generator.  
\[L_{adv}^{\cG_{b}}=\frac{1}{N}\Sigma_{i=1}^{N}-\log D_{b}(G_{b}(RoI_{i})) \tag{5}\]  
Here, \(N\) is the mini-batch size, and \(G_{b}(RoI_{i})\) represents the \(i\)-th bounding box prediction. The discriminator is critical in assessing the quality of bounding boxes proposed by the generator. However, evaluating the boxes based solely on their coordinates may not provide a thorough assessment. To overcome this, a method is suggested where feature maps for both authentic and generated boxes are presented together, allowing for a more nuanced evaluation of the bounding box quality. The discriminator's loss function, as laid out in Equation (6), is crucial for its training. It integrates three Convolutional Neural Networks (CNNs) that collaborate to enhance the discriminator's ability to differentiate between authentic and fabricated bounding boxes, thereby aiding overall training efficacy.  
\[L_{D}=\frac{1}{N}\Sigma_{i=1}^{N}-[\log(D_{b}(bb_{i}^{St}))+\log D_{b}(1-D_{b}( G_{b}(RoI_{i})))] \tag{6}\]  
In this formulation, \(G_{b}(RoI_{i})\) denotes the actual parameters for the \(i\)-th bounding box, while \(D_{b}\) indicates the likelihood that the image is authentic. The adversarial loss \(L_{adv}^{\cG_{b}}\) incentivizes \(G_{b}\) to create bounding boxes convincing enough to mislead \(D_{b}\).
## 2 Materials and Methods  
### Overview of the Onion Transplanter Prototype under Development  
A 3-D model of the 12-kilowatt self-propelled riding-type automatic onion transplanter is shown in Figure 1a. It consists of two major parts, the operator-driven four-wheel vehicle and onion transplanter. The physical dimensions of the onion transplanter are summarized in Table 1. The onion transplanting process is divided into three major steps (mechanisms): seedling picking (extraction mechanism), seedling supplying (conveyor mechanism), and seedling planting (transplanting mechanism). At first, onion cell trays were placed into the seedling trays. The onion seedlings are extracted from the growing cell tray using the pushing rods and placed in the conveyor. Under the conveyor mechanism, seedlings were conveyed into the planting hopper, and seedlings were transplanted in six rows through three separated units of seedling planting mechanisms. All these units are synchronized together in such a way that each unit can supply 12 seedlings per cycle to the conveyor mechanism. Contrarily, each conveyor mechanism receives 6 seedlings per cycle, which are fed into the hopper and transplanted in the soil bed through the rotary planting mechanism. Twelve axillary pressing wheels (two for each row) press the soil finally to maintain the proper uprightness of seedlings and low damage to the mulch film. As momentum wheel, rotating devices, and powertrain devices are considered as the main sources of vibration, these devices along with the mounted and thin plate parts would be highly affected and damaged by vibration [16; 42; 43].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Specification of Four-Wheel Vehicle**} & \multicolumn{2}{c}{**Specification of Onion Transplanter**} \\ \hline Length, width, height (mm) & 2602, 1716, 1648 & Length, width, height (mm) & 875, 1626, 2036 \\ Ground clearance (mm) & 363 & Ground clearance (mm) & 260 \\ Wheelbase (mm) & 1196 & Number of rows & 6 \\ Wheel track (mm) & 1200 & Row spacing (mm) & 200 \\ Front wheel radius (mm) & 650 & Hill spacing (mm) & 174 \\ Rear wheel radius (mm) & 950 & Power (rpm) & 74 \\ Transmission level & HST & Transplanting speed (ms\({}^{-1}\)) & 0.24 \\ Maximum power (kW/rpm) & 16.2/3400 & Transplanting mechanism & Mechanical \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the 12-kilowatt self-propelled riding-type automatic onion transplanter under development.
## 3 Results and Discussion
### In Vitro Drug Release Test  
In vitro drug release tests were carried out to investigate the releasing properties of levodopa from the scaffolds for 336 h (14 days). The test was performed in the phosphate-buffered saline (PBS) (pH 7.4, 37 \({}^{\circ}\)C) to simulate the physiological conditions of living organisms. Firstly, the linear standard calibration curve for levodopa was constructed by UV spectroscopy. Then, the EE of the scaffolds that were loaded with levodopa at different concentrations (0.01, 0.03, and 0.05 mg/mL) was measured to analyze the drug amount loaded in scaffolds. They were found to be 87.63, 89.77, and 91.56%, respectively.  
All these scaffolds exhibited a sustained release for 14 days. Levodopa was released at a final cumulative release of 41.23 %, 36.90 %, and 27.95 % from scaffolds, where it was present in concentrations of 0.01, 0.03, and 0.05 mg/mL at the end of the 1 st day, respectively. While levodopa was released over 50% on the 3 rd day from scaffolds with 0.01 and 0.03 mg/mL concentrations, levodopa release reached over 50% on the 6 th day for the scaffold with 0.05 mg/mL. These results proved that the drug release behavior was decelerated by increasing the drug concentration in the scaffolds [47] (Figure 5).
## 3 Data and Methods
### Calculation of Fault Slip Rate and Locking Depth  
Slip rate and locking depth are important parameters for quantitatively studying the characteristics of fault movement and deformation, as well as the regional seismic hazard. During interseismic periods, fault zones are seismically locked above a certain depth. Below this locking depth, the fault zone is characterized by continuous sliding motion. The two-dimensional elastic dislocation model method [61; 62; 63] can effectively connect the velocity of crustal deformation during the interseismic period with the long-term slip rate of the fault and is widely used for quantitative analysis of the slip rate and locking depth of the fault. Based on the elastic dislocation model [61; 62; 63], the Bayesian Monte Carlo inversion method is used to calculate the slip rate and locking depth of 20 sections, each about 200 km long and 10 km wide, on the XSHFZ (the fault distribution is shown in Figure 1) to obtain  
Figure 3: Mean line-of-sight (LOS) velocities derived using the Small Baseline Subset (SBAS) technique for (**a**) the T26 ascending track and (**b**) the T135 descending track [22]. (**c**) The GNSS velocities with respect to the Eurasia plate during the period of 1991–2016 [21] and the vertical velocities were derived from the leveling data using a Helmert joint adjustment method [57].  
the spatial distribution characteristics of the slip rate and locking depth of the XSHFZ. In this method, the strike-slip component parallel to the fault is fitted using formula (1), while the slip rate perpendicular to the fault and the relative vertical slip rate on both sides of the fault are obtained by taking the average of the slip rates on both sides of the fault and then differentiating [60; 64; 65].  
\[V_{para}=\frac{s}{\pi}\tan^{-1}\left(\frac{x}{d}\right)+a \tag{1}\]  
where \(V_{para}\) is the rate component of the parallel fault of the surface site, \(a\) is the migration constant, \(s\) is the long-term strike-slip rate of the fault zone, \(d\) is the locking depth of the fault, and \(x\) is the distance from the site to the fault.
## 3 Results and Discussion
### Sanitary Safety of TPW  
Although raw materials used in food production are required to comply with established MRLs for contaminants, it remains essential to monitor food processing residues for contaminant levels as well.  
In this aspect, TPW was analyzed for various contaminants--mycotoxins (patulin, ochratoxin, aflat); heavy metals (Pb, Cd, As); and perchlorate and obtained values were compared with MRLs set by Commission regulation [24] for different food categories, including fruits and vegetables, which are directly relevant to tomato-derived materials (Table 1). The amount of pesticide residues is presented in Table S1 and the obtained values were compared with MRLs for tomatoes set by the European Pesticide Database [46].  
The investigation of contaminant residues in TPW is crucial due to the high prevalence and persistence of pesticide residues in tomatoes and their derivatives. Despite industrial processing steps like washing, peeling, and thermal treatment, some pesticide residues remain detectable in processed tomato products such as extracts, sauces, and ketchups, albeit often below regulatory MRLs [47]. Studies have demonstrated that worldwide, a significant proportion of tomato samples contain multiple pesticide residues, with over 60% of samples exceeding the current European Union MRLs for pesticides such as acetamipid, chlorpyrifos, lambda-cyhalothrin, and DDT [48]. In tomatoes grown in the EU, the situation is significantly better; according to the 2022 EU report on pesticide residues in food, approximately 1.6% of tomato samples analyzed under the EU-coordinated multiannual control program (EU MACP) exceeded the MRLs, with 0.9% being non-compliant after considering measurement uncertainty [49]. The European Food Safety Agency (EFSA) particularly highlights that chlorates, which can be found in tomatoes, have been detected at levels exceeding MRLs in leafy crops and tomatoes, warranting continued surveillance. The results of our analysis show that the residues of all monitored pesticides were below the current MRLs, including the residues of perchlorates (Table S1 and Table 1). The occurrence of heavy metals and pesticides in tomatoes and their by-products is less frequently studied. However, metals and certain mycotoxins are resilient to common industrial processing methods like heating and pasteurization, leading to their persistence in tomato products and potentially in processing wastes. As presented in Table 1, the levels of mentioned contaminants were below MRLs in the analyzed TPW, indicating its suitability for further valorization.
## 3 Results
### Kinetic Behaviors
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Parameter** & **Unit** & **Value** & **Standard Error** \\ \hline  & k\({}_{1}\) & L/min & 0.078 & 0.006 \\  & qe & mg/g & 25.4 & 0.416 \\  & R\({}^{2}\) & — & 0.982 & — \\  & \(\chi^{2}\) & — & 1.270 & — \\ PSO & k\({}_{2}\) & g/mg\(\times\)min & 0.004 & 2.2 \(\times\) 10\({}^{-4}\) \\  & qe & mg/g & 27.4 & 0.228 \\  & R\({}^{2}\) & — & 0.997 & — \\  & \(\chi^{2}\) & — & 0.221 & — \\  & \(\alpha\) & mg/g \(\times\) min & 18.1 & 7.954 \\  & \(\beta\) & g/mg & 0.253 & 0.024 \\  & \(\kappa^{2}\) & — & 0.966 & — \\  & \(\chi^{2}\) & — & 2.356 & — \\ Avrami & K\({}_{\rm AV}\) & min\({}^{-1}\) & 0.071 & 0.004 \\  & n\({}_{\rm AV}\) & — & 0.661 & 0.045 \\  & q & mg/g & 26.1 & 0.306 \\  & & t1/2 & min & 8.1 & — \\  & & R\({}^{2}\) & — & 0.996 & — \\  & & \(\chi^{2}\) & — & 0.347 & — \\ Intraparticle- & & K\({}_{\rm KP}\) & mg/g/min\({}^{1/2}\) & 0.926 & 0.169 \\ diffusion & First & C & mg/g & 2.88 & 2.07 \\  & step & R\({}^{2}\) & — & 0.908 & — \\  & & \(\chi^{2}\) & — & 0.162 & — \\  & Second & K\({}_{\rm IP}\) & mg/g/min\({}^{1/2}\) & 0.048 & 0.008 \\  & step & C & mg/g & 20.59 & 0.628 \\  & & R\({}^{2}\) & — & 0.953 & — \\  & & \(\chi^{2}\) & — & 0.121 & — \\ Third & K\({}_{\rm IP}\) & mg/g/min\({}^{1/2}\) & 0.008 & 0.004 \\ step & C & mg/g & 24.35 & 0.905 \\  & R\({}^{2}\) & — & 0.902 & — \\  & & \(\chi^{2}\) & — & 0.213 & — \\ \hline \end{tabular}
\end{table} 
Table 4: Kinetic parameters of CV dye adsorption onto DSD material at C\({}_{0}\) = 30 mg/L. film diffusion mechanism that the dye molecules immediately enter the solid surface. The second and third portion represent the intraparticle diffusion mechanism and equilibrium condition, respectively [43,44]. Similar researches reported that the adsorption of dyes onto mineral clays involves the intraparticle diffusion mechanism [1,45].
## 4 Experimentation and Evaluation
### Multiclass Classification
#### 4.2.7 Choice of the Best Model
* _Dinplkt_: From slightly above average values to the lowest possible values, the influence for classifying as anomalous traffic is observed. Values outside this range have little or no influence on classifying the trace as anomalous. After selecting the model for binary classification, the best model for anomaly-type classification was chosen when a trace was classified as non-normal behavior. For this study, Figure 16 was used, comparing the TPR and FPR values of the models. Due to the relatively similar values across all models, the system used in previous sections could not be followed. For this reason, the average TPR and FPR values were calculated as shown in Table 4.  
The MLP, logistic regression, and KNN models were discarded due to their low average TPR values compared to the remaining models. For the three remaining models, \begin{table}
\begin{tabular}{l l l} \hline \hline
**Model Type** & **MEAN TPR** & **MEAN FPR** \\ \hline DT3\_B (20) & 0.60 & 0.012 \\ XGB3\_B (multisoftmax, 25, gbtree, 0.1.30) & 0.59 & 0.011 \\ RL1\_SSB (11, liblinear) & 0.26 & 0.019 \\ KNN1\_B (5) & 0.48 & 0.014 \\ RFI\_B (100, gini) & 0.61 & 0.017 \\ MLP1\_B (7.100, 8) & 0.35 & 0.023 \\ DT3\_B (20) & 0.60 & 0.012 \\ XGB3\_B (multisoftmax, 25, gbtree, 0.1.30) & 0.59 & 0.011 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the results for the proposed algorithms. Figure 16: TPR and FPR metrics for the best multiclass classification models. the difference between their TPR and FPR values was calculated to determine the most comprehensive model, with random forest emerging as the best model for multiclass classification. Following the same procedure as in binary classification, a similar analysis for each class can be obtained. As an example, the classification of an anomalous trace as a DoS attack or exploit is studied in Figure 17. It is noteworthy that an extremely low value for the _Sttl_ attribute is crucial for classifying an attack as DoS, while an extremely high value prevents it from being classified as such, with the opposite behavior for _Exploit-type attacks_.
## 3 Backward Reachability-Based Schedulability Test  
The idea of our backward reachability analysis for the exact schedulability test comes from model checking techniques for safety properties: if it is impossible to reach initial states of a model from \"bad\" model states using the inverse of the transition relation of this model, then \"bad\" states are unreachable and the model is safe. Note that the inverse of the transition relation may not be total, since the set of the model states may include states that are not reachable from the initial states. The set of bad states defined above can be represented as a union of particular bad states: \(Bad\_States=Bad_{1}\cup\ldots\cup Bad_{n}\), where \(Bad_{i}=\{s\in Bad\mid s.bad_{i}=\mathbf{1}\}\). Note that in real-time systems with non-preemptive tasks under a GFP scheduler, when the number of processors is one less than the number of tasks \(Bad_{i}=\{s\in Bad\mid s.bad_{i}=\mathbf{1}\wedge\forall j\in[1..n]:j\
eq i \to s.bad_{j}=\mathbf{0}\}\), i.e., exactly one task can enter into a bad state, because otherwise there are some points where multiple released tasks do not occupy a free processor. To perform the exact schedulability test in these systems, it is important to check the unreachability of each set \(B_{i}\). We also suppose that \(P_{j}=D_{j}\) (actually, in real-time systems, it is often not necessary for a job to complete before it can release again. \"This requirement is consistent with the throughput requirement that the system can keep up with all the work demanded of it at all times\" ([3] p. 41).). In the rest of our paper, we consider only such systems.  
Let us fix a task \(i\) whose job misses its deadline, and the corresponding set \(Bad_{i}\). In every state \(s\in S^{T}\) which goes to a bad state \(b_{i}\in Bad_{i}\) (\((s,b_{i})\in R^{T}\)), all the processors are busy; this is shown as \(s.busy=m\), because in the opposite case, a \(i\)-job can capture some processors and start. Based on this observation, we conduct a backward reachability-based analysis. We consider 14 critical cases with different relative occurrences of tasks' release times and execution points closest to the bad state \(b_{i}\). To carry this out, we analyze \"lifelines\" of the corresponding tasks in the next section. This analysis shows whether the bad state is reachable from the initial state or whether backward steps from the bad state lead to a state that is unreachable from the initial state. The former means that the real-time system is unsafe (infeasible) and the latter means that the system is safe (schedulable).
# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
## 3 Resource Management With Fixed Power Control
### User Minimum Capacity Requirement Based Sub-Channel Algorithm
 \tag{35}\]  
By setting \(\frac{\partial\mathcal{L}(\alpha,\lambda,\eta)}{\partial\alpha}=0\), we get  
\[\frac{1}{\ln_{2}}\bigg{(}\frac{P_{r}|h_{i,m}|^{2}}{|h_{i,m}|^{2}(\alpha P_{r} +p_{i,m}^{*})}-\frac{P_{r}|h_{j,m}|^{2}}{|h_{j,m}|^{2}((1-\alpha)P_{r}+p_{j,m}^ {*})}-\frac{P_{r}|h_{j,m}|^{2}}{|h_{j,m}|^{2}(\alpha P_{r}+p_{i,m}^{*})+n_{0} }\bigg{)}-2\lambda P_{r}-\eta=0, \tag{36}\]  
After some straightforward simplification, we obtain  
\[ \begin{split}& P_{r}|h_{i,m}|^{2}(((1-\alpha)P_{r}+p_{j,m}^{*})|h_{ j,m}|^{2})\times((\alpha P_{r}+p_{i,m}^{*})|h_{j,m}|^{2}+n_{0})-P_{r}|h_{j,m}|^{2}(( \alpha P_{r}+p_{i,m}^{*})|h_{i,m}|^{2})\\ &\times((\alpha P_{r}+p_{i,m}^{*})|h_{j,m}|^{2}+n_{0})-P_{r}|h_{j, m}|^{2}((\alpha P_{r}+p_{i,m}^{*})|h_{i,m}|^{2})(((1-\alpha)P_{r}+p_{j,m}^{*})|h_{j,m}|^{ 2})\\ &=(2\lambda P_{r}\ln_{2}+\eta\ln_{2})((\alpha P_{r}+p_{i,m}^{*})|h _{i,m}|^{2})(((1-\alpha)P_{r}+p_{j,m}^{*})|h_{j,m}|^{2})((\alpha P_{r}+p_{i,m}^{ *})|h_{j,m}|^{2}+n_{0}).\end{split}  \tag{37}\]Finally, the \(\alpha\) is obtained from solution of (37). To get the optimal value of Lagrangian multipliers \(\lambda\) and \(\eta\), we employ sub-gradient method which can be iteratively updated as [30]:  
\[\lambda(\phi+1)=\lambda(\phi)+\delta(\phi)\big{(}2\alpha P_{r}+p_{i,m}^{*}-P_{r} -p_{j,m}^{*}\big{)}, \tag{38}\]  
\[\eta(\phi+1)=\eta(\phi)+\delta(\phi)(\alpha-0.5). \tag{39}\]  
where \(\phi\) denotes the iteration index and \(\delta\) is the non-negative step size. In each iteration, the multipliers \(\lambda\) and \(\eta\) are updated using the calculated values of \(\alpha\). Then, in the next iteration, these updated values of \(\lambda\) and \(\eta\) are used to calculate the optimal \(\alpha\). Note that the said process will repeat until convergence.
## 4 Proposed Path Planning Techniques
### Hybrid A* Algorithm  
This path-planning algorithm is one of the most efficient path-planners for mobile robots or autonomous vehicles [38]. It consists of two parts; the first one is named the forward search stage. The algorithm is guided by a cost function named \(g(n)\); it works from the starting node to the current node (\(n\)). It has two more functions named h\({}_{1}(n)\) and h\({}_{2}(n)\) respectively. h\({}_{1}(n)\) is a heuristic function that uses the obstacle map to compute the cost of the distance from the current node to the goal in the discrete state. Another heuristic function named h\({}_{2}(n)\) which considers the kinematics of the used mobile robot. The robot kinematics is used to predict the path that considers the direction, steering angle and speed of the mobile robot in the continuous state. Then, the algorithm selects the right successor in continuous coordinate and lets the mobile robot follow it. The overall objective function of the planner algorithm at each node \(n\) is shown in Equation (3).  
\[F(n)=g(n)+\text{h}_{1}(n)+\text{h}_{2}(n) \tag{1}\]  
The second stage is called analytic expansion, which ensures that the algorithm achieves the continuous coordinate of the goal state.
## 3 Methodology
### Data Preprocessing
#### 3.2.3 Normalization  
The datasets used in this study comprise diverse features with varying scales and units. Features with larger scales have a greater impact on the model, potentially causing it to overlook features with smaller scales. In order to address this issue, it is crucial to normalize the feature values within a defined range. In this paper, the min-max normalization method is utilized to rescale the feature values into the range of [0, 1]. The method for min-max normalization is given as follows:  
\[x_{n}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{1}\]  
where \(x\) represents the original feature value, \(x_{\text{min}}\) represents the minimum feature value, \(x_{\text{max}}\) represents the maximum feature value, and \(x_{n}\) represents the normalized feature value.